American Diabetes Association – 72nd Scientific Sessions

June 8-12, 2012; Philadelphia, PA; Full Report– Draft

Executive Highlights

In this final report, we provide our coverage of the 72nd Scientific Sessions of the American Diabetes Association, held at the Pennsylvania Convention Center in Philadelphia, PA. The conference drew 17,890 attendees, representing a continued increase in attendance from the 17,600 total attendees in 2011 and 17,300 attendees in 2010. 59% of attendees were international this year, with a total of 111 countries represented as well as all 50 states in the US – we expect the international attendance to continue to increase at this premiere meeting of diabetes and obesity research. The five-day meeting consisted of eight tracks, 169 exhibits (a small increase from the 166 at last year’s meeting), and 2,156 oral and poster sessions (down 1% from 2,177 in 2011 and 12% from 2,441 in 2010). As we understand it, the acceptance rate for abstracts and late breakers this year was quite high, with 2,156 oral and poster sessions accepted from the 3,028 submitted, and a higher-than-usual 153 of 311 submitted late breakers accepted.

Our final report includes in-depth commentary on symposia, lectures, oral presentations, corporate symposia, and a really valuable new addition this year – meet the expert sessions. You’ll notice while reading that insulin safety and novel insulins were major areas of focus during the meeting, while incretins, CGM, and SGLT-2s also generated significant interest. As detailed in our comprehensive table of contents below, the complete meeting notes are organized into 12 sections: (1) ADA themes – the big picture; (2) Artificial pancreas, CGM, pumps, and SMBG; (3) Meet the Expert; (4) Incretins; (5) Oral therapies (excluding incretins); (6) Novel drug development and basic science; (7) Insulin therapies; (8) Cardiovascular disease and other complications; (9) Healthcare, management & education, and epidemiology; (10) Obesity and obesity therapies; (11) Mobile health and telemedicine; (12) Type 1 therapies – cure related; (13) ORIGIN; and (14) Exhibit hall report. Coverage of select posters, corporate symposia, and investor events are included within each section. Directly below, we describe what we see as the major themes from ADA 2012.  

  • ADA 2012 was full of insight on the next generation of CGM technology. Dr. David Price (Dexcom, San Diego, CA) shared new accuracy data from Dexcom’s pivotal study of the G4 sensor: a mean absolute relative difference (MARD) of 13%, 80% of points in the CEG A-Zone, and 94% of sensors lasting up to seven days (1-OR). As a reminder, the G4 was submitted to FDA at the end of 1Q12 and management hopes for approval before year-end (see Dexcom 1Q12 at In the same session, Dr. Steven Russell (Massachusetts General Hospital, Boston, MA) unveiled data from the team’s closed-loop experiments comparing the Dexcom G4, the Medtronic Enlite, and the FreeStyle Navigator (as an aside, it’s great to see independent comparative accuracy data!). The G4 was the most accurate (MARD of 11.3%), although we were also glad to see the improvements in the Enlite sensor relative to the Sof-Sensor (MARD of 16.0-17.2% vs. 20.3% for the Sof-Sensor) (4-OR). New looks at next-generation CGM technology also included 24-hour pilot study data from Echo Therapeutics’ transdermal CGM (MARD of 12.6%) (7-OR) and a poster summarizing the pivotal six-day trial of Medtronic’s Enlite CGM sensor (depending on the calibration scheme, a MARD of 13.6-14.7% and CEG A-zone 81-86%) (30-LB). As a reminder, Enlite was recently submitted to FDA along with the MiniMed 530G low glucose suspend pump; approval is expected in 1H13 (see our report at [Editor’s note: CGM accuracy data noted above may not be comparable due to differences in study design, calibration schemes, etc.]
  • We also noticed broader and more refined thinking about CGM – and a lot more use of CGM in clinical trials. Behavioral factors came up much more often in presentations and Q&A, especially from perplexed questioners wondering why so many people quit CGM and how they might be encouraged to stay on the technology. We appreciated this heightened focus on behavioral and psychological factors and believe it will: (1) take the field to a higher level in terms of product development; (2) help fine-tune patient selection criteria and education; and (3) help put (and keep) more patients on the technology, and ultimately get to the point where CGM represents the new standard of care. Also noteworthy were two excellent presentations on interpreting CGM downloads from Drs. Bruce Bode (Emory University, Atlanta, GA) and Howard Wolpert (Joslin Diabetes Center, Boston, MA) – we believe this is another area with significant potential to move the needle on CGM adoption at both a micro level (e.g., improving individual patients’ glycemic control through better retrospective analysis) and a macro level (e.g., boosting the utility of CGM for HCPs). The broader level of thinking about CGM also featured illuminating talks on CGM cost and coverage issues (Dr. Michael O’Grady), glycemic variability (Drs. David Rodbard, Robert Vigersky, Han DeVries, James Krinsley, and Thomas Danne), and how study design and analytic techniques affect reported CGM accuracy (Dr. David Price). We also came away from this year’s ADA with the sense that CGM is becoming increasingly used in clinical trials for drugs. This extra data should offer a plethora of additional insight over just A1c or SMBG profiles, especially to differentiate basal insulins, GLP-1 receptor agonists, and ultra-rapid acting insulins. Just as when GLP-1 came into the fold and suddenly everyone was asking about, with novel new drugs, “What was the change in weight?”, we feel like we are getting to the point where when novel drug results are presented, it is increasingly being asked, “Did you use CGM?” or, “What happened with CGM?”
  • Highlights on SMBG were mostly in the exhibit hall, with a major focus on better software. The three meters “launched” at ADA – Sanofi’s iBGStar, Abbott’s FreeStyle InsuLinx, and LifeScan’s OneTouch Verio IQ – were all heavily marketed with easier data review and analysis in mind (for our thoughts on the three meters, see Closer Look reports at and and diaTribe reviews at and SMBG software has been suboptimal for the longest time, so this next-generation of easier-to-download meters, more actionable data reports, and on-meter statistics and pattern recognition is very refreshing to see from a patient perspective. In their corporate-sponsored events, Sanofi, Abbott, and LifeScan did a particularly good job attracting some of the field’s notable speakers to advocate for their products: Drs. Bruce Bode (Emory University, Atlanta, GA), Steve Edelman (UCSD, San Diego, CA), Bill Polonsky (Behavioral Diabetes Institute, San Diego, CA), and Ralph DeFronzo (University of Texas Health Science Center, San Antonio, TX). Bayer’s exhibit hall booth showcased the recently approved Contour Next EZ and Next Link, which were introduced at ADA in anticipation of the big upcoming US launch in late summer or early fall. The strips have achieved a very impressive level of accuracy: 99% of the time within 10% of lab standards (>75 mg/dl) and 100% within 10 mg/dl (<75 mg/dl). This is a major achievement in our view and was some of the very biggest news at the exhibit hall overall at ADA Presentations on SMBG were primarily in a joint ADA/EASD symposium, highlighted by a debate on the utility of SMBG for type 2s on oral agents (more data needed, though structured testing is important) and a talk on accuracy standards (the upcoming new ISO 15197 requirements are a move in the right direction [95% within 15 mg/dl (<100 mg/dl) or 15% (>100 mg/dl)]).
  • Valued information on insulin pumps came from the exhibit hall, headlined by the launch of Tandem’s t:slim. Tandem held a well-attended product theater to introduce its new touchscreen pump, which included presentations from CEO Kim Blickenstaff, expert educator Ms. Jen Block (Stanford University, Stanford, CA), and Dr. Timothy Bailey (Advanced Metabolic Care and Research, Escondido, CA). The company began taking orders on June 11 and will begin shipping the t:slim in August (we learned at ADA that the new pump will also ship with the LifeScan OneTouch Verio IQ blood glucose meter). Although Insulet did not secure FDA approval for the second-generation OmniPod in time for ADA, the new pod was on display in the exhibit hall accompanied by ample marketing materials and signage. As of Insulet’s 1Q12 results call in early May (see our report at, approval for the next-gen pod is expected in the coming months. The center of Medtronic’s exhibit hall booth was devoted to the mySentry remote monitor (see our report on the approval at, the only product of its kind right now in the pump/CGM arena. Despite its $3,000 price tag, this device is starting to gain support from patients (~1,000 users right now) and even some small payer support. In the expansive J&J LifeScan/Animas booth, the Animas Vibe was on display, though reps were quick to note that the device has not yet been submitted for FDA review (as of Dexcom’s 1Q12 call, this is on hold for the moment). Roche had a kiosk devoted to its Accu-Chek insulin delivery products, while Cellnovo told us it hopes to launch in the US in 2013. Finally, Valeritas’ V-Go (recently launched in April) was also on display in the exhibit hall, giving us an up-close look at the company’s disposable insulin delivery device for people with type 2 diabetes.
  • Artificial pancreas (AP) highlights included more focus on portability for outpatient studies, new products from Medtronic and Animas, and bi-hormonal control. With prototype in hand, Dr. Edward Damiano (Massachusetts General Hospital, Boston, MA) demonstrated the iPhone-based, bi-hormonal (two Tandem t:slim pumps for insulin and glucagon) artificial pancreas system that will hopefully be used in an upcoming five-day, semi-outpatient closed-loop study (222-OR). Portability for outpatient studies was also frequently mentioned at a closed-loop research meeting sponsored by the JDRF and NIDDK. The greatest minds in the field presented updates on their research, capped off by a “science fair” to show off the increasingly portable and patient-friendly systems being developed. There’s no question in our minds that the hardware aspect of AP research has really moved by leaps and bounds in the past couple years. On the more near-term front, we were very interested in two orals from Dr. Satish Garg (University of Colorado Denver, Aurora, CO) on the in-patient ASPIRE study of Medtronic’s low glucose suspend (LGS) pump/CGM system (22o- and 221-OR). (As noted above, the combination of the Enlite and MiniMed 530G pump was recently submitted to FDA; see our report at Most notable was the finding that “hypoglycemia begets hypoglycemia,” reminding us of the urgent need to help patients break the cycle of hypoglycemia unawareness and the potential afforded by LGS and predictive-suspend systems. The fact that Medtronic was able to submit LGS faster than expected is likely a very good sign for other pump manufacturers working on the closed loop – Animas in particular. Speaking of the latter, we finally saw data on Animas’ Hypoglycemia-Hyperglycemia Minimizer System – despite two high-carb meals and deliberate under- and over-bolusing, the system kept patients in zone (70-180 mg/dl) nearly 70% of the time, with only 0.2% of the time spent in hypoglycemia (917-P) – a very big deal. We look forward to seeing more data, especially when the device becomes more portable and is tested even more extensively. Other notable presentations focused on factors that affect closed-loop control (“controller effort” was the biggest one) and a record four AP presentations that discussed use of both insulin and glucagon. Glucagon in particular is getting a lot more attention than it used to … promising news for patients who have been working with decades-old technology but for whom new and much better “glucagon rescue” pens are in the works.
  • What is the biggest obstacle to the artificial pancreas? While the usual answer we hear at conferences is “the speed of insulin” or “CGM accuracy,” we were intrigued to hear Dr. William Tamborlane (Yale University, New Haven, CT) propose that safety actually represents the biggest obstacle (i.e., ensuring that a system’s malfunctions will not result in harmful over-delivery of insulin). We believe safety is FDA’s biggest worry as well (e.g., the delay in the Veo…), and we hope as CGM accuracy improves and algorithms get smarter, this will be less of a concern. We believe FDA may move to actually asking patients to take on more risk, in order to move things faster – it will be interesting to watch this.
  • We were encouraged to see FDA representatives more involved in device Q&A sessions than we’ve ever seen in the past. Not only were they actively asking questions and engaging with speakers, but they were publicly identifying themselves at the microphones! This is another sign to us that things may be picking up steam and moving along now, especially as JDRF and industry put more pressure on the Agency to define study requirements and expectations.
  • Incretins were again a topic of significant enthusiasm and interest at this year’s ADA meeting. While there wasn’t much groundbreaking new data presented for GLP-1 agonists (topline results were previously reported for a number of the trials), there was a predominant focus on distinguishing the class from other anti-diabetic agents. In particular, several oral presentations highlighted the efficacy and safety of adding GLP-1 agonists (GlaxoSmithKline’s albiglutide and Sanofi’s lixisenatide) to basal insulin therapy (55-OR, 62-OR, 983-P). Four-year results from DURATION-1 for Amylin/Alkermes’ Bydureon (1156-P) and three-year results from EUREXA highlighted the durable effects of GLP-1 agonists on glycemic control and weight – this data is serving to make GLP-1 an even more established part of clinical care, rather than a “new” option, as it has been since its introduction in 2006. A pooled analysis of liraglutide’s (Novo Nordisk’s Victoza) LEAD development program found a significantly greater proportion of patients achieved a composite endpoint of A1c < 7.0%, no weight gain, and no hypoglycemia with 1.8 mg liraglutide (34%) and 1.2 mg liraglutide (30%) than with exenatide (Amylin’s Byetta, 24%), sitagliptin (Merck’s Januvia, 14%), sulfonylureas (9%), TZDs (3%), and insulin glargine (Sanofi’s Lantus, 9%) (1041-P). Several speakers, including Dr. Steven Marso (University of Missouri Kansas City, Kansas City, MO) and Dr. Ralph DeFronzo (University of Texas Health Science Center, San Antonio, TX), discussed the potential cardiovascular benefits of GLP-1 agonists and their potential utility as prediabetes treatments. We had been expecting to see Dr. DeFronzo’s trial using GLP-1, metformin, and TZDs that formed the basis of his Banting Lecture in 2008  – we found out that this was not accepted as a late breaker (presumably due to timing) and that it will be shown at EASD – good news for the EASD organizers, as many others will be looking forward to hearing this data. With three GLP-1 agonists currently marketed and several candidates in late-stage development, emphasis was also placed on comparing and contrasting the different drugs within the class. Dr. Filip Knop (Gentofte Hospital, Hellerup, Denmark) addressed the incretin competition topic head-on, emphasizing efficacy and tolerability differences based on duration of action, size, and structure – this was not new, but synthesized nicely. Additionally, further results from the HARMONY-7 trial comparing albiglutide to liraglutide were presented, which favored liraglutide in terms of glycemic efficacy and weight, but albiglutide in terms of GI tolerability. Finally, we were excited to get our first look at the PK/PD profiles of the exenatide once weekly suspension formulation, which appeared to closely resemble the PK/PD profiles of Bydureon. During its investor event at ADA, Amylin announced that the first phase 3 trial (named DURATION-NEO-1 – great name) for this new formulation was expected to initiate in 3Q12 (previously mid-2012), and that the trial would be similarly designed as DURATION-5 (head-to-head vs. Byetta).
  • In contrast to GLP-1 agonists, we felt that there was less new interesting data presented for DPP-4 inhibitors at ADA 2012. Overall, there appeared to be widespread agreement among KOLs that little differentiated the DPP-4 inhibitors on the market today. While there have been product advances in GLP-1 (twice daily to once daily to once weekly), there haven’t been as many changes with DPP-4 inhibitors, although there’s an additional combination available this year, Merck’s Juvisync, which combines DPP-4 inhibitor Januvia and with statin simvastatin. In discussing the class’s  commercial success (the class sold over $5 billion in 2011, up from $42 million in 2005), speakers emphasized the class’s strong safety and tolerability profile, and several highlighted the potential use of DPP-4 inhibitors as prediabetes agents. Clearly, the next frontier for the DPP-4 inhibitor class will be once weekly dosing –it will be interesting to see how well this does since once-weekly dosing implies monotherapy as there are no combinations that offer once-weekly dosing. There was no new data presented for the once weekly DPP-4 inhibitors under development by Takeda (SYR-472; phase 3 in Japan) or Merck (MK-3102; phase 3 to initiate in 2012). With regards to new data that were presented, we were most eager to see data demonstrating the efficacy and safety of combination therapy with BMS/AZ’s dapagliflozin and Merck’s sitagliptin (Januvia; 1071-P). Still, we believe Lexicon’s dual SGLT-1/SGLT-2 inhibitor LX4211 holds greater potential for combination use with DPP-4 inhibitors given the effects of LX411 on GLP-1 secretion (Lexicon recently released phase 2b data on Lx4211 – please see our Closer Look from June 25, 2012 that showed a 0.95% A1c reduction from an ~8% baseline A1c).  We were also excited to see continued efforts in examining the effects of incretin therapies in the setting of type 1 diabetes. In particular, Dr. Bo Ahren (Lund University, Malmo, Sweden) reported results from a small (n=28) four-week study that showed a reduced glucagon response following a meal with vildagliptin treatment (Novartis’ Galvus), but no significant reduction in the glucagon response during hypoglycemia – potentially quite exciting. Finally, back on the GLP-1 front, Dr. Gayatri Sarkar (DEOB, NIDDK, NIH, Bethesda, MD) highlighted intriguing results from another small study (n=14) that found lower daily insulin requirements and enhanced insulin sensitivity with exenatide plus basal insulin therapy compared to insulin monotherapy in people with long standing type 1 diabetes. We wonder how complicated dosing would be – we have heard some serious enthusiasm to date from type 1 patients on GLP-1 agonists off label.
  • A significant highlight of ADA 2012 was hearing the highly-anticipated phase 3 data for J&J’s canagliflozin, which the company recently submitted to the FDA (for details, please see our May 31st, 2012 Closer Look at ). Of note was 26-week data (81-OR) showing significantly greater A1c reductions with canagliflozin 100 mg (-0.77%) and 300 mg (-1.03%) compared to placebo (+0.14%), as well as significant improvements in fasting plasma glucose, two-hour postprandial glucose, body weight, systolic blood pressure, and HDL. There were also three posters of particular interest on canagliflozin. 41-LB demonstrated the glycemic efficacy and tolerability of using canagliflozin in patients with moderate renal impairment. 38-LB found non-inferior glycemic control with canagliflozin vs glimepiride, but with greater improvements in weight and lower rates of hypoglycemia. 50-LB highlighted superior A1c reductions with canagliflozin over sitagliptin (Merck’s Januvia) with similar rates of hypoglycemia and urinary tract infections – although risk for genital mycotic infections was higher with canagliflozin. Overall, the canagliflozin data looked good, with the highest dose showing A1c reductions slightly higher than expected, at about 1.2%, from a ~8.5% baseline. Unsurprisingly, canagliflozin treatment led to higher rates of genital infections and urinary tract infections (UTI) than other classes of agents. However, unlike with BMS/AZ’s dapagliflozin, the drug did not appear to increase the risk of breast or bladder cancer, at least based on the data presented thus far – there were big sighs of relief not only from J&J, but from others happy to hear that this was looking less and less like a potential class issue. In reviewing the potential therapeutic use of SLGT-2 inhibitors, Dr. Robert Henry (University of California San Diego, San Diego, CA) noted that patients who developed UTIs while treated with an SGLT-2 inhibitor likely already had an underlying infection that was exacerbated by the therapy. Overall, the KOL chatter seems to be that UTIs and genital infections won’t be a deal-breaker for patients. We’re interested in learning more about “lifetime” risk of UTIs and genital infections if a patient is on an SGLT inhibitor – if they don’t get a UTI after one year, what is the risk the following year? These are still unanswered questions, given that trials do not have patients on the therapy for much longer than a couple of years. We also noticed continued interest in targeting SGLT-1, with Dr. Henry presenting data on canagliflozin’s transient inhibition of intestinal SLGT-1 (79-OR) and Dr. Ralph DeFronzo’s (University of Texas Health Science Center, San Antonio, TX) discussion on the potential drawbacks and even greater merits of SGLT-1 inhibitors. Notably, after the ADA conference, Lexicon announced positive phase 2b data for its SGLT-1/SGLT-2 dual inhibitor, LX4211 (for details, see our June 25, 2012 Closer Look ) – we expect to see some big interest in this little company throughout 2012, and a major partnership to be announced perhaps early next year.
  • There were less notable data presented on BMS/AZ’s dapagliflozin compared to previous years. Two oral sessions examined the drug’s effect on renal glucose kinetics (83-OR) and efficacy across different baseline A1cs (82-OR). There were also five noteworthy posters on dapagliflozin, including data demonstrating dapagliflozin’s effectiveness when added onto insulin therapy over 104 weeks (1042-P) as well as to sitagliptin (1071-P). A separate poster highlighted the safety and efficacy of using dapagliflozin in patients with a history of cardiovascular (CV) disease (1114-P). Finally, two safety-focused posters with pooled data from 12 or more studies demonstrated that dapagliflozin was not associated with impaired renal function (1098-P), was not associated with increased risk for CV or liver damage, and confirmed that the drug did lead to greater incidences of genital infections, UTI, and breast and bladder cancer (1011-P). We wonder if BMS and AZ performed these meta-analyses to address the FDA’s concerns; if so, data from 1011-P on liver function may work towards mitigating the FDA’s unease about the drug’s potential hepatotoxicity. We will be keeping an eye out for new results at subsequent conferences, since the FDA requested in their complete response letter “additional clinical data”, presumably from studies on-going at the time – whether they are asking for new data is the key question.
  • Information on other SGLT-2 inhibitors was sparse, with one oral session on Chugai Pharmaceutical’s tofogliflozin (80-OR) and one poster on 90-week data for BI/Lilly’s empagliflozin as a monotherapy or add-on to metformin (49-LB). We hope to see more trials of SGLT-2 inhibitors as adjuncts to other therapies, as a critical question is how this drug class could be combined with other diabetes medications, in particular TZDs and DPP-4 inhibitors (though this dual-therapy would be expensive).  Make-it-or-break-it events for SGLT-2 inhibitors will be the FDA’s reevaluation of dapagliflozin once it is resubmitted and evaluation canagliflozin, which will hopefully clarify the agency’s position regarding safety risks and their preferred amount of clinical data. Looking toward EASD, we expect that greater regulatory movement, including a possible approval by the EMA for dapagliflozin, will provide more informed opinion on the benefit:risk profiles of SLGT-2 inhibitors. 
  • While incretins, insulins, and SGLT-2 inhibitors were the highlights of ADA, there were several notable talks on additional diabetes medications. Of interest was the debate on the future survival of PPARγ agonists (TZDs) with Dr. Steven Nissen (Cleveland Clinic, Cleveland, OH) arguing “Yes” and Dr. George Grunberger (Grunberger Diabetes Institute, Bloomfields Hills, MI) claiming “No.” Barring any serious safety concerns, we wonder if small changes in the perception of TZD safety will influence prescribing patterns – payors may heavily push for TZD therapy following metformin due to the low cost of the drug starting now (pioglitazone goes generic in August). Further contributing to our understanding of TZD safety was a poster with data from a six-year follow-up of the PROactive trial showing that pioglitazone did not increase the risk of bladder cancer or other malignancies (928-P). Given the emergence of GLP-1 agonists and DPP-4 inhibitors with more favorable effects on weight and hypoglycemia, we’ll be interested in seeing whether TZD continue as a staple of diabetes therapy, and why – to date, of course, the fact that TZDs address insulin resistance has been the primary advantage. As we discuss elsewhere in this report, Metabolic Solutions Development Company (MSDC) reported promising 12-week phase 2b data for its lead PPAR-sparing insulin sensitizer MSDC-0160 (966-P) – this was great to see on the PPAR front, an equally effective but perhaps safer and more tolerable therapy.
  • Beyond SGLT-2 inhibitors, there were relatively few abstracts on clinically-staged novel therapies for the treatment of type 2 diabetes. Still, we were excited to hear new data presented for several candidates in development. In particular, 48-week results were reported from Stage 2 of the TINSAL-T2D study, which evaluated the efficacy and safety of the non-steroidal anti-inflammatory drug (NSAID) salsalate. Although some anti-inflammatory effects were conferred, 48 weeks of treatment resulted in only a modest reduction in A1c (0.24% beyond placebo), a trend toward increased SBP, significantly increased total cholesterol and LDL, significantly increased urinary albumin, an increased risk for mild hypoglycemia, and a modest increase in weight (~2 lbs) – overall, we don’t believe this bodes well for the therapy, especially given the weight gain. Separately, Metabolic Solutions Development Company (MSDC) reported promising 12-week phase 2b data for its lead PPAR-sparing insulin sensitizer MSDC-0160 (966-P). Overall, the data demonstrated similar improvements in A1c and FPG with MSDC-0160 as with high-dose pioglitazone, but with less hemodilution and less weight gain. MSDC also revealed for the first time the mitochondrial target of its insulin sensitizers – a mitochondrial pyruvate carrier system that contains the proteins MPC1 and MPC2 (1096-P). Additionally, Eli Lilly highlighted additional clinical results for its glucagon receptor agonist LY2409021 (981-P). In a 12-week phase 2 study (n=87), treatment with LY2409021 led to robust reductions in A1c with a low risk for hypoglycemia and no changes in average weight, blood pressure, lipid levels, or triglyceride levels. However, similar to what was reported in a trial for LY2409021 at ADA 2011, dose-dependent increases in hepatic transaminases (elevations in these enzymes may be an indicator of liver damage) were observed. While transaminase levels returned to baseline after four weeks of washout and no indications of liver injury were detected, we wonder how clinically relevant this side effect will become during longer periods of treatment. Similar increases in hepatic transaminases were also observed with Merck’s former glucagon receptor agonist MK-0893 (also reported at ADA 2011), suggesting that the effect may be class related. Separately, we heard intriguing phase 1/2 results for a cord-blood derived multi-potent stem cell therapy (the Stem Cell Educator), which provided significant reductions in A1c, improvements in insulin resistance, and improvements in beta cell function four weeks following a single treatment (287-OR). Finally, additional data was presented from a multiple ascending dose study for Advinus’ liver selective glucokinase activator GKM-001, which showed dose-dependent and significant reductions in FPG and PPG with no hypoglycemia or changes in liver transaminases or triglycerides.
  • While basic science is not a major focus of ours at ADA, there were a few presentations of particular interest at this year’s meeting. One of our favorite presentations over the course of the conference was the Banting Lecture delivered by Dr. Bruce Spiegelman (Harvard Medical School, Boston, MA). Dr. Spiegelman gave a captivating lecture on the work conducted by his lab to elucidate the biology of brown fat regulation (including the discovery of irisin) and to develop brown-fat based therapeutics for the treatment of metabolic disease. Clearly passionate and tirelessly driven, we found listening to Dr. Spiegelman highly inspirational, and the lecture served as yet another reminder of how fortunate we are for the brilliant researchers and clinicians throughout the world working to advance diabetes and obesity care. We also enjoyed listening to the Outstanding Scientific Achievement Award Lecture given by Dr. David Altshuler (Broad Institute, Boston, MA). In front of a packed audience, Dr. Altshuler detailed his work using genome-wide association studies (GWAS) to identify novel genetic variants commonly associated with type 2 diabetes. Of greatest note, he asserted that attempting to use these genetic variants for prediction and prevention could be futile given the complex genetic basis for type 2 diabetes. Instead, he advocated for identifying loss-of-function mutations that confer protection against diseases to identify new drug targets, given that drugs more commonly inhibit rather than activate. Other basic science topics we found interesting included the roles of mitochondrial dysfunction, epigenetics, and inflammation in insulin resistance, the effects of insulin in the brain, and the molecular mechanisms underlying the beneficial effects of exercise.
  • Some of the most exciting new data at this year’s ADA was on insulin analogs, including phase 2 results on Lilly’s proprietary basal analog, PEGylated lispro (LY2605541) and Halozyme’s PH20-supplemented rapid-acting insulin products. This was great to see in our view since we believe there is much more improvement needed in prandial insulins than in basal insulins, though both obviously need improvement. Compared to insulin glargine (Sanofi’s Lantus), LY2605541 conferred lower nocturnal hypoglycemia and intra-day glycemic variability in type 2 diabetes (347-OR) and better reduction in A1c, daily mean blood glucose, and nocturnal hypoglycemia (though with higher overall hypoglycemia) in type 1 diabetes (1026-P). Intriguingly, patients taking LY2605541 experienced weight loss (in contrast to glargine’s weight gain) as well as relatively higher levels of the liver enzymes ALT and AST – support for the hypothesis that PEGylated lispro has more liver-specific action than other analogs, also suggested by preclinical studies. That’s really interesting. Halozyme showed some very strong results from studies pitting injections of its PH20/insulin analog coformulations against insulin lispro (Lilly’s Humalog) – notably, PH20-analog gave better reductions in postprandial glycemic control in both type 1 (353-OR) and type 2 diabetes (882-P). Additionally, Halozyme gave encouraging results from phase 4 research on injection of standalone PH20 (Hylenex) at the time of insulin pump infusion site, as well, though only interim results were available (34-LB). As for the next-gen basal analog closest to market, presentations on insulin degludec (Novo Nordisk’s Tresiba) indicated that a flexible dosage schedule can provide effective glycemic control in type 1 diabetes (348-OR) and that U-200 degludec has a similar action profile to the U-100 version (349-OR). We are excited to see what the combination of degludec combined with new prandial insulins show – we were very keen earlier to hear that Novo Nordisk will soon choose a new “ultra-ultra” rapid acting analog for phase 3 and although there wasn’t new information at this meeting, the thought of the combination of this new prandial insulin with degludec (or in a pump!) is pretty exciting. Also new to this year’s ADA were results of the EASIE study, in which glargine outperformed sitagliptin (Merck’s Januvia) with regard to 24-week A1c decline (presented during the joint ADA/The Lancet symposium) – in our view, these results were incredibly unsurprising (no one would expect Januvia to beat Lantus since it is a therapy designed for use much earlier in disease progression) and we were slightly surprised this trial was selected for this high-profile symposium, though we note the Q&A was very meaty. We do look very forward to results from the extension study of EASIE, in which a subgroup of patients used a triple-therapy of sitagliptin, glargine, and metformin.   
  • ADA 2012 featured a large number of talks on complications, ranging from hypoglycemia to cardiovascular disease and retinopathy to sleep disorders. There were several presentations focused on how to better predict complications – some of the methods presented included hypoglycemia, hyperinsulinemia, type of therapy, advanced glycation end products, genetics, oxidative stress, and inflammation. These epidemiological studies should prove valuable in generating hypotheses. ADA brought relatively little discussion of new complications therapies in the pipeline, though Reata’s bardoxolone methyl (currently enrolling in phase 3; identifier: NCT01351675) was a notable exception – clearly there is great excitement about this therapy in development. Other talks focused on the risk of complications using sub-analyses from large trials such as ACCORD (hypoglycemia), DCCT/EDIC (inflammation and advanced glycation end products), and BARI-2D (use of insulin-providing or insulin-sensitizing therapies). We also noticed a particular focus on the negative implications of hypoglycemia and severe hypoglycemia on both a short-term (alterations in QTc interval, rises in inflammatory cytokines and markers of endothelial dysfunction, clot resistance to lysis, diminished vagal tone, and cardiac arrhythmias) and long-term (potentially cardiovascular disease and mortality) basis. With this in mind, we’re glad to see the emergence and expansion (and, indeed, establishment) of therapies for type 2 diabetes that don’t cause hypoglycemia (e.g., DPP-4s, GLP-1s, SGLT-2s). Of course, this is still a major challenge for type 1s, though we believe CGM has already helped many in this respect, and a bihormonal (insulin-glucagon) artificial pancreas might do so in the future.
  • The growing diabetes epidemic, barriers to addressing it, and ways to optimize diabetes care were widely discussed at this year’s ADA. Both the President, Health Care & Education Address (delivered by Ms. Geralyn Spollett [Yale School of Medicine, New Haven CT]), and the President, Medicine & Science Address (delivered by Dr. Vivian Fonseca [Tulane University School of Medicine, New Orleans, LA]) included natural disaster metaphors (a tsunami and a hurricane, respectively) that were meant to convey how the diabetes epidemic may overwhelm the healthcare system, end millions of lives early, decrease quality of life, and have a large negative impact on GDP if not appropriately addressed. Ms. Spollett and Dr. Lynne Levitsky (Massachusetts General Hospital, Boston, MA) both highlighted, in passionate speeches, the need to increase the diabetes workforce in order to meet the rising demand for care. Dr. Levitsky also discussed the economic pressure diabetes care providers are under, arguing (very convincingly) that the current fee-for-service system under-reimburses diabetes practices. After presenting arguably suboptimal data on achievement of ADA A1c goals from the T1D Exchange, Dr. Joseph Wolfsdorf (Children’s Hospital of Boston, Boston, MA) argued that we need improved treatment methods that are acceptable, affordable, and generalizable across the socioeconomic spectrum in order to improve outcomes – again, hard to argue with this, but we feel we’re increasingly unlikely to see movement on this front.
  • Talk about diabetes care was not all somber at ADA, with many speakers presenting innovative ideas. Dr. Silvio Inzucchi  (Yale University, New Haven, CT) discussed the new ADA/EASD position statement, which emphasizes an individualized approach to type 2 diabetes treatment – we look forward to seeing how this important perspective, developed by many of the world’s leading thinkers on diabetes, will be taught to primary care. Ms. Wanda Montalvo (Montalvo Consulting, New York, NY) and Ms. Judith Fradkin (National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD) discussed the goals and new initiatives of the National Diabetes Education Program, which provides diverse educational and support materials for patients and healthcare providers. We think these tools could have a big impact on outcomes and care delivery if used to their full potential, but wonder how recognized they are in the diabetes community. Ms. Montalvo also touched upon use of self-management tools, noting that “much of primary care happens at home. The patient is a core member of the primary care team.” We don’t know that the average patient feels this way and would love to see more ideas on patient motivation and incentives. Ms. Spollett meanwhile outlined three steps for dealing with the diabetes tsunami: (1) increase awareness; (2) begin preparation in terms of workforce training and research; and (3) educate the general public. We were heartened to see such broad discussion of shortcomings and opportunities in diabetes care at this year’s ADA, and were especially glad to see the issues of diabetes reimbursement and workforce availability brought to the forefront. These issues are going to strongly shape quality and availability of diabetes care in the coming years, and so we hope this year’s presentations sparked interest or discussion that could eventually translate into a policy impact – although we continue to worry that it is increasingly challenging to change policy.
  • Compared to previous years, the 2012 Scientific Sessions had little focus on the role of pharmacotherapy in the management of obesity. Similar to last year, there were no oral sessions on obesity drugs. There were, however, three posters on Vivus’ Qnexa (phentermine/ topiramate), two of which provided new data from SEQUEL on the drug’s effects in people with prediabetes and type 2 diabetes (1985-P and 2022-P). Notably, Qnexa provided a 7% placebo-adjusted weight loss (lower than the 8.7% weight loss found in the entire study cohort), reduced the need for anti-diabetic medications, and reduced the progression from prediabetes to diabetes. There have been large regulatory strides in the obesity arena within the past year, with both Qnexa and Arena’s Lorqess (lorcaserin) receiving overwhelmingly positive (20-2 and 18-4-1 [abstained] respectively) votes during their advisory committee hearings. A turning point for obesity drugs will likely be the FDA’s upcoming decision on Qnexa (expected on July 17, 2012), which will provide insight into the agency’s evaluation of benefit and risk and what type of data will likely be requested during the development of future obesity medications. We look forward to hearing about the designs of the cardiovascular outcome trials that the FDA will likely require should it approve Qnexa and Lorqess.
  • There were several notable talks regarding the prediction, management, and effects of weight loss. Ms. Nonas reviewed the five critical questions outlined in the National Heart, Lung, and Blood Institute’s 2012 obesity guidelines. In describing how weight loss effects cardiovascular (CV) risk, Dr. Robert Eckel (University of Colorado, Aurora, CO) noted that losing less than the previously-established threshold of 10% body mass can still result in positive, permanent changes in lipoprotein levels. This speaks positively for drug such as lorcaserin that barely meet the FDA’s efficacy requirements. Although we think the average impact of the drugs are important, since there are so many people that could benefit from therapy, we also keep in mind that there are always “super-responders” to every therapy and this is likely a high absolute number of people with regard to obesity. In terms of bariatric surgery, Dr. Mary-Elizabeth Patti (Joslin Diabetes Center, Boston, MA) discussed ways to predict and manage post-operative hypoglycemia while the highly-regarded (and longtime bariatric surgery expert) Dr. David Cummings (University of Washington School of Medicine, Seattle, WA) argued that gastric bypass surgery improves type 2 diabetes through mechanisms independent of weight loss. Furthermore, there is continued interest in the role of genetic testing in managing obesity, with Dr. Richard Grant (Kaiser Permante Northern California, Oakland, CA) asking whether genetic testing can motivate people to lose weight and Dr. Paul Franks (Lund University, Malö, Sweden) considering how a person’s genetic profile can predict his/her response to treatment. While the use of genetic information to individualize therapy is relatively recent, it may be a useful tool to predict whether a patient will not respond to a treatment, allowing him/her to bypass the associated risks and move more quickly to alternative medications.
  • Mobile health and telemedicine are subjects we typically hear more about in exhibition halls than conference rooms, and this ADA was no exception. While we had hoped to see more research that was discussed at the podium and at plenary sessions, this was not to be that ADA. Overall, the biggest talking point was Sanofi’s iBGStar, the iPhone and iPod touch compatible blood glucose monitor that was launched in early May (see our ADA 2012 Devices category document for our coverage of the iBGStar product theater and our Exhibit Hall report for more on Sanofi’s booth). We see this meter as an exciting departure because it makes such strides on the software front in particular and gives more data to patients much more easily, such as the ability, on an iPhone screen, to see overall and before and after meal averages and standard deviations in one button push! While clearly not every patient will have access to such a sophisticated meter, Sanofi was very upfront about not trying to be all things to all patients – certainly the lines at its booth suggested strong interest in the changes it is making possible.
  • Disappointingly, in symposia and oral sessions, clinical trials and pilot projects evaluating the use of mobile health and telemedicine for diabetes showed inconclusive results. We were interested to see a unique Skype-based behavioral therapy intervention for people with diabetes (345-OR), though it fared no better than face-to-face contact on glycemic, self-management, and adherence outcomes. More encouraging was a study examining a telephone-based DPP-style weight loss intervention (341-OR) – at one year, participants lost nearly 5% of their body weight and between 5.1 and 6.0 cm from their waist circumference (no control group was used). Another interesting trial compared home telemonitoring by nurses to conventional nurse case management (312-OR). The study found that telemonitoring was no better than standard care, and in fact, required more nursing time. WellDoc also had an announcement at ADA that suggested when HCPs were in more frequent touch with patients though texting, they prescribed more medication changes (in our view, a proxy for optimizing therapy) compared to those who used regular care alone. Although not statistically significant, we believe this was probably clinically relevant, especially given that over time, it probably could be a doctor directing more allied healthcare professionals. These mobile health results at ADA broadly signal two overall challenges that we see in mobile health and telemedicine: (1) the need for well-designed studies that demonstrate a clinical benefit to which payers will respond; and (2) the critical need for interventions to reduce costs and/or HCP time. We have been thinking and hearing for some time that healthcare delivery must change, due to the very high costs that are simply unsustainable, to say nothing of unsettling patient outcomes; we hope to see more change on this front come faster.
  • There were a few interesting data nuggets and updates on type 1 therapies presented at this year’s ADA, but not too much movement overall. Most notably, Dr. Tihamer Orban (Joslin Diabetes Center, Boston, MA) presented three-year results from a phase 2 study with abatacept in subjects newly diagnosed with type 1 diabetes (150-LB). Following treatment for two years with either abatacept or placebo and observation for an additional year, subjects treated with abatacept had significantly higher mean AUC C-peptide levels than those treated with placebo. We’ll look forward to learning more about abatacept’s (BMS’ Orencia, currently used for rheumatoid arthiris) potential in phase 3 testing, particularly as there was no difference in insulin dose between the study’s two groups at 36 months, despite C-peptide differences. Results from the DELAY Trial meanwhile excitingly suggested that the effect of teplizumab on C-peptide loss in type 1 diabetes is not limited to the new-onset period (85-OR). We wonder about the impact on Macrogenics from these results. Studies with IL-1 targeted therapies were not as successful. In phase 2 studies, the IL-1 receptor antagonist anakinra did not preserve C-peptide secretion in adults with recent onset type 1 diabetes, while the anti-IL-1 monoclonal antibody canakinumab did not have an effect in newly diagnosed subjects ages 6-45. As at last year’s ADA, presenters voiced interest in exploration of combination therapies for treatment of type 1 diabetes – we know there are quite a few hurdles (ranging from preclinical to regulatory) to overcome before such testing commences, but we’ll be eagerly waiting for word on what surely seems to be the next step for type 1 immune therapies. On the islet transplantation front, we found presentations on the CIT-07 islet transplantation protocol (156-OR), benefits of using liraglutide in transplantation (158-OR) and 11-year islet transplantation trends quite interesting – overall, this year’s ADA emphasized that islet transplantation field is certainly not speeding along, we are getting slowly getting smarter on what therapies and procedures might be helpful for improving engraftment, insulin independence, and outcomes. 
  • By far, the results from the ORIGIN trial were the most highly anticipated data at this year’s meeting. While the results were largely viewed as neutral, unsurprising, and unlikely to change clinical care, they did teach us a great deal about insulin. As a reminder, ORIGIN was designed to investigate the effects of insulin glargine therapy vs standard care and omega-3 fatty acids vs placebo on cardiovascular (CV) outcomes in people at high risk for CV events with either impaired fasting glucose, impaired glucose tolerance, or early type 2 diabetes. The trial demonstrated that insulin glargine confers no benefit or increased risk with regards to macrovascular and microvascular outcomes vs standard care over a median 6.2 years. Apart from the relatively short follow up period, these neutral findings were also unsurprising given that A1c levels were similar between groups and maintained at ≤6.5% throughout the trial. We think follow up, given such good control, would likely have to be far longer. We hope to learn more about differences in macrovascular and microvascular risk in the follow up study, ORIGINALE, although we believe it unlikely given the short planned follow up period (at least two years). Insulin glargine did, however, reduce the risk of progressing to diabetes by 28% vs standard care. Despite the delay, we feel the effect needs to be linked to improvements in long-term complications to initiate change in clinical practice. ORIGINALE may also help assess the durability of insulin therapy’s effect on diabetes prevention after discontinuing treatment, though again, the short follow up period limits this possibility. As expected, insulin glargine significantly increased weight and incidence of hypoglycemia. We believe these effects (in addition to the complexity of insulin treatment) will limit the clinical use of insulin in early type 2 diabetes, particularly given the comparable effectiveness of standard care and insulin glargine in controlling the worsening of glycemia in ORIGIN. In our view, this later result highlights the importance of diagnosing and treating individuals early in the course of type 2 diabetes regardless of what therapy is used, as long as it is shown to be reasonably safe.
  • The risk for cancer with insulin glargine treatment was also a topic of significant focus, and the data presented from several studies at this year’s meeting moved us closer to understanding whether a true association exists. Of greatest note, the ORIGIN trial demonstrated no increased risk for cancer with insulin glargine over six to seven years of treatment. While these data are certainly encouraging given the randomized-controlled nature of the study and the relatively long follow-up, some experts have pointed out that a definitive answer regarding insulin glargine’s cancer association would come over a longer period of time (i.e., 10 plus years of exposure). We’ll be interested to see data on cancer risk by level of exposure to insulin in ORIGIN as well as results from the ORIGINALE extension trial in the coming years. In the symposium “Cancer Link with Insulin – Data from the U.S. and Northern Europe”, three database analyses were also presented that found no increased risk for cancer associated with insulin glargine, although average exposure in these studies was still relatively short (1.2 to 3.1 years) – presumably such databases will be able to show us more in the years to come. Reflecting on these studies as well as ORIGIN, Dr. James Meigs (Harvard Medical School, Boston, MA) commented that the results effectively “put the stake in the zombie that is insulin glargine and cancer risk.” While we wouldn’t go quite that far yet, the preponderance of the accumulated evidence to date does appear to suggest no increased risk.
  • A number of talks at this year’s ADA got us thinking harder about prediabetes and a prediabetes approval pathway. Results from ORIGIN, which showed a 28% reduction in risk of progression to diabetes with glargine vs. standard treatment, were particularly interesting. While we agree with the diabeterati that it would not likely be feasible to treat the large numbers of patients diagnosed with prediabetes each year with a therapy as complex as glargine, ORIGIN did get us pondering whether we should be using drug therapy more regularly in prediabetes, for those at highest risk, and how to best identify those who are most likely to progress to diabetes. It also reminded us how much we hope a prediabetes pathway is put in place in the near future – we think such a pathway would make companies significantly more likely to explore their compound in prediabetes and would therefore allow for better, more standardized comparisons of efficacy, cost effectiveness, and long-term benefits of various compounds (including those that have already been studied in prediabetes, like metformin, pioglitazone, and acarbose). Positive data on the use of phentermine/topiramate extended release in prediabetes (2022-P), long-term DPP data, Dr. Ralph de Fronzo’s (University of Texas Health Science Center, San Antonio, TX)’s talk on pharmacotherapy in prediabetes, and his suggestion during a BMS/AZ Corporate Symposium that companies should start exploring SGLT-2 inhibitor use for IFG all strengthened our feeling that a prediabetes pathway would be extremely valuable. Encouragingly, as we understand it, pursuit of a prediabetes indication for metformin is currently underway by three professional associations and a company (we have heard it is BMS but this has not been confirmed). Given how inexpensive metformin is compared to long-term diabetes treatment, we think this effort could have a huge impact, both clinically and politically. The question will be how durable metformin is in prediabetes and if we can we properly identify who really needs it. Regardless, we applaud this effort and hope to see more movement and discussion on the prediabetes front in the coming years. 
  • Discussion of pediatric diabetes at this year’s ADA spanned the treatment, epidemiology, and healthcare organization fronts alike. During a packed Saturday afternoon symposium, we got more details on the TODAY trial  (see our report on the trial’s publication in NEJM at Among the numerous insulin sensitivity, sociodemographic, beta cell function, and comorbidity data presented, we found quite interesting that metformin + rosiglitazone therapy significantly improved insulin sensitivity and stabilized beta cell function vs. metformin alone and metformin + lifestyle intervention and that only baseline A1c and insulin secretion were significantly associated with treatment failure. Broadly, the session emphasized for us the benefits of combination therapy (what exactly the combination should be is unclear, since rosiglitazone is no longer a viable treatment option) and individualization of treatment (a growing theme in type 2 diabetes, as we discuss in our report on the new ADA/EASD guidelines at  On the type 1 diabetes front, data from the T1D exchange gave us interesting insight into pediatric achievement of A1c targets and use of SMBG and CGM. We were also moved by a talk by Dr. Lynne Levitsky (Massachusetts General Hospital, Boston, MA) that described the economic challenges of providing pediatric diabetes care and a presentation by star endocrinologist Dr. Anne Peters  (University of Southern California, Los Angeles, CA) on the challenges of transitioning “emerging adults” with diabetes to adult healthcare providers. These latter two presentations reminded us that significant work remains in the pediatric diabetes space in terms of making the diabetes care provided to children and young adults organizationally strong and sustainable, in addition to clinically optimal.
  • We were surprised not to hear more discussion of biosimilars at this year’s ADA, despite growing chatter about them in the regulatory and commercial arenas. We assume this absence of talks relates to continued uncertainty around the requirements for submitting biosimilars for regulatory approval, especially in the US, and more limited interest in the topic in the academic arena – but, we were surprised not to hear even any debates! Rather, discussion was largely focused on insulin analogs (including Eli Lilly’s PEGylated lispro (LY2605541), Halozyme’s PH20-supplemented rapid-acting insulin products), and Novo Nordisks’s degludec. Given how big of a deal biosimilars are almost certainly going to become as soon as glargine’s patents expire in 2014-2015 and how positively both patients and educators feel about these products (at least early views, as we wrote about in our poster 1225-P on the topic), we will hope for a presentation or two on the topic in 2013 and beyond, as data begins to emerge.
Table of Contents 



Oral Sessions: Continuous Glucose Monitoring

Impact of Study Design and Analytic Techniques on the Reported Accuracy of Continuous Glucose Monitoring (CGM) Systems (1-OR)

David Price, MD (Dexcom, San Diego, CA)

Dexcom’s Dr. David Price gave an excellent, Wizard-of-Oz-inspired review of how CGM data is reported, emphasizing how “turning handles and twisting levers” can skew the data. The highlight was topline data from Dexcom’s pivotal G4 study (n=72; 9,093 matched pairs): a mean absolute relative difference of 13%, 80% of points in the CEG A-Zone, and 94% of sensors lasting up to seven days. Turning to study analysis, Dr. Price advocated for taking CARE when interpreting CGM data: Calibration (frequency, method, instrument), Analytic techniques (A+B Zones vs. A Zones, median vs. mean, lag adjustment), Range (glucose and rates of change), and Excluded data (outlier values, outlier sensors, Day #1). He especially emphasized that CGM studies should present data based on a product’s intended use. We appreciated Dr. Price’s examples of how changing the data can drastically affect accuracy, especially when multiple questionable approaches are stacked (e.g., using YSI for calibration AND calibrating four times per day). Overall, this presentation was as eye opening as we expected and it was quite clear that Dexcom has a high level of confidence in the performance of their new sensor. AS a reminder, the G4 was submitted for FDA approval in 1Q12; see our most recent Dexcom earnings report at

  • The pivotal study of the G4 (n=72) demonstrated a mean absolute relative difference (MARD) of 13%, with 94% of sensors lasting seven days and 80% of points in the Clarke Error Grid A-Zone. The overall percentage of sensors within 20% of reference (>80 mg/dl) and 20 mg/dl of reference (<80 mg/dl) was 82%, with 83% within 20 mg/dl at 40-80 mg/dl. The precision between two sensors as measured by coefficient of variation was 7%. On time percentage within days (288 possible) was 97%. Dr. Price noted the improved accuracy of the G4 compared to the Seven Plus – while 73% of individual Seven Plus sensors had a mean ARD within 20%, this increased to 93% in the G4 pivotal study. The pivotal study had 9,093 matched pairs and 15% of YSI values were less than 80 mg/d.

G4 Pivotal Study Results


Within 20%/20 mg/dl of reference

Clarke Error Grid A-Zone


Two daily real-time calibrations with SMBG by subjects




YSI Calibration




Four daily SMBG calibrations




Retrospective calibration (two times per day)




Retrospective YSI calibration (four times per day)





  • “An MARD of 13% does not equal a MRD of 13%.” Dr. Price characterized these common CGM accuracy acronyms as “confusing” and gave an example to illustrate how mean relative difference (MRD) can be biased. For a YSI reading of 200 mg/dl and corresponding CGM readings of 150 mg/dl and 250 mg/dl, the mean relative difference (MRD) is 0%. The MARD, however, is 25%. This illustrates the major bias of MRD, where positive and negative biases average each other out.
  • Clinical studies must be designed to reflect intended use. Dr. Price noted that studies should enroll an adequate number of intended users and patients in studies must act like patients do in real life (self-deploy sensors, self-calibrate at the labeled frequency, calibrate with glucose moving up or down, use the sensor in values across the glucose range). Studies should also include in-clinic days throughout a sensor session (i.e., at the beginning, middle, and end of a sensor session). YSI values should also be matched to corresponding CGM values.
  • Calibration: Dr. Price showed how increasing the number of calibrations, the method of calibrations, and post processing CGM data can dramatically improve reported CGM accuracy. The first row in the table below illustrates what Dexcom observed in the G4 pivotal study, while subsequent rows illustrate the G4 pivotal study data with certain post-processing tactics.
  • Analytic: Certain analytic techniques can be misleading, such as reporting Clarke Grid A+B-Zone data vs. A-Zone data alone. Dr. Price showed how the GlucoWatch 2B and Dexcom G4 (on day seven) have similar A+B Zone data (95% and 97% respectively), while reporting the A-Zone data alone really shows the accuracy difference between the sensors: 51% of points for the GlucoWatch data vs. 85% of points for the Dexcom G4 on day seven. This difference was also highly noticeable on the Clarke Grid Plot, where the Dexcom G4 had a tight clustering around the 45-degree line, while the GlucoWatch 2B had a wider and more scattered dispersion.
  • Range: Excluding low glucose data lowers Clarke Error Grid D-Zone points and improves a sensor’s MARD. The Dexcom G4 pivotal study included points YSI values between 40 and 400 mg/dl, resulting in 9,093 matched pairs, a MARD of 13%, 80% of points in the A-Zone, and 2% of points in the D-Zone. Limiting the YSI points between 81 and 400 mg/dl reduces the number of matched pairs to 7,742, improves the MARD and A-Zone points by a single percentage point (to 12% and 81% respectively), and decreases the number of D-Zone points to just 0.5%. The reason is because Clarke Error Grid and MARD analyses amplify errors at low glucose values. For instance, at a YSI of 60 mg/dl, a CGM reading of 60 mg/dl will fall into the A-Zone, while a reading of 75 mg/dl will fall into the D-Zone. Because the denominator in the MARD calculation is the reference glucose (e.g., 60 mg/dl in the previous example), a small error in the low glucose range (e.g., 15 mg/dl), can inflate the MARD (25% in this case).
  • Excluding: Removing outlier sensors can improve a sensor’s reported MARD. Dr. Price explained that a MARD can be reported as the average error across all sensors in a study, or it could be reported as the histogram of individual sensor MARDs. Removing outlier sensor data from Seven Plus data reduced the mean ARD from 15.9% to 13% and the median ARD from 14.1% to 12.7%. He further explained that reporting medians diminishes the impact of outliers. Dr. Price argued for greater transparency in the data, especially because reporting all values better reflects the experience users will actually have.

Questions and Answers

Q: As clinicians, do you have a comparison between the G4 and Medtronic’s Enlite?

A: Medtronic’s new product performance has improved a great deal. I would say that our G4 is really revving on days four to seven. I’ve not seen data on the Enlite besides what has been presented. Based on the Enlite poster, I’m not quite sure how calibration was done, whether there was lag adjustment, etc. You’ll have to ask them at their presentation.

Q: A question on median and mean ARD. If you look at the glucose data measured by CGM, it’s not a normal distribution. You should be reporting median. You often report mean because that’s what we’re used to, but if you look at the data, it should be a median.

A: The problem with looking at the median is that it negates the impact of outlier sensors. I don’t disagree with you. But those outlier sensors are important.

Comment: So maybe we should add a measure of distribution.


A Comparative Analysis of Three Continuous Glucose Monitors: Not All Are Created Equal (4-OR)

Steven J. Russell, MD (Massachusetts General Hospital, Boston, MA)

Dr. Russell presented head-to-head comparisons of multiple CGM systems (including some not yet approved in the US), with data drawn from 48-hour bi-hormonal closed-loop experiments (which offered a wide range of hyperglycemic values and rates of glucose change, but few points in the hypoglycemic range). First he showed data in which the FreeStyle Navigator performed with higher accuracy and lower variability compared to the Dexcom Seven Plus and Medtronic REAL-Time Guardian – a finding his team has discussed previously. He went on to discuss a new three-way comparison of the FreeStyle Navigator with Dexcom’s Gen 4 Sensor and Medtronic’s Enlite based on seven 48-hour experiments. The Enlite (MARD 17.2±9.5%) showed an improvement over the REAL-Time Guardian, with much of the remaining inaccuracy and variability due to some outlier values (Medtronic engineers are working on an improved algorithm, which Dr. Russell said that he and his colleagues will be testing). Meanwhile the Gen 4 (MARD 11.3±4.3%) posted better results even than the Navigator (MARD 11.9±4.3%) and – unlike the Navigator or Enlite – got better on the second day of wear, implying that subsequent days of wear might make the Gen 4 look even better (a prospect Dr. Russell’s team will “soon” investigate in five-day closed-loop studies).   

  • Dr. Russell began by discussing a three-way comparison of Abbott’s FreeStyle Navigator, Dexcom’s Seven Plus, and Medtronic’s REAL-Time Guardian (12 48-hour experiments; 2,360 reference blood glucose measurements [GlucoScout]). The FreeStyle Navigator had the lowest mean absolute relative difference (MARD) and the smallest standard deviation in MARD (11.8%±3.8%), as well as the highest percentage of matched pairs in the Clarke Error Grid A Zone (81%). The results were less favorable for the Seven Plus (MARD 16.5±6.7%; CEG A 76%) and REAL-Time Guardian (MARD 20.3±6.8%; CEG A 64%). Dr. Russell also presented data on sensor reliability, which was notably higher for the Navigator and Guardian (99.8% and 98.5% of measurements captured, respectively) than for the Seven Plus (75.9%).
  • Dr. Russell further illuminated sensor performance in various less-conventional ways, including a breakdown of MARD by glucose range. (The hypoglycemic range was excluded due to the paucity of data; the closed-loop system maintained control so effectively that only 0.7% of the total measurements were below 70 mg/dl.) In each of 70-120 mg/dl, 120-180 mg/dl, and 180-250 mg/dl, the Navigator performed better than either the Seven Plus or Guardian, whereas at >250 mg/dl, the Seven Plus had the lowest MARD (the Navigator consistently under-reads in the high glucose range, Dr. Russell said). The Seven Plus had better MARD than the Guardian in all glucose ranges. Dr. Russell also presented Bland-Altman plots showing that standard deviation for the Navigator was tighter than for the other CGMs across the glycemic spectrum studied.
  • The Boston artificial pancreas researchers have conducted 17 experiments comparing just the FreeStyle Navigator (MARD 13.2±3.6%) and Medtronic’s Enlite sensor (MARD 16.0±7.4%). Dr. Russell noted that the Enlite’s standard deviation was raised by several sensors with atypically high MARD – a problem that Medtronic engineers think they can address with an improved algorithm (which Dr. Russell and his colleagues will help test once it has been developed).
  • In Dr. Russell’s group’s studies, the Dexcom Gen Four had a MARD below 10.0% on the second day of sensor wear – an improvement from day one, whereas the Navigator and Enlite performed worse on the second day. (Dr. Russell noted that his group found better Gen Four MARD than had been presented by Dexcom’s Dr. David Price earlier in the session – he attributed this in part to calibration with the GlucoScout rather than with the less-accurate BGM devices used in the Dexcom clinical study). Dr. Russell speculated that the Gen Four’s performance might be still more superior on subsequent days of wear. He and his colleagues are planning to conduct five-day closed loop experiments “soon” – we are curious whether the system will be ‘driven’ by the Navigator (the group’s historical sensor of choice) or, in light of these new data, the Gen Four. 

Questions and Answers

Q: Were you able to compare during/after exercise and overnight?

A: We haven’t looked at those subsets of the data.

Q: But you have not noticed any gross differences?

A: Overnight we have tighter glucose control and lower rates of change, so lag becomes less of an issue; I would expect control to be better then. During exercise we see more rapid changes, so I would anticipate worse accuracy then.

Q: Were there any artifacts in sleep from patients rolling on the sensors?

A: In our closed-loop system, the sensors are all worn on the abdomen, so people can’t sleep on their stomach.

Dr. Roman Hovorka (University of Cambridge, Cambridge, UK): I look forward to seeing the Gen 4 in our studies.


Accuracy and Large Inaccuracy of Two Continuous Glucose Monitoring (CGM) Systems (3-OR)

Lalantha Leelarathna, MBBS, MRCP (University of Cambridge, UK)

Dr. Leelarathna presented Abbott FreeStyle Navigator and Dexcom Seven Plus CGM data from five closed-loop studies in a total of 52 patients at Cambridge. The accuracy and inaccuracy of both CGMs were compared on a wide variety of metrics, including MARD (9.9% for the FreeStyle Navigator vs. 12.6% for the Dexcom Seven Plus), Clarke error Grid (78% in the A-Zone for the FreeStyle Navigator vs. 71% for the Dexcom Seven Plus), frequency of large sensor errors, and error duration. Large sensor over-reading occurred two to three times more frequently with the Dexcom Seven Plus than with the FreeStyle Navigator. Additionally, at higher error levels (50% and 60%), errors of one hour or longer were absent with the FreeStyle Navigator. This led Dr. Leelarathna to conclude that the FreeStyle Navigator “may be more valuable for closed loop operation than Dexcom’s Seven Plus.” Of course, Dr. Steven Russell’s subsequent presentation demonstrated that Dexcom’s new G4 sensor seems to have closed the accuracy gap with the FreeStyle Navigator. Nevertheless, we appreciated the researchers and session’s focus on more than just top-line CGM accuracy data – reducing large sensor errors will not only improve AP performance, but we suspect it might also lead less patients to become frustrated and quit using CGM. We were particularly intrigued by the multiple questions from FDA during the Q&A. It seems the agency is wrestling with this idea of statistically defining and analyzing large sensor error data.

Questions and Answers

Q: Have you modeled the number of severe lows that would have occurred as a result of over readings?

A: We do have some simulation data. After 40% or more errors, that would cause hypoglycemia.

Q: Did you adjust for the fact that Navigator doesn’t show the very first few hours of data?

A: We used the one-hour Navigator in our studies. We also inserted both sensors one day before. They were identically 24 hours into their sensor life.

Q: How did you calculate error duration? What happens if a sensor went from 40% to 50% back to 40% error? Did you take individual time points and call that a minute or was it continuous duration? Did you use arterialized or venous blood?

A: We inter-collated into one-minute data. We took YSI every hour, but inter-collated into one-minute data.

Q: What if it moved back and forth?

A: That would be classified as a second error. If an error came out of an error zone, the error ended. The second time we went back it was counted as a second error. And we used venous plasma glucose.

Q: How did you define an event? Say you went from 30% to 40% to 30% to 40% error. Would three separate events be counted?

A: That’s correct.

Q: And the event was a CGM point compared to blood glucose?

A: Yes, reference glucose vs. CGM glucose.


Transdermal Continuous Glucose Monitoring Following Skin Permeation

Wayne Menzie (Echo Therapeutics, Philadelphia, PA)

Wayne Menzie discussed the performance of Echo’s transcutaneous continuous glucose monitoring device, the Symphony tCGM, in a 24-hour pilot study of people with diabetes (n=20; 12 with type 1 diabetes, 8 male, mean age 51.5); sensors were calibrated four times a day. As announced in December 2011, mean absolute relative difference was 12.6%, and 94.4% of matched data pairs were within the A zone of the Continuous Glucose Error Grid Analysis. Mr. Menzie noted that the sensors used in the study had a bimodal distribution of accuracy: the 15 best-performing sensors had a MARD of 11.3%, whereas the five worst-performing sensors had MARD of 20.1% (Echo’s team has identified the source of inaccuracy in these sensors as a hardware problem and believe they have addressed the issue). Next steps include pilot studies in the ICU (ongoing), tests with additional skin types and body locations (the study in diabetes included arms and abdomens), more data in the hypoglycemic range (Mr. Menzie acknowledged that this was still a “blind spot” in Echo’s dataset), studies that last beyond 24 hours, and further studies of potential interferents (though Mr. Menzie said that neither acetaminophen nor ascorbic acid seem to significantly interfere with the Symphony, based on preliminary research). 

  • Although Echo’s near-term target market is critical care patients, healthy people with diabetes offered the company a chance to test the system across a wide range of glucose levels (2,680 measurements with mean glucose 157±62 mg/dl; range 42-333 mg/dl).

Questions and Answers

Q: Do you have data on repeated use?

A: Some subjects have been volunteers in multiple studies, but these are usually separated by months. The site itself takes several days to regenerate. Skin permeation is a precise technique, so we can’t go to same site until three-to-four days have passed.

Q: Have you seen any adverse events?

A: No, the worst thing we’ve seen is minor skin irritation; the biggest cause of irritation is actually the adhesive.

Q: On the last slide you presented data that you said was without calibration… how do you account for background current and interference?

A: Calibration was done every four hours – since blood glucose is taken more frequently in hospitals, we would look to those measurements to improve performance. Our calibration algorithm is still under review, however. No specific measures taken other than design of device, there was no signal processing or correction for interference. In limited studies we haven’t seen that problem but we will study the interference question further; we know this is issue especially for the hospital.

Q: I recommend you study ascorbic acid and acetaminophen to see what happens with those.

A: We’ve tried both of those preliminarily and haven’t seen problems.


A1c and Mean Glucose (MG) in Insulin Treated Diabetes Using the Dexcom SevenPlus Continuous Glucose Monitor (CGM): Correlation and Intra-Patient Consistency Over Time (2-OR)

Nicholas Argento, MD (Maryland Endocrine and Diabetes Center, Columbia, MD)

Dr. Argento discussed “high glycolators” and “low glycolators” – people whose CGM-measured mean glucose (CMG) differs widely from the estimated average glucose (eAG) based on A1c. To study the relationship between CMG and A1c, his team analyzed A1c and CGM (Dexcom Seven Plus) data from several dozen patients in Dr. Argento’s own practice. Similar to the ADAG and JDRF CGM studies, on average a CMG of 154 mg/dl translated to an A1c of 7.0% – a CMG/A1c ratio of 21.7. But (also as seen in the JDRF CGM trial), the CMG/A1c ratio varied widely across the population and was stable within individuals. This phenomenon means that “high glycolators” (patients in the highest decile of CMG/A1c ratio – i.e., below 19.2 mg/dl·%) actually have lower mean glucose than their A1c would indicate, and so are in danger of frequent hypoglycemia. By contrast, “low glycolators” (patients in the lowest decile of CMG/A1c ratio – i.e., above 24.9 mg/dl·%) actually have higher mean glucose than their A1c would indicate, and so are at much greater risk of complications than they might realize. Dr. Argento proposed that in both high and low glycolators, CGM-measured mean glucose is a better measure than A1c for setting glycemic targets.

Questions and Answers

Q: Could you comment on mean glucose vs. A1c as a target? We know that at the same glucose level, A1cs are higher in older people and in blacks.

A: Black patients in our study appeared to have residual negative – A1c is higher than would be expected from mean glucose. I’m not saying we shouldn’t use a1c; as best we can tell it is a surrogate marker of mean glucose, but it seems not to be a great marker in all patients. Ten genetic loci with high variability have been found to influence A1c; only three of those seem to have to do with glycemic control. One would think that other factors that influence A1c, such as iron metabolism and red cell life, don’t have effect on diabetic complications. Should we look only at mean glucose? No, because variability is important too – of course, CGM is also probably the best way to look at this too. If you are outlier in CCMG/A1c, you may want to modify the way your glycemic targets are set.

Q: I am trying to understand the significance of glycation. Maybe if you are a low glycator, you would glycate renal proteins less also.

A: That is a great question. If you make hemoglobin at a lower rate, perhaps you are protected elsewhere. But there so many other factors – like iron metabolism and red cell life – seem that they would be unlikely to protect kidneys or endothelial tissue. There has been talk of this, but I am not aware of evidence. Perhaps some patients have partial protection in some tissues; perhaps others have none. I think the way to go is mean glucose, since we know what that means.


Long-Term Effects of Sensor-Augmented Pump Therapy in Type 1 Diabetes: A 3-Year Follow-Up Study (8-OR)

Signe Schmidt, MD (Copenhagen University Hospital, Hvidovre, Denmark)

Dr. Signe Schmidt presented three-year follow-up data on 24 Danish patients from the Eurhythmics trial of sensor-augmented pumping (SAP). The patients that were still using SAP at three years (n=16) maintained the vast majority of the glycemic improvement that they had achieved during the 26-week study (three-year A1c below 7.5%, from a baseline of roughly 8.5%). These patients also reported improvements in the Diabetes Treatment Satisfaction Questionnaire (p<0.01), the Problem Areas in Diabetes survey (p<0.02), and the Hypoglycemia Fear Survey (not statistically significant). Notably, however, the patients that stopped using CGM and switched to either pumping alone (n=4) or MDI (n=2) achieved extremely similar three-year glycemic control. Dr. Schmidt noted that the small sample size makes true analysis difficult, but she speculated that perhaps these six patients had gained important diabetes insights from their time wearing sensors and that they continued to apply these insights even after ceasing CGM use (a phenomenon that Dr. Robert Vigersky, who commented during Q&A, reported in a study of intermittent CGM use in patients with type 2 diabetes not using mealtime insulin [Vigersky et al., Diabetes Care 2011]).

Questions and Answers

Q: There was a striking lack of improvement in the hypoglycemic fear index, even though we often think of SAP as a way to address hypoglycemia. Why do you think that was the one measure that didn’t improve?

A: Actually it did improve, the improvement was just non-significant. This depends on sample size.

Q: In the SWITCH study, people using SAP who stopped wearing the sensor saw a decline in their A1c.

A: SWITCH was designed to address the question of SAP vs. pumping; it suggests that there is a difference between them.

Q: I have two questions. In the initial randomization between MDI and SAP, did the MDI patients get as much contact and education as the SAP group?

A: I was not part of the Eurhythmics team, so I am not sure how randomization worked. I can say that when MDI patients started SAP in our clinic, they got the same education as the SAP group in the Eurhythmics trial.

(Eurhythmics Investigator): We used much more time to train the SAP patients compared to MDI – it was obvious that we needed to train them.

Q: When you switch from MDI to SAP you make two changes. Can you speculate which part of SAP was more important?

A: To that question I can only refer to meta-analysis by John Pickup, which suggested that both components have great impact.


Glycemic Variability Is Higher in Type 1 Diabetic Patients with Microvascular Complications Irrespective of Glycemic Control (5-OR)

Jan Soupal, MD (Charles University, Prague, Czech Republic)

Dr. Soupal detailed an interesting CGM study comparing glycemic variability in type 1 patients with and without microvascular (MVC) complications. Thirty-two patients (mean age: 43 years, mean A1c: 9.5%, mean duration of diabetes: 19 years, n=16 with MVC) wore blinded CGM for two weeks and performed at least four fingersticks per day. There were no significant differences in baseline criteria between patients with and without MVCs. Glycemic variability (SD, MAGE) calculated from CGM was significantly higher in type 1 diabetes patients with retinopathy (p=0.02), microalbuminuria (p=0.035), and impaired vibration perception threshold (p=0.01). Moreover, patients with any MVC had significantly higher glycemic variability than patients without complications, although they didn’t differ in glycemic control (p=0.019). Of course, this data is merely correlational and does not demonstrate that higher glycemic variability caused the complications. Nevertheless, we do think it is interesting and we wish that a real trial could be done long-term that would show the impact of glycemic variability on long-term outcomes. In our view, most notable was that when glycemic variability was calculated using SMBG data from the study, these relationships did not hold. At past conferences, we’ve often seen SMBG data used to calculate glycemic variability statistics, though we’ve heard a few speakers characterize this as inappropriate; Dr. Soupal’s analysis may indeed suggest it is questionable, since patients may be paying more attention to their blood glucose when they know they will be tested. Overall, we’re glad to see increasing focus on these issues and we look forward to prospective glycemic variability studies like Dr. Irl Hirsch’s FLAT-SUGAR, which could turn out to be a landmark study in some years from now.


Questions and Answers

Q: Complications take years to develop. Many become born again diabetics when complications show up. Did you look at historical values?

A: That’s an important limitation of each observational, case control study. We don’t know the past. It was a limitation of this study and we have to think about it. This study should be considered for a larger study. It should be multicenter and obtain a lot of patients. It must be prospective. That’s the only way to get definitive results – does glycemic variability really increase the risk of microvascular complications. We can demonstrate correlation, but not causation.

Q: Did you do subgroup analyses? Was there a significant correlation with high A1c patients?

A: We did multivariate analyses but it didn’t affect the results. The group of patients was very homogenous.

Q: Did you adjust the models for A1c?

A: Yes, we did multivariate analysis.


Performance of a Microdialysis-based Continuous Glucose Monitoring (CGM) System (6-OR)

Eric Zijlstra, PhD (Profil Institute for Metabolic Science, Neuss, Germany)

Dr. Zijlstra presented the results from a 10-48-hour study of an intravenous microdialysis-based CGM in 21 healthy individuals. The system achieved an MARD of 9.4% and 91.4% of points in the Clarke Error Grid Zone A. Calibration occurred once per day and the glucose range was 42-267 mg/dl. An advantage of the system is that there is no blood loss to the patient. The accuracy looks decent, but nowhere approaching the Dexcom/Edwards GlucoClear2 data we saw at ATTD (MARD of 5.2%).

  • Dr. Zijlstra described the intravenous microdialysis-based CGM used in the study, which measures glucose every one to two minutes without blood loss. To use the microdialysis unit, a standard blood catheter is inserted into the vein of the forearm. For each measurement, the system automatically draws blood into the sampling line. Glucose from the blood diffuses over a thin membrane, is perfused with saline solution, and transported to a glucose sensor for monitoring. The blood is then flushed back and returned to the patient. A glucose value is recorded every one to two minutes.
  • The accuracy and reliability of the CGM was assessed in 21 healthy volunteers (mean age: 29 years, mean BMI: 23.7 kg/m2). Experiments were generally ten hours long and four volunteers did 48 hours. The glucose sensor was calibrated once every 24 hours. The system was calibrated before the experiment using a two-point calibration. Reference blood samples were taken manually and analyzed using a laboratory glucose analyzer every 10-60 minutes. The volunteers consumed meals or glucose was administered orally or intravenously to analyze the accuracy of the CGM system over a range of blood glucose concentrations.
  • The system achieved a mean absolute relative deviation of 9.4% (n=1796 matched pairs; glucose range of 42-267 mg/dl). Mean absolute deviation was 10.7 mg/dl. At glucose values <75 mg/dl, 94.6% of points were within 15 mg/dl. For glucose values >75 mg/dl, 90.4% of points were within 20% of reference. A Clarke Error Grid Analysis showed 91.4% of points in Zone A, 8% in Zone B, 0.6% in Zone D, and 0.1% in Zone E.

Reference BG


Mean Absolute Deviation

Mean Absolute Relative Deviation

All (42-267 mg/dl)


10.7 mg/dl


<70 mg/dl


6.7 mg/dl


70-180 mg/dl


9.6 mg/dl


>180 mg/dl


25.9 mg/dl


48 hours (53-184 mg/dl)


8.2 mg/dl



Questions and Answers

Dr. Bruce Buckingham (Stanford University, Stanford, CA): Is there a lag with this system?

A: There’s a little lag time due to the transportation of the sample to the sensor. Probably two minutes lag time.

Dr. Steven Russell (Massachusetts General Hospital, Boston, MA): How did you avoid blood clotting?

A: In this setup, the microdialysis is integrated into the sampling line. We had two-way pumps. The pump would draw blood for 30 seconds and then flush. The microdialysis membrane would be flushed after every measurement.

Q: These were healthy subjects. Did you induce hypoglycemia?

A: Yes, we did induce hypoglycemia.

Q: How much dialysate was done every day? Is this a glucose oxidase sensor?

A: Yes, it’s glucose oxidase. On the amount of dialysate, I’m not sure. It’s relatively little. I think on average 100-125 microliters per minute.


Oral Sessions: Expanding the Domains of Diabetes Education

Impact of a Pocket Insulin Dosing Guide on Utilization of Basal/Bolus Insulin by Internal Medicine Resident Physicians (77-OR)

Michael Jakoby, MD, MBA (Southern Illinois University School of Medicine, Springfield, IL)

Dr. Jakoby discussed Southern Illinois University’s efforts to distribute a pocket insulin-dosing guide among internal residents, to increase the appropriate use of basal/bolus insulin therapy (as opposed to sliding scale insulin therapy, which Dr. Jakoby found was used in 90% of patients in the Carle healthcare system in Urbana, IL – scary!). A pilot program including all internal medicine residents began in November 2010, and an eight-month extension study began in July 2011 (when new internal medicine interns arrived and a second staff presentation on the guides was given). Compared to historical controls (November 2009 to October 2010), distribution of the pocket guides significantly increased basal-bolus insulin prescriptions by 2.5-fold on average. This change appeared durable, though the highest rates of prescription seemed to occur after the staff presentations in November 2010 and July 2011. No increase in hypoglycemia was observed, and glycemic control among patients that had spent several days in the hospital was better following the introduction of the guide. Also following the guide, statistically non-significant improvements were seen in length of stay (4.8 vs. 5.7 days, p=0.08). Next steps include refining the card’s orders (no modifications were made during the study itself, to preserve the integrity of the data), increasing acceptance of the system among physicians and other clinicians at SIU and beyond, and developing a computer-based algorithm based on the card (Dr. Jakoby noted that pocket-based guides are not as useful in this modern age, when physicians input orders via computer and thus rarely reach into their pockets for pens).


Questions and Answers

Q (Physician from the UK): Do I understand that for patients that come in on oral hypoglycemics, you would recommend they go on basal-bolus insulin in the hospital?

A: Unequivocally. We have different philosophies on different sides of the Atlantic. Dr. Umpierrez at Emory and Dr. Baldwin at Rush have shown that patients are better managed on insulin than on orals.

Q: Do you think your junior doctors are safe enough?

A: When we give them tools, they are certainly not as successful as the other authors I’ve cited, but there was steady improvement over time without an increase in hypoglycemia. I would posit that our results show house staff can be trained to manage diabetes with reasonable efficacy.

Q: We did a snapshot day where we looked at all people in hospital; glycemic control for those on insulin was a very big problem. This is across the whole of England, with 12,000 patients. I have concerns about our current use of insulin regimes in the UK; that is all I will say. I hope you are doing better.

A: We have a database of about 1,200 patients and data from my time at the University of Illinois and Champaign-Urbana, and we are teasing this issue out. With regard to patients that remain on sulfonylureas, they have actually higher rates of hypoglycemia than those on basal-bolus insulin.

Q: I appreciate your study of one area where failure can occur, residents. What about the nursing staff? Sometimes the basal insulin will be incorrectly held because glucose is normal. Have you factored that in?

A: We did not. We will go back and look at that. Obviously successful inpatient management depends on many factors. We have a team with an NP, educator, dietitian; we are tracking that and finding better compliance. Another problem we identified is poor coordination between delivery of trays and prandial insulin dosing. Tray deliverers often drop off the tray and then run to the next patient without notifying nurses that the tray has arrived. We will be trying a new system to link the processes better.

Q: What regimes are patients going home on? Did you look at all at readmission at patients that had used this kind of dosing?

A: We are fairly aggressive about sending patients home on basal-bolus insulin. We sent about 85% home on basal-bolus insulin and tracked them with the Carle Clinic system. We found compliance was very high and A1c improved from 8.1% to 7.2% over three months. (Editor’s note – wow!) As mentioned earlier in the session, there is nothing like a severe acute illness to clarify someone’s thinking, and a lot of downtime during the day in the hospital can be used for education.

Q: Is this analysis published anywhere?

A: That is next on the agenda. 

Q: We tried a similar pocket dosing guide but found we had to keep updating the card – for instance, to include specific guidance on renal failure.

A: We had instructions for how to handle patients with renal failure on the order set but not card; we will change this in version 2.0, but for the integrity of the study we kept the card the same throughout the study duration. Implementing the dosage guide more widely will be challenging – we have to convince a variety of groups that it is in their self-interest. So first we are trying to demonstrate success at SIU.


Educator Use of Masked Continuous Glucose Monitoring Device (CGM) in a Clinic Population of Youth with Type 1 Diabetes (T1D) (75-OR)

Kerry M. Milaszewski, BS, RN, CDE (Joslin Diabetes Center, Boston, MA)

Kerry Milaszewski analyzed the A1c-lowering effects of a blinded CGM intervention among youth with type 1 diabetes (n=122; mean age 14 years, A1c 8.5%, 61% pumpers, diabetes duration 7.5 years). During their three-days of blinded CGM use, patients and their families also maintained a log of glucose values, insulin, food, and activity. Based on the log and the sensor data, Joslin clinicians gave patients therapeutic recommendations (mean 3.1 recommendations per patient). Although no significant A1c effect was seen in the population as a whole two-to-three months later, 39 patients improved their A1c by at least 0.5%. Compared to those with lesser benefits, these ‘responders’ were older (15.5 vs. 13.9 years), with longer diabetes duration (8.7 vs. 6.9 years) and higher baseline A1c (8.9% vs. 8.2%). Improvement was especially likely among those that received the recommendation to use advanced bolus techniques and/or to attend to active insulin – these people were fourfold likelier to improve significantly. Notably, advanced bolus techniques were taught to both pumps and MDI users – mode of insulin delivery did not correlate with success in the Joslin intervention. We would have been interested to see follow-up CGM data on other glycemic measures besides A1c (e.g., hypoglycemia, hyperglycemia, glycemic variability) – as suggested during Q&A, the three-day wear intervention may well have improved patients’ time in zone even though it did not influence mean A1c.

Questions and Answers

Q: Did standard deviation get narrower? Did they have less hypo reactions?

Ms. Milaszewski: We did not look at hypoglycemia at all, and I’m not sure of answer in terms of SD.


Oral Sessions: Can We Rein in the Costs of Diabetes with Better Diabetes Care?

Self-Monitoring Blood Glucose Test Strip Utilization in Canada (132-OR)

Jason Yeaw, MD (IMS Consulting Group, Redwood City, North Carolina)

Dr. Yeaw presented his study of blood glucose test strip utilization in Canadian diabetes patients with insulin injections. The study found that the average Canadian with diabetes on insulin injections uses 1,094 test strips per year (~three per day), which cost $860 Canadian dollars. Test strips on average make up 41.6% of total diabetes-related pharmacy costs.

  • The Canadian patients in this study averaged three test strips per day. (We note that this meets the ADA’s recommendation that MDI users test three or more times daily.) The average cost per strip was $0.79 CAD. As described by Dr. Yeaw, the study used IMS Brogan drug plan data for the period of July 1, 2006 to June 30, 2010, and only considered patients who were expected to self-monitor blood glucose. The study included 142,551 patients with type 1 or 2 diabetes who had at least two prescriptions of insulins (basal only, basal-bolus, bolus only, or premix).





Number of Subjects

Number of test strips per patient per year

Mean annual test strip cost per patient (Canadian dollars)

Test strips as a proportion of total diabetes-related pharmacy cost
















Bolus only











Questions and Answers

Q: Can you tell us anything about how the strips are actually used in the patients who are only using basal insulin? Have you had focus groups, conversations with providers, etc., to look at what strips are used for?

A: I agree this is a helpful extension to this study and a good future project.

Q: In Europe, it’s about one Euro also per strip. In Canada you pay almost one dollar. How can we push for better or lower prices?

A: We really don’t have an idea why the price is what it is. The technology is indeed no longer new or complex. The price does seem fairly uniform across the world, though.

Q: Is the share of costs similar in the US? Is there any effort to rein that in? 30-40% seems very high.

A: Yes, I remember that a study in the US showed that the costs of strips there is also in the 30-40% range Of course, the percentage is of diabetes pharmacy costs, which are basically test strips and insulins. I’m not aware of any current endeavors to rein in the costs of blood glucose test strips.



Accuracy and Acceptability of the 6-Day Enlite Continuous Subcutaneous Glucose Sensor (30-LB)

Timothy Bailey, Ronald Brazg, Mark Christiansen, Andrew Ahmann, Robert Henry, Satish Garg, Elaine Watkins, Francine Kaufman

This poster reported results from the pivotal six-day trial of Medtronic’s Enlite CGM sensor, which was conducted at seven US research centers and enrolled 90 adults with type 1 (n=65) or type 2 (n=25) diabetes. For the 61 patients that wore the Enlite for the full six days, accuracy data were reported from the trial’s frequent sample testing (FST) periods – i.e., roughly 12-hour in-clinic visits on days one, three, and six when YSI reference blood glucose values were taken. Two calibration regimes were compared: “actual use calibration” (three-to-four prescribed calibrations per day, mean calibration frequency 2.8±0.9 per day) and “minimal calibration” (calibration prescribed every 12 hours, mean calibration frequency 1.2 ± 0.9 per day). Accuracy results appeared slightly better with the actual use calibration than the minimal calibration (MARD 13.6% vs. 14.7%; consensus error grid A-zone 86% vs. 81%) and when calibration occurred during a glucose rate of change that was slow (<1 mg/dl) rather than rapid (≥2 mg/dl) (MARD 13.6% vs. 16.3%). We thought these accuracy results were favorable overall, though we are uncertain if sensors were calibrated prospectively or retrospectively (we assume the former but the poster did not specify). Mean responses to a seven-point-scale survey indicated that study participants had favorable views on the Enlite’s ease of insertion (5.9), comfort (6), and ease of use (5.8), and patients also reported high likelihood of recommending the sensor to others (5.8).

  • The pivotal trial of the 6-day Enlite continuous glucose monitoring (CGM) sensor enrolled 90 adults with diabetes (65 with type 1 and 25 with type 2 diabetes; mean age 44 years, range 18-71 years) wearing one to two sensors on their abdomen. Sixty-one of the 90 participants wore Enlite sensor(s) on their abdomens for six days, while 29 patients only wore buttock sensors. The poster only considers data from the 61 patients who wore abdominal sensors. Of this group, 29 wore two abdominal sensors simultaneously and 32 wore one abdominal sensor.
  • This poster described results from clinic visits for frequent sample testing (FST), which occurred on days one, three, and six, lasted roughly 12 hours each, and included intentionally induced hypo- and hyperglycemia. Sensors were calibrated either three-to-four times per day (“actual use calibration”) or every 12 hours (“minimal calibration”), and calibration could occur even during rapid glucose changes. (We are not sure whether calibration was conducted retrospectively or prospectively.) A Yellow Spring Instruments (YSI) analyzer was used to collect reference plasma glucose values every 15 minutes and every five minutes if blood glucose <75 mg/dl. A 0-5 minute (left inclusive and right exclusive) window was used for sensor values paired with YSI values.
  • Accuracy during FST was reported in terms of mean absolute relative difference (MARD), defined as the mean absolute difference between paired sensor glucose and blood glucose values (MAD) divided by the mean blood glucose, with the result multiplied by 100% to give a percentage. The poster also included data on bias, defined as the mean difference between paired sensor glucose and blood glucose values. The standard deviations for MARD and bias were calculated per paired point.
  • Performance data during FST were broken down by a variety of categories, as shown below. Accuracy with minimal calibration (mean MARD 14.7%, mean bias -2.4 mg/dl) was only slightly lower than with actual use calibration (mean MARD 13.6%, mean bias -1.2 mg/dl). We see this result as encouraging, since many patients’ real-world use may be more similar to the minimal calibration regime (1.2 calibrations a day, rather than 2.8 for the actual use condition). Regardless of calibration condition, MARD during FST was lower on day three than either day one or day six. As for glucose ranges, MARD was greatest during hypoglycemia and MAD was lowest, as would be expected. The standard deviation of bias was quite high (above 25 mg/dl overall, for each calibration condition), which we interpret to mean that some sensors were consistently biased high and others were consistently biased low. The high variation in bias was in line with Dr. Steven Russell’s comments during his ADA 2012 presentation about a head-to-head study of next-gen CGM sensors (4-OR). As a reminder, Dr. Russell said that several Enlite sensors in his group’s study were outliers with much higher MARD than usual, and he added that Medtronic engineers think this problem can be addressed with an improved algorithm.  
  • By rate of change (ROC) between SMBG calibrations, during FST





Glucose ROC (mg/dl/min)


1 to <2






MARD, % (mean ± SD)

13.6 ± 13.9

12.9 ± 11.8

16.3 ± 14.8

MARD, % (median)





  • By day, actual use calibration (calibration frequency 2.8±0.9/day), during FST. Consensus error grid scores for all actual-use-calibration data pairs were as follows: 86% A, 13% B, 1.1% C, 0% D.


Day 1

Day 3

Day 6




Bias, mg/dl


Bias, mg/dl


Bias, mg/dl


Bias, mg/dl

Mean ± SD

15.9 ± 14.9

-0.3 ± 28.3

11.8 ± 10.9

0.9 ± 22.8

13.2 ± 14.4

-4.3 ± 34.3

13.6 ± 13.6

-1.2 ± 28.9











  • By day, minimal calibration (calibration frequency 1.2±0.9/day), during FST. Consensus error grid scores for all minimal-calibration data pairs were as follows: 81% A, 17% B, 1.3% C, 0.1% D.


Day 1

Day 3

Day 6




Bias, mg/dl


Bias, mg/dl


Bias, mg/dl


Bias, mg/dl

Mean ± SD

15.3 ± 14.0

0.3 ± 27.8

13.4 ± 11.6

-0.2 ± 27.1

15.5 ± 15.4

-7.8 ± 43.1

14.7 ± 13.7

-2.4 ± 33.3











  • By glucose range, actual use calibration, during FST. The percentage of sensor readings during the in-clinic portion broke down as follows: 9.6% (<75 mg/dl), 53.3% (75-180 mg/dl), and 37.1% (>180 mg/dl).

YSI Reference Range



MAD, mg/dl

Bias, mg/dl

≤75 mg/dl

Mean ± SD

17.4 ± 17.9

10.8 ± 10.9

5.6 ± 14.3





>75-180 mg/dl

Mean ± SD

12.6 ± 12.0

15.3 ± 14.9

2.3 ± 21.2





>180 mg/dl

Mean ± SD

12.0 ± 11.0

31.0 ± 31.6

-12.6 ± 42.5






Mean ± SD

13.6 ± 13.6

18.7 ± 22.0

-1.2 ± 28.9






  • By glucose range, minimal calibration, during FST

YSI Reference Range



MAD, mg/dl

Bias, mg/dl

≤75 mg/dl

Mean ± SD

18.4 ± 15.8

11.5 ± 9.6

3.2 ± 14.6





>75-180 mg/dl

Mean ± SD

14.2 ± 12.4

17.3 ± 15.1

2.2 ± 22.8





>180 mg/dl

Mean ± SD

12.8 ± 13.4

32.8 ± 38.4

-12.7 ± 48.9






Mean ± SD

14.7 ± 13.7

21.0 ± 26.0

-2.4 ± 33.3






  • Mean responses to a 30-question survey indicated patient satisfaction across all four of the domains assessed (on a scale of 1 to 7): ease of insertion (5.9), comfort (6), ease of use (5.9), and likelihood of recommending Enlite to others (5.8). The poster included a table of representative statements and responses ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). In the absence of comparison to Medtronic’s Sof-sensor and/or another sensor, these results are somewhat difficult to interpret, but attitudes certainly seem to be favorable overall.






The sensor was comfortable under my skin.





I did not feel the sensor underneath my skin





The sensor was easy to remove from my skin





The sensor started up reliably





The sensor performed well on the final day





The sensor insertion device was easy to use





Sensor insertion was pain-free





I like that I do not have to see the needle





I like that the retractable needle protects me from injury






Meet the Expert Sessions

Continuous Glucose Monitoring Challenges

Howard Wolpert, MD (Joslin Diabetes Center, Boston, MA)

Dr. Wolpert gave a whirlwind tour of interpreting CGM downloads and insulin delivery data, including an excellent handout illustrating his tips, thought process, and what he sees most commonly in his patients (those interested in a copy can email him). Dr. Wolpert had a number of recommendations for clinicians to “make sense” of CGM reports, including: (1) identifying frequent hyperglycemia/hypoglycemia excursions and occurrences of variability using the hourly glucose statistics report; (2) checking for rebound hyperglycemia due to reduced basal rates or overreaction to CGM lag time; (3) evaluating whether boluses are sufficient to correct hyperglycemia; (4) checking to see if delayed infusion set changes are contributing to high or erratic glucose; and (5) identifying early/late postprandial hyperglycemia due to meal content and bolus timing. For optimizing alarm thresholds, Dr. Wolpert recommended a two-step process: deciding on initial settings (usually a conservative 55-60 mg/dl for lows and 250 mg/dl or more for highs) and refining them over time to optimize benefits. We were struck by the number of examples that required both CGM and insulin delivery data – in addition to Medtronic’s pumps and well-regarded CareLink software, we look forward to more integrated systems coming to market from Insulet/Dexcom, Animas/Dexcom, Tandem/Dexcom, and Roche/Dexcom.

  • Hourly glucose statistics reports can “give important insights into highs and lows.” Dr. Wolpert showed the Dexcom Hourly Statistics report, which summarizes two or more weeks of data into 24 hourly bars with mean, median, and 25-75th percentile glucose values. Dr. Wolpert first recommends drawing in target thresholds (e.g., 80 mg/dl and 180 mg/dl). Then, one can clearly see where hourly bars exceed the threshold lines, indicating frequent occurrences of hypoglycemia and hyperglycemia. The report is also useful for identifying times of the day where there is high variability, prompting a discussion with the patient on contributing factors. Finally, if an HCP identifies frequent hyperglycemia, then he or she can easily look at the following period to assess for subsequent hypoglycemia. If there is none, the insulin dose can be safely increased.
  • CGM data can help identify some of main reasons for exaggerated rebounds from hypoglycemia: patient overreaction to CGM lag time and temp basals/pump suspensions. Dr. Wolpert showed the Medtronic Quick-View summary, which combines fingerstick, CGM, and insulin delivery data. In one example, hypoglycemia consistently prompted a patient to reduce his basal rates, resulting in a marked hyperglycemic rebound. Dr. Wolpert characterized this behavior as “a common practice in CGM users that leads to glycemic variability.” Turning to lag time, he explained that many patients treat a low with carbs, see no upward trend on the CGM, and eat further carbs. The problem is compounded by neurocognitive function, where patients continue to “feel” low (and the CGM continues to read low) even though their glucose has normalized. To prevent this type of rebound hyperglycemia, Dr. Wolpert recommends patients take a fingerstick before eating more carbs.
  • Looking at a patient’s basal/bolus proportion and the frequency of infusion set changes is also helpful to drill down into causes of hyperglycemia. Dr. Wolpert again used the Medtronic Quick-View summary to illustrate how insulin delivery data and CGM data can be mutually informative. He showed what to look for when patients under-bolus despite persistent hyperglycemia. Reasons for this behavior might include fear of hypoglycemia and concerns about weight gain. Dr. Wolpert specifically mentioned a study from Drs. Bill Polonsky and Barbara Anderson conducted in Joslin waiting rooms. One-third of women surveyed acknowledged intentionally under-dosing insulin due to concerns about weight gain. Turning to infusion set changes, Dr. Wolpert explained that delayed replacement of infusion sets is sometimes a “common cause of high/erratic glucoses and is a teachable moment!” If this is the case in certain patients, Dr. Wolpert recommends changing sets more regularly.
  • Key steps in the initial CGM/pump download review:           
    • 1. Check priming history to assess frequency of infusion system change
    • 2. Check bolus history to detect possible missed meal boluses (“surprisingly common”)
    • 3. Check percentage of basal to bolus insulin.
      • If frequent hypoglycemia with basal > bolus, it may indicate that bolus doses are frequently being missed.
      • If frequent hyperglycemia with basal < bolus, it may indicate that basals are too low.
      • If frequent hypoglycemia with basal > bolus, it may indicate that high basals are contributing to hypoglycemia.
      • If frequent hypoglycemia with basal < bolus, it may indicate that excessive boluses are contributing to hypos.
    • 4. Check for pump suspension or basal rate reduction.
  • “When it comes to optimizing post-breakfast control, it comes down to looking at the food.” CGM can offer excellent insights into early and late postprandial hyperglycemia – common causes include meal type (carb and fat content) and bolus timing. Dr. Wolpert honed in on the breakfast period, where patients are more insulin resistant and typically eat high carb content, high glycemic index meals (“a carb isn’t a carb”). He recommends switching to lower glycemic index carbs, increasing protein/fat content of the meal, bolusing before breakfast (often hard for people to do in practice), or taking a larger bolus and cutting late-morning basal rates (what we’ve sometimes heard characterized as a “super bolus”). The latter allows the tail end of the bolus to cover the late-morning basal rate, preventing delayed hypoglycemia.
  • “It’s more than just fat delaying gastric emptying. There is a different insulin to carb ratio for a high fat meal vs. low fat meal.” Dr. Wolpert reviewed the importance of dietary fat on postprandial glucose control, which can increase insulin resistance after a meal. He gave a short preview of a closed-loop study to be presented at this year’s ADA (OR-266). The crossover design examined the effect of high and low fat meals on glycemic control. A high fat dinner with identical carb content to a low fat dinner caused more hyperglycemia despite increased insulin coverage by the closed-loop system. He noted the high degree of inter-individual variability as well – one patient needed twice as much insulin for a high fat meal with an identical carb load to a low fat meal. With this in mind, Dr. Wolpert recommends obtaining a diet history and focusing specifically on fat intake. Foods like peanut butter and cheese – traditionally characterized as “free foods” for type 1s – “can cause big problems.”
  • “Setting alarm thresholds on the sensor are like setting basal rates on the pump.” Dr. Wolpert emphasized that the goal is to “derive benefit, but reduce the risk for alarm/sensor burn-out.” When patients first start on CGM, he called for setting conservative initial alarm thresholds: 55-60 mg/dl for lows and 250 mg/dl or more for highs (80 mg/dl or higher as a low alarm for those with severe hypoglycemia or hypoglycemia unawareness). This step should be followed by further tightening and refinement as necessary. Key questions to remember at follow-up include: Did the alarm alert the patient of all low and high glucoses? Did the patient hear/feel the alarm? How many false alarms is the patient experiencing?

Optimizing Hi Alarm Threshold

Optimizing Low Alarm Threshold

If fasting glucoses are often high, but no overnight high alarm:

(1) Check whether the patient hears/feel the alarm

(2) Decrease the Hi alarm threshold.

If frequent hypos but no low alarm:

(1) Check whether the patient hears/feel the alarm

(2) Increase the Lo alarm threshold.

If the Hi alarm threshold is going off too often and frequently when glucose is not high:

(1) Increase the Hi alarm threshold or snooze duration to minimize repeat alarms.

If the Low alarm threshold is going off too often and frequently when glucose is not low:

(1) Decrease the Low alarm threshold


Questions and Answers

Q: Do you recommend a 50/50 split between basal and bolus?

A: That’s somewhat of a generalization. It’s valuable as a starting point, but patients are individuals and you need to get a sense of where a particular problem area might be. Some patients are highly sensitive in the liver and their basal requirements are quite low. I don’t look at it as a treatment endpoint at all. It’s a starting point for defining where problem areas are.

Q: Is there a way on the report to see how long the temp basal or suspend is? Or how many are being done?

A: On the new version of the Medtronic Professional, there’s a column that outlines how many times a person is suspending. Otherwise, you need to look at it on a day-by-day basis.

Q: Do you recommend suspension of the pump for exercise?

A: In practice, that’s a nice feature of pumps. It gets around the issue of weight gain and is one of the advantages of pump therapy vs. MDI – people can reduce their caloric intake around exercise. But you must consider the type of exercise. Isometric exercise gives a big epinephrine surge, and you don’t need a reduction. It’s mainly for just aerobic exercise. With insulin pumps, you have a depot of insulin when you suspend insulin delivery. It can take 30-60 minutes for insulin levels to decline. In those without diabetes, insulin drops very quickly. While you can suspend the basal with pump therapy, to mimic normal physiology is quite complicated. Patients must suspend their basal 30-60 minutes ahead of exercise. The other side of suspending pumps with exercise is the post-exercise period. Some patients rebound up quite high from unrestrained hepatic glucose production. They might need a mini bolus right after exercise. The two main clinical issues are then early basal suspension and to what extent they need a bolus after.

Q: With the fat, is it a combination of duration and total insulin?

A: That’s what the data suggest. My co-investigator Gary Steil is modeling the data and coming up with an optimized bolus delivery pattern. People need more insulin. The confound is there are different effects of different fats. Saturated fats are more of a culprit here. But with some patients eating salad dressings with vegetable oil, we still see this late postprandial hyperglycemia.

Q: What about protein?

A: The literature on that is somewhat mixed in terms of protein’s effect on glucose control. Mechanistically, the amino acids could trigger glucagon release. Or there are more gluconeogenic acid substrates. We didn’t formally study that.

Q: Are there clinical studies on the type of bolus for high fat meals?

A: There have been studies on coverage of pizza and the type of bolus delivery pattern. That was more relating to delayed gastric emptying. There are no studies looking at bolus amounts. Diabetes Tech and Therapeutics reported an article on bolusing that incorporates fat and protein. It was out of Poland. But it was not a crossover study design. The practical confound is inter-individual variability.


Symposium: Continuous Glucose Monitoring - Practical Aspects

Cost and Coverage Issues

Michael J. O'Grady, PhD (NORC, Bethesda, MD)

Dr. O’Grady gave a very valuable talk on CGM reimbursement, brilliantly integrating clinical knowledge with clear expertise in cost effectiveness. He explained the two major cost effectiveness analyses ($/QALY and budgets), noted the levels established in the JDRF CGM Trial, and showed how payer coverage policies are generally based on that data. One major theme from his talk was that not all CGM manufacturers are created equal, and those who can drive down costs (especially with longer sensor wear) will have stronger leverage with insurers. He also noted that “we’re moving from the clinical side to the cost side” with CGM – usually, this move of medical devices is accompanied by lower efficacy in the real world and thus worst cost effectiveness. However, Dr. O’Grady, believes CGM may be the opposite – if sensors can get down to a cost of two fingersticks per day, they would be cost saving.  In that case, “Insurance companies will hug and kiss you on the cheek.” Certainly something to hope for as the new generations of sensors are being developed.

  • Major coverage decisions from payers have generally tracked data from the JDRF CGM trial. Aetna and United cover all patients with type 1 diabetes >25 years and those <25 years with recurrent severe hypoglycemia. CIGNA covers type 1s not achieving optimal control or experiencing hypoglycemia unawareness. He highlighted that these are three of the five largest payers in the US. Medicare does not cover CGM for type 1s, though Dr. O’Grady explained that appeals with lots of documentation can be successful.
  • “Not all manufacturers are created equal.” Dr. O’Grady reviewed the different thresholds for the manufacturers shown below, explaining that companies who bring technologies at a lower cost and similar efficacy will have more pull with payors. “If you’re Aetna, you start the discussion with manufacturer #3,” although that “doesn’t mean you don’t cover #1 and #2.” Dr. O’Grady suggested later in the presentation that longer sensor wear drives costs down, so we would broadly speculate that #1 is Abbott (five-day wear), #2 is Medtronic (three-day wear), and #3 is Dexcom (seven-day wear) in the below table. Dr. O’Grady also pointed out that the mean cost effectiveness ratio is just barely less than $100,000 per QALY (the rule-of-thumb threshold in the US and far below the NICE criteria in the UK of £20,000 pounds/QALY ($30,600).

From the CGM Cost Effectiveness Analysis


Manufacturer #1

Manufacturer #2

Manufacturer #3


Total Daily Cost






Incremental Cost Effectiveness Ratios from the JDRF CGM Trial


Manufacturer #1

Manufacturer #2

Manufacturer #3


Cohort #1

(A1c >7% and age >25)





Cohort #2

(A1c <7%)






  • Insurers ~85% share of the cost of CGM suggests manufacturer discounts may sway coverage decisions. Dr. O’Grady showed a slide with the typical CGM claims for a 90-day supply of sensors The billed amount is $1,380 and insurers receive a 39% discount over retail. Of the remaining $840, the insurance company picks up 85% of the cost ($714) and the patient pays 15% ($126). According Dr. O’Grady, “You can see that this is going to get their attention.” He suggested that the discounts offered by manufacturers may drive payers to choose one manufacturer over another.
  • The length of sensor wear “makes a tremendous difference in cost.” Dr. O’Grady showed an analysis using data from manufacturer #3 in the aforementioned table. By switching the sensor site every seven days, it costs $9.89 per day for a 90-day supply. However, switching the site every 14 days brings it down to $5.38 per day. He emphasized repeatedly that that was just based on anecdotal reports and he is not publishing this data. As a reminder, Dexcom noted in their 1Q12 earnings call that they will pursuing extended durability claims for the G4 sensor (see page five of our report at The current version is for seven-day wear. Medtronic’s new Enlite sensor is six-day wear. We’re not sure about Medtronic’s plans for longer durability, though we assume they are working on it.
  • Dr. O’Grady believes CGM has the potential to be cost saving in the long run. He showed a comparison between the incremental cost effectiveness ratios of two fingersticks per day (ratios of -$1,494 and -$15,725) and use of CGM according to the label ($98,679 and $78,943 from the mean values in column three of the bottom table above). Two fingersticks per day were actually cost saving, suggesting that if they are inexpensive enough, sensors could be too. Dr. O’Grady emphasized that it’s “very rare” to see cost saving interventions and this would be very powerful and persuasive for payors and Congress.
  • “There are reasons to believe that CGM may be more cost effective than in the peer reviewed literature” – we need different analyses moving forward. Dr. O’Grady believes we need more real-world evidence of CGM’s benefits beyond anecdotal accounts. He was especially in favor of claims analyses to track patient use. 
  • The two major cost effectiveness analyses (cost per QALY and budgets) help give decision makers a better idea of the return on investment for a particular intervention. Dr. O’Grady explained that in the US, the general threshold for reimbursement is $100,000 per quality adjusted life year, while the “Brits are the hardest” at £20,000 pounds/QALY for the NHS ($30,600). The other criteria typically used by the federal government is budget and cost estimates. Such analyses project a spending stream under a current and proposed alternative. Notably, quality of life is not considered in these analyses and they are typically ten-year estimates. Dr. O’Grady explained that this is problematic for a disease like diabetes, where offsetting savings from avoiding complications is probably delayed beyond ten years. Medicare estimates go out 75 years, which he believe should be taken with “a grain of salt.”

Questions and Answers

Q: In the UK, there are a few cases where you can get NHS reimbursement for CGM. It’s for recurrent hypoglycemia. There is flexibility but it’s not great.

Q: Thanks for your presentation. I’d like to comment on coverage by payers. Many of the payer in the US are covering people with type 2 diabetes on insulin. Your slide on coverage policies reflected only type 1s. Many payers are moving into the type 2 world.

A: Good.

Q: Has industry collected data on using sensors for 10-14 days?

A: I don’t know. That’s a hard one. You can see anecdotal evidence, but we haven’t tested this. It is testable. People are clearly doing it and not noticing a deterioration in accuracy that’s encouraging them to switch the sensor. We know our friends at FDA want 100% accuracy all the time and then you swap out. I don’t know if they’ve talked to CMS, who must pay for this.

Q: Nice presentation, thank you. Could you educate us on the reasoning for the lack of coverage of CGM by Medicare? In this day and age, more and more type 1s are in the Medicare population. We know from the T1D Exchange that it’s adults who seem to have a high prevalence and occurrence of severe hypoglycemia. My mother is 81 and has horrible, brittle diabetes. She was hospitalized for days at a high cost. This could have been prevented by a CGM, which she now wears. What do you do in these cases? Is it possible to appeal and get coverage in cases like this? There is a clear documented need. Why is Medicare not covering this when the data suggests otherwise?

A: Our fault as investigators is part of it. In the JDRF CGM Trial, we didn’t have enough seniors. For something like a sample size of 20, they are not going to make a national coverage decision. We could have done better at providing data. But they’re still behind the times. They don’t think many type 1s make it to Medicare age. They just have to run the claims. You see how old people are, you see complications, and you can see how many are billed to type 1 and type 2. The claims indicate, on the actuary side of it, that there are hundreds of thousands of type 1s that are Medicare eligible. They know this. But that’s not on the coverage side though. That is a real sort of education campaign. This is not just 200 people. It’s really hundreds of thousands of people. As the boomers retire, and god knows they’re aware of this, you’ll see more and more every year. You also have people in very tight in control who are 70 years old. Sometimes they have a hard time loosening up on the tight control. Of course they don’t have to worry about blindness in 20 years when they’re 80 years old.


Interpreting and Applying the Download

Bruce Bode, MD (Atlanta Diabetes Associates, Atlanta, GA)

Dr. Bruce Bode gave an outstanding case-based presentation on interpreting CGM downloads and getting paid to do so. He advocates using a macro-micro approach, first using the modal report (14 days or less) to look for broad trends and then looking at individual days. In terms of new CGM users, he finds that about one-third of people figure out CGM on their own and really like it, another one-third really need help and guidance, and another one-third just stop using it. Dr. Bode also ran through the reimbursement criteria to interpret CGM downloads and strongly encouraged the audience to get paid to do it. He concluded with six excellent case studies using the macro-micro approach, which really made download interpretation look simple, fast, and led to some impressive changes for patients in terms of hypoglycemia, A1c, daily insulin doses, and weight. The most common themes were using CGM to identify lows throughout the day and highs after meals. It was really clear that the combination of insulin delivery and CGM data is extremely helpful in this process, and we certainly look forward to more sensor augmented pumps in the next couple years.

  • Dr. Bode advocates taking a macro-micro approach to interpreting CGM downloads. The macro view looks at the big picture to diagnose problem areas. Dr. Bode recommends using the modal day (14 days or less), meal overlay curves (“are they covering meals?”), and overnight curves to take a 30,000-foot approach. On the micro front, Dr. Bode delves into individual days, meals, and other events. These are especially helpful to provide patients with teachable moments. Dr. Bode highlighted the “very valuable” importance of wear time statistics (“If you wear it, you usually do well”).
  • When it comes to CGM, “You must try to get paid for it. We do have codes.” Dr. Bode reviewed the reimbursement criteria for CGM, noting the limitation that an MD, DO, NP, or PA must interpret the download to get paid for it. He recommends having a CDE do the initial interpretation and then having an advanced HCP review it, sign off on it, and generate a bill. Dr. Bode also recommended speaking directly with local carriers.
    • Interpretation of CGM download (code: 95251) – The report must be generated and interpreted, it does not require face to face contact, and it requires an MD, DO, NP, or PA.
    • Technical component (code: 95250) – Includes placement of sensor and training, does not require high a high level HCP.
    • CGM is generally accepted under the following codes: diabetes out of control (250.02, 250.03), diabetes out of control with hypoglycemia (250.82, 250.83), pregnancy out of control (640.80-84), and insulin pump adjustment (V45.85).
  • A number of themes emerged from Dr. Bode’s concluding download interpretations from six different case studies. Using the aforementioned approach, Dr. Bode usually interprets CGM download reports in just five to ten minutes. Three of the cases involved hypoglycemia, which was easily identified on the modal day report. Dr. Bode either lowered basal rates or reduced meal coverage depending on when the lows were occurring. Highs after meals were also quite easy to spot on the modal day report (especially on the Medtronic reports because insulin delivery and bolus calculator data was included) – Dr. Bode recommended increasing mealtime insulin doses (though in one case, doing so required a consequent drop in a patient’s basal), carb counting, and pre-meal bolusing. The micro, daily view was particularly helpful for drilling down into these meal issues. One interesting type 1 patient, a 57 year-old type 1 with a BMI of 36 kg/m2, had unexplained hyperglycemia in the morning. The reason was actually caused by sleep apnea and was subsequently diagnosed and corrected. Overall, A1c typically improved by ~0.5% in most cases shown, especially solid results given reductions in hypoglycemia.

Questions and Answers

Q: You are an expert in interpreting downloads. What about someone that’s new to this?

A: If someone is new, they should come back to see a specialist. I will also spend longer with new patients and teach them how to upload at home. We encourage everyone to upload at home. Looking out to CDEs, if you don’t bill for it, you’ll never get paid. Insurance companies love it when you don’t bill, but you’ve got to bill them to teach them to pay for this.

Q: Do you ever use blinded CGM? And do you use dual wave boluses?

A: I’m more of a believer in real-time CGM. I do use professional real time and we bill accordingly for that. We use masked CGM all the time in trials. We have multiple ongoing trials with masked CGM. There are pros and cons to both. With masked, you don’t have to teach anything. But when a patient sees and experiences CGM, they can come back and learn. One-third can figure out on their own. Another one-third need your help. Dual wave boluses are easy to see on CGM. After eating, blood sugar is normal or going low. Then, two to three hours later, it’s going high due to high fat. We recommend adding more insulin to the meal and covering it over another 2 hours. A split of 60/40 or 70/30. There’s not a lot of science here.

Q: Most of your downloads were in type 1s. Any key clinical points for type 2s?

A: We’ve done a lot of downloads. I’m doing a study with Dr. John Buse on type 2 diabetes and downloads. There is little published there. We really think that type 2s are much more predictable. You see a much lower standard deviation and much more predictable curves. We’re hoping when you do type 2 diabetes, you’ll clearly see the problem – is it a fasting problem, a hepatic problem, a bolus problem? Hopefully next year at this time we’ll have data to share. We’ve also used lots of masked CGM in type 2 diabetes studies. There has been a published paper in a single center study from Walter Reed. It looked at CGM in type 2 diabetes in real time. If patients wear it, they do very well and make behavior changes.

Q: How do you tease out basal adjustment and bolus adjustment? You mentioned the three-hour window. Are there other things to use?

A: For basal or bolus, you need a meal marker. Medtronic has the simple to use bolus calculator. Dexcom must be put in to the receiver. Within three hours, I call it a bolus problem. After three hours, and certainly greater than four hours, it’s a basal problem. When making a change in the basal, you must do it two hours before the event. So if you’re going low at three in the morning, you need to make a change at one in the morning.


CGM: Choosing the Right Patients

Larry A. Fox, MD (Nemours Children’s Clinic, Jacksonville, FL)

Dr. Fox reviewed a variety of continuous glucose monitoring papers in different populations, from landmark studies like the JDRF CGM Trial and STAR 3 to more recent research (e.g., in young children and toddlers [Mauras et al., Diabetes Care 2012] and for the purpose of reducing hypoglycemia [Battelino et al., Diabetes Care 2011]). The technology’s potential has been shown potential in both pumpers and MDI users, and in both well-controlled type 1 diabetes (who might get a benefit in beta-cell function) and poorly controlled type 1 diabetes (at least for those already under intensive therapy – the value is less clear in less-motivated patients, a population Dr. Fox and his colleagues are currently studying in an NIH-funded trial). He concluded that adults generally seem to be better candidates than children and that greater frequency and longer duration of use correlate with better results, and he acknowledged that his talk did not even attempt to address a plethora of other important populations (e.g., pregnancy, type 2 diabetes, hypoglycemia unawareness) or to help clinicians identify individual patients that are likely to benefit from CGM (which we had hoped would be a bigger theme, based on the presentation’s title). It’s important to note that all these “rules” will keep changing as the products develop further.  

Questions and Answers

Q: You presented a German/Austrian registry study in which people that had been using CGM for less than 30 days had a higher rate of hypoglycemia than the overall population. Might this have been a case of selection bias – perhaps patients were prescribed CGM because they had frequent hypoglycemia?

A: Absolutely – at the end of the day, that study was just a registry review.


Patient Self-Management Algorithms

Rosanna Fiallo-Scharer, MD (University of Colorado Denver, Aurora, CO)

In this ‘CGM 101’-style presentation, Dr. Fiallo-Scharer introduced the audience to an algorithm for helping patients make the most of numerical and especially trend information. During Q&A, she emphasized the importance of basing therapeutic decisions on confirmatory fingersticks rather than sensor readings.

Questions and Answers

Q: Can you give a timeframe for suspending basal rates in hypoglycemia? Patients need to understand that suspension is not a rescue therapy – suspending basal rates won’t take care of it if they are already too low, like 45 mg/dl.

A: But when you wear a pump that is one feature you can use – to suspend the basal rate until glucose becomes stable again. We don’t recommend replacing with a temporary basal rate – the original rate will automatically kick back in once the suspension is over.

Q: How do you interpret data over the next two days after a patient has a hypoglycemic episode?

A: I am not sure what you mean by that. Frequent hypoglycemia can cause patients to become unaware of their lows due to counterregulatory failure. Some patients tend to overtreat lows – I don’t know if that is what you mean. They can get a rebound hyperglycemic event and then have to catch their tails. One beautiful thing about CGM is that they get that feedback and can fix it next time.

Comment: In my experience hypoglycemia results in a lot of overtreatment – patients often aren’t aware that yesterday’s hypoglycemia can cause today’s hyperglycemia.

Q: Can you clarify whether with the algorithm you are having people respond based on sensor glucose or confirmatory blood glucose?

A: Patients were asked to confirm any alarm with a fingerstick and to base treatment on that. The sensors are more useful for looking at trends. Obviously very often the alarms are associated with a real low or high. But if they are going to act based on a number outside the target range they should use a confirmatory blood glucose check.


Symposium: Behavioral Interventions in Routine Clinical Care - What Works?

Supporting and Maintaining Continuous Glucose Monitoring Use

Timothy T. Wysocki, PhD (Nemours Children’s Clinic, Jacksonville, Florida)

Dr. Wysocki provided an overview of the behavioral aspects underlying CGM use, especially among children and adolescents using the technology. He first reviewed the long list of behavioral barriers to successful CGM use, ranging from unrealistic expectations to youth-parent conflicts about CGM data. He also noted the disconnect between young patients that have grown up in a world where everything works, versus the fact that CGM is still a new technology (“these individuals are immensely intolerant of glitches”) – this was an interesting characterization that we have not heard before and one with which we agree. The remainder of Dr. Wysocki’s presentation described the NIH/NIDDK funded study his team is undertaking ( identifier: NCT00945659). The nine-month trial hopes to enroll 150 poorly controlled adolescents (mean A1c is 9.1% in the 97 enrolled thus far), who will be randomized to one of three groups: standard care, CGM, or CGM plus behavior therapy. The behavior therapy is aimed at promoting the benefits of CGM by addressing each adolescent’s unique barriers. This will take the form of motivational interviewing, problem solving training, and communication training. Sessions will guided by a manual and will have a defined structure, although Dr. Wysocki emphasized that families will dictate the content. Data collection is expected to end in November 2013, with A1c as the primary outcome. Additionally, we believe that Medtronic’s new Enlite sensor and Dexcom’s new G4 sensor will significantly improve the CGM experience for many patients. To boot, we are wary of studies that use old technology that are not always characterized as such. To boot, with this trial, we’re very glad to see such smart and committed focus on the behavioral aspects underlying CGM use. In our view, better understanding of this area will be an integral part of improving the technology and climbing up the adoption curve in the years to come.

  • Dr. Wysocki  pointed to a variety of behavioral barriers that prevent patients from successful use of CGM:
    • Unrealistic expectations
    • Inconsistent or infrequent use
    • Deficient calibration technique (“garbage in equals garbage out”)
    • Treating without verifying blood glucose level
    • Non-response to trend alarms
    • Disabled alarms
    • Youth-parent conflict about CGM data
    • CGM associated pain or discomfort
    • Hypoglycemia fear or avoidance
    • Device loss or damage (“Including one run over by a mother’s Cadillac Escalade. It didn’t work but the company retrieved the data”)
    • Peer, school, and fashion issues
    • Information overload
  • While we would absolutely agree there are barriers, and while we believe these barriers are driving lower than optimal satisfaction among patients today, it’s also key to point out that this list has improved over time and will continue to do so as long as the therapy is used. If it is not, that is troubling for all patients who would benefit from future versions of CGM.

Questions and Answers

Q: How do you find this intervention integrating into the clinic?

A: We specifically recruited master’s level social workers. We thought they would be more commonly available in ordinary diabetes clinic settings. They are often called upon to do some of the tasks we’re talking about. Our first effort is to demonstrate efficacy. Then, we can talk about dissemination. But like the previous speaker, I’ve had experience teaching these skills to highly experienced individuals and I really struggle. It’s hard to get people to give up things they’re comfortable with. But the majority of pediatric diabetes centers have people like this around. What remains to be seen is (a) is it effective? and (b) can we disseminate it practically.

Q: You mentioned online data collection – are you doing that in-clinic or on a computer?

A: All of these people had to have a computer in order to download the CGM data. About 92% of families have completed at least some questionnaires. About 70% have completed all. And they love it.


Symposium: Glycemic Variability

Methods of Quantifying Glycemic Variability

David Rodbard, MD (Biomedical Informatics Consultants Potomac, Maryland)

Dr. Rodbard gave an excellent overview of the ongoing debate about measuring glycemic variability – his central thesis was that percent coefficient of variation (%CV), which is the standard deviation divided by the mean multiplied by 100 (SD/mean X 100), is the best measure of glycemic variability. Dr. Rodbard provided a variety of arguments to support this assertion: %CV is easily calculated, is independent of A1c and mean glucose, can be measured more precisely than MAGE or CONGA, and is one of the best predictors of hypoglycemia. He also argued in favor of establishing reference ranges for %CV: <32% is excellent, 32-37% is good, 37-42% is fair, and >42% is poor (Rodbard et al., Postgrad Med 2011). These cut-points also align well with other data sets from Dr. Irl Hirsch (%CV <33% is ideal, while %CV >50% is poor) and the CACT1 Study (<34%, 34-40%, 40-46%, and >46%). As the %CV gets below 25% or 20%, Dr. Rodbard noted that there is no hypoglycemia. Encouragingly, %CV is applicable to both SMBG and CGM and people with type 1 or type 2 diabetes. However, specific cutoffs may need to be established for certain populations (early stage type 2s, pregnant women). Dr. Rodbard was negative on MAGE because “it throws out half the data” by only counting upstrokes or downstrokes but not both. He also highlighted the importance of time horizon, as glycemic variability can be measured within day, between day, and overall. He recommends taking the inter- and intra-day standard deviations as well as the SD of daily means. He also showed the Ambulatory Glucose Profile (AGP), noting that our subjective interpretation of a patient’s glycemic control correlates quite well with %CV (As a reminder, the AGP is what the Helmsley Charitable Trust plans to use to standardize glucose reporting; see our report on the recent panel at

Questions and Answers

Q: I have a comment on a simpler measure: range divided by mean. That’s something that clinicians can look at for very small sample numbers. It tells you something about the risk of hypoglycemia. I agree it’s extremely crude, but it’s quick to do in your head.

A: Range is proportional to SD. But it’s affected by outliers. Those outliers may be clinically interesting. Range based on four to five measurements may be unstable. If you have 288 values or 1000 values, it may be more stable. It might be better to use 10th and 90th percentiles to throw out the lowest and highest values.

Q: Do you have data on whether there is a correlation between this measure and outcomes in the general pop or the pregnant population?

A: No sorry I do not.

Q: Do the values need to be different for type 1 diabetes vs. type 2 diabetes?

A: The values for interpretation will be different. In normal subjects, there is about 17% variability, which rises to a %CV of 20-25% for obese, non-diabetic patients with A1cs in the normal range. We need to define the ranges for interpretation for early type 2s and type 1s.

Q: Is there data correlated the quartiles and quality of life measurements?

A: No, I don’t have that. On the earlier question relating to the correlation between %CV and outcome – there was a paper that appeared Diabetic Medicine on coronary calcium. Some of the different types of SD were correlated retrospectively. I believe these measures will be useful for correlation with outcomes.


Methods of Minimizing Glycemic Variability in Type 2 Diabetes

Robert Vigersky, MD (Walter Reed National Military Medical Center, Bethesda, MD)

Addressing a nearly full room on Day #5 of ADA 2012, Dr. Vigersky pointed out that the “turnout on this last day of the meeting is a testimony to how important this topic has become.” Most interesting was Dr. Vigersky’s discussion of CGM in type 2s, which included a review of his study published in Diabetes Care earlier this year (see our report at Showing examples from the trial, Dr. Vigersky cautioned that CGM downloads have gaps where no glucose data is collected – in his view, these can give “very spurious variability data unless you do something about it.” His team chose to use an interpolation approach that approximated the lost values based on the pre- and post-gap CGM values. He then discussed the analysis we first saw at Clinical DTM a few months ago (see pages 11-12 of our report at, which classifies CGM users in his study based on their observed response to the device. Dr. Vigersky focused on those who learned over time, burned out, or achieved and maintained tight control. Interestingly, the number of receiver screen views was related to each group: those who burned out over time looked at the receiver increasingly less often over the course of the study, while those who achieved tight control increased their number of screen views over the course of the study. The latter group also had Problem Areas in Diabetes (PAID) scores that were low at baseline and remained low throughout the study, potentially offering a window into identifying types 2s likely to benefit from CGM. He concluded that real time CGM can be used as a behavioral modification tool for type 2 diabetes, although the dose, frequency, and patient selection criteria need to be further studied. Dr. Vigersky also covered SMBG in non-insulin using type 2s and argued that use of structured testing (STeP, ROSES, St. Carlos studies) to selectively prescribe medications can improve both A1c and glycemic variability. Finally, he mentioned some nutritional strategies to reduce glycemic variability: eating carbs at the right time of day (lunch was best in one study), eating lower glycemic index foods, and eating vegetables before carb intake (rather than after).

  • Gaps in sensor readings were fairly common in Dr. Vigersky’s study and were dealt with using an interpolation approach. All 47 patients in the CGM study arm experienced gaps in data. Dr. Vigersky and colleagues looked at CGM data in three-day cycles in the middle of the week (we note that participants were wearing the Dexcom Seven in this study for two weeks on, one week off). In all three-day cycles, the total number of gaps of over five minutes was 14,173, representing 6.6% of the data. This translated to an average number of gaps per subject of 302 and a mean gap of 26 minutes. Dr. Vigersky noted that gaps make it difficult to calculate statistics such as MAGE. To fill in the missing data, Dr. Vigersky’s team took the before- and after-gap values and interpolated. In other words, if the CGM read 120 mg/dl, then a ten-minute gap (one missed reading), then 130 mg/dl, the missing point would be 125 mg/dl. (According to management, Dexcom’s G4 has a significantly improved transmitter relative to the Seven Plus. In product’s recently completed pivotal study, the G4 sensor captured 99% of data points. It also has a typical transmission range up to 30 feet and up to 50 feet if it’s in line of sight, compared to a range of five feet cited in the label for the Seven Plus.)
  • Dr. Vigersky characterized patients with an “immediate effect” response pattern as learning over time, burning out, or achieving and maintaining tight control. Thirty-eight of the 47 patients fell into the immediate effect group. Screen views were defined as discrete episodes of viewing the display with one or more minutes between views.
    • Those who learned over time saw an improvement in mean glucose, standard deviation, and MAGE throughout the study. Their mean number of daily screen views was similar throughout the study, averaging 16-17 per day (i.e., one episode of screen viewing every waking hour). Their Problem Areas in Diabetes (PAID) scores decreased from 32 to 25.
    • Those who burned out got worse over time. Their mean blood sugar increased and their screen views decreased over time (from 12 to less than five). This group’s PAID scores did not change. For us, the central question here is one of causality – are the reduced screen views causing the worse outcomes, is frustration with the device causing reduced screen views and therefore worse outcomes, or is there some other factor at work. We think this would be an interesting qualitative question to study among ex-CGM users; better understanding why people quit using the technology should improve it. More importantly, we believe that churn will decline as product ease of use, reliability, accuracy, etc. improve, which is only a matter of time – a “when” not an “if”.
  • Those who achieve and maintained tight control got immediately better and stayed better. Mean blood glucose improved and standard deviation dropped from 20 mg/dl to 14 mg/dl. The group’s screen views also doubled from about 15 to 30 per day by the end of the study. This group’s PAID scores were low at first and did not change much by the end of the study.


Perspective on Outcomes

J. Hans DeVries, MD, PhD (Academic Medical Center, Amsterdam, Netherlands)

Dr. DeVries delivered a skeptical and thoroughly cited review of the proposed link between glycemic variability (GV) and harm in diabetes. Two of the field’s foundational findings (a link between GV and microvascular complications in type 1 diabetes, seen in a 1995 analysis of DCCT data; a link between GV and oxidative stress in type 2 diabetes patients not on insulin) have been retracted or weakened upon follow-up analysis. Dr. DeVries proposed that future studies of GV concentrate on mean absolute glucose change (MAG), a measure developed by his group that accounts for the frequency of glucose fluctuation (rather than just the dispersion of values, a la standard deviation). To close he acknowledged that at the very least, unpredictable values are very disturbing to patients and are associated with risk of severe hypoglycemia. He recommended continuous glucose monitoring as a tool for reducing both A1c and variability (though he indicated that any glucose-lowering intervention will tend to address both A1c and variability at the same time since the measurements inherently related).

  • Dr. DeVries said that substantial doubt has been cast on two of the foundational papers linking glycemic variability to harm in diabetes. These papers were the analysis of DCCT suggesting relationship between GV and microvascular complications in type 1 diabetes, independently of A1c (Diabetes Care 1995) and the famous Monnier study of 20 non-insulin-dependent people with type 2 diabetes, which suggested that 74% of variation in oxidative stress could be explained by glucose variability (JAMA 2006). The authors of the former retracted their paper after finding that the GV/complications link was a statistical artifact due to erroneous modeling assumptions. As for the latter, when Dr. Monnier expanded the study size to 60 total patients, GV explained only 15% of variability in oxidative stress (Diabetologia 2010). Also problematic for the Monnier findings was Dr. DeVries’ group’s failure to confirm the relationship with a more accurate assay for oxidative stress (Siegelaar JDST 2011). However, Dr. DeVries acknowledged that his study’s population had lower mean A1c (7.0% vs. 9.0% in the Monnier cohort); other analyses suggest that high GV may be harmful only in those with high mean glucose. Epidemiological data on the question are mixed, and post-hoc analysis of the randomized clinical trial HEART-2D fails to support GV’s relevance as a risk factor independently of mean glucose (with which variability is inherently associated, since people with high mean glucose have a wider range over which to fluctuate).

Questions and Answers

Q: How do we go through this maze? Do we need something like ORIGIN, or can we make sense of the data available?

A: What’s available is secondary analysis. This gives us a hint that large ORIGIN-like study may not give definitive answer, since it would be so difficult to design. The APOLLO study compared basal to prandial insulin and didn’t show a difference, but it didn’t measure oxidative stress, and it wasn’t big enough.

Comment: The FLAT-SUGAR study might address this issue. It is not as long-term as ORIGIN, but it will go on for a year – so we will probably have an answer in the next year or two. (Editor’s note: As we understand it, the yearlong FLAT-SUGAR trial is technically a feasibility study to establish whether different therapeutic regimens can separate cohorts of patients by glycemic variability while causing equivalent effects on mean glucose. The independent clinical relevance of GV change would not be assessed until a larger follow-up study.)  

Q: It is thought that exposure to hyperglycemia may have a non-linear effect – more pathology is generated by a few high sugars rather than many lower sugars, even if the mean glucose is the same. The theory behind MAG is different – that harm is related to the act of going up and down.

A: Intuitively it makes a lot of sense that a repeated insult is more harmful than a single, long-standing insult. By definition a stable glucose must be better than variable, since nature preserves stable glucose so strongly. But the additional harm of variability (relative to mean glucose) is probably minimal.


Glycemic Variability in the Critically Ill Patient

James S. Krinsley, MD (Stamford Hospital, Stamford, CT)

Dr. Krinsley reviewed the substantial and growing body of evidence that establishes glycemic variability as a risk factor for ICU mortality, independent of hypoglycemia and hyperglycemia. Ongoing research needs include characterizing the risks of GV by subpopulations (e.g., diabetes vs. non-diabetes, medical vs. surgical ICU), and Dr. Krinsley is leading a massive (n~42,600) international observational study that he expects will aid in this effort. (Preliminary analysis suggests that GV is associated with increased mortality risk whether or not patients have diabetes, but the slope of the risk increase is steeper for people without diabetes.) Dr. Krinsley argued that inpatient glucose control must address all three domains of glycemic control – hyperglycemia, hypoglycemia, and glycemic variability – and he looked forward to benefits from wider use of new technologies (e.g., CGM, insulin-dose-recommendation software). He also offered the audience some relatively low-tech recommendations from his own clinical experience (e.g., supplement intravenous insulin with twice-daily subcutaneous injections of basal insulin as a way to increase glycemic stability; use 10% dextrose solution rather than 50% dextrose solution as hypoglycemia rescue therapy, in order to avoid rebound hyperglycemia).

Questions and Answers

Q: Can you comment on the incremental importance of GV as a factor separate from hypoglycemia?

A: The data suggest that these derangements are cumulative – the most recent paper on this is Mackenzie et al., Intensive Care Medicine 2011.

Q: I am a little lost. I think we are completely lacking in causal effect. The only randomized controlled trial data involved targeting a BG so low that no one is going for it anymore, at least not in the US.

A: Yes, this is a challenging issue, and it is impossible to ethically randomize anyone to hyperglycemia or increased glycemic variability. As to the guidelines, I believe that the one-size-fits-all approach is not necessarily correct, even though it is based on the largest randomized controlled trial on the topic. I think that study, NICE-SUGAR, was the end of Chapter One of inpatient glycemic control. With new studies – like the large observational I mentioned – and with new technologies, we will enter Chapter Two.

Q: I am fascinated by your new multinational data looking at the differences among those with diabetes and without. If I am remembering your slide right, at high glycemic variability levels the mortality was less for people with diabetes than people without diabetes. Did you look at the diabetes medications the diabetes patients were on before entering the hospital?

A: This was huge database study, so we didn’t have that information. Diabetes patients might show up in the ICU with a broader variety of comorbidities, but numerous studies show that diabetes is not an independent risk factor.

Q: Have you started using incretins in the ICU as was proposed at a conference in Brussels two years ago?

A: I haven’t, and I’m not aware of any widespread use.


Symposium: Beyond Insulin and A1C in the Management of Pediatric Type 1 and Type 2 Diabetes

Is It Time for Routine Monitoring of Other Measures of Glycemic Control?

Thomas Danne, MD (Kinderkrankenhaus auf der Bult, Hannover, Germany)

Dr. Danne discussed “Something that I feel quite strongly about: Looking beyond A1c and insulin.” He began by showing how A1c does not tell a complete enough story, which provides rationale for using glycemic variability. Although there are a wide variety of glycemic variability statistics, Dr. Danne prefers using standard deviation – it’s easy to understand and other measures don’t seem to offer benefits. However, there is certainly an allure to having one number, so something like the Glucose Pentagon may be warranted (combining A1c and four other numbers). Although the evidence is mixed on glycemic variability, Dr. Danne believes it is clinically relevant and using CGM can help improve it. He concluded by summarizing studies of the Veo and the DREAM project, explaining that glycemic variability will help us assess if these closed-loop therapies are beneficial. The Joslin Diabetes Center teaches the three pillars of diabetes are insulin, exercise, and diet; similarly for Dr. Danne, the three pillars of glucose management are CGM, A1c, and SMBG. We’re glad to see more and more focus on looking beyond A1c. And in our view (and, Dr. Danne’s, we’re sure), the key will be convincing FDA and other regulatory agencies that this is the way to go.

  • “A1c does not give us any idea about glucose fluctuations…knowledge of glycemic variability is important for adjusting diabetes therapy.” To illustrate the shortsightedness of A1c and the importance of glycemic variability, Dr. Danne showed a patient’s modal day report with a high average blood sugar (translating to a high A1c) and lots of glycemic variability. He then graphically showed how intensifying therapy to lower the average would bring the patient into hypoglycemia. A different patient with the same average but less glycemic variability would be brought into target after therapy was intensified. We found this simple graphical illustration very persuasive.
  • “I still believe standard deviation is very easy to understand and maybe the best parameter. Even though all my math friends tell me it’s the wrong thing to do.” Theoretically speaking, Dr. Danne explained that standard deviation is hard to use because upswings are larger than downswings (i.e., there is a glycemic floor). However, he believes standard deviation does as good of a job as other measures and is much easier to understand and calculate. For a more complete review of other glycemic variability measure, he told audience members to read Dr. Hans DeVries’ paper in Endocrine Reviews (2010). Dr. Danne conceded that We always want one single value,” so something like the Glucose Pentagon may be warranted (Thomas et al., Diabetes Technol Ther 2009). This measure combines A1c, GPR, standard deviation, time spent >160 mg/dl, and AUC >160 mg/dl.
  • Regarding the clinical relevance of glucose variability, Dr. Danne stated, “It’s still controversial. We need more data.” He first reviewed the famous Monnier study linking oxidative stress to MAGE, which has not been duplicated in a number of other studies. To provide some more clinical data, Dr. Danne next discussed an interesting analysis of 1,000 insulin pumpers. The number of boluses per day was linearly associated with A1c – as boluses per day increased from two, four to seven, and then to over 12, A1c declined in a step-wise fashion from 10.4% to 8.2% to 7.3%. He likened this to driving a car and steering it several times – the more times you steer it, the more likely you are to stay on the road. Since this study did not look at glycemic variability, Dr. Danne believes it would be interesting to combine the pump data with CGM. One would expect a high number of daily boluses would lead to a drop in glycemic variability.
    • Dr. Danne also highlighted the ONSET trial (see pages 107-109 of our EASD 2011 Full Report), which compared conventional pump therapy plus SMBG to sensor-augmented pump therapy from the onset of diabetes. Those on SAP therapy had higher C-peptide levels, potentially suggesting that the reduced glycemic variability associated with CGM may have reduced glucose toxicity and helped preserve more beta cells. This was an interesting hypothesis and we hope that the TrialNet Metabolic Control in New Onset Diabetes ( identifier: NCT00891995) study can help answer it. There is a poster on the trial at this year’s ADA (891-P), though the full C-peptide data isn’t expected until November 2013.
  • Studies from Dr. Danne’s group suggest glycemic variability improves with CGM. Dr. Danne discussed two CGM studies using the Abbott Navigator CGM (Danne et al., Diabetologia 2009) and another with the Dexcom Seven Plus. Patients used both a masked and unmasked sensor. Independent of baseline A1c, patients were able to lower glycemic variability with use of the real-time CGM.

Questions and Answers

Q: I have a small pediatric diabetes program and we have lots of kids on sensors. My problem is they don’t stay on sensors. How do we keep kids using sensor? Also, after listening to all of the closed loop and CGM data for two days, I’m worried about kid’s real estate. We saw a glucagon pump, an insulin pump, and a sensor. How are we going to keep this real estate intact?

A: Imagine that while you were driving your car, you had to insert two needles into your body and you got several alarms all the time – you should have slowed down because of that red light. Or there’s a bicycle over your shoulder. After three days, you’d probably take the bus or walk to work. [Laughter] I’m not surprised many patients are saying, “I’m fed up with the sensors. They’re alarming and not helping me.” It’s a technology issue. Yes, there is a real estate issue. But we are burdening them with information. Patients want simple solutions. Something like LGS, which suspends without an alarm. Or overnight closed loop – you hook onto it in the evening and wake up in the morning with great control. That’s what our patients want. I think the real estate issue will be smaller.

Q: You showed that taking 12 boluses a day was associated with a lower A1c. I actually see that kids bolusing a lot have a lot more glycemic variability. Was there any measure of glycemic variability in that data?

A: I would love to have that data. Taking 12 boluses per day is like having a second basal rate. It simply came out that way. There was a clear relationship between the number of daily boluses and A1c. Seven to ten is a good number. That’s certainly much higher than injection therapy. I would not ask any patient to take ten injections per day.


Symposium: Joint ADA/EASD Symposium - Capillary Glucose Monitoring in 2012

Self-Monitoring of Blood Glucose is Useful in Patients with Type 2 Diabetes Mellitus on Oral Agents - Pro

Lutz Heinemann, PhD (Science & Co, Dusseldorf, Germany)

Dr. Lutz Heinemann made a thoughtful defense of SMBG in non-insulin-treated type 2 diabetes (NITT2) patients, including a meta-analysis of 12 meta-analyses on the topic and some ‘meta-’ discourse about the controversy itself. Meta-analyses tend to show a six-month A1c improvement of 0.3%, and recent randomized controlled trials like STeP, ROSES, and St. Carlos have demonstrated benefits of 0.5% (Dr. Heinemann argued that these studies, all conducted after 2008 and all emphasizing SMBG as an educational/motivational tool, are designed better than historical trials – a positive result of pressures from cost-controlling agencies such as Germany’s IQWiG). He believes that the field still needs more long-term data as well as a neutrally funded, conclusive study that clearly quantifies the study effect (i.e., one that includes a placebo group receiving no intervention at all); he proposed that the major SMBG companies could divert 10% of their marketing budgets for a few years toward such a study. He also expressed optimism about phone-connected meters to improve the integration of SMBG into daily life, and – as a bolder way to encourage proper testing – he proposed that SMBG could require a driver-license-like process of training and certification (Heinemann et al., JDST 2012).


Self-Monitoring of Blood Glucose is Useful in Patients with Type 2 Diabetes Mellitus on Oral Agents - Con

Jeffrey W. Stephens, MBBS, PhD (Swansea University, Swansea, United Kingdom)

Dr. Stephens took the con side of the debate and argued that SMBG does not improve A1c to a clinically significant level in non-insulin treated type 2s. He ran through a series of professional guidelines and recent studies on the topic, concluding that the evidence is not clear on the subject. In RCTs and meta-analyses that did find a benefit of SMBG in non-insulin using type 2s, the average A1c improvement was typically ~0.25% (“We would not approve this if it was a new therapy for type 2 diabetes”). Dr. Stephens also emphasized the high cost of glucose monitoring, the decrease in well-being, and the potential for an increase in depression. In addressing Dr. Bill Polonsky’s STep Study, Dr. Stephens noted that the benefit of structured testing over the control group was only an additional 0.3% benefit – again, not clinically meaningful in his view. He closed, however, by emphasizing that a structured approach to testing that uses the blood glucose data meaningfully may be warranted in certain patients: those who are educated, motivated, and at risk of hypoglycemia, have inter-current illnesses, are fasting, or when using sulfonylureas. (One editorial perspective is this – we believe the traditionally lauded RCT design of most trials hurts BGM, since it is so “not” real-world in our view. We hope for more realistic trials moving forward.)


Accuracy Standards for Self-Monitoring of Blood Glucose - Are They Attainable?

George S. Cembrowski, MD, PhD (University of Alberta, Edmonton, Canada)

Patients with pumps and hypoglycemia unawareness, among others, require relatively high accuracy, as do people in the ICU – a particular emphasis of his talk. (As a side note, Dr. Cembrowski said that hospitals often favor central laboratory glucose testing because it is the least expensive option, even though it is also the slowest.) For hospital systems, he favors the CLSI’s proposed targets of within 12 mg/dl for values below 100 mg/dl and within 12.5% for higher values; he indicated that the upcoming new ISO 15197 requirements (95% of results within 15 mg/dl for values below 100 mg/dl or within 15% for higher values) are also a move in the right direction. Briefly reviewing two anonymous hospital-use meters, he concluded that BGM products seem to be accurate enough in the hyperglycemic and upper-normoglycemic ranges, and “probably” in the lower-normoglycemic range as well. He said that the picture is less certain in hypoglycemia, in part because accuracy testing in hypoglycemia is often “contrived” – i.e., based on altered blood samples rather than blood from actual hypoglycemic patients.  


Panel Discussion

Lawrence Blonde, MD (Ochsner Health System, New Orleans, Louisiana); Andrew J.M Boulton, MD (University of Manchester, UK); Lutz Heinemann, PhD (Science & Co, Dusseldorf, Germany); Jeffrey W. Stephens, MBBS, PhD (Swansea University, Swansea, United Kingdom); George S. Cembrowski, MD, PhD (University of Alberta, Edmonton, Canada)

Comment: We have to be careful to look at studies with biases. In the Farmer et al. study, there was no transfer of information to meaningful therapy decisions. You have to look at those studies differently. If you think about SMBG and insulin treatment, there’s no doubt it is of value. The information is directly transferrable to insulin. This principle can also be applied to last year’s STeP study – glucose information was transferred into a medically meaningful modification of drug therapy or lifestyle intervention. The absolute effects were comparable to effect sizes with drugs.

Dr. Stephens: There’s been uncertainty in the previous studies.

Dr. Heinemann: This is clear. The more recent studies are the ones that we should take more into consideration. Meta-analyses have limitations and they cannot be better than the studies they include. I believe in the STeP study and it was an important step in the right direction.

Q: Talking about outcome, there is a fixation on A1c – a measure of average glycemic control. Glucose variability has not been looked at. My second question is about testing sugars in sulfonylurea patients. You said that’s reasonable, but it may not be for non-hypoglycemia causing therapies. Is there any study looking at that specific question?

Dr. Stephens: I’m not aware of any studies. The majority of studies performed were before the era of DPP-4 inhibitors and GLP-1 analogs. If any study is designed now, it should take into account use of these agents. Perhaps it should have more than one arm.

Q: I would argue in one of studies, it was said that newly diagnosed had a higher reduction in A1c with blood glucose testing. But they were probably on treatments that did not cause hypoglycemia.

Dr. Boulton: In patients using therapies that don’t cause hypoglycemia, testing might be beneficial due to better adherence to lifestyle recommendations.

Comment: Surely the answer is somewhere between yes and no. If I do a lot of testing and find that my sugar goes up after supper, aren’t we better off for that?

Dr. Blonde: Individualization was a big theme of the ADA/EASD position paper, and it seems like this is a key area where therapy can be individualized. The patient and healthcare provider can decide together if SMBG can be used to improve care.


Economic Aspects of Self-Monitored Blood Glucose

Philip Clarke, PhD (The University of Melbourne, Melbourne, Australia)

Dr. Clarke told a waning crowd in this afternoon symposium that while it is established that SMBG is beneficial for those with type 1 diabetes, the evidence is less clear for type 2 diabetes. Despite the evident short-term benefit SMBG has on A1c levels in people with type 2 diabetes, there have been mixed results on its long-term benefit in terms of mortality, quality of life, and cost-effectiveness. He explained that while the evident costs of SMBG are quite clear – in 2002, SMBG cost Medicare B nearly half a billion dollars – it is more difficult to define and measure the benefits. For example, Dr. Clarke said that the information provided by SMBG may be positive (e.g., encouraging more exercise) or negative (e.g., making someone more anxious) depending on the circumstances. However, UKPDS did find a reduction in mortality due to SMBG in people with type 2 diabetes, but it took 12 years for the reduction to become significant. The results have been particularly mixed when researchers attempt to determine the impact SMBG has on quality adjusted life years (QALYs; one QALY equals a year of full health and zero QALYs equals dead) of people with type 2 diabetes. Dr. Clarke next turned to costs, arguing that the United States must start thinking about which health care technologies are worth funding and which are too expensive. He concluded by providing several suggestions for alternative ways of measuring the cost-effectiveness of SMBG. 

  • The impact of SMBG on the quality of life adjusted years (QALYs) in people with type 2 diabetes has been inconclusive, making it difficult to do a comprehensive cost-benefit analysis. To see if type 2 diabetes patients have better quality of life due to SMBG, Dr. Clarke looked at participants’ quality adjusted life years using the open, parallel group, randomized Diabetes Glycemic Education and Monitoring trial) DiGEM. During the trial, QALYs were actually reduced in the treatment arms receiving less intensive SMBG (-0.008 QALYs) and more intensive SMBG (-0.035 QALYs) than in the usual treatment arm. Dr. Clarke said the reduction in QALY was mainly due to anxiety and depression. In contrast, the Center for Outcomes Research (CORE) assumed effectiveness based on effects observed in the Kaiser Permanente diabetes registry. This registry included 5,867 patients who were newly beginning SMBG and were only on oral anti-diabetic medications. They found that an A1c reduction of 0.32% results in an additional 0.103 QALYs. Cameron et al. (CMAJ 2010) found similar results: an A1c reduction of 0.25% (baseline not reported) translates to 0.024 QALYs gained.
  • Dr. Clarke said that given the current state of healthcare spending, the United States must start making decisions about which therapies and treatments are cost effective. In his view, disinvestment from ineffective therapies needs to be a critical component of efforts to control healthcare expenditures in the United States. He emphasized that when it comes to health care systems, early interventions are generally going to be easier than waiting until a crisis hits and only hard choices are left on the table. Thus, there needs to be a definitive study on the cost effectiveness of SMBG in treating type 2 diabetes. One method he proposed for such a study would be to assign some predetermined target as effective (for example, an A1c reduction of 0.5% in three to five years, with no reduction in quality of life). If SMBG demonstrated that it was able to meet this target then it should be kept; otherwise, it should not. Such a study, of course, would be expensive, and coming to a consensus on what is effective would be challenging as well.  That said, such a study, especially if it incorporated CGM and regular tests on whether therapy was “working” might help us move toward more consensus on what personalized and individualized medicine means.
  • Dr. Clarke proposed trying to lower the costs associated with SMBG by researching if a cheaper test-strip is equally effective, or by motivating meter and strip  manufacturers to help people with diabetes see the benefits of better outcomes. He proposed a system in which one would only pay the manufacturer a portion of the meter and strips’ costs initially and then only pay the rest if the individual’s levels stayed below a wanted target. We think that while making diabetes management more cost-effective is critical, there are too many variables to make this suggestion practical – some patients, for example, have the funds to eat good and healthy food while others do not; some have jobs and families that prompt stress and in turn cause diabetes management to be more difficult than others – etc!  We do appreciate critical thinking on the part of Dr. Clarke, however, and hope that his talk spawns other suggestions of note.


Symposium: Which Technologies Have Impact on Clinical and Behavioral Outcomes in Diabetes?

Behavioral and Clinical Outcomes of Continuous Glucose Monitoring - What is the Evidence After a Decade of Continuous Glucose Monitoring?

John Pickup, MD, PhD (Kings College London School of Medicine, London, UK)

Dr. Pickup gave a very comprehensive literature review of the clinical and behavioral evidence supporting use of CGM. He began by showing a number of studies to suggest that A1c improves with CGM, but only if patients wear the sensor (after showing the famous summary slide of the JDRF CGM trial, Dr. Pickup humorously quipped, “I’ve seen this slide four times today. I’m bored of my own talk”). In addition to sensor usage, his 2011 meta-analysis also found that baseline A1c and age were related to the magnitude of A1c benefit. Dr. Pickup asserted that there is good RCT evidence to support improvements in glycemic variability and mild/moderate hypoglycemia with CGM; however, there is no RCT evidence to suggest that CGM improves severe hypoglycemia – trials have not been designed or powered to test this, they had very low levels of severe hypoglycemia at baseline, and did not test CGM specifically for those with disabling hypoglycemia. He showed that there is data to support use of CGM in special circumstances like pregnancy, type 2 diabetes (“an emerging application for CGM”), and in the hospital (good accuracy with current devices). Although observational studies have seen a positive effect of CGM on behavioral outcomes, “surprisingly and disappointingly,” RCTs have not shown a benefit. Dr. Pickup believes insensitive psychosocial measures, high baseline quality of life, and low baseline hypoglycemia and A1c may play a role. Turning to frustrations, Dr. Pickup highlighted that good coping skills (stoicism, problem solving), good use of information (trend and pattern recognition), and significant other involvement are important for success with CGM. Concluding, he stated that all behavioral and quality of life outcomes need to be studied in the target groups where CGM is indicated (continued disabling hypoglycemia and continued high A1c).

Questions and Answers

Q: Your individual patient meta-analysis was an extremely interesting application. Have you thought to use the same techniques in analyzing patient reported behavioral outcomes? Aggregated effects on patient reported outcomes may show little effect, whereas certain patients may have whopping effects.

A: The main reason we did not look at behavioral outcomes is we didn’t have that data. We had to write to all the study investigators and tried to get hypoglycemia measures and A1c. That was a moderate struggle in its own right. It would be good to move on to behavioral outcomes next. That’s a very good idea. I agree with you on the danger of looking at mean values.

Q: Most of the studies you referenced used many products and sensors that are no longer in use. Benefits from CGM are dependent on patients using devices, trusting the information, and acting on the information. There are significant differences between products and across generations of products. A meta-analysis does not give an accurate depiction of the way CGM is.

A: That’s a fair comment. That’s a constant complaint of meta-analyses – they look at the past and don’t capture the present. You have to wait until there is a significant number of trials before doing a meta-analysis. But the message remains the same. Sensor usage and baseline A1c and the reduction of mild to moderate hypoglycemia. What changes is the accuracy – maybe the magnitude of A1c effect through increased training programs and learning to use it. All your points are fair.

Q: There is a group of patients that frustrates our team: we think they’re good candidates, they meet all the criteria, and yet they stop using this device. And it stops rather quickly, within two weeks or two months. And they drop way off. How do we do that way better?

A: It’s the same with pump therapy. There are frustrated patients that you think ought to do well on pumps. The same applies on CGM. There are behavioral issues. We need to learn much more about that. This is an improving technology; it’s not a mature technology. Think of the early days of the computer. They were a blooming nuisance to work with. Dramatic improvements in CGM is my hope for the future so we can help patients like that.


Technology and Youth - How Can Children, Adolescents, and Emerging Adults Benefit from New Technology?

Korey H. Hood, PhD (University of California, San Francisco, San Francisco, CA)

Dr. Hood based his talk on a model of successful diabetes technology for pediatric and young adult patients. He explained that many of these patients lack important diabetes knowledge and skills, many place diabetes as a relatively low priority in their lives, and many are tech-savvy and have short attention spans. Dr. Hood thus believes that in general, the best effects on patient outcomes will be brought about by solid, visually engaging technologies that reduce the burden of care and act as a “scaffolding” to help people improve their self-management skills. The most straightforward tools are “direct,” such as historical insulin pumps and glucose meters. “Direct-plus” technologies, like continuous glucose monitoring, are those that add a layer of pattern management or trend analysis. Dr. Hood said that such direct-plus tools have been shown to simplify pattern management and to improve glycemic control, but their effects on self-management skills and burden of care are still not well characterized. As for “facilitator” technologies such as mobile applications, Dr. Hood noted that a variety of products are user-friendly and have shown promising early results. However, he remains unsure about whether the use of these tools in their present forms is sustainable.

Questions and Answers

Q: Do you have some indications of which technologies are which useful for most patients? Is it consumer-driven?

A: I think there is a lot of self-selection, which suggests that we should more frequently include more youth and young adults with diabetes in these decisions.

Q: I am also very tech-savvy and would love to use systems to facilitate diabetes control. But we are inundated with new technology every three-to-six months – how will we know which tools and websites are effective and worthwhile?  

A: I don’t think there is an easy answer. At the pace of technological development, we cannot keep up – apps do not need to go through the FDA, so they can be developed quickly. I think a good strategy is for find apps that complement their philosophy of care and then recommend those (rather than reactively investigating tools that patients ask them about). 


Product Theaters

iBGStar Innovation, Integration, Inspiration (Sponsored by Sanofi)

Bruce Bode, MD (Emory University, Atlanta, GA)

After providing an overview of the history and importance of blood glucose meters, Dr. Bode discussed the new iBGStar meter, the first meter integrated with Apple’s iPhone and iPod touch (for our first take on the device and marketing strategy, see our report at Before launching into the specific benefits of the device’s smartphone integration, Dr. Bode mentioned other strong points of the device, including that it is currently the smallest blood glucose meter in the world, it requires a small (0.5 microliter) blood sample size (slightly larger than the 0.3 microliters required for Abbott’s FreeStyle strips), and it provides results within only six seconds. Dr. Bode also touted the device’s accuracy, which he attributed to its dynamic electrochemistry – as we understand it, this innovation makes the iBGStar better able to compensate for environmental, blood sample, and manufacturing variations than the static electrochemistry used in other meters. He then detailed how the iBGStar seamlessly integrates with the iPhone and iPod touch, allowing users to easily display, manage, and share their diabetes information. Dr. Bode also emphasized the intuitive, user-friendly nature of the interface, which in his view doesn’t even need an instruction manual to use. Turning to higher-level functions, Dr. Bode mentioned the meter’s ability to analyze blood glucose data, especially for identifying above, below, and in target range values over customizable time periods. He noted that the iBGStar automatically generates a logbook – these readings cannot be changed, but they can be tagged with additional notes and information. Dr. Bode further emphasized the iBGStar’s data sharing potential, as the iBGStar can automatically generate and email reports and spreadsheets to the user’s healthcare provider or whoever else he or she chooses. He closed by sharing a number of anecdotes and a case study to help illustrate the high potential utility of the iBGStar both as a more discreet and attractive blood glucose meter and as a way for people with diabetes to more effectively monitor and manage their glucose as part of their treatment program.

  • Dr. Bode explained that smartphone integration provides a way for patients to significantly increase their access to monitoring technology without even realizing it. Dr. Bode quoted a statistic that 60% of people with type 1 diabetes leave home without a needle, but he then suggested that virtually none of them go out without their cell phone. There are 331 million total cell phones in the United States – greater than the total US population of 313 million – of which 165 million are smartphones. Dr. Bode noted that Apple in particular has enjoyed exponential growth with its iPhone, growing from 2.1 million in 2008, to 6.4 million in 2009, and then ballooning to 53 million in February 2012. He estimated that about 1.6 million people with diabetes in the United States have either an iPhone or an iPod touch.
  • The iPhone and iPod touch provide several features that improve the iBGStar’s efficacy as a blood glucose meter. Dr. Bode explained that Sanofi sought a partnership with Apple partly because, no matter how the design of the iPhone or iPod touch may change, the actual USB port is the same for all devices and never changes. This gave them the confidence that the product would be usable and useful long-term.
    • He noted its ability to share data both with family members (see below) and with health care providers. Dr. Bode noted that if the vast majority of HCPs do not currently download and look at patients’ blood glucose data, the iBGStar helps remove one step by sending them all data directly (of course, only time will tell if this will overload HCPs and/or how easy reimbursement is).
    • Dr. Bode also explained that the Diabetes Manager App could be constantly tweaked and improved based on feedback both from patients and HCPs, and then existing users could simply upgrade the app automatically. The result is greater utility of the iBGStar to change and evolve than its counterparts. Indeed, many features iPhone users take for granted are of great use in blood glucose monitoring – as Dr. Bode noted, because the iPhone’s internal clock constantly syncs with satellites, data entry on the iBGStar will always have the correct time, something that is far from guaranteed in other meters.
  • The iBGStar is particularly useful in helping children with diabetes and their parents monitor blood sugar. Dr. Bode noted that most children nowadays barely ever put their cell phones down, which among other things means that parents of children with diabetes can rest easy that their children always have their meters close at hand. Moreover, the ability to share data between Apple devices means that parents can receive instant updates on their children’s blood glucose levels even when they are at school. He also remarked that parents don’t have to give a cell phone to their younger children with diabetes to use the product – Dr. Bode mentioned that he advised the mother of a five-year-old with diabetes, who was understandably wary of giving such a young child an iPhone, that the child could use an iPod touch instead and still enjoy all the functionality of the iPhone version as long as there was wireless access. Dr. Bode also shared the amusing but heartwarming anecdote of how a teenager, recently diagnosed with type 1 diabetes and feeling quite down, was informed that he would be getting his own iPhone so that he could use the iBGStar and immediately declared, “This is the best thing that has ever happened!”
  • People with diabetes have responded very favorably to the iBGStar, both in terms of reactions to the product itself and in terms of improved diabetes management. Dr. Bode shared a number of anecdotes and case studies meant to illustrate the iBGStar’s appeal. He remarked that during testing, when people were first shown the iBGStar, three out of five people pulled out their current meter and said, “You mean I don’t have to carry this around anymore?” He went into some detail with a case study of a 57-year-old man with type 2 diabetes who in February 2012 had a blood glucose of 358 mg/dl and an A1c of 10.9% who switched from metformin, SUs, and a GLP-1 to a metformin- and insulin-based regimen in conjunction with frequent blood glucose monitoring. The patient had specifically mentioned his concerns that he would not be able to regularly monitor his blood sugar due to his heavy work schedule, but he did already have an iPhone and thus could use the iBGStar. Dr. Bode then showed the patient’s data as gathered by the iBGStar – weekly and monthly glucose values and clear indications of his time spent in and out of his target range. The patient, now on a heavy 100 units per day basal dose of insulin, was able to meet his three-month glycemic target and bring his A1c down into the 6% range.

Questions and Answers

Q: Is this an exclusive agreement with Apple? Is this the only device that will be allowed to connect to Apple phones?

A: That’s up to Apple and who they take. I presume others will probably do it. But Apple is very picky. They have to accept you – you don’t accept them.

Q: Could there be an additional app to manage insulin pump settings?

A: Here’s the problem. When you get into a consumer device managing a medical device, you can’t do it. It can transmit data, so you can technically take your blood sugar and put it on your phone, as long as all the calculations are done in the transmitter. That’s what happening right now. You calculate in the transmitter and submit it to the phone.


Helping Patients Manage Patterns of Hights and Lows Between Office Visits (Sponsored by Lifescan and Aimas)

Steven Edelman, MD (University of California San Diego Medical Center, San Diego, CA)

Given how little time people with diabetes are able to spend with their HCPs, Dr. Edelman highlighted the importance of empowering individuals to independently manage their blood glucose. He explained that LifeScan’s new OneTouch Verio IQ blood glucose monitor, which  features PatternAlert technology, help solve many of the challenges faced by patients monitoring their blood glucose. Dr. Edelman, who uses the device himself, also focused on the product’s ease of use, the accuracy of the OneTouch Verio Gold Test Strips, and the device’s eco-friendly design. Ultimately, Dr. Edelman presented the Verio IQ as a way to optimize insulin therapy and stimulate insightful conversations to facilitate better self-management. To see our review of the meter in diaTribe, please visit

  • Pattern management leads to improved glycemic control, but most patients act in the moment rather than making decisions based on patterns. Dr. Edelman cited a study demonstrating a 2.6 times higher chance of severe hypoglycemia following clinical patterns of low blood glucose (Lee-Davey J. et al, LifeScan Scotland). Unfortunately, in a study of 315 individuals using insulin, 76-79% treat out-of-range blood glucose results immediately, whereas only 10-17% review other recent out of range results to see if there if there is a connection, trend or pattern. (We would also note that making decisions based on patterns is challenging – historically, software has not been designed to do this and few patients actually download their meters.)
  • The OneTouch Verio IQ automatically provides high and low blood glucose pattern alerts. The meter analyzes past blood glucose data as soon as it is measured. For example, if a user has two lows within the same three-hour period over the previous five days, the Verio IQ will immediately alert the user indicating a low pattern with the message, ‘Looks like your glucose has been running low around this time.’ Upon clicking on the alert, the patient can see the specific glucose values and times of the day used by the meter to identify the pattern. 
  • People with diabetes often don’t understand the cause or solution to high and low glucose patterns; the OneTouch Verio IQ Pattern Guide provides insights into their causes and offers potential guidance. Though this paper guidebook is not meant to replace the advice of a physician, Dr. Edelman believes that it can be a powerful informational tool for patients to learn how to be proactive when interpreting blood glucose data. One side of the guidebook addresses high patterns, while the other side deals with low patterns. The guide includes pullout tabs for different periods of the day (before breakfast, after dinner, etc.) and suggests potential causes and reasons why the pattern may have occurred. For instance, on the side for a low pattern in the pullout for “Before Breakfast,” potential causes include “Long periods of increased activity” and “Too much intermediate or long-acting insulin before bed.” It also lists potential actions to take, such as adding a bedtime snack or reducing insulin doses. We like the user-friendliness of this guide and are glad to see LifeScan easing the often-challenging connection between blood glucose values and therapeutic changes.
  • Dr. Edelman listed other innovations of the OneTouch Verio IQ, emphasizing its eco-friendly battery and the accuracy of its OneTouch Verio Gold Test Strips. He pointed out the illuminated testing area, high-resolution color screen, and one-step meal tagging to highlight the product’s ease of use. The Verio IQ’s battery is rechargeable (requires charging twice a month based on five tests per day) and has a convenient one-minute rapid charge feature if the power is too low for testing. The Gold Test Strip Smart Scan technology scans 500 times, correcting for common interferences and does not interact with commonly used drugs (except for xylose.)


Corporate Symposium: Introducing FreeStyle InsuLinx - The Latest Advancment for Insulin Users (Sponsored by Abbott)

Welcome and Objectives

Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, TX)

Dr. DeFronzo opened by noting the symposium’s focus: introducing the newly FDA-approved Abbott FreeStyle InsuLinx meter (see our take on the recent approval and our first experience with the device at Dr. DeFronzo characterized the new meter as “quite an innovative advancement in blood glucose monitoring” and a “unique glucose monitoring device.” He noted that patients face a number of barriers to achieving good glycemic control, and the FreeStyle InsuLinx has been designed to overcome some of these barriers. Before discussing each speaker’s topic and background, Dr. DeFronzo concluded with a reminder that “we still don’t do a great job in the US overall” – over half the people with diabetes in the US have an A1c >7% and 25% have an A1c >8% (a source wasn’t named). Dr. DeFronzo attributed some of this lackluster performance to patients and physicians failing to review blood glucose data. If we can improve patient-physician integration and information gathering (presumably by using the FreeStyle InsuLinx), Dr. DeFronzo asserted that we can “markedly improve the level of glycemic control.” We’ll certainly be interested to see future studies of the FreeStyle InsuLinx to that effect.


Features and Benefits of Freestyle Insulinx System

Bruce Bode, MD (Atlanta Diabetes Associates, Atlanta, GA)

In the symposium’s main talk, Dr. Bode gave an upbeat overview of the useful features in the FreeStyle InsuLinx meter. He honed in on the FreeStyle InsuLinx’s Auto-Assist software and emphasized its “very unique” on-meter storage (requiring no downloads or installation), the very useful Snapshot report, the excellent graphical display of paired pre-meal/post-meal testing (“the major benefit of the FreeStyle InsuLinx”), and integration of data with logged insulin doses. Dr. Bode acknowledged that the US version does not have the bolus calculator, saying this was “obviously something to do with FDA”. He asserted that the FreeStyle InsuLinx is designed for insulin users, though he also believes it’s “great for non-insulin users too.” The latter group on diet and oral therapy can especially benefit from the paired testing (reminding us of Roche and Dr. Polonsky’s work on structured testing). Dr. Bode was also a fan of FreeStyle InsuLinx’s testing reminders, its very quick setup, and the simplicity for patients to remotely send PDFs and results for feedback. Dr. Bode closed by presenting three case studies (a type 1, a type 2 on MDI, and a type 2 on basal-only) – the emphasis was on diagnosing problem areas and giving patients feedback. We thought he persuasively highlighted the real clinical value of the FreeStyle Auto-Assist Software in this part of the presentation. Although he was a fan of the touchscreen (“so simple”), Dr. Bode’s features discussion was very clearly centered on the FreeStyle InsuLinx’s software. We’ll be interested to visit Abbott’s booth to further understand how the company is positioning the meter for the US market.

  • Dr. Bode urged attendees: “The report you must print out is the Snapshot report. You must look at this.” This report includes glucose and logged insulin data. Dr. Bode highlighted the ability to customize target ranges based on patient type (pregnant vs. older with cardiovascular disease risk) and the clear display of mean blood glucose and standard deviation. He recommends that the mean blood glucose divided by two is an acceptable standard deviation. Dr. Bode also emphasized the importance of getting patients to log their insulin doses (on an interesting aside, one of Dr. Jane Seley’s patients has used the FreeStyle InsuLinx to log Symlin doses). The glucose data displayed includes a bar graph depicting time in- and above-target, standard deviation, average blood glucose, test frequency data, trends and averages over time, and notes (e.g., “100% of BG values above target range (70-140 mg/dl) in morning” or “BG standard deviation may not be the best indicator of glycemic control because the average is outside the range of 110-180 mg/dl”). The Snapshot report also displays logged insulin data, including total daily dose and basal-bolus ratio.
  • Dr. Bode concluded the presentation with three case studies using FreeStyle Auto-Assist reports – themes included the ease of diagnosing problems and changing therapy, the importance of showing patients a picture, the simplicity of report interpretation, and patients’ reluctance to do pre- and post-meal tests and log insulin. When all three patients initially began using the meter, they infrequently did paired testing (pre- and post-meal) and rarely logged insulin doses. The reports clearly revealed this and prompted a discussion with patients.
    • (1) In the first case study, Dr. Bode knew within 30 seconds that hypoglycemia was a problem, as average blood glucose minus standard deviation put the patient below 70 mg/dl. He emphasized that the entire process occurred digitally, as the patient uploaded the PDF, Dr. Bode reviewed it, and then called the patient to recommend lowering his basal and increasing his meal-time boluses. Dr. Polonsky chimed in that the graphical reports are really valuable from a psychologist’s perspective as well, and they may even prompt better cooperation and adherence.
    • (2) The problem in the second case study was readily apparent after looking at the modal day report: the patient’s glucose average steadily rose between dinner and bedtime. After Dr. Bode showed the patient the report, he admitted a tendency to overeat at dinnertime. The solution was reducing the glargine dose and increasing the bolus dose at dinner (The patient was resistant to trying GLP-1, though Dr. Bode acknowledged in the session that 5 mcg of exenatide would likely be easily tolerated; Dr. DeFronzo suggested pramlintide might also be useful).
    • (3) The third case was a patient with hyperglycemic values throughout the entire day. The suggestion was to increase basal insulin to get fasting glucose down. Dr. Bode also removed the glipizide and sitagliptin, as the patient had a history of hemochromatosis with cirrhosis and had just had a lacunar stroke.


Understanding the Barriers of Patient Logging and Pattern Management

William Polonsky, PhD, CDE (Behavioral Diabetes Institute, San Diego, CA)

Dr. Polonsky discussed how not enough people with diabetes are keeping complete glucose and insulin logs and the obstacles that are keeping them from doing so. He began the presentation by reminding the audience of the importance of regular SMBG to improve glycemic control. Yet, in one Danish/British study conducted in 2000 (Hansen et al., Diabetes Research and Clinical Practice), nearly a quarter of individuals with type 1 diabetes checked their blood glucose less than once a week and only 39% checked it at least once a day. Individuals with type 2 diabetes had similarly low rates of SMBG in the NHANES study from 1988-1994. Dr. Polonsky also explained that insulin users struggle to keep track of the daily activities that influence their diabetes. Even amongst patients who reported occasional SMBG, only half reported bringing their SMBG data to their medical visits and an equally low percentage actively responded to their SMBG data. Dr. Polonsky believes that the three major obstacles keeping patients from faithfully monitoring blood glucose are: (1) inconvenience; (2) patients do not view it as worthwhile; and (3) patients never go over the data with their HCP. Concluding, Dr. Polonsky asserted that the FreeStyle InsuLinx can help address some of these barriers, especially by making it easier to log data, see trends, and make therapeutic changes.

  • People with type 1 and type 2 diabetes test too infrequently and struggle to log their test results and insulin doses. Dr. Polonsky highlighted a 2011 self-reported survey of 500 people with type 1 diabetes and 504 people with type 2 diabetes. The study found that a large portion of insulin users are not logging their test results and even fewer are recording their insulin dosing. Importantly, the fact that the data was self-reported mean that the percentage of patients who do not log results is probably higher than the table below reflects.


Insulin users log their test results using (%)

Insulin users log their insulin doses using (%)

Do not log results



Electronic logbook



Paper logbook




  • Few patients bring their SMBG data in when they meet with their HCP – even when they do, few are actively responding to their blood glucose numbers. In a study of 869 individuals with type 2 diabetes who reported having at least occasional SMBG use, only 50% of noninsulin users (NIU) and 58% of insulin users (IU) regularly brought their SMBG data to their medical visits. Furthermore, these individuals did not take action in response to highs and lows: 56% of NIU and 54% of IU reported that they were just observing their numbers. Even when patients do bring their information in, Dr. Polonsky noted that the data is often sparse, disorganized, and hard to interpret. In response to this data, Dr. Bode asked the audience, “How many of you are still willing to meet with a patient if they do not bring their meter or a log?” Approximately a fifth of the audience sheepishly raised their hands – we would bet the number is actually far higher. We also note that this would be easier for patients if they could see their HCP download it for them, which doesn’t happen in many centers (there are some exceptions to this – we also see this changing through a Helmsley Charitable Trust initiative that you can see more about at  Dr. Bode said that if a patient does not bring their meter or a log to an appointment, he sends them home to get it. Dr. DeFronzo jokingly added that Dr. Bode’s office still charges the patient for their visit to ensure that they bring their meter or log the next time.
  • Dr. Polonsky stated that the three major obstacles preventing patients from regularly monitoring their blood glucose levels are: (1) inconvenience; (2) a perception that it is not worth the effort; and (3) their HCP either never looks at or discusses the data. Dr. Polonsky said, “The daily ‘job’ of diabetes is already demanding enough” for individuals before needing to spend the time to regularly check and record one’s blood glucose levels. When nothing is done with this information, patients become further frustrated and discouraged. He quoted one of his patients as saying, “When my results are too high, I just get so mad and disappointed with myself.” Dr. Polonsky repeatedly emphasized that the emotional valance patients attach to their blood sugar numbers causes them to avoid testing their blood glucose and/or act dishonestly about their numbers. To try and counter this, Dr. Polonsky gives his patients stickers to place on their meters, which say ‘Remember, it is just a number.’ Many HCPs also find meters are cumbersome to download and the data patients do record is often difficult to interpret. The result is data that is not reviewed at all, which leads patients to conclude that the data is not worth gathering (or that it can be gathered dishonestly). In one particularly unsettling case, Dr. Bode mentioned he had a young patient who used her lunch money to pay students at her school to prick their fingers. While she had perfect blood glucose numbers on the meter, her A1c was very high. Dr. DeFronzo concurred and said that he believes many patients want to impress their HCPs more than they want to take care of themselves. 
  • Dr. Polonsky believes that patients will check and log their blood glucose more faithfully if it is more convenient to do so and if their HCPs take the time to review the data. Dr. Polonsky’s STeP study found that if physicians have the time to sit with patients and review their data carefully, then patients were more likely to track their blood glucose and insulin dose. A1c levels decreased significantly relative to the control (Polonsky et al., Diabetes Technology & Therapeutics 2011). Dr. Polonsky emphasized that HCPs should use this data not just to answer their own questions, but questions that are important to the patient. Key in all of this is having data presented in a way that highlights trends – such representation would help HCPs and also increase the number of patient ‘ah-ha’ moments. Dr. Polonsky reviewed an example to illustrate this point. One of his patients had a similar meal each morning and measured her blood glucose right before the meal and two hours later for a week. She logged the data in a structured table, making it easy for her to determine her average blood glucose change. She was motivated by the obvious impact her data collection was having on modifying (or in this case maintaining) her treatment and wanted to know what questions they were going to look at next. Dr. Polonsky concluded by saying that he likes that the new FreeStyle InsuLinx system allows data to be collected in a way that causes trends to ‘pop’, and that it would have made his patient’s breakfast experiment even easier.


Panel Discussion

Panelists: Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, TX); Bruce Bode, MD (Atlanta Diabetes Associates, Atlanta, GA); William Polonsky, PhD, CDE  (Behavioral Diabetes Institute, San Diego, CA)

Dr. DeFronzo: These cases demonstrate how easy it is to use the instrument. It gives a clear picture and helps patients and providers see what’s going on. Now getting to the questions. In the cases, the patient had a low blood sugar at night and a high sugar during the day. Is that a common situation? How do you handle this?

Dr. Bode: There is no question that often people over basalize themselves. They never take enough meal bolus. It’s a knee jerk reaction to the blood glucose running high – up the basal. What that patient needed to do was lower the Levemir and increase the meal dose. It’s a very common problem in both type 1 and type 2s on MDI. It’s especially common in type 1s on a pump. It’s really a knee jerk reaction from the HCP – go up on the basal. Really, you need to cut the basal and go up on the meal dose insulin.

Q: What are your thoughts on Levemir versus glargine?

Dr. Bode: Our hospital just did a weight loss based switch study in which patients went from Humalog to Novolog and Lantus to Levemir, which made no sense. Both insulins work and it does not matter what type of insulin is prescribed. However, there are some special circumstances, such as young individuals with type 1 diabetes, who only need small doses of insulin. Therefore, you need to give them two small doses because the larger the dose you give, the longer it lasts. The smaller the dose, the shorter it lasts. Levemir can be prescribed for twice a day, while many others cannot

Q: How do you see this system integrated with pump therapy?

Dr. Bode: Lots of pumps are integrated with link meters. OneTouch has the Ping into the Animas system. Medtronic initially had BD, and then Novo Max came in. There was the J&J LifeScan meter and now the Bayer Contour Link. Then you have the FreeStyle meter in the OmniPod system. The major benefit of the FreeStyle InsuLinx system is the paired testing. I’d love to have it link up, but it’s really the ability to get the paired testing. And you don’t need anything – no software. The cable comes with the meter. You can put it on your iPad. You can put it on your Mac device or any PC device. I encourage you to talk to your Abbott rep and go to the booth. It’s amazing and an impressive thing that the software is built in.

Q: I have a patient who refuses to check their blood glucose more than once a day. How would you approach this patient?

Dr. Polonsky: Being a psychologist I would start by asking them questions. I would ask them ‘what is in your way from checking more often?’ Often you will find that your patient says that they do not see the value in monitoring their blood glucose. We need to be able to sell the value. We have to tell them ‘here is how I am going to use that data to make your quality of life better.’ In response to the earlier question I am concerned about how easy it is to overwhelm people. When I think about all the software on this device, and all the possible summary data and graphs, I am concerned that it may overwhelm either the HCP or patients, and we need to be careful about that.

Dr. Bode: You may just want one Snapshot report. Typically you just use a few reports. Meal average, Snapshot, and Logbook. I probably would get rid of the rest. But it only takes about three minutes to set it up.

Q: Dr. Bode, you said if your patients showed up without a meter, you said you sent them home. Can you expand upon this?

Dr. Bode: We mandate that you have a downloadable meter. If you bring in a log that’s perfect, we’ll certainly look at it. But we want the meter to validate it. If a patient doesn’t bring it, our medical assistant says ‘Go home and get it. We’ll fit you in the afternoon.” I cannot help them if they don’t bring the meter in. What am I going to do, socialize with them? If a patient’s A1c was 10%, I might say, ‘Well what were you overnight?’ The patient response might be ‘Well I don’t know.’ And I would say, ‘What were you during the day?’ And the patient might respond, ‘During the day, I’m doing well.’ What do I do in that case? I’ll put them on professional CGM if I need more data. I do that all the time. A picture is worth 1,000 words. Once you see it, it’s a game changer.

Dr. Polonsky: As the psychologist on the panel, I get queasy at the idea of sending patients home if they don’t bring in their blood glucose data. But it does send a message that it’s so valuable. In qualitative studies, we ask patients why they don’t check their blood glucose. The response is ‘No one looks at the data.’ It’s so powerful when patients know that this is so core to what we’re doing that I cannot talk to you without it. That sends a powerful message.

Dr. DeFronzo: You showed it in the STeP study. When doctors looked at the data, the improvement was bigger. Bruce is saying the same thing. That offers a big whack on the side of head when the patient shows up.

Dr. Polonsky: Those patients in the intervention group that didn’t bring in data didn’t show a benefit. That conversation between the patient and provider was critical.

Q: I believe that if the patient is not writing in a logbook, he is unlikely to log data in a meter either. What do you think?

Dr. DeFronzo: There is a big difference between having to write something down and just having to touch a button.

Dr. Bode: I agree with Dr. DeFronzo – with technology you can just touch a button and it will log the data for you; it is seamless. There is a big difference between writing something on paper and having technology record it for you. For example, when I write down notes at these meetings and then get home and look at them I go ‘what are all these notes?’ But if I was using a technological system like this then it keeps all those notes straight.

Dr. Polonsky: We have seen that making data logging easier and more convenient causes a big improvement in the rate at which patients record data.

Q: How often do you get complete data on insulin users?

Dr. Bode: The problem is when people have multiple meters that don’t talk to each other. We’d like patients to have one meter. We also encourage meter companies to talk to each other. People might have four meters, and if you could upload and get them to one database, that’s beneficial. I encourage patients to use one meter. You don’t want them using two or three FreeStyle InsuLinx meters. People always need to carry the same meter and work on that. Whatever makes it easy and simple for a patient, the compliance increases dramatically.

Dr. Polonsky: I almost never see patients with comprehensive data. The reason is that I typically see patients who are struggling. But I love hearing about patients that are doing well.

Q: Earlier in the talk, Dr. DeFronzo mentioned splitting the insulin dose when you get above 60-70 units. What do the other speakers think of this?

Dr. Bode: When you’re taking a dose of insulin and you get above 40 to 50 units of these long acting units, then you do not need to split it. However, when you get to over 100 units, then you clearly have to split it because it will require two syringes. In that case, I will definitely look at and probably modify what they are taking.

Dr. DeFronzo: Both glargine and detemir work for 24 hours. Did you see a tapering of the dose in the last six hours?

Dr. Bode: There is no question that there is dose tapering. It is prolonged a little more with Levemir than glargine. At about 0.1 units per hour, they last six to seven hours. Doses of 0.4-0.8 units per hour last much longer. For type 1s on a low dose, you have to split it a lot.

Dr. DeFronzo: For type 2s, you may be giving what you think is a reasonable dose of insulin, but they’re severely insulin resistant.

Q: Currently, many of the treatment guidelines do not use blood glucose values for adjusting patients’ dose; they focus on A1c. Since we are at the ADA, I was wondering if you would push the ADA to add blood glucose values to the guidelines?

Dr. Bode: Many studies that helped determine these guidelines, including ACCORD, were A1c driven. Yet, one A1c value in one individual may mean something very different in another individual. You have to look at blood glucose levels when making decisions about treatment. There is no question that you need glucose data.

Dr. Polonsky: Another reason to encourage blood glucose monitoring is that it is one of our least used motivational tools. Usually it is a de-motivational tool. But I think what we saw here is that it is valuable for physicians and valuable and impactful for patients when done well.

Dr. DeFronzo: I particularly like that you can look at blood glucose levels over the week because it is easy to see for both the HCP and patient what needs to be done.

Dr. Bode: I agree. When showing this data, it’s key to make sure that you are not blaming the patient or making them feel bad about the data.

Q: We use a dosing tool with an insulin adjusting formula. Will the reports ever be available as a .CSV file that can be turned around and sent back?

Dr. Bode: That’s a good point. That file would go into an Excel file or a database. We do this in our hospital with Glucommander. Actually, we recently got approval for outpatient use of Glucommander for subcutaneous insulin. We’re encouraging meter companies to do provide reports in this format. It’s key to trying to close the loop for patients. We think you need to touch base with people weekly and encourage positive reinforcement. I like the idea uploading from home and sending the PDF – I love the digital format and using an automated system for dosing. Most people just don’t know how to adjust insulin appropriately.


Corporate Symposium: Setting the Stage for Better Type 1 Diabetes Care - Live Clinician-Patient Conversations on the Challenges of Continuous Insulin Infusion (Sponsored by Lilly USA)

Faculty Presentations

Steven Edelman, MD (University of California, San Diego School of Medicine, San Diego, CA); Peter Chase, MD (Barbara Davis Center, Denver, CO); Howard Wolpert (Joslin Diabetes Center, Boston MA); Howard Wolpert (Joslin Diabetes Center, Boston MA)

Dr. Edelman gave a comprehensive overview of CSII, beginning with the history of insulin pumps and the main features of current technologies. He noted that pumps have a number of advantages over MDI, including enabling better glycemic control, less weight gain, and more lifestyle flexibility. However, pump therapy also lead to irritation at the infusion site, unexplained hyperglycemia, or improper medication due to technology malfunction or improper use. Dr. Chase discussed who should use insulin pumps, as well as contraindications for insulin pump therapy. He noted that those starting on pumps should be willing to self-monitor blood glucose, have motivation/desire to use a pump, have family support, and be knowledgeable about diabetes. According to Dr. Chase, contraindications to insulin pump therapy include lack of motivation, unwillingness to self-monitor, and non-adherence to injection regimens. Dr. Chase also touched upon suggestions for pump therapy in pediatric populations (including those under six years of age) and the benefits of pump therapy for preventing post-exercise hypoglycemia. Finally, Dr. Wolpert discussed the adoption and use of pump therapy and emphasized that physicians need to focus on engaging patients in their own self-care. He opened by noting that while diabetes technology can make treatment more manageable, it also imposes additional burdens that physicians should consider when individualizing treatment approaches. Dr. Wolpert cautioned that “early young adults,” people aged 18-25 years, balance competing priorities that can distract them from diabetes care and may not be strong candidates for CSII use. Like Dr. Chase, he underscored that patients who adopt pump therapy should be skilled at diabetes self-management. Dr. Wolpert concluded his talk with a brief discussion on the role of CGM in diabetes management, noting that while CGM use has several advantages, patients must have realistic expectations of the technology and an understanding of its drawbacks.


Live Clinician-Patient Conversations and Discussions

Dr. Chase’s patient, 17-year-old Monica, discussed her twelve-year experience with insulin pump therapy and the situations she expects to face upon entering college this fall. Monica stated that her favorite aspect of pump therapy was that it allowed her to snack often without worrying about injections. She noted that this advantage would be particularly useful during college because students often eat at irregular times.  Dr. Wolpert’s patient, Paul, then touched upon the burdens associated with use of CGM and pump use, how he uses the technologies in conjunction with exercise, and how he uses the data from his CGM.  Finally, Dr. Edelman’s patient 76-year old patient Barbara (who was misdiagnosed with type 2 diabetes at 64 by five physicians before being correctly diagnosed with LADA) described how she has adjusted to a CGM and pump, despite not being “tech savvy.” Throughout her comments, she stressed the importance of doctors listening to their patients.


Panel Discussion

Peter Chase, MD (Barbara Davis Center, Denver, CO); Howard Wolpert (Joslin Diabetes Center, Boston MA); Bruce Bode, MD (Emory University School of Medicine, Atlanta, GA); Steven Edelman, MD (University of California, San Diego School of Medicine, San Diego, CA); and their patients.

Q: How often would you start a patient on pump therapy with saline first?

Dr. Edelman: I typically do not and I don’t think I ever have. Because by the time I think a patient would do well on pumps, I think using saline not needed in my experience.

Dr. Wolpert: It varies. With some patients there’s value in using saline so that the patient can get used to using the pump technology and the controls. But for most patients, we put start them with insulin. But it’s something that should be individualized for each patient.

Monica’s Mom: Monica was five when she started the pump and before we let her use it, my husband and I wore the pump for a few weeks to understand how it works. That process helped us a lot. It was invaluable.

Dr. Bode: I encourage all HCPs to wear a pump and to wear a sensor so you understand what it is like. Especially when starting children on a pump, it’s great to put the parent on the pump first.

Dr. Chase: By our standard, almost everybody does a week or so of saline and performs a set change at home before they come back and start insulin with the pump . If they’re coming from out of town, maybe they’ll start insulin right away, but 98% of our children begin on saline.

Q: How early can you start pump therapy after diagnosis? Would you start a patient within six months of being diagnosed?

Dr. Chase: There are studies that I am involved in that are putting newly diagnosed patients immediately on the pump. The reason is because when someone is recently diagnosed, the liver is producing huge amounts of sugar because there is no insulin. We hypothesize that by turning off the liver early, we might prevent some of the glucotoxicity that destroys the islet cells and we can help people produce insulin for longer periods. If we show that we can shut off the destroying of the islet cells by glucotoxicity, everyone may go to the hospital after being diagnosed for a period of closed-loop pancreas before going home on the pump to keep their pancreas working. If they do that, they may be less likely to have hypoglycemia and develop complications.

Dr. Edelman: when it comes to type 1 diabetes, and maybe all diabetes, there really should be no hard and set rules for anything. I don’t think there should be a set time frame or age limit for pump therapy. I have a patient that has done extremely well – he’s blind and he’s had incredible control over his insulin pumps. I don’t think there should be any limits.

Q: Any data on pump therapy with the elderly?

Barbara: As the patient population matures and there are more of us diagnosed as type 1 later in life, there will be a greater need to include elderly in clinical trials.

Dr. Bode: Dr. Wolpert and I were in a JDRF group that was studying CGM and we only had 5% elderly who were on CGM and so it was hard for us to submit the data to FDA.

Dr. Edelman: I’ve been to the Medicare hearings in Baltimore and something I don’t understand why they think that diabetes is easier to control when you go from 64 to 65. When I went to San Diego as a fellow in 1987, there was a group called insulin pumpers. They are still a strong group and one thing I noticed the last time I was there was that many members are starting to reach retirement. It’s nice to know that people are living long enough to be in that age group, but the government isn’t prepared.

Q: In the previous presentation, one of the contraindications for pump therapy was hypoglycemia unawareness if you live alone. I have a patient had hypoglycemia unawareness who lives alone and I thought pump therapy would be good for her. Am I wrong?

Dr. Edelman: A meta-analysis of over 15 studies showed that the chances of having a severe hypoglycemic episode are lower with a pump. I think that the first step for that patient would be CGM. That would have a more beneficial effect than a pump.

Dr. Bode: I think that’s a mistake in the slide. Many people who live alone wear a pump. I agree that you should use a sensor. Always, when starting therapy, you should decrease the insulin dose just to be safe.

Q: There is no insulin approved for under age 3. What should pediatric endocrinologists do?

Dr. Chase: This has been a real problem in pediatrics. Previously there was no approval of any type 2 agent for pediatrics. So you use these medicines by “physician discretion”. Now the FDA is starting to tell companies that they have to prove that their medications are safe in children and I applaud this approach by the FDA. While it may make the development process longer, it’s a good precaution to take.

Dr. Bode: Insulins have be proven safe through multiple analyses and tests – that’s been done. The problem is that when people are younger than age three, it’s harder to get them to enroll in a trial.

Q: Pumps cost more than injections. The President’s address talked about tsunami of diabetes coming to the US and the rising cost of healthcare. Do you think you’re wasting the government’s money by being on a pump?

Barbara: I’m paying for my pump, so I’m not wasting the government’s money.

Monica: No. I think it’s definitely worth it. It helps me out.

Dr. Bode: So basically, it’s like saying, “Do you treat breast cancer in a women?” If you look at cost effectiveness data, the cost of CGM and pumps are clearly within the guidelines of paying for healthcare. If you have to live day to day with diabetes and try to manage with multiple daily injections, you see that it’s not an easy disease to live with. There’s quality of life involved. That’s one of the goals of diabetes management – trying to live a reasonable quality of life.

Q: Why does giving insulin 10-15 minutes before a meal increases glycemic control while giving it after the meal or just before the meal doesn’t?

Dr. Chase: If you’re really trying to lower A1c – and this is more of an issue in teenagers when hormones are increasing and the risk of complications goes up dramatically –giving a bolus 15 - 20 minutes before a meal helps the glucose levels stay below 180 mg/dl. The ADA said that people should not have glucose levels above 180 and there’s no way you can keep it below 180 without giving a bolus before you eat.

Q: Paul when you were younger, you rebelled and did not adhere to your regimen. Did it help when HCPs told you that if you don’t manage your disease, you’ll get major complications?.

Paul: No, not at all.  You don’t internalize it until you actually see something happening right in front of your face. For a lot of people, it takes a sign to motivate behavior change and that’s certainly true for diabetes. It’s the same decision balance model as any other sort of behavioral activity. Are the benefits going to outweigh the effort it takes to accomplish a task? Even now, I do things that I know will raise by blood sugar, but I make that decision.

Q: When will we have closed loop out there?

Dr. Chase: It will come in parts.  The first part with be turning off pumps in response to low glucose levels. That should occur in one to two years in the US. The second part will be turning off  the pump with predicted low sugar levels, which will likely take three to four years.. The third part is just having closed loop when someone is lying in bed at night. The mean A1c for teenagers in the type 1 diabetes registry during the past year has been 8.6-8.7% - this is where we were 20 years ago. We have not made a lot of progress. I think that if you can even control that for the hours that they are asleep, that will help. I think it will come in stages and it will be a couple of decades before we see it fully complete.

Dr. Wolpert. I agree that it will come in steps. The pumps today are essential the same as those 20 years ago, with changes in ease of use and delivery. I think for the next couple of years, we’re talking about proof of concept studies to show that the technology will be effective.

Dr. Bode:  I think capitalism is great – there’s a lot of competition out there. People are working to make faster insulin and different types of sensors. Outside the U.S., I think it the closing of the loop will happen a lot quicker. JDRF said it will have a closed loop system somewhere in the world within 4 years. It will probably not be in the U.S. though. Clearly, I think everything will get better and I think its great to have competition out there. Clearly you will see a lot happening in the next few years. Hopefully Barbara won’t have to pay for sensing in the future. We’re trying to show that sensing really saves money. The registry run by the Helmsley shows that when you go on pump or CGM, the A1c is 1 point lower than  with MDI, though the patients may be more compliant to begin with. 


Private Event: Hospital-Based Glucose Control (Sponsored by Echo Therapeutics)

Company Introduction

Pat Mooney, MD (CEO and President); Wayne Menzie (Director of Technology and Clinical Development); Rajko Ilic (VP Product Development, Echo Therapeutics, Philadelphia, PA)

Dr. Mooney welcomed roughly 30 attendees to Echo’s symposium, held in a spacious gallery at the Pennsylvania Academy of Fine Arts. Standing beside Thomas Eakins’ famous painting of Dr. Samuel Gross, Wayne Menzie reviewed the clinical need for glucose control in the ICU, as pioneered by Dr. Greet Van den Berghe and still widely endorsed by clinical organizations worldwide (even though targets are no longer as tight as proposed by Dr. Van den Berghe, following the unsuccessful performance of the intensive control arm in NICE-SUGAR). Rajko Ilic then discussed Echo’s product development efforts, which have been informed by multiple interviews and focus groups with nurses and physicians. He closed with a video demonstration of Echo’s Symphony transcutaneous continuous glucose monitor (tCGM) as it would be used in the ICU. After the nurse has mechanically abraded the skin with the Prelude SkinPrep system, the sensor is worn for 24 hours, during which it wirelessly transmits CGM data every minute to a nearby data display screen (which alarms audibly and visually in hyper- and hypoglycemia).  


Panel Discussion

Moderator: Pat Mooney (Echo Therapeutics, Philadelphia, PA)

Anthony Furnary, MD (Starr-Wood Cardiac Group, Portland, OR); Jeffrey Joseph, DO (Thomas Jefferson University, Philadelphia, PA); Stanley Nasraway, Jr., MD (Tufts Surgical Center, Boston, MA); Eileen Donnelly, RN, BSN (Thomas Jefferson University, Philadelphia, PA); Samir Farah (Echo Therapeutics, Philadelphia, PA)

The majority of the symposium consisted of a star-powered panel discussion featuring Dr. Anthony Furnary (the father of tight glucose control in cardiac surgery), Dr. Jeffrey Joseph (a founder of Animas and frequent consultant on glucose monitoring to the FDA), and Dr. Stanley Nasraway (head of Tufts’ surgical ICU, where he has led the center’s adoption of tight glycemic control). The panelists outlined various success factors for inpatient CGM, including: some combination of point and trend accuracy (with the details to be discussed at an FDA public workshop on June 25), invasiveness (an area where Dr. Furnary said Echo is the clear leader), cost, and interactivity (or “fussiness,” a quality with which clinicians cannot abide). Frequent analogies were drawn to pulse oximetry, which was initially used as an adjunct to point estimates of blood oxygenation in the ICU, then became adopted more and more widely in other areas of the hospital as providers perceive its clinical value and payors/administrators recognize its cost-effectiveness. The overwhelming sentiment from the panelists was that they and their colleagues are hungry for inpatient CGM and eager for regulatory approval – as Dr. Joseph noted, “This is the first technology I’ve been involved in where the end user wants the technology and we can’t get it through.”

Questions and Answers

Q: I think in the past few years, widespread consensus has developed that we need CGM in the hospital. A number of CGM sensors are already approved, at least for ambulatory use. There is a poster on Sunday showing the FreeStyle Navigator in the ICU. Multiple products will be commercialized for inpatient use. From your perspective as clinicians, what are the top three factors that would make you decide on which CGM sensor you would use? What do you think will make Symphony attractive in the coming shootout?

Dr. Nasraway: The issues you raise are true not just for CGM but for a wide panoply of devices in ICU patients. Often these are among the most expensive products in the hospital – how do we decide among devices, or whether to get devices at all? The advantage of the Prelude/Symphony system is that it’s completely noninvasive. When I was first asked to help Echo in 2005 the transcutaneous penetration used ultrasound, which was finicky and more expensive. Clinicians found it intimidating, awkward, and unlike other products they use. The new handheld applicator that vibrates like electric toothbrush is much easier to use. It is also a less expensive and more effective abrasive maneuver.

To influence us at Tufts, it would have to be combination of low-hassle – nurses will pitch fussy technologies out the window; in the past we have bought products and then not used them because the form factor wasn’t good enough. I think some sort of reasonable acquisition and maintenance cost will also be important. Healthcare is right up top in major issues facing Massachusetts, as it is nationwide. Just today the Massachusetts House of Representatives passed a measure to reduce paybacks to providers, and to assess a 20% luxury tax on hospitals who overcharge. So I think the two main issues are fussiness and cost, with reasonable accuracy.

Ms. Donnelly: I think the most noninvasive, non-cumbersome monitoring device will be key. Patients are already inundated with central lines, and often they are intubated and on ventilators. We don’t want something to make it more cumbersome.

Q: Are those real savings in your mind?

Ms. Donnelly: We now do Accu-Chek tests every two hours, and every one hour for patients on insulin. This is a huge amount of time, and the repeated fingersticks are painful for patients.

Q: What do you think it will take to convince both CMS and insurers to switch from direct blood sampling to the next generation of CGM?

Dr. Furnary: Just cost-effectiveness.

Your first question was the best one – what do you see the shootout looking like. I think it will come down to four factors: 1) invasiveness, 2) accuracy, 3) cost, 4) interactivity. Right now cost and interactivity are taking a back seat. Companies are either heading down accuracy and not worrying about invasiveness, or – like Echo – focusing on invasiveness and working to meet the current standard of accuracy. Once approved, each will go for the other element – the highly accurate sensors will move toward less invasiveness, and Echo will improve its accuracy. Only one company will win noninvasively, and that is Echo. With continuing modifications of the algorithm, it will become more and more accurate. Thus I think Echo is the most well positioned to be a competitor.

The other elements come down to cost-effectiveness for administrators and ease/interactivity for nurses. In a really good setting, a BG measurement takes five minutes – one twelfth of an hour. We are spending between $170 and $250 per patient per day to monitor blood glucose.

Dr. Nasraway: It’s not just in strips and the monetary price of nursing time – I think the greater gain is that if nurses are not using 1980s-era technology to measure intermittent glucose, they can take the time to be a real nurse at the bedside – this will add unmeasured benefit.

Ms. Donnelly: I think the time issue is critical, too – if you have to make an insulin drip infusion change based on a blood glucose reading that is supposed to be taken at 2 pm, you may not actually have a chance to take the measurement until 2:20 – whereas if you have something that takes a continuous readout, you could act faster.

Dr. Joseph: In our facility we take 32,000 glucose tests per month – 4% of these readings are below 70 mg/dl, and 20% are above 200 mg/dl. CGM trend data will become widely seen as critical. As it was with pulse oximeters in the ICU, once you can see trend data, you’ll never want to go back to ‘flying blindly’ with intermittent measurements.

Q: In the CGM oral presentations, a competitive company called into question the data of other companies [Editor’s note: see coverage of Dr. David Price’s oral presentation above]. Do you clinicians have opinions on the quality of Echo’s data and what will be good enough to get to market?

Dr. Furnary: I have been doing this for 20 years. I think anything as good as point-of-care glucose is good enough – I get point-of-care measurements every one or two or four hours, and we avoid hypoglycemia 99% of time with a target range of 70-110 mg/dl. For me, even the current accuracy is enough with our protocol. This will vary by center… but what really matters is what FDA says. Go to Maryland on June 25th to find out – we still don’t know what benchmark the agency will set. [Editor’s note: for more on the FDA’s upcoming public meeting on clinical study design and performance of hospital glucose sensors, see]

Dr. Joseph: Regulatory agencies have recognized that it’s not just about the point accuracy, it’s about the trend accuracy. We have to figure out how to show trend accuracy to get approval. Once a system is approved, clinicians will use it and develop protocols. The FDA is on our side; they would love to see inpatient CGM approved.

Dr. Mooney: You are on the FDA panel on June 25. Dr. Joseph was on a panel at ISICEM in Brussels that seems that it will lead to recommendation that point accuracy be in the 12%-13% range, on the basis that frequent measurements would reduce the need for point accuracy.

Dr. Furnary: Where do you think FDA will come out on continuous monitoring requirements?

Dr. Joseph: They are not sure where they stand, looking for us to give them advice, hence this workshop. It will be a combination of point and trend accuracy, to be defined.


Artificial Pancreas and Insulin Pumps

Oral Sessions: Closed Loop Systems

State of the Art Lecture - Pitfalls in the Development of a Closed Loop

William V. Tamborlane, MD (Yale University, New Haven, CT)

After reviewing the state of closed-loop research (including his own group’s recent work with Insuline’s infusion-site-heating InsuPatch), Dr. Tamborlane proposed that the biggest remaining obstacle is to ensure that a system’s malfunctions will not result in harmful overdelivery of insulin. Underdelivery of insulin has also been raised as a concern, however, most notably in the FDA’s review of Medtronic’s low glucose suspend (LGS) Veo pump/CGM. To investigate whether erroneous insulin suspensions could reasonably cause diabetic ketoacidosis (DKA), Dr. Tamborlane and his colleagues conducted a crossover-design experiment in pump-using adolescents and young adults with type 1 diabetes (n=14), in which participants’ pumps were suspended for two hours on random nights. On the mornings after these suspension events, significant rises were seen in the levels of both blood glucose (199 vs. 151 mg/dl) and the ketone beta-hydroxybutyrate (0.11 vs. 0.07 mmol/l). Encouragingly, however, the mean rise was not clinically significant, and the highest single ketone reading (0.6 mmol/l) corresponded to only mild ketonuria. Dr. Tamborlane said that every sane person in diabetes supports LGS, provided the system is assured to be safe. Based on his own group’s results and the findings from the inpatient ASPIRE study (presented by Dr. Satish Garg later in the session), so far he is taking a positive view on the safety and efficacy of the Veo as the first small step toward the goal of an artificial pancreas (which to him means a system that provides fully automated, 24-hour control as well as remote data transmission). 

Questions and Answers

Q: You said that the ultimate obstacle is overinfusion of insulin. Is that really the ultimate obstacle? I would wonder if it’s actually cost, given what is happening with payors for type 1 diabetes.

A: Dr. Hirsch is obviously from a blue state, where the lowest common denominator of care is delivered for everyone. I am really not prepared to discuss cost, though I think all the company people need to pay attention to this. We all want to make things available and hopefully this won’t be an obstacle.

Q: It is gratifying to see that overnight LGS is safe. But I am still concerned that people will make behavior changes if they think they are protected, and perhaps give higher boluses at dinnertime than they otherwise would. Then if the system is reading low, there could be problem.

A: There is a big turnout at this session because we all envision automatic glucose control. This was Medtronic’s smallest step they thought they could get through the FDA – we would hate to get hung up on the Veo. We would like to have suspension for predicted lows, so you don’t even get an alarm – alarm fatigue is a big problem. Another FDA concern with the Veo was that A1c would increase A1c. But even in the JDRF CGM trial, we said we would accept an A1c rise of 0.3% in well-controlled patients if it meant less hypoglycemia. Another consideration is that use of CGM in individual patients is a delicate balance between benefits and hassles (which are the main reason people stop using it now). Increasing the benefits would increase the likelihood that patients would use these technologies. There are lots of downstream consequences, intended and unintended, of closed-loop research.

Q: Might GLP-1 agonists be a way to suppress glucagon in type 1 diabetes?

A: We are very excited. I see friends from JDRF in the audience. We have proposal to titrate GLP-1 receptor agonist dose in both open- and closed-loop. That was a very nice softball question.


Automatic Insulin Pump Suspension for Induced Hypoglycemia: The Aspire Study (221-OR)

Satish Garg, MD (University of Colorado Denver, Aurora, CO)

Dr. Garg reviewed the in-patient ASPIRE study of Medtronic’s low glucose suspend (LGS) pump/CGM system. As a reminder, the crossover-design study showed that for patients undergoing exercise-induced hypoglycemia, suspending insulin delivery restored normoglycemia faster (and with similar negligible risk of rebound hyperglycemia) compared to when the LGS feature was not activated and insulin continued to be delivered (see additional details in our coverage of Dr. Garg’s ATTD 2012 presentation at


The Order Effect of the In-Clinic Aspire Study: Hypoglycemia Begets Hypoglycemia (220-OR)

Satish Garg, MD (University of Colorado Denver, Aurora, CO)

Immediately following his review of top-line ASPIRE data, Dr. Garg explained that patients who underwent LGS-on experiments on their first study day (Group A) recovered from hypoglycemia much faster than those whose LGS-on experiments came on the second study day (Group B). Indeed, the between-group difference was 63.7 minutes (107.8 vs. 171.5 mg/dl; p<0.01). The difference seems to be due to the fact that on the second experimental day, patients were still affected by the hypoglycemia induction from the first experimental day, even following the 3-to-10-day washout period. (The mechanism may have involved depletion of glycogen stores or loss of counterregulatory response – unfortunately data were not collected to investigate either of these hypotheses.) Dr. Garg proposed that future crossover-design studies of hypoglycemia induction should use a longer washout period to avoid order effects. He also hypothesized that real-world use of LGS, by reducing exposure to low blood glucose, could potentially help halt the vicious cycle of hypoglycemia begettting hypoglycemia. However, as Dr. William Tamborlane noted during Q&A, exercise-induced hypoglycemia is quite different from low blood sugar during deep sleep, so the only way to truly understand the latter is through the ongoing outpatient ASPIRE study (the pivotal trial for the US version of the Veo, which recently began the FDA review process; see our coverage of Medtronic’s recent analyst day at Dr. Garg said the outpatient results could be ready in time for next year’s ADA – we’re already looking forward!

  • The Medtronic-funded ASPIRE study was designed to assess the low glucose suspend (LGS) feature of the Veo pump/CGM in response to exercise-induced hypoglycemia. The inpatient study’s participants (n~50, mean age 34, mean A1c ~7.9%, BMI 27 kg/m2) first underwent a six-week washout period involving several clinic visits. As for the protocol itself, patients entered the clinic early in the morning, after an overnight fast. (In order for an experiment to begin, patients were required to have blood glucose of 100-140 mg/dl.) Patients then exercised until YSI-measured plasma glucose fell to 85 mg/dl, at which time they stopped exercising and were observed. Once the sensor glucose hit 70 mg/dl, the low glucose suspend feature was activated, suspending insulin delivery for two hours. (If the patient failed to drop to 85 mg/dl within 10-15 minutes of exercise, he or she would take a break before re-initiating exercise up to five more times in the visit.) If a patient’s YSI blood glucose value ever fell below 50 mg/dl or rose above 300 mg/dl, the experiment was immediately stopped and treatment administered – Dr. Garg said that between the various requirements, roughly one-third of the inductions had to be repeated. The other arm of the crossover study design used the same exercise protocol, but without the LGS turned on. Patients were randomized to receive either Group A (LGS-on first) or Group B (LGS-off first), and the visits were separated by a washout period of 3-10 days.
  • Participants who underwent LGS-on experiments on their first in-clinic day (Group A) had significantly shorter durations of hypoglycemia than those whose LGS-on experiments occurred on the second day (Group B) – the between-group differential was a whopping 63.7 minutes (107.8 vs. 171.5 mg/dl; p<0.01). That’s a big drop – and also a good reminder of how prevalent hypoglycemia is. People in group A also required significantly fewer inductions before a successful LGS-on experiment occurred (0.36 vs. 1.57 prior inductions1, respectively; p<0.001). Related to their having fewer prior inductions, those in group A also had less cumulative duration of YSI-measured hypoglycemia prior to a successful LGS-on experiment (16.6 vs. 204.6 minutes; p<0.01). Statistical analysis showed that the between-group difference in hypoglycemia duration could not be attributed to sensor glucose rates of change, exercise duration, or spontaneous hypoglycemia (as measured by the area of the curve spent with glucose below 70 mg/dl in the two days prior to successful induction): the p-value was below 0.3 for all three.
  • Dr. Garg presented a graphic of every-30-minute mean YSI values to illustrate that LGS-On gave a better trajectory than LGS-Off no matter which days the experiments were conducted; however, other measures seemed to make the order effect appear more severe. At ATTD 2012, Dr. Thomas Danne showed that duration of hypoglycemia was not statistically different between the LGS-On and LGS-Off experiments run on day two (171.5 min for Day 2 LGS-On vs. 167.7 min for Day 2 LGS-Off min; p=0.4)). For more details from Dr. Danne’s talk, see page 5 of our coverage at

[1] As a reminder, if a patient failed to drop to 85 mg/dl within 10-15 minutes of exercise, he or she would take a break before re-initiating exercise up to five more times in the visit. Also, if a patient’s YSI blood glucose value ever fell below 50 mg/dl or rose above 300 mg/dl, the experiment was immediately stopped and treatment administered. At ATTD 2012 Dr. Garg said that due to these various requirements, roughly one-third of the inductions had to be repeated.

Questions and Answers

Q: You hypothesize that the order effect is due to impaired counter-regulation. That raises the question of efficacy in those already known to have impaired counter-regulation. In deep sleep, we have shown – and Dr. Cryer has confirmed – that you don’t get a counterregulatory response, even if you otherwise would. This finding does support the need for an at-home experiment to see what happens when people are sleeping and not on a treadmill, which is totally different.

A: We hope maybe by next year’s ADA to have results from the in-home study.

Q: Your data captured only events where YSI crossed below 85 mg/dl. I guess this means that events where the sensor might have erroneously suspended at a higher glucose level are thus not captured. Can you explain this rationale?

A: I wish I could go into the details of the regulatory hurdles. The protocol was written so strictly that observation could start only if YSI glucose was below the given number. However, it is likely that many times the sensor glucose would caused LGS events earlier or later. In hindsight we should have had more leverage.

Q: So for the LGS-Off condition, how was basal insulin delivery handled?

A: It was continued, even when the sensor glucose fell below 70 mg/dl. There was no choice in that.


Automated Management of Blood Glucose in Children with Type 1 Diabetes Using a Bi-Hormonal Bionic Pancreas (222-OR)

Edward R. Damiano, MD (Boston University, Boston, MA)

Dr. Damiano gave us an “anecdotal” perspective on his team’s research in this thrilling talk, which included a first-ever look at the iPhone-driven system that will be used in upcoming five-day closed-loop experiments. His group has developed a universal algorithm that can be initialized with only one variable (body weight) and thereafter is robustly adaptive to account for the wide inter- and intra-individual variability in insulin needs (two-to-three-fold changes in insulin dosage are possible within 12-to-24 hours!). Pre-meal priming boluses (which require the patient simply to input whether they are about to eat a low-, mid-, or high-carb meal) allow more robust adaptability, but systems without pre-meal priming offer performance as well. Dr. Damiano looks forward to trying out the group’s latest system in a five-day transitional study as early as late fall/early winter 2012, pending FDA approval of the device (the IDE will be submitted in six-to-eight weeks). He piqued the audience’s interest still further by showing off the actual physical system that will be used in the study, which he said was turned on for the first time earlier that morning (Dr. Damiano was actually wearing a Navigator CGM during the presentation and showed the audience his streamed, real-time glucose information). The hardware consists of: two Tandem t:slim insulin pumps delivering insulin and glucagon, an iPhone to run the controller algorithm and communicate with the pumps via low-energy Bluetooth (no laptop or tablet required), and a Navigator CGM. (The researchers will also run experiments with a platform using a Dexcom G4 CGM, based on their findings that the G4 has superior day-two accuracy vs. the Navigator – see Dr. Steven Russell’s presentation from ADA 2012 Day #1 at We’ve always been impressed with Dr. Damiano’s data-driven, nose-to-the-grindstone approach and hope FDA is receptive to the new device and ambitious study.


Comparison of Two Closed Loop Algorithms with Open Loop Control in Type 1 Diabetes (224-OR)

J. Hans DeVries, MD (Academic Medical Center, Amsterdam, Netherlands)

Building on Dr. Eric Renard’s preliminary presentation of findings at ATTD 2012 (see page 19 of our coverage at, Dr. DeVries presented the intent-to-treat analysis of the CAT Trial – a crossover-design comparison of 24 hours under open-loop control, closed-loop control with the Padova/Pavia/UVa (iAP Consortium)’s MPC algorithm, and closed-loop control with the Cambridge University team’s MPC algorithm. Both algorithms performed similarly and were not statistically significantly different from open loop therapy with respect to the study’s primary endpoint, time in range (70-144 mg/dl in fasting periods; 70-180 mg/dl in postprandial): 58% (Cambridge), 59% (iAP), and 62% (open loop). The good news was that both MPC algorithms offered significant improvements for hypoglycemia <70 mg/dl: 2% vs. 2% vs. 6%. Less encouragingly, the MPC algorithms also caused statistically significantly higher mean glucose (149 vs. 148 vs. 126 mg/dl) and significantly more time spent in hyperglycemia. However, Dr. DeVries noted that recent advances (e.g., more accurate CGM sensors) could allow researchers to make their algorithms more aggressive without compromising safety.

  • The CAT Trial compared closed-loop control using MPC algorithms from Cambridge and Padova/Pavia/Montpellier (iAP). Participants with type 1 diabetes used the Insulet OmniPod, a Dexcom Seven Plus, and either the UCSB artificial pancreas system or manual validation from a nurse. Patients were studied in three non-consecutive 24-hour periods under three different conditions (open-loop, closed-loop with Cambridge algorithm, closed-loop with iAP algorithm; order of the conditions was randomized). Six centers took part in the study, with eight patients per center participating, and a total of 142 experiments were performed.
  • The study included meal, rest, and exercise periods with time in range as a primary endpoint. Participants entered in the evening, ate dinner, spent the night, had breakfast and lunch the following day, and concluded the final afternoon with an individualized exercise session. Target range was defined as 3.9-10 mmol/l (70-180 mg/dl) in the first three hours after a meal and 3.9-8 mmol/l (70-144 mg/dl) otherwise. In this presentation Dr. DeVries discussed only intent-to-treat data; per-protocol results (excluding data contaminated by errors from the CGM, pump, or study personnel) will be shown at EASD 2012.
  • Compared to open-loop control, both the Cambridge and iAP algorithms conferred significantly less time in hypoglycemia below 70 mg/dl, though this came at the expense of statistically significant increases in mean glucose and time spent in hypoglycemia. A trend toward significance was observed in time spent at or below 50 mg/dl, and no significant change was observed in the primary endpoint of time in zone. Dr. DeVries noted that these results made sense in light of the higher insulin doses used with open-loop control. (The iAP and Cambridge algorithms were not statistically significantly different with regard to any of the glycemic measurements analyzed, though more insulin was dosed with the iAP algorithm (1.7 vs. 1.6 U/hour; p<0.05).


Open Loop



Overall P-value

% Time in Range





% Time < 70 mg/dl





% Time ≤ 50 mg/dl





Mean Glucose (mg/dl)





Mean Insulin dose (U/hr)






  • The CAT Trial suggested an inverse relationship between hypoglycemia and mean glucose, but Dr. DeVries said that recent technological advances might enable closed-loop systems to improve both hypoglycemia and mean glucose simultaneously. He explained that in CAT, both the Cambridge and iAP algorithms had been “detuned” as a safety measure to account for the effects of exercise and the “suboptimal” function of the Dexcom Seven Plus. He said that fortunately, the greater accuracy of newer CGMs (e.g., the Dexcom Gen 4) could enable the algorithms to be made more aggressive in reducing hyperglycemia while still also preventing hypoglycemia. As Dr. Hirsch noted during Q&A, data from the Helmsley Charitable Trust suggests that hypoglycemia is similarly prevalent across the type 1 diabetes population regardless of A1c. Thus, a great deal of patients could benefit from a system that simultaneously improves both mean glucose and incidence of lows. 

Questions and Answers

Q: In the data from the T1D Exchange, we actually don’t see an increased prevalence of hypoglycemia at lower A1c – the rate of hypoglycemia is similar across all A1c ranges. If we assume that this is a true observation, would closed-loop systems maybe be better for those specifically with higher A1c?

A: I think the algorithms could be adapted to more-accurate CGM systems in a way that makes it possible to lower mean glucose with these algorithms. Such a possibility has been suggested in earlier research as well as some that is about to be published.


Factors Affecting Performance of Overnight Closed-Loop Insulin Delivery in Type 1 Diabetes (T1D) (219-OR)

Marianna Nodale (University of Cambridge, UK)

Ms. Nodale presented an interesting retrospective review of closed loop studies from Cambridge. The study looked for correlations between overnight closed loop performance and various factors: age, A1c, total daily dose, insulin absorption rate (time to peak of plasma insulin; Tmax), controller effort (the ratio of total insulin delivered during closed loop to the basal profile pre programmed on a patient’s pump). Controller effort was the factor most correlated with overnight closed-loop performance – more controller effort was associated with a higher mean plasma glucose, a higher standard deviation, and less time in target range (70-145 mg/dl). Ms. Nodale explained that the Cambridge controller algorithm has a safety feature that is based on the basal profile of patient – when controller effort was high, the safety feature prevented aggressive insulin dosing to bring down hyperglycemia. Aside from controller effort, better time in target was associated with older patients and those with high total daily doses, while more time spent in hypoglycemia (<70 mg/dl) was associated with a higher A1c and a higher Tmax. Ms. Nodale also emphasized the wide interindividual variability in controller effort (25-200%) and Tmax values – this is now a clear theme we’ve heard in closed-loop talks and we’re glad to see researchers better characterizing and hopefully dealing with this challenge.

  • Overnight closed-loop performance from seven closed-loop studies (1,612 hours) in 79 patients was analyzed. All studies occurred in a clinical research setting and both manual and automated closed loop delivery were included. An adaptive MPC algorithm was used. Factors considered included age, A1c, total daily dose, insulin absorption rate (time to peak of plasma insulin), and controller effort (the ratio of total insulin delivered during closed loop to the basal profile pre programmed on patient’s pump). Overnight closed loop performance was defined as midnight to 8am.
  • Combined, the seven studies’ participants included 11 children, 44 adolescents, and 24 adults; median controller effort was 110% (i.e., the controller delivered 10% more insulin relative to the pump’s pre-programmed basal rate). Mean age was 20 years, mean A1c was 8%, mean BMI 22.5 kg/m2, mean duration of diabetes was 10 years, mean duration on a pump was two years, and mean total daily dose was 49 units per day.
  • Controller effort was significantly positively related to mean plasma glucose and standard deviation of glucose and significantly negatively related to time in target range (70-145 mg/dl), time below 70 mg/dl, and low blood glucose index (LBGI; a measure of severity and frequency of hypoglycemia). Generally, the controller algorithm did not need to give much more insulin than patients had pre-programmed, but Dr. Nodale explained that there was wide inter-individual variability. In some cases, the controller delivered only 25% as much of the preprogrammed basal profile, while other patients needed almost twice as much insulin. She explained that the controller algorithm has a safety feature that is based on the basal profile of patient. In cases of high controller effort, the safety feature likely prevented the controller from being more aggressive and bringing down hyperglycemia.
  • In addition to controller effort, age, A1c, total daily dose, and Tmax were significantly associated with closed loop performance statistics. Age and total daily dose were significantly associated with time in target range. A1c was significantly associated with time <70 mg/dl, and Tmax was significantly associated with time <70 mg/dl and LBGI.

Spearman’s rho correlations


Mean Plasma Glucose

SD of Plasma Glucose

Time <70 mg/dl

Time in target range














Total Daily Dose






Controller Effort












                  *p<0.05, **p<0.01

Questions and Answers

Q: You pointed out that increased controller effort was associated with higher glucose concentrations. Does that mean that your control algorithm was set quite conservatively?

A: Yes, there is a tradeoff between aggressiveness and risk. The controller is conservatively tuned. In patients who are resistant to insulin or have their basals underestimated, the safety aspect makes the controller too conservative.

Q: Has the algorithm learned iteratively? More recently, is it looking better?

A: Each patient was only studied on one occasion. We had few on more than one night, so we cannot assess how the algorithm will adapt to changing insulin requirements. We suspect it will do better. We will have opportunity to study patients for several days.

Q: Is it valid to compare insulin Tmax between patients who received varying levels of  insulin from the controller?

A: Please refer to the poster for how we calculate Tmax. I think so.


Dual-Hormone Closed-Loop (CL) System in Adults with Type 1 Diabetes

Ahmad Haidar, MSc (Ecole Polytechnique de Montreal, Quebec, Canada)

Mr. Haidar presented results from a 15-patient bi-hormonal overnight closed-loop study from Canada. (We cannot recall having ever seen a presentation from this closed-loop team). He mentioned that this was the first randomized, crossover design comparing open loop control to bi-hormonal closed loop. Participants undergoing closed loop spent 71% of time in target range (72-180 mg/dl), compared to 57% of the time during open loop therapy (p=0.003). Time spent <72 mg/dl declined significantly from 10% during open loop to 0% during closed loop (p=0.01). This is encouraging data and we look forward to hearing more about this team’s work. Due to large differences in study design and methods (e.g., meal size, type, schedule, sensor calibration, algorithm interaction), it is hard to compare this trial to the bi-hormonal work from Dr. Ed Damiano and colleagues in Boston. However, we’re glad to see so much interest in dual-hormone control and look forward to even better performance, a stabilized glucagon in solution, and dual-chamber pumps down the road.

  • This 15-hour randomized, crossover study included 15 adults with type 1 diabetes (9 female, mean age 47 years, mean A1c 8%, mean BMI 26 kg/m2). Patients were admitted to the clinical research center twice and received either closed loop or open loop treatment. The intervention started at 4 pm, 30 minutes of exercise (60% VO2 max) occurred at 5:50pm, a dinner was eaten at 7:20 pm (80g carbs for males and 60g carbs for females), and then a 15g carb snack was eaten at 10 pm followed by sleep until 7 am the next morning. Meals were announced to the algorithm.
  • The algorithm’s input was sensor readings from a Medtronic Sof-Sensor and subcutaneous insulin and glucagon doses were recommended every ten minutes. The algorithm was based on a fuzzy-supervised model-based predictive controller combined with an extended Kalman filter and a set of heuristic rules. Mr. Haidar did not describe how portable or software integrated the system was. Plasma glucose was measured using YSI 2300 STAT Plus Analyzer. Study outcomes are based on plasma glucose readings.
  • Time in target range (72-180 mg/dl) improved from 57% during open-loop therapy to 71% during closed-loop therapy (p=0.003). Time spent under 72 mg/dl declined from 10% to 0% during closed loop (p=0.01), and time spent under 60 mg/dl dropped from 2.8% during open loop to 0% during closed loop (p=0.006). Time spent above target was not significantly different between the two groups. Standard deviation of glucose was 60% lower during closed loop visit (20 mg/dl vs. 34 mg/dl). Insulin delivery and concentration were not significantly different between the two groups.
  • The number of patients that had at least one hypoglycemic event (<54 mg/dl) dropped from 53% during open loop to 7% during closed loop (p=0.02). Nocturnal events declined from 32% to 0% (p=0.07). Exercise induced hypoglycemia was not significantly different between the groups.

Questions and Answers

Q: Thank you for this very important and very impressive study. In some cases, we noticed in our studies that giving small doses of glucagon, resulted in no reaction. Glucose continued to fall. Did this happen in your study?

A: I don’t remember any times where that happened. There was one case of hypoglycemia. The sensor algorithm was not aggressive, so we didn’t have cases of hyperinsulinemia.

Q: Given the instability of glucagon, how did you prepare it in this study?

A: We prepared it at the beginning of the study. It was in the pump for 16 hours.

Q: Since we’re learning more about dual hormones, can we see glucagon resistance develop?

Dr. Ward: When insulin levels are high, the response to glucagon is less. The key is to keep insulin dosing to a minimum. We’re also looking to see if repeated doses of glucagon deplete hepatic glycogen. We’ll be looking at this in a study.

A: Glucagon is a safety layer. With exercise, we had large incidence of hypoglycemia. We had to give glucagon to prevent it.


Oral Sessions: Late-Breaking Abstracts

Closed-Loop Insulin Therapy Improves Nocturnal Glycemic Control in Children <7 Years (153-LB)

Andrew Dauber, MD, MMSc (Boston Children’s Hospital, Boston, MA)

Dr. Dauber presented results of an overnight (10 pm – noon), crossover-design closed-loop study in children under seven years old (n=10; mean A1c 8.1%, age 5 years old, diabetes duration 2.1 years; C-peptide levels undetectable). Compared to open-loop therapy, closed-loop control with a FreeStyle Navigator and a PID algorithm led to statistically significantly shorter duration of overnight hyperglycemia > 300 mg/dl (0.18 vs. 1.3 hours) without an increased risk of hypoglycemia. The post-breakfast spike was sharper after breakfast since no pre-meal priming boluses were used, but closed-loop control also improved mean pre-lunch blood glucose values (190 vs. 270 mg/dl). Dr. Dauber believes that the young pediatric population is well-suited to closed-loop control (due to their predilection for hypoglycemia and their unpredictable eating and activity habits) but is underrepresented in current artificial pancreas research.

  • The crossover-design study required study participants (n=10) to spend two consecutive nights in the hospital wearing an Animas Ping pump and two Abbott Navigator sensors, on each leg. (The second sensor was included in the study only as a failsafe and turned out not ever to be necessary – in every experiment, the system was run based solely on input from the sensor worn on the patient’s right leg). During the day between experimental nights (noon to 10 pm), patients were able to use open-loop control, disconnect themselves from the intravascular line used to test blood glucose over night, and walk around the hospital.
  • In the closed-loop condition, insulin dosage was determined based on physician entry of sensor glucose values into an algorithm that targeted 150 mg/dl from 10 pm to 6 am and 120 mg/dl from 6 am to noon. Over night, the algorithm adjusted basal insulin rates every 20 minutes. In the morning, the system delivered microboluses as often as every minute (the system was fully reactive: no pre-meal priming boluses were given). During Q&A, Dr. Dauber said that future versions of the system might incorporate every-minute dosing at night, hopefully enabling lower rates of hypoglycemia at the same mean glucose target. 
  • Overnight time in hyperglycemia > 300 mg/dl was statistically significantly reduced with closed-loop control (0.18 vs. 1.3 hours), and overnight time in the target range of 110-200 mg/dl was non-significantly greater (5.3 vs. 3.2 hours). Exposure to overnight hyperglycemia (area under the curve above 200 mg/dl) was also significantly reduced with closed-loop control. Because no pre-meal priming boluses were given, the post-breakfast glucose rise occurred faster among closed-loop patients. However, both groups reached similar peak postprandial glucose values (367 vs. 353 mg/dl), and the closed-loop patients’ glucose fell back toward normal much faster –mean glucose values at noon were 270 mg/dl and 190 mg/dl, respectively. The groups were comparable in instances of hypoglycemia < 70 mg/dl during the observational period (four vs. five events; treated with juice), though five instances of hypoglycemia also occurred in the open-loop rest period between experimental nights (potentially related to the relatively higher rates of insulin given under the closed-loop than open-loop condition, as suggested during Q&A).

Questions and Answers

Q: Did you use the data from both sensors?

A: We used only one – the second was in case the children ripped off one of them. By convention we always used the sensor on the right leg.

Q: You showed a lot of hyperglycemia after breakfast.

A: There is no doubt that pre-meal priming boluses will improve closed-loop therapy. But there are reasons to be concerned about this. It is extremely difficult to predict how much children will eat.

Q: Do you think you should raise the overnight target even higher to avoid hypoglycemia?

A: The 150 mg/dl overnight target was already right in the middle of the ADA’s recommended range. Based on the information from this study, we will refine the parameters for our closed-loop algorithm. I think we will see improvement by switching to once-a-minute adjustments overnight, instead of every 20 minutes. My aim would not be to raise the target but rather to improve insulin delivery.

Q: You wound up giving a lot more insulin in the closed-loop condition. In the open-loop ‘rest’ period, was there a difference in glycemic control after lunch?

A: That was not included in our analysis. Five episodes of hypoglycemia occurred outside the outcome period of the study [i.e., outside of the 10 pm – noon range when the experiments were actually running]. I think a morning snack might help blunt the postprandial hypoglycemia, but we checked blood sugars only at noon, three, and prior to dinner.



Feasibility Study Assessing Hypoglycemia-Hyperglycemia Minimizer (HHM) System in Patients with Type 1 Diabetes (T1DM) in a Clinical Research Center (CRC) (917-P)

Linda Mackowiak, Daniel Finan, Thomas McCann Jr., Ramakrishna Venugopalan, Howard Zisser, Henry Anhalt

This feasibility study is the first data we’ve seen since the JDRF/Animas partnership formed in 2010 to develop and commercialize a first-generation artificial pancreas device. The Animas system uses a OneTouch Ping pump, a Dexcom Seven Plus CGM, and a hypoglycemia-hyperglycemia minimizer (HHM) algorithm running on a laptop (i.e., controlling to a range of 90-140 mg/dl). The investigational device was tested in the clinic in 13 patients with type 1 diabetes. The 20-hour study included two important challenges to the algorithm: (1) at the breakfast meal, insulin was under-bolused (up to 50%) and (2) at the lunch meal, insulin was over-bolused (up to 50%). The system achieved a mean blood glucose of 165 mg/dl, correlating to a respectable A1c of 7.4%. Time in the range of 70-180 mg/dl was 70% overall, over 80% at night, and less than 1% at values <70 mg/dl. There were no events of DKA or severe hypoglycemia. Although this was just a small feasibility study, we think the data is encouraging and are glad to see the partnership moving forward. While some might say 165 mg/dl is not perfect control, and while we very much agree with this, we believe it’s directionally solid considering the average A1cs in the Helmsley T1D Exchange (over 8%) and the high prevalence of hypoglycemia and severe hypoglycemia (see our ATTD 2012 report at Additionally, we note that participants ate fairly high-carbohydrate meals and the system was thrown curveballs with real world insulin dosing mistakes. Besides fine-tuning the algorithm, we believe the most critical future step is making this Animas system portable – we’ve already seen two cellphone based portable systems that have been tested in trials: Medtronic’s Portable Glucose Control system and the iAP consortium’s Diabetes Assistant (for the basics on both devices, see pages 3-6 of our DTM 2011 report at

  • This study used Animas’ hypoglycemia-hyperglycemia minimizer (HHM) System, which consists of an OneTouch Ping insulin pump and meter, a Dexcom Seven Plus CGM, and the HHM algorithm. The algorithm runs on a laptop and uses the UCSB/Sansum Artificial Pancreas System (APS) platform. The HHM targets a glycemic zone setting of 90-140 mg/dl. The control algorithm automatically adjusts insulin delivery in response to changes in CGM values, as well as predictions of future CGM trends. The algorithm is designed to take action in order to reduce or prevent glucose excursions outside of the target zone.
  • This nonrandomized, uncontrolled, inpatient feasibility study tested Animas’ HHM system over a 20-hour period ( identifier: NCT01401751). Participants’ pump settings were assessed and optimized by investigative staff during the week before the CRC visit. CGM sensor insertion occurred two to three days prior to the study. Participants arrived in the early evening on day one of the study and closed-loop control was initiated at midnight and lasted 20 hours. Breakfast on day two was eaten around 07:00 am and included a manually given under-bolus of insulin (up to 50% in some cases). Lunch on day two was eaten around 01:00 pm and included a manually given over-bolus of insulin (up to 50% in some cases). (Rather than just giving the maximum amount of each over- or under-dose, we wish that patient-specific bolus amounts had been given.) Both meals included one gram of carbohydrate (CHO) per kilogram of body weight, up to a maximum of 100 grams of carbohydrates. Regular YSI monitoring was performed.
  • Thirteen participants with a mean age of 42 years and a mean A1c of 7.4% took part in the study. Of the 13 patients, 11 were female. Mean BMI was 24.7 kg/m2, mean duration of diabetes was 27.2 years, mean duration of pump use was 9.6 years, and insulin brands included seven patients on Humalog, five patients on Novolog, and one patient on Apidra.
  • The HHM kept participants in target range (70-180 mg/dl) nearly 70% of the time and achieved a mean blood glucose of 165 mg/dl (standard deviation: 39 mg/dl) (statistics as measured by YSI). As expected, time in range rose significantly over night (over 80%) and was most challenging after breakfast (55%). We were glad to see time in range metrics reported for both CGM and YSI values – this is not often done in AP studies but is useful from a CGM accuracy perspective. Additionally, we note that the two analysis methods were largely congruent except in the post-lunch period (see table below). Drs. Aaron Kowalski and Boris Kovatchev have often said that current CGMs are accurate enough for control to range, a sentiment that is supported by this study. Overnight was defined as midnight to 7:00 am, post-breakfast was 7:00 am to 1:00 pm, and post-lunch was 1:00 pm to 8:00 pm.

Mean Percentage of Time Spent at Different Glucose Levels Based on CGM and YSI Measurements




Overall 70-180 mg/dl



Overall <70 mg/dl



Overall >180 mg/dl






Overnight 70-180 mg/dl



Overnight <70 mg/dl



Overnight >180 mg/dl






Post-breakfast 70-180 mg/dl



Post-breakfast <70 mg/dl



Post-breakfast >180 mg/dl






Post-lunch 70-180 mg/dl



Post-lunch <70 mg/dl



Post-lunch >180 mg/dl




Performance Metrics of a Hypoglycemia-Hyperglycemia Minimizer (HHM) System in a Closed-Loop Feasbility Study (922-P)

Ramakrishna Venugopalan, Daniel A. Finan, Thomas W. McCann Jr., Linda Mackowiak, Eyal Dassau, Stephen D. Patek, Henry Anhalt

This follow-up poster to the study described above details the performance and behavior of the control algorithm used in the 13-patient, in-clinic study of Animas’ hypoglycemia-hyperglycemia minimizer (HHM) System. The controller appropriately increased and decreased basal infusion to mitigate below-zone and above-zone glucose excursions (it targets a range of 90-140 mg/dl). For below-zone excursions, the system reduced pre-programmed basal rates by 85.7% on average; for above-zone excursions, basal rates were increased by 42.2% on average (as assessed by CGM values). The controller also took preemptive action to avoid below-zone excursions (defined as adjustments to insulin infusion 15 minutes prior to an excursion) 100% of the time when performance was assessed using CGM values and 83% of the time for YSI values. Of course, one of the major challenges with any control-to-range system is the slow speed of current rapid-acting analogs (we’ve heard it characterized as turning the steering wheel on a car and then waiting an hour or more for the car to respond). With this in mind, we especially look forward to faster insulins (e.g., Novo Nordisk’s NN1218, Halozyme’s PH20, Biodel’s BIOD-123, Novo Nordisk’s upcoming decision on the ultra rapid acting insulin to move into phase 3) and faster insulin delivery technologies (e.g., BD’s intradermal needles, InsuLine’s InsuPatch). There is a great deal happening on this front, and we’re very glad since we believe the biggest weaknesses in insulin are by far with prandial vs. basal.

  • The algorithm of the HHM System comprises two components to control glucose to a target zone. A model predictive controller (MPC) uses a mathematical approximation of insulin-glucose dynamics in the participant to predict near-future glucose trends from recent CGM measurements and insulin dosage amounts. The algorithm is designed to deliver insulin as needed with an objective of maintaining glucose levels within the target zone of 90-140 mg/dl. The safety module uses (other) mathematical approximations of insulin-glucose dynamics to continually assess and mitigate the risk of near-future hypoglycemia. It acts on the MPC’s recommended insulin infusion amount, and is designed to provide an additional safeguard against predicted near-future hypoglycemia.
  • As assessed by both CGM and YSI values, the HHM System’s algorithm adjusted insulin infusion to mitigate below-zone and above-zone excursions. Values below are reported as the average of all 13 participants from the feasibility study. The table below excludes all data up to 60 minutes after meals, during which above-zone excursions are anticipated.


Ability of the HHM System algorithm to mitigate below-zone and above-zone excursions


Below-Zone Excursions

Above-Zone Excursions


Average Basal Rate (units/hr)

Average HHM System Rate (units/hr)

% Change from Basal Rate

Average Basal Rate (units/hr)

Average HHM System Rate (units/hr)

% Change from Basal Rate
















  • The HHM System’s algorithm took preemptive control action nearly 100% of the time when below-zone excursions were predicted. Preemptive action was defined as algorithm adjustments to insulin infusion (relative to basal) in the 15 minutes prior to the start of a below-zone excursion. The table below represents the total number of excursions for all 13 patients in the trial.

Below-zone Prediction Capabilities of the HHM System Algorithm
(number of times preemptive action taken/number of below-zone excursions)

Based on CGM

Based on YSI

9/9 (100%)

5/6 (83.3%)


Feasibility of Adjacent Insulin Infusion and Glucose Sensing Via the Medtronic Combo-Set (901-P)

David N. O’Neal, Sumona Adhya, Alicia Jenkins, Gayane Voskanyan, Glenn Ward, John B. Welsh

This feasibility study assessed the performance of the Medtronic Combo-set, which incorporates an insulin infusion catheter and a CGM sensor separated by a short distance (i.e., two skin punctures but a single insertion device). The study showed that insulin pharmacodynamics and CGM accuracy with the Combo-set were comparable to standard, independently located CGM and insulin infusion sites. This is encouraging news and certainly an improvement over current devices, though we hope Medtronic could eventually reduce the Combo-set to just a single skin puncture. (The poster notes that in earlier studies in dogs, the CGM sensor was built in to the infusion catheter wall but it showed interference during both insulin and diluent (placebo) infusion.) We note that Medtronic does not have any ongoing studies of the Combo-set on and a combined insulin infusion/CGM set was not mentioned in the pipeline discussion at the recent analyst day (see our report at

  • The Combo-set incorporates a CGM sensor and insulin infusion catheter separated by a short distance (i.e., two skin punctures but a single insertion device). The poster showed a single inserter for the Combo-set (the “Combo-serter”). An exterior picture of the Combo-set displayed a Medtronic Quick-Set infusion set with a Mini-Link transmitter attached to the top of it. An in situ view showed a skin cutaway version of the Combo-set – the CGM sensor and insulin catheter appeared to be separated by about half an inch.
  • This study aimed to determine (1) if real-time CGM readings are affected by nearby insulin infusion and (2) if insulin pharmacodynamics are affected by a nearby glucose sensor. Ten individuals with type 1 diabetes (mean age: 47 years, mean diabetes duration: 22 years, mean pump use: 6.4 years) participated in the study. Each patient had a Combo-set inserted in the abdomen, a contralateral Sof-sensor attached to an iPro recorder as a control, and a contralateral infusion set for routine insulin delivery. The Combo-set delivered insulin diluent except during meal tests on days one and three, when boluses of insulin lispro were delivered via the Combo-set. Post-bolus venous lispro levels were determined at 0, 30, 60, 120, and 180 min. Capillary blood glucose readings were collected with Bayer Contour Link meters.
  • The accuracy of sensor glucose readings was not affected by nearby insulin infusion. Mean absolute relative deviation was 17% with the Combo-set vs. 18.9% with the Sof-sensor (p=0.63).


Combo-set (n=10)

Sof-sensor (n=10)

Mean ARD



Mean ARD Range



Median ARD



Clarke A + B




  • Pharmacodynamics of insulin were not affected by a nearby glucose sensor. Insulin via the Combo-set showed the expected post-bolus peak time of 66.6 minutes. Postprandial glycemia after both test meals using the Combo-set was comparable to profiles obtained on day two, when participants were on their usual diet and received insulin via the control infusion set. One "No Delivery" alarm occurred during the 21 patient-days of use, similar to the historical control rate of other infusion sets (1 per 24 patient-days in the CareLink database of 99,857 patients in 2010).


Symposium: Beta Cell Replacement Therapies for Severe Hypoglycemia Unawareness

A Bionic Pancreas Delivering Insulin and Micro Dose Glucagon

Steven Russell, MD, PhD (Massachusetts General Hospital, Boston, MA)

Dr. Russell reviewed the last couple years of bi-hormonal closed-loop work at Mass General. Most intriguing to us was the concluding portion of the presentation, which featured discussion of the team’s upcoming trials in adults and pediatrics. A five-day outpatient closed-loop study is slated to start in late 2012, which may reflect a slight delay from the “summer” timeline we heard at ATTD in February (see pages 18-19 of our report at; as of right now, the mobile AP system is still awaiting FDA approval (an iPhone 4S as the controller and two Tandem t:slim insulin pumps to deliver glucagon and insulin), and we certainly hope the Agency keeps the team’s ambitious work moving. What’s noteworthy about this five-day trial is that participants will have free roaming around the Massachusetts General Hospital Campus, no set schedule or diet, and free access to the gym. More ambitious is the planned two-week (!) outpatient closed-loop study at a diabetes camp – this is still slated for summer 2013 (unchanged from ATTD). Although not mentioned today, Dr. Russell and colleagues are also planning a 12-day study of Massachusetts General Hospital staff with type 1 diabetes (2013) and a study in newly diagnosed patients (2013-14). Down the road, Dr. Russell believes the ultimate commercial AP product will ideally have a form factor similar to the current t:slim pump with a second reservoir for glucagon infusion and a built in CGM receiver and controller algorithm (two patch pumps would also be feasible in his view). Also possible would be a sleeve that would fit on the back of a smartphone where the logic and wireless radios would reside. In Dr. Russell’s opinion, the phone would be used only as an interface so that it wouldn't fall under regulation as a medical device (e.g., a similar strategy to Sanofi’s iBGStar). Ultimately, Dr. Russell hopes for a dual infusion set for glucagon and insulin and the potential for the team’s algorithm to work with multiple smartphones, CGMs, and pumps.

  • Dr. Russell emphasized that insulin-only control will not be enough to close the loop. Just one example is exercise, where his team typically sees declines in blood glucose of 2-5 mg/dl per minute. In other words, someone at 70 mg/dl dropping at 2 mg/dl per minute will be hypoglycemic in just 15 minutes. Given this scenario, turning off subcutaneous insulin “will not cut it.”
  • Dr. Russell reviewed previous bi-hormonal studies from Mass General (see pages 18-19 of our ATTD report at and pages 57-59 our ADA 2011 report at He focused on the second study of the system, which used an Abbott Navigator CGM input to the control algorithm, a GlucoScout to measure reference blood glucose, partial meal priming boluses (weight based), structured exercise of around 30 minutes, adults and children ages 12-17 years (no C-peptide), and two control algorithms separately controlling glucagon (PD) and insulin (MPC) delivered through two Insulet OmniPods. The closed-loop system achieved a solid average blood glucose ~158 mg/dl, which correlates to an A1c of 7.1%, 68% time in the range in 70-180 mg/dl (93% at night), and time <70 mg/dl of just 0.7%. The system was also robust to technical failures (e.g., failed OmniPod delivery, computer crash), though the team has asked FDA to modulate some of the algorithms to eliminate even more of the hypoglycemia.

Questions and Answers

Q: I was looking at your blood glucose excursions during meals. You attempted to give more insulin before the meals to reduce excursions. Do you have any comparisons to blood glucose levels during meals in people who don’t have diabetes? How is insulin released before blood glucose starts to rise?

A: We have done a number of experiments. We brought people without diabetes in, had them eat the same meals, and undergo the same monitoring. After a meal of that size, blood glucose may rise to 140-150 mg/dl in someone without diabetes. You also see some cephalic insulin release before food even begins to be absorbed. The pancreas also dumps insulin into the portal vein, so you see sharp spikes in insulin. We do have a disadvantage giving insulin subcutaneously. I would focus on that fact that we can attain pretty good average blood glucose control and avoid the risk of intravenous and intraperitoneal insulin. As far as we know based on the DCCT, if you achieve a mean blood glucose below 154 mg/dl, there is little signal for microvascular complications. That’s a tremendous improvement over where we are now – cross sectional studies suggest we’re at a mean A1c of ~8.5%.


Symposium: Clinical Aspects of Hypoglycemia in Diabetes - Consequences and Prevention

Glucagon and Closed-Loop Insulin Replacement in Diabetes

W. Kenneth Ward, MD (Oregon Health and Science University, Portland, Oregon)

Dr. Ward provided a thorough scientific overview of stabilizing glucagon for the closed-loop. He especially drilled down into the fibrillation of glucagon, which increasingly occurs as pH is lowered. Dr. Ward emphasized that fibrillation is nearly absent at a pH of 10. This is illustrated in a late-breaking poster [48-LB] from Dr. Ward’s team at this year’s ADA, in which the researchers pumped various glucagon formulations using an OmniPod pump. Glucagon at a pH of 2.5-3 delivered without a pump occlusion for only 47 hours, whereas glucagon at a pH of 10 had not caused occlusion at the 72-hour pump expiration (p = 0.036; n=5). Encouragingly, an in-press study (Ward et al., Clinical Drug Investigation 2012) demonstrates that subcutaneous injection of glucagon at a pH of 10 only led to a slight increase in injection site discomfort (“I don’t think pain in an of itself will prevent us from going forward.”). Dr. Ward believes the most promising glucagon formulation uses a glycine buffer, a stabilizing agent of lactose or bovine albumin, and alkaline preparation at a pH of 9.6-10. Dr. Ward and colleagues also have a number of upcoming studies to better understand and develop a stabilized glucagon in solution. As we noted in our recent report on Xeris (the company is using non-aqueous solvents to stabilize glucagon in solution), the glucagon competitive landscape includes at least four companies using different approaches (for more information, see our report at

  • There are a variety of reasons why glucagon fails to prevent hypoglycemia ~25% of the time in the closed-loop setting. First, glucagon fails when there are high concurrent insulin levels. Dr. Ward (with funding from JDRF) us undertaking a study to better understand this. Second, depletion of hepatic glycogen may also be a problem. Studies are needed to develop better models to more accurately anticipate the effects of glucagon. Another cause of glucagon failure is overestimation of glucose by the sensor (not specifically glucagon related, but still a problem). Finally, the chemical instability of glucagon is particularly challenging – glucagon is “not pumpable in its current form” due to polymerization into amyloid fibrils (probably cytotoxic) and degradation of the native glucagon molecule.
  • There are a number of remaining “to-do’s” that Dr. Ward hopes to study going forward: quantify the effect of stabilizing agents on glucagon bioactivity, shelf life stability studies during refrigeration, the biological effect of glucagon in pigs (fresh vs. aged), and the biological effect and tolerability of glucagon in humans (fresh vs. aged).


Product Theaters

Introducing the T:Slim Insulin Delivery System (Sponsored by Tandem Diabetes)

Kim Blickenstaff (CEO and President, Tandem Diabetes Care, San Diego, CA)

Tandem CEO Kim Blickenstaff led off the t:slim product theater with a major focus on simplicity (for our take on the t:slim approval and experience with the device, please see our November 2011 reports at and To start, he highlighted a number of well-designed and poorly designed products (well designed: the ATM; poorly designed: BMW’s iDrive, the Metro ticket machines in Washington, DC, and the Baxter Colleague Infusion Pump), explaining that the design process MUST involve the intended users of a device. Mr. Blickenstaff also shared data from the pump’s summative study for FDA approval, in which users typically learned the pump’s interface in about 90 minutes and 70% never referred to the user manual. A narrated t:slim promotional video followed, which was similar to those shown at JPM 2012 (see our report at and have also been posted on Tandem’s website. Highlighted features included the simple user interface, the “slimmest, most compact design,” and “the first touchscreen insulin delivery system cleared by FDA.” Last, Mr. Blickenstaff asserted that the t:slim is easier for educators to teach, easier for patients to learn and use, and may even reduce the support burden placed on diabetes practices. In supporting the last five months of clinical studies, Tandem has also built out its customer support. Tandem announced yesterday that it will begin taking orders for the t:slim on June 11 and the first orders will ship in August 2012.


Jen Block, RN, CDE (Stanford University, Stanford, CA)

The compelling educator-extraordinaire Ms. Jen Block walked audience members through the t:slim’s interface using an on-stage iPad. She first noted how the pump helps her achieve goals as both an educator (simple to train users on) and a patient (less time interacting with diabetes and more time to live life). The majority of her presentation included quick navigation through a variety of activities on the t:slim interface, including the well-designed home screen, the setting up and duplicating of user profiles, and seven-day averages and statistics.


Timothy Bailey, MD (Advanced Metabolic Care and Research, Escondido, CA)

Dr. Bailey concluded the product theater with a summary of t:slim’s three-day home use study prior to FDA approval (n=29) and the ongoing 30-day pre-launch study (n=100). The major takeaway was high patient enthusiasm for the t:slim in both studies. In the pre-launch study, ease of use scores across 14 critical t:slim functions averaged a 6.6 on a seven-point Likert scale (1=impossible to use, 7=very easy to use), well exceeding typical scores for devices. The reception was equally positive in the pre-launch study: 100% of subjects reported being satisfied with t:slim’s size, 95% of patients were satisfied with t:slim’s color screen, and 95% of patients were satisfied with t:slim’s home screen. Patients were also asked to compare the t:slim to their current pump on several metrics: convenience, easy to learn, ease of taking insulin, ability to keep blood glucose stable, and willingness to recommend t:slim to friends. Patients rated t:slim statistically significantly higher than their current pump for all the aforementioned metrics.


Panel Discussion

Timothy Bailey, MD (Advanced Metabolic Care and Research, Escondido, CA); Jen Block, RN, CDE (Stanford University, Stanford, CA); Kim Blickenstaff (CEO and President, Tandem Diabetes Care, San Diego, CA); Linda Parks, RN, CDE (Tandem Diabetes Care, San Diego, CA)

Q: The pump has a rechargeable battery. Do patients need to sit next to an outlet?

Ms. Block: Some people choose to disconnect, but you don’t have to.

Q: How long does it take?

Ms. Block: It depends on how long you’ve gone. The pump can last seven days on a full charge. We recommend you charge for a few minutes each day.

Q: So the user must sit there with the pump connected to an outlet?

Ms. Block: You could charge it while showering. One thing I’m excited about as an educator is that when you plug t:slim into a computer, it not only charges but it also asks you if you want to upload data. So it could be plugged in, uploading, and charging at the same time.

Q: What about a camping trip? What do you do there?

Ms. Block: It does come with a car charger.

Ms. Park: You can charge in a car, or buy one of those battery packs – it’s just like a cell phone. There are lots of different options.

Q: Is the device waterproof?

Ms. Parks: It’s rated to IPX-7. That means it can be in water for 30 minutes at three feet. We don’t encourage swimming with it, but it’s okay if you’re in the shower or thrown into a pool.

Q: Will this pump have integration with CGM?

Ms. Parks: The first pump will not. But we have an agreement with Dexcom.

Q: Have you seen any issues with the font size?

Ms. Parks: All those crazy things you can do with the iPhone are heavily patented. The font cannot be made bigger.

Q: Were there comments from patients that they couldn’t read it?

Ms. Park: The contrast is bright. We had very few comments on this.

Q: What about customer service issues. Will there be an 800 number available 24/7?

Dr. Bailey: Guaranteed.

Q: Will it be some god awful recorded voice on the other side or a live human being?

Ms. Parks: First you will get a recording to send you to the correct department, and then you will speak to a human.

Q: What about on nights and weekends? Will the lines be staffed by nurses or will it be technicians who have to get a nurse?

Ms. Parks: A mix. We have clinical people and salespeople in the field. We also have nurses and patients with diabetes in customer support.

Q: What is the turnaround time if a present pump doesn’t work.

Dr. Bailey: Fed-Ex the next day.

Ms. Parks: Within 24 hours.

Q: I was confused when you were selecting the Gym profile. How does that work?

Ms. Block: It’s activated with the touch. You just go into the options menu and to personal profiles. From there, I can select which one I like.

Dr. Bailey: This also has the cutting and pasting. In pumps, you always had to do everything from scratch. So now you can have a complicated profile and then tweak it.

Ms. Block: And as you’re tweaking it, the profile will appear shadowed.

Q: Can you show a cartridge change?

Ms. Parks: We don’t have a video here. But come by the booth and we can show you how to change it. It’s very easy.

Q: What if the pump has been used for a full seven days and it has zero battery life?

Ms. Parks: With a completely dead battery, it will take one hour to fully charge. It will turn back on in 15 minutes after it’s plugged in. We’re trying to encourage people to top it off every day. For instance, while you’re taking a shower, plug it in and keep the battery charged.

Q: It the touchscreen pressure-based or electrostatic-based?

Ms. Parks: It’s capacitance. You cannot use it with gloves on.

Q: How long is the durability of the touchscreen.

Ms. Parks: It’s durable and shelf tested.

Q: Can your pump show basal rates with a graph?

Ms. Block: Not on the pump, but it can in the t:connect software.

Q: So in the pump you can only see text?

Ms. Block: Yes.

Q: Can you expand on infusion sets?

Ms. Block: You can connect to any infusion set that has a luer lock.

Q: When you set the Gym profile by duplicating the other profile – what I did not see was when that basal rate stops and the other resumes. You changed it at 5 pm. But that basal rate continued indefinitely to midnight.

Ms. Block: it did and that was my intention.

Q: You said you are approved for ages 12 and over. But you said the participants in your trial were 22 years and older. How did you get approval?

Ms. Parks: In the summative studies for FDA, the youngest child was 12. In the current user evaluation studies, we limited it to 18 years and older. We wanted to test the usability of the pump and prepare for launch.

Q: Can you charge the pump with any USB charger?

A: Any micro USB charger.

Q: Do you have other languages?

Ms. Parks: It’s English only. We have plans to look at other countries after the US launch.

Q: What’s the delivery amount?

Ms. Parks: Every five minutes. Increments up to 0.001 units. The lowest basal is 0.1 units per hour.

Q: How do you calculate insulin on board?

Ms. Parks: Curvilinear.

Q: Is there a carb diary in the pump?

Ms. Parks: There is no carb diary in the pump.

(Editor’s note – the tone of this panel conversation was less than constructive at points and we were surprised and disappointed the audience would ask some openly antagonistic questions.)


Corporate Symposium: Waiting for Closed Loop - Tapping the Full Potential of Advanced Diabetes Technology in Today's Clinical Practice (Sponsored by Medtronic)


Irl Hirsch, MD (University of Washington School of Medicine, Seattle, WA)

Dr. Hirsch opened with a question, “What is the holy grail of type 1 diabetes?” The answer to this question has changed tremendously over time – from improving insulin, to the islet, to the holy grail of today, the artificial pancreas. The latter was the topic of the night’s Medtronic-sponsored symposium. Dr. Hirsch outlined the schedule, which included his presentation on the evidence behind sensor-augmented pump therapy, Dr. Thomas Danne on reducing the risk of hypoglycemia, and Dr. Timothy Jones on the latest progress towards closing the loop.


Advanced Diabetes Technologies: The Weight of Evidence

Irl Hirsch, MD (University of Washington School of Medicine, Seattle, WA)

In the symposium’s opening presentation, Dr. Hirsch reviewed the path we’ve taken to developing the closed loop. He emphasized the importance of the Star 1 trial (Hirsch et al., Diabetes Technol and Ther 2008), particularly because it revealed how to design a better CGM trial, how to select appropriate patients for CGM, and how to use CGM. Notably, the Star 1 trial also pointed to the relationship between patient adherence to CGM and A1c reduction – a common thread throughout the CGM studies Dr. Hirsch presented and the take home message of his talk. Turning to the JDRF CGM Study (NEJM 2008), he emphasized that we learned our lesson from Star 1. The trial showed a large separation in A1c between adults (>25 years) using real-time CGM and the control group. But again, success was moderated by how often patients wore the sensor. (He quipped to the audience’s amusement, “You can lead a horse to water, but that doesn’t mean he will drink or wear his CGM”.) This relationship surfaced once more in the Star 3 study and data from the Helmsley Charitable Trust’s T1D Exchange. He explained that regulatory officials and payers must realize that success in CGM is dependent on patient behavior, and that a trial using CGM is not comparable to a trial where all you have to do is take a pill. “That’s not the way we do diabetes and that’s not how patients take care of themselves.” Dr. Hirsch concluded his talk by announcing the latest progression in closed loop technology from Medtronic – the MiniMed 530G, which was recently submitted to the FDA and he believes will be available in the US market in 12-18 months (Medtronic management estimated 12 months during the recent Medtronic Analyst Day; see our coverage at


Reducing the Risk (and Fear) of Severe Hypoglycemia

Thomas Danne, MD (Kinder- und Jugendkrankenhaus Auf der Bult, Hannover, Germany)

Dr. Danne gave a broad overview of hypoglycemia, emphasizing the critical importance of identifying it in clinical practice, ascertaining its causes, and preventing it. Dr. Danne first reviewed data and professional guidelines suggesting the use of pumps and CGM can help prevent hypoglycemia and severe hypoglycemia. He next discussed Medtronic’s Veo insulin pump with low glucose suspend (LGS), recently submitted to the FDA as the MiniMed 530G (for more information, see the Medtronic 2012 Analyst Day report). We appreciated his review of the multi-center crossover study (LGS on or LGS off) of the Veo published last year (Danne et al., Diabetes Tech Therapeutics 2011). The main finding was that average glucose control did not change with LGS turned on vs. off, while all measures of hypoglycemia improved when patients had the LGS feature turned on (“You’re preventing hypoglycemia and your average glucose control is not getting worse. That’s a lot of good news”). Consistent with previous data, the vast majority of full two-hour LGS episodes occurred at night, the average blood glucose rise during pump suspension was 35 mg/dl/hour, and most alarms occurred during the day and were of short duration. Dr. Danne emphasized that there was no real risk of severe hyperglycemia from a two-hour suspension (one of the FDA’s major concerns with the Veo). In closing, Dr. Danne briefly mentioned the next step, predictive LGS management, which he is “taking a closer look at.” [We learned in the aforementioned Medtronic Analyst Day that the MiniMed 640G, a hypoglycemia minimizer, is expected in early 2013 (EU) and 2014-2015 (US).]


Closing in on Closed-Loop Insulin Delivery: Near and Long-Term Imperatives

Timothy Jones, MD (University of Western Australia, Perth, Australia)

Dr. Jones continued the focus on hypoglycemia in the symposium’s concluding presentation. He asserted that excessive fear of hypoglycemia leads to inappropriate diabetes management: in one survey at his clinic, fear of hypoglycemia caused 13% of children to eat a large snack at bedtime, while 11% of parents often avoided leaving their child alone (age >13 years). In these groups, mean A1c was 0.8% higher. Most unique was his slide showing handwritten responses from children to the question, “What worries you most about hypoglycemia?” Answers shown included “passing out and dying [sic]” “Going so low that I go into a comba [sic] and not waking up,” and “being alone.” Talk about reasons to get an LGS here in the US and predictive LGS as soon as possible... Dr. Jones next reviewed the Australian study of the Medtronic Veo (Ly et al., Diabetes Care 2012), which has demonstrated similar data to the Danne et al. study. The most common problems experienced by patients with the Veo system have been CAL Errors (calibration error between the sensor glucose and meter glucose), weak signal errors, and sensor errors. After six months of using the Veo, 85% of participants elected to continue using it for a second six months; moreover, 94% said they would recommend the system to other patients. While these numbers may reflect a trial effect, it is encouraging to see such high satisfaction with the product and we look forward to its arrival in the US likely sometime in 2013. Dr. Jones concluded with a brief slide showing Medtronic’s portable glucose control system (“the first step to home studies?”), which consists of two MiniLink transmitters with Enlite sensors, a BlackBerry smart phone, a Paradigm Veo insulin pump, a 915 MHZ translator, and a remote monitor (we first saw the system at the 2011 Diabetes Technology Meeting; see page five of our report at


Panel Discussion

Panelists: Irl Hirsch, MD (University of Washington School of Medicine, Seattle, WA); Thomas Danne, MD (Kinder- und Jugendkrankenhaus Auf der Bult, Hannover, Germany); Timothy Jones, MD (University of Western Australia, Perth, Australia)

Dr. Hirsch: We have had CGM for 12 years. A question for both of you: ‘Where will we be a decade from now?’

Dr. Danne: Pediatricians are always optimists. Ten years from now, we will not talk about closed loop being around the corner. Closed loop will be here. We’ll have a glucose sensor that’s redundant, using two different technologies. It will be so small that you will be able to wear it easily. It will work on its own. It will be a question of whether you want a Porsche or a Mercedes.

Dr. Hirsch: Appropriate from a German colleague [laughter].

Dr. Jones: I think that as human beings, we’re good at technology. Once we start using them, they will improve. Ten years is a long time. I’m confident that it’ll be normal therapy by then.

Dr. Hirsch: How are pumps paid for in Australia and Germany? Please compare and contrast with what happens in the United States with employer-based insurance.

Dr. Danne: We have the lucky situation in Germany that if we put in a good application, the insurance company will pay for the pumps, but we have a difficult time with CGM in Germany.

Dr. Jones: Paying for this is a challenge. Insurers will pay for pumps, but only 50% of people are insured.  The National Health system does provide coverage. 

Q: Germany has had a lot of international publicity about payment of insulin analogs. Is that still an issue?

Dr. Danne: Payment is always an issue. Yes, we have payment for rapid-acting analogs. But in Germany, only 50% of type 1 diabetes patients are actually using rapid acting analogs. In Germany, most patients are on human insulin. The financing is actually not so much of a problem.

Dr. Hirsch: By far the number one question has to do with reimbursement, not technology. In Australia, is CGM covered?

Dr. Jones: The sensors are not covered at all. They can get it through other means.

Q: Dr. Jones, why do you think that 15% didn’t want to stay on the LGS?

Dr. Jones: They don’t like the alarms, they don’t like wearing something, some are teenagers. It’s the reasons people don’t want to use CGM.

Dr. Hirsch: I actually think that’s a surprisingly low number.

Dr. Hirsch: There are a lot of questions, specifically for pediatricians, about problems with allergies and skin reactions, both in terms of sensors and pumps. What are the tricks you use?

Dr. Danne: I am amazed by the ideas parents come up with. There have been a range of solutions, including adhesives and sprays. I don’t remember a lot of cases where we couldn’t find a solution. It is more size problems and size rotation problems than allergy issues.

Dr. Jones: When we started we saw a lot of those types of problems and it has improved with time.

Q: Is there a validated tool to assess fear of hypoglycemia?

Dr. Danne: There is a fear of hypoglycemia questionnaire. I don’t think it is that sensitive. In my clinical experience, fear of hypoglycemia is much easier to get at with talking to a patient rather than a questionnaire.

Dr. Hirsch: Up to how many times would you let a child correct hypoglycemia with carbohydrate intake? The example you showed did it several times. What do you recommend for that?

Dr. Danne: It depends on patient tolerance. I don't have too many patients who pull out their pump when they have hypoglycemia. Most correct with carbohydrates. This was an extreme patient who had had many of these alarms. Most patients who are not on a sensor will do it by clinical symptoms.

Q: How did you define hypoglycemia unawareness? Did you see any differences in hyperglycemia based on duration of suspend time?

Dr. Jones: We used the Clarke’s questionnaire. You can use the Clarke’s or Gold’s questionnaire. The Clarke’s is more complicated. There was no relationship in terms of hyperglycemia.

Dr. Hirsch: Have either of you observed differences in LGS in relation to proximity of prior insulin bolus? Do you counsel patients with LGS systems to behave differently in these conditions after bolus?

Dr. Danne: Patient intervention is the rate-limiting step in all types of closed loop systems. The problem we have in the closed loop approach is if you over bolus, you can have the best closed-loop system in the world but it still can’t pull insulin out of your body. Patients who don’t interact with system have the best management. If you severely over bolus, there isn’t much you can do.

Q: When are the best times to calibrate CGM? Pre-prandial, after fasting, during a plateau? Is it best to calibrate during hypoglycemia?

Dr. Hirsch: The way I understand it is to calibrate when the glucose is relatively flat.

Dr. Francine Kaufmann (Medtronic Diabetes, Northridge, CA): See the Enlite poster.

Dr. Hirsch: See the late-breaking Enlite poster. I should mention Enlite is not approved in the US. Fran, what do you recommend with your current sensor?

Dr. Kaufman: Stable glucose with a three to four times per day calibration.

Dr. Hirsch: That’s what we recommend at our hospital. 

Dr. Jones: We recommend calibrating at a stable time between meals. Four times per day.

Dr. Hirsch: Dr. Jones you found two-hour suspend events on 10% of the nights.  Since 50% of patients respond to the alarms, does that mean 20% of patients would have two-hour suspend events on any night?

Dr. Jones: 143 patients did not respond to the alarms and had a full two-hour suspend.

Dr. Hirsch: What’s so interesting is so many of these questions deal with reimbursement. It seems like we don’t have to convince people that the technology is good and it works. The convincing part of it is with the payers.

Dr. Jones: It doesn’t matter who the payers are, they’re reluctant to pay.

Dr. Danne: We also need to be making sensors convenient enough to wear on a continuous basis. We do have cases where there is reimbursement and the sensors are sitting on a shelf at home.

Dr. Hirsch: This is for the pediatricians with regards to very young kids and the topic of real estate. Where on the body can you use this technology for insulin delivery or sensors?

Dr. Danne: I am amazed with what people come up with. The buttocks is excellent, while the abdomen generally is something the kids hate. The thigh works and the arm works. There are many places that are not the official way that still work, so wherever you have subcutaneous fat, try it.

Q: If sensor-augmented pump therapy is state of the art, but only at most half of patients are willing to participate, shouldn’t we investigate more about diabetes coping strategies? How did you address that in STAR-1?

Dr. Hirsch: We did not do well in those people. Those are not the best patients to use this technology. If they don’t use the CGM, they won’t improve. That’s a critical point. One of the conclusions of this session is that no matter how good the technology is, if you don’t have patients willing to participate and use the therapy, it doesn’t matter that much. I don’t know the best word – motivated is one. You have to have a willingness to participate. The original question is about better understanding how to get patients to be more interested in managing their own diabetes. This is a huge, huge issue…

Dr. Kaufman: It’s also with type 2s. They don’t even take their pills.

Dr. Hirsch: It’s with any medication. Look at HIV patients who don’t take their medication. Transplant patients that don’t take their meds.

Dr. Danne: If you had a car that alarmed all the time, that hurt to drive because you were inserting two catheters all the time, that gave you alarms when you have misbehaved, you would probably say to your wife after half a week, “I’ll take the bus.”

Dr. Hirsch: I would turn off the alarms.

Dr. Danne: I really don’t think it’s motivational. It’s a burden on the patient right now. It’s too much. I wouldn’t drive a car either. I would also take a bus.

Dr. Hirsch: Let’s go back to the beginning of insulin therapy. Since then, we have increased the burden by making diabetes more complex. Back even thirty years ago, the majority of patients were on once or twice daily insulin. They took insulin and forgot about it – there was no SMBG. Nothing happened until the eye doctor looked in their eye and said “We have problem.” Or there was edema showing. Until that happened, there was no burden at all. We’ve added burden and it’s come at a great cost, but when you look at the data in the T1D Exchange, the population-based risk of complications has gone down.

Comment: I would disagree. It was still a burden back then. I think back to the patients with type 1 who stood in front of our group when I was a medical student. They said, “The doctor says we should have a normal life.” But they had this huge emotional burden to testing their urine. What you had to do every day back then was not as many steps. But I would disagree that it was not burdensome.

Dr. Hirsch: How many patients did their urine testing?

Comment: These patients did.

Dr. Hirsch: Well, most did not. Most people are not very successful. Look at A1cs in the T1D Exchange. On the other hand, we’re not seeing the complications.

Dr. Danne: Absolutely, Irl – we have seen progress. I’ve saw complications in young adults when I started. I hardly see them now. But we’re still not there, and it’s not right to blame the patient. We haven’t made the advances to be good enough at this point. We’re on the verge of a breakthrough with the closed loop. Five years from now, we might look back and say what we did in 2012 was Stone Age.


Private Event: JDRF/NIDDK Closed-Loop Control Research Meeting

This special meeting has been a fixture at ADA for a few years, starting with only 10-15 people in 2007. Today, the meeting was another who’s-who of diabetes, filling an entire hotel ballroom with members of academia, industry, funders, and FDA. Nineteen separate groups in the US, Europe, Middle East, and Australia are now being funded by JDRF, NIH, or the AP@Home project (EU only). The format for today’s meeting was a panel discussion with six key principal investigators followed by a ‘science fair’ with hands on demonstrations of the technology being used by 12 of the groups. Wow!

Outpatient Closed-Loop Study Progress and Panel Discussion

Panelists: Stuart Weinzimer, MD (Yale University, New Haven, CT); Roman Hovorka, PhD (University of Cambridge, UK); Boris Kovatchev, PhD (University of Virginia, Charlottesville, VA); Bruce Buckingham, MD (Stanford University, Stanford, CA); Ed Damiano, PhD (Massachusetts General Hospital, Boston, MA); Ken Ward (Oregon Health and Science University, Portland, OR)

Dr. Weinzimer: Before we begin the panel discussion, everyone will give an orientation explaining what they’re doing.

  • Roman Hovorka, PhD (University of Cambridge, UK). The Cambridge group has been developing the “FlorenceD” home closed-loop prototype. The system consists of the Abbott Navigator (currently “the most accurate CGM available in Europe”), the Companion (a device to assist communication with the Navigator), a small laptop running an MPC algorithm, and a Dana R Diabecare pump (with a Bluetooth connection). The switch to the Diabecare pump is a departure from presentations in the past, where the team always seemed committed to the Abbott Aviator pump. Since May, the Cambridge group has been approved to conduct overnight outpatient trials by the UK regulatory authority (MHRA). A typical trial design is a crossover study comparing several nights of open and closed-loop periods. For the first few closed-loop nights, a nurse stays nearby in case of problems. Dr. Hovorka wishes for more robust wireless connectivity, access to the communication protocols, and more pump functionality such as a short basal infusion time and storage of bolus wizards.
  • Boris Kovatchev, PhD (University of Virginia, Charlottesville, VA): Dr. Kovatchev reviewed the iAP study group’s (UCSB/Sansum, Montpellier, Padova/Pavia, and the University of Virginia) DiAs system that we first saw at DTM 2011 and again at ATTD 2012 (see our report at The system includes a Dexcom Seven Plus, an Insulet OmniPod with an iDex PDM, and two cell phones (one to run the control algorithm and another to transfer the signal to the iDex). The system has been used successfully used in an outpatient setting in 17 patients with type 1 diabetes for ~700 hours (see our ATTD 2012 report for more detailed accounts). From the first 250 hours of outpatient use, error rates were 3% on the sensor, 2% on the pump, 0.8% with remote monitoring, and only 0.2% with the DiAs running on the cell phone. This led Dr. Kovatchev to conclude, “It does look like a cell phone can run closed loop control.” He concluded with a number of recommendations and thoughts on the future: (1) portable software should be built from the OS level to meet medical device standards; (2) human factor studies are “extremely important” for transition to outpatient use; (3) inter-device communication is still a major problem; (4) algorithm development should include hybrid control; (5) current sensor accuracy is sufficient for control to range. The team plans to use the Dexcom G4 sensor and t:slim insulin pump in upcoming studies.
  • Bruce Buckingham, MD (Stanford University, Stanford, CA). The Stanford group (in conjunction with the University of Colorado, Rensselaer, and the Jaeb Center) have been working on a number of initiatives, including (1) in-home predictive low glucose suspend using a Kalman filter and a Medtronic pump and sensor; (2) inpatient metabolic control at the onset of diabetes using a Medtronic PID controller; (3) control to range studies with an MPC controller and the Sansum APS platform; (4) treat-to-range studies with a modified ePID controller; (5) safety studies to detect infusion site and sensor failure; and (6) investigating the University of Virginia’s DiAs system (with remote monitoring) at a children’s diabetes camp. The (1) predictive low glucose suspend work has FDA approval for 1,600 in-home nights with 44 patients, and has already amassed 250 nights of data with 20 subjects. Patients sleep with a laptop at the their bedside. This system has already halved the number of nights at which patients go below 60 mg/dl. Dr. Buckingham wished for more robust wireless communications and a single, simple handheld device with a minimal number of alarms.
  • Ed Damiano, PhD (Massachusetts General Hospital, Boston, MA). Dr. Damiano provided an overview of the Boston group’s bi-hormonal closed-loop research, starting with animal studies in 2006 and followed by the first experiments in humans in 2008-2009 using venous blood glucose as the controller input. From 2010-2012 (>2500 hours of control), the team has used the Abbot Navigator CGM as the input to a laptop-driven controller – studies have included six carb-heavy meals and a meal priming bolus. Dr. Damiano then reviewed the three outpatient studies planned for the next year. All will use either the Abbott Navigator CGM or Dexcom G4, an iPhone 4s controller with an algorithm running in C++, and two tandem t:slim insulin pumps to dose glucagon and insulin. Dr. Damiano showed the room the Navigator/iPhone handheld, which is enclosed in one unit but includes the Navigator receiver hardwired to the iPhone through the 30-pin connector. The system uses low-energy Bluetooth to avoid too much battery usage. This system will be submitted to FDA for an IDE in the next six to eight weeks, while Dr. Damiano hopes to build a G4 version of the system by the end of the year. He hopes to start the transitional five-day study on the MGH campus this fall and finish in fall 2013. A study is also planned for summer 2013 in campers at Clara Barton/Camp Joslin and for 2013-2014 in MGH staff.
  • Ken Ward (Oregon Health and Science University, Portland, OR): The Oregon group has been focusing on the issue of sensor redundancy to eliminate ‘large sensor errors’ (“egregious” errors of over 50%). Studies show that a single Dexcom Seven Plus sensor chosen at random has on average 19 hours per month of large errors. Using two sensors can reduce this to seven hours per month. While this is a lot better, it is still not perfect and can cause problems. The group is also performing inpatient studies with two Dexcom Seven Plus sensors, two Insulet OmniPod pumps (for insulin and glucagon) and a tablet computer. The group is testing the system for eventual outpatient use, and improving the user interface – they expect to move to a Motorola ES400 smartphone and a special belt. The team is also trying to stabilize glucagon for use in bi-hormonal systems and has had success using reducing sugars to dramatically reduce the breakdown (see Dr. Ward’s presentation on glucagon from Day #1 of ADA 2012 at Dr. Ward’s #1 wish list item is a single site device that combines insulin and glucagon infusion on the same catheter with sensors on the outside surface.

Questions and Answers

Dr. Weinzimer: As we move towards outpatient studies, it sounds like some are sticking with one sensor and some are using multiple sensors.

Dr. Ward: That’s an important question Ed and I have been talking about. As sensors get more accurate, it’s quite possible that the value of redundancy declines. But the question is will you still be able to keep the risk of large errors to a minimum. We’ll continue to look at this.

Dr. Damiano: Yes, it’s about the improvement in MARD that’s to be gained from multiple sensors.

Dr. Hovorka: We presented at this year’s ADA that the Navigator had no over-reading of 40% or higher lasting more than one hour. We believe one sensor is sufficiently safe .

Q: This is a fantastic discussion. In the studies from the consortium, we’re seeing great efficacy. But where the rubber hits the road is in system failure and reporting of system failure. Ed, you and I have talked about this. When we clump together patients and present means and averages, it hides what we’re all concerned about: outliers when there are system failures. It’s something to consider as a community. As we move to outpatient studies, how frequently are we defaulting to basal rates? Why are they defaulting? It would help funders, FDA, and industry. To start to narrow down and clump together errors.

Dr. Weinzimer: Maybe there should be agreed upon common reporting metrics.

Dr. Kovatchev: Aaron, what you’re saying is extremely important. There was a quick table in my presentation that showed the frequency of errors for various components. That may be just the first approach to defining what it means for the system to fail. In addition to by component, the duration, and gravity of the failure should also be reported.

Dr. Hovorka: In our studies, we focus on efficacy, safety, and utility. Utility is how long the system is operational. This was our attempt to capture this important issue. I agree, it should be captured.

Dr. Kowalski: Somebody brought up the communications. The diabetes community has struggled with a variety of different ways to download devices. We’ve all seen the pictures at clinics with many cables to download. JDRF is funding a project in Toronto, Canada to get the ball rolling on communication standards that can be used. We’ve reached out to industry. I would urge all of you to introduce yourselves to Joe. The Canadian clinical trials network is working on this.

Q: I wear a CGM and an insulin pump and I have a love-hate relationship with my Dexcom. Have you tried to incorporate other types of data? What kinds of things might trigger failure in the sensor? Heart rate monitors, activity monitors, dietary data, schedules, calendars, would those types of things help inform sensor accuracy?

Dr. Kovatchev: The short answer is all of the above. First, the system itself as its running now doesn’t rely on the sensor. There are two sources of information – one is the sensor and one is the insulin pump. The output of the insulin pump can actually flag sensor errors. Mark Breton is incorporating heart rate in closed loop funded by NIH. There are also studies looking at meal profiles and behavior profiles to flag events daily and take patterns into consideration. That will help when considering sensor accuracy. All that you mentioned is under consideration.

Bruce: If you went to the exhibit, there are patch pumps that have accelerometers built into them. Anything stuck on the body can easily do that. Where I think we need to go is if a cell phone can do face recognition, it should do food recognition.

Dr. Ward: Sweating, showering, jarring the device, those will cause rapid rises and rapid falls in the signal. The big problem is the slow drift you see. We don’t understand the cause of that drift.

Q: Each group has done phenomenal work. Now that we’re moving to the outpatient setting, studies are bigger, more expensive, and more complicated. Could you do joint clinical studies that share economies of scale. Using multiple different camp sites and even a shared control group? Would regulatory bodies consider that?

Dr. Kovatchev: I reported on behalf of a group of four different centers.

Dr. Hovorka: The European commission project has six centers that work together.

Dr. Damiano: I second that. A lot of our initial studies were in CRCs. CRCs can only allow us to do so much. Once out of the CRC, collaboration really becomes the obvious thing to do. Parallel studies and running at multiple sites. We intend to do that.

Dr. Buckingham: The JDRF consortium has wanted that and we should be doing that and sharing the data. A shared control group make sense. Also sharing set failure data.

Dr. Weinzimer: We’re still at transitional studies and not to the point where we can agree on something. Things are still in a weeding out direction and we’re still casting a wide net.

Dr. Weinzimer: Another question on human factors. Have any groups gone out and done some focus grouping activities to look for what users want out of a device? I imagine that many adults with type 1 diabetes may have different expectations than an adolescent or child.

Dr. Kovatchev: In preparation for our outpatient studies, we went through a rigorous cycle of focus groups and human factors. We cannot give that device to a patient without these types of studies.

Dr. Ward: We’re doing these to establish clarity. When you press a button, what does that mean?

Dr. Hovorka: We did an initial focus group two years ago. We’ve haven’t done formal human factors work but it’s on our list.

Dr. Damiano: We’ve been building out the interface for the device I just showed you. In our five-day study, we hope to include a human factors study as part of it. There will be good oversight and one to one nursing. We can run in that a human factors study. Up until now, subjects have not interacted with the device. They could look at it at their bedside, but it’s the first time where they’re interacting with mobile platforms. We’ll learn a great deal over the next 12 months.

Dr. Buckingham: That’s one of the things FDA wants from us. We’re all doing it. I as a physician am also a beta tester for engineers. We’re a hard group.

Comment: I’d like to echo Aaron’s comment. I come from the auto-antibody field, and progress depended on everybody getting together and saying you cannot publish a paper unless you do these things. The group needs to get together and standardize. It really helped in antibodies.

Comment (from FDA): I’d like to make a comment about egregious errors. As you people submit data to us, it’s harder to get data for errors as studies go to the outpatient settings. There is no reference standard. Is there any way to analyze severe CGM errors and see what was the impact of that? That would be extremely helpful for us to understand the risks and how to mitigate them.

Dr. Ward: A number of us are interested in studying closed-loop devices using systems that are not yet approved. The Dexcom G4, Enlite, etc. Is there a special request you have for bringing IDEs for devices that are not yet approved.

Dr. Courtney Lias (FDA, Silver Spring, MD): We definitely encourage the study of new devices. That’s where things are going. As far as outpatient studies, maybe we need more information. You don’t need to have approved devices to get an IDE. You just need to show that there are mitigation factors in case of malfunction.

Q: How much effort is being put forth into creating a system that is more cross platform? Rather than having a patient with their regular phone plus a phone for closed loop.

Dr. Kovatchev: Our control algorithm is written in JAVA. That conversation is long and we should have that outside.

Dr. Hovorka: I would mention the project running in Canada. It’s trying to find a common means to communication. That cross platform connectivity is so essential.

Comment: Regarding the standards, lots of work has been done by industry. There are drafts for insulin pumps and CGM. The intent is to accelerate those projects.

Dr. Kowalski: Before everyone gets out of the room, I’d like to leave a JDRF thought. It stems from data that are very important – the Helmsley Charitable Trust data. It’s very, very important to say that there is a significant unmet medical need. In the Helmsley data, average A1cs are above 8.5%. Then you look at the prevalence of severe hypoglycemia in adults with type 1 diabetes. We still have a tremendous amount of work to do. So many in the audience are affected by type 1 diabetes. There are challenges here. But there is a huge, huge potential to help many, many people.



Oral Sessions: Incretin Therapies

Once-Weekly GLP-1 Receptor Agonist Albiglutide vs. Titrated Prandial Lispro Added on to Titrated Basal Insulin Glargine in Type 2 Diabetes (T2D) Uncontrolled on Glargine Plus Oral Agents: Similar Glycemic Control with Weight Loss and Less Hypoglycemia (55-OR)

Julio Rosenstock, MD (University of Texas Southwestern Medical School, Dallas, TX)

Dr. Rosenstock presented additional results from the phase 3 study Harmony 6 for GSK’s once weekly GLP-1 agonist albiglutide. As a reminder, the study randomized participants to receive either albiglutide or thrice-daily insulin lispro added onto insulin glargine with or without metformin and/or a TZD for 52 weeks. Topline data announced in March reported: 1) non-inferiority with regard to A1c reduction at week 26 with albiglutide (0.82%) vs. insulin lispro (0.66%, p <0.0001 for non-inferiority); 2) weight loss with albiglutide (1.61 lbs) vs. weight gain (1.79 lbs) with insulin lispro at week 26; and 3) an increased rate of nausea (13% vs. 2.1%) and vomiting (7% vs. 1.4%) with albiglutide vs. insulin lispro. (For our coverage of this topline data, please see the April 6, 2012 Closer Look at The new data provided today by Dr. Rosenstock revealed that the A1c reductions achieved at week 26 were sustained through week 56, more participants in the albiglutide arm achieved an A1c <7.0% and <6.5%, weight changes continued to diverge between the groups through week 56, hypoglycemia rates were reduced in the albiglutide arm (2.12 vs. 1.01 events per patient year), injection site reactions were slightly higher in the albiglutide arm (9.5% vs. 5.3%), and antibody formation was low with albiglutide treatment in the trial. During Q&A, Dr. Rosenstock remarked that there were no significant changes in lipids, blood pressure, or pulse rate with albiglutide treatment. 

  • The Harmony 6 trial was a 52-week, randomized, open-label, active comparator controlled study that randomized individuals with type 2 diabetes to receive 30 mg of once weekly albiglutide (n=279) or thrice-daily insulin lispro (n=278) added on to insulin glargine with or without metformin and/or a TZD. The primary efficacy endpoint of the study was A1c reduction at 26 weeks. At baseline, average age was 55 years, weight was 92 kg, BMI was 33 kg/m2, duration of diabetes was 11 years, A1c was 8.5%, and FPG was 153 mg/dl. Approximately 70% of participants continued to receive metformin therapy and 2% continued to receive TZD therapy in the study. Insulin glargine treatment was titrated to a target FPG of 80-130 mg/dl. Insulin lispro therapy was adjusted per a pre-specified algorithm based upon BG monitoring. Albiglutide therapy could be up-titrated at week eight to 50 mg if an A1c of <8.0% was not achieved. An ITT-LOCF analysis was employed.
  • Non-inferior improvements in glycemic control were demonstrated in the albiglutide arm in comparison to the insulin lispro arm at weeks 26 and 52. A1c reductions at week 26 were 0.82% in the albiglutide arm vs. 0.66% in the insulin lispro arm (p <0.0001 for non-inferiority). Dr. Rosenstock indicated that the result was close to demonstrating superiority for albiglutide (p=0.053). These reductions in A1c were largely maintained through week 52, with a change in A1c of 0.75% in the albiglutide arm and 0.66% in the insulin lispro arm. Changes in FPG at week 26 were also not significantly different between the arms (-17.9 mg/dl for albiglutide vs. -12.9 mg/dl for insulin lispro), and these changes were maintained out to week 52. Finally, more individuals in the albiglutide arm achieved an A1c <7% (30% vs. 25%) and <6.5% (11% vs. 8.5%) at the end of 52 weeks than individuals in the insulin lispro arm. Interestingly, the mean insulin glargine dose was increased in comparison to baseline in both the albiglutide (+5 units to 52 units) and insulin lispro (+7 units to 50 units) arms. 
  • On average, individuals in the albiglutide arm experienced weight loss while individuals in the insulin lispro arm experienced weight gain. At the end of 26 weeks, weight loss in the albiglutide arm was 0.73 kg (1.61 lbs) and weight gain in the insulin lispro arm was 0.81 kg (1.79 lbs). The resultant treatment difference was 1.54 kg (3.40 lbs; p <0.0001). At the end of 52 weeks, the treatment difference was even greater at 2.61 kg (5.75 lbs), with a weight loss of 0.96 kg (2.12 lbs) with albiglutide and weight gain of 1.66 kg (3.66 lbs) with insulin lispro.
  • Hypoglycemia occurred less frequently while GI side effects occurred more frequently in the albiglutide arm. The number of hypoglycemic events per patient year were doubled in the insulin lispro arm (2.12 vs. 1.01 events per patient year). However, the occurrence of severe hypoglycemia was reported to be negligible in both arms. No major imbalance was observed between the arms with regards to adverse events, except for an increased rate of GI side effects in the albiglutide arm (41.8% vs. 21.4%). More specifically, 13% of participants experienced nausea in the albiglutide arm vs. 2.1% in the insulin lispro arm. Vomiting occurred in 7% of the  participants in the albiglutide arm vs. 1.4% of participants in the insulin lispro arm. Dr. Rosenstock remarked that these rates for albiglutide, while relatively higher than insulin lispro in the study, were lower than rates observed with other GLP-1 agonists in separate studies. A slightly higher rate of injection site reactions were also observed in the albiglutide arm (9.5% vs. 5.3%), although the majority were deemed to be mild in intensity (83.7% vs. 95.6%, respectively). Few systemic allergic reactions occurred with both albiglutide (1.4%) and insulin lispro (0.7%) treatment, and the treatment difference was not statistically significant. Additionally, antibody formation to both therapies was noted to be low and similar between the groups.

Questions and Answers

Q: Can you comment on whether there were any changes in lipids, blood pressure, and pulse rate?

A: There were no changes in the lipid profile. In terms of blood pressure, there was nothing major there. There were also not major changes in heart rate. There were initial minor changes in heart rate, but nothing overall.


Efficacy and Safety of Once-Daily Lixisenatide Added on to Titrated Glargine Plus Oral Agents in Type 2 Diabetes: GetGoal-Duo 1 Study (62-OR)

Julio Rosenstock, MD (University of Texas Southwestern, Dallas, TX)

Dr. Rosenstock presented results from the phase 3 study GetGoal-Duo 1 for Sanofi’s once daily GLP-1 agonist lixisenatide. As a reminder, the study evaluated the safety of efficacy of adding lixisenatide to recently initiated and optimally titrated insulin glargine therapy in individuals with type 2 diabetes. Topline results reported for the trial in December 2011 highlighted that lixisenatide led to further reductions in A1c over placebo and minimized the weight gain associated with basil insulin therapy, but was associated with increased rates of GI adverse events and hypoglycemia – although the hypoglycemia rate remained low. In today’s presentation, Dr. Rosenstock discussed additional safety and efficacy data from the trial. Of greatest note, he revealed: 1) the specific A1c reductions observed in each treatment arm (-0.71% with lixisenatide vs. -0.40% with placebo); 2) that significantly more participants in the lixisenatide arm were able to achieve the composite endpoint of A1c <7.0%, no hypoglycemia, and no weight gain (28% vs. 18%); 3) that injection site reactions were elevated in the lixisenatide arm (6.7% vs. 2.2%); and 4) that the discontinuation rate due to adverse events was higher in the lixisenatide arm (8.5% vs. 3.6%), largely driven by GI side effects.

  • The GetGoal-Duo 1 study evaluated the safety of efficacy of adding lixisenatide to recently initiated and optimally titrated insulin glargine therapy in individuals with type 2 diabetes. The randomized, double-blind, placebo-controlled study included a 12-week run in period in which insulin glargine therapy was initiated and titrated to a target FPG of 80-100 mg/dl. Following this 12-week period, participants (n=446) who had not achieved an A1c <7.0% despite achieving the FPG target were randomized to receive lixisenatide once daily or placebo for 24 weeks while continuing on insulin glargine therapy (continuously titrated) and background metformin and/or TZD therapy. The primary endpoint of the study was change in A1c at week 26. At baseline, patient demographics were similar in both groups. Average age was 56.2 years, type 2 diabetes duration was 9.2 years, BMI was 31.8 kg/m2, and A1c was 8.6%.
  • Lixisenatide treatment improved glycemic control over placebo when added onto insulin glargine treatment. During the run in period, A1c was reduced in both treatment groups from 8.6% to 7.6%. At week 26 of the treatment period, lixisenatide (-0.7%) provided further improvements in A1c over placebo (treatment difference of 0.32%; p <0.0001). The percentage of individuals achieving an A1c <7.0% (56% vs. 39%) and <6.5% (32% vs. 16%) was statistically greater in the lixisenatide arm than the placebo arm, respectively. Additionally, two hour OGTT values were significantly improved with lixisenatide over placebo (mean treatment difference of 56 mg/dl; p <0.0001). Dr. Rosenstock noted that insulin glargine doses were only minimally titrated in the treatment period, increasing from 44 units to 50 units per day in both arms.
  • Lixisenatide had a beneficial effect on body weight, but was associated with an increased risk for hypoglycemia. In the lixisenatide arm, there was 0.3 kg (0.7 lbs) of weight gain vs. 1.2 kg (2.6 lbs) of weight gain in the placebo group (p=0.0012). Meanwhile, the number of hypoglycemic events per patient-year was 0.98 for lixisenatide vs. 0.44 for placebo – although Dr. Rosenstock argued that this was still a very low rate of hypoglycemia. Significantly more participants in the lixisenatide arm were able to achieve the composite endpoint of A1c <7.0%, no hypoglycemia, and no weight gain (28% vs. 18%).
  • As expected, GI side effects were the most frequently reported adverse events in the lixisenatide arm. In particular, the rates of nausea (27.4% vs. 4.9%) and vomiting (9.4% and 1.3%) were elevated with lixisenatide treatment over placebo, respectively. On average, nausea appeared to subside after six to eight weeks of treatment. The percentage of participants discontinuing from the trial due to adverse events was also higher in the lixisenatide arm (8.5% vs. 3.6%), which was driven by nausea and vomiting (4% vs. none).  Injection site reactions occurred in 6.7% of individuals in the lixisenatide arm in comparison to 2.2% in the placebo arm. However, injection sites reactions did not play a major causal role in study discontinuation (0.9%). Only one pancreatitis case was reported in the study, and it occurred in the placebo arm.

Questions and Answers

Q: Was there any effect on heart rate, blood pressure or lipids?

A: There were no lipid changes in this study. There doesn’t appear to be a signal for pulse increases with lixisenatide. There was no change in blood pressure either.

Q: The effects of lixisenatide were quite impressive. When was the drug administered?

A: Lixisenatide was administered in the morning. The mixed meal tests were conducted in the morning.

Q: Do you have any data for PPG following other meals?

A: No, we don’t in this study.


Exenatide Treatment for six Months Improves Insulin Sensitivity in Adults with Type 1 Diabetes Mellitus (58-OR)

Gayatri Sarkar, MD (DEOB, NIDDK, NIH, Bethesda, MD)

Dr. Sarkar presented the results of a small study that explored the effects of exenatide on glucose metabolism in adults with type 1 diabetes with a focus on insulin sensitivity. The cross over study randomized 14 adults with long-standing, well-controlled type 1 diabetes to receive either insulin or insulin and exenatide (dose gradually increased to 10 mcg four times daily) for six months before switching treatments. Exenatide provided decreases in weight and postprandial plasma glucose levels, an increase in fasting plasma glucose levels, and no change in A1c (data presented in Table 1, below). While taking exenatide and insulin, participants had lower daily insulin requirements and enhanced insulin sensitivity compared to treatment with insulin monotherapy. Dr. Sarkar reviewed several potential explanations of the results, which are summarized in Table 2 below. In particular, she noted that while exenatide’s mechanism of enhancing insulin sensitivity in humans is currently uncertain, results from preclinical studies have suggested some possibilities, including increased vagus nerve activity and altered cytokine production. Dr. Sarkar concluded by stating that the results from this study should prompt further investigation into whether insulin sensitizing effects can also be achieved with other GLP-1 agonists in people with type 1 diabetes.

        Table 1. Study Results


On Exenatide

Off Exenatide

P value

Weight (kg)




A1c (%)




Fasting plasma glucose (mg/dl)




Postprandial plasma glucose (mg/dl)




Insulin requirement (units/kg/day)




Insulin Sensitivity Index* (SI) (mg/m2/min per mU/ml)




* The Insulin Sensitivity Index (SI) is defined as the glucose disposal rate normalized for body surface area and plasma insulin concentration. It was determined using hyperinsulinemic euglycemic clamp studies lasting 180 minutes with an insulin dose of 120 mU/m2/min.


        Table 2: Potential Explanations of Results

Observed Result

Potential Explanation

Decreased body weight

Suppressed appetite

Lack of lowering fasting plasma glucose levels

Lack of inhibition of glucagon

Decreased postprandial plasma glucose levels

Slower gastric emptying

No change in A1c

Net effect of fasting and postprandial plasma glucose levels

Lower insulin requirement

Improved insulin sensitivity

Improved insulin sensitivity

Mechanism is not entirely clear in humans.

Results of preclinical studies suggest three possibilities:


1.     {C}

Increased insulin-stimulated glucose uptake in muscle and fat cells via a PI-3 kinase dependent mechanism


2.     {C}

Centrally increased vagal activity


3.     {C}

Anti-inflammatory effects and altered cytokine production.


Questions and Answers

Q: With respect to the increased fasting plasma glucose levels, you said it may be due to a lack of glucagon inhibition. Do you have any data on postprandial glucagon measurements?

A: No, our group didn’t see any difference in glucagon. However, other groups have shown inhibition of glucagon after exenatide use. So this remains controversial. Now there are postulates for some mechanisms for the lack of glucagon suppression. Because there is no ability to produce insulin in type 1 diabetes patients, the cross talk between glucagon and insulin may be lost. Also, there is no postprandial expression of amylin in people with type 1 diabetes, and amylin has been shown to suppress glucagon. So the topic is controversial.


Vildagliptin Reduces Glucagon During Hyperglycemia and Sustains Glucagon Counter-Regulation in Type 1 Diabetes (59-OR)

Bo Ahren, MD, PhD (Lund University, Malmo, Sweden)

Dr. Ahren presented results from a small (n=28), randomized, placebo-controlled, crossover study in people with type 1 diabetes that examined the effects of vildagliptin treatment on glucagon responses during meal induced hyperglycemia and insulin-induced hypoglycemia. Following four weeks of treatment with vildagliptin or placebo, a mixed meal test and a subsequent hyperinsulinemic hypoglycemic clamp were used to measure glucagon responses to a meal and induced hypoglycemia. Overall, the study found that vildagliptin treatment was associated with a reduced glucagon response following a meal (73% reduction; p=0.022), but no significant reduction in the glucagon response during hypoglycemia (11% reduction; p=0.895). Changes to other counter-regulatory hormones (i.e., epinephrine, norepinephrine, and cortisol) were also not observed. Interestingly, despite no detected alterations in counter-regulatory responses to hypoglycemia, glucose levels remained lower in the vildagliptin arm (6.4 mmol/l [115 mg/dl]) than in the placebo arm (7.6 mmol/l [136.8 mg/dl]; p=0.040) during recovery from hypoglycemia. Dr. Ahren was unclear of why this result occurred, but hypothesized that it might have been related to the lower average blood glucose achieved by participants treated with vildagliptin in the study. Vildagliptin treatment led to a 0.3% reduction in A1c vs. no change with placebo (p <0.001). Finally, vildagliptin therapy was reported to be safe and well tolerated, with no differences in rates of adverse events in comparison to placebo, including hypoglycemia. Based on this data, Dr. Ahren concluded that vildagliptin could serve as an effective adjunct to insulin therapy in people with type 1 diabetes and called for more extensive studies to more robustly examine DPP-4 treatment in this patient population.

  • This placebo-controlled, crossover study examined the effects of vildagliptin treatment on glucagon response during meal-induced hyperglycemia and insulin-induced hypoglycemia in people with type 1 diabetes. 28 individuals with type 1 diabetes with no measurable C-peptide response, an A1c of 6.5% to 8.5%, and a diabetes duration between two and twenty years were randomized to receive 50 mg of vildagliptin twice daily or placebo for four weeks. All participants continued with their previous insulin therapy. Following a four-week wash out period, trial participants received the alternative study treatment for an additional four weeks. A mixed meal test and subsequent hyperinsulinemic hypoglycemic clamp at 2.5 mmol/l (45 mg/dl) were used to measure glucagon responses to a meal and induced hypoglycemia at the end of both treatment periods. Blood glucose values, intact GLP-1 levels, and other counter-regulatory were also measured (i.e., epinephrine, norepinephrine, and cortisol). At baseline, average age was 30 years, BMI was 24.8 kg/m2, A1c was 7.7%, and diabetes duration of 11 years.
  • Vildagliptin treatment was associated with a reduced glucagon response following a meal, but no significant reduction in glucagon response during hypoglycemia. Following the consumption of the mixed-meal, glucose levels were observed to be higher in the placebo arm than the vildagliptin arm. This result was at least partly attributed to a 73% reduction in glucagon levels in the vildagliptin arm (p=0.022). During hypoglycemia (induced using the hyperinsulinemic hypoglycemic clamp) the glucagon counter-regulatory response was not found to be significantly impaired with vildagliptin treatment (11% reduction, p=0.895). Similarly, the counter-regulatory responses in epinephrine, norepinephrine and cortisol were also not different between the groups. Interestingly, despite no detected alterations in counter-regulatory responses to hypoglycemia, glucose levels remained lower in the vildagliptin arm (6.4 mmol/l [115 mg/dl]) than in the placebo arm (7.6 mmol/l [136.8 mg/dl]; p=0.040) during recovery from hypoglycemia.
  • Vildagliptin treatment provided statistically significant reductions in A1c over placebo but no change in insulin dose requirements. After four weeks of treatment, average A1c reduction in the vildagliptin arm was 0.3% in comparison to no change in placebo (p <0.001).

Oral Sessions: The Clinical Management of Diabetes

Comparison of 24-Week Treatment with Exenatide, Insulin and Pioglitazone in Newly Diagnosed ad Drug-Naive T2DM (12-OR)

Wen Xu, MD (Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China)

In the CONFIDENCE trial ( ID: NCT01147627; study conducted at 25 sites in China), newly diagnosed and drug-naïve patients with type 2 diabetes (n=416) were randomized to receive exenatide (5ug BID; titrated up to 10 ug BID after four weeks), mixed protamine zinc recombinant human insulin lispro 25R injection (initial dose 0.4 IU/kg/day), or pioglitazone (30 mg; titrated to 45 mg as appropriate) for a 48-week period in order to compare the efficacy of exenatide versus the other agents on glycemic control and β-cell function. In this preliminary analysis of 24-week data available as of November 2011 (n=127), the exenatide group (n=40) and the insulin group (n=43) experienced significantly greater (p=0.036) reductions in A1c (1.8% from baselines of 8.0% and 8.2%) compared to the pioglitazone group (n=44) (1.3% from a baseline of 8.0%). Exenatide treatment resulted in significantly more weight loss (5.0 kg [11 lbs]) than with insulin treatment (0.7 kg [1.6 lbs]) or pioglitazone treatment (2.6 kg [5.7 lbs]). No cases of severe hypoglycemia were reported during the 24-week period. In the exenatide group, GI side effects were the most commonly reported adverse event (60%); one case of acute pancreatitis was reported in this treatment arm. The trial is expected to complete later this year. (Editor’s note – we didn’t catch baseline weights.)

  • In the CONFIDENCE trial, 416 newly diagnosed, drug-naïve individuals with type 2 diabetes were randomized to exenatide, insulin, or pioglitazone treatment. These patients were enrolled from 25 centers in China, were 30-70 years of age, had a BMI of 20-35 kg/m2, had baseline A1c between 7.0% and 10.0%, and had stable body weight (≤10% fluctuations for at least three months). In this analysis of the available 24-week data as of November 2011 (n=127), patients were on average 52 years old, with a BMI of 26 kg/m2

Questions and Answers

Q: Nearly 60-65% of patients with exenatide experienced GI side effects. What was the discontinuation rate in this group?

A: Only five patients withdrew from the study because of severe GI effects. Most were able to tolerate these adverse events. About 20% of patients had to go back to the 5 ug BID dose; at that dose, they could tolerate the adverse events, and had very good glucose control at that dose.

Q: Could you comment on the rationale behind your insulin choice, and tell us how it was titrated?

A: We chose it because multiple daily injections would not have been practical, since all patients were treated for 48 weeks. So, we used a twice-daily insulin. We didn’t have a standard titration algorithm; we just asked investigators to titrate insulin according to their experience.

Q: Was the dose of pioglitazone increased to 45 mg?

A: Yes, it was – if patients had A1c greater than 9%, pioglitazone was titrated to the maximum dose of 45 mg.

Q: Some of the results you presented seemed surprising, just based on what I think I know from previous trials – you observed a 1.8% decrease in A1c with exenatide, and you didn’t see any weight gain with pioglitazone or insulin, right?

A: It is not common to see weight reduction with pioglitazone. In this preliminary data, we showed that patients treated with pioglitazone lost weight. Two potential explanations are the lifestyle modification program, and the small sample size in this analysis. Only 127 patients had 24-week data available for analysis at the time – just 40 patients in each group. Before I came here, I analyzed all the most recent data we could get from the database, and patients treated with exenatide had about a 3.5 kg (7.7 lb) weight reduction, patients treated with pioglitazone had about a 0.5 kg (1.1 lb) reduction, and patients treated with insulin had a 0.17 kg (0.37 lb) increase. We think that with a larger sample size and longer follow-up period, these weight changes may reflect what previous trials have shown; maybe patients treated with pioglitazone will have weight gain.


Real-world Outcomes of Initiating Injecable Therapy with Insulin Glargine or Liraglutide among Patients with Type 2 Diabetes (11-OR)

Philip Levin, MD (Mercy Medical Center, Baltimore, MD)

Dr. Levin presented the results from a retrospective analysis of the IMPACT managed care database that assessed and compared the real-world clinical outcomes (at one-year) and costs associated with injectable treatment initiation with liraglutide or insulin glargine among people with type 2 diabetes. Inclusion criteria for the study included age over 18, initiation of either therapy between January and June 2010, and A1c over 7%. Stringent propensity score matching (1:1 ration) was used to minimize any differences among baseline characteristics between the two study groups. A total of 336 individuals were included in the final study (168 individuals in each group). Overall, differences in treatment persistence, A1c reductions (1.02% vs. 0.95%), prevalence of overall hypoglycemia (7.7% vs. 4.7%), and prevalence of severe hypoglycemia (1.1% for each) were found to be non-significant between the insulin glargine and liraglutide groups, respectively. However, insulin glargine was associated with lower yearly study drug costs ($1198 vs. $2784; p <0.0001), which also drove lower diabetes related drug costs ($2958 vs. $3988) and lower total diabetes related costs ($5600 vs. $7900) for insulin glargine. Dr. Levin concluded that these results suggest that among people with type 2 diabetes, the initiation of injectable therapy with insulin glargine in comparison to liraglutide is associated with similar clinical outcomes but lower diabetes care costs in a real-world setting. Still, he noted that there were several notable limitations to the study, including a small sample size, a lack of weight loss data, the short time span of the study, the observational and retrospective nature of the study, and the use of clinical data from a claims database that may contain errors regarding diagnosis and treatment codes. Further details from the study will be presented in poster 1108. We note that in the LEAD-5 study, liraglutide was demonstrated to provide statistically significant improvements in A1c (1.33% vs. 1.09%; p=0.0015) and weight (treatment difference -3.43 kg; p <0.0001) over insulin glargine when added onto metformin and glyburide therapy. Rates of major and minor hypoglycemia were reported to be similar between the two arms.


Oral Sessions: Human Immunology and Diabetes

Glucagon-like Peptide-1 - A Key Regulator of Innate Immune Function with Clinical Efficacy in a Range of Inflammatory Diseases (90-OR)

Andrew Hogan, PhD (St. Columcille’s & St. Vincent’s Hospitals, Dublin, Ireland)

Dr. Hogan explored the role of GLP-1 as a regulator of innate immune function. In particular, he highlighted the anti-inflammatory effects of GLP-1, which he suggested were mediated by the hormone’s effects of on invariant natural killer T (iNKT) cells. As background, iNKT cells are a subset of lymphocytes that possess potent immunoregulatory activities, with the ability to modulate biological processes ranging from inflammation, autoimmunity, and tumor rejection to antimicrobial immunity. Consequently, iNKT cells are thought to play a role in the pathology of a number of different inflammatory, autoimmune, and infectious diseases. Dr. Hogan highlighted results from both rodent and human studies to detail the known impacts of GLP-1 on iNKT cells. In mice, liraglutide at 50 ug/kg (a significantly higher dose than currently approved for use in humans [1.2 or 1.8 mg]) had no effect on weight and food intake, but did decrease fat pad mass and white adipose tissue adipocyte size. Liraglutide was also found to increase the number of circulating and thymic iNKT cells, and it exhibited several anti-inflammatory effects, including the modulation of white adipose tissue macrophage polarization. These effects were conferred in an initial, very small human study, which examined the effects of liraglutide treatment in two people with type 2 diabetes and psoriasis (an inflammatory disease). After six weeks of treatment, the Psoriasis Area and Severity Index improved in both patients. These improvements were associated with increased numbers of iNKT cells in circulation and decreased numbers in psoriatic plaques. Separately, in vitro studies showed that the GLP-1 receptor was expressed on iNKT cells, and that GLP-1 could induce dose-dependent inhibition of iNKT cell cytokine secretion. While acknowledging the need for much further investigation in this area, Dr. Hogan concluded that GLP-1 appears to directly interact with the immune system, potentially raising the possibility for therapeutic applications of GLP-1 in inflammatory diseases.


Oral Sessions: Diabetic Dyslipidemia

Exenatide, A Glucagon-like Peptide Receptor Agonist, Acutely Inhibits Intestinal Lipoprotein Production in Healthy Humans (49-OR)

Changting Xiao, PhD (University of Toronto, Toronto, Canada)

Dr. Xiao described the effects of exenatide on triglyceride-rich lipoprotein (TRL) production and clearance in healthy men. A cohort of normolipidemic, normoglycemic men (n=15) was administered 10 ug of exenatide while a pancreatic clamp was used to mitigate exenatide-induced changes in insulin and glucagon production. During the study, a fed state was also maintained by infusing a high-fat, mixed macronutrient, liquid formula at a constant rate into the duodenum. No significant differences were observed in plasma triglyceride, TRL, or free fatty acid levels. Exenatide did, however, acutely inhibit intestinal lipoprotein (apoB-48) production, but not hepatic lipoprotein (apoB-100) production. While exenatide administration did result in a transient rise in insulin and C-peptide levels, Dr. Xiao indicated that the prolonged suppression of apoB-48 observed in the trial was thought to be a direct result of exenatide. Previous clinical trials have demonstrated the ability of GLP-1 agonists to improve plasma lipid profiles and postprandial lipemia; yet, the mechanisms underlying these effects have remained unclear. Based upon these results, Dr. Xiao suggested that these effects could be mediated by the direct effects of GLP-1 agonists on intestinal lipoprotein production independent of any effects on gastric emptying, weight loss, and satiety.

Questions and Answers

Q: You ran into an issue of lack of complete suppression with the pancreatic clamp – it could be that the intestine is more sensitive than the liver is. Could a change in the site of absorption be a factor?

A: This is possible, however we can’t tell if adsorption site changed with GLP-1 based on the current data.



Efficacy and Safety of Once-Daily Lixisenatide in Type 2 Diabetes Insufficiently Controlled with Basal Insulin ± Metformin (983-P)

Matthew Riddle, Ronnie Aronson, Philip Home, Michel Marre, Elisabeth Niemoeller, Lin Ping, Julio Rosenstock

The 24 week, double-blind, placebo controlled phase 3 trial (n=495) randomized participants with type 2 diabetes previously treated with basal insulin with or without metformin to add placebo or 20 µg of lixisenatide, Sanofi’s once daily GLP-1 agonist. Lixisenatide in addition to basal insulin led to significantly greater reductions in A1c compared to placebo (-0.74% vs -0.38%, p=0.0002), post-0prandial glucose after breakfast (-75 mg/dl vs -6 mg/dl, p<0.0001), and mean seven-point self-monitored blood glucose (-27 mg/dl vs -11 mg/dl, p <0.0001). However, no significant change in fasting plasma glucose levels were observed.  Additionally, after 24 weeks, participants on lixisenatide lost 1.8 kg (3.97 lbs) of weight on average compared to 0.5 kg (1.10 lbs) with placebo (p <0.0001). Gastrointestinal disorders accounted for the majority of adverse events in the lixisenatide arm, and were significantly more frequent (40.2%) than in the placebo arm (20.4%). Treatment with lixisenatide resulted in no significant increases in hypoglycemia compared to placebo. A double-blind extension study of at least 52 weeks is currently underway.

  • In this 24 week, double-blind, randomized and placebo controlled trial, people with type 2 diabetes were randomized to receive placebo (n=167) or 20 µg of lixisenatide (n=328) in addition to their previous basal insulin treatment, with or without metformin. Lixisenatide was added in a two-step dose increase regimen (i.e., 10 ug at trial start, 15 ug at week one, and 20 ug at week two). The study drug was delivered subcutaneously once daily, less than an hour before breakfast. Participants had diabetes for an average duration of 11 years, were on basal insulin at a stable dosage (>30 U/day) for at least three months, and received diet and lifestyle coaching every three months.
  • Lixisenatide led to significantly improved glycemic control compared to placebo in terms of A1c, postprandial glucose, and seven-point self-monitored blood glucose. Lixisenatide reduced A1c from a mean 8.4% baseline significantly more than placebo (-0.74% vs. -0.38%, p=0.0002). Additionally, a greater proportion of participants achieved a 7% A1c target at 24 weeks on lixisenatide (28.3%) compared to placebo (12%, p <0.01). Lixisenatide also led to significantly greater reduction of postprandial glucose increment following breakfast (-75 mg/dl from 138 mg/dl at baseline) than placebo (-6 mg/dl from 130 mg/dl at baseline, p <0.0001) and greater mean reduction of seven-point self-monitored blood glucose (-27 mg/dl vs. -11 mg/dl, p <0.0001). Change in fasting plasma glucose was not significantly different between the two groups (-11 mg/dl with lixisenatide vs. -10 mg/dl with placebo, baseline of 145 mg/dl, p=ns). With regards to weight, significantly greater reductions were observed with lixisenatide (1.8 kg [4.0 lbs], baseline of 87.4 kg [192.7 lbs]) compared to placebo (0.5 kg [1.1 lbs], baseline of 89.1 kg [196.5 lbs], p<0.0001).
  • The most common side effects associated with lixisenatide treatment in combination with basal insulin were gastrointestinal in nature. The incidence of gastrointestinal disorders was higher in the lixisenatide treatment arm (40.2%) compared to placebo (20.4%), and discontinuation due to GI adverse events was also higher with lixisenatide (4.3% vs 1.2%). The most common adverse events in the lixisenatide arm, excluding hypoglycemia, were nausea (26.2%), vomiting (8.2%), and diarrhea (7.3%). In contrast, 8.4% experienced nausea, 0.6% vomiting, and 5.4% diarrhea in the placebo arm. The study also reported one sudden cardiac death in the lixisenatide arm unrelated to the study treatment. Finally, while symptomatic hypoglycemia was r elevated in the lixisenatide arm over the placebo arm (27.7% and 21.6%, respectively), the difference was not found to be statistically significant.


Identifying Predictors of Response to Liraglutide in Type 2 Diabetes Using Recursive Partioning Analysis (1041-P)

Robert Ratner, Jason Brett, Naum Khutoryansky, Vanita Aroda

Analyzing pooled data from the six LEAD trials and Lira-DPP-4 study, this study identified characteristics that predict the likelihood of responding to therapy, defined as achieving an endpoint A1c of <7%, with no weight gain and no hypoglycemia over 26 weeks. Overall, 34% of individuals on liraglutide 1.8 mg achieved this composite endpoint (n=1,530), compared to 30% of those on liraglutide 1.2 mg (n=1075), 24% on exenatide (n=221), 14% on sitagliptin (n=210), 9% on sulfonylureas (n=475), 9% on glargine (n=224), and 3% on TZDs (n=226). The most significant predictor of response to therapy was baseline A1c, as 46% of individuals with a baseline A1c <8.5% achieved the composite outcome compared to only 19% of individuals with baseline A1c ≥8.5% (p <0.0001). Recursive partitioning analysis was then used to identify a total of six homogenous subgroups with varying probabilities of achieving the composite endpoint with liraglutide 1.8 mg treatment. The subgroup consisting of female participants with baseline A1c <8.5%, previous treatment with diet or monotherapy, and diabetes duration <4.9 years had the highest likelihood of achieving the composite outcome (74%). Individuals with a baseline A1c ≥8.5% who were previously on combination oral antidiabetic therapy had the lowest likelihood of achieving the endpoint (14%). In general, the type of analysis used in this study may be valuable for guiding clinicians in individualizing treatment approaches. 


Pharmacokinetics and Pharmacodynamics of a New Exenatide Formulation, Exenatide Suspension (1099-P)

Leigh MacConell, Stephen Flore, Brenda Cirincione, Matthew Zierhut, Christopher Biwald, Wenying Huang, T.K. Booker Porter, Lisa Porter

This phase 2, randomized, controlled, 2-cohort clinical trial examined the PK/PD profile of exenatide suspension (EQWS). EQWS utilizes the same extended release microspheres as once weekly exenatide (EQW), but is reformulated with a medium chain triglyceride (MCT) vehicle. As a pre-suspended product, EQWS would be compatible with a pen device and would reduce the initial release that appears to affect gastrointestinal tolerability. In an in vitro release assay, EQWS was shown to have a blunted initial release compared to EQW, but a similar release profile overall. This trial’s first cohort of 30 healthy volunteers, which was given a single 10 mg dose of EQWS, confirmed the release profile seen in vitro. This profile was characterized by exenatide concentrations increasing gradually over time and peaking at weeks six to seven. The extent and duration of exposure following one dose of EQWS was similar to what has been seen with EQW, suggesting that EQWS could provide similar efficacy with improved delivery. This trial’s second cohort included 35 individuals with type 2 diabetes who received weekly injections of 2 mg EQWS or MCT-only as a control.  Those on EQWS achieved mean steady-state concentrations by eight to ten weeks. At week 12, there was a significant difference in mean A1c between those receiving EQWS compared to MCT-only (-0.9% versus 0.1%, p<0.05; mean baseline A1c ~ 8%). EQWS was generally well tolerated; the majority of observed treatment-emergent adverse events were of mild intensity, and no major or minor hypoglycemia was observed. Overall, the safety profile, release profile, and improvements in glycemic control seen with EQWS were comparable to EQW, supporting development of exenatide suspension.

  • At twelve weeks, participants receiving 2 mg EQWS experienced an A1c reduction of 0.9% compared to a 0.1% A1c increase in the control group. Significantly more participants reached an A1c goal of <7% on EQWS (61%) compared to MCT-only (17%). EQWS treatment also led to a significant reduction in fasting plasma glucose (32 mg/dl) in comparison to MCT-only, which led to an 8 mg/dl increase after 12 weeks. Additionally, individuals on EQWS lost an average of 1.4 kg (3.1 lbs), compared to 0.4 kg (0.9 lbs) with MCT-only.
  • Apart from injection site reactions, EQWS was generally well tolerated, with no major or minor hypoglycemia events observed. Injection site reactions accounted for the majority of adverse events, with 52.2%, 47.8%, and 39.1% of participants on EQWS experiencing erythema, pruritus, and haematoma, respectively. In comparison, participants on MCT-only experienced these reactions at rates of 8.3%, 8.3%, and 50%, respectively. Patients treated with 2 mg EQWS also reported decreased appetite, injection site pain, and nausea (21.7% each). In contrast, nausea, injection-site pain, and diarrhea rates were 8.3%, 0.0%, and 0.0%, respectively in control patients. Overall, the adverse event profile of EQWS was similar to that previously seen with EQW.


Efficacy and Safety of Lixisenatide Once Daily Versus Placebo in Patients with Type 2 Diabetes Insufficiently Controlled on Pioglitazone (Getgoal-P) (1010-P)

Michel Pinget, Ronald Goldenberg, Elisabeth Niemoeller, Isabel Muehlen-Bartmer, Ronnie Aronson

This randomized, double-blind, placebo-controlled, multicenter study evaluated the efficacy and safety of lixisenatide (20 μg), a once-daily selective GLP-1 receptor agonist, compared with placebo in patients with type 2 diabetes (n=484) insufficiently controlled with pioglitazone (≥30 mg daily) ± metformin. At the end of the study’s 24-week treatment period, lixisenatide produced a significantly greater reduction in A1c from baseline (-0.90%) than placebo (-0.34%) (p <0.0001). Furthermore, 52% of the lixisenatide treatment population achieved an A1c level of less than 7.0% compared with only 26% of the placebo population (p <0.0001). Overall, lixisenatide was well tolerated, and significantly fewer participants on lixisenatide vs. placebo required rescue therapy (3.8% vs. 11.3%). Incidence of adverse and severe side effects was comparable in both groups, and there were no cases of severe hypoglycemia in either group. This data suggests that lixisenatide effectively and safely improves glycemic control in patients with type 2 diabetes experiencing insufficient control with pioglitazone ± metformin.

  • Lixisenatide (n=323) and placebo (n= 161) treatment arms were well matched for A1c, fasting plasma glucose, and body weight at baseline. Baseline A1c, fasting plasma glucose, and body weight were 8.08%, 164.5 mg/dl, and 92.9 kg (204.8 lbs), respectively, in the lixisenatide group. In the placebo group, baseline A1c, fasting plasma glucose, and body weight were 8.05%, 164.2 mg/dl, and 96.7 kg (213.2) lbs, respectively. About 51% of subjects in both groups had an A1c of ≥8%. Demographic characteristics, including average age (56.0 years (lixisenatide group) vs. 55.3 years (placebo group), gender (% male = 53.3% lixisenatide group) vs. 50.9% (placebo group)), and duration of diabetes (8.1 years), were also comparable between the two groups. Notably, over 80% of all participants in both populations were Caucasian.
  • Compared with placebo, lixisenatide produced significantly greater reductions in A1c and fasting plasma glucose levels. At week 24, lixisenatide treatment lowered A1c by 0.90% versus 0.34% with placebo (p < 0.0001). A significantly greater proportion of patients in the lixisenatide arm vs. the placebo arm attained an A1c of ≤6.5% (28.9 vs. 10.1%; p <0.0001) and an A1c of <7.0% (52.3 vs. 26.4%; p <0.0001) at 24 weeks. Similarly, lixisenatide was associated with a significantly greater reduction in fasting plasma glucose vs. placebo (-20.9 mg/dl vs. -5.7 mg/dl).  Significantly fewer lixisenatide vs. placebo-treated patients required rescue therapy during the study’s 24-week treatment period (3.8 vs. 11.3%; p = 0.0011).
  • There was no significant difference in mean body weight reduction with lixisenatide vs. placebo treatment. From baseline to week 24, body weight decreased on average by -0.21 kg (0.5 lbs) in the lixisenatide group compared with an average increase of 0.21 kg (0.5 lbs) in placebo-treated patients.
  • Incidence of adverse events and rates of discontinuation were comparable between both treatment arms. Rates of treatment emergent adverse events (72.4% with lixisenatide vs. 72.7% with placebo) and serious TEAEs (2.5% with lixisenatide vs. 1.9% with placebo) were similar between the study’s arms. Discontinuation rates due to TEAEs were also similar between groups (6.5% vs. 5.0% for the lixisenatide vs. placebo treated groups, respectively). Notably, symptomatic hypoglycemia rates were low in both groups, and there was no incidence of severe hypoglycemia in either treatment population.


Exenatide Once Weekly Resulted in Sustained Improvement in Glycemic Control with Weight Loss Through Four Years (1156-P)

Leigh MacConell, Yan Li, Rich Pencek, Christine Schulteis, Lisa Porter

The comparator-controlled, open-label, randomized DURATION-1 study examined the safety and efficacy of exenatide once-weekly (EQW) in patients with type 2 diabetes over 30 weeks. Persistence of glycemic control in patients receiving EQW was then studied in a four-year extension trial (n=258; 176 patients completed four years of treatment). Long-term EQW treatment was associated with a significant reduction in A1c (-1.7% [95% CI: -1.9, -1.5]). At four years, mean A1c was 6.9% ± 0.1% among those receiving EQW. Four year EQW treatment was also associated with clinically significant improvements in fasting plasma glucose (FPG) (-37 mg/dl), weight (-2.5 kg (5.5 lbs)), systolic blood pressure (-1.6 mmHg), total cholesterol (-10.9 mg/dl), LDL cholesterol (-8.0 mg/dl), and triglyceride levels (-13%). Nausea was the most common adverse effect associated with four-year EQW treatment; however, its incidence decreased substantially over time. 20% of patients experienced adverse events during the four-year study duration. However, there was no identifiable pattern of serious adverse events. No major hypoglycemia was observed, and minor hypoglycemia was mainly associated with SFU use.

  • At baseline, the four-year completer population (n=176) were on average 56 years old, weighed 222 lbs, had a BMI of 35 kg/m2, an A1c of 8.2%, a fasting plasma glucose of 166 mg/dl, and an average duration of diabetes of 7 years. These values were comparable to baseline data for the intent-to-treat population (n=295).
  • At the end of four years, 55% of patients receiving EQW achieved an A1c of less than 7.0%, and 36% achieved an A1c less than 6.5%, quite striking results, especially the percentage that got to an A1c of less than 6.5%, given the medium to high baseline A1c. We do note that 34% of the ITT population withdrew between weeks 30 and 212 and, presumably, their response to EQW might have not been as favorable; however, the results from those who did stay in the study are encouraging.  Notably, improvements in beta cell function with EQW treatment were also observed after four years (HOMA-B and HOMA-S increased an average of 26% and 13%, respectively).
  • Nausea and injection-site infection were the most common adverse side effects observed in the trial. Within the first 30 weeks of treatment, incidence of both nausea and injection-site events was ~30%. Subsequently, the incidence reduced dramatically, and during the second 30-week period of treatment, about 10% or less of patients reported either nausea and injection-site events. The annual event rate for nausea with EQW was 15/100 years patient exposure over the four-year study duration, while injection-site pruritus occurred at a rate of 6/100 years patient exposure. Rates of vomiting and diarrhea also declined over time. 20% of EQW patients experienced 89 serious adverse events during the study’s four years, without any pattern among these events.
  • Notably, no major hypoglycemia was observed with EQW over the study’s four years. Minor hypoglycemic events occurred mainly in patients using concomitant sulfonylureas (event rate of 26/100 years patient exposure over four years). We find it very encouraging, though not surprising, that EQW’s favorable hypoglycemia profile persists with long-term treatment.


Efficacy and Safety of Once-Weekly (QW) Albiglutide vs. Once-Daily (QD) Liraglutide in T2D (Type 2 Diabetes) Inadequately Controlled on Oral Agents: Harmony 7 Trial (947-P)

Richard Pratley, Anthony Barnet, Mark Feinglos, Ilana Harman-Boehm, Michael Nauck, Fernando Ovalle, Susan Johnson, Murray Stewart, June Ye, Julio Rosenstock

Harmony 7 was a 32-week, randomized, open-label phase 3 trial (n=841) in which participants on one to three oral anti-diabetic medications received GSK’s albiglutide once-weekly titrated to a maximum of 50 mg (n=404) or liraglutide once-daily titrated to a maximum of 1.8 mg (n=408). As previously reported  (see our coverage at, albiglutide failed to meet the non-inferiority criteria of A1c reduction compared to liraglutide – compared to a 0.99% A1c reduction with liraglutide (baseline 8.15%), those on albiglutide experienced an average A1c reduction of 0.78% (baseline 8.19%). Additionally, participants receiving albiglutide lost less weight than those on liraglutide (0.64 kg [1.41 lbs] vs. 2.19 kg [4.83 lbs]). Fasting plasma glucose change from baseline was significantly greater with albiglutide than liraglutide (-22.1 mg/dl with albiglutide vs. -30.4 mg/dl for liraglutide; p = 0.005). Notably, fewer patients experienced nausea/vomiting on albiglutide than on liraglutide (9.9% vs. 29.2% nausea, and 5.0% vs. 9.3% vomiting) - this was a striking tolerability finding and makes us wonder about the dose of albiglutide used, especially since efficacy was lower. Hypoglycemia occurred more frequently in the liraglutide group than the albiglutide group (though was very low in both), while subjects on albiglutide experienced higher incidences of injection site reactions.

  • Additional data on GI adverse events and A1c lowering support the strong tolerability but reduced A1c lowering efficacy of albiglutide compared to liraglutide. Although albiglutide significantly reduced A1c from baseline to week 32 (p <0.001), significantly more patients achieved an A1c of <7% at 32 weeks with liraglutide (51.7%) than albiglutide (42.2%) treatment (p=0.0323). Similarly, 19.6% vs. 28.1% (p=0.0009) of albiglutide vs. liraglutide treated patients achieved an A1c of <6.5% at 32 weeks. Additionally, there was a statistically significant difference between albiglutide and liraglutide for time to hypoglycemia rescue (p= 0.005), with a higher probability of hyperglycemia rescue from week 12 to 32 in those taking albiglutide vs. liraglutide. Notably, rates of GI adverse events were lower in the albiglutide vs. liraglutide group (35.9% vs. 49.0%) - if the efficacy had been as strong, this might have been quite a notable finding. The incidence of GI events was generally stable through the 32 weeks in the albiglutide group, compared to a high peak in GI events with liraglutide in the first two weeks followed by a plateau after 12 weeks. Rates of overall adverse events were similar between the two groups.
  • Albiglutide showed a favorable tolerability profile in terms of pancreatitis and hypoglycemia, but was associated with a higher incidence of injection site reactions (12.9%) than liraglutide (5.4%). Pre- or post-rescue hypoglycemic events occurred more frequently in the liraglutide group than the albiglutide group (20.8% vs. 16.3%), with the majority of hypoglycemic events occurring in patients also taking sulfonylureas. Importantly, albiglutide was associated with no pancreatitis events, compared to five incidences of on-therapy pancreatitis in the liraglutide group (although one was likely not related to study drug).


Meet the Expert Sessions

Newer Agents and Their Use in Clinical Practice

John Buse, MD, PhD (University of North Carolina, Chapel Hill, NC)

In this very well attended Meet the Experts session (over 200 by our approximation!), Dr. Buse began by briefly lauding the use of DPP-4 inhibitors (because of their “extreme” tolerability) and GLP-1 agonists (because of their substantial glycemic efficacy and weight loss) in the treatment of type 2 diabetes. After highlighting the need for anti-diabetic agents that address beta cell function and cardiovascular risk, Dr. Buse opened the remainder of the session to questions from the audience. During the subsequent Q&A session, Dr. Buse touched on a number of hot topics in the pharmacological treatment of diabetes, including the prospects of SGLT-2 inhibitors, the safety of TZDs and GLP-1 agonists, the use of type 2 therapies to treat type 1 diabetes (i.e., GLP-1 agonists, SGLT-2 inhibitors), and the need for earlier treatment in the course of disease, including during prediabetes. Of particular note, Dr. Buse revealed that his default second line therapy for people with type 2 diabetes was a GLP-1 agonist, stating that, “if [he] had diabetes, that is what [he] would take.” He also voiced optimism regarding the SGLT-2 inhibitor class, noting that all drug classes have associated issues, and that SGLT-2 inhibitors offer the powerful combination of glucose reduction, weight loss, and blood pressure lowering. While not appearing overly unconcerned with the potential bladder cancer risk associated with pioglitazone, Dr. Buse expressed hesitation regarding the early use of this therapy in the course of diabetes (including prediabetes) given the effects of the drug on bone loss.  

Questions and Answers

Q: Can you tell us about the current status of SGLT-2 inhibitors?

A: This class is newer than new. Both dapagliflozin and canagliflozin are at one level of another under FDA review. From my understanding, dapagliflozin has received a preliminary approval decision from the EMA. It is not yet marketed in Europe, but it should be available soon. I personally think that this is an exciting class of medications, especially in the post ACCORD world, which found excess mortality in the intensively treated arm. There is some supposition, although no proof, that the mortality may have been related to adverse effects associated with the drugs used, such as hypoglycemia and weight gain. I really like the possibility of using metformin, GLP-1 agonists, SGLT-2 inhibitors, and maybe DPP-4 inhibitors to lower glucose levels without causing an increase in weight (potentially even providing weight loss) or hypoglycemia risk. 

Q: I heard a rumor that sometime in our future there may be combo SGLT-2/SGLT-1 drug. These drugs would not only block reabsorption in the kidney, but also have an acarbose like effect and block the rate of carbohydrate absorption.

A: SGLT-1 is involved in glucose absorption in the GI tract and SGLT-2 is involved in glucose reabsorption in the kidney. There are several combined agents in development. If you inhibit the absorption of carbohydrates in the intestine, you might get flatulence and diarrhea like with acarbose. This is why many thought targeting SGLT-2 was more ideal. That said, there is some data around combined agents being effective with regard to weight loss and glucose lowering, at least from small studies. With acarbose, you are not getting a net malabsorption of carbohydrates, absorption is just delayed. With SGLT-1 inhibition, however, there would be malabsorption of carbohydrates. 

Q: I find the mechanism of action of SGLT-2 inhibitors to be bizarre. Can you talk about your opinions on this class a little more?

A: It is bizarre. When I first heard the notion, I thought it was a bad idea. When I reflect back to when I was a fellow and graduate student, I remember seeing a publication on the use of phlorizin in rodents. I tried to hold back from my initial response. I looked at the rodent data and came to realize that there were humans with SGLT2 mutations. These individuals lived normal lifespans without kidney disease. In clinical trials, in general, people seem to tolerate SGLT-2 inhibitors quiet well. One of the issues that came up was that urinary tract and genital infections occur more frequently with treatment, particularly yeast infections in women. The severity of these side effects were reasonable modest. There were no patients in the development program for dapagliflozin who developed pyelonephritis.  Like other classes of drugs, this class also has some associated issues. However, the ability of these drugs to lower glucose, blood pressure, and weight is very compelling in the field of diabetes. Another issue that may occur is that some patients may become dehydrated, especially those that develop flu like symptoms. There will lose an obligate loss of fluid. But again, all drug classes will have their issues.

Q: Is the pancreatitis observed with sitagliptin a class effect?

A: To be provocative, I will say that it is a class effect. However, the class effect will be that there is probably no increase in pancreatitis risk with these drugs. There have been three epidemiological studies published that have examined the incidence of pancreatitis requiring hospitalization. These were pharmacy benefit manager type data sets. Overall, the incidence of hospitalization due to pancreatitis associated with GLP-1 or DPP-4 therapy (most of the data was for exenatide and sitagliptin) appeared similar to the incidence with metformin and glyburide. That said, there were confidence intervals and there could be a small increased risk. However, the vast evidence seems to suggest right now that there is no increased risk.

Q: Can you discuss how you use colesevelam and bromocriptine in clinical practice?

A: With colesevelam, I use it with the occasional patient who is on high doses of statins or is statin intolerant and needs some additional LDL and A1c lowering. It provides both and is fairly well tolerated with no serious adverse effects. That said, I haven’t used it a lot. The A1c reductions achieved in clinical trials is modest, about 0.5%. You shouldn't think of it as something that will solve the diabetes problem, but it will provide you with some LDL and A1c lowering that might be beneficial for a minority of patients. Bromocriptine is a bit more of a mystery. It would provide any greater efficacy than colesevelam, maybe even a bit less. I have not tried this in my patients yet, but that doesn’t mean that I don’t wish to do so. I just haven’t had the occasion. You do need to take multiple tablets a day and there is only modest efficacy. I think bromocriptine might be a drug that begins to find its way as we get more data on efficacy and tolerability through studies other than company-sponsored studies. It does have a novel mechanism of action and is not associated with weight loss or hypoglycemia.

Q: Bromocriptine has had astonishing CV disease data published. Maybe the drug will have a special role in treating high CV risk patients? 

A: My only concern is that the data is from a short-term study. It is really not equivalent to the data that is being collected from the CV outcomes studies that are being conducted today for DPP-4 inhibitors and GLP-1 agonists. In the short-term, it is suggestive of benefit. For many drugs, however, shorter studies have suggested potential CV benefit, such as the meta-analyses of development program data for DPP-4 inhibitors and GLP-1 agonists. Fundamentally, I just don't believe it. I believe that these short-term studies may be leading us astray. I’m not sure how, but I do believe they are leading us astray.

Q: Can you discuss the link between bladder cancer and pioglitazone?

A: We have a serious problem in the American society right now. Epidemiological studies are only hypothesis generating. Multiple datasets have looked at this issue, but we cannot conclude based on the results of one, two, or three such publications that pioglitazone causes bladder cancer. It is really only hypothesis generating. That said, there was a bit of an issue in rodents during the development program. My understanding of it, although this is not my area of expertise, the regulatory bodies decided that the rodent bladder cancer risk was different from what was expected in humans. In the PROactive study, which was an randomized controlled trial, there was an imbalance in bladder cancer risk. However, there weren’t routine urinalyses in the study. Pioglitazone can cause edema, so these individuals may have received urinalyses to evaluate edema more often than controls, leading to a possible detection bias for bladder cancer in the trial. There were two large pharmaco-epidemiology studies that examined bladder cancer risk with pioglitazone. The Kaiser study was very well conducted. The French study had less data on potential baseline confounding variables. They both come to similar conclusions – with long term use, there is a potential increased risk for bladder cancer with pioglitazone. This is by no means proof. The second thing is that bladder cancer is quite rare. The last time I reviewed this data, it was about 1 case per 20,000 individuals per year. If the elevated risk found in the epidemiological studies is proven accurate, then the incidence would increase to 1.5 cases per 20,000 patients per year. In comparison to the benefits of pioglitazone, that risk may not be terribly dangerous. The third issue is that in the French study, a reduction in risk for head, neck, and breast cancers and a trend toward reduced risk for other cancers. Patients don’t care what type of cancer they get. If there is an absence of increased risk for overall cancer or cancer mortality, the increased risk for bladder cancer may not matter that much. There may be an increased risk for some cancers and a decreased risk for others. It might be random. Basically, I don't think this is a big issue.

Q: What about the effect of pioglitazone on bone loss and osteoporosis?

A: I think this is a bigger issue. I believe there is a great opportunity for the earlier diagnosis and earlier management of type 2 diabetes. Pioglitazone has one of the best data sets available for prevention of beta cell failure or beta cell failure progression. However, the early use of pioglitazone is problematic for me. I usually use pioglitazone in patients that are already using agents that increase insulin levels and are inadequately controlled.

Q: Regarding SGLT-2 inhibitors, what study design would you like to see to discern whether SGLT2 inhibitors do no cause hypoglycemia exacerbation in people with type 1 diabetes or people with type 2 diabetes using insulin?

A: I do like the idea of using SGLT-2 inhibitors in people with type 1 diabetes. It almost certainly will work. I like the idea that there is an autoregulatory component of SGLT-2 inhibition. You will get more glucose excretion at higher glucose levels and less glucose excretion at lower glucose levels. What type of study would be best would entail a much longer discussion.

Q: What about cell therapy for type 2 diabetes?

A: Some companies are doing studies that are looking at cell-based therapies. I’m not wildly optimistic that they are going to work. That is me being skeptical in general. But I do think someone will learn to take advantage of cell therapy for type 1 and type 2 diabetes one day. I’m not sure we are there yet.

Q: What about the use of alpha-glucosidase inhibitors?

A: These just never worked out in the US. I’ve started hundreds of patients on these agents in my career. A few patients have thought it was great. They get lower blood glucose and not as much constipation. In the type 1 diabetes setting, I sometimes give a small dose to my patients for nighttime use alongside a small snack to avoid nocturnal hypoglycemia. But what I found over time is that very few patients continue on the drug. In my practice, I discuss the benefits and potential harms of each drug. My patients are deciding that they are not getting enough benefits given the drug’s side effects. I know that it is popular in other parts of the world. Regarding the ADA/EASD algorithm, we discussed including it the first, second, and third time. We decided to not put it in as a core recommendation. That said, my belief is that it will lower blood glucose in anyone if it is carefully titrated upward. If flatulence is not a major psychosocial issue for a patient, it could be good.

Q: If early treatment is desirable, is there a role for new drugs that are better than metformin in the early treatment or prevention of diabetes?

A: I do think that prevention is a tremendous strategy and a real opportunity in overall diabetes care. Lifestyle therapy applied early in the course of disease appears to have its advantages. Metformin doesn’t appear to work very well with really early prediabetes. Other agents may have greater benefit. I am still concerned about the use of pioglitazone in the early stages of diabetes or prediabetes given the effects of the drug on bone loss. Incretin-based therapies and SGLT-2 inhibitors could be compelling choices if we had data. The important issue here is that someone needs to do a study. The earlier we want to treat, especially during prediabetes when not everyone will progress to diabetes, the more clinical evidence we need that a therapy is safe and effective.

Q: In the new ADA/EASD position statement, there great leniency on which second line therapy to use. What is your drug of choice?

A: My default is a GLP-1 agonist. If I had diabetes, that is what I would take. It is a bit of an act of faith since we don’t have long-term data to guarantee its safety and efficacy. My sense of the data that we have available today is that these drugs are safe and effective. Liraglutide and exenatide once weekly are unparalleled in terms of glycemic efficacy. We wrote in the position statement that insulin had the highest efficacy, but you can also make the argument that GLP-1 agonists are just as powerful as basal insulin in reducing A1c. This is my personal bias. The maybe patients who would not prefer to use an injectable therapy as a second line option. Other orals are all fair game. Sulfonylureas are dirt-cheap. They can fill the need for those who have difficulty paying for more expensive therapies. DPP-4 inhibitors are ridiculously well tolerated. Many would prefer this as their second line therapy. 

Q: What about the risk for C-cell carcinoma with GLP-1 agonists?

A: I think people have largely bought into the notion that the regulation of C-cells in humans is different from the regulation of C-cells in rodents. Medullary thyroid cancer occurs much more frequently in rodents than in humans. I’m not terribly worried about thyroid cancer with these agents. There will be studies to address this concern in the future. With regards to pancreatic cancer, the human data is remarkably bad. By that, I mean that the studies have not been conducted in a way that should be reported in medical literature. Pancreatic cancer risk will also be addressed with studies to come. I neither believe nor can I prove that GLP-1 agonists don't cause cancer. However, the evidence that they do is essentially zero.

Q: What about Afrezza and the use of GLP-1 agonists to treat obesity in people with type 1 diabetes?

A: I know very little about Afrezza. The pulmonary insulin route is interesting. It has both issues and possibilities. If it is approved, I would do my best to evaluate the drug. With regards to GLP-1 agonists in type 1 diabetes, there are many pieces of anecdotal evidence to support their use. In some individuals, blood glucose has stabilized and weight loss was achieved. Small studies have also suggested some benefit. My belief is that if we are considering using a $4,000 a year therapy for an already $10,000 a year condition, we need to conduct robust studies to clearly show benefit. We can spend an almost unlimited amount of money managing type 1 diabetes. This is an expensive therapy and we only have anecdotal evidence so far.

Q: Is it worth it to start with combination therapy to preserve beta cells? 

A: We don't have a lot of data. It's a wonderful theory. It is based on better living through chemistry. I have had many hundreds of patient who were on 10-15 drugs for type 2 diabetes. I just wonder if 100 years from now, we’ll look back and say, “what did these people think they were doing?” We are basically creating a chemical swamp in people with diabetes to compensate for bad lifestyle. It is compelling, but I worry about the slippery slope of thinking that three therapies will be more effective than two therapies, etc. It doesn’t turn out that way most of the time. I understand the theory, but I am resistant to globally adopting it.

Q: What about the combination use of GLP-1 agonists and basal insulins?

A: I think this combination is great. I struggled to get Lilly and Amylin to apply for this indication for five years. I think it is a remarkable combination. We will have new drug therapies, but my default pathway is metformin, a GLP-1 agonist, then a basal insulin. The big question right now is whether long-acting GLP-1 agonists like liraglutide and exenatide once weekly will provide similar benefits when used alongside basal insulin therapy as exenatide twice daily. I believe that there might be a special niche for the short acting GLP-1 agonists to be used alongside basal insulins. Will have to see.

Q: What are the relative safety and efficacy issues among GLP-1 receptor agonists?

A: Exenatide twice daily is the most powerful post-prandial agent we have. Liraglutide is the most powerful A1c reducing agent we have. Bydureon is the best-tolerated GLP-1 agonist we have. The rest with cancer risk, antibodies, pancreatitis risk, etc., there are some small potential differences, but the clinical significance is what we just reviewed. Though there have been efforts to distinguish safety issues between the three, I do not think there are compelling issues in that comparison.


Symposium: Incretin-Based Therapies (Supported by an Unrestricted Educational Grant from Merck)

GLP-1 Receptor Agonists - Are They All the Same?

Filip Knop, MD, PhD (Gentofte Hospital, Hellerup, Denmark)

In front of a packed audience, Dr. Knop highlighted the similarities and differences between the GLP-1 agonists exenatide (Byetta), liraglutide (Victoza), exenatide once weekly (Bydureon), lixisenatide (Lyxumia), and albiglutide (Syncria). After reviewing the structures and pharmacokinetic profiles of each drug, Dr. Knop discussed results from several head-to-head studies that examined these agents, including DURATION-1, -5, and -6, LEAD-6, and HARMONY 7. Overall, Dr. Knop emphasized that: 1) longer-acting GLP-1 agonists (i.e., liraglutide, exenatide once weekly, albiglutide) tend to provide greater glycemic efficacy and fewer GI side effects than shorter-acting GLP-1 agonists (exenatide BID and lixisenatide); 2) exendin-based drugs (i.e., exenatide, exenatide once weekly, lixisenatide) are associated with a higher incidence of antibody formation (the clinical relevance of which remains unclear, although some studies have suggested minimal impacts on efficacy and safety); and 3) large GLP-1 agonists (i.e., albiglutide) have a reduced effect on weight as well as lower associated rates of GI adverse events potentially resulting from restricted movement across the blood-brain barrier. Thus, addressing the question posed by the title of his talk, Dr. Knop concluded that all GLP-1 receptor agonists are not the same.

Questions and Answers

Q: Based on your understanding of differences between GLP-1 agonists, would you expect there to be any differences with regards to more serious side effects?

A: That is a very hard question to answer, and I cannot back it up with any data, which is why I chose not to include this discussion in my presentation. Theoretically, since GLP-1 may help preserve or proliferate beta cells, a continuous acting dose may have a higher associated  risk for malignancy. Yet, the short-acting agonists, because of their frequent exposure peaks,  may have a higher associated risk for pancreatitis. Saying that, this is all theoretical.


DPP-IV Inhibitors - Are They All The Same?

Adrian Vella, MD (Mayo Clinic, Rochester, MN)

To open this Merck-supported session, Dr. Adrian Vella reviewed DPP-4 inhibitors in terms of their mechanism of action, effects (glucose- and non-glucose-related), and safety. He noted that most agents in the class have 24-hour effect with >80% DPP-4 inhibition (except for vildagliptin, which has a half-life of two-to-three hours), most have a large volume distribution and bind poorly to plasma protein (except for linagliptin), and most do not have metabolic activity with cytochrome P450 (other than saxagliptin). Compared to the field, vildagliptin and saxagliptin are somewhat less selective for DPP-4 inhibition relative to DPP-8 and -9, and linagliptin is less selective relative to fibroblast activation protein (FAP) – these differences have not yet been shown to have safety or efficacy effects, though Dr. Vella implied that the possibility has not been ruled out. Notably, he disputed the clinical relevance of linagliptin’s lack of renal clearance (or, as he framed it, the fact that 85% of linagliptin is excreted in the feces): he thinks that DPP-4 inhibitors are rarely appropriate in patients whose renal dysfunction is severe enough to require dose adjustment. 

Questions and Answers

Q: One clinical difference is the lack of need to screen with creatinine to select a dose with linagliptin. Is that of clinical import to you?

A: No. It is true that due to linagliptin’s large volume distribution and the fact that 85% of the secretion is in the feces, there is no need for checking renal function. But it is the rare patient with diabetes and significant renal failure who would be on a DPP-4 inhibitor, at least in my practice. I will grant you that this is a difference among members of the class in pharmacokinetics.


Cardiac Effects of Incretin-Based Therapies

Steven Marso, MD (University of Missouri Kansas City, Kansas City, MO)

Dr. Marso delivered an engaging lecture on the potential impact of incretin-based therapies on the cardiovascular (CV) system. Reviewing a series of results from preclinical and clinical studies, Dr. Marso highlighted the following observations as indications that incretin based therapies might have a beneficial impact on the CV system: 1) small clinical studies have demonstrated a potential benefit of native GLP-1 on endothelial function; 2) GLP-1 agonists have positive impacts on several established CV risk markers (LDL, total cholesterol, triglycerides, weight); 3) both native GLP-1 and GLP-1 agonists minimize myocardial ischemia/reperfusion injury and decrease infarct size in rodents and possibly in humans; and 5) meta-analyses have demonstrated a reduced risk for MACE with GLP-1 agonists and DPP-4 inhibitors (Ratner et al., Cardiovascular Diabetology 2011; Marso et al., Diabetes and Vascular Disease Research, 2011). Still, he expressed concern that GLP-1 agonists have been documented to increase pulse rate in humans and emphasized that it remains unclear whether these findings will translate into actual CV benefit. He concluded by notating that a number of dedicated CV outcomes studies were currently underway for GLP-1 agonists and DPP-4 inhibitors that would help clarify the CV impacts of these drugs. As a reminder, these CV outcomes studies include ELIXA (lixisenatide; expected completion in 2014); LEADER (liraglutide; 2016), EXSCEL (exenatide once weekly; 2017), ELIXA (lixisenatide; 2014), REWIND (dulaglutide; 2019), TECOS (sitagliptin; 2014), SAVOR-TIMI 53 (saxagliptin; 2014), EXAMINE (alogliptin; 2015), and CAROLINA (linagliptin; 2018).


Symposium: The Role of Pharmacology in Managing Prediabetes

Lifestyle Changes and Metformin Are Not Adequate

Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, TX)

After making the case for why lifestyle alone is not sufficient to manage prediabetes (e.g., inevitable weight regain, the impracticality of implementing intensive lifestyle interventions in the real world, the body’s metabolic adaptation to reduced caloric intake), Dr. DeFronzo discussed the role of pharmacotherapies in managing prediabetes. In particular, he expressed enthusiasm for two classes of drugs – thiazolidinediones (TZDs) and GLP-1 agonists – as potent drugs that could be used to manage prediabetes. While TZDs have been proven to be effective in preventing the progression from prediabetes to diabetes in clinical trials, GLP-1 agonists remain unproven in this domain. Nonetheless, when taking the totality of currently available data for GLP-1 agonists into consideration, Dr. DeFronzo is quite convinced they’ll be effective. Highlighting that those in the highest tertile of IGT are already maximally insulin resistant and have lost 80% of their beta cell function, Dr. DeFronzo stressed that we must intervene with drugs earlier in dysglycemia to stop the progressive loss of beta cells and beta-cell function.

  • Dr. DeFronzo highlighted that pharmacotherapies, especially thiazolidinediones, have been shown to be effective in preventing type 2 diabetes. In the IDPP and the DPP, metformin treatment decreased progression to type 2 diabetes by 26% and 31%, respectively. Meanwhile, thiazolidinediones were even more impressive, reducing progression to type 2 diabetes by 55-75% in clinical trials (55% with troglitazone in TRIPOD, 60% with rosiglitazone in DREAM, 72% with pioglitazone in ACT NOW, and 75% with troglitazone in the DPP). Other agents, including acarbose (36%; STOP-NIDDM), voglibose (40%), and orlistat (37%; XENDOS) have also been shown to reduce progression to type 2 diabetes.
  • He further emphasized the effectiveness of pioglitazone in diabetes prevention, reviewing results from the ACT NOW trial. In the ACT NOW trial, pioglitazone treatment reduced the progression from IGT to type 2 diabetes by 72% (DeFronzo et al., NEJM 2011). Only 18 patients with IGT would need to be treated for one year with pioglitazone to prevent one case of type 2 diabetes; for comparison, 35-40 patients would need to be treated with a statin to prevent one myocardial infarction. Dr. DeFronzo noted that with all the recent emphasis on cardiovascular disease, we’ve forgotten about microvascular complications – each day, diabetes causes 55 people go blind and 120 to go on dialysis. He stated that given the fact that pioglitazone is soon going generic, one could probably extrapolate significant cost-benefit or cost savings.
  • Dr. DeFronzo noted that data suggest that GLP-1 agonists would be very effective in preventing the progression from IGT to type 2 diabetes. He expressed excitement, because in overt diabetes, GLP-1 agonists lower A1c, promote weight loss, address many of the pathophysiological defects of diabetes, do not cause hypoglycemia, and have excellent safety profiles. In a study investigating the effects of liraglutide on weight in obese patients without diabetes, the vast majority of patients with prediabetes reverted back to normoglycemia; patients experienced noticeable reductions in weight, A1c, and blood pressure as well (Astrup et al., IJO 2011). 

Questions and Answers

Q: I’m interested in your patient population. You said that a lot of them are over 100 kg (220 lbs). I’m really interested in your standpoint on bariatric surgery for prevention versus Victoza?

A: That goes a little bit beyond pharmacology. I think that there are several nice articles that came out back to back in the NEJM, and we have long-term data from the SOS showing a decrease in mortality over 20 years. If you are morbidly obese, whether you have diabetes or sleep apnea or some other disorder, bariatric surgery is really the only thing that works on a long-term basis. I think it’s a whole different situation for those with BMIs of 32-33 kg/m2 who have not taken metformin or intensive lifestyle interventions. I don’t want you to think that I disagree with Dr. Hamman. We have to try as best we can to intervene from a lifestyle standpoint. I like metformin; I just think we can do a lot better than that. Bariatric surgery is something I would look at much further down the line. What seems to be so effective about bariatric surgery and normalizing glucose tolerance is the astronomical levels of GLP-1 that you get in these people, and the marked improvements in beta cell function. It’s similar to what I tried to show you with liraglutide. I believe you would see this effect not only with liraglutide, but also with Bydureon or exenatide. GLP-1 agonists are a unique group of drugs. They should be considered not only in treatment but also in prevention. 

Comment: I appreciated you saying that you don’t disagree much with Dr. Hamman. I don’t disagree much with you. I was a strong supporter of trying troglitazone in the DPP. I think there is a huge role for the right medicine for prevention. We may see the glass as half empty or half full right now, but regardless, we need to take cost effectiveness and safety into consideration. The physiological standpoint is where we need to start, but we don’t know where that will take us 5-10 years later. There are some concerning side effect data, especially for TZDs. GLP-1 may be the best way to go, but before we give it to a 20 year-old with a BMI of 30 kg/m2, who would take the medication for 30-40 years, we would want to know what it will do in the long term. Absolutely, we’re going to have to take some risk, but we should try to characterize the cost-effectiveness and safety as much as we can.  

Q: You avoided mentioning DPP-4 inhibitors – was that intentional?

A: I avoided mentioning them because we don’t really have any data with them in terms of prevention of IGT. We have very nice studies with liraglutide. We have done a head-to-head study with exenatide and sitagliptin, and I’m sorry to say for DPP-4 lovers that exenatide just destroyed sitagliptin in anything you would want to look at. In my practice, I actually only use GLP-1 analogs because they have much more powerful effects on the beta cell. Probably the most important effect of DPP-4 is to inhibit glucagon, not to augment insulin secretion. But, I wouldn’t be opposed if someone wanted to conduct an intervention trial and get some serious data. I do believe that the earlier we start, when there is still significant beta-cell function, the more likely they will be effective.


Joint ADA/The Lancet Symposium

Exenatide Twice a Day Versus Glimepiride for Prevention of Glycaemic Deterioration in Patients with Type 2 Diabetes with Metformin Failure (Eurexa) - An Open-Label, Randomised Controlled Trial

Guntram Schernthaner, MD (Rudolfstifstung Hospital, Vienna, Austria)

Dr. Schernthaner presented efficacy data on EUREXA – “the longest GLP-1 study in the literature at the moment” – a three-year comparison of exenatide 10 mcg BID to glimepiride (titrated to a mean of 2 mg/day) in type 2 diabetes patients on background metformin therapy (Gallwitze et al., Lancet 2012 in press). At baseline, the study population (n=1,029) had a very low mean A1c (~7.4%), short diabetes duration (~5.5 years), and high BMI (~31 kg/m2). Exenatide was superior to glimepiride in the primary efficacy endpoint, time to treatment failure as defined by A1c (median 180 weeks for exenatide and 142 weeks for glimepiride). The favorable result for exenatide was certainly encouraging, though we think a median failure timeline of less than three years highlights the need more effective, durable, and easy-to-use therapies – still, SFUs can fail in less than a year as we understand it. Compared to glimepiride, exenatide caused more favorable change in body weight (3.9 kg decrease vs. 1.5 kg increase [-8.6 vs. +3.3 lbs]), higher prevalence of GI side effects (47% vs. 24%), and rarer hypoglycemia (1.5 vs. 5.3 episodes per year). Several questioners sought more granular data (e.g., on liver function and heart rate), but Dr. Schernthaner said we would all have to wait until his group’s presentation at EASD 2012 – which will also include results from a substudy of re-randomization to triple therapy (exenatide+metformin+TZD or exenatide+metformin+glimepiride). 

  • Mean baseline A1c was low at 7.42%-7.45% (inclusion criteria allowed baseline A1c between 6.5% and 9.0%), mean BMI was roughly 31 kg/m2, and mean fasting plasma glucose was 8.6-8.9 mmol/l (155-160 mg/dl) – a fairly low value that Dr. Schernthaner said may have been due to preservation of beta-cell function. Mean age was ~56 years old, mean diabetes duration was 5.5-5.6 years, and mean metformin dose was over 1.9 g/day. The population was mostly male (50-55%) and predominantly Caucasian (>90%) or Hispanic (7%).
  • Rates of discontinuation were higher in the exenatide group (though the difference, due largely to GI side effects, was statistically significant only in the first six months of the study), and treatment failure was more common in the sulfonylurea group. The EUREXA investigators randomized 515 patients to exenatide and 514 to sulfonylurea. Of the intent to treat population (490 and 487), 174 and 128 patients discontinued, respectively, and 203 and 262 failed treatment, respectively. Thus at study end 138 patients remained in the exenatide group, compared to 124 in the sulfonylurea group.
  • Exenatide was both non-inferior and superior to glimepiride in the study’s primary endpoint, time to treatment failure (median 180 vs. 142 weeks; p=0.032 by log-rank test). Along similar lines, rate of treatment failure at three years was lower with exenatide (41% vs. 54%; HR 0.748, p=0.002). (Treatment failure was defined as when a patient on the maximally tolerated dose of drug had either a) two consecutive A1c measurements >7.0% taken by at least three months apart or b) a single A1c measurement >9.0%.) Exenatide and glimepiride conferred similarly rapid glycemic improvements down to an A1c nadir of roughly 6.7% during the first few months of the study; Dr. Schernthaner noted that this was an earlier glucose normalization than seen in many previous exenatide studies. HOMA-B was not significantly different between groups at any of the three years, but HOMA-IR and disposition index were significantly better for exenatide at the end of each year in the study.
  • Baseline A1c had a highly statistically significant relationship with time to treatment failure – i.e., faster at higher baseline A1c (p <0.001). Failure was rarest among those with A1c ≤7.3%, in whom rates were similar regardless of treatment (which Dr. Schernthaner said made sense, since these patients presumably tended to have sufficient beta-cell function). However, exenatide performed better than glimepiride in people with A1c >7.3% and ≤8.2%, and in those with A1c above 8.2%.
  • Adverse event findings were in line with previous studies; Dr. Schernthaner noted that exenatide was not associated with any added risk of pancreatitis (one in each group), nor did any participants get pancreatic cancer. Gastrointestinal side effects were much more prevalent with exenatide (47% vs. 24%), and adverse injection site reactions were more common as well. However, the incidence of hypoglycemia was lower compared to sulfonylurea, across the range of hypoglycemia definitions studied: any events (36% vs. 67%), nocturnal (10% vs. 16%), glucose confirmed below <3.9 mmol/l (< 70 mg/dl) (20% vs. 47%), etc. Hypoglycemic events were less frequent with exenatide (1.5 vs. 5.3 episodes per year), and the time to first hypoglycemic event was longer.
  • Dr. Schernthaner closed by discussing the study’s limitations, including the low baseline A1c and the relatively low rates of glimepiride, despite titration to the maximum protocol-recommended dose. However, during Q&A Dr. Schernthaner noted that higher glimepiride doses might not have been much more effective; he referenced his own double-blind comparison of glimepiride and gliclazide (in which 3 - 6 mg doses did not seem more effective than 2 mg) and Dr. Alan Garber’s LEAD-3 trial (in which the efficacy of high-dose glimepiride was roughly in line with that seen in EUREXA). Another study limitation was the ethnic homogeneity of the enrolled population, most of whom came from Europe (with some also from Mexico and Israel). During Q&A Dr. Schernthaner tentatively forecasted that results would have been positive in East Asian patients as well, based on previous successful studies of GLP-1 receptor agonists in these patients. 

Questions and Answers

Q: It looked from the graph like the earliest failure was at one year in either group. Usually treatment failure occurs sooner – did you look for treatment failure at three or six months?

A: There were very few failures this early. Probably this was because A1c was very low at the start, so you don’t see treatment failure at the beginning. The result could be totally different if starting with higher baseline.

Q: Did you monitor liver enzymes? With weight difference of more than 5 kg, perhaps Byetta has a direct effect on liver disease?

A: The data were accepted for presentation at EASD, so I cannot present them. But I can talk to you personally.

Q: The BMI was fairly high in your group. I just wonder if you tested this in a lower BMI or lower body-weight cohort that looks more like an Asian population. Do you suspect that exenatide would also be better then sulfonylurea in such a lower-BMI population?

A: I just returned from lecturing in Asian countries; I saw that the highest there was about 28 kg/m2. But there is much more visceral and liver fat in Asia. But GLP-1 receptor agonists are at least as effective in Asian vs. Western populations, so I wouldn’t expect to see a difference.

Q: 53% of the group failed glimepiride. How did you define maximum-tolerated dose in this study, in which obviously glimepiride was not the study drug.

A: I did a double-blind study of gliclazide vs. glimepiride that we cited in this paper; 3, 4, 5, and even 6 mg were not better than 2 mg. Alan Garber used up to 8 mg in LEAD-3, and the A1c-lowering was in line. Probably higher doses are not as beneficial as previously thought.

Q: Did you see geographic variation in how glimepiride was prescribed and up-titrated?

A: The highest risk of hypoglycemia was seen at the 1-2 mg doses. This confirms previous research in which the most events were seen at lower doses –probably because there is more beta-cell function remaining in these patients.

Q: Do you have the information about effect on heart rate?

A: Yes, or course. We will present this data at EASD in Berlin; since the manuscript is already accepted I cannot bring this data today.


Corporate Symposium: Long-Acting GLP-1 Receptor Agonist Thearpy: Improving Efficacy , Adherence, and Weight in T2DM (Sponsored by Amylin)

A Holistic View of Type 2 Diabetes Pathophysiology and Management

Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, TX)

Dr. DeFronzo reviewed the basic physiology and epidemiology of diabetes to set the stage for discussing GLP-1 agonist therapy. Driving home the impact of diabetes in the US, he noted that every 24 hours there are 4,000 new cases of diabetes, 810 deaths from diabetes, 230 amputations, 120 kidney failures, and 55 blinded from the disease. He expressed particular sympathy for individuals with diabetes suffering from the microvascular complications of the disease due to the lifelong debilitation with which they are afflicted. A theme of Dr. DeFronzo’s talks is that the treatment of diseases should be based on their pathophysiology, and that a multifactorial disease like diabetes will require a combination of therapies to address its various etiological facets. On this note, he reminded us of his “ominous octet” of dysfunction in diabetes (he first introduced this term at his Banting lecture during ADA 2008): decreased insulin secretion, increased glucagon secretion, increased hepatic glucose production, neurotransmitter dysfunction, decreased glucose uptake, increased glucose reabsorption, increased lipolysis, and of course, decreased insulin effect. He emphasized that GLP-1 agonists address aspects of the ominous octet uniquely from any other class – in addition to reducing A1c, they preserve beta-cell function, promote weight loss, and do not cause hypoglycemia. He said that, in his opinion, the most dramatic study carried out in people with type 2 diabetes was one in which liraglutide (7.5 ug/kg) drastically increased the rate of insulin secretion in a glycemic-dependent manner almost to the extent where they were indistinguishable from normal controls (Chang et al. Diabetes 2003).


Long-acting GLP-1 Receptor Agonist Clinical Trial Data

John Buse, MD, PhD (University of North Carolina, Chapel Hill, NC)

Dr. Buse reviewed some of the landmark trials involving GLP-1 agonists to support their clinical utility. The currently approved GLP-1 medications are exenatide (Amylin’s Byetta), exenatide once weekly (Amylin/Alkermes’ Bydureon), and liraglutide (Novo Nordisk’s Victoza). He began with a recap of the DURATION-1 trial, which compared exenatide once weekly versus the twice-daily formulation. The once-weekly formulation reduced A1c significantly more than the twice-daily formulation; switching all patients to the once-weekly formulation halfway through the study allowed both groups to achieve a ~2.0% A1c reduction after 52 weeks of treatment (we did not get the baseline A1c). Nonetheless, he noted that exenatide twice-daily is the most powerful postprandial glucose lowering drug available today. He also emphasized that liraglutide is the most powerful GLP-1 receptor agonist, based on the surprising DURATION-6 study result that showed a 0.2% greater A1c reduction in liraglutide compared to exenatide once-weekly. Importantly, he emphasized that longer-acting formulations are associated with less nausea than the shorter-acting exenatide; in addition, they are also associated with less hypoglycemia. In summary, Dr. Buse concluded that GLP-1 agonists are unparalleled in their A1c-lowering abilities, that they improve a variety of endpoints relevant to diabetes, and that the safety profile includes some risks but for most patients is safe and appropriate. Profiling the individual drugs, he said that exenatide showed the best postprandial control, that exenatide once weekly is the best tolerated, and that liraglutide has the best glycemic efficacy and weight loss.

  • Dr. Buse elaborated on the safety issues associated with GLP-1 agonists. Although he said that pancreatitis isn’t a huge worry, he always tells his patients to stop their therapy if they experience vomiting or other worrying symptoms like back pain. Another potential concern with GLP-1 agonists is their use with renal impairment. There is not enough clinical experience in patients with stage 4/5 kidney disease, since nausea/vomiting can exacerbate an already unstable hemodynamic clinical picture. He briefly overviewed the C-cell tumor black box warning for the GLP-1 agonist class, noting that the human relevance to animal trials could not be determined with clinical or nonclinical studies, and that the only real consequence was that this class is contraindicated in patients with multiple endocrine neoplasia 2 (a genetic condition predisposing patients to thyroid cancer) or personal family history of medullary thyroid cancer. Moving on to cardiovascular risk, Dr. Buse noted that DURATION-2 showed roughly similar risk factor changes (HDL, LDL, systolic blood pressure, C-reactive protein) with exenatide, sitagliptin and pioglitazone treatment. The exceptions were that exenatide showed superior systolic blood pressure improvement compared to sitagliptin and superior HDL improvement compared to pioglitazone.


Extraglycemic Effects and Clinical Applications of Long-Acting GLP-1 Receptor Agonists

David D’Alessio, MD (University of Cincinnati, Cincinnati, OH)

Dr. D’Alessio discussed the physiology of GLP-1 in the body and how the basic science correlates to the clinical effects of GLP-1 agonists. Overall, the most clinically notable central nervous system effects of GLP-1 agonists are decreased food intake and nausea. Dr. D’Alessio broke this down by the effects of GLP-1 in individual areas of the brain. Subsequently, he explained that despite the fact that proteins and peptides don’t cross the blood-brain barrier, there are areas in which the blood-brain barrier is leaky called circumventricular organs, which typically function to detect toxins and allow for peptide sensing of the bloodstream. These are the areas in which GLP-1 agonists can exert their greatest effect. He described how changes in food intake are partially due to a decrease in gastric emptying that promotes satiety and lower peak blood glucose. In addition, Dr. D’Alessio also described the direct impacts of GLP-1 agonists on the cardiovascular system, and the potential for GLP-1 agonists as weight-loss drugs. He highlighted that liraglutide 3.0 mg has demonstrated a ~9 kg (~20 lb) weight loss after 20 weeks. In closing, he advocated for greater use of GLP-1 agonists and commented that novel applications may expand the clinical utility of this class.

  • Dr. D’Alessio broke this down by the effects of GLP-1 in individual areas of the brain. If GLP-1 is injected into the brainstem and paraventricular nucleus of the hypothalamus, a consistent decrease in food intake is always observed. A moderate aversive response is observed when GLP-1 is injected into the central amygdala, an area associated with fear and stress. Administration to the arcuate nucleus decreases hepatic glucose production. An autonomic and cognitive stress response is seen with injection into any of these areas.
  • Subsequently, he described the direct impacts of GLP-1 agonists on the cardiovascular system. The GLP-1 receptor is expressed in vascular endothelial cells, arterial muscles, myocardium and the endocardium. GLP-1 knockout mice show increased septal and posterolateral wall thickness but a decreased cardiac size with an impaired response to stress hormones. It seems that GLP-1 serves to increase insulin sensitivity in the heart, while improving myocardial relaxation and protecting from ischemia. After a myocardial infarction, GLP-1 has been shown to increase left ventricular ejection fraction (a measure of cardiac performance) and to cause beneficial vasodilation, reducing the cardiac burden that can exacerbate future CV events.


Panel Discussion

Ralph DeFronzo, MD (University of Texas, San Antonio, TX); John Buse, MD, PhD (University of North Carolina, Chapel Hill, NC); David D’Alessio, MD (University of Cincinnati, Cincinnati, OH)

Q: Does it make sense to use exenatide BID three times a day, with each meal?

Dr. Buse: It hasn’t been studied that way, but one well-known clinician (who has type 1 diabetes himself) does prescribe exenatide that way.

Q: How are patients doing on exenatide once weekly? Are there problems with the injections?

Dr. Buse: We’ve been experimenting with this for years and we don’t really have any problems, but people in other centers say that they do have problems with the injection kits. I think the important thing is education to make sure that your patients know how to use the kit.

Q: Do you recommend a combo of GLP-1 agonists and DPP-4 inhibitors?

Dr. Buse: There is a rationale for this, but the cost is too great to support this regimen currently. There are other hormones that are broken down by DPP-4, and those may be involved.

Q: What do we know about preservation of beta-cell function on a long-term basis with GLP-1 agonists?

Dr. DeFronzo: With exenatide the effects are maintained past three years. But shortly after discontinuing therapy, the protection was lost.

Q: What is the evidence for DPP-4 and beta-cell dysfunction?

Dr. DeFronzo: It’s relatively weak. With sitagliptin we couldn’t show an improvement in beta-cell function. If you go through the literature you can find varying effects. The strongest effect was seen with vildagliptin. But with GLP-1 analogs you always see a powerful effect.

Q: How do you explain bladder cancer to your patients on pioglitazone?

Dr. DeFronzo: I tell them it’s not solidly shown what the relationship is between pioglitazone and bladder cancer. If you look at the incidence of people off pioglitazone the incidence was 7/10,000; on any dose of pioglitazone it was 8/10,000. That’s pretty minimal. When you compare that to a 2-3% decrease in myocardial infarction, it’s not even comparable in terms of risk-benefit. And for many cancers there’s actually a decrease.

Q: Do you have a follow-up of your triple therapy study?

Dr. DeFronzo: Yes I do. We submitted that study as a late-breaker at ADA. And let me say that it just destroyed the ADA algorithm. Better weight loss, better hypoglycemia, better everything. But mysteriously it wasn’t accepted as a late-breaker at ADA this year. I found that rather strange considering the late-breakers I saw, but apparently they didn’t think that it was worth showing.

Q: Is the vasodilation effect of GLP-1 agonists receptor mediated?

Dr. D’Alessio: It seems to be specific to acetylcholine-mediated vasodilation as opposed to nitric oxide-mediated vasodilation.

Q: Is the insulin-GLP-1 combo cost effective?

Dr. D’Alessio: We don’t have enough data yet to say either way.

Q: What’s happened to the CV studies?

Dr. D’Alessio: The last one I showed you was from 2004, so why haven’t they done a big clinical trial? I’ve wondered that. I think there are going to be some studies. GLP-1 agonists have been studies in clinical trials with heart failure. It is a curiosity why this hasn’t been studied more.

Q: What’s the mechanism of GLP-1-associated nausea, and is it related to gastric emptying?

Dr. D’Alessio: We’re not sure how it happens in humans. We can inject it into an area of mouse brains to make them sick, but we still can’t extrapolate an exact mechanism from those studies.

Q: Is liraglutide on the road to a once-weekly formulation?

Dr. Buse: They’re developing a modified version of liraglutide that’s longer acting, but it’s going to be a different compound so I don’t think it’s actually fair to call it liraglutide any more.


Corporate Symposium: Role of GLP-1 and DPP-4 in Type 2 Diabetes (Sponsored by Novo Nordisk)

Role of GLP-1 and DPP-4 in Type 2 Diabetes (Sponsored by Novo Nordisk)

Richard Pratley, MD (Florida Hospital Diabetes Institute, Orlando, FL)

Dr. Pratley’s promotional presentation focused on the mechanism of action of GLP-1 analogs, and the advantages of Victoza (liraglutide) over sitagliptin (Merck’s Januvia). After reviewing the method of action of the GLP-1 hormone and DPP-4 enzyme, he explained that the incretin effect is seriously diminished in people with type 2 diabetes. Dr. Pratley then reviewed Victoza’s structure, effects on various glycemic metrics, safety data, and performance in a head to head trial with sitagliptin. He noted that from average baseline A1c levels of 8.4, participants in the trial on the 1.2 mg and 1.8 mg doses of Victoza showed larger declines in A1c than taking sitagliptin (A1c declines were -1.2%, -1.5%, and -0.9%, for the Victoza 1.2 mg, Victoza 1.8 mg, and sitagliptin groups, respectively) . He also emphasized Victoza’s positive effects on fasting plasma glucose and weight loss compared to sitagliptin. For more details on this study, see our April 10, 2012 Closer Look at Audience questions mainly focused on choosing among therapies, reducing nausea and injection site reactions, and the underlying mechanism in the observed weight loss and nausea.


Amylin Investor Reception

Daniel Bradbury (CEO) and Christian Weyer, MD (Senior Vice President, R&D)

Amylin CEO Daniel Bradbury led the company’s investor presentation, which focused heavily on its exenatide franchise. Reviewing data presented at ADA 2012, management emphasized Bydureon’s robust and durable effects on glycemic control, associated weight loss, low risk for hypoglycemia, improved tolerability, and convenience. In the DURATION-1 study, participants receiving Bydureon treatment for four years maintained strong glycemic control (average A1c of 6.9%) and exhibited improvements in beta cell function markers and weight (1156-P). A 26-week study found significantly greater improvements in A1c and weight with Bydureon vs. Levemir as well as low rates of associated hypoglycemia (40-LB). Furthermore, in the DURATION program, participants treated with Bydureon exhibited similar reductions in A1c and weight regardless of baseline weight (1152-P). Turning to Byetta, management highlighted the results of the EUREXA study, which demonstrated a greater durability of glycemic effect with Byetta vs. glimepiride at the end of three years (CT-SY22). Finally, regarding Symlin, data was reviewed from an in silico study that suggested a Symlin to insulin ratio of 9 mcg to 1 U in a coformulation would maintain 90% of the study’s virtual patients within optimal glycemic control without hypoglycemia (1057-P).

Subsequently, management provided several updates regarding the company’s development pipeline. Most notably, management revealed that Amylin was pursuing a line extension regulatory approach for their exenatide once weekly suspension formulation, building upon already available data for Byetta and Bydureon – a similar strategy was used in the development of Bydureon. The first phase 3 trial for the new formulation (named DURATION-NEO-1) is expected to initiate in 3Q12 (previously mid-2012), and the trial will be similarly designed as DURATION-5 (head-to-head vs. Byetta). During the Q&A session, management reaffirmed that the submission timeline for Bydureon’s dual chamber pen had been delayed until early 2013 because of requests from the FDA for additional test data and longer stability data for the device. Amylin confirmed with the agency, however, that it could file an sNDA for the pen, allowing for a four-month review. Separately, management indicated that the first clinical study to test various co-formulations of pramlintide and insulin would commence in 3Q12. Particular excitement was expressed for the use of this coformulation in an artificial pancreas device.


Panel Discussion

Moderator: David Maggs, MD, MRCP (Vice President for R&D Strategic Relations, Amylin, San Diego, CA); Virginia Valentine, CNS, CDE (University of New Mexico, Albuquerque, NM); Anne Peters, MD (Keck School of Medicine, Los Angeles, CA); Carol Wysham, MD (University of Washington, Seattle, WA)

Dr. Maggs: Have you seen patients with lipodystrophy? What do you think about the potential of metreleptin?

Dr. Wysham: Traditionally, we have thought of lipodystrophy as a very rare condition, something we only read about in textbooks. Only when the metreleptin studies and the NIH reports came out to give some guidance on partial forms of lipodystrophy did I find out that it occurs much more commonly. On several occasions, I’ve taken the advice of my colleagues to ask my patients to show me their calves or give me a view of what their arms look like, and recognized that I have those patients in my practice. Although metreleptin will come out as an agent for what we think is a rare disease, I suspect we have a significant number of patients with partial lipodystrophy for which this agent will be very helpful.

Dr. Maggs We’re excited about the pramlintide/insulin coformulation at Amylin. What are your thoughts about the coformulation?

Dr. Peters: Everybody who is on insulin, particularly people with type 1 diabetes, should be on the coformulation product once it’s available. I can’t wait, could you hurry up?

Dr. Maggs: What would this mean for your patients on insulin pumps?

Ms. Valentine: It’d be huge to have physiological replacement of both hormones. The limiting factor for patients on pumps or using injections is the hassle factor. So, having a pump automatically deliver both insulin and pramlintide would make it more usable, and way more utilized than it is now.

Dr. Peters: And maybe better than insulin alone in many ways.

Dr. Maggs: Could you reflect on the type 2 diabetes epidemic and what type 2 diabetes currently looks like in your clinic?

Dr. Wysham: I have been in this field for 30 years now. I’m currently working in a county hospital taking care of many people with diabetes. I’m very astounded by how little we had to offer patients thirty years ago. The advance we have made in glucose testing and treatment have had a dramatic impact on patients. I keep wondering to myself how my patients got so old, and I’m thrilled that they are doing well. My biggest concern is with my younger patients. Some of these patients come in and have several risk factors for type 2 diabetes. They develop diabetes in their 30s. They are in their reproductive age. These are patients we hope enjoy a normal life expectancy. But if they are not well controlled, they will exhibit disease characteristics in their 40s that are typically present in 60-year olds. We need agents that not only help our patients achieve good glycemic control, but also alter their long term risk for progression to complications. 

Dr. Maggs: Regarding the new ADA/EASD position statement, Anne, you were one of the key authors. Can you describe what this position statement means? What do you mean by patient-centered approaches to diabetes care?

Dr. Peters: That concept didn’t come up until later in the committee’s discussions. If patients don't use the their therapies, they are not going to work. There are a lot of drugs that we give our patients that they don’t take. It is import to consider the patient when making treatment recommendations. What is the patient going to want to do? How do we guide them to individualized targets? From ACCORD, we learned that the one size fits all approach might not be that good. There should be individualized targets and individualized treatment protocols. I may use drugs in different orders and in different places given different patient factors. The algorithm was meant to capture that. Some people wanted a more prescriptive algorithm. As an organization, the ADA wants to give more options for the patient and the provider. It wants what is best for the patient.

Dr. Maggs: What has your early experience been like with Bydureon? What do your patients think of it?

Ms. Valentine: It has gone extremely well. In my experience, people have been very positive about it. They are taking it, and they are loving not having to take multiple injections. Having had diabetes for 32 years myself, I of course immediately went on it too. I’m amazed how you really don’t have to think about it, that it’s only once a week. Patients are expressing the same feeling. It was easier to start than I thought it would be. The injection is amazingly easy, and what I’ve discovered is that after learning how to use Bydureon, it takes minimal effort afterwards. With the other GLP-1 agonists, the work up front is lower, but then it keeps going up because of titration and side effects. It’s just been amazingly smooth, people are liking it, and I’ve been very impressed with the persistence. People are sticking with it. That’s been wonderful. 

Dr. Wysham: I agree with everything Virginia said. I participated in an earlier trial before the current device was available, and back then, it was a little bit more burdensome, so I was somewhat concerned. I have an older population so you’d think it’d be more challenging, but Bydureon has been well accepted by my patients. I gave myself a dry injection with the needle, and was not uncomfortable at all. I expected it to be worse, and that’s what most physicians are concerned with – the difference in the size of the needle. But it’s not an uncomfortable injection. Persistence is an important issue – when we look at persistence with some of the previously available GLP-1 agonists, levels are less than 50%. Insulin wasn’t much better than that. It is very important to measure and beware of what the persistence data is for Bydureon. 

Dr. Peters: I agree entirely. I’m a cynic. I actually thought that I wouldn’t like Bydureon because I thought my patients would hate the whole fiddling thing with dosing as well as the huge needle. Not only did my patients like it, I’m sold as well. The product really just sold itself. My patients have had great experiences. They hate insulin. It has this taboo around it. But this is so different from insulin. They only have to take it once a week. For some patients that I wasn’t able to control on insulin, they’ve been willing to try it and they are getting amazing results. Yes, we all do the same thing. We all try shots. It is bigger and thicker, but it doesn’t hurt. I have been pleasantly surprised about my experience with Bydureon.

Dr. Maggs: We no have three products in the GLP-1 space. Is there such a thing as short-acting and long-acting GLP-1 agonists?

Dr. Peters: Absolutely. I have some patients on Byetta who tried Bydureon and wanted to switch back. They like Byetta more. They feel that Bydureon is more stealth. They get more of an immediate post-prandial effect with Byetta. Bydureon provides a slower effect. I think that there is a role for both. If you combine Byetta with a long acting insulin, you get a great response. We need more studies, however, to look at all the possible GLP-1 agonist/basal insulin combinations. We don't know yet what combination will work best. I like that there is more than one available. We’ll just have to see how it plays out clinically.

Ms. Valentine: I’ve had one patient who went back from Bydureon to Byetta. How come? My patient said, “Byetta speaks to me, whereas Bydureon whispers.”

Dr. Maggs: The GLP-1 agonist class as a whole seems pretty potent, and now we’re seeing that it’s durable, with three-year data for Byetta, and four-year data for Bydureon. Any thoughts on this?

Dr. Wysham: If you asked us all what we want, it’s to start a patient on a therapy and not have to worry about them for as long as possible. Durability is really key. Primary care is going to have a lot of difficulty trying to keep up with new medicines, titration, et cetera. Having something that you can just start patients on and have a durable effect is very important. One interesting observation from my own practice is that in the original Byetta clinical trials, the patients who were only on metformin are still only on metformin and Byetta. We had to stop prescribing metformin to a few patients with renal insufficiency, but they are doing just fine on Byetta, and they’ve been taking it for about eight years now. That’s something we just don’t see. I think it really points to the fact that these agents are extraordinarily effective early on in disease, and may very well change the natural history of the disease. If managed care insists on trying one or two additional agents before using GLP-1 agonists, we might lose the durability effect.

Ms. Valentine: What I hear from primary care doctors is that they don’t have to do any titration. In the diabetes club, we want to see our patients every three months or so. But for primary care doctors, however, they like that they don’t have to see their patients every three months in order to help them with titrations. 

Dr. Maggs: I want to ask a little bit more about the comparison to basal insulin therapy. We’ve seen some nice data going out to three years comparing Bydureon to Lantus. What do these results mean to you as you make clinical decisions in your practice?

Dr. Peters: I prefer not to use insulin in people with type 2 diabetes. They seem to do less well on insulin than anything else. If I don't have to go to insulin, I prefer GLP-1 agonists because, similar to Carol’s experience, I’ve found that initiating these therapies early leads to sustained benefit. I’ve been in clinical practice for 30 years, and after three months of treatment, it’s really those on insulin who have a lot of issues. Clinical trials are the best-case scenarios for inulin. I think GLP-1 agonists can have a huge role in delaying insulin therapy or to be used in combination with insulin therapy. When I think about the pathophysiology of diabetes, GLP-1 therapy seems to make a lot of sense, whether or not it is used in combination with insulin.

Dr. Wysham: And we do it. Patients are able to get control with insulin with endocrinologists, but that just doesn’t happen in the primary care arena, and that’s who’s taking care of most of diabetes.

Ms. Valentine: Everybody in my practice with type 2 diabetes is going to be on an incretin of some sort or other until proven otherwise. The benefits so outweigh any issues. My patients really don’t like to go on insulin because of weight gain and fear of hypoglycemia.

Dr. Maggs: There is now quite some literature looking at the wider cardiometabolic effects of GLP-1 receptor agonism. What are your thoughts on the cardiovascular effects of GLP-1 agonists?

Dr. Wysham: I find myself thinking back to my discussions with primary care academic colleagues, who in 1992 said there was no proof that good glycemic control changed microvascular or macrovascular outcomes. I felt like there was so much data we accumulated between animal data and what we understood about the effects of good control that it was just inconceivable to me that primary care docs didn’t get it. We’re in the same situation with GLP-1 agonists and cardiovascular effects. The animal studies, the meta-analyses, the markers that we have of oxidative stress – that’s overwhelming evidence to me that it really looks like this is going to be a changer in terms of cardiovascular disease. But, we really need the outcomes studies to prove that, and to push that towards our payers to say that this is why these drugs need to be preferred over less expensive medications that solely lower glucose and don’t have an impact on cardiovascular risk factors.

Dr. Maggs: Any last comments?

Dr. Peters: I think our ability to take care of patients has really improved and will get better over time. I think GLP-1 agonists will help, and I am really looking forward to seeing data on how this class impacts outcomes.  


Questions and Answers

Q: There has been a lot of controversy around Byetta since you launched Bydureon. Byetta sales have remained relatively flat. I’d like to know from your market research, is the universe of prescribing physicians expanding? Are prescriptions expanding? How do these factors explain the dynamics of Bydureon vs. Byetta prescriptions?

Vincent Mihalik (Senior Vice President, Sales and Marketing, Chief Commercial Officer): As you can expect, there has been some erosion for Byetta from patient switches. Our data lags by about two months, but we are seeing some erosion from Victoza and from Bydureon. Yet, we are seeing nice growth of Byetta on top of insulin. The Byetta market share is very dynamic right now. Looking at patient data, about 50% of Bydureon prescriptions are from patient switches. The other 50% are naïve to GLP-1. In terms of prescribers, if you look at total prescriptions, there is continual growth in the prescriber base. It is up from the first year of Byetta’s launch. It is even up from Victoza due to Bydureon’s launch. We are seeing very good productivity from Bydureon compared to Victoza with endocrinologists.

Q: How much impact do you think the pen will have beyond the current formulation and dosing process? I went to your booth and saw a demo on how to inject yourself, and it seemed pretty simple to me.

Ms. Valentine: I think for the diabetes club it won’t make any difference because we’ve just jumped right in. I think that for primary care, it might make a difference for some people. 

Dr. Wysham: The perception of any injectable therapy – insulin, pens, et cetera – is way worse in the primary care office than in reality. So, anything that decreases the complexity in their minds is going to improve the likelihood of prescription. I think the pen is really important. Again, for those of us who feel really comfortable with the current formulation, I’ve had literally no problems when presenting Bydureon to patients. They’re very excited. We unfortunately cannot take care of all patients with diabetes, so we really have to address the concerns of primary care.

Dr. Peters: The lack of titration is key no matter how it is given. One of the real barriers for primary care physicians is that they can sometimes have difficulty communicating to patients what to do with their medications. This needs to be a point of stress. I tell providers who are hesitant to start prescribing Bydureon that once you teach a patient how to use it for the first time, you will know how to dose the drug. The guide is very easy to use, and I find it fun to teach patients how to use the drug. The pen may make it more easily accessible, but the fact that it requires no titration, is administered once weekly, and is not associated with hypoglycemia will make it a pretty well accepted therapy.

Mr. Mihalik: I would just like to build on Anne’s point. One thing that I keep hearing from primary care physicians and endocrinologists is that they like the increased tolerability. They keep telling us that they are getting no call backs from patients, and that they are not used to that with GLP-1 agonists. This is not just with the medication, but the kit too. It’s the increased tolerability that is really helping acceptance. That bodes very well for the primary care community. They want a simple drug that does not require blood glucose monitoring or titrations. Providers appear to like the simplicity that Bydureon offers.

Q: Can you compare how teaching patients to use Bydureon versus insulin (e.g., starting a Lantus regimen) are in terms of time and ease in physicians’ practices?

Ms. Valentine: With Bydureon, all the work is up front, and then there is nothing after that. Teaching a patient how to inject insulin is maybe a few minutes shorter. But then there is teaching them what their dose is, teaching them about hypoglycemia, teaching them how to check blood sugar, and teaching them how to titrate. You’ve got to teach them to titrate, or they have to call you about their blood sugars, or come in to the practice and have you titrate insulin for them. And then you have to deal with side effects – hypoglycemia and weight gain. With Bydureon, it’s very simple to teach, and they’ve done an extraordinary job of designing a very simple system that is well supported.

Dr. Wysham: Probably the most important part of insulin titration is that even though we teach patients and give ideas on how to do it, it’s not uncommon for half of the patients not to titrate insulin at home by themselves, because they don’t feel comfortable doing so.

Ms. Valentine: I’d say in my practice it’s more than half.

Dr. Wysham: When this happens, the efficacy of medications is reduced because patients are not able to take the steps necessary to make the medicine work. Whereas, with Bydureon, we tell our patients that we’ll step them through their next injection if they have problems, but we haven’t had any patients come back to us for help.

Ms. Valentine: They never call, they never write.

Dr. Wysham: They never take us up on that even though we offer. We’re happy to go through the next injection. I have a patient who is 80 years old – one of my original Byetta patients, who just needed a bit more A1c reduction. We taught him one time in five minutes, and he felt like he could do it after that. It’s not difficult, and it doesn’t affect our workflow whatsoever.

Q: Can you give us more granularity about what the FDA is requiring that led to the delay for Bydureon’s pen?

Mr. Bradbury: We announced last week that we now plan to submit the pen in 2013 and that we expect an approval in 2013. Regarding the application itself, we received clarity from the FDA that we would be able to submit an sNDA, allowing for a four-month review. The FDA asked for additional testing and stability data for the device. The stability data they asked for was a little bit longer than what we already had in place. These activities are already ongoing and will allow us to submit in 2013 with an expected approval in 2013.


SGLT-2 Inhibitors

Oral Sessions: SGLT-Inhibitors

State of the Art Lecture - Interpreting Adverse Signals in Drug Development Programs (OR-07)

Clifford Bailey, PhD (Aston University, Birmingham, UK)

Dr. Bailey highlighted the difficulties of interpreting adverse signals in drug development programs, using safety issues associated with newer diabetes drugs – cardiovascular risk (thiazolidinediones), pancreatitis (DPP-4 inhibitors), thyroid c-cell cancer (GLP-1 agonists), bladder cancer (thiazolidinediones; SGLT-2 inhibitors), and breast cancer (SGLT-2 inhibitors) – as springboards for discussion. He emphasized that it is incredibly challenging to determine what constitutes a real signal, given the low incidence (less than one case per 1,000 patient years) of most of the aforementioned adverse events, the differences in interpretation of risk at different time points, and the uncertainties in the translatability of risks identified in animal models to humans. Notably, Dr. Bailey did not seem particularly concerned about the potential cancer risk with dapagliflozin treatment.

  • Dr. Bailey emphasized that it is incredibly difficult to determine when an imbalance in the incidence of an adverse event constitutes a real signal. Using rosiglitazone as an example, Dr. Bailey displayed the results of its clinical trials, asking the audience to identify when they were able to identify an imbalance in cardiovascular events. He noted that in real life, even when 30 trials had reported results, no one shouted out saying that they saw a signal; it wasn’t until 2007 when Dr. Nissen and Dr. Wolski identified increased cardiovascular risk with rosiglitazone in the NEJM meta-analysis. Dr. Bailey noted that interestingly, in an updated meta-analysis in 2010, it was shown that when only data from shorter trials (less than one year in duration) was considered, rosiglitazone was associated with a higher event rate, but when only data from longer trials (more than one year in duration) was analyzed, rosiglitazone actually did not increase cardiovascular risk. As such, Dr. Bailey commented that in short-term trials, the pathophysiology of patients prior to entering the trial is probably one of the key factors influencing the outcomes. Using Steno-2 as an example of how the timing of risk assessment can dramatically affect interpretation, Dr. Bailey noted that if one had only followed out Steno-2 until the four-year time point, one might have concluded that intensive therapy increased CV risk, whereas it showed benefit 10-15 years down the road.
  • With the 2008 cardiovascular risk assessment guidelines for diabetes drugs in effect, numerous companies are now conducting large outcomes trials. Dr. Bailey noted that even though some companies had ruled out an acceptable level of cardiovascular risk in their phase 2 and phase 3 programs (i.e., the upper bound of the 95% CI was less than 1.3), they nonetheless decided to conduct large outcomes trials. He stated that with the improvements in medical care (e.g., fewer CV events), one has to factor in a longer amount of time to accrue enough events to characterize a drug’s cardiovascular profile. Dr. Bailey noted that companies are adopting different strategies in their CV outcomes trial designs, highlighting the differences in the duration and size of the studies. 









>3 years





~4 years





~4 years





~5 years





>5 years





~5 years





~8 years





>6 years



  • Dr. Bailey noted that it remains difficult to interpret whether GLP-1 agonists increase the risk of thyroid c-cell tumors. While c-cell hyperplasia and neoplasia were observed in rodent studies, there was only one case of medullary thyroid carcinoma in the LEAD clinical development program for liraglutide. Given that GLP-1 receptors are prominent in thyroid c-cells in rats, but are not nearly as prominent in human thyroid c-cells (Knudsen et al., Endocrinology 2010), it remains difficult to predict to what degree the results can be translated from animal studies to humans. Dr. Bailey noted that there are only about 600 cases of thyroid c-cell cancer in the entire US every year, so it would be nearly impossible to design a clinical trial to spot this potential risk. He explained that the FDA is particularly cautious on this risk, as the agency saw that the levels of calcitonin (the best biomarker available for this risk) were raised marginally with liraglutide treatment (Parks and Rosebraugh, NEJM 2010).
  • He downplayed concerns about the risks of cancer with pioglitazone treatment and dapagliflozin. He noted there was very little difference between pioglitazone versus comparator in the Kaiser Permanente Northern California database – a difference of 0.13 cases per 1,000 person years. Dr. Bailey emphasized that faint signals such as this would take a long time to identify. Although there was an imbalance of bladder cancer in the development program for dapagliflozin, Dr. Bailey did not seem particularly concerned about the risk, for several reasons: 1) for the most part, patients were randomized in a 2:1 fashion (and even 3:1 in early trials) to drug treatment, so the observed 9:1 imbalance should actually be thought of as a 4.5:1 imbalance; and 2) most patients who developed bladder cancer while on treatment had signs of hematuria at or before the time of diagnosis, suggesting the presence of preexisting tumors. In addition, Dr. Bailey noted that the imbalance in breast cancer incidence in the dapagliflozin development program could likely be explained by detection bias.  
  • Dr. Bailey pointed out a number of shortcomings of pharmacovigilance in detecting faint signals of risk: 1) pharmacovigilance does not accurately characterize risk (since it captures a numerator without a denominator); 2) there is relative over-reporting of signals for new agents (but usually under-reporting otherwise); 3) it is biased by media coverage, ease of recognition, and familiarity; 4) diagnosis, causation, and severity of adverse events are often unconfirmed; 5) data from individuals are pooled and analyzed as groups, and then applied back to individuals; and 6) pharmacovigilance only focuses on relative, not absolute risk. 


Canagliflozin Lowers Postprandial Glucose and Insulin by Delaying Intestinal Glucose Absose Absorption in Addition to Increasing Urinary Glucose Excretion (79-OR)

Robert Henry, MD (University of California San Diego School of Medicine, San Diego, CA)

Canagliflozin has a much stronger affinity for SLGT-2 than for SGLT-1 and its inhibition of SGLT-2 increases urine glucose excretion (UGE). Previous data showed that high doses of canagliflozin reduced postprandial glucose and insulin levels more than could be explained by UGE alone, prompting the hypothesis that following drug delivery, canagliflozin transiently inhibits intestinal SGLT-1 and reduces glucose absorption. Dr. Henry presented data from a two-period crossover trial in which healthy male participants received either canagliflozin 30 mg or placebo before a mixed meal tolerance test (MMTT) with oral 14C glucose. Participants received 3H-glucose IV infusion three hours before MMTT initiation and during the six-hour post-MMTT observation period. In the first two hours following MMTT initiation, canagliflozin decreased plasma levels of insulin by 43%, endogenous glucose by 35%, and the oral 14C glucose tracer (no reduction given) while having little effect on plasma levels of the IV 3H-glucose tracer. The simultaneous increase in UGE was not large enough to explain the reduction in postprandial glucose, suggesting that canagliflozin decreases plasma glucose levels through inhibition of both SGLT-1 and SGLT-2. Dr. Henry ended by noting that canagliflozin does not cause glucose malabsorption seen with certain SGLT-1 mutations because the drug is rapidly absorbed and likely inhibits SGLT-1 only transiently. 

  • Canagliflozin is a potent and selective SGLT inhibitor with a much stronger affinity for SGLT-2 (IC50 of 4 nM) than for SGLT-1 (IC50 of 663 nM). SGLT-2 is the main transporter for renal glucose. Inhibition of the transporter by canagliflozin increases urinary glucose excretion, reduces plasma glucose, and reduces A1c levels in people with type 2 diabetes. There is no meaningful systemic SGLT-1 inhibition predicted by plasma canagliflozin concentrations; however during drug absorption, canagliflozin levels in the gut lumen may be sufficiently high to inhibit SGLT-1.
  • Previous data showed that at doses above 200 mg, canagliflozin reduced postprandial glucose and insulin levels more than could be explained by urine glucose excretion alone. This finding prompted the study on whether higher doses of canagliflozin transiently inhibit intestinal SLGT-1 during drug absorption.
  • The two-period crossover trial assigned 20 healthy men to a single oral dose of canagliflozin 30 mg or placebo with the primary endpoint defined as the rate of appearance in plasma of orally ingested glucose (RaO). The study cohort had a mean age of 26.4 years, body weight of 78.1 kg (172 lbs), and BMI of 23.9 kg/m2. Participants received either canagliflozin or placebo 20 minutes before a mixed meal tolerance test (MMTT) with oral 14C glucose in 75 g glucose solution, solid food, and 960 mg acetaminophen solution. Participants received 3H-glucose IV infusion for three hours before MMTT initiation and during the six-hour post-MMTT observation period. 
  • During the first two hours following MMTT initiation, canagliflozin 30 mg decreased plasma levels of insulin by 43%, endogenous glucose by 35%,  and the oral 14C glucose tracer (no reduction given), while having little effect on plasma levels of the IV 3H-glucose tracer. However, canagliflozin provided no meaningful effect on glucose absorption over the total six-hour observation period since the reduction in orally ingested glucose during the first two hours was balanced by an increase in absorption over the last four hours. 
  • While mean urine glucose excretion increased with canagliflozin (18 g) compared to placebo (0.1 g), this effect was not large enough to explain the decrease in postprandial plasma glucose observed in the first two hours. During this period, the 11-gram reduction in oral glucose absorption was nearly two-fold greater than the six grams of glucose lost to renal excretion.


Canagliflozin, A Sodium Glucose Co-Transporter 2 Inhibitor, Improves Glycemic Control and Lowers Body Weight in Subjcets wth Type 2 Diabetes Inadequately Controlled with Diet and Exercise (81-OR)

Kaj Stenlof, MD, PhD (Sahlgrenska University Hospital, Gothenburg, Sweden)

This 26-week trial evaluated the efficacy and safety of the SGLT-2 inhibitor canagliflozin (CANA)  as monotherapy in patients with type 2 diabetes inadequately controlled with diet and exercise. Patients were randomized to receive placebo (n=192), canagliflozin 100 mg (n=195), or canagliflozin 300 mg (n=197); at baseline, patients in the three arms had respective baseline A1cs of 8.0%, 8.1%, and 8.0%. At the 26-week time point, patients in the placebo, canagliflozin 100 mg 300 mg arms experienced average A1c changes of +0.14%, -0.77%, and -1.03%, respectively. In addition, canagliflozin treatment resulted in significant improvements in fasting plasma glucose, two-hour postprandial glucose, body weight, systolic blood pressure, and HDL. The drug was generally safe and well tolerated; however, treatment was associated with higher rates of genital mycotic infections and urinary tract infections, as well as adverse events related to osmotic diuresis and intravascular volume.

  • In this 26-week trial, patients were randomized to receive placebo (n=192), canagliflozin 100 mg (n=195), or canagliflozin 300 mg (n=197). The study enrolled participants between the ages of 18 and 80 with type 2 diabetes who either: 1) had an A1c between 7.0% and 10.0% at screening without the use of glucose-lowering therapies; or 2) had an A1c between 6.5% and 9.5% and were on oral antidiabetic monotherapy at screening, and A1c between 7.0% and 10.0% and fasting plasma glucose ≤270 mg/dl after an eight-week washout period.


Placebo (n=192)

CANA 100 mg (n=195)

CANA 300 mg (n=197)

A1c (%)




Fasting Plasma Glucose (mg/dl)




Weight (kg [lbs])

87.6 (193)

85.8 (189)

86.9 (192)

BMI (kg/m2)




Duration of Diabetes (years)




Systolic Blood Pressure (mm Hg)




HDL (mg/dl)





  • Canagliflozin 100 mg and canagliflozin 300 mg resulted in significant reductions in A1c, fasting plasma glucose, and two-hour postprandial glucose beyond placebo after 26 weeks of treatment. At the 26-week time point, patients in the placebo, canagliflozin 100 mg, and canagliflozin 300 mg arms experienced average A1c changes of +0.14%, -0.77%, and -1.03%, respectively.


Placebo (n=192)

CANA 100 mg (n=195)

CANA 300 mg (n=197)

Change in A1c (%)




Proportion of Patients Reaching A1c <7.0% (%)




Proportion of Patients Reaching A1c <6.5% (%)




Change in Fasting Plasma Glucose (mg/dl)




Change in Two-Hour Postprandial Glucose (mg/dl)




  • In addition, canagliflozin 100 mg and canagliflozin 300 mg significantly improved body weight, systolic blood pressure, and HDL versus placebo.


Placebo (n=192)

CANA 100 mg (n=195)

CANA 300 mg (n=197)

Change in Weight (kg [lbs])

-0.5 (-1.1)

-2.5 (-5.5)

-3.4 (-7.5)

Change in Systolic Blood Pressure (mm Hg)




Change in HDL (%)




Questions and Answers

Q: You indicated that HDL increased with canagliflozin treatment. You might have said this, but what was the average HDL at the beginning?

A: It was quite low in this population.

Q: Your study population ranged from 20 to 80 year-olds. Was age associated with changes in HDL?

A: I think there’s really no specific age range that experienced greater changes in HDL – the variation was quite large in this population.

Q: Were the urinary tract infections and genital mycotic infections spontaneous, or actively solicited?

A: These were spontaneously reported; most of the urinary tract infections and genital mycotic infections were treated by the patients themselves or by the investigators. 

Q: There were changes in serum creatinine – small changes are clinically significant. What accounted for those changes in creatinine?

A: The changes were quite small, as you pointed out. I’m not sure if they’re clinically significant. It’s probably related to osmotic diuresis and intravascular volume.

Q: Were there any differences in GI side effects?

A: We didn’t observe any.

Q: Were weight loss and glucose lowering correlated?

A: As I remember, there was significant correlation between reductions in weight and glucose.


Tofogliflozin, A Novel and Selective SGLT-2 Inhibitor Improves Control and Lowers Body Weight in Patients with Type 2 Diabetes Mellitus Inadequately Controlled on Stable Metformin or Diet and Exercise Alone (80-OR)

Takashi Kadowaki, MD, PhD (University of Tokyo, Tokyo, Japan)

Dr. Kadowaki presented the results of a phase 2 dose-ranging study of Chugai Pharmaceutical’s tofogliflozin in patients with type 2 diabetes as monotherapy or as an add-on to metformin. In the 12-week study, 398 participants were randomized to receive placebo, or 2.5 mg, 5 mg, 10 mg, 20 mg, or 40 mg tofogliflozin. At baseline, participants in all treatment arms had average baseline A1c of 7.9%-8.0%, and weight between 82 kg (180 lbs) and 86 kg (189 lbs). A1c was significantly reduced with tofogliflozin compared to placebo in all but the 2.5 mg treatment group (p <0.0001, except for the 5 mg treatment arm [p <0.001]). There was a clear dose-dependent effect, with 40 mg tofogliflozin bringing about the greatest A1c reduction (0.83% from baseline, versus 0.27% with placebo). Tofogliflozin also brought about significantly greater weight loss than placebo, also in a dose-dependent manner (40 mg tofogliflozin led to a 2.8 kg [6.2 lb] loss from baseline, versus 0.7 kg [1.5 lbs] with placebo). Tofogliflozin was generally well tolerated, and adverse event rates were not significantly different from placebo. Phase 3 trials of tofogliflozin are in progress.

At 12 weeks, there was a dose-dependent reduction of A1c across treatment arms. A1c reduction with tofogliflozin treatment was significant compared to placebo at all doses except for 2.5 mg (p 0<.0001, except for the 5 mg dose [p <0.001]). The greatest A1c reduction occurred in the 40 mg tofogliflozin group, highlighting the dose dependent relationship.


Baseline A1c

Reduction in A1c

Placebo-Subtracted A1c Reduction





2.5 mg




5 mg




10 mg




20 mg




40 mg





  • Tofogliflozin treatment also brought about significantly greater weight loss than placebo in a dose-dependent manner. Most notably, participants on 40 mg tofogliflozin lost 2.8 kg (6.2 lbs) by 12 weeks compared to baseline. Dr. Kadowaki proposed that the increased urinary glucose excretion (UGE) from SGLT-2 inhibition could lead to body weight reduction through two mechanisms: 1) increased UGE translates to calorie loss; and 2) increased UGE may be shifting energy metabolism from glucose to lipids.


Baseline Weight (kg [lbs])

Weight Loss (kg [lbs])



 84 (185)

0.7 (1.5)


2.5 mg

85.5 (188)

1.6 (3.5)

p <0.05

5 mg

82.1 (181)

1.9 (4.2)

p <0.001

10 mg

83.4 (184)

2.2 (4.8)

p <0.001

20 mg

84.9 (187)

2.6 (5.7)

p <0.001

40 mg

81.6 (180)

2.8 (6.2)

p <0.001


  • Tofogliflozin was well tolerated with no notable differences in adverse events between groups. Genitourinary tract infections were the most common adverse event, but Dr. Kadowaki stated that there was no significant difference between placebo and tofogliflozin treatment arms; no patient terminated the study due to genitourinary tract infection. Hypoglycemic events likewise did not discriminate between treatment arms and was low across all groups – only the placebo, 2.5 mg and 10 mg tofogliflozin arms had any patients with a hypoglycemic event.
  • Dr. Kadowaki proposed a comprehensive mechanism of action for SGLT-2 inhibitors to affect multiple systems in the body by increasing urinary glucose excretion (UGE). Increased UGE could decrease both fasting and postprandial blood glucose, which could potentially confer renal protection. UGE could also shift energy metabolism from glucose to lipids. The shift away from glucose could decrease insulin demand and protect beta cells, while the shift to lipids would increase lipolysis and beta-oxidation, leading to improved insulin resistance and weight loss (which could also result from calorie loss due to UGE). Finally, increased UGE could cause osmotic diuresis, leading to decreased blood pressure (data from this study was inconclusive as to the effect of tofogliflozin on blood pressure).


Renal Glucose Kinetics in Response to Dapagliflozin (83-OR)

Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, TX)

Dr. DeFronzo presented results from a study examining the effects of dapagliflozin (DAPA) on renal glucose kinetics in people with type 2 diabetes. Twelve people with type 2 diabetes (A1c 6.5%; BMI 30 kg/m2) were compared to 12 matched controls with normal glucose tolerance (NGT) (A1c 5.5%; BMI 27 kg/m2). The study used a model to determine maximum tubular transport for glucose (TmG), renal threshold (the area under the curve between the theoretical glucose titration curve and what is observed) based off of pancreatic stepped hyperglycemic clamp (SHC) data. SHC values were taken at baseline and after seven days of treatment with dapagliflozin (10 mg/day). TmG and splay were increased in individuals with type 2 diabetes compared to NGT controls, whereas renal threshold was similar between groups. All parameters decreased following dapagliflozin treatment across both groups, but renal threshold had the most notable decrease. He pointed to the latter as the primary factor, not TmG, for decreasing fasting plasma glucose in patients with type 2 diabetes.

  • Dr. DeFronzo reviewed the kidney’s role in glucose regulation. SGLT-2 inhibitors have the potential to moderate reabsorption in the proximal kidney by affecting renal glucose kinetics. (Dr. DeFronzo noted that while he thinks SGLT-2 inhibitors will soon become a therapeutic intervention, we are lagging behind in the US.) He reminded us of three important kidney parameters: 1) TmG – the maximal absorptive capacity of the tubules; 2) renal threshold – the level at which plasma glucose first appears in the urine; and 3) splay – the area under the curve between the theoretical glucose titration curve and what is observed.
  • Renal glucose kinetics were assessed by stepped hyperglycemic clamp (SHC) at baseline and after seven days of dapagliflozin (10 mg/day). Medications that the participants were previously on were continued from baseline, but none were administered on the morning of the SHC. SHC followed an overnight fast with insulin treatment as necessary to reduce fasting plasma glucose to 100 mg/dl in people with type 2 diabetes. Twelve people with type 2 diabetes (A1c 6.5%, BMI 29.8 kg/m2, 108 mg/dl FPG, 53 years of age) were compared to 12 matched controls with NGT (A1c 5.5%, BMI 27 kg/m2, 85 mg/dl FPG, 41 years of age). The study used a model to translate data obtained from the SHC to individual values for TmG, renal glucose threshold, and splay. While Dr. DeFronzo did note the limitation of using a model, he estimated that it could predict values for renal threshold to within 5-10 mg/dl accuracy.
  • In patients with type 2 diabetes, TmG and splay increased compared to NGT controls, whereas threshold for glucosuria is similar between both groups. All parameters decreased after dapagliflozin treatment. TmG was 420 mg/min for patients with diabetes versus 317 mg/min for NGT controls. However, the absolute drop in TmG was greater for patients with diabetes after dapagliflozin than for NGT controls. Splay was decreased in both groups by about 30-35% after dapagliflozin treatment. The threshold for glucose excretion changed from 196 mg/dl to 21 mg/dl in people with type 2 diabetes after dapagliflozin treatment and from 182 mg/dl to 37 mg/dl in NGT controls.
  • Dr. DeFronzo pointed to the marked reduction in threshold for glucosuria as the primary factor responsible for decreasing fasting plasma glucose concentration. He explained that while the reduction in TmG following the dapagliflozin treatment in this study could partly explain improvement in postprandial glucose levels in people with type 2 diabetes, it could not explain the decrease in fasting plasma glucose concentration.

Questions and Answers

Q: In view of this finding, do you have concern about the safety of this drug if people stop eating or drinking? Could they have more osmotic diuresis or dehydration?

A: There are potential complications that I’m sure will end up as warning on the box. People with dysentery, nausea, or diarrhea who can’t eat should be warned to stop the medication. The number of reports is really quite small. There is a quick on-and-off effect. Stopping the drug should mitigate against that potential side effects.

Q: Are people going to the bathroom all night?

A: Nocturia is very common with this drug. But after the first 24-48 hours, you don’t see it. The kidney has an incredible ability to reabsorb sodium. You don’t get excessive losses of salt and water and don’t experience significant nocturia.

Efficacy Increases with Increasing Baseline HbA1c Category with Dapagliflozin Therapy (82-OR)

Elise Hardy (AstraZeneca, Wilmington, Delaware)

Ms. Hardy presented pooled data from five studies on BMS/AZ’s dapagliflozin in people with type 2 diabetes. Dapagliflozin promotes urine glucose excretion (UGE) and leads to mild osmotic diuresis. While its mechanism is independent of insulin activity, dapagliflozin’s ability to promote urine glucose loss depends on the filtered load of glucose, which is determined by plasma glucose levels and the glomerular filtration rate. Thus it is expected that blood sugar concentration, as measured by baseline A1c level, will influence dapagliflozin’s effect on weight, blood pressure, and glycemic control. Pooled data from the five clinical trials showed that higher baseline A1c was associated with greater improvements in A1c, blood pressure, and UGE/creatinine ratio caused by dapagliflozin 10 mg. Surprisingly, there was no relationship between drug-induced weight loss and baseline A1c.

  • Dapagliflozin promotes urine glucose excretion and leads to mild osmotic diuresis and the loss of approximately 300 calories a day. The drug’s effects are independent of insulin secretion and activity, suggesting that dapagliflozin will be effective in the absence of beta cell function. However, urine glucose excretion depends on the filtered load of glucose, which is determined by plasma glucose levels and the glomerular filtration rate. Thus it is expected that blood sugar concentration, as measured by baseline A1c level, will influence dapagliflozin’s effect on weight, blood pressure, and glycemic control. To assess this impact, data was pooled from five phase 3 clinical trials in patients with type 2 diabetes: one monotherapy study, three trials investigating dapagliflozin in combination with metformin, glimepiride, or pioglitazone and one study of dapagliflozin as an add-on in insulin therapy.
  • Dapagliflozin 10 mg increased urine glucose excretion (UGE)/creatinine ratios and reduced A1c, weight, and blood pressure across all five studies. The one exception was the study of dapagliflozin in combination with pioglitazone, in which dapagliflozin mitigated the pioglitazone-induced weight gain, but did not results in an overall weight loss.
  • Across the five clinical trials, higher baseline A1c was associated with greater improvements in A1c, blood pressure, and UGE/creatinine ratio caused by dapagliflozin 10 mg. There was no relationship between drug-induced weight loss and baseline A1c (data summarized in table below). Ms. Hardy speculated that body mass may not be as sensitive as A1c to small differences in glucose excretion that are due to differing baseline A1c levels.

Table 1: Effects of dapagliflozin by baseline A1c category  - pooled data from five phase 3 clinical trials in people with type 2 diabetes.


Baseline A1c Categories


A1c < 8%

A1c 8% to <9%

A1c  9%

Urine glucose excretion/creatinine ratios




A1c (%)




Weight (kg) [lb]

-2.03 [-4.48]

-2.12 [-4.67]

-2.09 [-4.61]

Systolic blood pressure (mmHg)




Diastolic blood pressure (mmHg)






Canagliflozin, A Sodium Glucose Co-Transporter 2 Inhibitor, Improves Glycemia and Is Well Tolerated in Type 2 Diabetes Mellitus Subjects with Moderate Renal Impairment (41-LB)

Jean-Francois Yale, George Bakris, Liwen Xi, Kate Figueroa, Ewa Ways, Keith Usiskin, Gary Meininger

This double-blind, placebo-controlled, phase 3 study randomized 269 patients with type 2 diabetes and moderate renal impairment (estimated glomerular filtration rate [eGFR] 30 and <50 ml/min/1.73 m2) to canagliflozin 100 mg or 300 mg daily, or placebo. At 26 weeks, canagliflozin 100 mg and 300 mg provided significant, placebo-adjusted reductions in A1c (-0.30% and -0.40%, respectively), as well as improvements in weight, fasting plasma glucose, blood pressure, and lipid profiles. While the incidence of adverse event were similar across all treatment groups, canagliflozin therapy led to higher rates of genital infections and the 300 mg dose resulted in a higher incidence of urinary tract infections compared to the 100 mg dose and placebo. Furthermore, canagliflozin provided modest changes in renal function parameters relative to placebo.

  • The 26-week, phase 3 study randomized 269 participants with type 2 diabetes and moderate renal impairment (estimated glomerular filtration rate [eGFR] 30 and <50 ml/min/1.73 m2) to canagliflozin 100 mg (n=90), 300 mg (n=89), or placebo (n=90). The participants had, at baseline, mean age of 68.5 years, A1c of 8.0%, BMI of 33.0 kg/m2, and eGFR 39.4 ml/min/1.73 m2. Characteristics and treatment history were comparable across treatment arms (see table below).


Placebo (n=90)


100 mg (n=90)

Canagliflozin  300 mg (n=89)

Total (n=269)

Sex (male%)





Mean age (years)





Race (% white)





Mean BMI (kg/m2)





Mean duration of T2D (years)





Median ACR (ug/mg)






  • At 26 weeks, canagliflozin 100 mg and 300 mg provided placebo-adjusted improvements in A1c (-0.30%, p <0.05; -0.40%, p <0.001, respectively), weight (-1.60% and -1.80%, p value not given), and lipid profiles (more details in table below). Notably, canagliflozin 300 mg produced a slight reduction in LDL-C levels whereas the other treatment groups showed a similar increase. The proportion of participants to achieve A1c <7.0% and A1c <6.5% was greater in the canagliflozin 100 mg group (27.3% and 8.0%, respectively) and 300 mg group (32.6% and 9.0%) compared to placebo (17.2% and 3.4%). Furthermore, fewer participants received rescue therapy on canagliflozin 100 mg (4.4%) and 300 mg (3.4%) compared to placebo (14.4%).


Change from baseline at week 26

Placebo (n=90)


100 mg (n=90)


 300 mg (n=89)

A1c (%)




Fasting plasma glucose (mg/dl)




Body weight (%)




Systolic BP (mmHG)




HDL-C (%)




LDL-C (%)





  • While the overall incidence adverse events (AE) and study discontinuations due to AEs were similar across the groups, canagliflozin led to higher rates of genital mycotic infections compared to placebo. Furthermore, slighter higher rates of urinary tract infections and AEs related to osmotic diuresis and intravascular volume were found with canagliflozin 300 mg compared to canagliflozin 100 mg and placebo.
  • Modest changes in renal function parameters were observed with both canagliflozin doses compared with placebo. Specifically, estimated glomerular filtration rate was reduced more with canagliflozin 100 mg (-8.3%) and 300 mg (-8.9%) compared with placebo (-3.8%). Furthermore, canagliflozin 100 mg and 300 mg provided modest decreases in urine albumin:creatinine ratio (median changes of 29.9%, -20.9%, respectively) relative to placebo (-7.5%).


Efficacy and Safety of Canagliflozin, A Sodium Glucose Co-Transporter 2 Inhibitor, Compared with Glimepiride in Patients with Type 2 Diabetes on Background Metformin (38-LB)

William Cefalu, Lawrence Leiter, Leo Niskanen, John Xie, Dawn Millington, William Canovatchel, Gary Meininger

Dr. Cefalu presented the results of a 52-week phase 3 trial comparing canagliflozin 100 mg and 300 mg with the sulfonylurea glimepiride in participants with type 2 diabetes inadequately controlled on metformin. Both doses demonstrated non-inferiority to glimepiride in reducing A1c, with the 300 mg dose showing superiority. Furthermore, canagliflozin 100 mg and 300 mg were associated with greater improvements in weight and lower rates of hypoglycemia compared to glimepiride.

  • In this double-blind, active-controlled phase 3 study, 1,450 patients with type 2 diabetes inadequately controlled on metformin monotherapy were randomized to canagliflozin 100 mg (n=483) or 300 mg  (n=485) daily, or glimepiride (n=482), all on a background of metformin.  Glimepiride was up-titrated from one mg to a maximum dose of six to eight mg daily (average dose of 5.6 mg). Baseline characteristics were similar across the groups – the study cohort was 52.1% male, 67.4% Caucasian, and had mean ages of 55.8 - 56.4 years, BMI of 30.9 – 31.2 kg/m2, and A1c of 7.8%.
  • At 52 weeks, canagliflozin 100 mg and 300 mg provided noninferior reductions in A1c (-0.82% and -0.93%, respectively) compared to glimepiride (-0.81%), with the canagliflozin 300 mg dose showing superiority. The percentage of participants to achieve A1c <7.0% and A1c <6.5% were comparable for canagliflozin 100 mg (53.6% and 25.5%, respectively), 300 mg (60.1% and 30.6%), and glimepiride (55.8% and 30.7%). Greater reductions in fasting plasma glucose were observed with canagliflozin 100 mg (-24.3 mg/dl) and 300 mg (-27.5 mg/dl) compared to glimepiride (-18.3 mg/dl). Furthermore, canagliflozin therapy provided significant improvements in body weight (-4.2% for 100 mg dose and -4.7% for 300 mg; p <0.001 for both) compared to the weight increase experienced with glimepiride (+1.0%). More patients on glimepiride required rescue therapy (given with pioglitazone; 10.6%) compared to those on canagliflozin 100 mg (6.6%) and 300 mg (4.9%).
  • The proportion of participants with documented hypoglycemia episodes was dramatically lower with canagliflozin 100 mg and 300 mg (5.6% and 4.9%, respectively; p <0.001 for both) relative to glimepiride (34.2%). However, canagliflozin treatment had higher rates of genital mycotic infections (6.7% [males] and 11.3% [females] for 100 mg and 8.3% [males] and 13.9% [females] for 300 mg) compared to glimepiride (1.1% [males] and 2.3% [females]). Rates for urinary tract infections were also modestly elevated with canagliflozin (6.4% for both doses) compared to glimepiride (6.7-13.9%), as well as rates of adverse events due to osmotic diuresis.


Efficacy and Safety of Canagliflozin, A Sodium Glucose Co-Transporter Two Inhibitor, Compared with Sitagliptin in Patients with Type 2 Diabetes on Metformin Plus Sulfonylurea (50-LB)

Jorge Gross, Guntram Schernthaner, Min Fu, Sharmila Patel, Masato Kawaguchi, William Canovatchel, Gary Meininger

The 52-week, active-controlled, phase 3 study compared canagliflozin 300 mg to sitagliptin 100 mg (Merck’s Januvia) as an add-on therapy in patients with type 2 diabetes (n=755) inadequately controlled on metformin and a sulfonylurea. At 52 weeks, canagliflozin provided noninferior, as well as superior, reductions in A1c compared to sitagliptin (-1.03% and -0.66%, respectively) from a baseline A1c of 8.1%. A greater proportion of participants taking canagliflozin compared to sitagliptin achieved A1c <7.0% (47.6% vs. 35.3%, respectively) and A1c <6.5% (22.5% vs. 18.9%). While incidences of adverse events, hypoglycemia, and urinary tract infections were comparable between canagliflozin and sitagliptin, canagliflozin led to higher rates of genital mycotic infections. 

  • Patients with type 2 diabetes (n=755) inadequately controlled on metformin and a sulfonylurea were randomized to add once-daily canagliflozin 300 mg or sitagliptin 100 mg to their regimen for 52 weeks. Baseline characteristics were comparable across treatment groups - patients were on average 56.7 years old, weighed 88.3 kg (195 lbs), and had a BMI of 31.6 kg/m2, A1c of 8.1%, and an average disease duration of 9.6 years. Of the 755 participants, 22.5% in the sitagliptin group and 10.6% in the canagliflozin group did not complete the study based on glycemic discontinuation criteria (there was no rescue therapy, per protocol).
  • At 52 weeks, canagliflozin provided noninferior, as well as superior, reductions in A1c compared to sitagliptin (-1.03% and -0.66%, respectively). A greater proportion of participants taking canagliflozin compared to sitagliptin achieved A1c <7.0% (47.6% vs. 35.3%, respectively) and A1c <6.5% (22.5% vs. 18.9%). Canagliflozin also provided greater improvements in fasting plasma glucose, weight, blood pressure compared to sitagliptin (results summarized in table below). However, canagliflozin showed a greater increase in LDL levels.

Change from baseline over 52 weeks

Canagliflozin 300 mg

Sitagliptin 100 mg

A1c (%)



Fasting plasma glucose (mg/dl)



Body weight (%)



Systolic blood pressure (mmHg)



Triglycerides (%)



HDL-C (%)



LDL-C (%)




  • Incidences of adverse events, hypoglycemia, and urinary tract infections were comparable between the canagliflozin and sitagliptin groups. However, canagliflozin led to higher rates of genital mycotic infections in males (9.2% vs. 0.5% for sitagliptin) and females 15.3% vs. 4.3%), as well as increased incidence of adverse events related to osmotic diuresis.


Safety and Efficacy of Empagliflozin as Monotherapy or Add-On to Metformin in a 78-Week Open-Label Extension Study in Patients with Type 2 Diabetes (49-LB)

Hans Woerle, Ele Ferrannini, Andreas Berk, Stefan Hantel, Sabine Pinnetti, Uli Broedl

This phase 2 trial for empagliflozin was a 78-week extension study of two previous 12-week phase 2 studies. At 90 weeks, empagliflozin (10 or 25 mg) was demonstrated to provide sustained reductions in blood glucose levels and weight. As a monotherapy, empagliflozin was slightly less efficacious in lowering A1c, slightly more efficacious in reducing fasting plasma glucose, and more efficacious at reducing weight than metformin. As an add-on to metformin, empagliflozin was slightly more effective at reducing A1c and fasting plasma glucose and significantly more effective at lowering weight than sitagliptin. Finally, with regard to safety and tolerability, empagliflozin was generally well tolerated, with a minimal associated risk for hypoglycemia, a comparable risk for UTIs as sitagliptin plus metformin, and an elevated risk for genital tract infections in comparison to metformin alone and sitagliptin plus metformin. As a reminder, empagliflozin’s phase 3 development program is already well underway, with initial results expected later this year (for more information, see our Lilly 4Q11 report at

  • In this open-label extension study, participants in one of two 12-week phase 2 studies who had received 10 or 25 mg of empagliflozin (monotherapy or add-on to metformin), metformin monotherapy, or sitagliptin as an add-on to metformin were continued on the same treatment for 78 additional weeks. Participants in the original two trials who had received 1, 5, or 50 mg of empagliflozin or placebo were randomized to receive empagliflozin 10 or 25 mg as monotherapy or as an add-on to metformin. In total, 106 individuals were enrolled in the 10 mg empagliflozin monotherapy arm, 109 individuals in the 20 mg empagliflozin monotherapy arm, 166 individuals in the 10 mg empagliflozin plus metformin arm, 109 individuals in the 20 mg empagliflozin plus metformin arm, 56 individuals in the metformin only arm, and 56 individuals in the sitagliptin plus metformin arm. 
  • As a monotherapy, empagliflozin was slightly less efficacious in lowering A1c, slightly more efficacious in reducing fasting plasma glucose, and more effective at reducing weight than metformin. In participants continuing their treatment from the preceding trial, A1c reductions achieved at week 90 in the 10 mg empagliflozin, 25 mg empagliflozin, and metformin arms were 0.34%, 0.47%, and 0.56%, respectively (from baselines of 7.9%, 8.0%, and 8.2%, p-values not provided for any data). These A1c reductions appeared to be durable throughout the study, although an upward trend in A1c was observed in each group beginning around week 66.  The fasting plasma glucose reductions achieved in each group were 30.4 mg/dl, 27.8 mg/dl, and 26.0 mg/dl, respectively (from baselines of 179.0 mg/dl, 178.1 mg/dl, and 175.5 mg/dl). These reductions were largely achieved by week 30 and maintained through week 90. The weight loss in each respective group was 2.24 kg (4.9 lbs), 2.61 kg (5.8 lbs), and 1.28 kg (2.8 lbs) from a baseline of approximately 84 kg (185 lbs). Again, maximal weight loss was achieved around week 12, although a slight upward trend in weight was observed with 25 mg empagliflozin beginning at week 54.
  • As an add-on to metformin, empagliflozin was slightly more effective at reducing A1c and fasting plasma glucose and significantly more effective at lowering weight than sitagliptin. In the 10 mg empagliflozin plus metformin, 25 mg empagliflozin plus metformin, and sitagliptin plus metformin arms, the A1c reductions achieved at week 90 were 0.34%, 0.63%, and 0.40%, respectively (from baselines of 7.9%, 7.9%, and 8.0%). An upward trend in A1c was observed starting at week 18 in the sitagliptin group and week 54 in the 10 mg empagliflozin group, while A1c reductions were maintained in the 25 mg empagliflozin arm through week 90. The reductions in fasting plasma glucose were 21.3 mg/dl, 31.8 mg/dl, and 15.6 mg/dl, respectively (from baselines of 177 mg/dl, 179 mg/dl, 179 mg/dl).  These changes were largely sustained through week 90. The weight loss in each respective group was 3.14 kg (6.9 lbs), 4.03 kg (8.9 lbs), and 0.41 kg (0.9 lbs) from a baseline of approximately 89 kg (196 lbs). While weight loss was sustained with sitagliptin treatment through week 90, a trend toward increasing weight was observed starting at week 66 with 10 mg empagliflozin and a trend toward further weight loss was still detected with 25 mg empagliflozin at week 90.
  • Over the 78-week extension study, empagliflozin was generally well tolerated, with a minimal risk for hypoglycemia, a comparable risk for UTIs as sitagliptin, and an elevated risk for genital tract infections versus sitagliptin. In each of the empagliflozin treatment arms, between 63.2% and 74.1% of participants experienced an adverse event in the extension period. This compares to 69.6% of participants in both the metformin alone arm and the sitagliptin plus metformin arm. Participant-reported hypoglycemic events were less frequent in the empagliflozin monotherapy arms (0.9% to 1.8%) than with metformin monotherapy (7.1%). The same was true in the empagliflozin plus metformin arms (2.4% to 3.6%) vs the sitagliptin plus metformin arm (5.4%). Between 3.8% and 6.4% of participants in the empagliflozin monotherapy arms and 3.6% in the metformin only arm reported a UTI. Notably, the rates of UTIs between empagliflozin and sitagliptin as add-on therapies were comparable, at 9% and 12.7% in the empagliflozin plus metformin arms and 12.5% in the sitagliptin plus metformin arm. As expected, however, genital tract infections were elevated in the empagliflozin arms (3.0% to 5.5%) compared to the metformin arm (1.8%), and sitagliptin plus metformin arm (zero). No participants were reported to discontinue the study due to UTIs or hypoglycemic events; however, four patients treated with dapagliflozin discontinued the study due to genital infections. Across treatment groups, there were no clinically relevant changes in renal function.


Long-Term Effectiveness of Dapagliflozin Over 104 Weeks in Patients With Type 2 Diabetes Poorly Controlled with Insulin (1042-P)

J.P.H. Wilding, V. Woo, K. Rohwedder, J. Sugg, S. Parikh

Dr. Wilding presented the results of a 104-week study investigating the long-term efficacy, safety and tolerability of dapagliflozin. The study randomized 808 participants poorly controlled with insulin to placebo or dapagliflozin 2.5 mg, 5 mg, or 10 mg, all on a background of insulin ± ≤2 oral glucose lowering drugs. The analysis of efficacy and safety at 24 and 48 weeks were previously reported (Wilding et al., Ann Intern Med, 2012). At 104 weeks, treatment with dapagliflozin and insulin provided more stable insulin requirements, greater reductions in A1c and weight (weight reductions in the dapagliflozin groups achieved at 48 weeks were maintained for the rest of the trial while weight increased progressively in the placebo group throughout the study), and lower rates of peripheral edema compared to placebo plus insulin. Though dapagliflozin was not associated with an increased risk of adverse events, hypoglycemia, or malignancies, as expected, it led to greater rates of genital infection and urinary tract infections.

  • In this double-blind multicenter 104-week trial, participants with type 2 diabetes poorly controlled on insulin (n=808; mean baseline A1c 8.53%) were randomized to placebo (n=193) or dapagliflozin 2.5 mg (n=202), 5 mg (n=211), or 10 mg (n=194) on a background of insulin (mean baseline dose 77 U/day) ± 2 oral glucose lowering drugs. At 48 weeks, participants receiving dapagliflozin 5 mg were switched to 10 mg (creating the 5/10 mg group), excluding 28 participants from the UK who discontinued the study due to lack of UK regulatory approval. Insulin was uptitrated if participants’ A1c was >7.5% between weeks 52-65 or >7.0% between weeks 78-104.
  • Participant baseline characteristics were similar across treatment arms. On average, participants were 58.8 - 59.8 years of age, 44.8 - 49.5% male, had been on insulin for 5.8 - 6.3 years, and weighed 93.0 - 94.5 kg (205-208 lbs). Mean A1c was 8.46-8.62% and BMI averaged 33.0-33.4 kg/m2. Regarding adherence, 63.6% of participants completed the 104-week study period.
  • Greater reductions in A1c, weight, and daily insulin dosage were observed in the dapagliflozin groups compared to the placebo group over 104 weeks. Dapagliflozin provided placebo-adjusted A1c reductions of -0.21% for the 2.5 mg group, -0.39% for 5/10 mg, and -0.35% for 10 mg. Both placebo-adjusted reductions in weight and in insulin requirement were greater for dapagliflozin 10 mg (-3.19 kg [7.0 lbs]; -19.2 U/d), followed by the 5/10 mg dose (-2.74 kg [6.0 lbs]; -16.8 U/d) and the 2.5 mg dose (-2.70 kg [6.0 lbs]; -14.3 U/d ). Weight reductions in the dapagliflozin groups achieved at 48 weeks were maintained for the rest of the trial while weight increased progressively in the placebo group throughout the study. At 104 weeks, insulin requirements increased by 18.3 U/d from baseline in the placebo group but remained steady in all dapagliflozin groups. Over the 104 weeks, the proportion of participants requiring insulin up-titration or who discontinued the study due to poor glycemic control was higher in the placebo group (50.4%) compared to the dapagliflozin 10 mg group (25.5%), 5/10 mg group (26.5%) and 2.5 mg group (29.1%).
  • Serious and non-serious adverse events, hypoglycemic episodes, and malignancies were comparable across treatment groups, but genital infections and urinary tract infections were more common in the dapagliflozin groups. At 104 weeks, 78.2% of participants on placebo experienced at least one adverse event compared to 80.2% on dapagliflozin 2.5 mg, 78.3% on 5/10 mg, and 60.1% on 10 mg. Between 60.7% and 69.3% of participants in all groups experienced at least one hypoglycemic episode. Of the 15 malignancies reported with dapagliflozin, four occurred within 90 days of starting treatment, three were bladder cancers, and three were breast cancers. At 104 weeks, 3.0% of participants on placebo experienced events suggestive of genital infection compared to 7.4% of those on dapagliflozin 2.5 mg, 12.7% on the 5/10 mg dose, and 14.3% on the 10 mg dose. Similarly, a lower proportion of participants on placebo (5.6% ) experienced events suggestive of urinary tract infections, compared to the dapagliflozin 2.5 mg group (8.4%), 5/10 mg group (13.2%) and 10 mg group (13.8%). According to the investigators, genital and urinary tract infections responded to routine care and rarely led to interruption or discontinuation of dapagliflozin treatment.


Dapagliflozin is Effective as Add-On Therapy to Sitagliptin With or Without Metformin: A Randomized, Double-Blind, Placebo-Controlled Study (1071-P)

Serge Jabbour, Elise Hardy, Jennifer Sugg, Shamik Parikh

This 24-week, double blind, placebo controlled study recruited 451 patients with type 2 diabetes inadequately controlled on a DPP-4 inhibitor with or without metformin. Participants were randomized to dapagliflozin 10 mg daily or placebo, all on a background of sitagliptin (Merck’s Januvia) ± metformin. Adding dapagliflozin to sitagliptin therapy led to a significantly greater reduction in A1c (-0.48%, p <0.0001) compared to placebo, and participants with baseline A1c ≥8% experienced the greatest improvements (~ -0.8%; p value not given). Genital infections and urinary tract infections were more frequently reported in the dapagliflozin arm (9.3% and 5.8%, respectively) than the placebo arm (0.4% and 3.5%). Additional adverse events, including hypoglycemia, dehydration, and hypotension were balanced between the groups.

  • The 24-week, placebo controlled study with a 24-week blinded extension period recruited 451 participants with type 2 diabetes inadequately controlled on a DPP-4 inhibitor ± metformin. Participants were randomized to dapagliflozin 10 mg daily or placebo, all on a background of sitagliptin (Merck’s Januvia; 100 mg daily) ± metformin (≥1500 mg daily) Baseline characteristics were similar across the dapagliflozin and placebo groups, with a mean age of 54.8 and 55 years, respectively, A1c of 7.9% and 7.97%, weight of 91.0 kg (201 lbs) and 89.2 kg (197 lbs), and duration of diabetes 5.7 and 5.64 years.
  • At 24 weeks, dapagliflozin as an adjunct to sitagliptin ± metformin therapy provided significantly greater improvements in A1c (-0.48% vs. ~+0.1% for placebo; p <0.0001) and in weight (-1.89 kg [4.16 lbs]; p value not given) compared to placebo. This effect was observed in both the dapagliflozin+sitagliptin group (A1c reduction of -0.56%, p <0.0001; weight loss of 1.85 kg [4.08 lbs]) and the dapagliflozin+sitagliptin+metformin group (A1c reduction of -0.40%, p <0.0001; weight loss of 1.87 kg [4.12 lbs]). In the dapagliflozin+sitagliptin group, A1c levels rose progressively from week 24 to reach baseline levels by week 48, while those taking dapagliflozin+sitagliptin+metformin maintained the A1c reduction achieved at week 24 until the study end. Over 24 weeks, fewer participants on dapagliflozin (18.8%) compared to placebo (41.5%) discontinued treatment due to lack of efficacy or were rescued.
  • Participants receiving dapagliflozin experienced slightly more adverse events, and had a higher frequency of urinary tract infections (5.8%) and genital infections (9.3%) compared to placebo (3.5%, 0.4%, respectively). Dapagliflozin led to more adverse events linked with renal impairment compared to placebo (3.6% vs 1.8%), though the events were generally non-serious and reversible, suggesting a mild diuretic effect. In addition, hypotension and dehydration events were balanced between the groups, as was the incidence of participant-reported hypoglycemia (5.3% for dapagliflozin and 6.2% for placebo).


Safety of Dapagliflozin in Clinical Trials for T2DM (1011-P)

Agata Ptaszynska, Kristina Johnsson, Anne Marie Apanovitch, Jennier Sugg, Shamik Parikh, James List

Dr. Ptaszynska and colleagues analyzed pooled short- and long-term safety, death, and rare event data from phase 2b/3 trials in the dapagliflozin clinical development program. Among these trials, hypoglycemia rates were higher in the dapagliflozin treatment arms than control arms, but the incidence of severe hypoglycemia was balanced across treatment groups. A greater proportion of patients receiving dapagliflozin versus placebo treatment experienced genital infections; these were more common among females than males. Similarly, a higher proportion of patients in the dapagliflozin 5 and 10 mg groups reported urinary tract infections compared to those in the dapagliflozin 2.5 mg and placebo groups. Dapagliflozin treatment was associated with clinically meaningful decreases in seated blood pressure (especially systolic blood pressure) versus placebo, while AEs of fracture and renal impairment were similar across dapagliflozin across groups. Long-term data (up to two years of follow-up) was used to analyze cardiovascular and  liver safety, and cancer risk. Cardiovascular risk and measures of liver safety were comparable between the dapagliflozin and the comparator groups. While the overall incidence of cancers was balanced among dapagliflozin and comparator groups,  bladder and breast cancer incidence was substantially higher with dapagliflozin treatment..

  • Overall safety, mortality, and rare events were studied using data pooled from more nineteen phase 2b/3 studies in the dapagliflozin clinical development program. To assess overall safety, data was pooled from three 12-week phase 2b studies and nine 24-week phase 3 studies. Subjects in these studies were between the ages of 18 and 85 and had inadequately controlled type 2 diabetes. More than twice as many patients were randomized to the dapagliflozin vs. control arms in these studies (n=3291, n= 1393). Data on deaths were pooled from 14 phase 2b/3 trials. Data used to examine cardiovascular (CV) and liver safety, and malignancy were pooled from 19 phase 2b/3 trials (from which long-term follow-up data of up to two years was available).
  • Baseline characteristics were similar across the studies’ placebo and the three dapagliflozin groups (dapagliflozin 2.5 mg, 5/10 mg, and 10 mg). Data was analyzed from 3,291 placebo-treated patients, and 1,393, 814, and 1,145 patients treated with dapagliflozin 2.5 mg, 5 mg, and 10 mg, respectively.  Mean baseline A1c across the groups analyzed ranged from 8.11% to 8.39%, while systolic BP ranged from 129.0 mmHg to 131.9 mmHg, and diastolic BP ranged from 79.1 mmHg to 79.8 mm Hg. Average type 2 diabetes duration ranged from 5.3 to 6.7 years, and mean BMI was greater than or equal to 30 kg/m2 in over 50% of participants across treatment groups. Overall 33.2% of individuals receiving dapagliflozin, and 42.3% of those receiving control had a prior history of CV disease. Similarly, 67.6% of those taking dapagliflozin and 74.0% of those on the placebo had a prior history of hypertension.
  • The overall proportion of patients reporting serious adverse events was higher among dapagliflozin than placebo treated patients. Serious adverse events (AE) were rare and balanced across groups. Similarly small, proportions of patients experienced AEs that led to study discontinuation. Deaths were infrequent across the clinical program.
  • Hypoglycemia was reported in a higher proportion of dapagliflozin than placebo-treated patients. However, episodes of major hypoglycemia were similar across all treatment groups. Rates of hypoglycemia were similar for dapagliflozin 10 mg and placebo in monotherapy, add-on to metformin, and add-on to pioglitazone trials. Higher rates of hypoglycemia with dapagliflozin 10 mg vs. placebo were seen in the add-on to sulfonylurea and insulin studies. Hypoglycemia was ten-times less common with dapagliflozin than glipizide in a dapagliflozin vs. glipizide + metformin study. No hypoglycemia events led to study discontinuation.


Hypoglycemic events in the short-term double-blind pool (% patients)



Dapagliflozin 2.5 mg

Dapagliflozin 5 mg

Dapagliflozin 10 mg



       Total (%)










 *Major episode defined as a symptomatic episode requiring 3rd party assistance due to severe impairment in consciousness or behavior with a capillary or plasma glucose value <54 mg/dl and prompt recovery after glucose or glucagon administration

  • The proportion of patients with genital and urinary tract infections was higher with dapagliflozin versus placebo treatment; both genital and urinary tract infections were more frequent in females than males. Most genital infections (including vulvovaginitis, balanitis, and related infections) were mild to moderate in intensity, and responded to standard treatment. Additional treatment was required for 6.5% and 4.5% of those receiving dapagliflozin 5 mg and 10 mg, respectively. Similarly, most urinary tract infections were mild to moderate in intensity and responded to an initial course of standard treatment; 5.7%, 1.3%, 15.9%, and 14.3% of patients in the dapagliflozin 2.5 mg, 5 mg, 10 mg, and placebo groups required additional treatment. Discontinuations due to genital infections and urinary tract infections were infrequent. Pyelonephritis was rare and balanced across treatment groups (0.2, 0.1, 0, and 0.1% in the dapagliflozin 2.5 mg, 5 mg, and 10 mg and placebo groups, respectively).

Adverse events of special interest in the short-term double-blind pool


Percentage of patients



Dapagliflozin 2.5 mg

Dapagliflozin 5 mg

Dapagliflozin 10 mg

Genital infections















Urinary Tract Infections*















*Includes data after rescue.   

  • Relative risk for the primary composite endpoint of CV death, myocardial infarction, stroke, and hospitalization for unstable angina with dapagliflozin vs. comparator treatment was 0.819 (95% CI: 0.583, 1.152). Event rates (patients with events/1,000 patient years) for cardiovascular death, myocardial infarction, stroke, and hospitalization for unstable angina, were 16.4 in the DAPA group and 19.9 in the comparator group.
  • There were no clinically meaningful differences in liver function tests between dapagliflozin and control groups. The proportion of participants with elevated liver tests, based on measured laboratory values, was similar in dapagliflozin vs. control patients (4.4% versus 4.2%). Hepatic disorder AEs led to study discontinuation in 0.2%, 0.1%, and 0.3% of patients in the dapagliflozin 2.5, 5, and 10 mg groups vs. 0.1% of patients in the control groups. There was one patient receiving dapagliflozin who was diagnosed with possible drug-induced hepatitis; the event was judged as having a 25-49% likelihood of being related to study drug.
  • Overall cancer rates were similar across treatment arms (1.39/100 patient years in the comparator group and 1.34/100 in the dapagliflozin group), but bladder and breast cancer were more common in the dapagliflozin treatment arms. Of the patients receiving dapagliflozin, nine were diagnosed with bladder cancer compared to only one control participant. However, the majority of these people had hematuria at baseline, suggesting that some of the cancers may have been pre-existent. Breast cancer was reported in ten members of the dapagliflozin group, in contrast to three members in the comparator group. According to the study’s authors, the small number of overall events and tumor types limits the ability to assess causality of cancer imbalances.
  • Dapagliflozin treatment was also associated with decreases in seated BP (especially systolic BP) and a higher incidence of volume depletion compared to placebo. Meanwhile, renal impairment and fracture adverse events were balanced across groups. Changes from baseline to week 24 in seated heart rate were not clinically relevant in any treatment group (-1.1, -0.7, -0.4 beats per minute (bpm) in the dapagliflozin group vs. + 0.5 bpm in the placebo group). The proportion of patients with volume depletion and AEs of fracture was small across all treatment groups.


Effect of Dapagliflozin on Renal Function (1098-P)

Agata Ptaszynska, Alexandros-Georgios Chalamandaris, Jennifer Sugg, Kristina Johnsson, Shamik Parikh, James List

This study analyzed dapagliflozin’s effect on renal function using data from 12 24-week, placebo-controlled trials (six of which had data up to 102 weeks).  Across these studies, dapagliflozin treatment resulted in a rapid, small decrease in estimated glomerular filtration rate (eGFR) by week one that was restored to baseline by week 24. Renal adverse events were similar between dapagliflozin and placebo treated groups, and dapagliflozin did not have any major effect on other tubular functions or albuminuria. Overall, dapagliflozin not associated with acute renal toxicity or deterioration of renal function.

  • This study assessed the effects of dapagliflozin on renal function using data from 12 placebo-controlled randomized dapagliflozin studies. Trials included in the meta-analysis examined dapagliflozin monotherapy, dapagliflozin use in initial combination with metformin, and use of dapagliflozin as an add-on to insulin, metformin, TZD, or sulfonylureas. In these trials, participants were randomized to receive either placebo, dapagliflozin 5 mg, or dapagliflozin 10 mg therapy. 24-week data was derived from all 12 trials, while 102 week data was derived from six of the twelve.
  • Participants all had inadequate glycemic control at baseline and had similar baseline characteristics: across the treatment arms and data groups, participants were on average 55.1-56.9 years old, 79.2-86.5% Caucasian, and had mean A1cs of 8.11-8.39%. Participants were categorized based on estimated glomerular filtration rate (eGFR) levels at both 24 and 102 weeks. At 24 weeks, ~38% of participants across included studies were classified as having normal renal function (eGFR >90 mL/min/1.73m2), ~53% as having mild renal impairment (eGFR: ≥60 to <90 mL/min/1.73m2), and ~8% as having moderate renal impairment (≥30 to <60 mL/min/1.73m2). There were similar proportions of participants in each of these categories at 102 weeks. Participants with severe renal impairment (eGFR <30 mL/min/1.73m2) were excluded. More than 80% of study participants in all groups were Caucasian, and ~50% were female.
  • Dapagliflozin therapy resulted in a rapid, small decrease in mean eGFR in the first week of treatment, followed by a return to baseline at 24 weeks that was maintained at 102 weeks. At 24 weeks, mean eGFR change was +0.85 mL/min/1.73m2 (from a baseline of 86.0 mL/min/1.73m2) in the placebo group, +0.77 mL/min/1.73m2  (from a baseline of 85.3 mL/min/1.73m2 ) in the dapagliflozin 5 mg group, and +0.33 mL/min/1.73m2 (from a baseline of 86.7 mL/min/1.73m2 ) in the dapagliflozin 10 mg group. At 102 weeks, mean change from baseline was +1.31 mL/min/1.73m2 (from a baseline of 83.1 mL/min/1.73m2) in the placebo group, +2.52 mL/min/1.73m2 (from a baseline of 81.9 mL/min/1.73m2) in the dapagliflozin 5 mg group, and +1.38 mL/min/1.73m2 (from a baseline of 84.3 mL/min/1.73m2) in the dapagliflozin 10 mg group. For the 12 studies with data at 24 weeks, >90% of participants maintained normal albuminuria (0 to <30 mg/g).  
  • Renal adverse events were similar between dapagliflozin and placebo-treated groups. At 24 weeks, 0.9% of 1,393 participants in the placebo group had experienced an adverse event related to renal function, compared to 1.3% of 1,145 patients taking 5 mg dapagliflozin and 0.9% of 1,193 patients taking 10 mg dapagliflozin. At 102 weeks, 1.7%, 1.8%, and 1.9% of placebo, dapagliflozin 5 mg, and dapagliflozin 10 mg treated patients, respectively, had had such an event. Additionally, dapagliflozin had no major effect on other tubular functions (as assessed by electrolyte levels, potassium levels, and serum phosphorous and magnesium levels, among several measures). No cases of acute tubular necrosis were reported with dapagliflozin treatment and no patient taking dapagliflozin required dialysis. Dapagliflozin additionally had no adverse effect son albuminuria. However, dapagliflozin treatment was associated with modest mean blood pressure reductions and a slight increase in episodes of hypertension.

Corporate Symposium: New Frontiers and Evolving Therapeutic Paradigms for Kidney-Mediated Glucose Homeostasis in Type 2 Diabetes (Sponsored by BMS/AZ)

The Role of the Kidney in Glucose Homeostasis: Focus on SGLT-2 Inhibition as a New Physiological Interface for T2D Management

George Bakris, MD (University of Chicago Medicine, Chicago, IL)

Dr. Bakris announced at the beginning of his talk that he was not a “gluco-centric” thinker, but rather a “nephro-centric” thinker. He then reviewed basic renal physiology to introduce the role of SGLT-2 inhibitors in glucose homeostasis. In addition to lowering blood glucose, SGLT-2 inhibitors relieve the stressor of pumping out excess glucose during periods of hyperglycemia. Because people with diabetes have excess glucose production in renal epithelial cells, their intracellular glucose is already at elevated levels. Dr. Bakris noted that the increased renal gluconeogenesis seen in diabetes is too often overlooked, and further adds to the burden on the cells.


SGLT-2 Inhibition: A Novel Treatment Strategy for Type 2 Diabetes Mellitus

Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, Texas)

Dr. DeFronzo spoke on the value of SGLT-2 inhibitors in treating diabetes and diabetic nephropathy. He reminded the audience that, in addition to being an endocrinologist, he was a board-certified nephrologist. In describing the rationale for SGLT-2 inhibitors, he noted that people filter 160 grams of glucose daily, yet only excrete a fraction of that amount. Promoting renal glucose excretion over reabsorption results in glucosuria, which leads to a decline in plasma glucose and a reversal of glucotoxicity. Dr. DeFronzo noted that the benefits of SGLT-2 inhibitors lie in their simplicity and their effectiveness in a wide variety of patients, even those with advanced-stage diabetes. He emphasized that since the expression of SGLT-2 transporter is increased in diabetes, inhibiting the protein is a practical therapeutic approach. In discussing the safety of SGLT-2 inhibition, Dr. DeFronzo noted that people with a genetic condition called familial renal glucosuria have no SGLT-2 transporters, yet maintain normal fasting glucose levels and are completely asymptomatic, except for glucosuria. To Dr. DeFronzo, this condition suggests that SGLT-2 inhibition itself is unlikely to cause adverse effects, though patients may still exhibit drug-specific side effects. Looking forward, he predicts that SGLT-2 inhibitors’ role in diabetes care will be complicated, despite their safety and efficacy. Regarding their development, he noted, “the smart pharma company is the company that starts a trial tonight to study SGLT-2 inhibitors for impaired fasting glucose, because sooner or later the FDA is going to get it that we need to start treating glycemia earlier.” Dr. DeFronzo concluded his talk by addressing the controversial use of SGLT-1 inhibitors, suggesting that such therapies may be helpful.  Of concern is the possibility that SGLT-1 inhibitors increase the risk for glucose-galactose reuptake syndrome and reduce intestinal GLP-1 secretion, since L-cells depend on SGLT-1 function. Dr. DeFronzo noted that a significant degree of SLGT-1 inhibition can occur before the onset of glucose-galactose reuptake syndrome. Furthermore, SGLT-1 inhibitors provide several benefits, including inhibiting glucose absorption within the GI tract and increasing GLP-1 secretion via increased glucose delivery to, and metabolism within, the distal small bowel. Overall, Dr. DeFronzo suggests that pharmaceutical companies investigate combination SGLT-1/SGLT-2 inhibitors to better understand their cost/benefit profile. 


Achieving Glycemic Control in T2D Through Kidney-Mediated Mechanisms and Therapeutics: Aligning SGLT2 Inhibitors with Patients Profile, Guidelines, and Unmet Clinical Needs

Robert Henry, MD (University of California San Diego, San Diego, CA)

Dr. Henry provided a review of certain SGLT-2 inhibitors in phase 3 clinical trials and their potential future use. Dapagliflozin will likely be the first SGLT-2 inhibitor to enter the market, followed by canagliflozin, empagliflozin, ipragliflozin, and other drugs in early-stage development, he said (others would say that canagliflozin will be first, due to regulatory delays associated with dapa). These drugs have generally been studied in doses ranging from 25-40 mg/dl. Data show that at high doses, SGLT-2 inhibitors provide comparable or improved glycemic control compared to sitagliptin (Merck’s Januvia). Dr. Henry then focused his presentation on dapagliflozin. Although dapagliflozin provided a smaller reduction in A1c at 18 weeks compared to the sulfonylurea glipizide, its glycemic effect was consistently sustained over 104 weeks, unlike glipizide. In patients taking glipizide, A1c levels increased to nearly baseline levels over the two-year study period due to beta-cell burnout. In addition, dapagliflozin provided an approximately 2-3 kg (4.4-6.6 lb) weight loss at 12-16 weeks that was sustained throughout the treatment. Dr. Henry stipulated that patients who developed urinary tract infections (UTI) while taking an SGLT-2 inhibitor likely already had an underlying infection that was exacerbated by the therapy. However, he acknowledged that the risk of UTI is currently not well understood. Regarding the possibility that dapagliflozin increases breast cancer risk, Dr. Henry noted that the clinical trials had too few events to establish causality. While no carcinogenicity or mutagenicity was found in animal studies, the potential risk needs to be further investigated for dapagliflozin and the entire drug class. Dr. Henry concluded his talk by speculating that, given the clinical trial data on the efficacy and safety of SGLT-2 inhibitors, this drug class could become first, second, and third-line treatment options, although he doubts that it will displace metformin as the standard first-line treatment.


Additional Oral Therapies

Current Issue: Will PPAR Agonist Therapy Survive?


Steven Nissen, MD (Cleveland Clinic, Cleveland, OH)

In an engaging and entertaining presentation (complete with a theme song), Dr. Nissen argued that PPAR agonists still have a place in the diabetes treatment armamentarium. He posited that PPAR agonists are not a class of drugs; rather, they’re individual compounds with individual effects on many different genes, and some are good (pioglitazone [Takeda’s Actos]), some are bad (muraglitazar [BMS]), and some are quite ugly (rosiglitazone [GSK’s Avandia]) in terms of their benefit/risk profiles. Dr. Nissen commented that he believed the whole rosiglitazone controversy could have been avoided if GSK had made the ethical decision to stop developing the drug when they first found out about its adverse effects; it could have saved the whole field a lot of grief (and he might still have a full head of hair, Dr. Nissen said). He believes this controversy colored everyone’s thinking about the class, but it’s time to get it out of our heads and move on, he urged. He argued that pioglitazone’s ability to reduce insulin resistance, provide a durable glucose-lowering effect, improve lipids, confer anti-inflammatory effects, improve non-alcoholic fatty liver disease (NAFLD), and potentially reduce cardiovascular events trump the downsides of weight gain, congestive heart failure, bone fracture, and the potential risk of bladder cancer (he believes the bladder cancer issue will go away soon). Dr. Nissen commented that currently available thiazolidinediones should not be used as first-line agents because of the associated safety concerns, but nonetheless they remain useful in many patients not controlled on metformin. During the subsequent panel discussion, Dr. Nissen explained that he wanted to take the “yes” position on this debate, because he wanted to let it be known that it was not his intent to torpedo the class, but rather, to emphasize that benefit/risk must be adequately assessed for each drug candidate.

  • (“The Bad.”) While muraglitazar initially held promise, ultimately its cardiovascular risks outweighed its benefits. In clinical trials, muraglitazar lowered A1c, lowered triglycerides, raised HDL and had little effect on LDL. The drug looked like it was going to be good, but one can’t predict what is going to happen based on biochemical profiles. Six weeks after the Advisory Committee meeting for muraglitazar (September 8, 2005), Dr. Nissen and his colleagues published a meta-analysis that showed that the risks of all-cause mortality, nonfatal myocardial infarction, or stroke more than doubled (as well as virtually every other composite cardiovascular endpoint) with muraglitazar. Dr. Nissen noted the PPAR agonist class is one in which it’s hard to predict what the balance of benefit and risk are going to be, but the fact that it’s hard doesn’t mean we shouldn’t try.
  • (“The Ugly.”) Dr. Nissen asserted that rosiglitazone was “born bad,” and should have never been approved in the first place. Following Dr. Nissen’s 2005 meta-analysis of muraglitazar, Dr. John Buse (University of North Carolina, Chapel Hill, NC) conducted a similar analysis of the data GSK submitted to the FDA for rosiglitazone, and found a similar association with increased cardiovascular events (Dr. Nissen said that  Dr. Buse was forced to sign a document stating that he could no longer talk about the safety of the drug). Looking at the totality of data submitted to the FDA prior to approval, rosiglitazone had an 80% increase in cardiovascular risk versus comparator. In addition, rosiglitazone had adverse effects on the lipid profile – rosiglitazone increased LDL 18.6% (Dr. Nissen acknowledged that although lipid can’t predict cardiovascular risk, they are a sign). In a secret, non-published, integrated analysis of 42 randomized controlled trials (n=12,183) conducted by GSK in 2004, the company found the overall hazard ratio of cardiovascular events with rosiglitazone to be 1.31 (95% CI: 1.01-1.70); looking at the trials, it was clear that rosiglitazone was a bad PPAR agonist. Dr. Nissen noted that these data were submitted to the FDA; however, neither the company nor the FDA made any public statement warning physicians or patients of the findings. In Dr. Nissen’s now infamous 2007 meta-analysis, it was found that rosiglitazone increased the risk of myocardial infarction by 43%, and the risk of cardiovascular death by 64% (Nissen and Wolski, NEJM 2007). Finally, on September 23, 2010, what Dr. Nissen coined as “pharmageddon” for rosiglitazone, European regulators decided to ban the drug entirely, while the FDA proposed restricting the drug’s use. In 1999, the drug giant SmithKline Beecham secretly conducted a study to determine whether rosiglitazone was safer for the hear than pioglitazone, but the study’s results were “disastrous” – senior management mandated that the data “should not see the light of day” to anyone outside the company. Dr. Nissen emphasized the difference between the “bad” and the “ugly” – whereas it was somewhat understandable that the cardiovascular risk of muraglitazar remained undetected for a period of time, it was inexcusable that GSK did not just stop developing rosiglitazone in 1999 when they learned of the drug’s increased cardiovascular risk, he said. 
  • (“The Good.”) In Dr. Nissen’s opinion, pioglitazone’s benefits outweigh its risks.  Specifically, its ability to reduce insulin resistance, provide a durable glucose-lowering effect, improve lipids, confer anti-inflammatory effects, improve non-alcoholic fatty liver disease (NAFLD), and potentially reduce cardiovascular events trump the downsides of weight gain, congestive heart failure, bone fracture, and the potential risk of bladder cancer (he believes the bladder cancer issue will go away soon). Dr. Nissen noted that from the very beginning, it was readily apparent that rosiglitazone and pioglitazone had very different profiles – rosiglitazone increased triglycerides by 14.9% and increased non-HDL cholesterol by 18.6%, whereas pioglitazone decreased triglycerides by 12.0% and only increased non-HDL cholesterol by 3.8% (p <0.001) (Goldberg et al., Diabetes Care 2005). In the PROactive study, pioglitazone treatment decreased the risk of major adverse cardiovascular events (MACE) by 16% (p=0.273) (Dormandy et al., Lancet 2005). Dr. Nissen acknowledged that while MACE was a secondary endpoint and does not meet the regulatory standard for cardioprotection, it is useful for informing us about the relative safety of the drug, stating that pioglitazone appears to be cardioprotective. In two subsequent meta-analyses, pioglitazone was associated with an 18-20% reduced cardiovascular risk. Dr. Nissen argued that just because regulators will not give a label claim based on meta-analyses, it doesn’t mean we shouldn’t take them into consideration; he asserted that pioglitazone is the only drug that has solid evidence supporting that the drug reduces cardiovascular risk.
  • Dr. Nissen emphasized that the known safety issues (e.g., congestive heart failure, bone fractures, weight gain) with PPAR agonists can be mitigated through good clinical practice. He stated that physicians should be careful not to give TZDs to patients with low ejection fractions to mitigate the risk of heart failure, to avoid prescribing the drugs to elderly women at high risk of osteoporosis to mitigate the risk of bone fractures, and to promote lifestyle interventions and counseling to mitigate weight gain. Dr. Nissen commented that these aforementioned safety issues are real concerns, but not overwhelming. In the context of an anti-atherogenic drug like pioglitazone that he believes reduces cardiovascular morbidity and mortality, Dr. Nissen asserted that on balance the drug provides much more benefit than harm. Dr. Nissen firmly stated that he does not believe pioglitazone increases the risk of bladder cancer, noting that the just-released eight-year interim results of the Kaiser Permanente North California database epidemiological study demonstrated neutral bladder cancer risk for pioglitazone (HR=0.98).
  • Looking to the future, Dr. Nissen was hopeful that next-generation PPAR-based agents could maintain efficacy while reducing side effects and adverse events. Dr. Nissen reviewed data from the SYNCHRONY phase 2 trial for Roche’s dual PPAR-α/γ agonist aleglitazar, noting that it was the first PPAR agonist documented to decrease LDL. Specifically, the 150 ug dose of aleglitazar looked similar in efficacy to the 45 mg dose of pioglitazone, with the same favorable patterns of lipid effects, if anything with perhaps more favorable reductions in triglycerides (30%), increases in HDL (25%), and decreases in LDL (10%) (Henry et al., Lancet 2009). Dr. Nissen said he didn’t know whether the drug would make it or not, but pointed out that it is currently in a large cardiovascular outcomes trial. He was glad that a company was willing to invest hundreds of millions in the drug to find out, and that frankly, we need more and better drugs for diabetes. Dr. Nissen noted that another company (Metabolic Solutions) is currently working on a mitochondrial target, and has thus far been shown to provide many of the same benefits without the negative effects of edema. Dr. Nissen expressed optimism that future PPAR agonists may find broader usage, especially if development efforts can uncouple efficacy from adverse effects; he stressed that we still need PPAR agonists in the therapeutic armamentarium for diabetes. 



George Grunberger, MD (Grunberger Diabetes Institute, Bloomfield Hills, MI)

In this heated but good-humored session, Dr. Grunberger opened by declaring that he thought it must have been “a practical joke” that the ADA wanted him to argue that PPAR agonist therapy will not survive, against Dr. Nissen of all people, commenting that he wanted to back out of the commitment but it was already too late to do so. Dr. Grunberger argued that the currently available PPAR-γ agonists will not survive given the recent safety concerns and American propensity toward media-incited hysteria. He believes that patients and primary care providers, who care for 95% of diabetes patients and lack specialized knowledge about diabetes drugs, are increasingly unlikely to choose thiazolidinediones (TZDs) based on the bad press they have received. He argued that the side effects and safety concerns associated with TZDs – weight gain, edema, and risk of fractures, cardiovascular disease, and bladder cancer – are not outweighed by the relatively ordinary glucose-lowering effects of the drug compared to other anti-diabetic agents. In conclusion, he does not see PPAR-γ  agonists surviving in their current iteration, but he does see promise for the future of PPAR-α agonists.

  • Dr. Grunberger began by putting the audience in a primary care provider’s shoes, asking if we would prescribe a thiazolidinedione to an average middle-aged, obese, type 2 diabetes patient with suboptimal glycemic control. He argued that a busy PCP having to make a snap decision based on a quick overview of the available literature would take the safer, easier route and avoid prescribing TZDs to reduce risk of lawsuits and late-night patient phone calls. He suggested that the data for risks associated with TZDs is presented in such a way that might incite unnecessary hysteria in patients that come across news releases or commercials for malpractice lawyers.
  • He recounted the well-known history of the approval and subsequent recognition of the CV risks associated with rosiglitazone and bladder cancer risks associated with pioglitazone, contending that the benefits of these drugs did not outweigh the risks.  He discussed the patient-provider interaction again, arguing that patients would refuse the drug after learning about the associated weight gain, edema, and risks of fractures, heart failure, and bladder cancer. Additionally he asserted that most anti-diabetic agents have similar glucose-lowering efficacy, are cheaper, and that TZDs do not have truly preventative properties because their glucose-lowering effect disappears after patients come off of these drugs. In our opinion, he may have understated the benefits, though, as TZDs are the only drugs currently available that truly target the underlying cause of type 2 diabetes, insulin resistance, and have been shown to have a more durable effect than other alternatives.
  • Dr. Grunberger relayed allegations from a Takeda contract physician of drug safety indicating that the company reclassified adverse events during clinical trials in order to make pioglitazone seem safer. This informant claimed that the company knew about the bladder cancer risk associated with pioglitazone during preclinical animal studies and in subsequent phase 3 trials, amended patient enrollment criteria using a urine cytology test as an exclusion criterion. We are reporting this to let readers know what was said in the session; there is no way, of course, to verify this.
  • The PPAR-γ agonist class is likely on the decline, but Dr. Grunberger sees hope in pioglitazone’s PPAR-α activating function as a start to exploring alternative PPAR agonist mechanisms. Thus, his argument was subtler than a simple declarative “no.” He did not foresee a bright future for the current PPAR-γ agonists on the market, but did not condemn this entire therapeutic strategy. In fact, he sounded hopeful that future PPAR-based therapies (e.g., MSDC-0160, MSDC-0602) could provide the same insulin-sensitizing effects of TZDs without their associated adverse effects.


Panel Discussion

Steven Nissen, MD (Cleveland Clinic, Cleveland, OH); George Grunberger, MD (Grunberger Diabetes Institute, Bloomfield Hills, MI)

Dr. Nissen: As I think many people in the room are now aware, diabetes has a relation to cancer that is very clear – the longer patients have diabetes, the more likely they are to have cancer. One of the problems with signals is that drugs like pioglitazone are used later in the course of diabetes. You create a bias – if you have a drug used later in the course of the disease, it may appear to have a higher rate of cancer when it really doesn’t. That’s why observational studies can’t be used. The FDA were the first to tell you not to use AERS to compare drugs. Differential reporting is something everyone is aware about. You can’t use AERS data to answer questions about drug safety; it’s just there to highlight signals.

Dr. Grunberger: I was just trying to play the devil’s advocate. With insulin and any other drugs that you use later in the game with overweight or obese patients, you have to be careful about the cause and effect relationships. 

Comment: Pioglitazone is not the right track for everyone. It is important to select the ideal patient to use this drug.

Dr. Grunberger: If you are a good practitioner, then yes, you would be selective with the patients you prescribe this drug to. I agree that personalized medicine is the way to go.

Dr. Nissen: I would not give pioglitazone to an elderly female with prior fractures, but that’s not whom we’re talking about. You said, George, that it has no unique advantages, but show me another oral drug that has such durability.

Dr. Grunberger: That is an important point to bring up. With sulfonylureas, you see that drop in A1c, but that it comes back up. Of course that is a concern, and especially in young patients durability is important. Now I think GLP-1 agonists might have prove to have the same durability.

Q: Do we have any information on PPAR-α/β agents? Would those be good drug candidates?

Dr. Grunberger: I’m not aware of any drug candidates dealing with those.

Dr. Nissen: The point both of us need to acknowledge is that this is one of the hardest classes to develop drugs in that we’ve ever seen. The number of PPAR drugs that have died during development is enormous. While Dr. Grunberger listed a number on his slides, there are at least 50.

Dr. Grunberger: I ran out of space.

Dr. Nissen: Saying that it’s hard doesn’t mean it can’t be dome. If we never had pioglitazone, I would have been much more gloomy. We have a drug, albeit with a few warts, that has a lot of unique advantages, including an anti-atherogenic effect we haven’t been able to see with other classes of drugs. Whether well see these effects with GLP-1 agonists, I don’t know. To have a class of drugs lower CV risk and mortality in a good meta-analysis is pretty important.

Comment: It seems like it is going to be a pretty daunting challenging from a regulatory standpoint for the next generation of PPAR agonists.

Dr. Nissen: One thing that makes me stay an optimist is that we have such a large unmet need. It is so difficult to control diabetes with existing agents, so the incentive to develop better drugs is very high. Yes it is daunting and the regulatory hurdles are huge, but there is an enormous medical need, and that is why we don’t give up.

Dr. Grunberger: I agree, but in the current environment in which we work, there are requirements for all these extra steps because, in America, everything has to work 100% perfectly before approval, and if anything goes wrong there will be a lawsuit.

Comment (Takeda): I just want to provide some information. I’m not here to influence the debate. First, we’re deeply interested in the safety of our products on the market, and have ongoing studies to find out what the risk/benefit profile of pioglitazone is. Dr. Nissen pointed out the imbalance in bladder cancer. We have an ongoing observational study following patients what impact bladder cancer shows with follow up. The results were presented in poster format at this meeting, and I recommend that those interested can look at that. There’s also the Kaiser Permanente Northern California ongoing 10-year study. The interim five-year analysis was published last year, and we’re continuously updating the data. When new data becomes available, I would suggest looking into that. Perhaps the most important reason is for information purposes. Dr. Grunberger presented an analysis from the UK General Practice Database that was just published in BMJ. That was using what is known as case-control. Using propensity matching scores from the exact same database, a separate analysis found the other conclusion. Different studies have to be looked at in the context of the database used, and the type of analysis.

Dr. Nissen: To my understanding, the report from Kaiser released today is an eight-year interim analysis. What you showed in the five-year completely goes away when you do the more comprehensive analysis. The unadjusted hazard ratio is 0.98. I’m not sure the bladder cancer is real at all. If it is real, the magnitude of effect in terms of the number of people affected would still be small. I don’t think we should practice medicine according to court attorneys; I think our job is to take care of patients. If there is a bladder cancer problem, it is so small in comparison to the other causes of mortality in these patients. We have to be careful not to throw the baby out of the bathwater. I’m worried this got so much hype that it scared people away from what they should be doing for patients. Even though I’m a cardiologist, I’ve had patients on pioglitazone for years. They’re doing well, and I’m going to continue treating patients with the drug. I don’t use it as first-line therapy; I use it in a thoughtful way.

Dr. Grunberger: I’m supposed to argue the con side. The problem we have is the environment we’re currently in. The absolute risk doesn’t make the headlines – only the relative risk does. The day when the rosiglitazone meta-analysis in NEJM hit the airwaves, I was in the examine room seeing a patient, and was called out of the exam room by my MA, because a patient of mine was on the phone and was asking to speak to me right away. My patient said, “How dare you give me a drug that increases the risk of heart attacks by 43%?!?” I had no idea what he was talking about. It turned out that it was the hazard ratio of 1.43 that the patient heard on the news. When you look at the absolute risk in the meta-analysis, it is actually miniscule. But when you publish percentages, the average patient or practitioner just doesn’t look at it that way, unfortunately. 

Dr. Nissen: But it’s our job to communicate that to the patient. It’s always the case that the best doctors are always the ones who sit down and explain the risk and benefits to their patients each day – “every drug has risks, and I’m giving you this one because I believe the benefits outweigh the risks.” It’s about finding the right people to treat; PPAR agonists do belong in the armamentarium. I’m hoping there are companies in the audience that will give us PPAR agonists to use; it is still a target I think is viable.

Q: I’m one of those who is convinced that the cardiovascular benefits of pioglitazone outweigh the risks, but I wanted to say a word about the “ugly” of TZDs: if you look at the ACCORD study, what is striking is the immense amount of rosiglitazone used in the intensive treatment group. Do you think that was a major factor in that study?

Dr. Nissen: The authors said they looked at this as well as they could and said that it was not an effect of rosiglitazone. When you have a study design where you’re considering the intensity of control, you’re really looking at an observational and not a randomized control trial in the conventional sense, so I don’t think you can answer that question. But everyone agrees that in comparison to pioglitazone, rosiglitazone has a much higher risk of ischemic cardiovascular outcomes. The point that I wanted to make is that this controversy, unfortunately, did not need to happen because the evidence was so clear within a few years, or even before, approval of rosiglitazone that it was a bad drug. I’m heartsick about it, and I was obviously the one who dropped the bomb on it, but it was in the interest of getting the drug off the market, not torpedoing a promising class of drugs. A bad signal came out of ACCORD for rosiglitazone because these people were very sick. In a way it was contributed to rosiglitazone, but we will never find out.

Q: George, as a testament to your ability to speak on this subject – I know you use pioglitazone.

Dr. Grunberger: 95% of people with diabetes are not seen by diabetes specialists, so it is difficult for them to maintain good knowledge about it. If you’re not specialized in it, how would you know? For most doctors, it is a lot easier to prevent future phone calls by giving an easier prescription.

Comment: I just wanted to enforce the point pioglitazone not only brings down blood glucose, but also improves beta cell survival.

Dr. Nissen: Though I’m not a diabetologists, I’ll try to play one here. I wonder about using a drug like pioglitazone in a low dose as an early way of preserving glycemic control for a longer period of time. I guess I’m wondering whether the 15 mg dose given as the first drug might be useful. The durability is so great, maybe we’re waiting too long to use it.

A (Dr. Ralph DeFronzo [University of Texas Health Sciences Center, San Antonio, TX]): We did an analysis of Takeda data, and published in Diabetes Care about six years ago. At very low doses (15-30 mg) you actually have quite potent effects on both beta cell function and preservation over a long period of time, and also major improvements in insulin sensitivity. I think that one of the things that has been overlooked by endocrinologists is the powerful effect of TZDs on the beta cell. Quite frankly, several times I actually thought I was giving the lecture when I heard you speak. I would agree with everything you said. This issue about bladder cancer is a joke. You go back to PROactive and you see maybe six or seven more cases of bladder cancer, but maybe six or seven fewer cases of breast cancer with pioglitazone. Why aren’t all women going back to their senators and demanding for pioglitazone to be used for breast cancer? The French study is a joke. The raw ratio for bladder cancer was something like 0.82. I don’t know what adjustments they made to get it above 1.2. Every cancer other than bladder cancer decreased; many decreased significantly, and quite frankly, I think it’s a disgrace the other data have not been made public. Even if we look at the Kaiser five-year interim analysis, there was a background rate of seven cases of bladder cancer per 10,000 patient years in people who were taking drugs other than pioglitazone. If we took the worst-case scenario, pioglitazone, when used for 24 months or longer, would have increased it to 10 cases per 10,000 patient years – an extra three cases. If I go back to PROactive, there was a 16% reduction in heart attack, stroke, and death. What’s really important is the benefit far outweighs the risks, and if we’re careful and use low doses, we can also mitigate weight gain. When we’re examining patients, the presence of edema is a good clue as to who will develop heart failure. Those people respond well to diuretics. Heart failure is really a problem with physicians for not treating. Weight gain is obviated at low doses. Actually, the more weight you gain, the better improvements you see in beta cell function, insulin sensitivity, and cardiovascular risk factors. Obviously, I am a strong believer in pioglitazone. I actually use it as first-line therapy in combination with metformin for all of my patients. I’m glad you were here to participate in this debate. I agree with everything you said, and I think it’s a great drug. [Audience applause.]

Q: When I listen to you, I have the feeling that both of you are probably correct, yet both are probably incorrect in some respects. Earlier Cliff Baily gave a talk on the interpretation of safety signals in clinical trials, and one thing he said was that the difference between a drug and a poison is in the dose. When I listen to both of you speak, the most important issue here might be dose. Is it possible that it is impossible to say we need to lower doses for everyone, but that individuals require different doses?

Dr. Grunberger: Personalizing treatment makes sense, but there is no systematic data looking at individual sensitivity, so how would you optimize that? There is no assay to use to predict the dose for individual patients.

Dr. Nissen: What seemed to happen during development of this class of drugs was that we went through an era when the FDA said, “show us this drug lowers blood sugar, and we’ll approve it.” Companies developing TZDs pushed the doses to the brink in order to say “my drug lowers A1c more than yours,” and I think we may have killed some drugs in the process. I’m saying we should move away from that metric and also take into account cardiovascular benefit. I worry that we lost some opportunities because companies were so focused on lowering A1c, and maybe low doses of TZDs used earlier, as Dr. DeFronzo pointed out, is a superior strategy for management.

Q: Would you consider the absence or presence of steatosis a criterion for starting pioglitazone as second-line therapy?

Dr. Grunberger: If you go back and look at what we published in Endocrine Practice in 2009, there is a page that looks at the risks and benefits of diabetes therapies – go to We list patient characteristics on the vertical, and all drug classes on the horizontal axis. Non-alcoholic steatosis is actually listed as a positive indication for using TZDs. Really, TZDs are the only class of drugs that are insulin sensitizing. If you believe non-alcoholic steatosis is a consequence of insulin resistance, then TZDs are beneficial. It all goes back to the individual patient. I just wanted to put something out – now, we have more emphasis on avoiding adverse events, rather than just safety. The risks of hypoglycemia and weight gain are clearly big concerns. We now have drugs on the market that did not exist when pioglitazone and rosiglitazone were approved. DPP-4 inhibitors and GLP-1 agonists make it easier for clinicians to come up with different drugs on top of metformin, and you can avoid this whole nonsense with a lot of noise. As practitioners, it’s so much easier to prescribe incretins than to deal with all these different controversies around using TZDs. 

Dr. Nissen: Many family practitioners start their patients on metformin, and then add a DPP-4 inhibitor when they do not achieve adequate control. And then they stop. And then I see these patients because they already have cardiovascular disease, and they still have an A1c of 8%. I think practice has kind of gotten dumbed down to the point where some physicians only give the drugs that cause the least worry and the least grief. Unfortunately, you have to think a little more than that sometimes. We are losing the battle here frankly in part because we have drugs like DPP-4 inhibitors that area really safe drugs, but just aren’t that effective. We can’t do it with just that and metformin for many people, but many family docs stop there. I don’t think that’s acceptable for patients.

Q: Do you foresee anything changing when pioglitazone goes generic later this year? Do you think that public will possibly be more motivated to verify how risky the drug actually is when the drug will be much less expensive than DPP-4s and GLP-1s?

Dr. Nissen: People are still getting sulfonylureas pushed at them pretty hard even though we know they’re not going to work for a long time. We’re living in an era of medicine where cost drives insurers to do things that are not always rational. When pioglitazone goes generic I think it will have additional appeal for some people that are very cost sensitive. I think it will make a difference.


Oral Sessions: Glucagon and Glucagon-Like Peptides - Animals

The GLP-1 Gastin Dual Agonist ZP3022 Increases Beta-Cell Mass in DB/DB Mice (210-OR)

Dorthe Almholt, PhD (Zealand Pharma, Copenhagen, Denmark)

Dr. Almholt presented animal data on Zealand Pharma’s GLP-1/gastrin dual agonist, ZP3022, which contains sequences of endogenous exendin-4 and gastrin-6. The eight-week, stratified study was conducted in diabetic db/db male mice and compared  ZP3022 to vehicle or liraglutide – equimolar doses of liraglutide and ZP3022 were administered at 2x50 nmol/kg/day. The endpoints included A1c, fasting blood glucose, and pancreas histology. ZP3022 provided similar reductions in A1c and fasting blood glucose compared to liraglutide. Notably, while liraglutide and ZP3022 led to a significant increase in beta-cell mass compared to vehicle at week 4, ZP3022 treatment provided a greater sustained increase at week eight compared to both vehicle and liraglutide (p<0.001 and p<0.05, respectively). This encouraging finding indicates that ZP3022 may have additional mechanisms beyond that of beta cell preservation, and could possibly promote beta-cell regeneration.

  • ZP3022 is a peptide containing a sequence of endogenous exendin-4 and Gastrin-6. The peptide is composed of amino acids 1-28 of exendin-4 and the gastrin-6 amino acid sequence, connected by a linker.
  • ZP3022 provided similar reductions in A1c and fasting blood glucose compared to liraglutide. Final A1c levels at week eight were  5.6 ± 0.6% for ZP3022, 5.3 ± 0.4% for liraglutide, and 7.4 ± 0.5% for vehicle (p<0.001 for ZP3022 and liraglutide vs. vehicle). Notably, at week eight ZP3022 caused an significant increase in beta cell and islet cell mass compared to both vehicle and liraglutide (p<0.001 and p<0.05, respectively). These findings indicate that ZP3022 may have additional mechanisms beyond that of beta cell preservation.

Questions and Answers

Q: How do the receptors targeted in mice compare to those in humans?

A: We have tested the mouse receptors and they are similar to those found in humans

Q: How does ZP3022’s action compare to that of current GLP-1 agonists?

A: Given what we have seen regarding the increase in beta cell mass, there is a difference in ZP3022’s effect on the beta cells and we think this is due to gastrin.


Clinical Proof of Concept with a Prototype MTOT Modulating Insulin ensitizer (966-P)

Jerry Colca, James VanderLugt, Wade Adams, Joanne Liang, Rong Zhou, David Orloff

Metabolic Solutions Development Company (MSDC) presented 12-week phase 2b clinical data (n=258) for their lead PPAR-sparing insulin sensitizer MSDC-0160 (an isomer of a pioglitazone metabolite). Overall, treatment with MSDC-0160 provided comparable improvements in A1c as pioglitazone (placebo adjusted reductions of -0.79% for 100 mg MSDC-0160, -0.86% for 150 mg, and -0.98% for 45 mg pioglitazone). However, MSDC-0160 was associated with smaller increases in body weight ( 0.6 kg [1.3 lbs], 1.2 kg [2.6 lbs], and 1.5 kg [3.3 lbs], respectively; p-value not provided) and smaller reductions in circulating red blood cells (p <0.05) – evidence of less fluid retention. Additionally, increases in adiponectin (a marker of white adipose tissue) levels were less with MDSC-0160 than with pioglitazone (p <0.0001), suggesting greater effects of pioglitazone on white adipose tissue. Consistent with this finding, while pioglitazone reduced circulating triglyceride levels, MSDC-0160 did not. Finally, MSDC-0160 was found to provide similarly positive impacts on HDL and (to a lesser extent) LDL cholesterol levels as pioglitazone. We note that other general tolerability and safety data were not provided. Altogether, these findings provide some additional support for MSDC’s hypotheses that: 1) the insulin sensitizing effects of PPAR gamma agonists are not dependent on activation of PPAR gamma, but rather an inner mitochondrial target (mTOT); and 2) the activation of PPAR gamma may be responsible (at least in part) for the observed side effects of TZDs (i.e., weight gain, fluid retention, and bone loss). We’ll be interested to see which dose MSDC chooses to advance into phase 3 development, and we’ll be eager to see longer term efficacy and safety data given that weight gain and some fluid retention were still observed with the drug. For more detailed information on MSDC-0160, please see page 104 of our ADA 2011 Full Report at, page 26 of our EASD 2011 Full Report at, page 26 of our GTCbio 2011 Full Report at, and page 53 of our JP Morgan 2011 Full Report at Additionally, further details regarding mTOT can be found in our coverage of poster 1096-P, “Identification of a Mitochondrial Target of Thiazolidinediones (mTOT).”

  • The 12-week, randomized, double-blind, phase 2b clinical trial compared three doses of MSDC-016 (50 mg, 100 mg, and 150 mg), 45 mg of pioglitazone, and placebo in people with type 2 diabetes (n=258).  Baseline characteristics were similar across treatment groups. Average age ranged from 53 to 56 years, A1c was approximately 8%, FPG ranged from 165 to 178 mg/dl, and weight ranged from 86 kg (189.6 lbs) to 94 kg (207.3 lbs). Of the participants, 80% had a stable metformin treatment, while 20% were treatment naïve.
  • The 100 mg and 150 mg doses of MSDC-0160 provided comparable reductions in A1c as pioglitazone, but with less weight gain and fluid retention. Placebo adjusted mean reductions in A1c were -0.79% with 100 mg MSDC-0160, -0.86% with 150 mg MSDC-0160, and -0.98% with pioglitazone; p=ns). Statistically significant reductions from baseline in fasting plasma glucose were also reported for each of the above doses (placebo adjusted mean reductions were approximately -19 mg/dl with 100 mg MSDC-0160 [p=0.006], -28 mg/dl with 150 mg MSDC-0160 [p <0.0001], and -30 mg/dl for pioglitazone [p <0.0001]). Weight gain was approximately 0.6 kg (1.3 lbs) with 100 mg MSDC-0160, 1.2 kg (2.64 lbs) with 150 mg MSDC-0160, and 1.5 kg (3.3 lbs) with pioglitazone. Fluid retention as evidenced by reduction in hematocrit, red blood cells, and total hemoglobin was also 50% less in the MSDC-0160 arms.
  • MSDC-0160 was also associated with lower levels of high molecular weight adiponectin (a marker of white adipose tissue) than pioglitazone, suggesting that MSDC-0160 has a smaller effect on white adipose tissue than pioglitazone. Consistent with this finding, no dose of MSDC-0160 significantly reduced circulating triglycerides (unlike pioglitazone). Comparable increases in medium HDL and decreases in small HDL were found with MSDC-0160 treatment and pioglitazone. While decreases in small LDL and increases in large LDL were observed MSDC-0160, the effect appeared to be larger with pioglitazone.


12-Week Treatment with Glucagon Receptor Antagonist LY2409021 Significantly Lowers A1c and Is Well Tolerated in Patients with T2DM (981-P)

Christof Kazda, Parag Garhyan, Ronan Kelly, Chunxue Shi, Chay Ngee Lim, Haoda Fu, Mark Deeg

In this double blind, phase 2 study (n=87) of Lilly’s glucagon receptor antagonist LY2409021, adults were randomized to receive placebo (n=10), LY2409021 10 mg (n=17), LY2409021 30 mg (n=34), or LY2409021 60 mg (n=26) once daily. Because a previous clinical study for the drug found dose-dependent increases in liver transaminases (see page 94 of our ADA 2011 Report at, this 12-week study examined the margin between efficacy and hepatic safety of LY2409021. Therefore, the study excluded anyone with symptoms of liver disease, diagnosis of hepatitis B or C, or high average alcohol consumption (>2 units/day in men, >1 unit/day in women). At all doses of LY2409021, participants experienced a greater reduction in A1c levels (0.83%, 0.65%, 0.66% for 10 mg, 30 mg, and 60 mg, respectively) compared to placebo (0.11% increase). However, treatment with LY2409021 also produced dose-dependent increases in glucagon levels and hepatic transaminase levels, which returned to baseline after four weeks of washout. No signs of liver injury accompanied the hepatic transaminase elevations, and incidences of hypoglycemia were low across treatment groups. Additionally, no changes in body weight, lipids/triglycerides, and blood pressure were observed with LY2409021 treatment. Altogether, these results lead the study’s investigators to conclude that the efficacy, safety, and tolerability of LY2409021 supports further clinical development. We note that elevated hepatic transaminases were also observed with Merck’s former glucagon receptor antagonist MK-0893 (see page 99 of our ADA 2011 Report at, suggesting that the effect might be class related. How clinically relevant this side effect is with LY2409021 remains unclear, and we look forward to data from longer durations of exposure in future clinical trials. It is encouraging, however, that increases in weight, lipids, and blood pressure have not been observed thus far with LY2409021, unlike with MK-0893. 

  • After a one-week single-blind placebo lead-in period, the double blind study randomized participants into one of four treatment arms for 12 weeks: placebo, LY2409021 10 mg, LY2409021 30 mg, or LY2409021 60 mg. The average age of participants was approximately 52 years, with baseline A1c levels of 7.8%, 8.0%, 7.5%, 7.6%, and 7.7% for placebo and LY2409021 10 mg, 30 mg, and 60 mg, respectively.  Most participants were also on metformin therapy (52% to 70% of each arm), and had similar BMIs across treatment groups (approximately 32 kg/m2). Average duration of diabetes ranged from 3.7 to 5.1 years. Meanwhile, average weight ranged from 86.4 (190.5 lbs) to 91.8 kg (202.4 lbs).
  • In the study’s primary endpoint, participants experienced a significantly greater reduction in A1c levels at all doses of LY2409021 (0.83%, 0.65%, 0.66% for 10 mg, 30 mg, and 60 mg, respectively) compared to placebo (0.11% increase; p <0.03, p <0.04, p <0.05, respectively). At the 30 mg and 60 mg doses, participants also achieved significantly greater reductions (p <0.05) in mean fasting plasma glucose compared to placebo at most visits. Mean change from baseline in self-monitored blood glucose readings were significantly greater (p <0.05) at week 12 in all treatment groups vs placebo at pre-breakfast, pre-lunch, pre-evening meal, post-evening meal, and bedtime, but not post-breakfast (with all doses) and post-lunch (30 mg and 60 mg doses).
  • The study showed dose dependent increases in glucagon levels, which returned to baseline after the four-week washout period.  Compared to placebo, participants on 30 mg or 60 mg doses of LY2409021 experienced statistically significant (p <0.05) increases in mean glucagon levels at 12 weeks, while those on 10 mg did not show significant changes from placebo. Dose dependent increases in fasting GLP-1 were observed during the treatment period with all doses of LY2409021, but these levels also returned to baseline after four weeks of washout. No significant changes in active GLP-1 were detected.
  • Treatment with LY2409021 produced a dose-dependent increase in mean aminotransferase (ALT) levels. These levels returned to baseline after four weeks of post-treatment follow-up. One participant in the 10 mg group experienced a significant increase in ALT at week one (14 U/L to 259 U/L). Treatment was stopped, and ALT levels returned to baseline after two weeks. Four participants treated with LY2409021 developed ALT levels three times ULN. However, elevated bilirubin or other signs of liver injury did not accompany these findings. Furthermore, eight participants experienced a mild hypoglycemic event during the 12-week trial: three in the 10 mg arm, two in the 30 mg arm, four in the 60 mg arm, and none in the placebo arm. There were no events of severe hypoglycemia. The proportion of participants experiencing a treatment emergent adverse event was reported to be similar across all the groups, and there were no serious treatment emergent adverse events in the trial.


Identification of a Mitochondrial Target of Thiazolidinediones (MTOT) (1096-P)

William McDonald, Gregory Cavey, Serena Cole, Danielle Holewa, Angela Brightwell-Conrad, Cindy Wolfe, Jean Wheeler, Kristin Coulter, Rolf Kletzien, Jerry Colca

Metabolic Solutions Development Company (MSDC) reported the identity of the mitochondrial target of their PPAR-sparing insulin sensitizers MSDC-0160 and MSDC-0602, previously referred to as mTOT. MSDC discovered mTOT using a selective, photo-catalyzable affinity probe and mass spectrometry-based proteomics. mTOT is a component of a phylogenetically conserved complex in the inner mitochondrial membrane. It contains two proteins, Mpc1 and Mpc2, whose identities and role in mitochondrial pyruvate uptake were recently published in two papers in Science (Bricker et al., Science 2012; Herzig et al., Science 2012). Overall mTOT acts as a molecular “sensor switch”, connecting mitochondrial metabolism to cellular activities such as carbohydrate, amino acid, and lipid metabolism. MSDC believes that all active TZDs bind to mTOT, but vary greatly in their activation of PPARgamma. MSDC has hypothesized that anti-diabetic drugs which target mTOT and avoid PPAR gamma activation may provide similar glycemic benefits as TZDs, but without the class’s associated side effects (i.e., weight gain, fluid retention, bone loss). Further evidence to support this hypothesis was provided in a phase 2b study reported at this meeting (966-P), which demonstrated comparable reductions in A1c with MSDC-0160 as with pioglitazone over 12 weeks, but with less weight gain and fluid retention.

Comparison of Efficacy and Safety of Co-Administration of Sitagliptin and Low-Dose Pioglitazone with High-Dose Pioglitazone Monotherapy (1068-P)

Vivian Fonseca, Helmut Steinberg, Robert Henry, Bart Staels, Margaret Chou, Rujun Teng, Gregory Golm, Ronald Langdon, Keith Kaufman, Barry Goldstein

In this double-blind, placebo-controlled trial, Dr. Fonseca and colleagues compared the effects of initial sitagliptin + pioglitazone (submaximal dose) combination therapy (sitagliptin 100 mg + pioglitazone 15 mg, 30 mg, or 45 mg) with those of higher dose pioglitazone monotherapy (pioglitazone 15 mg, 30 mg, or 45 mg).  Participants between 18 and 78 years of age with type 2 diabetes inadequately controlled by diet and exercise were randomized equally among seven treatment arms (three combination treatment arms, three pioglitazone monotherapy arms, and one sitagliptin monotherapy arm)  At 24 weeks, sitagliptin + submaximal dose pioglitazone combination therapy had generally better glycemic efficacy (measured by A1c, fasting plasma glucose, and post-prandial glucose) than maximal doses of pioglitazone monotherapy. Safety was comparable or better with sitagliptin + pioglitazone combination therapy than with pioglitazone monotherapy.

  • This double-blind, placebo-controlled trial randomized patients (n= 1332) equally among seven treatment arms. Four groups received combination therapy: sitagliptin 100 mg/pioglitazone 0 mg, 15 mg, 30 mg, or 45 mg (n=186, 193, 190, 198, respectively); Three groups received pioglitazone monotherapy: pioglitazone 15 mg, 30 mg, or 45 mg (n=183, 194, 188, respectively). One group (n=186) received sitagliptin 100 mg monotherapy. Eligible patients were between 18 and 78 years of age, had type 2 diabetes that was inadequately controlled by diet and exercise, had A1cs of ≥7.5 and ≤11.0%, and were either drug naïve or on metformin or sulfonylurea monotherapy at screening. Treatment was conducted for 24 weeks, followed by a 20-week extension. A patient population (n=283) that had originally been randomized to receive overencapsulated pioglitazone or matching placebo was excluded from all analyses due to evidence that overencapsulated and non-overencapsulated pioglitazone were not bioequivalent.
  • Baseline characteristics were similar across treatment arms. On average, participants were about 51 years old, had a BMI of 30-31 kg/m2, and baseline A1c of 8.7-8.9%. Fewer females than males were enrolled in all treatment arms. 
  • Both same-dose and cross-dose comparisons suggest a greater effect of combination sitagliptin (100 mg) + pioglitazone therapy on glycemic control compared to pioglitazone monotherapy. As seen in cross-dose comparisons, combination therapy with sitagliptin and submaximal doses of pioglitazone had generally greater effects on glycemic control than pioglitazone monotherapy at maximal doses.

Same-dose comparisons


Δ A1c (%) from baseline

Δ FPG (mg/dl) from baseline

Δ 2h postprandial glucose (mg/dl) from baseline

sitagliptin + 15 mg pioglitazone


15 mg pioglitazone alone

-0.7 (p<0.001)

-21.6 (p<0.001)

-37.8 (p<0.001)

sitagliptin + 30 mg pioglitazone


30 mg pioglitazone alone

-0.4 (p<0.001)

-16.2 (p<0.001)

-32.4 (p<0.001)

sitagliptin + 45 mg pioglitazone


45 mg pioglitazone alone

-0.6 (p<0.001)

-14.4 (p=0.002)

-27.0 (p<0.001)


Cross-dose comparisons


Δ A1c (%) from baseline

Δ FPG (mg/dl) from baseline

Δ 2h post prandial glucose (mg/dl) from baseline

sitagliptin + 15 mg pioglitazone


30 mg pioglitazone alone

-0.3 (p=0.008)

-10.8 (p=0.018)

-16.2 (p=0.029)

sitagliptin + 15 mg pioglitazone


45 mg pioglitazone alone

-0.3 (p=0.007)

-3.6 (p=0.444)

-1.8 (p=0.743)

sitagliptin + 30 mg pioglitazone


45 mg pioglitazone alone

-0.4 (p<0.001)

-9.0 (p=0.045)

-18.0 (p=0.019)


  • No significant differences in hypoglycemia or edema rates were observed in prespecified between-group comparisons (combination therapy vs. monotherapy at the same component doses). Overall adverse events were largely balanced among the various treatment arms. The pioglitazone 45 mg group experienced the highest rate of clinical adverse AEs (62.8%) whereas the sitagliptin + pioglitazone 45 mg combination therapy experienced the lowest rate (56.1%). Incidence of asymptomatic and symptomatic hypoglycemia over 54 weeks ranged from 7.8% (sitagliptin + pioglitazone 15 mg combination therapy) to 11.1% (sitagliptin + pioglitazone 45 mg combination therapy). Incidence of edema ranged from 3.0 - 3.2% across the combination therapy arms, compared to 2.7 - 5.3% in the pioglitazone monotherapy arms and 0.5% in the sitagliptin monotherapy arm.


Pioglitazone and Bladder Malignancy During Observational Observational Follow-Up of Proactive: 6-Year Update (928-P)

E Erdmann, E Song, R Spanheimer, A-R Van Troostenburg, A Perez

As a reminder, in the PROspective pioglitazone Clinical Trial in macroVascluar Events (PROactive) trial, there was an imbalance in the incidence of bladder cancer in the pioglitazone and placebo arms, warranting follow-up studies to further characterize the potential risk. The results of this pre-specified six-year interim analysis of the PROactive trial demonstrated that pioglitazone did not increase the risk of bladder cancer or other malignancies. Specifically, patients previously on pioglitazone in the double-blind period of PROactive (n=1,820; mean exposure of 2.5 years) did not have a significantly increased risk of bladder cancer compared to patients previously on placebo (n=1,779) after six years of follow-up (HR=1.06; 95% CI: 0.59-1.89). We find these data to be encouraging, and hope that longer-term analyses from the 10-year Kaiser Permanente Northern California (KPNC) epidemiological study will further substantiate pioglitazone’s safety (at ADA 2012, Dr. Steve Nissen [Cleveland Clinic, Cleveland, OH] stated that eight-year results from the KPNC study showed no increased risk for bladder cancer with pioglitazone; however, we could not confirm this information). Regardless, even if pioglitazone is found to increase bladder cancer risk, the absolute risk would be low; the benefits of the drugs use may well outweigh the risks for a sizable subset of the population, given that the drug is such a potent insulin sensitizer and has a durable effect. That being said, older women at high risk for osteoporosis and patients with low ejection fractions should not take pioglitazone, in order to mitigate the risks of bone fractures and heart failure – two proven safety issues with thiazolidinediones.

ADA Diabetes Care Symposium

Beta Cell Function Preservation After 3-5 Years of Intensive Diabetes Therapy

Ildiko Lingvay, MD (University of Texas Southwestern Medical Center, Dallas, TX)

Dr. Lingvay discussed the 3.5-year results of a study comparing the preservation of beta cell function with insulin + metformin (INS) treatment versus triple oral therapy (TOT; glyburide, metformin, pioglitazone) in treatment-naïve patients with newly diagnosed type 2 diabetes. Following a three-month run-in period, patients in the INS and TOT arms achieved average A1cs of 6.0% and 5.9% and weights of 102 kg (224 lbs) and 101 kg (222 lbs), respectively, and sustained glycemic control out to 3.5 years regardless of treatment. Beta cell function (as assessed by C-peptide divided by glucose AUC) remained stable in both groups over 3.5 years. Meanwhile, patients in both arms gained weight – an average of ~4 kg (~9 lbs) in the INS arm, and ~10 kg (~22 lbs) in the TOT arm. Dr. Lingvay concluded that that based on the results of this study, beta-cell function can be preserved for at least 3.5 years after diagnosis of type 2 diabetes if intensive therapy is initiated early. She championed early pharmacological intervention to achieve glycemic normalization rapidly after diagnosis, and voiced strong support for combination treatment with complementary mechanisms of action to maintain glycemic control in the long term.

  • Following a three-month run-in period on insulin and metformin treatment, patients were randomized to insulin + metformin (INS; n=29) or triple oral therapy (TOT; n=29) treatment. At the time of screening, participants had to be newly diagnosed (within two months) with type 2 diabetes, and treatment naïve. In the INS group, Novolog 70/30 was initiated at 0.2 U/kg (split 2/3 before breakfast and 1/3 before dinner), and titrated as appropriate throughout the study based on home capillary glucose monitoring; metformin was initiated at 500 mg daily and titrated up by 500 mg every week until the maximum dose of 1,000 mg twice daily was reached. In the TOT group, glyburide was initiated at 1.25 mg twice daily and titrated as needed up to the maximum dose of 5.0 mg twice daily, metformin was administered at the 1,000 mg twice daily dose (or the maximally tolerated dose), and pioglitazone was initiated at 15 mg daily and titrated monthly by 15 mg until the maximum dose of 45 mg was attained. Treatment failure was defined as A1c >8%, confirmed by repeat measurement after titrating to the maximum doses of all therapies. Following treatment failure with TOT, patients were switched to INS; meanwhile, patients who failed INS continued insulin therapy, but had the option to change the type or frequency of insulin treatment. Treatment failures were included in the ITT analysis for this study.
  • Dr. Lingvay highlighted several limitations of the study: 1) while 3.5 years is a long time for a randomized study, it is still only a small fraction of the diabetes disease course (six-year results will be available shortly); 2) there was no conventional treatment group; and 3) the effects in the run-in period could not be separated from those in the treatment period. She noted that this study was designed before incretin therapies are available, so the therapies are no longer as relevant (especially given the recent decrease in the popularity of pioglitazone).

Questions and Answers

Q: Was baseline A1c predictive of treatment failure?

A: Few patients failed treatment (A1c >8%); baseline A1c did not predict ultimate treatment failure. At the time of randomization, everyone had tight glycemic control. Even at the end of the study, 70% of patients were still within ADA guidelines. What predicted it was the presence of side effects to drugs like pioglitazone; those who experienced side effects were more likely to have treatment failure.

Comment: You reported hypoglycemia as events per patient month. While one event sounds minor, it equates to about 12 per patient year. I think it is OK to be aggressive, but we need to be cautious about safety as well.

A: We defined hypoglycemia very conservatively, so most of the events were in the 65-70 mg/dl range.

Comment: It’s nice to do an ITT analysis, but once you had patients switch treatment, you need to censor the data.

A: When we censored the data, there was no significant difference between the groups. I would argue that patients who switched from triple oral therapy to insulin after treatment failure actually improved A1c quite significantly, so the fact that we left them in their original arm for analysis favored the triple oral therapy group. If we moved them to the insulin group, there might have been a difference, but because we left them in their original assigned group, there was no difference between groups.

Q: When you started people on triple oral therapy, were patients started at the maximum doses for each drug, or were they titrated depending on fingerstick readings?

A: We started patients on 15 mg pioglitazone, and increased the dose by 15 mg each month for the next two months, until patients reached the max 45 mg dose. Patients were already on the maximum dose of metformin when they entered the study, so they stayed at that dose. Glyburide was the only drug that was titrated in the study, from 1.25 mg twice a day, up to the maximum dose of 5.0 mg per day as appropriate.


Product Theater

Discussions and Insights into the Benefits and Risks Associated with Actos Therapy for Type 2 Diabetes

Robert Busch, MD (The Endocrine Group, LLP, Albany, NY)

Dr. Busch reviewed the benefit and risks of pioglitazone, emphasizing on several occasions that even though the increased relative risk of bladder cancer with pioglitazone sounds concerning, the absolute risk of bladder cancer with pioglitazone treatment is very low. Specifically, while pioglitazone use for over 12 months in duration was associated with a 40% increased relative risk of bladder cancer in the five-year interim analysis of the Kaiser Permanente Northern California (KPNC) database, in absolute terms, that risk would translate into an additional three cases of bladder cancer per 10,000 patient years (from a background rate of seven cases per 10,000 patient years in people with diabetes). Dr. Busch noted that the FDA package insert is very specific in saying that pioglitazone only should not be used in patients with active bladder cancer, or a history of bladder cancer. Dr. Bosch highlighted the benefits of pioglitazone – strong A1c-lowering efficacy (up there with GLP-1 agonists and sulfonylureas), strong insulin-sensitizing effects, durability of effect, improvements in lipids (lower triglycerides and higher HDL), and low risk of hypoglycemia. In terms of side effects, Dr. Bosch mentioned that pioglitazone does cause weight gain, edema, and bone fractures. In closing, he told the audience to stay tuned for the upcoming release of an eight-year interim analysis from the KPNC database, which he hinted would have more favorable data on bladder cancer and bone fractures with pioglitazone treatment.


Novel Drug Development

Oral Session: Novel Agents for Diabetes Management

Targeting Insulin Resistance via the Immune Modulation of Cord Blood-Derived Multipotent Stem Cells by the Stem Cell Educator Therapy (287-OR)

Yong Zhao, MD, PhD (University of Illinois at Chicago, Chicago, IL)

Dr. Zhao presented the results from an open-label phase 1/2 study that examined the safety and efficacy of the Stem Cell Educator therapy in people with type 2 diabetes (n=25). The Stem Cell Educator circulates a patient’s blood through a closed loop device that separates lymphocytes from whole blood, co-cultures the lymphocytes with human cord blood-derived multipotent stem cells (which are believed to modulate immune responses), and returns the lymphocytes to the patient. Dr. Zhao indicated that the procedure duration was approximately eight hours. At baseline, average A1c was 8.5%, age was 50 years, and duration of diabetes was nine years. Following a single treatment with the Stem Cell Educator, A1c was statistically significantly reduced at week four (-0.6%; p=0.022) and at week 12 (-1.4%; p<0.0001). Furthermore, more than 80% of participants treated with the Stem Cell Educator achieved an A1c <7.0% at week 12. Measures of both insulin resistance (HOMA-IR) and beta cell function (HOMA-B) were statistically significantly improved at week 12, as was AUC glucose following an OGTT. Although detailed safety data were not provided, Dr. Zhao indicated that the therapy was found to be very safe overall. We note that positive results from a phase 1/2 study in people with type 1 diabetes were published for the Stem Cell Educator therapy in January (Zhao et al., BMC Medicine 2012). Overall, this study demonstrated that single administration of the therapy could significantly improve A1c and C-peptide levels in both individuals with residual beta cell function and without residual beta cell function.  

Questions and Answers

Q: Is there any introduction or transfer of the stem cells to patients?

A: No, there is no transfer. Both flow cytometry results and examinations of the device following treatment have suggested that the stem cells remain adherent to the bottom of the device.


GKM-001, A Liver-Directed/Pancreas-Sparing Glucokinase Modulator (GKM), Lowers Fasting and Post-Prandial Glucose Without Hypoglycemia in Type 2 Diabetic (T2D) Patients (293-OR)

Rashmi Barbhaiya, PhD (Advinus Therapeutics, Bangalore, India)

Dr. Barbhaiya reported results from a multiple ascending dose study for Advinus Therapeutics liver selective glucokinase activator GKM-001. As a reminder, this 14-day study randomized 60 people with type 2 diabetes to receive GKM-001 (ranging from 25 mg to 1000 mg) or placebo. At baseline, mean A1c was 9.0%, FPG was 176 mg/dl, and BMI was 26 kg/m2. Topline data reported in December showed that GKM-001 was effective at lower glucose levels across all doses tested without any incidence of hypoglycemia or other clinically relevant adverse events (for more details, see the December 15, 2011 Closer Look at In his presentation, Dr. Barbhaiya highlighted that treatment with GKM-001 provided: 1) dose dependent and significantly greater reductions in FPG, glucose excursions to a mixed-meal tolerance test, and 24-hours glucose over placebo. More specifically, the reductions in mean plasma glucose achieved were 9% with the 25 mg dose, 20% with the 1000 mg dose, and no change with placebo. Furthermore, Dr. Barbhaiya indicated that C-peptide responses to mixed-meal and oral glucose tolerance tests remained constant throughout the study in all GKM-001 treated arms, suggesting no activation of pancreatic glucokinase and induction of insulin secretion with the candidate therapy. On safety, Dr. Barbhaiya stated that no hypoglycemia, changes in liver transaminases, or changes in triglycerides were observed at any dose of GKM-001. Finally, regarding pharmacokinetics, he revealed that GKM-001 had a long half-life (~ 21 hours), that it was eliminated through both feces (75%) and the urine (25%), and that there were no food effects. Dr. Barbhaiya concluded by noting that a phase 2b study was currently in preparation, that Advinus would make a go/no go decision on global phase 3 development following the completion of phase 2 development in India, and that Advinus would seek a development and commercialization partner in the US and EU if GKM-001 progressed into phase 3 development.

Questions and Answers

Q: Did you measure body weight?

A: There was no change, but the study was only 14 weeks in duration. 

Q: Glucokinase is also expressed in the hypothalamus. Was their any brain exposure? 

A: The blood to brain ratio is very small. The brain concentration is vey small. I don’t have the exact concentration, but it can be defined as poor.

Q: Were there any changes to lactate or LDL?

A: There were no changes.


Symposium: Glucagon - Renaissance of an Old Hormone

Hyperglucagonemia: The Untreated Half of Type 1 Diabetes

Roger Unger, MD (University of Texas Southwestern, Dallas, TX)

Using data from preclinical studies conducted in his lab, Dr. Unger (who was unable to attend the meeting and had to call in remotely) provided a compelling argument for the need to target glucagon suppression in the management of type 1 diabetes. In particular, he demonstrated that leptin (a known glucagon suppressor) administration alongside insulin therapy in a mouse model of type 1 diabetes normalized glucose levels and eliminated glycemic variability. Additionally, glucagon receptor knockout mice exhibited normoglycemia, even after exposure to streptozotocin, and only developed elevated blood glucose upon introduction of glucagon receptors via an adenovirus containing glucagon receptor cDNA. Based on these findings, Dr. Unger concluded that type 1 diabetes, at least in mice, cannot exist without glucagon, suggesting that glucagon suppression in addition to insulin therapy should form a core part of type 1 diabetes treatment. He foreshadowed that GABA (an oral agent that suppresses glucagon) could become the first oral treatment for type 1 diabetes. 

Questions and Answers

Q: How does glucose get taken up by cells and metabolism restored to normal without both insulin and glucagon?

A: We are currently studying that. In mice, we performed OGTTs and used 13-C labeled glucose and mass spectrometry to trace glucose in the body. Again, this is ongoing work. What we do know is that glucose ends up being stored in the liver as glycogen. However, it is made from a three-carbon chain precursor. Where those precursors come from we don’t know yet. Our hypothesis is that the glucose is getting into the liver without insulin. Once it is in there, it is getting chopped up into three-carbon fragments. From there, it gets transformed into glycogen. Perhaps in the absence of glucagon, you don’t need insulin. That is our hunch. Our work will tell us whether that is correct.

Q: Hormone independent transport of glucose plays a larger role in rodents than in humans. Do you have any studies planned that will examine this in humans?

A: I don't see how we can study this concept in humans besides suppressing glucagon action and stropping insulin treatment. I don’t think we’d ever get permission to do that study.

Q: In some humans, glucagon secretin is lost alongside insulin secretion. Those patients, however, develop diabetes. How does this fit in with your theory? 

A: Studies have suggested that alpha cells also exist in the fundus of the stomach. In these individuals, if they are using insulin therapy, you will not detect glucagon release from these cells until insulin treatment is stopped. If you stop insulin treatment, you will see glucagon.


Future of Pharmacomodulation of the Glucagon Receptor in Diabetes Therapy

Jens Holst, MD, PhD (University of Copenhagen, Copenhagen, Denmark)

Dr. Holst provided a broad overview of the data supporting the use of glucagon pharmacomodulation in the treatment of type 2 diabetes, expressing concern over the side effects associated with glucagon receptor antagonists and optimism for glucagon secretion suppressants and glucagon receptor agonists.

  • With regard to glucagon receptor antagonism, Dr. Holst highlighted data for two small molecule glucagon receptor antagonists presented at ADA 2011, Merck’s MK-0893 and Eli Lilly’s LY2409021. He noted that while providing robust glycemic control, one or both of the candidates were associated with a delay in hypoglycemia recovery and increases in LDL, hepatic transaminases, and weight, all of which he found concerning and problematic if they turned out to be class effects. (For our coverage of these results, please see pages 94 and 99 of our ADA 2011 Report at Notably, during Q&A, a representative from Eli Lilly remarked that data would be presented later at the meeting for LY2409021 demonstrating no associated delay in hypoglycemia recovery or increases in LDL or weight with treatment (981-P, 1002-P).
  • Turning to the suppression of glucagon secretion as a treatment strategy, Dr. Holst demonstrated that the glucose lowering effect of GLP-1 is equally attributed to insulin secretion stimulation and glucagon secretion suppression. Because of the positive safety profile, weight effects, and lipid effects of GLP-1 receptor agonists, he believed that glucagon secretion suppression could form an attractive alternative to glucagon receptor antagonism for the treatment of type 2 diabetes.
  • To close his presentation, Dr. Holst highlighted the promise held by glucagon receptor agonism for the treatment of type 2 diabetes. In particular, he pointed to a study in DIO mice in which treatment with a dual GLP-1/glucagon receptor agonist led to superior weight loss, lipid lowering activity, and similar blood glucose lowering activity as a GLP-1 receptor agonist (Pocai et al., Diabetes 2009). Citing other preclinical studies as well as data from a small infusion study in healthy volunteers, he suggested that the weight loss effects of glucagon agonism may be due to both decreased food intake and increased resting energy expenditure. We note that several companies are currently developed dual GLP-1/glucagon receptor agonists, including Transition/Eli Lilly (TT-401; phase 1) and Zealand Pharma/BI (ZP2929; preclinical).


Leptin Therapy in Type 2 Diabetes

Bethany Cummings, PhD (University of California – Davis, Davis, CA)

After discussing studies in which leptin therapy ameliorated hyperglycemia in type 1 diabetes in mouse models, Dr. Cummings explored the potential of leptin therapy in type 2 diabetes in UCD-T2DM mice, and in closing briefly touched on its potential clinical applications. In UCD-T2DM mice, exogenous leptin administration normalized fasting plasma glucose by: 1) decreasing circulating glucagon concentrations, likely leading to decreased hepatic glucose production; 2) improving lipid metabolism; and 3) improving insulin sensitivity, likely mediated by decreases of endoplasmic reticulum stress signaling. Dr. Cummings noted that while a clinical study showed no significant improvement in insulin sensitivity with leptin therapy for obese subjects recently diagnosed with type 2 diabetes, the therapy could perhaps be useful in combination therapy. She briefly flashed clinical data for pramlintide/metreleptin as an example of potential combination therapy; we note that while the efficacy of the combination is compelling, Amylin/Takeda discontinued development of the drug in 2011 (see our August 25, 2011 Closer Look at Looking ahead, we would really love to hear researchers explore the potential use of leptin for weight maintenance…


Basic Science

Banting Lecture

Transcriptional Control of Adipogenesis - Toward a New Generation of Therapeutics for Metabolic Disease

Bruce Spiegelman, PhD (Harvard Medical School, Boston, MA)

In this year’s Banting Lecture, Dr. Spiegelman delivered a fascinating presentation on the work conducted by his lab to elucidate the biology of brown fat regulation and to develop brown-fat based therapeutics for the treatment of metabolic disease. After describing the different functions of brown and white fat in the body, Dr. Spiegelman reviewed a series of in vitro, animal, and human studies that demonstrated that: 1) a distinct form of UCP-1 expressing thermogenic adipocytes from classical brown fat exists in rodents (which he termed beige fat); 2) increased beige fat in rodents improves glucose intolerance; and 3) beige fat exists in humans and appears to be the predominant thermogenic adipocyte in adults. Backtracking momentarily, he highlighted results from mice studies linking the expression of the transcriptional co-activator PCG1-alpha to beneficial effects associated with exercise (i.e., mitochondrial biogenesis, prevention of muscle atrophy). Interestingly, he showed that increased expression of PCG1-alpha in the muscle of transgenic mice led to the browning of white fat into beige fat. Subsequently, his lab discovered that a secreted moiety (which was named irisin) cleaved from FNDC5 (a protein whose expression was induced by PCG1-alpha) was responsible for this browning. To examine the in vivo effects of irisin, an adenovirus containing FNDC5 was injected intravenously into mice, which resulted in increased expression of UCP-1 in subcutaneous fat and an improvement in glucose homeostasis. Dr. Spiegelman next demonstrated that the major effect of irisin was the promotion of beige fat precursor cell maturation rather than the transdifferentiation of white fat into beige fat. Finally, he concluded by discussing the development an FC-fusion irisin therapeutic in his lab that was shown to have a half-life of nine days and the ability (with a single injection) to reduce fasting glucose and insulin levels in HDF mice. He stressed, however, that the drug was yet not optimized and not nearly ready for clinical development – although it could serve as a useful research tool. We note that Ember Therapeutics in-licensed irisin from the Dana-Farber Cancer Institute earlier this year. For more information on Ember, please see the December 22, 2011 Closer Look at

  • Dr. Spiegelman opened his presentation with a brief description of the differences between white adipose tissue and brown adipose tissue. White adipose tissue stores energy in a single lipid droplet, has relatively low mitochondrial content, expresses no uncoupling protein-1 (UCP-1), and is pro-inflammatory in the context of obesity. He noted that PPAR gamma has been identified as a key regulator of white adipose tissue development. In contrast, Dr. Spiegelman explained that brown adipose tissue plays an anti-obesity, anti-diabetes, and anti-hyperthermia role in most mammals, including humans. Through high mitochondrial content and the expression of the mitochondrial protein UCP-1, brown adipose tissue is capable of dissipating chemical energy content in the form of heat. Several key regulators of brown fat development have been discovered to date, including PPAR gamma, PGC1 alpha, PCG1 beta, and PRDM16.
  • Dr. Spiegelman highlighted that the presence of brown fat in adult humans was “rediscovered” through the work of PET imaging in the field of oncology. When PET imaging was used alongside radioactive glucose to detect metastases, a ring of symmetric hot spots of glucose uptake were frequently observed that did not appear to be metastatic tumors. These hot spots were particularly apparent following exposures to cooler temperatures. Open biopsies were performed on several individuals, and these hot spots were identified as areas of brown adipose tissue. Based on these findings, Dr. Spiegelman remarked that most individuals likely had some stores of brown adipose tissue in their bodies. He indicated that major questions facing the scientific community today include: 1) what role does brown adipose tissue play in overall energy balance, and 2) are there ways in which to increase the amount and/or function of brown adipose tissue in the body?
  • Brown adipose tissue is derived from a separate lineage than white adipose tissue.  Dr. Spiegelman detailed results from his lab that demonstrated that suppression of the protein PRDM16 transformed primary cultures of brown adipose tissue into muscle tissue. Furthermore, driving the expression of PRDM16 in muscle tissue resulted in the formation of brown adipose tissue. Altogether, these results demonstrated not only that PRDM16 is a key regulator of brown adipose development, but that brown adipose is derived from the same lineage as muscle, not white adipose tissue.
  • There are two separate types of brown adipose tissue in the body (classical brown fat and beige fat), and beige fat helps improve the metabolic health of animals. Following the work above, studies in his lab noted that pockets of brown fat still emerged in white adipose tissue under conditions of extreme cold or extreme beta-adrenergic signaling. These results suggested that a separate type of thermogenic, UCP-1 expressing adipose type existed that shared the same lineage as white adipose tissue. In a separate experiment, the transgenic expression of PRDM16 in mice adipose tissue resulted in increased formation of beige fat in white fat tissue stores. Classical brown fat stores were not altered. In comparison to high-fat fed control mice, these transgenic mice exhibited significant improvements in glucose AUC following a glucose challenge.
  • PGC-1 alpha may help regulate the benefits of exercise in mammals. PGC-1 alpha is a transcriptional activator that is found in higher levels in brown fat and red oxidative muscle. Its expression is increased during exercise in rodents, mice, and humans. In muscle, PCG-1 alpha stimulates mitochondrial biogenesis, glucose uptake, neuromuscular junction formation, angiogenesis, muscle fiber type switching, and fatting acid oxidation – many of the benefits associated with exercise. Most strikingly, expression of PGC-1 alpha prevented muscle atrophy in mice unable to move following the severing of their sciatic nerve. Thus, by expressing PGC-1 alpha in cultured muscle tissue, Dr. Spiegelman noted that an experimental system could be created (at least to some extent) in a petri dish to study the molecular mechanisms underlying exercise.
  • The beneficial effects of PGC-1 alpha on adipose tissue are imparted by the action of the hormone Irisin. Intriguingly, the expression of PGC-1 alpha in the muscle of transgenic mice led to increased levels of beige fat in white adipose tissue stores. Through a series of experiments, Dr. Spiegelman’s lab identified a secreted soluble molecule (which they named Irisin) that is cleaved off the muscular transmembrane protein Fndc5 that appeared to be responsible for this “beigeing” of white fat. Of note, Irisin is nearly 100% identical between different mammalian species, indicating a high-degree conservation and restriction in molecular changes to the compound. This compares to 85% homology in insulin and 90% homology in glucagon between humans and mice. Irisin was found to circulate in both mice and humans, and the levels of circulating Irisin increases with exercise.
  • Elevated Irisin levels were associated with improvements in glucose homeostasis. An adenovirus vector expressing full length Fndc5 was injected into the tail vein of mice. After uptake and expression of the vector in the liver, circulating levels of Irisin increased two- to three-fold at 10 days following the injection. Subsequently, increased patches of UCP-1 expressing cells were detected in the subcutaneous fat of these mice, suggesting a “beigeing” effect. In high-fat fed obese mice transfected with this vector, significant improvements in fasting plasma glucose and glucose tolerance were observed, indicating that even modest elevations in Irisin can have positive effects on glucose homeostasis.
  • Dr. Spiegelman pondered what other therapeutic benefits Irisin may provide beyond its impact on adipose tissue. He noted that exercise also impacts the brain, the liver, and skeletal muscle. Focusing on the brain, he highlighted that exercise helps induce neurogenesis and has been shown to benefit individuals with Parkinson’s disease and Alzheimer’s disease. Thus, Dr. Spiegelman was particularly excited about the potential applications of Irisin or other similar compounds as a treatment for neurodegenerative diseases, especially given that many individuals with neurodegenerative diseases are unable to effectively exercise. Similarly, he believed that the opportunity to impart the benefits of exercise through a drug would also provide substantial benefit to individuals with morbid obesity and paraplegia.
  • Dr. Spiegelman detailed the work of his lab to develop an Irisin-based therapeutic for the treatment of metabolic disease. In particular, his lab fused the Fc fragment of immunoglobulin to the N-terminus of Irisin to enhance the hormone’s stability. When cultured murine adipose cells were exposed to this compound, a significant upregulation in UCP-1 was observed. The half-life of the compound in the blood of mice was notable nine days, and a single injection in high-fat fed mice led to a significantly increased expression of UCP-1 as well as significant improvements in fasting glucose and fasting insulin. Dr. Spiegelman stressed, however, that this was a proof of concept molecule and that it was not close to human use.
  • Dr. Spiegelman discussed other work in his lab aimed at isolating and characterizing beige adipose cells. Clonal cell lines were derived from the stromal vascular faction of murine subcutaneous adipose tissue, 23 of which underwent adipose differentiation. After analyzing the gene expression in these cell lines, the lines were clustered and were found to fall into two separate groups. One of these groups clustered more closely to classical brown adipose cells, which were cloned separately. Dr. Spiegelman’s group believed that these cell lines represented precursors to beige adipose cells, and the other group of cell lines (the ones that did not cluster as closely to classical brown adipose cells) represented precursors to white adipose cells. Interestingly, it was demonstrated that the beige precursor cells only began to express UCP-1 and other markers characteristic of brown adipose tissue when stimulated with cAMP (a thermogenic stimulus). Prior to cAMP exposure, the beige cells largely resembled the white adipose cells. These results suggested that beige adipose cells or their precursors could hide in a white adipose tissue like state, but could rapidly be converted into thermogenic cells when exposed to the proper stimulus.
  • Returning to the PET scan findings discussed at the beginning of his lecture, Dr. Spiegelman reviewed work conducted by his lab and others to show that these identified pockets of thermogenic adipose tissue were actually beige adipose cells. Biopsy samples from patients were obtained the PET scan detected hot spot areas. Unambiguously, it was shown that these adipocytes expressed beige adipocyte specific markers (CD137, Tmem26, Tbx1) previously identified in mice beige adipocytes, but none of the classical brown adipocyte specific markers (Ebf3, Eva1, Fbxo31). These results suggested that the predominant thermogenic adipocyte type present in adult humans is beige fat, not brown fat. Additionally, the findings provided evidence that adult human beige fat closely resembles murine beige fat, making murine beige fat a useful model with which to explore the biology of human beige fat. Dr. Spiegelman expressed great optimism that information from such studies in the coming decades would lead to the development of brown/beige fat-based therapeutics for diabetes prevention and/or treatment. He noted that that Irisin was a nice first candidate, but unlikely the only candidate that will emerge.
  • Finally, Dr. Spiegelman demonstrated that Irisin acts to induce the activation of beige adipocyte precursors into thermogenic beige adipocytes rather than convert white adipocytes into beige adipocytes. Using CD137 as a marker of beige preadipocytes, mouse white preadipocytes (low CD137) and beige preadipocytes (high CD137) were sorted. When the Fc-fused Irisin molecule was applied to the white preadipocytes, no effect was observed. In comparison, Irisin induced a markedly increased expression of UCP-1 and other markers of thermogenic adipocytes (Cox8b, Prdm16) in the beige preadipocytes. Thus, Irisin does not appear to cause transdifferentiation of white adipocytes, but rather encourages beige adipocyte precursor cells to mature down a preordained pathway.


Current Issues: Perspectives on Mitochondira Dysfunction in Insulin Resistance

Mitochondrial Deficiency is Associated with Insulin Resistance

Bret Goodpaster, PhD (University of Pittsburgh, Pittsburgh, PA)

Dr. Goodpaster took an evidence-based, chronological approach to supporting his claim that mitochondrial deficiency promotes insulin resistance. He opened by noting three points: 1) it is important to think about mitochondria based on their role as mediators of oxidative stress; 2) there are multiple modes of insulin resistance, which can develop through different pathways; and 3) evidence must be translatable to humans. Dr. Goodpaster mentioned early data showing that people with type 2 diabetes exhibit impaired fatty acid oxidation.. He emphasized the uncertainty in whether this impairment was due to suppressed oxidative activity or a change in preferred substrates to be oxidized.   Dr. Goodpaster then reviewed subsequent studies that found that mitochondrial oxidation activity declines in people with obesity and is especially reduced in patients with type 2 diabetes.  Notably, the combination of weight loss and exercise has been found to improve both mitochondrial oxidation capacity and insulin sensitivity in people with type 2 diabetes. Further data show that in obese people, increased mitochondria oxidation is the strongest predictor for improved insulin sensitivity. During the final portion of his presentation, Dr. Goodpaster explained the possible role of oxidative stress as a potential mechanism for how mitochondrial oxidative activity influences insulin resistance. In rodents, a high fat diet increases oxidative stress, which was linked to insulin resistance. Furthermore, antioxidant activity was associated with improved glucose uptake and mitochondria oxidase activity. Dr. Goodpaster concluded his talk by noting that mitochondrial deficiency may represent one of the several pathways which drive insulin resistance.

Questions and Answers

Q: It is tempting to think that coupled respiration is most important. What is known about uncoupled respiration or the proton leak? Does greater mitochondrial mass increase leak?

A: I don’t have an answer; we are looking into that right now. I think there is a difference in mitochondrial coupling that could be very important

Q: Is there any causal relationship between abnormal mitochondria causing insulin resistance?

A: In some instances I would say yes, data supports this relationship.

Q: There are papers describing how metformin might impair mitochondrial function; can you comment on insulin signaling in the liver?

A: I can’t comment on insulin signaling in the liver.

Q: Exercise will increase whole-body metabolism. Can exercise enhance beta cell function?

A: The real question is whether exercise can have systemic effects. Even the brain mitochondria increase with exercise, in addition to muscle mitochondria.


Mitochondrial Deficiency Does Not Mediate Insulin Resistance

John Holloszy, MD (Washington University School of Medicine, St. Louis, MO)

Taking the opposing view to Dr. Goodpaster, Dr. Holloszy argued that mitochondrial deficiency does not mediate insulin resistance. During his presentation, Dr. Goodpaster argued that the reduction in oxidation of fatty acids due to a deficiency of mitochondria is hypothesized to cause insulin resistance. In response, Dr. Holloszy referenced papers which show that a reduction in muscle mitochondria concentration neither precedes nor causes the development of insulin resistance, and that the muscles of type 2 diabetes patients do not oxidize less fat than those of people without diabetes. He concluded with the controversial statement, “people should stop wasting money on this” because the hypothesis is “nonsensical.” The audiences’ opinions on this topic started off as being roughly evenly split, and ended up slightly in Dr. Holloszy’s favor. The issue is still far from clear, however, and most audience members raised their hands when asked whether they were “totally confused.”

  • According to Dr. Holloszy, Dr. David Kelley’s report (Kelley et al., Diabetes, 2002) that the muscles of patients with type 2 diabetes contain fewer mitochondria than those of healthy individuals ignited interest in the relationship between mitochondrial deficiency and insulin resistance. It led to the hypothesis that mitochondrial deficiency reduces the oxidation of fatty acids in muscles, leading to increased insulin resistance. This theory was corroborated by subsequent studies which found that insulin resistant obese individuals and patients with type 2 diabetes have a roughly 30% reduction in mitochondria compared to people without insulin resistance.
  • Dr. Holloszy warned that correlation does not imply causality.  For the hypothesis to hold true, the mitochondrial deficiency should result in decreased fatty acid oxidation in the muscle and should precede and lead to insulin resistance.. Such outcomes are difficult to measure in humans because insulin resistance develops years before a clinical diagnosis. However, Dr. Holloszy noted that rodent models are viable tools for evaluating the hypothesis, since rodents on high fat diets predictably develop obesity and insulin resistance in a laboratory setting.
  • Dr. Holloszy cited numerous studies that found that mitochondrial deficiency does not precede insulin resistance. According to the rodent models of Turner (Diabetes, 2006) and Hancock (PNAS, 2008), a high fat diet increases, rather than decreases, muscle mitochondria while causing insulin resistance. Furthermore, Garcia-Roves (PNAS, 2007) found that raising the free fatty acid concentration in rodent muscles through direct injection also increased mitochondrial concentration. Dr. Holloszy agreed with Dr. Gracia-Roves’ proposed mechanism for this effect – that free fatty acids lead to increased PPARδ binding to the carnitine palmitoyltransferase 1 promoter, which increases mitochondrial biogenesis.
  • Dr. Holloszy then argued that mitochondrial deficiency does not lead to insulin resistance. Addressing the hypothesis that mitochondrial deficiency causes insulin resistance through decreased oxidation of fatty acids, Dr. Holloszy explained that the 30% decrease in mitochondria concentration found in type 2 diabetes patients should not impair the ability of muscles to oxidize fatty acid in a significant manner –the capacity of muscles to oxidize fatty acids far exceeds the amount required to supply energy in the resting state. When mitochondrial activity deteriorates enough to actually reduce oxidation, increased glucose uptake and insulin action, instead of reduced insulin action, was observed in mice models (Colbert et al., Nature, 1996; Felber et al., Journal of Bioenergetics and Biomembranes, 1987). Dr. Holloszy also noted the studies of Ritov (AJP - Endo, 2010) and Han (PLoS One, 2011), which both showed that knocking out key parts of the mitochondrial electron transport chain in rodent muscle tissue did not lead to insulin resistance.
  • Finally, Dr. Holloszy pointed out that fatty acid oxidation was not reduced in diabetes patients. He emphasized the Nair study (Diabetes, 2008), which showed that oxidative phosphorylation in Asian Indians with type 2 diabetes is the same as in non-diabetic Indians, and higher than in healthy European Americans. Dr. Holloszy also mentioned the studies of Ara (Nature, 2011), Larson (Diabetologia, 2009), and Boon (Diabetologia, 2007), which all showed that diabetes patients have similar or elevated rates of fatty acid oxidation compared to people without diabetes.

Questions and Answers

Q: You have shown a series of studies in rodents, but few studies in humans. In humans, it is my understanding that insulin resistance leads to decreased mitochondrial activity.

A: I think such decreases in mitochondrial activity are temporary. High fat intake does indeed lead to a decrease in mitochondrial reduction, but a subsequent increase follows shortly.

Q: Is the definition of mitochondria dysfunction only ATP oxidation?

A: The mitochondria have many functions, but the most important one in this case is ATP oxidation. Nevertheless, resting muscle cells have a very low rate of oxidative metabolism. They use ATP for mainly maintenance functions, so you need minimal mitochondrial production for that.


Oral Sessions: Diabetic Dyslipidemia

Signaling Through GPR119 Increases HDL Cholesterol and Reduces Post-Prandial Triglycerides (53-OR)

Kathleen Brown, PhD (American Diabetes Association, Research Triangle Park, NC)

Dr. Brown described the results of a randomized, crossover study (n=9) in high fat fed mongrel canines that looked at the effects of the selective GPR119 agonist GSK1292263 on lipid, cholesterol, and fatty acid endpoints. 6 mg/kg of GSK1292263 or vehicle was administered, and study endpoints included fasted glucose, total cholesterol, HDL, triglycerides, glycerol, free fatty acids, and body weight. Total cholesterol and HDL cholesterol were increased for the four-week treatment period (21.5% and 16.4%, p=0.0008 and 0.0025, respectively). Furthermore, reduced triglyceride levels were observed following a two meal tolerance test, although there was no effect on fasting plasma triglyceride levels. Overall, we are excited to hear additional encouraging GPR119 agonist data, and look forward to seeing how these data translate to humans. We note that as of February 2012, GSK1292263 was no longer listed in GlaxoSmithKline’s development pipeline. For more information on GPR119 agonists, including the competitive landscape, please see page six of our GTCbio 2012 Day #2 report at


Special Lectures and Addresses: National Scientific and Health CAre Achievement Awards Presentation and Outstanding Scientific Achievement Award Lecture

Outstanding Scientific Achievement Award Lecture - Using Genetics to Explore the Biological Basis of Type 2 Diabetes in Human Populations

David Altshuler, MD, PhD (Broad Institute, Boston, MA)

Dr. Altshuler presented, to a packed audience, his work using genome-wide association studies (GWAS) to identify 54 novel genetic variants commonly associated with type 2 diabetes and suggestions for how to best utilize data gleaned from GWAS for disease intervention. He asserted that attempting to use these genetic variants for prediction and prevention could be futile given the complex genetic basis for type 2 diabetes. Instead, he advocated for identifying loss-of-function mutations that confer protection against diseases to identify new drug targets, given that drugs more commonly inhibit rather than activate. Finally, he proposed that GWAS could be incorporated more routinely into the traditional drug development process to avoid expensive investigations into questions that can be answered by existing variation in human genetics.

  • In collaboration with Dr. Leif Groop’s (Lund University, Malmo, Sweden) team, Dr. Altshuler’s group discovered 54 new gene variants commonly associated with type 2 diabetes using genome-wide association studies (GWAS). The majority of these had not previously been identified; their identification has increased the fraction of variability in diabetes that can be explained genetically by about 10%. Dr. Altshuler also reported that GWAS has been used extensively in studying lipid disease; for example, new variants in HMG-CoA reductase, the rate-limiting enzyme in the cholesterol synthesis pathway, were discovered using GWAS.
  • Despite researchers’ great capacity to identify these genetic variants, following up with actionable indications is a challenge for three reasons: 1) most variants found by GWAS are found in regions of the DNA that do not encode proteins, so it is difficult to identify the function of each region and what specific gene(s) they affect; 2) since these data are purely observational, it is impossible to use these studies alone to investigate the effect of perturbing the function of these gene regions; and 3) there is a general lack of information about these gene regions because few had previously been implicated in type 2 diabetes.
  • Genetic variants discovered from GWAS can help us learn about the pathophysiology of diseases in order to better inform therapeutics. Conventionally, determining the genetic basis for disease has been thought to be useful in prediction and prevention. However, the genetic basis for diabetes is so complex that accurate prediction is difficult. Dr. Altshuler argued that we should focus instead on using GWAS to develop treatments, and since most drugs are inhibitory rather than activating, the most direct route in therapy development is identifying loss-of-function mutations that are protective against diabetes and have no other adverse effect. As an example, Dr. Altshuler highlighted the gene PCSK9, whose loss of function is protective against coronary artery disease; people who naturally have a homozygous loss of PCSK9 are healthy and fertile, suggesting that pharmacologically inhibiting it would be safe. In diabetes, a stop codon in the gene SLC30A8 has been identified to be potentially protective against diabetes, and studies are currently underway to confirm that its loss of function does not cause other adverse effects.
  • Dr. Altshuler proposed that GWAS information can also be used to re-evaluate current approaches to drug therapy development. As an example, he cited studies investigating whether raising HDL levels reduces the risk of heart attack. He pointed out that three such drug candidates in development that raised HDL failed to lower cardiovascular risk. Instead of spending “billions of dollars” on more drug trials, Dr. Altshuler suggested using GWAS  to identify a gene modulating levels of HDL and examine the risk of heart attack in populations with naturally high levels of HDL. Such a study was done, and demonstrated that there was no difference for risk of heart attack for people carrying the high-HDL variant; this resulted in at least two companies stopping development of HDL drugs.


Symposium: Inflammation in Type 2 Diabetes and Results of the TINSAL-T2D Trial


Allison Goldfine, MD (Joslin Diabetes Center, Boston, MA)

Dr. Goldfine presented the results of Stage 2 of TINSAL-T2D, a trial evaluating the efficacy and safety of the non-steroidal anti-inflammatory drug (NSAID) salsalate as a treatment for type 2 diabetes. Overall, the results were not very exciting. After 48 weeks, salsalate treatment (n=146) resulted in a modest but significant A1c reduction (0.24%) beyond placebo (n=140) from a baseline of 7.7%; Dr. Goldfine suggested that the magnitude of the difference was smaller than originally anticipated, but could likely be explained by the changes in concomitant medications in the two arms. We also note the very low baseline A1c. She explained that the salsalate treatment conferred anti-inflammatory effects, as evidenced by lower white blood cells (p <0.001), neutrophils (p=0.003), and lymphocytes (p <0.001), and significantly increased adiponectin; there was no significant change in CRP. Salsalate increased fasting insulin and decreased C-peptide, which Dr. Goldfine believed is likely due to changes in insulin clearance. There was a trend toward increased systolic blood pressure with drug treatment, and significant increases in total cholesterol (6.6 mg/dl) and LDL (8.2 mg/dl) compared to placebo (p <0.001). Salsalate increased urinary albumin, which reversed following discontinuation. Mild hypoglycemia was more common with salsalate treatment, and salsalate use was associated with a modest increase in weight (~1 kg [~2 lbs]) (p<0.001) – both of these are major negatives given that newer agents are now available that do not prompt hypoglycemia or weight gain. As expected, tinnitus occurred more frequently with salsalate. In the TINSAL-FMD substudy, which assessed flow-mediated vasodilation as a marker of cardiovascular health, no changes in endothelial function were observed. Dr. Goldfine noted that the longer-term effects of salsalate need to be better assessed before using it as a treatment for type 2 diabetes. We doubt that there will be much pursuit of this.

  • In Stage 2 of TINSAL-T2D, patients were randomized to placebo (n=140) or 3.5 g/day salsalate (n=146). Individuals between the ages of 18 and 75 with type 2 diabetes and baseline A1c between 7.0% and 9.5% who were on diet and exercise or stable pharmacotherapy (up to three agents) were included in the study; patients who were on TZDs, insulin, and/or GLP-1 receptor agonists were not eligible to participate in the study. Patients randomized to salsalate were started at a 3 g/day dose for the first two weeks, and escalated up to 3.5 g/day for the rest of the trial if tolerability wasn’t an issue. Changes in dosing on concomitant medications for diabetes, lipids, and blood pressure were avoided if possible over the first 24 weeks of the trial, then were adjusted based on good clinical practice, a questionable trial design decision in our view since we would assume this would make it more challenging to show the differences prompted by the main drug being studied. At baseline, participants had an average age of 56 years, diabetes duration of ~5 years, weight of 96.2 kg (211 lbs), BMI of 33.3 kg/m2, A1c of 7.7%, and fasting plasma glucose of 151 mg/dl. 5% were not on any medications, 41% were on monotherapy, 49% were on dual therapy, and 6% were on triple therapy.
  • After 48 weeks, salsalate treatment resulted in a modest but significant A1c reduction (0.24%) beyond placebo. Dr. Goldfine noted that the magnitude of the difference was a little smaller than originally anticipated, but could likely be explained by the changes in concomitant medications in the two arms. More patients taking salsalate achieved an A1c reduction ≥0.5% after 48 weeks – ~40% in the treatment arm versus ~22% in placebo. In addition, salsalate treatment reduced fasting plasma glucose by 11 mg/dl beyond placebo after 48 weeks of treatment (p<0.001).

Questions and Answers

Q: Were there any differences in baseline characteristics that may have explained the modest A1c-lowering effect?

A: Patients in the two treatment arms were well matched for all of the parameters I showed. At the time of randomization, one-quarter of the patients randomized had baseline A1c below 7.1%. I think that could contribute to the diminished effect in magnitude in the whole group.

Q: The insulin effect is really intriguing. Is this in fact related to some of the cases of reducing concomitant medications? At what level do you think salsalate is modulating insulin clearance – the liver, or the periphery?

A: The change in insulin clearance was not seen in animal models, so it is hard to tease apart. It could be at the level of the liver; it could also be other places. It’s difficult to assess.

Q: What specifically drove the decreases in concomitant medications?

A: The increase in mild hypoglycemic events led physicians to decrease concomitant medications, whereas poor glycemic control led to increases in concomitant medications.


Systematic Inflammation

Steven Shoelson, MD, PhD (Harvard Medical School, Boston, MA)

Dr. Shoelson began with an overview of inflammation in obesity. He explained that lean adipose tissue differed from obese adipose tissue in both the number and type of macrophages present. In obese mice, adipose tissue had a greater number of macrophages and more M2 macrophages – the proinflammatory type. In lean mice, more M1 macrophages were present. However, he noted that macrophage type has been difficult to identify in human adipose. Immune cell concentration, including T and B cells, is also greater in obese mice. Next, Dr. Shoelson provided a brief introduction on salicylate and its anti-inflammatory properties. In particular, he discussed the results from stage 1 of the TINSAL-T2D study. After 14-weeks of treatment with salsalate (3-4 g/day), participants with type 2 diabetes achieved provide placebo-corrected reductions in A1c of 0.4-0.6% from a baseline of 7.7%. Additionally, treatment with salsalate led to improvements in fasting blood glucose, C-peptide, glucose utilization, free fatty acids, triglycerides, C-reactive peptide, and adiponectin. While Dr. Shoelson noted the exact mechanisms behind salsalate’s effects are unknown, NF-kB inhibition and decreased metabolic clearance rate of insulin are potential candidates. Also of interest, Dr. Shoelson pointed to mouse studies suggesting salicylate could moderate the inflammatory cascade leading to atherosclerosis.

  • Lean adipose tissue has different macrophage characteristics than obese adipose tissue. Dr. Shoelson showed pictures highlighting the difference between macrophage character in lean mouse vs. obese mouse. In lean mice, macrophages were scarcely present and individually located in adipose tissue. In obese mice, adipocytes typically had a ring of macrophages surrounding them. Dr. Shoelson hypothesized that this occurred because: 1) macrophages were responding to dead adipocytes; or 2) individual adipocytes were producing substances causing monocytes to hone in on them. Dr. Shoelson noted that either way, the cell would die once the macrophages targeted it. Moreover, Dr. Shoelson explained lean adipose tissue usually contained M2 macrophages, while obese adipose tissue typically contained M1 macrophages . Importantly, M1 macrophages are pro-inflammatory. However, he noted it has been difficult to identify macrophage type in humans and that there is a fair amount of controversy on this matter.
  • Leukocyte concentration increases in response to obesity. Compared to negative controls, mice on a high fat diet had greater concentrations of inflammatory immune cells in their adipose tissue including CD3 T cells, CD4 T cells, CD8 T Cells, and B cells. On the other hand, T regulatory cell concentration decreased. Dr. Shoelson noted that this was particularly interesting because T regulatory cells are anti-inflammatory.
  • Stage 1 of the TINSAL-T2D trial suggested lowering effects of salsalate on fasting blood glucose and A1c. Dr. Shoelson explained why they chose salsalate over other salicylic acid derivatives: salsalate is insoluble at stomach pH, is hydrolyzed and absorbed in the duodenum, does not change bleeding time, is generic and inexpensive, and has a long term safety profile in humans. At the three doses tested (3.0 g/d, 3.5 g/d, and 4.0 g/d), A1c, fasting blood glucose, and glycated albumin decreased over 14 weeks, whereas adiponectin increased (for more details, please see our ADA 2009 report).
  • While many mechanisms of action for salicylate have been proposed, Dr. Shoelson focused on NF-kB inhibition and the alteration of the metabolic clearance rate of insulin. In mice, obesity activated NF-kB in circulating monocytes, whereas salicylate inhibited NF-kB. Dr. Shoelson presented data from Goldfine et al. (CTS 2008) suggesting similar NF-kB responses to obesity and salsalate in humans. NF-kB activity in peripheral blood mononuclear cells decreased in obese humans with diabetes after salsalate treatment. In addition to NF-kB inhibition, Dr. Shoelson indicated that salsalate might also reduce the metabolic clearance of insulin. In individuals treated with high doses of salsalate, insulin levels remained high even when C-peptide levels decreased.
  • Salicylate could moderate the inflammatory cascade leading to atherosclerosis. In mice predisposed to developing atherosclerosis, salicylate reduced CD45 adhesion and infiltration in the aortic arches at two weeks. Dr. Shoelson was argued that if salicylate could be used to reduce inflammation, the cascade from obesity to atherosclerosis might be preventable.

Questions and Answers

Q: Not all obese patients are insulin resistant. Have you found any difference in M1 vs. M2 macrophages in the fat of these two distinct populations?

A: In our studies, we haven’t taken tissue samples to analyze fat. In TINSAL-T2D, we didn’t take fat samples, so we don’t have access to that. It would be nice to have samples, particularly of abdominal tissues, but those would be very hard to get from patients.


Targeting IL-1B and IL-6 in Type 2 Diabetes

Marc Donath, MD (Universitätsspital Basel, Basel, Switzerland)

Dr. Donath presented on the role of IL-1 and IL-6 in people with type 2 diabetes. Beta cells have IL-1 receptors and produce IL-1β themselves. He suggested IL-1β plays a deleterious role in people with type 2 diabetes, as IL-1 antagonists have shown consistent success improving insulin secretion and glycemia. IL-6, on the other hand, appeared to have a beneficial effect in people with diabetes. Dr. Donath referred to a mouse model implicating IL-6 in mediating GLP-1 secretion in pancreatic alpha cells and intestinal L cells. He reminded the audience that IL-6 is one of the best predictors of type 2 diabetes. Notably, IL-6 also increases with exercise in mouse models. Dr. Donath proposed that IL-6 could serve different purposes. He suggested that in obesity, IL-6 is secreted from adipose to help the body respond to increasing insulin resistance and hyperglycemia. However in exercise, IL-6 may function to prepare the body for a post-exercise meal.

  • An elevated glucose concentration induces IL-1β. Beta cells are replete with IL-1 receptors and produce IL-1β in response to glucose. Dr. Donath suggested that the high expression of IL-1 receptors in pancreatic islets – more so than in all other tissues – indicates that these receptors play an inflammatory role in type 2 diabetes.
  • Eight studies have shown improved insulin secretion and glycemia with IL-1 antagonist intervention. Dr. Donath pointed to the overwhelming evidence suggesting IL-1 antagonists like canakinumab and anakinra (Amgen’s Kineret) confer improvements in patients with diabetes. He presented data from a gevokizumab study in people with type 2 diabetes in press for the July 2012 issue of Diabetes Care. Three months after a single injection of gevokizumab, participants showed a placebo-adjusted A1c reduction of 0.85% (the baseline was not available but this seems like a great response). Notably, gevokizumab also increased C-peptide secretion and decreased C-reactive protein levels.
  • Dr. Donath proposed that IL-6 reprograms pancreatic alpha cells to secrete GLP-1. Dr. Donath explained that proglucagon is processed into several end products, including GLP-1 and glucagon.. Pancreatic alpha cells produce prohormone convertase 1/3, which coverts proglucagon to GLP-1 in the intestine. In a rodent model, intermittently elevated IL-6 increased GLP-1 synthesis in both pancreatic alpha cells and intestinal L cells and increased. Prohormone convertase 1/3 levels in the pancreas. Dr. Donath suggested this pathway may be responsible for the glycemic benefits observed in IL-6-treated mice. In reviewing his previous work (Ellingsgaard et al., Nat Med, 2001), Dr. Donath noted that obese mice showed increased insulin levels and decreased blood glucose levels in an OGTT test when injected with a bolus of IL-6 compared to controls.
  • IL-6 levels are chronically elevated in obesity and intermittently elevated with exercise.  Mice models indicate that exercise increases IL-6 and GLP-1 levels. Dr. Donath suggested that the increases in IL-6 during exercise and obesity result from distinct, non-conflicting pathways. Exercise-induced IL-6 secretion from skeletal muscle likely prepares the body for post-exercise meals while obesity-induced IL-6 secretion helps the body compensate for increased insulin resistance and hyperglycemia.


Symposium: Glucagon - Renaissance of an Old Hormone

Role of the Glucagon Receptor in the Regulation of Hepatic Fat Metabolism During Fasting

Christine Longuet, PhD (University of Toronto, Toronto, Canada)

In an overflowing auditorium for the symposium on glucagon, Dr. Longuet argued that the glucagon receptor (Gcgr) is essential in lipid homeostasis and underscored that in rodent models, knocking out (KO) Gcgr leads to significant increases in hepatic triglyceride (TG) content  and free fatty acid (FFA) production and secretion. Dr. Longuet found that KO mice had significantly elevated levels of plasma free fatty acids (FFA) (about 23.4 mg/dl; p <0.001) and TG (about 100 mg/dl; p <0.001) after a 16-hour long fast compared to WT mice (about 13.5 mg/dl and 30 mg/dl, respectively). Furthermore, KO mice were more susceptible to hepatosteastosis after exposure to a high fat diet (liver triglyceride content was about 11 mg/dl in KO mice but only about 5 mg/dl in WT mice; p <0.05) (Longuet et al., Cell Metabolism 2008). She commented that while there are very few studies looking at the impact of glucagon suppressors on lipid homeostasis, Xiao et al. (Diabetes 2011) found that glucagon inhibits hepatic lipoprotein secretion in humans. She indicated this finding suggests that her rodent studies may be translatable to humans.

Questions and Answers

Q: Do you think that the effect of glucagon on lipid metabolism would make it dangerous to use to treat diabetes?  

A: Data in humans is still very preliminary. There is no guarantee that the phenotype in humans will be the same as it is in rodents. The impacts of glucagon suppression may be worse in the treatment of type 2 diabetes because it is already associated with lipodemia.

Q: How can we overcome hypoglycemia when suppressing glucagon?

A: We did not look at any way of overcoming that. I am not sure. I am sorry.

Q: Has anybody measured fatty acid metabolites?

A: Not that I know of.

Q: There is a case report of a human with an inactive glucagon receptor, and no liver abnormalities were reported. Wouldn’t this speak against your hypothesis that there should be this deleterious effect if you block Glcr?

A: These results were obtained in animal models, mainly rodents. We are not trying to say that this is what will happen in humans. But we need to monitor this in humans to make sure that this does not happen.


Symposium: Inflammation and Insulin Resistance

Session Overview

Steven Shoelson, MD, PhD (Harvard University, Cambridge, MA)

Dr. Shoelson opened the session on inflammation and insulin resistance with an overview of the epidemiological, biochemical, pharmacological, and immunological research to date that has helped characterize this relationship. Relevant drug targets he discussed were TNF-alpha, salicylates, and IL-1. Overall, Dr. Shoelson appeared ambivalent toward the applicability of TNF-alpha to metabolic disease therapy, presenting both positive and negative studies using TNF-alpha blockers. He expressed greater excitement for salicylates, noting that he was one of the lead investigators for the TINSAL and TINSAL-T2D trials. As a reminder, TINSAL-T2D was a clinical trial that examined the use of salsalate (a prodrug to salicylate) to treat type 2 diabetes. In the first stage of the study, salsalate (3-4 g/day) was demonstrated to provide placebo-corrected reductions in A1c of 0.4-0.6% from a baseline of 7.7% in people with type 2 diabetes over 14 weeks. Additionally, treatment with salsalate led to improvements in fasting blood glucose, C-peptide, glucose utilization, free fatty acids, triglycerides, C-reactive peptide, and adiponectin. Results from the second stage of the trial (a 52-week phase 3 study examining the efficacy and safety of salsalate in people with type 2 diabetes) were presented by Dr. Shoelson at a Monday symposium at this year’ s ADA entitled “Inflammation in Type 2 Diabetes and Results of the TINSAL-T2D Trial.” Dr. Shoelson commented that salsalate could serve as a valuable therapy for type 2 diabetes given its low cost and ability to target several of the inflammatory components of diabetes. Finally, Dr. Shoelson closed his talk with a brief overview of the role of IL-1 in type 2 diabetes pathogenesis. In particular, he highlighted positive results from a study by Larsen et al. (NEJM 2007) that found IL-1 antagonism with anakinra (Kineret) in people with type 2 diabetes resulted in reductions in A1c (-0.46% placebo-adjusted), improvements in beta cell secretory function, and reductions in systemic markers of inflammation (IL-6 and C-reactive protein). As a reminder, XOMA’s anti-IL-1 beta antibody XOMA 052 exhibited weak glycemic control efficacy (marginal, non-significant reductions over placebo), but provided significant reductions in C-reactive protein in both a six-month phase 2a and six-month phase 2b trial in people with type 2 diabetes in 2011 (see the June 10, 2011 Closer Look at


Inflammasome and Metabolism

Jenny Ting, PhD (University of North Carolina, Chapel Hill, NC)

Dr. Ting discussed innate immune receptors and the inflammasome and their relevance to metabolic disease. Her research has focused on nucleotide binding-leucine rich repeat proteins (NLRP), which respond to a variety of signals by activating inflammasome genes. There are at least seven inflammasome genes, the protein products of which form multi-protein complexes (called inflammasomes) that contribute to cell death through the activation of caspases (particularly caspase 1) and the secretion of pro-IL-1B and IL-18. Dr. Ting suggested that the secretion of IL-1 from inflammasomes could induce insulin resistance in surrounding tissues by inhibiting the action of IRS1 on Akt. Fatty acids appear to be directly involved in this pathway by inhibiting AMPK in macrophages, which in turn activate the inflammasome pathway. The pharmacologic agent AICAR reverses the deactivation of AMPK in response to fatty acids, and in in vitro studies, adding AICAR to cell cultures reduced IL-1 secretion and inflammasome activation in response to fatty acids. Dr. Ting believes that a deeper understanding of the inflammasome pathway could help elucidate the mechanisms and effects of a number of metabolic pharmaceuticals. Delineating how IL-1 affects insulin sensitivity, she cultured macrophages with fatty acids and an inflammatory promoter (lipopolysaccharide), then exposed this medium to hepatocytes. In response to this inflammatory environment, the hepatic cells responded with a loss of nearly all insulin sensitivity as measured by Akt phosphorylation. Knocking out NLRP genes ameliorated this effect. Providing further evidence for the role of inflammation in the development of insulin resistance, she demonstrated that the knockout of IL-1 and TNF-alpha improved glucose control in mice fed a high-fat diet.


Macrophages, Inflammation, and Obesity - Induced Insulin Resistance

Mark Febbraio, PhD (Baker International Diabetes Institute, Melbourne, Australia)

Dr. Febbraio discussed his research involving macrophages, obesity, and insulin resistance. In severe metabolic syndrome and obesity, macrophages are typically observed engulfing apoptosing adipocytes in the histology of adipose tissue. Dr. Febbraio suggested that this occurs due to a positive feedback loop between overgrown adipocytes that secrete inflammatory cytokines and macrophages, eosinophils, and other immune mediating cells that are recruited toward these adipocytes, which in turn release additional inflammatory factors. To further investigate macrophages’ role in insulin resistance, Dr. Febbraio engineered a mouse knockout of CD36, a lipid transporter found primarily on macrophages. He and his colleagues observed a decreased inflammatory response to a high-fat diet with this genetic alteration. Macrophages did not accumulate in adipose tissue, and there was superior insulin signal transduction in adipocytes. To block macrophage chemotaxis therapeutically, he highlighted IL-6 antagonism as an approach that may be effective. IL-6 is a mixed pro- and anti-inflammatory cytokine involved in systemic and local inflammation. It plays a particularly important role in recruiting immune cells to areas of inflammation. Tocilizumab (Roche) is an antibody that neutralizes IL-6 and is currently used to treat rheumatoid arthritis. However, Dr. Febbraio noted that IL-6 antagonism has been associated with increased triglyceride levels, weight, and insulin resistance. Dr. Febbraio suggested that scavenging and deactivating a particularly active compounded form of IL-6 (IL-6 bound to a soluble receptor, IL-6R), rather than antagonizing the lone cytokine itself, could reduce macrophage recruitment to areas of inflammation while avoiding these metabolic side effects. To explore this hypothesis, Dr. Febbraio developed a mouse model that was genetically engineered to express a soluble receptor for the soluble IL-6/IL-6-receptor complex, which binds to and removes the complex from circulation. The introduction of this protein resulted in normal adipose tissue histology in high-fat-diet mice as well as greater peripheral insulin sensitivity in skeletal muscle. Interestingly, there was no effect on the insulin sensitivity in adipose tissue as well as the liver. Dr. Febbraio concluded by commenting that the next evolution in IL-6 therapy should involve developing ways to block the IL-6/IL-6-receptor complex rather than just the IL-6 receptor itself.


Hypothalamic NFKB and Obesity and Autophagy

Dongsheng Cai, MD, PhD (Albert Einstein College of Medicine, Bronx, NY)

Dr. Cai discussed the role of NF-kB expression in the hypothalamus in the pathology of type 2 diabetes, obesity, and hypertension. He explained that the hypothalamus was a logical area in which to investigate this connection, since it releases hormones that affect a wide range of metabolic and cardiovascular functions. NF-kB activates an inflammatory cascade within a cell that leads to changes in cell morphology, secretion of and response to immune mediators, and a wide range physiological functions. Typically, NF-kB is released in response to stressors such as metabolite excess, oxidative stresses, ER stress, and autophagic defects. In a study conducted in his lab, active IKK-beta/NF-kB was injected into the hypothalamus of rats. The rats subsequently developed hypothalamic neuronal insulin resistance. Dr. Cai indicated that under normal conditions, hypothalamic neurons respond to increases in insulin by reducing hunger and increasing peripheral metabolism. Conversely, suppressing the expression of hypothalamic IKK-beta/NF-kB in high-fat fed rats led to reduced weight gain, an anti-aging effect, an improvement in memory, and increased longevity (average lifespan of 1200 days vs. 1000 days in the control group). Dr. Cai hypothesized that a decrease in sympathetic output (which raises heart rate, blood pressure, and metabolism) triggered by the decreased levels of IKK-beta/NF-kB and inflammation in the hypothalamus might have contributed to these findings. He revealed that a measurable decrease in blood pressure was detected in the study, providing some evidence to support his hypothesis.  

Questions and Answers

Q: What do you think is driving this increased inflammation in the hypothalamus? Have you repeated your studies in specific cytokine-knockout mice?

A: We haven’t studied these specifically, but the cytokine knockouts could help to elaborate the key players.

Q: In the human trials examining salicylate, there was an improvement in glucose and triglyceride levels, but there was no effect on hypertension. How do you rectify that with your data?

A: That is indeed a discrepancy; it may be related to the animal model as opposed to humans.

Q: Have you tried to study the downstream pathways in metabolic syndrome?

A: Downstream is very broad, particularly when we’re talking about the brain. We have some other studies with nutrient sensing and exocytosis of peptide neurotransmitters. But in terms of immediate downstream, we have not done much in that regard. We try to focus on NF-kB itself.

Q: Did you look at APK activity in your animal models in the hypothalamus?

A: No, we didn’t.


Symposium: Epigenetic Control of Insulin Action

Epigenetic Control of Insulin Action in Humans

Allan Vaag, MD, PhD (Copenhagen University, Copenhagen, Denmark)

Dr. Vaag presented evidence that insulin resistance may be under substantial epigenetic control. Epigenetics are the changes in gene and protein expression beyond what is encoded in the primary DNA sequence. For example, methylation of gene promoters can silence their transcription and miRNA expression can prevent the translation of protein from mRNA. Insulin resistance in type 2 diabetes is associated with impaired mitochondrial function in muscle, and data show that methylation of PGC-1α, a gene necessary for the synthesis of mitochondria, is increased after bed rest and after five days of overfeeding on a high fat diet. This increase in methylation was reversed after returning to a normal diet. Furthermore, the association between low birth weight (LBW) and risk for type 2 diabetes may be explained by differences in PGC-1α methylation: LBW individuals have a higher rate of PGC-1α methylation and do not respond to changes in diet (high fat or normal) by increasing or decreasing the methylation status of PGC-1α. Dr. Vaag concluded that the ability to respond to overfeeding with changes in DNA methylation may be protective against insulin resistance in normal individuals. Furthermore, LBW is associated with increased expression of various miRNAs that play a role in fat deposition and the insulin signaling pathway. Thus, LBW may be associated with type 2 diabetes via the epigenetic changes that occur in utero when the fetus is provided with insufficient nutrition.

  • Insulin resistance in type 2 diabetes is associated with impaired mitochondrial function in muscle tissue. This finding suggest that the differential regulation of genes involved in muscle mitochondrial function may explain the development of insulin resistance. Genes expressing mitochondrial proteins involved in oxidative phosphorylation are found to be expressed at a lower level in people with type 2 diabetes and in insulin-resistant first-degree relatives.
  • Increased methylation of PGC-1α, a gene necessary for the synthesis of mitochondria, in response to physical inactivity and high fat diet may help explain why these two habits lead to muscle insulin resistance. Methylation of genes at CpG sites in their promoter regions is typically associated with decreased gene expression. A mutation introducing a CpG site into the PGC-1α promoter region would result in increased methylation and decreased expression of the gene. It has been shown that nine days of bed rest leads to a significant increase in insulin resistance, and Dr. Vaag presented data demonstrating that bed rest was associated with increased methylation of PGC-1α close to the transcription start site of the gene. Furthermore, a study which fed a high fat diet to 11 young men for five days led to significant increases in methylation at two of the same four CpG sites, which was reversed by returning to a controlled diet. An examination of several other genes found that multiple CpG sites (25% of the ones studied) in human muscle biopsies exhibited altered methylation patterns (p<0.05) during the five days of high fat overfeeding, but these were very modest changes (most increased by only 2%, and none by more than 10%).
  • DNA methylation could explain the association between low birth weight (LBW) and risk for type 2 diabetes due to LBW individuals’ reduced ability to adjust their DNA methylation patterns when exposed to short-term, high-fat feeding. A 1 kg increase in birth weight translates to a ~45% reduction of diabetes risk. PGC-1α was methylated to a greater degree in LBW participants, but a high fat diet did not result in a subsequent increase in PGC-1α methylation as it did for their normal birth weight counterparts. Furthermore, an array study examining methylation of multiple genes after exposure to high-fat overfeeding showed that LBW individuals responded with only a 5.7% difference in DNA methylation, whereas normal birth weight individuals responded with a 25.3% difference in DNA methylation (p<0.001). Based on these findings, Dr. Vaag concluded that the ability to show flexibility in gene methylation in response to overfeeding may be protective against insulin resistance in normal individuals.
  • LBW is associated with increased expression of miR-483 in fat (which influences adipose tissue expandability and lipotoxicity) and with increased expression of miR-15b and miR-16 in muscle, which affects insulin signaling. miRNAs target complementary mRNA sequences, preventing the translation of the encoded protein. miR-483’s target mRNA plays a role in the proliferation of subcutaneous tissue, thus an increase in miR-483 could result in the inability to store fat subcutaneously. This increases the storage of fat in the liver or muscle tissues, contributing to insulin resistance. Additionally, LBW has been associated with increased expression of miR-15b and miR-16. The most prominently predicted target for the miR-15 family is the insulin receptor pathway.

Questions and Answers

Q: Have you done any experiments to demonstrate that methylation actually reduces the activity of PGC-1α? For example, you could introduce methylation sites into the promoter and measure luciferase activity – then you could actually conclude that increased methylation changes PGC-1α activity.  PGC-1α is very complicated, and many things regulate its expression.

A: That is a wonderful question. We do not have this data.

Q: My impression from a basic molecular biology standpoint is that DNA methylation is a relatively stable, long term change, so seeing such short term changes is astonishing. Do you know what is regulating these relatively rapid changes?

A: This is what I am most fascinated about. I think we should be very cautious not to conclude too much because the changes we see are small and there are some difficulties in working with these tissues. There are a lot of issues to address here.

Q: You stated that the change in methylation status was quite small in response to overfeeding – is there any hope of seeing this translate into biologically significant changes in expression?

A: We overfed the participants for only five days - what if you overfed people for a year, 10 years, or 20 years? At that stage, gene expression might change. Perhaps the changes right now are too small to see. Additionally, this was all done in resting muscle, and I think it is a bit naïve to think we can notice changes in this state. I think the muscle needs to be active – we need to challenge the muscle.


Symposium: Insulin Action via the Brain

Novel Roles of Brain Insulin in Regulating Whole-Body Amino Acid and Lipid Metabolism

Christoph Buettner, MD, PhD (The Mount Sinai Hospital, New York, NY)

Dr. Buettner discussed his work examining the role of central insulin signaling in the regulation of adipose tissue lipolysis and lipogenesis in a rodent model. An important distinction was first made between hepatic glucose production (HGP) and lipolysis: HGP is a somatostatin-dependent phenomenon, whereby blood glucose levels are only affected when insulin and somatostatin are dosed. In contrast, lipolysis is controlled by brain insulin independent of somatostatin. Two inducible insulin receptor knockout mice were used to test the effects of insulin on lipolysis: 1) a peripheral insulin receptor knockout, and 2) a whole body knockout. In the peripheral knockout, brain insulin was still able to suppress lipolysis, while in the whole body knockout, insulin could no longer suppress lipolysis. Furthermore, the whole body knockout was accompanied by a marked increase in several pro-inflammatory markers, while the peripheral knockout exhibited anti-inflammatory effects. Overall, Dr. Buettner commented that it appeared clear that brain insulin has direct effects on fat (at least in animals); however, the physiological importance of this relationship in humans remains to be explored.


Symposium: Novel Concepts in the Pathophysiology of Hypoglycemia in Diabetes

Novel Mechanisms Implicated in Hypoglycemia-Associated Autonomic Failure

Stephen Davis, MBBS (University of Maryland School of Medicine, Baltimore, Maryland)

Dr. Davis discussed mechanisms for hypoglycemia-associated autonomic failure, mechanisms for exercise-associated autonomic failure, and approaches to enhance autonomic nervous system and neuroendocrine responses during hypoglycemia. Though much is still not known about the potential mechanisms involved, there are several novel approaches to amplifying the counterregulatory response during hypoglycemia. Dr. Davis noted that while these approaches are still under investigation, they demonstrate early promise.

  • Individuals who take GABA agonists (GABA being an important neurotransmitter in the central nervous system) before experiencing hypoglycemia the next day experience autonomic failure and a reduction in endocrine responses. Taking a GABA-a agonist and experiencing hypoglycemia in the same day leads to an increased glucagon response the following day, indicating the complexity of the pathways of endocrine responses and how much is still not understood.
  • People experience a similar reduction in endogenous glucose production, epinephrine, glucagon, and direct sympathetic activity when they have an episode of hypoglycemia after exercising. Cortisol infusions antecedent to exercise also significantly reduced epinephrine, glucagon, norepinephrine, and pancreatic polypeptides.
  • A number of approaches to enhancing these responses have been identified. Though one week of troglitazone therapy resulted in hepatic toxicity, there is a significant increase in both epinephrine and glucagon during subsequent hypoglycemia. Catalytic amounts of fructose have been found to increase epinephrine and endogenous glucose production. Administration of Dehydroepiandrosterone (DHEA) preserves the glucagon and epinephrine response during repeated hypoglycemia . Dr. Davis discussed plans to perform clinical trials to investigate the effects of DHEA on hypoglycemia.


Central Nervous System vs. Intra-Islet Regulation of Glucagon Secretion

Owen Chan, PhD (Yale University, New Haven, CT)

Dr. Chan’s presentation examined both the intra-islet and central nervous system (CNS) factors affecting glucagon secretion as they relate to diabetes. He also emphasized the importance of the neurotransmitter gamma-aminobutric acid (GABA) in associating CNS and peripheral glucagon regulation. While precise mechanisms for the relationship between insulin and glucagon regulation at both the CNS and periphery have yet to be entirely elucidated, Dr. Chan provided an excellent analysis of this complex network and the current understanding of these regulatory pathways.

  • Dr. Chan reviewed the intra-islet hypothesis, which purports that insulin release during increasing glucose levels can inhibit glucagon secretion from alpha cells. Additionally, Dr. Chan described how the ventromedial hypothalamic nucleus (VMH) is the primary glucose sensor in the brain – glucose delivery into the VMH suppresses the counterregulatory responses during hypoglycemia. In diabetes, glucagon release is determined by the net effect of both CNS and peripheral inputs to the alpha cell.
  • Dr. Chan also emphasized the importance of the neurotransmitter gamma-aminobutric acid (GABA) in associating CNS and peripheral glucagon regulation. GABA receptors are expressed on alpha cells, with GABA acting to inhibit glucagon secretion. Interestingly, insulin has been shown to increase GABA receptor translocation, illustrating another pathway through which insulin and glucagon regulate each other.


Symposium: Protein Acetylation and Other Post Translational Modifications as Metabolic Sensors

SIRT3 in Control of Hepatic Lipid Metabolism

Matthew Hirschey, PhD (Duke University Medical Center, Durham, NC)

In this fascinating talk, Dr. Hirschey discussed a single nucleotide polymorphism (SNP) in the SIRT3 gene that has been implicated in metabolic syndrome. Participants in the Non-alcoholic Stetatohepatitis Clinical Research Network (n=834) and the Metabolic Syndrome in Men study (n~8000) were found to be at a statistically significantly higher risk of developing metabolic syndrome if they possessed this particular SNP in SIRT3. Dr. Hirschey therefore suspects that this SNP inhibits SIRT3, which leads to metabolic syndrome via decreased metabolic rate and fatty acid oxidation. 

  • SIRT3 belongs to a class of proteins called sirtuins that regulate acetylation of mitochondrial proteins, especially those involved in fatty acid metabolism and the generation of energy. Chronic high-fat diet feeding suppresses SIRT3, but the over-expression of PGC-1 alpha, an important metabolic cofactor, rescues SIRT3 expression during high-fat feeding. Dr. Hirschey has found that mice that lack the SIRT3 gene develop several metabolic complications, including obesity, diabetes, insulin resistance, hyperinsulinemia, inflammation, and fatty liver disease.
  • A single nucleotide polymorphism (SNP) in SIRT3 is correlated with increased risk for the metabolic syndrome. 12 SNPs in SIRT3 were interrogated in the NASH-CRN (Non-alcoholic Stetatohepatitis Clinical Research Network), which included 834 participants (mean age 48.9, 36:64 male:female, 34.6 kg/m2), of whom 30.6% had diabetes and 47.7% had the metabolic syndrome. rs11246020, a SNP in the SIRT3 gene, was found to be correlated with increased risk for the metabolic syndrome with p=0.008. The same SNP was implicated in higher risk of metabolic syndrome in the METSIM (Metabolic Syndrome In Men) study, which included approximately 8000 Finnish male participants. This SNP encodes a mutation in SIRT3 that significantly reduces enzymatic efficiency, inhibiting the deacetylating activity of SIRT3. Dr. Hirschey suspects that these effects result in decreased metabolic rate and fatty acid oxidation, leading to higher lipid content, insulin resistance, and obesity, culminating in the metabolic syndrome.
  • Dr. Hirschey then investigated the effects of high-fat diet feeding on other mitochondrial protein acylations, but more research needs to be done. High-fat diet feeding resulted in increased hepatic propionylation, which is similar to acetylation but adds an additional carbon atom. The relevance of propionylation to metabolism and metabolic disease remains to be seen.

Questions and Answers

Q: Are you, or is anyone in the field, trying to work on an algorithm to figure out which lysine residues are most likely to be acetylated?

A: The crystal structures of the sirtuins appear to be binding to the backbone rather than the specific amino acid side chain, so there might be promiscuity to the sirtuins, which is what a lot of people have seen. I know that here is some work going on right now to identify which sites are SIRT-3 dependent and which are not.

Q: As a clinician, we are thinking about using these signatures to phenotype patients and understand what’s happening to them. We see there is a negative energy balance and non-alcoholic fatty liver disease. But, if you put them all on unsaturated fat diets, liver fat goes down. What’s driving these enzymes? Is it the energy balance? Could this be used to assess the status of these proteins?

A: I’d say there is a paucity of work regarding the actions of SIRT3 on the transcriptional level. But it is not known if or how the lipids are regulating this. Thinking past SIRT3 and the regulation status of mitochondrial proteins – we’ve tried to do this in some human patient samples, but it’s difficult because to get good mitochondria, you’d have to purify immediately. The strategy on of my colleagues has used to make a site-specific acetylation utilizes antibodies which can read out if you had a panel of these antibodies. The panel could read out the acetylation status of a key number of proteins that would perhaps be indicative of the energy balance, but the specific sites that you would want to target antibodies against is still unclear.


Symposium: The Basic Science of Exercise - Implications for diabetes

PGC-1 Alpha, Exercise Mimetics, and Diabetes

Christoph Handschin, PhD (University of Basal, Basel, Switzerland)

Dr. Handschin presented an in-depth analysis on the physiology of PGC-1α, focusing on its relevance to glucose response and the potential therapeutic benefits from modifying its expression. In conducting several studies on transgenic PGC-1α mice, his group showed that PGC-1α plays a significant role in muscular adaptations to exercise – specifically, it enhanced mitochondrial proliferation in response to endurance exercise. The same studies found that PGC-1α promotes intramuscular lipogenesis or lipolysis (depending on if the mouse was sedentary or active, respectively), partially by modulating the pentose phosphate pathway. PGC-1α also increased muscular glucose uptake, hypothetically to replace the metabolized intracellular fatty acids.

  • Dr. Handschin cautioned the audience at the end of his talk that PGC-1α may not be a beneficial drug to replace exercise in a sedentary population. Studies show that sedentary mice on high fat diets with elevated PGC-1α had poor glycemic profiles compared to mice with no PGC-1α increases. He attributed this to a PGC-1α-stimulated increase in fatty acid concentrations in the muscle cells. Dr. Handschin explained that exercise mimetics such as PGC-1α may be effective when combined with exercise but may increase pathologies in sedentary populations eating Western diets. Still, some questions were raised regarding this conclusion in relationship to previously conducted studies, indicating a need for additional research.


Molecular Targets of Exercise in the Muscle

Laurie Goodyear, PhD (Joslin Diabetes Center, Boston, MA)

Dr. Goodyear’s presentation summarized several studies that her team conducted by transplanting subcutaneous adipose tissue (AT) from exercise-trained and untrained mice into the visceral cavity of other mice. They found that transplanting AT from mice that had been exercise-trained greatly improved glucose tolerance compared to transplanting AT from untrained mice. Dr. Goodyear’s team also conducted a similar study in mice that were fed high fat diets before receiving the transplant. In these studies, mice that received the exercise AT transplant were able to reverse the impaired glucose tolerance induced by the high fat diet.  

  • After transplanting subcutaneous adipose tissue (AT) from exercise-trained and untrained mice into untrained mice, Dr. Goodyear and her team concluded that there are significant changes that occur in AT along with exercise, and that these changes affect systemic glucose tolerance. The results from this transplantation show very apparent (>30%) glucose improvements at nine-days post transplant, but are negligible at 12 weeks. The mice that receive AT from exercise-trained mice had better glucose tolerance, lower insulin release, and increased brown adipose tissue activity. Dr. Goodyear noted that this study demonstrated that exercise has marked effects on AT that in turn influence systemic physiology.


Product Theater (Sponsored by Boehringer Ingelheim and Eli Lilly)

From Signs to Glycemic Impact: Exploring the Role of Glycosuria in T2DM

Mohammad Abdul-Ghani, MD, PhD (University of Texas Health Science Center, San Antonio, TX) and Carol Wysham, MD (University of Washington, Seattle, WA)

Drs. Carol Wysham and Muhammad Abdul-Ghani attracted a standing-room only crowd, despite the noise level of the exhibit hall, with a basic science presentation focusing on the mechanistic roles of SGLT-2 receptors, particularly in type 2 diabetes. Dr. Wysham began the presentation with an overview of the impact of glucotoxicity on beta cells and noted that people with diabetes carry a risk of cardiovascular events similar to people with cardiovascular disease and no diabetes. Dr. Abdul-Ghani then explained the role of SGLT-2 transporter, noting that glycosuria results when its capacity (about 180 mg/dl) is exceeded at normal transporter expression levels. However, SGLT-2 protein levels are increased in type 2 diabetes, as shown by the over-expression of renal SGLT-2 in rats with diabetes. Furthermore, studies of human exfoliated proximal tubule epithelial cells show increased SGLT-2 and GLUT2 transporter expression. Dr. Abdul-Ghani noted that in mouse models with a SGLT-2 gene deletion, the absence of SGLT-2 transporters dramatically lowered fasting plasma glucose by roughly 60-70 mg/dl (p <0.001). Furthermore, diabetic mice with this deletion showed improved beta cell function compared to control  diabetic mice, as shown by a two-fold increase in insulin secretion under hyperglycemic clamp. Dr. Abdul-Ghani further explained that the benign spontaneous loss of SGLT-2 function in a rare condition called familial renal glycosuria provides evidence that SGLT-2 inhibition therapy will be safe.


Insulin Therapies

Oral Sessions: Insulin Analogs

Reduced Nocturnal Hypoglycemia and Weight Loss with Novel Long-acting Basal Insulin LY2605541 Compared with Type 2 Diabetes (347-OR)

Richard M. Bergenstal, MD (International Diabetes Center, Minneapolis, MN)

Dr. Bergenstal presented a 12-week 2:1-randomized, open-label, parallel-design comparison of Lilly’s basal insulin analog LY2605541 (PEGylated insulin lispro, hereafter LY) vs. insulin glargine in 188 patients with type 2 diabetes who were poorly controlled on basal insulin. LY and glargine conferred similar improvements in mean fasting blood glucose (118 vs. 117 mg/dl, from baselines of 147 and 140 mg/dl) and mean A1c (7.0% vs. 7.2%, from baselines of 7.7% and 7.8%). However, patients using LY experienced relative improvements in both nocturnal hypoglycemia (48% baseline-adjusted rate reduction; p=0.021) and intra-day glucose variability as measured by standard deviation of eight-point SMBG (34 vs. 39 mg/dl). People on LY also had significant weight loss (-0.58 kg [1.27 lbs]) in contrast to the significant weight gain with glargine (0.31 kg [0.68 lbs]). LY patients also ended the study with relatively higher levels of triglycerides than the glargine group (172 vs. 147 mg/dl; p<0.01) and significant relative increases in the liver enzymes AST and ALT – though these remained in the normal range (p<0.01). Dr. Bergenstal said that the weight loss and triglyceride elevation suggest that LY might have more liver-specific effects than other insulins – a hypothesis supported by other early research on the new analog. During Q&A, an audience member noted that Lantus might have performed better in this regard if the analogs had instead been dosed in the evening – a protocol that will be included in phase 3 testing.

  • Lilly’s novel basal insulin analog consists of insulin lispro attached to a 20 kDa PEG group (i.e., it is “PEGylated”). This modification increases the actual molecular size to 26 kDa and the functional size to 71-98 kDa (i.e., functionally larger than albumin). LY has a slow absorption profile, with a peak-to-trough ratio of just 1.5 (ADA 2012 1063-P) and favorable steady-state profile across a range of studied doses (0.33 to 1.0 U/kg; ADA 2012 1000-P). Other ADA 2012 presentations describe how LY seems not to be affected by renal impairment (1148-P) and how it has demonstrated a preferential hepatic effect compared with human insulin in a somatostatin-infused/glucagon-replaced conscious dog model (1609-P).
  • This analysis included people with type 2 diabetes that were not at glycemic goal on basal insulin therapy (n=248 on glargine, n=39 on NPH). Patients were randomized to AM dosage of LY (n=195) or glargine (n=93) from their previous basal insulin therapies. At baseline the groups were comparable in mean ± SE A1c (7.7±0.1% vs. 7.8±0.1%), fasting blood glucose (147±3 vs. 140±4 mg/dl), weight (90.7±1.4 vs. 89.7±2.1 kg), BMI (roughly 32 kg/m2), diabetes duration (roughly 12 years), and prior mean glargine daily dosage. The majority of participants were Caucasian (93-94%).
  • At 12 weeks, the LY and glargine groups had statistically equivalent mean fasting glucose as measured by SMBG (1118±2 vs. 117±3 mg/dl), the study’s primary endpoint. The groups were also similar in mean 12-week A1c (7.0±0.06 vs. 7.2±0.9%). Central lab measurements of fasting glucose were slightly higher than the self-measured values, which Dr. Bergenstal said is common due to the lag between sample collection and analysis. The central lab values were also not significantly different between groups at 12 weeks (147 vs. 151 mg/dl).
  • LY and glargine did not significantly differ in 30-day event rates of hypoglycemia, either overall (1.34 vs. 1.52, p=0.80) or nocturnal (0.25 vs. 0.39, p=0.18), though LY-using patients had a 48% rate reduction in nocturnal hypoglycemia events after adjusting for baseline rates. Intra-day glycemic variability (standard deviation of seven-point SMBG profiles) was statistically significantly smaller with LY than glargine (34.4 vs. 39.1 mg/dl; p=0.03). The two analogs were not statistically significantly different with regard to inter-day variability (SD of eight-point profiles: 10.8 vs. 13.7 mg/dl; SD of daily SMBG 18.6 vs. 20.0 mg/dl), though Dr. Bergenstal noted that the LY group experienced numerically less inter-day variability.
  • Notably, patients on LY underwent statistically significant weight loss from baseline (0.58 kg [(1.28 lbs], p=0.007), as compared to non-statistically significant weight gain in the glargine group (0.31 kg [0.68 lbs], p=0.66). These divergent effects made for a statistically significant between-group LS mean difference of 0.84 kg (1.85 lbs).
  • Adverse event rates were balanced between groups, but significant between-group differences were observed in the levels of triglycerides and liver function enzymes. Baseline triglyceride levels in the LY and glargine groups were 163 and 160 mg/dl, respectively. By 12 weeks, triglycerides had posted a statistically non-significant rise in the LY group and a decline in the glargine group, making for a statistically significant gap between the two groups (172 vs. 147 mg/dl). Changes in cholesterol levels were similar between LY and glargine. As for liver function enzymes, in the LY and glargine groups, respectively, ALT levels were 26 vs. 27 U/l at baseline and 33 vs. 26 U/l at 12 weeks, and AST levels were 23 vs. 24 U/l at baseline and 26 vs. 24 U/l at 12 weeks. Two patients had significant (>3x normal) elevations: one experienced normalization four weeks later, and the other was lost to follow-up.

Questions and Answers

Q: Even in the studies with steady-state use of glargine, there is more variability at the end of the dose. The design of the study – giving Lantus in the morning – is specifically designed to find that flaw in Lantus. Maybe the between-group comparison would have been different with night-time dosing of Lantus.

A: We wanted to give both insulins at the same time every day. We could have done them both in the evening, but you could have said that would favor LY because Lantus has more of a bump and so you’d get nocturnal hypoglycemia. We had to pick one option, we did it carefully, and we will go into phase 3 – where there will be evening dosing and variable dosing – so this issue will be addressed.


The Novel Long-Acting Insulin LY2605541 is Superior to Insulin Glargine in Lowering Intra-Day Glucose Variability and Hypoglycemia Event Rate from Continuous Glucose Monitoring (CGM) in Patients with Type 2 Diabetes (346-OR)

Richard Bergenstal, MD (International Diabetes Center, Minneapolis, MN)

Dr. Bergenstal presented a CGM sub-analysis of the previously presented study (347-OR). Patients in the glargine (n=25) and LY2605541 (n=51) groups were put on three days of CGM (Medtronic iPro) during the weeks before week zero, six, and 12 study visits. Compared to glargine, LY2605541 demonstrated improved intra-day and similar inter-day glucose variability, lower incidence of hypoglycemia, less time spent in hypoglycemia, and a reduced low blood glucose index (LBGI; a measure developed by Dr. Boris Kovatchev). Dr. Bergenstal showed slides demonstrating that this subset of patients was similar to patients in the main trial, though some questioners took issues with the unbalanced group sizes and statistical comparisons. We look forward to the larger CGM analyses planned for hundreds of patients in the phase 3 trials.

  • This CGM subanalysis of a larger study (347-OR) included 51 patients on LY2605541 and 25 patients on glargine. Patients had a mean A1c of 7.7% and a mean age of 60 years. Baseline criteria were not significantly different between the two groups. Additionally, SMBG comparisons (mean blood glucose, fasting glucose by SMBG, and fasting glucose by laboratory) were not significantly different between the two groups in this CGM cohort and the two groups in the main study.
  • LY2605541 was associated with significantly lower within-day glycemic variability compared to glargine. Standard deviation of blood glucose during the day (6 am-12 am) was 45 mg/dl for glargine vs. 37 mg/dl for LY2605541 (p=0.04). At night (12 am-6am), the difference was borderline insignificant (p=0.06): 24 mg/dl for glargine vs. 18 mg/dl for LY2605541. Between-day glycemic variability was not significantly different between glargine and LY2605541.
  • Relative to glargine, LY2605541 had a significantly lower incidence of hypoglycemia, significantly less time spent in hypoglycemia, and a significantly lower low blood glucose index (LBGI). Hypoglycemia incidence at week 12 was significantly lower for glargine vs. LY2605541 over 24 hours (78% vs. 50%; p=0.04) and overnight (48% vs. 21%; p=0.03). Time spent in hypoglycemia (<70 mg/dl) over 24 hours was 83 minutes with glargine vs. 25 minutes with LY2605541 (p=0.01). The difference was not significant for time spent <50 mg/dl (p=0.11), though it trended in the right direction (20 minutes with glargine vs. 5 minutes for LY2605541). LBGI improved as well: 1.6 for glargine compared to 0.6 for LY2605541 (p=0.01). 

Questions and Answers

Q: Very nice data congratulations. I’m not quite sure you really excluded prolonged hypoglycemia. By nature, less hypoglycemia events means time spent in hypoglycemia should always be lower. What I suggest looking at is when you are <70 mg/dl, how long did it take to go up again. The average time per episode.

A: Thanks. We’re happy to do that. And thank you for contributing the graphics to the leading slides.

Q: I’m wondering about the PEGylation on lispro. What happens when the insulin hooks up to the receptor?

A: I’ll be honest, I cannot say what happens to the PEGylation of the molecule. Insulin still binds to the receptor. Somewhere along in the process, the PEGylation does get metabolized. It seems to be excreted in the biliary tract. The binding kinetics all look very promising when you say, ‘Does it have more mitogenicity?’ No. ‘Does it have longer dwell time,?’ No. It has a good safety profile.

Q: This was a subanalysis of the larger study. There were 25 patients in the glargine group vs. 50 patients in the LY group. By definition, you will have lower variability by doubling the patient size. It’s hard to compare the results. You should make the power similar.

A: That’s a good comment. We’ve corrected this as much as we can. The reason it’s doubled is there were three groups and 25 in each groups. Two of the groups happened to be lispro. I know we did not find differences in the first study. We’d be happy to break them out. My guess is there is no difference.

Q: How would this compare to degludec, which is probably several years ahead of you.

A: You can bet I’m going to answer that one. We are in need of continuing to improve basal insulins – longer, flatter, and less hypoglycemia. The more of them, the better. Let the best man win. Or rather, let all win. Let’s wait until they’re all available on the market we’ll test them and see.

Q: Rich, most surprising to me was the amount of time spent in hypoglycemia for the glargine group. I was surprised – it almost looked like a type 1 diabetes study. There was lots of hypoglycemia. Maybe you expected that? This was a small sample size. Did you have plans to do CGM in larger studies? It seemed like way too much hypoglycemia.

A: I don’t know if we expected it. But we’re seeing more hypoglycemia than people think is observed. These patients were in decent control at an A1c of 7%. Yes, phase 3 has CGM built in and several hundreds of patients.


Altering the Time of Day of Once-Daily Dosing of Insulin Degludec Achieves Similar Glycemic Control and Safety Compared to Dosing the Same Time of Day in People with Type 1 Diabetes (348-OR)

David L. Russell-Jones, MD (Royal Surrey Country Hospital, Guildford, United Kingdom)

Dr. Russell-Jones presented data from a 26-week treat-to-target trial comparing two dosing regimens of degludec (once-daily with dinnertime vs. a “forced flexible” schedule with alternating intervals of eight and 40 hours) for basal-bolus therapy with insulin aspart in people with type 1 diabetes (n=329; mean age 43-45 years, disease duration 17-20 years, BMI 26-27 kg/m2, ~80% white). The two degludec regimens were equivalent with regard to A1c reduction (0.4% from baseline of 7.7% in both groups) and similar in overall incidence of hypoglycemia below 56 mg/dl (82.4 vs. 88.3 events/patient-year), severe hypoglycemia (0.34 vs. 0.37 events/patient-year), and non-glycemic adverse events. The flexible regimen caused significantly smaller changes in mean fasting plasma glucose (-23.0 vs. -45.7 mg/dl, from baselines of 172.7 and 179.4 mg/dl) but produced significantly lower rates of nocturnal hypoglycemia below 56 mg/dl (6.2 vs. 9.6 events/patient-year, 37% rate reduction). Dr. Russell-Jones hypothesized that the forced-flexible group may have experienced less hypoglycemia and higher mean blood glucose because the study’s investigators and patients were relatively more cautious until they grew accustomed to the unconventional dosage schedule.

  • The presentation by Dr. Russell-Jones was on a prespecified subanalysis of the BEGIN Flex 1 study, which also included a once-daily glargine arm. In a 26-week extension period of the 26-week study, forced-flexible and once-daily degludec patients were allowed to use “free flexible” dosing. Basal-bolus therapy with the free flexible degludec regimen, relative to once-daily glargine, was demonstrated to provide equivalent glycemic control with less nocturnal hypoglycemia (ADA 2012 2162-PO).
  • Basal insulin dosage increased slightly from week 1 to week 26 in both groups: from 0.35 U/kg/day to 0.42 U/kg/day in the forced flexible arm, and from 0.34 U/kg/day to 0.38 U/kg/day in the once-daily arm. Titration was based on a simple formula targeting a pre-breakfast fasting glucose of 4.0-5.0 mmol/l (72-90 mg/dl), with daily dose adjusted by +/- two units if the two-to-three day average result was outside this glucose range.
  • Dr. Russell-Jones concluded that the viability of flexible degludec dosage could present a significant advantage to patients in terms of both convenience and likelihood of treatment adherence.  As a reminder, flexible dosage has been shown a safe and effective option in type 2 diabetes as well (see page 163 of our ADA 2011 full report at

Questions and Answers

Q: Patients on the forced-flexible regimen had higher FPG and reduced risk of hypoglycemia – might their glucose have run higher at night?

A: As I showed, the 4 am sample in the 9-point SMBG profile was not statistically significantly different. As an investigator or a patient in this study, you were told that there is a new long-acting dose that can be dosed alternately at 40 and 8 hrs. Under these circumstances there is a natural wariness on all parts, and I expect they were a little more careful until they got used to it.

Q: Did you compare blood glucose values between consecutive days in the forced-flexible regimen, to see whether there were differences when the interval was 40 hours vs. eight hours?

A: One of the safety points is that a lot of blood glucoses are measured and recorded. There were slight differences but these didn’t reach significance.

Q: The mean fasting glucose achieved was 138 – can you comment on the failure of treating to target [i.e., 90 mg/dl]?

A: I think treating to target is always difficult in multicenter / multinational studies. Also, there was some nervousness in using the new insulin. The important thing is that there was a significant reduction in glucose values.

Q: Could you comment on the high hypoglycemic rate vs. other studies in type 1 diabetes? In this study it was nearly 90 events per patient-year, as opposed to 40 events per-patient year in a recent study published in Lancet [Editor’s note: we think this is a reference to the 2012 Lancet paper by Heller et al. comparing glargine and degludec in basal-bolus dosing].

A: The event rate is indeed slightly higher than in some other studies. We are assuming that this is because patients they were made to measure glucose much more frequently than in other trials – when you measure more frequently you find higher rates of hypoglycemia, as has been previously shown. This was a very rigorous protocol; the insulin glargine arm had similar rates of hypoglycemia.


Insulin Degludec 200 U/mL is Ultra-Long-Acting and Has a Flat and Stable Glucose-Lowering Effect (349-OR)

Tim Heise, MD (Profil Institute for Metabolic Research, Neuss, Germany)

Dr. Heise discussed a small PK/PD study of U200 degludec in 16 patients with type 2 diabetes (mean BMI: 30 kg/m², mean A1c: 7.3%). The U200 formulation will allow up to 160 units of degludec to be administered in a single injection using a newly developed prefilled pen (vs. 80 units of degludec in the U100 formulation). This study examined U200 degludec delivered at 0.6 units/kg. The study found that the U200 formulation had very similar properties to the U100 formulation. Pharmacodynamics for degludec U200, measured as AUC GIR 0-12 hours/AUC GIR 12-24 hours was 54%, compared to 53% for the U100 formulation. Terminal half life for the U200 formulation was 26.2 hours, compared to 25.1 hours for the U100 formulation.

Questions and Answers

Q: There is a concern among clinicians that if you’re dosing an ultra long acting insulin that goes beyond 36-40 hours, and you’re doing it every 24 hours, you’ll see stacking of insulin and hypoglycemia. That did not seem evident from the data presented.

A: There is a misperception that if something is long acting, it will increase forever. As is true for drugs, once you reach a steady state, you stay at steady state. What you give in the body is what you get out of body. The levels remain the same.


Human Hyaluronidase + Rapid Analog Insulin (RAI) Improves Postprandial Glycemic Control in Type 1 Diabetes (T1DM) Compared to Insulin Lispro Alone (353-OR)

Irl Hirsch, MD (University of Washington, Seattle, WA)

Dr. Hirsch presented the results from a crossover design study of Halozyme’s PH20 in 117 patients with type 1 diabetes on MDI. It compared a coformulation of lispro or aspart plus PH20 (hence referred to as analog-PH20) to lispro alone. Patients were randomized to one of the two groups for 12 weeks of treatment and then crossed over to the other group for another 12 weeks of treatment. The study met its primary endpoint for A1c non-inferiority – A1c at screening was 7.4% in both groups, 7% at baseline four to six weeks later, and ended the study at 6.8% for lispro alone and 6.9% for analog-PH20. PH20 really shined in improving postprandial glucose excursions (an 82% overall reduction vs. lispro alone; p=0.004) and reducing hypoglycemia events ≤70 mg/dl by 5% (p=0.035) and events <56 mg/dl by 7% (p=0.045). Total daily insulin dose was similar between both groups (slightly trending in favor of PH20) and weight gain was negligible for both groups (again slightly trending in favor of PH20). The safety profile looks quite good, with no apparent sign of increased injection site pain, reactions, or adverse events. The two 12-week study periods recorded five severe hypoglycemia events when patients were on lispro-alone, compared to one event when on analog-PH20 – as PH20 moves to larger phase 3 studies, we think the differences will become even more noticeable. As a reminder, in addition to the PH20 coformulation strategy outlined in this study, Halozyme has also tested the effect of pre-administration of PH20 prior to insulin pump infusion set insertion; see abstract 34-LB for more details.

  • This double blind, crossover design study randomized 117 people with type 1 diabetes with a mean age of 43 years, a mean A1c of 7%, and a mean BMI of 27.3 kg/m2. Participants were 55% male, 95% white/Caucasian. The study occurred at 18 sites. Three patients withdrew consent to participate in the study and one was lost to follow-up, leaving 113 completers.
  • After a 4-6 week run-in period using prandial glulisine plus twice daily glargine, participants were randomized to use either lispro plus PH20 (intervention), aspart plus PH20 (intervention), or lispro alone (comparator) for 12 weeks. Patients then crossed over to the comparator group or intervention group as appropriate for a second treatment period of 12 weeks. All participants received twice daily insulin glargine throughout study. Randomization only occurred if patients were able to meet fasting blood glucose targets of 80-120 mg/dl. During treatment periods the focus was on postprandial targets <140 mg/dl. Prandial doses were given immediately before meals. Aspart plus PH20 consisted of normal insulin aspart (100 units/ml) formulated with rHuPH20 (5 μg/ml) and Lispro-PH20 consisted of normal insulin lispro (100 units/ml) formulated with rHuPH20 (5 μg/mL). Both were in preserved formulations suitable for storage at 77 degrees Fahrenheit (25 degrees Celsius) for up to four weeks.
  • The primary endpoint, A1c, was not significantly different between lispro alone and Analog-PH20. The study easily achieved the pre-specified noninferiority margin of 0.4%.


A1c at Screening

A1c at Baseline

A1c at Endpoint










  • Use of PH20 was associated with an 82% overall reduction in postprandial (90 min) glucose excursions over the 12 week period (p=0.004). This data was also summarized on slides showing a ten-point glucose profile for insulin lispro alone and analog-PH20. These graphs (and the table below) showed that the reduction in post-meal spikes was most noticeable after breakfast and after dinner.



% Reduction in Glycemic Excursion


73% (p=0.017)


34% (p=0.44)


219% (p=0.04)


82% (p=0.004)

*Overall mean postprandial glucose change (90 minutes) from pre-meal baseline using routine SMBG monitoring throughout each treatment phase.

  • Compared to using lispro alone, use of analog-PH20 reduced overall hypoglycemic events (≤70 mg/dl) by 5% (p=0.035) and events <56 mg/dl by 7% (p=0.045). Hypoglycemia include severe, documented symptomatic, asymptomatic, or probable symptomatic (as defined by ADA and FDA draft guidance). Over the full 12 weeks of treatment, five episodes of severe hypoglycemia occurred in the lispro only group compared to just one event in those on an analog plus PH20. The slide did not specify a p-value for this comparison, so initially we were not sure if this difference was statistically significant. However, this was certainly a valuable trend and we suspect statistical significance would arise in a larger study; in a talk to Dr. Hirsch after the presentation, he clarified that there were not enough events to do statistics on this.
  • Total daily insulin dose was similar between both groups (slightly trending in favor of PH20) while weight gain was negligible for both groups (slightly trending in favor of PH20). Total daily dose was 54 units for analog-PH20 vs. 56 units for lispro alone (p=0.06). From a baseline weight of 182 lbs, those in the lispro group gained 0.1 lbs on average while those in the PH-20 group lost 0.25 lbs on average (a difference of -0.57 lbs; p=0.27).
  • Adverse events were comparable between treatment periods. The analog-PH20 group had 53% of participants reporting at least one averse event, compared to 51% in the lispro alone group. The two categories where PH20 trended higher for AEs were nervous system disorders (7 patients in analog-PH20 (5.3%) vs. 3 patients for lispro alone (2.6%)) and skin and subcutaneous (6 patients in analog-PH20 (6.1%) vs. 3 patients for lispro alone (2.6%)). For the other eight categories, AEs were nearly identical. Treatment phase severe adverse events were limited to severe hypoglycemia, which occurred in two subjects treated with lispro alone. (This represents a discrepancy from a previous slide that stated five events in five patients were seen in the lispro alone group). 
  • No increase in injection site pain was observed with PH20. This analysis included all randomized patients.




Total Injections Assessed















Moderately Severe



Severe and Not Tolerable




  • There were no meaningful changes in insulin or PH20 immunogenicity profiles. Thirteen patients had positive antibodies at baseline (prior to PH20 exposure) with a geometric mean titer of 1:116. Twelve subjects had positive antibodies at endpoint with a geometric mean titer of 1:120. No neutralizing antibodies have been detected in any PH20 trial to date.

Study Sequence


% of Sub/Mean% Binding

End of Rx Period 1

% of Sub/Mean% Binding

End of Rx Period 2

% of Sub/Mean% Binding















Questions and Answers

Q: Why was there no significant difference in postprandial excursions at lunch time?

A: I cannot even speculate on that. You would expect to see it on all three meals. In our CGM data, we had these differences at breakfast and dinner. But there was no difference in A1c.

Q: Do you see this therapy as compatible with pump? Or does constant infusion present differences.

A: Obviously, some people are concerned with this mechanism of action with insulin pumps. We’ll have to wait and see. I’m excited to be involved in some pump studies. I am very hopeful that there are no problems with this.

Comment: I think there is a poster using this in pump therapy. It used an injection of PH20 before insertion of the catheter.

Q: When was insulin administered before meals?

A: The way we usually do it with insulin therapy: right before eating.

Q: For injection site reactions, you showed the data on pain. What about erythema?

A: No. There was none whatsoever.

Q: Regarding the data you showed on immunogenicity, there was no evidence of that?

A: We didn’t see it in this trial at all.


Evaluations of Modified Ultra-Rapid Acting Linjeta Formulations BIOD-105 and BIOD-107 in Patients with Type 1 Diabetes (350-OR)

Jessica R. Castle, MD (Oregon Health & Science University, Portland, OR)

Dr. Castle reviewed phase 1 data on four ultra-rapid-acting variants of Biodel’s Linjeta formulation: BIOD-105 and BIOD-107 (both dropped from development due to unfavorable pharmacokinetic data), BIOD-123 (the company’s lead candidate, which is slated to begin a 70-patient phase 2 study in 3Q12), and BIOD-125. Both BIOD-123 and BIOD-125 exhibited significantly faster time to half-max, non-significantly slower time to max concentration, and slower drop-off in concentration. (The study was not powered to evaluate pharmacodynamics, though the analogs’ slower drop-off seemed to translate to a statistically non-significantly slower drop-off in insulin action relative to lispro.) With regard to injection site tolerability – a concern with the original Linjeta formulation – discomfort with BIOD-123 was rated 3.6 on a scale of 100, comparable to 1.8 for lispro.

  • Dr. Castle described a single-center, randomized, double-blind, three-period crossover study in patients with type 1 diabetes (n=12) to compare BIOD-123 (Linjeta formulation stabilized with magnesium sulfate) and BIOD-125 (stabilized with a calcium salt) against Homolog (Lilly’s insulin lispro). (The magnesium and calcium, which can act as nerve-stabilizing agents, were added to the Linjeta formulation in order to reduce the Linjeta-related injection site discomfort that was attributed to disodium EDTA). The size of the subcutaneously delivered bolus was 0.2 U/kg for each analog. The study also included evaluation of injection site tolerability. Results, which the company first reported on April 16, are presented below (see additional details in the April 17, 2012 Closer Look at Dr. Castle noted that the study was not powered to detect a difference in pharmacodynamics, though lispro seemed to show a non-significant trend toward faster drop-off in action than either BIOD-123 or -125.


Pharmacokinetic Profiles


Early T½ -max in minutes

Tmax in minutes

Late T½ max in minutes


27.0 ± 2.7                 (25.9)

65.0 ± 7.0                  (67.5)

151 ± 11                       (150)

BIOD 123

9.8 ± 1.1*                    (9.6)

46.4 ± 14.9                  (25.0)

206 ± 35                     (170)

BIOD 125

12.4 ± 2.0*                 (9.4)

60.8 ± 15.2                  (30.0)

179 ± 41                      (140)


7.9 ± 0.5

29.4 ± 4.6


Data represent the mean ± SEM; median values are presented in parentheses. *p <0.001 vs. Humalog



Tolerability          (VAS 0 – 100 mm)

Absolute Severity Score

Relative Severity Score


1.8 ± 1.1

0.17 ± 0.11

2.92 ± 0.08

BIOD 123

3.6 ± 2.1

0.36 ± 0.15

2.91 ± 0.25

BIOD 125

6.8 ± 2.9*

0.50 ± 0.19

3.08 ± 0.26


22.0 ± 2.8

1.08 ± 0.21

3.54 ± 0.18

100 mm Visual Analog Scale: 0=no discomfort, 100=worst possible discomfort; Absolute Severity Scale: 0=None, 1=Mild, 2=Moderate, 3=Severe; Relative Severity (compared to usual meal-time injections): 1=Much less, 2=Less, 3=Equal, 4=Increased, 5=Greatly increased; *p <0.05 vs. Humalog

  • Dr. Castle also discussed two early-stage studies of BIOD-105 and BIOD-107, two fast-acting insulin formulations that Biodel dropped from development last fall in light of the evaluations’ negative results (see the October 27, 2011 Closer Look at Both studies were crossover-design comparisons to lispro in people with type 1 diabetes, the first (n=18) with subcutaneous dosing and the second (n=8) in pumps. Both BIOD-105 and -107 were absorbed significantly more rapidly than lispro (T1/2 early 15.3, 16.6, and 23.7 min), and neither caused greater local injection-site discomfort compared to lispro (which had been a problem for Linjeta, the candidate of which both BIOD-105 and -107 were variants). However, both formulations also had lower peak metabolic effects as measured by GIRmax (5.8, 6.4, and 7.2 mg/kg/min) and slower declines in action profile as measured by T1/2-GIR-late(279, 281, and 211 min) compared to lispro, which convinced Biodel to pursue BIOD-123 and BIOD-125 instead.

Questions and Answers

Q: The new candidates still show a two-to-threefold difference in tolerability relative to the comparator.

A: I would consider a 5 out of 100 on the discomfort scale to be very mild, but point well taken.

Q: I can’t get my head around how this insulin works. You suggested it should be monomeric. How can it then have a longer duration of action?

A: No one knows. Maybe after it is delivered subcutaneously, hexamers form to a small degree under the skin. But it is a very mild increase in offset; we will check on this.

Q: How much evidence that it is completely monomeric when you inject it?

A: Dr. Alan Krasner has in vitro data on this. [Editor’s note: Dr. Krasner is Biodel’s chief medical officer]  

Q: These formulations still don’t have the same degree of PK advantage relative to Linjeta. Might it not be better to go back to the original formulation where tolerability issues might be less, especially for applications where boluses would be small – such as in closed-loop research?

A: The PK profiles with the recent formulations are actually similar to the original Linjeta. We are also interested in using our formulations with insulin analogs rather than human insulin to improve from here.

Q: Did the original formulation have this delayed clearance effect?

A: Yes, it did.


Oral Sessions: The Clinical Management of Diabetes

Association of Treatment Persistence and Adherence with Real-World Outcomes Among Insulin-Treated Patients with Type 2 Diabetes Mellitus (T2DM) (15-OR)

Wenhui Wei, PhD (Sanofi, Bridgewater, NJ)

Dr. Wei presented the results of a study examining therapy adherence and persistence in patients with type 2 diabetes initiating insulin therapy. He and colleagues performed a pooled patient level analysis on published database studies. Subjects analyzed (n=5000) were adults with type 2 diabetes who had previously been on oral antidiabetic therapy or GLP-1 and who had initiated basal insulin analogs. Treatment adherence was defined as percent of doses taken over a one-year period as measured by a medication possession ratio (MPR), while persistence was measured by the number of days on treatment without switching or discontinuation during that one-year period. 65% of patients persisted on therapy during the course of the one-year follow-up, but the average MPR was only 45.3%. Patients who persisted on therapy had significantly lower A1cs at the end of the year than their non-persistent counterparts, and were less likely to experience hypoglycemia or an ER visit. Notable study limitations included the authors counting a claim for a filled prescription as medication consumption and their use of the MPR, which Dr. Wei noted is not an ideal measure of adherence for insulin.

  • A major question with insulin therapy is whether a patient actually takes it once you prescribe it. Poor medication adherence is a major issue in type 2 diabetes. It is very difficult to measure the treatment adherence and persistence in the real world. 
  • This study examined the association between insulin prescription versus adherence and persistence among type 2 diabetes patients initiating basal insulin therapy. It utilized a pooled patient level analysis from three published database studies that had all used the same national managed care claim database (IMPACT). Patients included in the study (n=5000) were adults with type 2 diabetes who were previously been on an oral antidiabetic therapy or GLP-1 and had recently initiated basal insulin analogs. Primary outcomes were treatment adherence (percent of doses taken as prescribed over a one-year follow up period, measured by a medication possession ratio over this period; adherence is defined as an MPR ≥ 80%) and persistence (defined as remaining on treatment throughout the one-year follow-up period or the number of days on treatment without discontinuation or switching during the one-year follow up). Additional outcomes included A1c reduction from baseline after one year, hypoglycemia related events during the period, and healthcare utilization costs.
  • 65% of patients persisted on therapy over the course of the year, while the average MPR was 45.3%. Patients remained on treatment for an average of 380 days. Patients who persisted on therapy during the one year follow-up period were significantly less likely to experience hypoglycemia or an ER visit. They additionally had significantly lower A1cs at one-year. Those who persisted on therapy had significantly higher drug costs, but slightly lower healthcare costs, than those who didn’t. Similarly, prescription drug costs were higher for adherent patients than their non-adherent counterparts. However, healthcare costs for the two groups were about the same. Patients who were adherent had a significantly lower risk of being hospitalized and going to the ER. Adherence was not associated with hypoglycemia rates or A1c change after a year. Notably, patients who experienced one hypoglycemic episode during the course of treatment were much less likely to persist on therapy.
  • The study had a number of notable limitations. Most significantly in our view, it used the presence of a claim for a filled prescription to calculate the MPR, although this measure does not accurately reflect consumption. Additionally, it was a retrospective database study, so causality can’t be established, and it used a claims database, which may be subject to coding error. Lastly, according to Dr. Wei, MPR is not an ideal measure of adherence for insulin, possibly limiting the adherence conclusions. 


Clinical Characteristics and Outcomes of T2DM Patients Adding 1 to 3 Stepwise Prandial Insulin Doses to Basal Insulin and Oral Therapy: Identifying a Problematic Subgroup (14-OR)

Matthew Riddle, MD (Oregon Health Sciences University, Portland, OR)

In the 60-week All to Target trial ( ID: NCT0038405), patients with type 2 diabetes failing on oral antidiabetics were randomized to twice-daily premixed 70/30 biaspart, insulin glargine with the option to add one dose of insulin glulisine, or insulin glargine with the option to add up to three doses of insulin glulisine. A1c reduction and A1c level differed little between the regimens at the end of the study; insulin glargine with up to three prandial injections was not clearly more effective than the other regimens. This recent subgroup analysis investigated why adding up to three injections of prandial insulin did not produce better results than one injection. At baseline, participants (n=191) had a mean age of 55 years, diabetes duration of 9.5 years, BMI of 32.7 kg/m2, and A1c of 9.4%; all used 2-3 oral antidiabetics). Those who took insulin glargine only (G+0), insulin glargine plus one (G+1), two (G+2), or three (G+3) injections of insulin glulisine at endpoint (week 60 or LOCF) had baseline A1cs of 9.1%, 9.2%, 9.6%, and 10.4%, respectively (mean A1c was higher for the G+3 group). At endpoint, mean A1c and insulin dose were higher in the G+3 group (8.3%; 1.83 U/kg) compared to G+0 (6.8%; 0.63 U/kg), G+1 (6.9%; 0.95 U/kg), and G+2 (7.3%; 1.22 U/kg). There were no significant differences in weight gain or hypoglycemia between the four groups. In conclusion, Dr. Riddle noted that those not attaining glycemic control (A1c <7%) with a simple regimen of basal insulin alone or basal insulin + one injection of prandial insulin pose therapeutic problems not routinely solved by full basal-prandial insulin regimens. He also mentioned that high baseline A1c is the most obvious predictor of poor outcomes, and that both physical and psychological factors contribute to the challenges this group faces.

Questions and Answers

Q: I was so taken aback by the figures you showed where A1c from 24 weeks onward did not budge, yet insulin dose continued to go up. You mentioned that there might be some level of insulin resistance we’re not catching, and some adherence we’re not catching, but what does your gut tell you is going on?

A: My gut tells me that more than one thing is going on. There are physiological differences between these people. Concurrent illnesses interfere for some people. I’m confident that behavioral problems with adherence also contribute.

Q: Since the patients started off with higher glucose levels, is it possible that arbitrary time intervals were less sufficient for those who had the highest baseline A1cs to start? If you have an arbitrary 12-week cut, maybe that amount of time wasn’t enough for some of those with the highest baseline A1cs. Was the percentage of patients who had fasting glucose less than 100 mg/dl on insulin glargine alone the same after that 12-week interval?

A: I don’t have the data to answer that question, but to answer your implied speculation, I don’t think changing time intervals or the duration of the study would have changed the results very much because of the plateau after 36 weeks. 



Initial Clinical Experience with Hyaluronidase Preadministration in the Treatment of Type1 Diabetes by Sensor-Augmented Analog Insuli Pump Therapy (34-LB)

Douglas Muchmore, Linda Morrow, Marcus Hompesch, and Daniel Vaughn

This poster presented interim results from an ongoing phase 4, double-blind, randomized, crossover study examining preadministration of rHuPH20 hyaluronidase (Halozyme’s Hylenex) in pump users. A single injection of Hylenex or sham was given at the time of each infusion set change. Although the study is small (n=11) and not yet completed, the data looks strong thus far: preadministration of Hylenex resulted in significantly faster insulin absorption, shorter duration of action, and significantly improved postprandial glucose values relative to sham injection. Adverse events were minor and do not seem to be an issue, although one patient reported infusion site pain and one reported a stinging sensation after Hylenex treatment. We believe the preadministration strategy has a major advantage over the insulin coformulation strategy, as Halozyme’s Hylenex is already FDA approved for other indications to speed subcutaneous drug delivery. Of course, one major downside to preadministration is the extra patient hassle, though this seems minor if it’s only once every three days. From a commercial perspective, the downside to preadministration is limiting the target market to pump users, though the regulatory advantages would somewhat offset this – although the fact that it isn’t yet approved for pump use would likely limit the payment for it. As we noted in Halozyme 1Q12 (see our report at, the company is developing its PH20 coformulation programs for MDI and pump users in parallel and is in partnership talks for each. An insulin pump indication for Hylenex preadministration is also being considered; a decision on this is expected in 2H12 – possibly at the company’s October 2 analyst meeting, which is slated to include phase 4 data on the pump program.  

  • This ongoing, phase 4 study examined the preadministration of hyaluronidase in pump users in an inpatient and outpatient setting. The double-blind, crossover design study randomized pump users to a sham injection or a single injection of 150 units in one cc of PH20 (Halozyme’s Hylenex; FDA approved for other indications) at the time of each infusion set change. Participants came to the clinical research setting for euglycemic clamps (0.15 units per kg bolus) at the study beginning (clamp one; performed within four hours), after three days of continuous use (clamp two), and returned every three days thereafter for insertion of a new infusion set and a test meal. All infusion set changes occurred in the clinical research setting. Participants used sensor augmented pumps for 12 days during each treatment phase. The inpatient data includes the two clamp experiments and the in-clinic meal challenges. The outpatient results come from CGM data.
  • Interim results include data from 11 individuals (in-clinic) and three individuals (outpatient) with type 1 diabetes. In-clinic participants to date have included eight females and three males with a mean age of 31 years, a mean BMI of 25 kg/m2, and a mean A1c of 7.8%. Outpatient results include data from: (1) a 22-year old Caucasian female (BMI: 26.7 kg/m2, A1c: 8.4%); (2) a 38-year old Hawaiian female (BMI: 29.9 kg/m2, A1c: 7.9%), and (3) a 21-year old Caucasian female (BMI: 21.9 kg/m2, A1c: 8.4%). All three patients were experienced pumpers, although patients (2) and (3) were new to CGM.
  • Relative to sham, preadministration with Hylenex resulted in a significantly faster insulin onset of action and shorter duration of action. The following table includes interim, in-clinic data from 11 participants. In the two tables shown below, p-values for treatment effect are mixed models with fixed effects for treatment, day, and interaction; compound symmetric covariance matrix among repeated measures was assumed.





Treatment Effect


Clamp 1

Clamp 2

Clamp 1

Clamp 2


Onset of Action (early T50%, mins)





-21 (p=0.005)

Duration of Action (mins)





-30 (p<0.0001


  • Following an solid meal challenges, Hylenex preadministration resulted in significantly improved postprandial glucose values. The following table includes interim data from eleven participants in the sham arm and ten in the Hylenex arm. Test solid meals were patient specific, standardized meals and insulin doses were given immediately prior to meal consumption. No details in the poster were given on meal size. We’re also not clear on why one patient was missing from the Hylenex group in the table below, though it may be related to the ongoing nature of the study.

Mean Postprandial Glucose (mg/dl) Following Solid Meal Challenge




Treatment Effect

One Hour



-44 (p<0.0001)

90 Minutes



-43 (p<0.0001)

Two Hours



-33 (p=0.01)


  • Outpatient interim CGM data from three patients treated suggests a slightly lower mean blood glucose and better time in range after using Hylenex. Data reported is an average of 24-hour CGM readings. Patient (1) had a mean blood glucose 5 mg/dl lower after Hylenex preadministration, patient (2) had a mean 10 mg/dl lower, and patient (3) had a mean 30 mg/dl lower.

Percentage of CGM Readings


Patient 1

Patient 2

Patient 3























Human Hyaluronidase + Rapid Analog Insulin (RAI) Improves Postprandial Glycemic Control in Type 2 Diabetes (T2DM) Compared to Insulin Lispro Alone (882-P)

Richard Bergenstal, David Klonoff, Timothy Bailey, Daniel Vaughn, Douglas Muchmore

This poster complemented the type 1 results presented by Dr. Hirsch on Day #4 of ADA. It compared a coformulation of lispro or aspart plus PH20 (hence referred to as analog-PH20) to lispro alone. Patients were randomized to one of the two groups for 12 weeks of treatment and then crossed over to the other group for another 12 weeks of treatment. The study met its primary endpoint for A1c non-inferiority – A1c at screening was 7.8% in both groups, 7.1% at baseline four to six weeks later, and ended the study at 6.7% for lispro alone and 6.6% for analog-PH20. PH20 showed a benefit in improving postprandial glucose excursions by 21% overall vs. lispro alone (p=0.0001; smaller than the 82% benefit in the type 1 study). Additionally, PH20 was associated with a significant 81% increase (p=0.014) in the number of patients achieving one and two-hour postprandial goals <140 mg/dl. Total daily insulin dose, weight change, injection site pain, rates of hypoglycemia, and number of adverse events were not significantly different between the two treatment periods. The data is nearly identical to the type 1 MDI results presented by Dr. Hirsch, although there was no benefit on hypoglycemia in this type 2 study (and a non-significant trend towards more severe hypoglycemia).

  • This double blind, crossover design study randomized 121 people with type 2 diabetes with a mean age of 49 years, a mean A1c of 7.1%, and a mean BMI of 35 kg/m2. Participants were 60% male and six patients did not finish the study, leaving 115 completers.
  • After a 4-6 week run-in period using prandial glulisine plus twice daily glargine, participants were randomized to use either lispro plus PH20 (intervention), aspart plus PH20 (intervention), or lispro alone (comparator) for 12 weeks. Patients then crossed over to the comparator group or intervention group as appropriate for a second treatment period of 12 weeks. All participants received twice daily insulin glargine throughout study. Randomization only occurred if patients were able to meet fasting blood glucose targets of 80-120 mg/dl. During treatment periods the focus was on postprandial targets <140 mg/dl. Prandial doses were given immediately before meals.
  • The primary endpoint, A1c ,was not significantly different between lispro alone and Analog-PH20. The study easily achieved the pre-specified noninferiority margin of 0.4%.


Mean A1c at Screening

Mean A1c at Baseline

Mean A1c at Endpoint










  • Use of PH20 was associated with an 21% overall reduction in postprandial (90 min) glucose excursions over the 12 week period (p=.0001). Data is from routine SMBG monitoring throughout each treatment phase. This data was also summarized on graphs showing three-day CGM data during the last two weeks of treatment. These graphs (and the table below) showed that the reduction in post-meal spikes was most noticeable after breakfast and after dinner.

Postprandial Glucose Excursions (mg/dl)


Lispro Alone


% Reduction in Glycemic Excursion




-22% (p=0.0005)




-9% (p=0.51)




-23% (p=0.033)




-21% (p=0.001)

*Overall mean postprandial glucose change (90 minutes) from pre-meal baseline using routine SMBG monitoring throughout each treatment phase.

  • A significantly higher percentage of patients during the analog-PH20 treatment period reached postprandial goals. Data in the table below refers to the percentage of patients able to meet postprandial goal for at least two-thirds of meals during three days of ten-point SMBG testing.


Lispro Alone


% Increase

2 hour <140 mg/dl



61% (p=0.0007)

Both 1 and 2 hour <140 mg/dl



81 (p=0.04)


  • There was no significant difference between lispro alone and analog-PH20 in terms of hypoglycemic events. Analog-PH20 had an non-significant increase in severe hypoglycemia events. This was in contrast to the type 1 diabetes study, where PH20 was associated with a benefit on hypoglycemia and a non-significant improvement in severe hypoglycemia.

Overall 12-week Hypoglycemia Event Rate
(rate per four weeks, events per patient)


Lispro Alone



<70 mg/dl




<56 mg/dl




Severe Hypoglycemia


1 patient with 1 event
1 patient with 3 events



  • Weight gain and total daily insulin dose were similar between both groups. Total daily dose was 123 units for analog-PH20 vs. 127 units for lispro alone. Body weight increased by 3.44 lbs during treatment with lispro alone and 3.35 lbs with analog-PH20.
  • Adverse events were comparable between treatment periods. The analog-PH20 group had 64% of participants reporting at least one averse event, compared to 51% in the lispro alone group. The three categories where analog-PH20 had more than a one-patient difference from lispro alone for AEs were gastrointestinal disorders (18 patients in analog-PH20 (14.9%) vs. 9 patients for lispro alone (7.6%)); metabolism and nutrition (four patients in analog-PH20 (3.3%) vs. one patient for lispro alone (0.8%)); and musculoskeletal/connective tissue (9 patients for lispro alone (7.6%) vs. 12 patients for analog-PH20 (9.9%)). Five subjects had severe adverse events: three cardiac disorders on aspart-PH20, one nervous system disorder on lispro alone, and one nervous system disorder on aspart-PH20.
  • No increase in injection site pain was observed during the analog-PH20 treatment period. This analysis included all randomized patients and was similar to that observed in the type 1 trial.




Total Injections Assessed















Moderately Severe



Severe and Not Tolerable




  • There were no meaningful changes in insulin or PH20 immunogenicity profiles. Four patients had positive anti-PH20 antibodies at baseline (prior to PH20 exposure) with a geometric mean titer of 1:12. Five patients had positive anti-PH20 antibodies at endpoint with a geometric mean titer of 1:15. No neutralizing antibodies have been detected in any PH20 trial to date.

Anti-Insulin Antibodies

Study Sequence


% of patients/Mean% Binding

End of Rx Period 1

% of Sub/Mean% Binding

End of Rx Period 2

% of Sub/Mean% Binding
















Better Glycemic Control and Weight Loss with the Novel Longacting Basal Insulin LY2605541 Compared with Insulin Glargine in Patients with Type 1 Diabetes (1026-P)

Julio Rosenstock, Richard Bergenstal, Thomas Blevins, Linda Morrow, Melvin Prince, Yongming Qu, Vikram Sinha, Daniel Howey, Scott Jacober

Dr. Rosenstock and colleagues presented the results of a phase 2, randomized, open-label crossover study to determine if LY2605541 (LY) was noninferior to glargine (GL) for daily mean blood glucose treatment of type 1 diabetes. LY is designed to delay insulin absorption and reduce clearance, resulting in prolonged duration of action. After eight weeks, LY was found to be superior to GL in daily mean blood glucose reduction (baseline 161 mg/dl to 144 in LY and 152 in GL), with reduced prandial insulin dose and, importantly, weight loss rather than weight gain. Mean A1c reductions over eight weeks were also slightly greater for LY than GL (-0.6 vs. -0.4, p <0.001). Overall hypoglycemia rates were higher with LY, but nocturnal hypoglycemia rates were lower. However, more gastrointestinal side effects were observed in participants receiving LY (15.3% vs. 3.8%). Phase 3 studies are underway to assess these observed benefits and evaluate the clinical significance of these side effects.

  • Participants with type 1 diabetes (n=137) received once-daily basal insulin, LY2605541 (LY, n=69) or glargine (GL, n=68), plus prandial insulin for eight weeks, followed by crossover treatment for eight weeks. Basal insulin doses were titrated according to treat-to-target algorithms, and prandial insulin doses were titrated at the discretion of the treating investigator. If the least squares mean difference (LY minus GL) for daily mean blood glucose was found to be <10.8 mg/dl after eight weeks, LY would be found noninferior to GL. If <0 mg/dl, LY would also be found superior to GL.
  • The group of participants receiving LY contained slightly fewer males (59% vs. 66% for glargine), was more recently diagnosed with diabetes, (16.8 years vs. 19.1 years for glargine), and had similar baseline A1c levels (7.7% vs. 7.8% for glargine). Otherwise, participants were of similar overall mean age (38.2 years), ethnicity (94% Caucasian), weight (83.0 kg or 183 lbs), and BMI (27.3 kg/m2).
  • After eight weeks, LY met statistical criteria for superiority vs. GL in reducing daily mean blood glucose (144 mg/dl vs. 152 mg/dl from baseline 161 mg/dl; p <0.001). Mean A1c reduction over eight weeks was greater for LY than GL (-0.6 vs. -0.4, p <0.001). Prandial insulin dose was lower for participants receiving LY than GL (0.19 ± 0.14 vs. 0.24 ± 0.18 U/kg/d, p<0.001.)  On average, individuals receiving LY for eight weeks lost 1.2 kg (~2.6 lbs), whereas individuals receiving GL for eight weeks gained 0.7 kg (~1.5 lbs). But, at crossover, individuals switched from LY to GL experienced a slight increase in mean A1c and weight after another eight weeks, partially reversing A1c reductions (0w: ~7.6%, 8w: ~7.1%, 16w: ~7.2%) and fully reversing earlier weight loss (0w: ~83 kg, 8w: ~82 kg, 16w: ~83 kg).
  • The risk of total hypoglycemia (BG <70 mg/dl) was greater for LY than GL (relative risk 1.12), but the risk of nocturnal hypoglycemia was lower for LY than GL (0.75). Mean liver enzymes were higher in LY than GL, though means remained within normal range. HDL-cholesterol levels reductions were greater in LY than GL (baseline 60±2 to 54 ± 2 vs. 59 ± 2 mg/dl, p <0.001) whereas LDL-cholesterol levels increased in LY but decreased in GL (baseline 96 ± 3 to 102 ± 3 vs. 92 ± 3 mg/dl, p=0.016). Adverse events were similar between groups, except LY was associated with more gastrointestinal events (15.3% vs. 3.8% of participants, p <0.001). 


Contrasting Weight Changes with LY2605541, a Novel Long-Acting Insulin, and Insulin Glargine Despite Similar Improved Glycemic Control in Type 1 Diabetes and Type 2 Diabetes (1023-P)

Scott Jacober, Julio Rosenstock, Michard Bergenstal, Melvin Prince, Yongming Qu, John Beals

The basal insulin analog LY2605541 is a novel, long-acting, PEGylated version of insulin lispro. It is designed to have a large hydrodynamic size that delays insulin absorption and reduces clearance, resulting in prolonged duration of action (t1/2 = 2-3 days). In two phase 2 studies, LY2605541 provided glycemic control comparable with or superior to insulin glargine, while causing weight loss in both type 1 and type 2 diabetes patients (versus weight gain with glargine). The first of these studies was a 12-week, randomized, open-label, parallel trial (n=288; 347-OR) which assessed weight fluctuation associated with once-daily LY2605541 vs. insulin glargine treatment in patients with type 2 diabetes. On average, participants lost 0.6 kg (1.3 lb) with LY2605541 and gained 0.3 kg (0.7 lb) with glargine. In a separate 8-week, randomized, open-label, 2x2 crossover study (n=137; 1026-P), participants lost an average of 1.2 kg (2.6 lb) on LY2605541 and gained 0.7 kg (1.5 lb) on glargine. Across both studies, more LY2605541-treated patients lost any weight or lost at least 5% of their baseline weight compared to glargine-treated patients – it will be very interesting to see in phase 3 if these advantages persist. There was no correlation between weight change and baseline BMI, hypoglycemia incidence, or GI adverse events.

  • Both type 1 and type 2 diabetes LY2605541-treated patients achieved mean weight loss, while glargine-treated patients experienced mean weight gain. Of the type 1 diabetes patients receiving LY2605541 , 66.1% experienced weight loss while only 40.3% of those on glargine treatment did (p <0.001). Similarly, of the type 2 diabetes patients receiving LY2605541 treatment, 56.9% experienced weight loss vs. 40.2% of patients receiving glargine treatment (p = 0.011). In the type 2 diabetes study, 0% of glargine treated patients versus 4.8% of LY2605541-treated patients experienced ≥ 5% weight loss after 12 weeks (p=0.033). In the type 1 diabetes study, 0.8% of glargine-treated patients vs. 11.9% of placebo-treated patients experienced ≥5% weight loss (p <0.001).
  • In the 12-week study, participants with type 2 diabetes receiving LY2605541 (n=188) showed improvements (albeit nonsignificant) in A1c, daily blood glucose and fasting plasma glucose compared to those receiving glargine (n=92). A1c levels dropped from 7.7% to 7.0% in Ly2605541-treated patients and 7.8% to 7.2% in glargine-treated patients. Daily mean blood glucose levels dropped from 170 mg/dl to 139 mg/dl with LY2605541 treatment and from 165 mg/dl to 145 mg/dl with glargine. FPG fell from 148 to 118 mg/dl with LY2605541 and from 140 mg/dl to 117  mg/dl with glargine. Basal insulin doses notably increased in both study groups over 12 weeks (from 0.73 to 0.50 units/kg/day with LY2605541 and 0.38 to 0.50 units/kg/day with glargine).
  • In the type 1 diabetes crossover study, treatment with LY2605541 resulted in significantly (p <0.05) lower A1c, mean daily blood glucose, and prandial insulin dose than glargine treatment after eight weeks. Patients receiving LY2605541 (n=124) saw improvements in A1c (7.7% to 7.1%), daily mean blood glucose (161 mg/dl to 144 mg/dl), and fasting plasma glucose (187 mg/dl to 153 mg/dl) over the eight-week period. Although the change was not as pronounced, patients receiving glargine treatment (n=130) also had improvements in A1c (7.7% to 7.2%.), daily mean blood glucose values (161 mg/dl to 152 mg/dl), and fasting plasma glucose values (187 mg/dl to 155 mg/dl) over the course of the study.  Basal insulin doses increased in both groups by the end of the study (from 0.36 to 0.42 units/kg/day in the LY2605541 group and 0.36 to 0.43 units/kg/day in the glargine group) while prandial insulin doses dropped in both groups (from 0.23 to 0.19 units/kg/day in the LY2605541 group and 0.23 to 0.24 units/kg/day in the glargine group).
  • There was no correlation between weight change with LY2605541 and baseline BMI, hypoglycemia incidence (p=0.65), or gastrointestinal adverse events in either study. GI adverse events were more frequent with LY2605541 in patients with type 1 diabetes (p <0.001), but not those with type 2 diabetes. However, there was no significant difference between rates of any single GI adverse event between the two treatment groups.


Insulin Degludec Reduces Hypoglycemia and Improves Health Status vs. Insulin Glargine in Insulin-Naive Type 2 Diabetes (1047-P)

Helena Rodbard, Yehuda Handelsman, Bertrand Cariou, Tina Johansen, Thorkil Christensen, Chantal Mathieu

Insulin degludec is an ultra-long-acting basal insulin that achieves a stable glucose-lowering effect with low day-to-day variability. Degludec has demonstrated effectiveness in achieving target levels of glucose control in patients with type 2 diabetes and significantly reduces rates of nocturnal hypoglycemia compared with insulin glargine.  This 52-week, open-label, randomized trial compared the health status of insulin-naïve patients with type 2 diabetes treated for 52 weeks with either degludec (n=773) or glargine (n=257) using the SF-36 v2 questionnaire. Questions in this questionnaire are grouped into eight domains, comprising a physical component summary and a mental component summary. Glycemic control and hypoglycemia were also assessed. At the end of 52 weeks, the overall physical component summary score was significantly improved with degludec versus glargine treatment (treatment difference: 1.03 [95% CI: 0.08, 1.97] p=0.03). This was primarily due to improvement in the physical functioning subdomain (treatment difference: 1.35 [95% CI: 0.26, 2.44] p=0.02). Other SF-36v2 subdomains (role-physical, bodily pain, general health, vitality, social functioning, role-emotional, mental health) were not significantly different with degludec treatment. Additionally, at the end of the 52-week period, degludec significantly improved A1c from baseline (7.1% vs. 8.2% at baseline); this improvement was noninferior to that observed with glargine treatment (7.0% vs. 8.2% at baseline). Confirmed nocturnal hypoglycemia was 36% lower (p=0.04) with degludec compared with glargine (relative rate: 0.64 [95% CI: 0.42, 0.98]; p= 0.04) and severe hypoglycemia was 86% lower (p=0.02) with degludec vs. glargine. Overall confirmed hypoglycemia was not significantly lower with degludec. 

  • Patients with type 2 diabetes in this randomized, open-label trial received either degludec (n=773) or glargine once-daily in combination with oral glucose-lowering drugs for 52 weeks. At baseline, both treatment groups were comparable with regards to mean age (59.3 vs. 58.7 years), A1c (8.2%), BMI (30.9 kg/m2 vs. 31.6 kg/m2), fasting plasma glucose (172.8 mg/dl vs.174.6 mg/dl), and duration of diabetes (~9.0 years). The SF-36v2 health status questionnaire, which was translated and linguistically validated into all languages relevant to the study, was administered at baseline and at the end of the trial.
  • For similar reductions in A1c, degludec resulted in a markedly reduced rate of confirmed nighttime and severe hypoglycemic events and improved fasting plasma glucose levels compared with glargine. Incidence of overall confirmed hypoglycemia was 18% lower with degludec than with glargine (p=NS). Relative risk of severe hypoglycemia with degludec vs. glargine was 0.14 ([95% CI: 0.03, 0.70] p= 0.02), while relative risk of nocturnal hypoglycemia was 0.64 ([95% CI: 0.42, 0.98] p=0.04). FPG was significantly lower with degludec versus glargine treatment (estimated treatment difference, degludec – glargine: -7.75 mg/dl [95% CI -13.33, -2.34] p <0.05).
  • While a significant improvement across all aspects of the physical health was associated with degludec, no difference was observed between the two treatment groups with regards to mental health.


Hypoglycemia Risk with Insulin Degludec Compared with Insulin Glargine in Type 2 and Type 1 Diabetes: A Prospective Meta-Analysis of Phase 3 Trials (387-P)

Robert Ratner, Stephen Gough, Chantal Mathieu, Stefano Del Prato, Bruce Bode, Henriette Mersebach, Lars Endahl, Bernard Zinman

Insulin degludec is an ultra-long acting basal insulin that achieves a stable glucose-lowering effect with low day-to-day variability. This pre-specified meta-analysis compared the rate of hypoglycemia between degludec once-daily and insulin glargine once-daily across seven phase 3a trials (two in patients with type 1 diabetes and five in patients with type 2 diabetes, total n=4330). Each trial consisted of a titration period and a maintenance period (together comprising a “total treatment period”). Insulin naïve patients with type 2 diabetes treated with degludec experienced significantly lower rates of overall confirmed, nocturnal confirmed, and severe hypoglycemic episodes (lower by 17%, 36%, and 86%, respectively) during the total treatment period compared with patients treated with glargine. In the complete type 2 diabetes population, overall confirmed and nocturnal confirmed hypoglycemic events were  both significantly lower (by 17% and 32%, respectively) with degludec versus glargine treatment. In the type 1 diabetes population, neither rates of overall confirmed, severe, nor nocturnal hypoglycemia differed significantly between degludec and glargine treated groups during the total treatment period. However rates of nocturnal hypoglycemia episodes were 25% lower (p < 0.05) with degludec vs. glargine during the maintenance period. In the pooled type 1 and type 2 diabetes population, subjects treated with degludec had significantly lower rates of overall confirmed hypgolycemia (9% reduction) and nocturnal (26% reduction) hypoglycemia than those receiving glargine. This meta-analysis provides evidence for the hypoglycemia-related benefits of insulin degludec in both type 1 and type 2 diabetes patients.

  • All seven trials included in this analysis were randomized, controlled, open-label, multicenter, phase 3a treat-to target trials of 26 or 52 weeks duration. Baseline characteristics for all participants (n=4330) were comparable between degludec (n=2899) and glargine (n=1431) treatment groups. Notably, nearly 40% of type 2 patients and 70% of the type 1 diabetes population involved in the study had previously used glargine therapy.
  • The difference in rate of hypoglycemia with degludec vs. glargine treatment was most apparent for nocturnal hypoglycemia. This metric was consistently lower among the type 1, type 2, and overall pooled populations, as well as across basal-only and basal-bolus populations.  In the type 1, type 2, and overall pooled populations, rates of nocturnal hypoglycemia were reduced by 17% (nonsignificant), 32% (significant), and 26% (significant) with degludec vs. glargine treatment – this effect was more pronounced in the maintenance period, where nocturnal rates were 38%, 25%, and 32% (all significant) lower with degludec vs. glargine in the type 1, type 2, and overall pooled populations, respectively. The effects of degludec on hypoglycemia were particularly evident in the type 2 insulin naïve population – in this group, overall confirmed hypoglycemia, nocturnal hypoglycemia, and severe hypoglycemia were all significantly reduced during the total treatment period (by 17%, 36%, and 86%, respectively).


Meet the Expert Sessions

Ongoing Challenges of Achieving Glycemic Control in Youth with Type 1 Diabetes

Georgeanna Klingensmith, MD (Barbara Davis Center, Denver, CO)

Dr. Klingensmith fielded practical, clinically focused questions on managing type 1 diabetes in youth.  Topics covered during the session ranged from insulin dosing before and after gym class, the benefits of MDI versus pumps in particular populations, and managing glycemia in children directly after the honeymoon period. She interestingly also touched on treatment of neonatal diabetes, providing care for children whose A1c and logbooks don’t jive, dealing with children who don’t bolus, and insulin recommendations for children taking steroids.


What We Know About Insulin and Cancer

John Buse, MD, PhD (University of North Carolina School of Medicine, Chapel Hill, NC)

Dr. Buse opened the symposium with a brief overview of the relationship between insulin and cancer. In particular, he highlighted that the four Diabetologia articles that incited the past two years’ controversy came in an already complex field – in addition to the association between diabetes and cancer risk observed in various meta-analyses (particularly breast, colorectal, endometrial, and pancreatic cancers), diabetes and cancer share similar risk factors (e.g., age, obesity, smoking), complicating analyses. Since those articles’ publication, he mused that much of the following research tended toward opinion rather than data, noting that of the 89 publications resulting from a Medline search of “glargine” and “cancer” 51 were editorials, commentary, or reviews. Additionally, he noted that the major circulating component after administration rather than glargine itself is the M1 metabolite – unlike glargine, M1 does not show increased affinity for the IGF-1 receptor, challenging preconceptions for a biological mechanism between the analog and cancer.


Northern Europe Database Study of Insulin and Cancer Risk

Peter Boyle, MD (International Prevention Research Institute, Lyon, France)

Dr. Boyle presented the results from an analysis on the cancer risk associated with insulin glargine use from data collected in Norway, Denmark, Sweden, Scotland, and Finland. A total of 447, 821 users of insulin were included in the study, with over 1.5 million person-years of exposure to insulin and over 17,500 new cases of cancer detected in the cohorts. There was an average of 3.1 years of follow-up for patients using insulin glargine. There was no increased risk for all cancers (HR=0.98; 95% CI 0.94-1.03), breast cancer (HR=1.12; 95% CI 0.93-1.29), colorectal cancer (HR=0.86; 95% CI 0.76-0.98), or prostate cancer (HR=1.11; 95% CI 1.0-1.24) with insulin glargine treatment vs. treatment with other insulins. Exploratory analyses included the risk for cancer with insulin glargine vs. all insulin among new insulin users, and no increased risk was demonstrated for all cancer (HR 1.02; 95% CI 0.94-1.10), prostate cancer (HR=1.04; 95% CI 0.83-1.30), lung cancer (HR=1.22; 95% CI 0.97-1.54), pancreatic cancer (HR=0.92; 95% CI 0.69-1.21), and colorectal cancer (HR=1.10; 95% CI 0.80-1.27). Increased risk for breast cancer in new users did trend toward significance with insulin glargine treatment (HR=1.29; 95% CI 1.01-1.63). However, the result was not consistently demonstrated in each country dataset, and a meta-regression of breast cancer risk with duration of insulin glargine treatment indicated no significant trend. Dr. Boyle remarked that further methodological work was required to further understand this risk. A meta-analysis was also conducted on 21 independent studies (including the above study) on cancer risk with insulin glargine treatment involving one million patients and three million person-years of insulin exposure. There was no detectable increase in risk for all cancers (HR=0.91; 95% CI 0.84-0.99), breast cancer (HR=1.11; 95% CI 1.00-1.24), colorectal cancer (HR=0.83; 95% CI 0.74-0.94), and prostate cancer (HR-1.14; 95% CI 0.85-1.11). 


Results from Kaiser-Permanente Collaboration and a Focus on Prevalent User Analysis

Laurel A. Habel, PhD (Kaiser Permanente, Oakland, CA)

Dr. Habel presented the results from an analysis of insulin glargine use and cancer risk in patients with diabetes treated in California’s Kaiser Permanente system. The analysis included all patients in the Kaiser system with diabetes and two or more fills of glargine or NPH within six months (n=27,418 “ever glargine” and 100,757 “ever NPH” users for prevalent user analyses; n=6,548 “new glargine” and n=39,708 “new NPH” users for new user analyses; median follow-up time 2.3 years with glargine versus 3.6 years with NPH). Results indicated a slightly increased risk (HR 1.3; 95% CI 1.0-1.8) of breast cancer in new glargine users, strengthened when only patients with 2+ years of use were included (HR 1.6; 95% CI 1.0-2.8); however, no increased risk of breast cancer was observed in prevalent user analyses (HR 0.9; 95% CI 0.7-1.2). Furthermore, analyses of prostate cancer (HR 0.9; 95% CI 0.7-1.2 in prevalent users and HR 0.6; 0.4-1.0 in new users), colorectal cancer (HR 0.7; 95% CI 0.5-1.0 in prevalent users and HR 1.1; 0.8-1.6 in new users), and all cancers combined (HR 0.9; 95% CI 0.8-1.0 in prevalent users and HR 1.0; 0.9-1.2 in new users) showed no increased risk with glargine use. Given the long time required for carcinogens to impact cancer development, Dr. Habel suggested the short duration of exposure limited results, as well as the lack of a biologically plausible mechanism for the difference between prevalent user and new user analyses in breast cancer results.

  • This analysis included all patients in the Kaiser system with diabetes and two or more fills of glargine or NPH within six months. Patients further had to be 18 years of age or older, without cancer history, and with continuous membership and pharmacy benefits past 12 months, leaving 27,418 “ever glargine” and 100,757 “ever NPH” users for prevalent user analyses (to investigate the effect of switching from NPH to glargine). This group was narrowed to include only those with no insulin fill in the past 12 months for new user analyses (to investigate whether initiation with NPH vs. glargine is associated with increased risk; n=6,548 glargine and n=39,708 NPH).
  • Dr. Habel indicated mostly similar characteristics between the two groups, though with slightly more obese patients on NPH (33.8% with BMI >35 kg/m2 vs. 27.2%) and slightly younger (19.2% vs. 11.1% of age 18-39) and more male (53.1% vs. 49.7%) and type 1 patients (6.9% vs. 2.8%) on glargine. Median follow-up time (until cancer diagnosis, end of study period, or termination of membership) was 2.3 years with glargine versus 3.6 years with NPH. Dr. Habel indicated a number of variables examined for cofounding, but results only required adjustment for region, calendar year of entry, age, metformin use, and short-acting insulin use.
  • Breast cancer results showed no increased risk of cancer with glargine use (HR 0.9; 95% CI 0.7-1.2) in prevalent user analyses but slightly increased risk (HR 1.3; 95% CI 1.0-1.8) in new glargine users. Increased risk in new users strengthened when only patients with 2+ years of use were included (HR 1.6; 95% CI 1.0-2.8) though risk in prevalent users remained neutral (0.9; 95% CI 0.6-1.4). Given the lack of a biologically plausible mechanism behind this variation between new and prevalent users, Dr. Habel suggested this could potentially only be a chance finding.
  • Further analyses showed no increased risk of cancer associated with glargine use. These included prostate (HR 0.9; 95% CI 0.7-1.2 in prevalent users and HR 0.6; 0.4-1.0 in new users), colorectal (HR 0.7; 95% CI 0.5-1.0 in prevalent users and HR 1.1; 0.8-1.6 in new users), and all cancers combined (HR 0.9; 95% CI 0.8-1.0 in prevalent users and HR 1.0; 0.9-1.2 in new users). Dr. Habel noted that a number of sensitivity analyses were performed as well (e.g., restricted to patients with at least 48 months of health plan membership prior to baseline, restricted to type 2 diabetes patients only) but that none altered results.
  • Given the long time required for carcinogens to impact cancer development, Dr. Habel indicated that the primary limitation of the study was short duration of exposure, suggesting both positive and negative results should be viewed with caution. She also noted a lack of information on some potential cofounders and the possibility that practice patterns in the Kaiser system may not be representative of other settings (though this would only impact results if patterns were related to unmeasured cancer risk factors).


Results from Claims Data and a Focus on Incident User Analysis

Til Sturmer, MD, PhD (University of North Carolina, Chapel Hill, NC)

Dr. Sturmer presented the results from a new user, active comparator analysis of the MedAssurant (Inovalon) healthcare database that examined the risk for cancer after initiation of insulin therapy with insulin glargine (n=43,306) vs. NPH (n=9,147). These individuals were selected from over seven million individuals diagnosed with diabetes in MedAssurant. Dr. Sturmer explained that by selecting only new users in the study and using an active comparator, confounding was minimized, making this type of analysis one of the most precise ways in which to evaluate risk in an observational manner. Criteria for inclusion into the study included insulin glargine or NPH insulin initiation between July 2004 and December 2010, no filled insulin prescriptions in the 19 months prior to insulin imitation, age 18 years or over, at least one prescription refill to demonstrate regular use, and no cancer at first prescription refill. Mean duration of treatment was 1.2 years in the glargine group and 1.1 years in the NPH group using an as treated analysis. Meanwhile, mean duration of treatment was 1.5 years in both groups using an intent to treat sensitivity analysis. Overall, in both analyses, no significant differences were observed for breast, prostate, bladder, colon, and all cancer risk between the insulin glargine and NPH groups, even when examining incidence rates by length of exposure (see below). Several important limitations of the study highlighted by Dr. Sturmer included the short duration of treatment, the observational nature of the study, the lack of data on potential confounders (such as BMI – although BMI was shown to be an unlikely cofounder based on external validation studies), and the lack of internal validation of cancer outcomes.

Table 1: Cancer Incidence Rates – As Treated Analysis





Crude HR

Adjusted HR

Breast Cancer










1.2 (0.7-2.0)

1.1 (0.7-1.8)







Prostate Cancer










1.0 (0.6-1.6)

1.2 (0.7-1.9)







Colon Cancer










0.8 (0.4-1.3)

0.9 (0.5-1.6)







All Cancer










1.1 (0.9-1.3)

1.1 (1.0-1.3)













Crude HR

Adjusted HR

Breast Cancer










1.2 (0.7-2.0)

1.1 (0.7-1.8)







Prostate Cancer










1.0 (0.6-1.6)

1.2 (0.7-1.9)







Colon Cancer










0.8 (0.4-1.3)

0.9 (0.5-1.6)







All Cancer










1.1 (0.9-1.3)

1.1 (1.0-1.3)







*Incidence rate per 100,000 person-years (PY).


Table 2: Breast, Prostate, Colon, and All Cancer Incidence Rates by Time after Treatment Initiation – As Treated Analysis





Crude HR

Adjusted HR

0-6 Months










1.3 (0.6-2.9)

1.0 (0.5-2.1)







6-12 Months










2.2 (0.7-7.3)

1.5 (0.5-4.3)







12-24 Months










0.8 (0.3-2.0)

1.1 (0.4-3.1)







> 24 Months










0.9 (0.2-3.3)

0.7 (0.2-2.5)







*Incidence rate per 100,000 person-years (PY).


Table 3: Cancer Incidence Rates – Intent to Treat Analysis





Crude HR

Adjusted HR

Breast Cancer










1.5 (1.0-2.4)

1.3 (0.8-2.0)







Prostate Cancer










1.0 (0.7-1.6)

1.2 (0.8-1.8)







Colon Cancer










0.8 (0.5-1.3)

1.0 (0.6-1.6)







All Cancer










1.1 (1.0-1.3)

1.1 (0.9-1.2)







*Incidence rate per 100,000 person-years (PY).


Implications for Practice and Future Research

James B. Meigs, MD (Harvard Medical School, Boston, MA)

In a comprehensive presentation, Dr. Meigs tied a bow on the insulin glargine and cancer story. Opening on a broad level, Dr. Meigs discussed the limitations of using pharmaco-surveillance data to analyze drug safety, noting that prevalent user analyses (ever use) do not properly account for past or cumulative exposure and that missing data impacts results; particular to the insulin and cancer story, he indicated that studies must allow time for proper disease development and account for confounding by shared risk factors as well. In light of these limitations, Dr. Meigs highlighted shortcomings in each of the insulin and cancer studies presented during the session, particularly the short duration of follow-up (MedAssurant: median <1 year; Kaiser median: 2.3-3.4 years; Northern European Database mean: 3.1 years); he suggested inconsistency in the results between studies limited applicability as well. On the clinical question of whether glargine was still safe to prescribe, Dr. Meigs answered with a resounding “Yes!” highlighting that ORIGIN (with both the clearest separation of insulin exposure and the longest duration of follow-up) showed no increased cancer risk with glargine use – “putting the stake in the zombie that is insulin glargine and cancer risk.” Given the dearth of such long-term trials, we will be interested to see if lessons learned from this story impact the future of pharmaco-surveillance as a whole, particularly as opinion continues to mount on the limitations of current methods.

  • Reviewing the basics of clinical study design, Dr. Meigs discussed the limitations of using pharmaco-surveillance data to analyze drug safety. Primarily, he noted that prevalent user analyses (ever use) do not properly account for past or cumulative exposure. Particular to the insulin and cancer story, he noted that studies must allow time for proper disease development and account for confounding by shared risk factors; stopping and switching treatment and competing hazards (e.g., fatality due to MI causing exposed patients to be dropped from the pool) complicate eliciting subtle effects of expos