June 21-25, 2013 Chicago IL Full Report – Draft

Executive Highlights

In this final report, we provide our full coverage of the 73rd Scientific Sessions of the American Diabetes Association (ADA), held at the McCormick Convention Center in Chicago, IL from June 21-25, 2013. The conference drew 17,737 attendees, on par with last year’s 17,890 attendees and up from 17,300 people in 2010. In our conversation with ADA organizers, they noted that ADA 2013 included over 14,000 medical professionals, a slight increase of ~3% over last year. A striking 59% of registered attendees were international, representing a total of 117 countries (up from 111 in 2012) – despite “American” in the name of the conference, this statistic truly symbolizes the global nature of diabetes. The largest international contingents hailed from Brazil, followed by Japan, India, China, and Canada (in order of attendance).

With 730 expert speakers this year, the five-day meeting spanned eight tracks and included 92 symposia, 387 oral presentations, 2,511 posters, over 150 exhibits, 60 guided audio poster tours, 16 meet the expert sessions, 11 corporate symposia, nine interest group discussions, five current issues, and four special lectures – a phenomenal array of brainpower, depth of discussion, and breadth of topics. We’ve sorted through the learnings in this final report and include our detailed commentary on 321 talks (up strikingly from 290 last year and 280 in 2010) – drawn from symposia, lectures, oral presentations, corporate symposia, and the meet-the-expert sessions – as well our coverage of 31 posters and 25 exhibits.

Below, we have organized our writing into 12 topic areas for convenient reading: (1) ADA themes – big picture; (2) Artificial pancreas, CGM, insulin pumps, and SMBG; (3) Incretin-based therapies; (4) SGLT inhibitors and other oral therapies (excluding incretins); (5) Novel drug development, basic science, and research funding; (6) Insulin therapies; (7) Cardiovascular disease and other complications; (8) Healthcare delivery, cost-effectiveness, lifestyle, prevention, and epidemiology (9) Obesity pharmacotherapies, metabolic surgery, and other obesity topics; (10) Type 1 therapies – cure related; (11) Treatment algorithms; and (12) our Exhibit hall report, which includes our first ever analysis of Social Media coverage during ADA. Directly below, we detail some of the major themes from ADA 2013 (organized by topic).

Our full report includes several dozen talks not published in our daily ADA reports, and the titles of these talks are highlighted in yellow. We’ve also highlighted in yellow any comments that we found particularly notable.

 

Diabetes Technology

• ADA 2013 showcased closed-loop progress in a big way, with outpatient trials becoming the norm rather than the exception. In a series of oral presentations during “The Journey to a Viable Artificial Pancreas” session, Dr. Revital Nimri (Schneider Children’s Medical Center of Israel, Petach Tikvah, Israel) presented encouraging results from the DREAM 4 closed-loop trial, which compared overnight glycemic control with the MD-Logic AP system to sensor-augmented pump control in the patients’ homes (13-OR). We also heard Dr. Steven Russell’s (Harvard Medical School, Boston, MA) brief update on the ongoing five-day, outpatientBeacon Hill study testing his and Dr. Edward Damiano’s (Boston University, Boston, MA) bihormonal bionic pancreas (15-OR) (to read a patient perspective on the trial, visit http://diatribe.org/issues/55/test-drive). Meanwhile, Dr. Kenneth Ward previewed an upcoming outpatient, hotel-based study exploring his group’s bihormonal closed-loop system (14-OR). Also at ADA 2013, we heard updates from Dr. Boris Kovatchev (University of Virginia, Charlottesville, VA) on two recent outpatient trials comparing open- and closed-loop control. Dr. Helen Murphy (University of Cambridge, Cambridge, UK) and the Cambridge team are even embarking on the very first outpatient feasibility study investigating overnight closed-loop control in pregnant women with type 1 diabetes. In contrast to the previous years, this year’s discussion around the closed-loop overwhelmingly had a sense of “when,” not “if.” While systems are very much still in the feasibility and research phase (e.g., on smartphones rather than in pumps), AP trials are now bolder than we’ve ever seen (e.g., missed meal boluses, exercise, simulated errors). There’s no question that a lot has to go right to bring a device to market, but the rate of progress in recent years has been very encouraging from a patient perspective. This is in large part due to the efforts of JDRF and the Helmsley Charitable Trust, as well as to many committed researchers. Now, we hope companies seize the opportunity and bring devices to market in the coming years.
• ADA 2013 made it abundantly clear that even closed-loop systems with the smallest amount of automation can make a huge difference in patient outcomes. This was most clearly demonstrated in new data on Medtronic’s MiniMed 530G/Veo pump with low glucose suspend. Most striking to us was Dr. Trang Ly’s (Princess Margaret Hospital, Perth, Australia) randomized controlled trial (228-OR) comparing low glucose suspend (LGS) with the Paradigm Veo to pump-only therapy over a six-month period in 95 patients with hypoglycemia unawareness. In what Dr. Hans DeVries called “the most important study at this whole meeting,” LGS eliminated (!) severe hypoglycemia without any increase in A1c in the 46 patients in the Veo group. Medtronic’s late-breaking poster (48-LB) on the ASPIRE in-home study echoed these strong results: big improvements in hypoglycemia without any increase in A1c. In our view, both studies provided strong real-world evidence that even low-glucose-suspend technology – literally a baby step in closed-loop development – will have a very big clinical impact on patients (and from a payer perspective, the severe hypoglycemia benefits are nothing to downplay either). For now, our waiting caps are on as the FDA continues to review the MiniMed 530G (as of Medtronic F4Q13, approval was expected this calendar year).
• The range of artificial pancreas systems described at ADA suggests that a “closed- loop” device will likely be as diverse and varied as any other diabetes technology. The oral session on “The Journey to an Viable Artificial Pancreas” displayed devices under development, ranging from low glucose suspend targeting nocturnal hypoglycemia to bihormonal closed-loop systems for 24/7 control. We heard multiple perspectives on the optimal design for closed-loop products: a debate headlined by Dr. Bruce Buckingham (Stanford University, CA) and Dr. Moshe Phillip (Tel Aviv University, Petah Tikva, Israel) explored whether treat-to-range techniques worked better than fully closed-loop control. Meanwhile, talks throughout the meeting made it clear that companies and academic groups are continuing to move products forward in different areas – everything from low glucose suspend to multi-hormonal, fully closed-loop control is being pursued.
  • We would not be surprised to eventually see different closed-loop products for different types of patients – those already in tight control might benefit the most from a predictive low glucose suspend system, while those who often forget meal boluses might see the most positive benefits with a fully closed-loop system. Certainly, the wide breadth of JDRF’s recent funding initiatives (supporting multiple closed-loopapproaches) suggests that there is no one “right” option for patients. This broad swath of initiatives was most clearly emphasized by JDRF’s Dr. Aaron Kowalski in the JDRF/NIH Closed-Loop Control Meeting. Like in other market spaces, we expect to see variability in what patients want from their technology (e.g., how much control will they be willing to give over to their technology? How important is reducing the number of devices [i.e., having an integrated sensing-infusion device]? Will patients prefer intraperitoneal delivery vs. subcutaneous delivery?). What will be particularly fascinating to see is how patient selection and reimbursement will work for these different devices.
  • Many are still debating whether insulin-only closed-loop systems are enough, and if not, what hormone should be added next. We note that the day before the start of ADA 2013, JDRF announced two partnerships (with Xeris and Latitude) to develop a stable, pumpable glucagon for the artificial pancreas. In our view, glucagon stands to add a lot to the artificial pancreas from a safety perspective, though it certainly increases a system’s complexity from an R&D, regulatory, and cost perspective. Stabilizing glucagon is definitely a challenging scientific hurdle to climb, as many companies have been working on it for some time. We are optimistic this is coming in the next few years, and we hope to see players like Novo Nordisk and Lilly jump into the game as well. Speaking of other hormones, we also have high hopes for amylin, as adding it to insulin in the closed loop would mimic natural physiology quite well. It will certainly be interesting to see which group/company with which hormone(s) will be the first to file for regulatory approval, as well as how one group’s experience with the FDA and on the market will impact other systems under development.
• The absence of Abbott and Bayer – both Big Four blood glucose monitoring (BGM) companies – in this year’s exhibit hall speaks to the growing business challenges in the BGM market. Competitive bidding was a topic of discussion among ADA attendees, as the meeting took place just days before the July 1 implementation date for the national competitive bidding program for diabetes supplies. The impact on the BGM industry and on patient care has been difficult to fully understand, and we were somewhat disappointed not to see formal discussion on the matter at the 73rd Scientific Sessions. At the 7th Annual Diabetes Forum (presented by TCOYD, Close Concerns, and diaTribe), Dr. James Gavin (Emory University School of Medicine, Atlanta, GA) captured the sentiment of many attendees: “I’m not asking ADA to take a position or put it’s neck on the line, but everyone who is interested in and committed to sensible things happening on behalf of patients in this space, is either at this meeting or could have been at this meeting. To organize a forum within this meeting that could speak in substantive ways about how we might develop a focus that could circumvent some of this trend – I’m surprised that this hasn’t happened and quite frankly disappointed.” We are already looking to the next major US diabetes conference, the American Association of Diabetes Educators (AADE) Annual Meeting & Exhibition, and hoping to see if there is space for such a forum to take place.
• In the world of CGM, comparisons between Dexcom and Medtronic were front and center. One major highlight was Dr. Steven Russell's (Massachusetts General Hospital, Boston, MA) presentation comparing the Dexcom G4 Platinum, Abbott FreeStyle Navigator, and Medtronic Enlite (171-OR). Dexcom’s accuracy (MARD) came in at an impressive 10.8%, slightly ahead of the Navigator at 12.3% and far ahead of the Enlite at 17.9%. We were most struck by the lower-than-expected Enlite accuracy results, which were far lower than the 13.6%-16.3% MARDs presented in the company’s poster at last year’s ADA. To us, this underscores the critical importance of independent, investigator-initiated, head-to-head evaluations of CGM – there are simply too many differences in study design, data processing, reference methods, patientpopulations, and accuracy metrics to directly compare accuracy across manufacturer studies. We look forward to seeing future accuracy data on Medtronic’s pipeline of CGM products in development: Enlite 2 (submitted for CE Mark), its optical sensor, and several redundant sensor products (see here and here of our ATTD 2013 report). As the first ADA with the FDA approved G4 Platinum, Dexcom’s exhibit hall booth and posters focused on data from the device’s pivotal study. Particularly notable were the improvements in hypoglycemia alarm accuracy and detection (391-P), an area enthusiastically highlighted in Dr. Irl Hirsch’s presentation on CGM and hypoglycemia. Last, Dexcom’s exhibit hall booth really underscored the comparisons between its G4 Platinum and Medtronic’s Sof-Sensor and Enlite, headlined by a one-page handout entitled “Accuracy matters.” Overall, we’re glad to the see healthy competition between both companies on CGM performance – it stands to bring better products to patients and improve the overall utility of CGM over the long-term.
• A few studies tested CGM in specific populations, though none showed strikingly positive results; we are optimistic that future trials using newer, next-gen devices will indeed demonstrate strong benefits. On the type 2 side, Dr. Richard Bergenstal (Park Nicollet International Diabetes Center, St. Louis Park, MN) presented results from one of the few studies comparing structured SMBG to CGM data in 104 patients with type 2 diabetes. Both SMBG data (Roche Accu-Check 360 View) and CGM data (ambulatory glucose profile) resulted in significant improvements in A1c and time in range compared to baseline (no between-group difference), but only CGM reduced the percentage of readings in the hypoglycemic range. Meanwhile, two other studies disappointingly showed little benefit to wearing CGM in specific populations: the HypoCOMPaSS trial in patients with impaired hypoglycemia awareness (387- OR) and Dr. Elizabeth Mathieson’s study in pregnant women with type 1 diabetes. Importantly, both used Medtronic’s Sof-Sensor and not the newer (though yet-to-be FDA-approved) Enlite. In the past, Dexcom has repeatedly mentioned the “lag time” between published clinical trial data and use of next-gen devices like the Enlite and G4 Platinum – we think this is an especially critical point for a field as new as CGM, where early perceptions of the technology and data seem to color the field for years to come. We have no doubt that more trials will emerge in the coming years testing newer-generation devices – the higher utility of these devices (e.g., better accuracy, reliability, and signal transmission) stand to show much stronger clinical benefits in our view.
• We did not see any landmark new data on investigational CGM systems in development, though it was encouraging to see movement from several companies. Roche (172-OR), Senseonics (176-OR), EyeSense (177-OR), and other groups presented data on their systems in development – in our view, none are ready for prime time quite yet, though it’s very positive to see continued focus on gathering data and presenting it publicly. There’s no doubt that much better products have emerged over time from the early days of CGM, though the perceived clinical utility of CGM is still too low, and doctors are still not adequately rewarded for helping patients get on CGM. While the product is well reimbursed, we believe doctor time isn’t – that’s a systemic problem that may be challenging for companies to address on a broad scale, though better next-gen products will certainly go a long way towards empowering parents to better utilize the technology in the long run. We also think better patient training from companies could go a long way towards properly setting expectations and optimizing use of CGM. We think an even bigger shift towards compelling self-training materials (e.g., videos, online assistance, live chat) would be welcomed by patients new to CGM.

 

Diabetes Drugs

• While last year’s ADA provided the bulk of the glycemic efficacy data for SGLT inhibitors, ADA 2013 provided a more nuanced view of their action profiles and benefits for specific patient populations. Notably, we saw the first clinical data on the use of the SGLT-2 inhibitors dapagliflozin (70-LB) and empagliflozin (1074-P) in type 1 diabetes. While still in the early stages, these investigations in type 1 appear quite promising and further support the movement toward using non-insulin agents (such as GLP-1 agonists) in this patient group. On the SGLT-2 inhibitor front, we also heard new data showing that dapagliflozin improves insulin sensitivity and secretion (242-OR), that combined A1c and weight reduction is achieved more frequently with dapagliflozin than with glipizide (236-OR), and that canagliflozin provides greater reductions in A1c and body weight compared to sitagliptin (238-OR). Lexicon Pharmaceuticals had a strong showing during the SGLT inhibitor oral sessions, presenting three abstracts. Most notably, preclinical data for the SGLT-1 inhibitor LX2761 indicated that the drug increases GLP-1 levels (with or without sitagliptin), decreases postprandial glucose excursions, and improves glycemic control (240-OR). The two presentations on Lexicon’s SGLT-1/SGLT-2 dual inhibitor LX4211 provided additional details on the phase 2 results that were first released last summer – i.e., that LX4211 reduced systolic blood pressure in a dose-dependent manner (241-OR) and decreased both body weight and triglyceride levels in patients with elevated weight and triglycerides at baseline (243-OR).
• The discussion of insulin therapy at ADA focused mainly on up-and-coming compounds, rather than on ways to optimize the use of current products. In a presentation on new insulin formulations, Dr. Thomas Donner (Johns Hopkins University, Baltimore, MD) outlined the advantages of both ultra-long-acting insulins (reduced nocturnal hypoglycemia and less weight gain – or potentially weight loss), as well as ultra-rapid-acting insulins (greater reductions in postprandial glucose excursions). His talk covered a range of candidates, including Novo Nordisk’s insulin degludec, Lilly’s pegylated insulin lispro, Biodel’s BIOD-123 (insulin lispro with citrate and calcium EDTA), and Novo Nordisk’s FIAsp. Speaking on alternative insulin delivery systems –i.e., transdermal, nasal, sublingual, buccal, oral, inhaled, and intraperitoneal – Dr. William Cefalu (Pennington Biomedical Research Center, Baton Rouge, LA) placed the greatest focus on oral insulin, noting that several candidates are currently in development: Biocon’s IN-105, Oramed’s ORMD-0801 (with new data in poster 1054-P), Diabetology’s Capsulin, Novo Nordisk’s oral insulin candidate, and Diasome’s hepatic-direct vesicles (we recently published a review of oral insulins, available at http://www.closeconcerns.com/knowledgebase/r/5439ad6a). While not presenting new data, Sanofi released a poster and a press release highlighting the results of EDITION I and EDITION II for it’s new U-300 insulin glargine formulation. Authored by Dr. Matthew Riddle (Oregon Health and Science University, Portland, OR), the EDITION I poster showed that U-300 was non-inferior to glargine U-100 in lowering A1c, and that participants on U-300 glargine experienced lower rates of severe or nocturnal confirmed hypoglycemia (36.1% with U-300 vs. 46.0% with U-100; p=0.0045; 43-LB). As noted in the press release, topline results from EDITION II were consistent with those from EDITION I with regard to glycemic efficacy and rates of hypoglycemia. Discussion of Novo Nordisk’s insulin degludec (Tresiba) was noticeably absent during the conference, as the drug received a Complete Response Letter from the FDA in January (our report on the CRL is available at http://www.closeconcerns.com/knowledgebase/r/ff79d66b) – we imagine that the next wave of data on this drug will be presented at EASD 2013 in Barcelona.
• There was little groundbreaking data for GLP-1 agonists as monotherapy for type 2 diabetes this year – the most exciting data for this drug class concerned their potential in type 1 diabetes (liraglutide [1007-P]) and in combination with basal insulin (IDegLira [65-OR]; see bullet on combination therapy). On the novel GLP-1 front, detailed results for dulaglutide’s AWARD 1, 3, and 5 were presented that expanded on the topline data for these trials disclosed in October 2012 (66-OR, 69-OR, 71-OR, 1004-P). The top dose of dulaglutide provided a ~1% additional A1c reduction over placebo, an additional 0.7% reduction over sitagliptin, and a 0.2% additional reduction (statistically significant) over metformin. Dulaglutide data seemed strong overall, with adverse event profiles similar to that of other long-acting GLP-1 agonists. Detailed results of GSK’s albiglutide’s HARMONY 8 were presented (68-OR) during which albiglutide provided 0.3% greater A1c reduction than sitagliptin in people with renal impairment. While Sanofi was supposed to share more information on the functionality of its Lyxumia/Lantus fixed-ratio combination device (and the problems that it encountered with the fix-flex device), the company disclosed disappointingly little during the conference call it held during ADA. Also notably, a phase 1 study of the safety and efficacy of the oral GLP-1 agonist TTP054 (115-OR), found consistently greater declines in fasting plasma glucose and two-hour postprandial glucose versus placebo-treated patients, with no hypoglycemic events and with a similar rate of GI adverse events to the placebo arm. No other oral GLP-1 agonist has reported results yet; we believe that the diabetes community is in the early stages of seeing GLP-1 use, as considerably more patient- and provider-friendly means of administering GLP-1 agonists are being developed and commercialized. This suggests the market will be very interesting to watch from a commercial perspective in the years ahead, barring any surprises on the pancreatitis/pancreatic cancer front.
• DPP-4 inhibitors received the spotlight on Sunday morning in an oral session that included a range of topics. Pooled data from three phase 3 studies indicated that linagliptin (BI/Lilly’s Tradjenta) has a favorable effect on the composite primary endpoint of cardiovascular (CV) death, non-fatal stroke, and non-fatal myocardial infarction (HR: 0.78; 95% CI: 0.55-1.12; 376-OR) – interestingly, Dr. Odd Erik Johansen spent only a fraction of time discussing the CV data, perhaps because linagliptin’s CV outcomes trial CAROLINA (comparing the drug to glimepiride) will provide more concrete data on the drug’s CV effects (though CAROLINA is not expected to finish until 2018). A study of linagliptin in type 2 diabetes patients on hemodialysis included the use of CGM and found that switching patients from insulin to linagliptin led to marked decreases in mean amplitude of glucose excursions (MAGE) while on hemodialysis (374- OR). On the preclinical front, Dr. Marco Bugliani (University of Pisa, Pisa, Italy) presented in vitro data showing that the DPP-4 enzyme is present in human pancreatic islet cells (primarily alpha cells) and that DPP-4 inhibitors such as Merck’s MK-0626 could have direct protective effects against glucotoxicity and lipotoxicity (378-OR). Outside of the oral session, a notable poster by Dr. John Buse’s (University of North Carolina, Chapel Hill, NC) group investigated the question of DPP-4 inhibitors and pancreatic cancer and found no increased risk of pancreatic cancer or any cancer with DPP-4 inhibitors compared to SFUs or TZDS (111-LB).
  • Looking forward, we see several major questions for DPP-4 inhibitors: first, what can we learn from the many CV outcomes trials? The SAVOR-TIMI trial for BMS/AZ’s Onglyza revealed a piece of the puzzle, indicating that Onglyza was non- inferior but not superior to placebo for reducing CV events. Interestingly, we did not hear as much discussion on DPP-4 inhibitors and cardiovascular effects as we expected, perhaps since SAVOR-TIMI did not achieve its ambitious endpoint of superiority or perhaps since most CVOTs are a few years away from reporting results. Broadly, we arenot surprised superiority was not reached, given the short time period studied. We wonder if it would be different had a smaller number of patients been studied over a longer time period (we recognize this is a fairly theoretical question given the commercial implications of a trial being longer). Secondly, we’re interested in the optimal approach for combining DPP-4 inhibitors with other therapies – we’re already seeing movement on this front with the BMS/AZ Alliance, the Lilly/BI partnership, and the Merck/Pfizer partnership (the latter which will aim to develop a ertugliflozin/sitagliptin fixed-dose combination), and likely with yet still others, and we’re curious about the possibility of combining a DPP-4 inhibitor with a GPR40 agonist such as Takeda’s TAK-875. (Takeda recently reported topline phase 3 data for the candidate – read our report at http://www.closeconcerns.com/knowledgebase/r/f66391fa.) Lastly, we’ll be keeping a close eye on whether and how the prescribing patterns for DPP-4 inhibitors change as SGLT-2 inhibitors enter the market – while SGLT-2 inhibitors come with the added side effect of genitourinary infections, KOLs have yet to reach a consensus on whether this issue will be a significant barrier for patients; for now, it certainly seems to be considered a more manageable side effect than nausea, weight gain, and other side effects associated with other drugs. .
• The potential association between pancreatitis and incretins was on the minds of many. Several talks during corporate symposia on GLP-1 agonists or DPP-4 inhibitors addressed the topic, reinforcing the opinion that current clinical data are insufficient to suggest taking patients off of these drugs. Most strikingly, Dr. Vanita Aroda (MedStar Health Research Institute, Hyattsville, MD) spoke at an independent session where, during Q&A an audience member asked if she herself would take her patients off of incretin-based therapies at this point. She turned the question around, and a poll of the very-packed audience found that no one would do this. While this sentiment likely prevails with endocrinologists, PCPs may find the decision less clear-cut; this question and other related ones are being explored in a new PCP survey from our sister company dQ&A (write Richard.wood@d-qa.com for more information). A related question, of course, is whether a PCP deciding to initiate incretin-based therapies for a patient would instead opt for an SGLT-2 inhibitor as we await more safety data on DPP-4 inhibitors and GLP-1 agonists. Clearly, there are competing forces at play since the long-term safety profile of SGLT-2 inhibitors is also more unknown relative to the incretin profiles.
• Combination therapy took center stage for type 2 diabetes drugs for the first time this year. We noticed more companies venturing beyond metformin combination therapy into the branded combination therapy realm – while data are still a bit sparse and in their early stages, what we have seen is impressive. The results for IDegLira (insulin degludec and liraglutide) were some of the most compelling we saw at the conference: Dr. John Buse presented data from IDegLira’s DUAL-1 (65-OR), during which over 80% of patients taking IDegLira achieved the A1c goal of 7.0% or below. IDegLira was better than degludec alone in terms of hypoglycemia (one third less than with degludec) and weight (slight weight loss, instead of weight gain with Tresiba). Dr. Ralph DeFronzo’s highly-anticipated Triple Therapy results (presented by Dr. Muhammad Abdul-Ghani [University of Texas Health Science Center, San Antonio, TX]) were another highlight in combination therapy (72-OR). We thought the results were encouraging (detailed below), though interpretation is complicated given how unusually well conventional therapy performed.
• ADA 2013 provided both high-level and nuanced analysis on navigating the type 2 diabetes treatment paradigm. Taking a targeted approach, Dr. Ralph DeFronzo’s study (which was presented twice during the conference) investigated whether triple therapy (metformin plus pioglitazone plus exenatide twice daily) provided greater glycemic controlcompared to the conventional step-wise method previously endorsed by the ADA (metformin followed by subsequent addition of an SFU and then basal insulin). The study showed compelling results for the triple therapy approach (an average A1c of 6.0% after 24 months, vs. 6.6% in the conventional therapy arm, from a baseline A1c of 8.6%) and corroborated the advantages of addressing the pathophysiological defects underlying diabetes rather than just hyperglycemia. The study also raised several questions beyond the glycemic efficacy of these two treatment approaches – i.e., how relevant are the results now that prescriptions for pioglitazone and exenatide twice daily appear to be decreasing? How can the results of the trial be replicated in a real-world setting? Should specialists and maybe even PCPs follow the deliberate titration scheme used in the study? We’re reminded of the ADA/EASD position statement – which highlighted the lack of head-to-head data comparing different therapies or treatment approaches – and hope that future trials will continue to evaluate treatment methods, as well as the dosing needed to optimize these approaches. On the broader level, we heard further discussion of the ADA/EASD position statement (by Dr. Anne Peters; our report on the position statement is available at http://www.closeconcerns.com/knowledgebase/r/4d95e564), as well as more commentary on the recently- released AACE comprehensive treatment algorithm (by Dr. Paul Jellinger; our report on the algorithm can be found at http://www.closeconcerns.com/knowledgebase/r/a331db88). In general, KOLs appear to agree that rather than opposing each other, the two documents serve different purposes and advantages for different types of HCPs.
• Overall progress for diabetes drugs was more incremental this year compared to the larger leaps seen in diabetes devices – highlights were Elcelyx’s NewMet and the use of type 2 drugs in type 1 diabetes. The late-breaking poster for Elcelyx’s NewMet garnered considerable acclaim (75-LB). This delayed-release version of metformin aims to address two of the biggest barriers for metformin uptake: renal insufficiency and gastrointestinal side effects. The company estimates that four million out of the seven million people in the US not on metformin that could benefit from it are not on treatment because of either of these two factors. Elcelyx’s market research, strikingly, suggests that US payers would support NewMet at price parity with Januvia. Furthermore, 60% of surveyed physicians blindly rated the product profile of NewMet as “significantly better” than metformin and indicated that they could see such a product replacing metformin in more than 40% of patients currently on metformin. We imagine the biggest potential for NewMet will be in oral combination therapies, in which the current metformin may muddy a otherwise cleaner side effect profile (e.g., in Janumet or in future combination with SGLT-2 inhibitors). On the topic of repurposing type 2 diabetes drugs for type 1 diabetes, posters on dapagliflozin (70-LB), liraglutide (1007-P), and Lilly’s glucagon receptor antagonist LY2409021 (64-LB) all suggested that these drugs that were initially developed for type 2 diabetes could considerably reduce insulin requirement for people with type 1 diabetes. We are encouraged to see this substantial progress here toward gaining an official type 1 diabetes indication for these drugs, since, as Children with Diabetes’ Jeff Hitchcock once said, “Life is lived off label.”
• Presentations on novel drug development aside from SGLT inhibitors were fairly sparse this year. No single mechanism in the earlier-stage pipeline stands out yet as the “next big thing,” though there were some promising results presented for Lilly’s glucagon receptor antagonist LY2409021, Takeda’s GPR40 agonist TAK-875, and Zealand Pharma’s liquid glucagon analog ZP-GA-1. Additional phase 2 data for LY2409021 were presented as an oral (112-OR) and poster (64-LB). In a phase 2 type 2 diabetes trial, the candidate provided a 0.6% or 0.8% placebo- adjusted A1c reduction at 10 mg and 20 mg doses, respectively, after 24 weeks and mild elevations in liver transaminases (consistent with previous observations). Meanwhile, an early-stage study in type 1 diabetes suggested it could have the potential to reduce daily insulin requirements. A poster on TAK-875 (1165-P) suggested that co-administration with glimepiride has the potential to produce synergistic effects. Zealand Pharma’s ZP-GA-1 was a surprise to many – the compound is a new glucagon analog shown to be stable in a liquid formulation (404- P). It was great to see Zealand now working on the challenging scientific problem of stabilizing glucagon in solution (joining Xeris, Biodel, Latitude, and others), an instrumental step in making the product available for the bihormonal artificial pancreas and more user-friendly rescue products. Also notable was a poster (1051-P) on Pfizer’s hepatoselective glucokinase activator (GKA) that was discontinued in phase 2. In this study, it demonstrated only modest A1c-lowering efficacy (~0.5% and 0.6% placebo-adjusted reduction on the top twice-daily and top once-daily doses tested, respectively) with a number of side effects (16-19% increase in triglycerides [TG] with three patients in the twice-daily group discontinuing treatment due to TG increases and slight elevations in ALT and AST liver enzymes).
• This year’s ADA featured a number of substantive debate-style events. The debates focused on common challenges for health care providers caring for diabetes patients, and were among the most clinically relevant events on the ADA docket. On Day #2, Drs. Martin Abrahamson (Joslin Diabetes Center, Boston, MA) and Saul Genuth (Case Western Reserve University, Cleveland, OH) discussed whether sulfonylureas should remain an acceptable add-on therapy for type 2 diabetes patients already on metformin. Dr. Abrahamson provided a very thorough representation of the “pro” argument given that his personal opinion is more qualified than the presentation topic he was assigned. After their partisan debate statements, the two KOLs came to a consensus that not all SFUs are created equal, and that most are only appropriate for a select group of patients early in the disease progression. On Day #4, Drs. Vivian Fonseca (Tulane University Medical Center, New Orleans, LA) and Parresh Dandona (State University of New York, Buffalo, NY) squared off regarding the appropriate course of action for type 2 diabetes patients “failing therapy” with oral agents (in the case of SFUs, we see it as closer to therapy failing patients). Dr. Fonseca argued that a GLP-1 agonist is the most prudent approach, while Dr. Dandona advocated adding basal insulin. Here, too, the debaters ended by agreeing on a number of points, namely that a combination of insulin and GLP-1 agonists could potentially prove most effective. Finally, on Tuesday, Drs. Cara Ebbeling (Children’s Hospital Boston, Boston, MA) and Michael Rosenbaum (Columbia University, New York, NY) debated whether low carbohydrate diets are helpful for maintaining weight loss. We look forward to more debates at future conferences, as they seemed to galvanize audience enthusiasm and bring forward some great questions from the floor. Nonetheless, we could not help but notice that the perceived strength of the debaters’ arguments was a function of their style as well as their arguments’ substance. We would also like to see the ADA encourage speakers to disclose their true opinions in addition to the side of the argument they are assigned to present.

 

Obesity

• ADA 2013 marked the first Scientific Sessions in 13 years with new FDA-approved anti-obesity medications on the market. Vivus and Eisai are each deep in the throes of commercializing their drugs (Qsymia and Belviq, respectively) in a market that is still in the early development stage. These milestone launches were signified by the presence of each company’s booths in the Exhibit Hall, Vivus- and Eisai-sponsored product theaters, and a Vivus-supported CME. Eisai’s presence was particularly noteworthy, as Belviq was only launched on June 7, 2013(nearly a year after being approved by the FDA due to the drug needing to be scheduled by the Drug Enforcement Agency).
• Though commercialization of the new anti-obesity drugs has been challenging, we saw several encouraging signs for the market. In Eisai’s product theater, Dr. Caroline Apovian (Boston University Medical Center, Boston, MA) convinced 53% of audience members to reconsider delaying obesity pharmacotherapy for obese or overweight patients. Vivus-sponsored and -supported events featured well-respected diabetes KOLs encouraging endocrinologists to consider prescribing anti-obesity pharmacotherapies. For example, Dr. James Gavin (Emory University School of Medicine, Atlanta, GA) led Vivus’ product theater on Qsymia, and Dr. Ralph DeFronzo (University of Texas Health Science Center, San Antonio, TX) joined obesity experts at the podium of Vivus’ CME. We believe diabetes KOLs’ growing presence at these events highlights that endocrinologists are likely to become thought leaders in the obesity field. Additionally, the highly respected family physician Dr. Michelle Look (San Diego Sports Medicine and Family Health Center, San Diego, CA) voiced strong support for the use of Qsymia and Belviq; we will be interested to see if more generalist KOLs will also join Dr. Look. PCPs are a vital group in the fight against obesity, and we were concerned leaving the American College of Physician’s annual meeting (Internal Medicine 2013) that PCPs were being dissuaded from prescribing Qsymia and Belviq by some presenters.

• Presentations on obesity pharmacotherapies tended to be factual recounts of Qsymia and Belviq’s labels, with few opinions conveyed and little focus on drugs in development. Several presentations repeated slides from one another or rehashed talks we saw at prior conferences. Indeed, Dr. Robert Eckel (University of Colorado, Aurora, CO) noted in the panel discussion following his presentation that he was careful to keep his description of each drug factual since he wanted attendees to decide for themselves which drug to use. Fortunately there were a few exceptions to this trend. For example, during the same panel discussion Dr. Eckel later remarked that he did not think 3.0 mg liraglutide’s (Novo Nordisk’s Victoza for obesity) side effects were worth its weight loss benefit, or at least were “limiting.”

•  Similar to the past two years, there were no oral sessions on obesity drugs, and  there was little discussion on new clinical developments. We did, however, see several posters on anti-obesity candidates, mostly post hoc analyses of the phase 3 programs for Vivus’ Qsymia (phentermine/topiramate), Orexigen’s Contrave (naltrexone/bupropion), and Arena/Eisai’s Belviq (lorcaserin). A post hoc analysis of the phase 3 CONQUER study for Qsymia found that in patients with baseline dysglycemia, Qsymia still led to a mean weight loss of 7.6%- 11.6% across all BMI categories (2103-P). A similar analysis found that Qsymia provided significant improvements in glycemic parameters, with greater weight loss associated with greater glycemic benefits (2121-P). Additionally, two post hoc analyses of Contrave’s four phase 3 trials found that Contrave was similarly efficacious across a broad range of baseline BMIs (1130-P) and that Contrave provided significant improvements in quality of life (1056-P). A post hoc analysis of Belviq’s phase 3 program found that patient responder status at week 12 (i.e., whether or not a patient achieved ≥5% weight loss) strongly predicted weight loss at week 52, thus providing evidence to support Belviq’s 12-week stopping rule in patients not achieving ≥5% weight loss (2117-P). With regard to novel obesity therapeutics, one notable poster presentation was on early phase 2 data for Zafgen’s MetAP2 inhibitor, ZGN-440 (beloranib). The highest dose of ZGN-440 provided up to 10 kg (22 lb) of weight loss after 12 weeks – a striking result for sure, though we’d note that the study population was fairly heavy at baseline and the poster presented results of the per-protocol  population.

• The Monday of ADA included a symposium dedicated to the Look AHEAD trial, which compared the effects of intensive lifestyle intervention (ILI) vs. diabetes support and education (DSE) on cardiovascular (CV) risk in ~5,000 obese or overweight type 2 patients. As a reminder, the study was terminated prematurely, as it failed to show a difference in the primary endpoint (major cardiovascular events) between the two trial arms. After an introductory presentation on the study design, Dr. Rena Wing (Brown University, Providence, RI) presented the CV results, highlighting that while ILI did not reduce the risk of cardiovascular morbidity or mortality, it did lead to significant and sustained weight loss, as well as improvements in fitness, A1c, systolic blood pressure, and HDL. Regarding microvascular complications, ILI reduced the risk of chronic kidney disease and provided favorable effects on several parameters of kidney function. The Look AHEAD symposium also included thought-provoking talks on the effects of ILI on healthcare costs and on quality of life – two key but often overlooked study endpoints. Regarding quality of life – specifically depression – ILI reduced the risk of progression to mild or more severe symptoms of depression by 20% versus DSE, as assessed by the Beck Depression Inventory (BDI) score. Dr. Lucy Faulconbridge (University of Pennsylvania, Philadelphia, PA) remarked that this data represents the strongest evidence to date that ILI may protect overweight and obese individuals from depression.

 

Additional Topics

• We were somewhat disappointed at the lack of substantive sessions on diabetes and obesity prevention. Remarkably little new information on the subject was shared, and the limited number of relevant presentations generally reiterated old information. There were a few bright spots: on Days #2 and #3, Outstanding Educator in Diabetes award winner Dr. Ann Albright (CDC, Atlanta, GA) gave an energizing set of talks on the importance of prevention. She promoted the National Diabetes Prevention Program, and argued that prevention can happen at the community as well as the individual level. A comprehensive Day #2 symposium on the cost benefits of diabetes prevention also provided convincing arguments in favor of prevention. Throughout the conference, various KOLs also brought up the results of the Diabetes Prevention Program and Look AHEAD studies. On the whole, however, prevention was far from a major topic at this year’s ADA. We feel that the endocrinology and diabetology community is responsible for leading the field in advocating prevention, and we hope to see more attention paid to the topic at future events.
• In their respective trials, the type 1 diabetes cure immunomodulatory therapies discussed at ADA all failed to meet their primary outcomes. In a distant corner of the conference hall (a sign en route encouraged attendees to “keep walking”), presenters in a symposia on candidates in clinical development dedicated much of their time to highlighting positive safety data and noteworthy sub-analyses as evidence that their therapies could still be efficacious for some patients. For example, in the early phase 2 START trial (n=66), anti- thymocyte globulin (ATG) therapy did not significantly delay two-hour C-peptide AUC decline over the course of 12 months. However, 22-35 year-olds old faired better on the treatment (compared to their younger counterparts) – they trended strongly toward C-peptide preservation, suggesting that statistical significance could potentially be achieved with a longer trial.
  • We were particularly frustrated to learn that poor trial execution could potentially explain why alefacept did not have a significant impact on C- peptide levels in the phase 2 T1DAL Study (n=66) – the trial only enrolled 75% of its target because the manufacturer (Astellas) decided to discontinue the drug forbusiness reasons. This led Dr. Mark Rigby (Indiana University School of Medicine, Indianapolis, IN) to suggest that the drug might be efficacious, as the trial was statistically underpowered. Unfortunately, there are few second chances for candidate therapies and we hope researchers will work to prevent tragic trial design and execution flaws, which preclude serious discussions of therapies’ benefit/risk profiles’ (i.e., one can not weight the risks and benefits of alefacept if the trial is not even completed and enrolled as designed!). At prior conferences, KOLs have commented that teplizumab and oral insulin might also have been victims to poor trial design. These presentations left us feeling all the more grateful for the artificial pancreas’ (AP) positive data, which stands to notably improve outcomes in the very near-term while patients wait for a biological cure. We also remember that this recent boom of progress in the AP followed many years of grueling and disappointing work, perhaps similar to what researchers in immunomodulation are experiencing now.

 

Exhibit Hall

• The ADA 2013 exhibit hall featured new product launches from a number of companies. Eisai’s Belviq was launched for the first time following nearly a year wait for the Drug Enforcement Agency (DEA) to schedule the drug. This year’s ADA also represented the true launch of Invokana, the company’s new SGLT-2 inhibitor – the drug was featured strongly in J&J’s large booth and was positioned as the “first and only SGLT-2 inhibitor approved in the USA.” On the device side, Insulet’s circular blue-and-green booth was all about the new OmniPod, with ADA 2013 representing the company’s first large US meeting following FDA approval last December. Finally, BD launched its EasyFlow pen needle technology at ADA (one of our favorite demonstrations in the exhibit hall), while NeuroMetrix showcased its recently approved Sensus device for the management of diabetic peripheral neuropathy.
• This Exhibit Hall lacked some of the spectacle of its predecessors, with the products and information supplemented with relatively little by way of giveaways or live entertainment. Although we’re now a few years removed from the “golden age” of Exhibit Hall giveaways – when attendees sometimes filled entire ADA bags to the brim with swag from various booths! – this year felt more austere by Exhibit Hall standards. There have been tightened restrictions on corporate giveaways so that main items given away seem to be ones that can be consumed onsite or those that are educational; in practice, this meant that espresso machines could seemingly be found every 20 feet. Frozen yogurt and bowls of apples were also spotted in multiple booths. We noted that there was also significantly less on-site entertainment than in years past – for instance, the splashy live quizzes that were such a big part of the experience at last year’s Exhibit Hall were now played on rather less obtrusive tablet devices. The focus is squarely on product awareness – though this is likely in response to tightening budgets, this is where the focus should be, as long as just as many HCPs are being drawn into booths. There was at least one highly entertaining exception that we kept hearing about (and then saw) all day, as Insulet enlisted the services of corporate magician Charles Greene III to perform card tricks – complete with bell sound effects, because “OmniPod has a ring to it” – in front of their exhibit.
• Regarding social media presence, we found that most companies have not yet tapped into the full potential of various social media platforms (Facebook, Twitter, and others) as part of their Exhibit Hall efforts. When we asked booth representatives about social media, they were more often than not unfamiliar with the broader social media presence of their company, which we speculate is because companies partition their sales team onthe ground from their marketing and outreach team; even in those booths that had their social media person present, we generally found that other reps could not speak to the online presence of their company. That social media related to their products was not part of the prepared talking point repertoire suggests to us that social media has yet to permeate the consciousness of most companies at ADA. Someday, we'll be at an ADA where contacts will tell us what they’re most excited about from their online outreach, but we’re not quite there yet. There were some exceptions to this – Sanofi and Medtronic were particularly impressive in their social media efforts – and these are noted in the company-specific entries, which also include brief analyses of the company’s Twitter and Facebook presences; all statistics are accurate as of June 22.

 

 

Artificial Pancreas

Oral Sessions: The Journey to a Viable Artificial Pancreas

Inpatient Evaluation of an Automated Closed-Loop Control-to-Range System (11-OR)

Howard Zisser, MD (Sansum Diabetes Research Institute, Santa Barbara, CA)

Dr. Howard Zisser shared much-awaited results from the inpatient Control-to-Range trial, a “Herculean effort” funded by JDRF and carried out at seven clinical centers around the world. The trial enrolled adults (n=27) and adolescents (n=25) with type 1 diabetes, who used a closed-loop system in two 29-hour admissions. Dr. Zisser focused on the first 23 hours of admission one, in which the main challenges were large, announced meals (≤100 g carbohydrate). The mean percentage of time in range (71-180 mg/dl) for adults was 59% during the day and 82% overnight; mean time in range for adolescents was 53% during the day and 82% overnight. Before coming to the clinical research center, patients wore blinded CGMs for two-to-three days, enabling a comparison between open- and closed- loop control. Dr. Zisser noted that the study was not designed to make this analysis, but the comparison certainly highlighted the imperfections of open-loop outpatient control – especially in adolescents, many of whom went “from 40 to 400 mg/dl on a daily basis.” The Control-to-Range study was an inpatient evaluation of how a particular closed- loop system would respond to various daily life events; Dr. Zisser explained that the goal of the study was to “challenge” the controller but not to “break” it. The study’s two 29-hour admissions included large, announced meals (1 gram of carbohydrate per body weight up to a maximum of 100 g), as well as overnight control. Other challenges included a missed meal bolus, a meal at which the bolus was too high (30% more than the calculated amount), and exercise (60 minutes, level 9 on the Borg rating of perceived exertion scale). During this presentation Dr. Zisser focused on the first 23 hours of the first admission, so we did not see data on the mis-bolused meals or the exercise session.

• The Control-to-Range study was an inpatient evaluation of how a particular closed- loop system would respond to various daily life events; Dr. Zisser explained that the goal of the study was to “challenge” the controller but not to “break” it. The study’s two 29-hour admissions included large, announced meals (1 gram of carbohydrate per body weight up to a maximum of 100 g), as well as overnight control. Other challenges included a missed meal bolus, a meal at which the bolus was too high (30% more than the calculated amount), and exercise (60 minutes, level 9 on the Borg rating of perceived exertion scale). During this presentation Dr. Zisser focused on the first 23 hours of the first admission, so we did not see data on the mis-bolused meals or the exercise session.

• The closed-loop control system featured a modular architecture of algorithms. The main algorithm, the range controller, was designed at the Universities of Pavia and Padova. Insulin delivery was modulated with a safety supervision module (SSM). The SSM was designed primarily at the University of Virginia and also included an insulin-on-board component developed at the University of California, Santa Barbara. The pump was the OmniPod, and the CGM was the Dexcom Seven Plus. All these components were connected via the Artificial Pancreas System, which was designed at the Sansum Diabetes Research Institute and UCSB.
• The study enrolled 27 adults and 26 adolescents with type 1 diabetes, with mean ages (standard deviation) of 4111 years and 1511 years, respectively. The mean durations of diabetes were 25 and 8, respectively, and mean A1c values were 7.7% and 8.1%, respectively.
• During the first 23 hours of the study, the control-to-range system met its primary pre-specified success endpoints. These endpoints included a time in range (71-180 mg/dl) of at least 50% overall, at least 50% during the day, and at least 60% overnight.
• Dr. Zisser said that in his opinion, the study’s take-home message was the clear advantage of inpatient closed-loop control compared to open-loop outpatient control (though he noted that the study was not powered for this comparison). For two-to-three days prior to the first inpatient admission, patients wore blinded continuous glucose monitors. With these outpatient blinded CGM data as a baseline, the YSI blood glucose measurements fromthe inpatient closed-loop sessions could be viewed in a new context. The closed-loop data looked better than the open-loop data for both adults and adolescents; Dr. Zisser pointed out that the benefits were especially clear in adolescents, whose blood glucose often swung “from 40 to 400 mg/dl on a daily basis.”

Glycemic Measures: Median (25th, 75th percentiles) or number

	Inpatient YSI		Outpatient Blinded CGM
	Adults	Adolescents	Adults	Adolescents
# participants	27	25	27	25
% values 71-180 mg/dl	70% (56%, 76%)	62% (46%, 82%)	56% (44%, 73%)	46% (25%, 65%)
Day (9 am to 11 pm)	60% (42%, 76%)	61% (32%, 76%)	61% (38%, 73%)	45% (23%, 68%)
Night (11 pm to 8 am)	81% (71%, 100%)	88% (70%, 100%)	59% (30%, 88%)	50% (9%, 76%)
% of days with glucose values ≤ 70 mg/dl	48%	20%	78%	58%

• Dr. Zisser closed with a series of summary thoughts on the Control-to-Range data that he had presented. He said that breakfast is the most difficult meal to control; by contrast, overnight control can be performed well with “most” controllers that are available. Based on experience in other recent studies, Dr. Zisser said that closed-loop control can be made more aggressive with new, more accurate sensors – he specifically mentioned the Dexcom G4. He suggested that closed-loop control could also be improved by optimizing patients’ pump settings prior to the start of studies. Despite the limitations of the Control-to-Range study, Dr. Zisser emphasized that closed-loop control was “far superior” to typical control in the outpatient setting, even among patients who knew they were being observed with blinded CGMs. He also emphasized that predictive hypoglycemia alert systems are “essential.”

​​​Questions and Answers

Q: I wanted to agree about the difficulty of controlling breakfast. Lunch is a lot easier, I think because there is more insulin on board. The closer a meal is to the prior meal, the more insulin on board.

A: So we should either eat all the time, or never. [Laughter]

Q: We are more insulin resistant in the morning than later in the day. Do your algorithms take this into account? Is it simply a matter of programming at that time of day to give more insulin per carb?

A: We were using the insulin-to-carb ratio of each patient, at that time of day. We also make the IOB constraints different at different times of day. Researchers at Mayo Clinic are looking into this dawn phenomenon, to improve our controllers.

 

Overnight Glucose Control with MD-Logic Artificial Pancreas System in T1DM Patient's Home (13-OR)

Revital Nimri, MD (Schneider Children’s Medical Center of Israel, Petach Tikvah, Israel)

On behalf of the DREAM consortium, Dr. Revital Nimri presented the results from the DREAM 4 closed- loop trial, which compared overnight glycemic control with the MD-Logic AP (MDLAP) system to sensor-augmented pump (SAP) control in the patient’s home. When considering the entire intent-to- treat study population (n=44), MDLAP significantly decreased the time spent in hypoglycemia (blood glucose [BG] <70 mg/dl; 2.9% vs. 5.6%; p=0.02) and increased the time spent in euglycemia (BG 70-180 mg/dl; 81.5% vs. 73.6%; p=0.01). Similarly, the MDLAP system improved nighttime control when the analysis was restricted to adolescents and children. Dr. Nimri concluded that the MDLAP system conferred improved overnight control, effectively reducing nighttime hypoglycemia with no severe adverse events reported.

• This randomized crossover study compared four consecutive nights of MDLAP control to four consecutive nights of SAP control (n=44). After an initial run-in period with the sensor and an assessment period to optimize pump settings, patients were randomized to four nights of either MDLAP or SAP control in a crossover design (daytime control was by SAP in both groups). The study had remote monitoring such that the physician would see the data but could not see whether patients were on open- or closed-loop control. As a reminder, MDLAP uses a fuzzy-logic control algorithm and is comprised of the Medtronic Paradigm Veo pump, Medtronic Enlite continuous glucose sensor, Bayer Contour-Link blood glucose meter, and a real- time remote monitoring system.
• The primary endpoint was time spent below 70 mg/dl and the percent of nights in which mean glucose was in range (90-140 mg/dl). Secondary endpoints included glycemic control variables, artificial pancreas technical performance, and psychological endpoints.
• The study included 44 patients with type 1 diabetes, consisting of 30 children and adolescents and 14 adults. Patient characteristics from the intent-to-treat group are outlined below.

Patient Characteristics
Age (years)	18
Gender (M/F)	22/22
BMI	0.55
A1c (%)	7.8%
Diabetes Duration (years)	9.4
Pump Therapy Duration (years)	5.6
Daily Insulin Dose (units/kg)	0.8

• The intent-to-treat analysis showed that overnight glycemic control improved significantly with MDLAP control (n=162 nights) compared to SAP control (n=160 nights) as measured by the percentage of nighttime with blood glucose less than 70 mg/dl (2.9% vs. 5.6%; p=0.02) and percentage of nighttime with blood glucose 70-180 mg/dl (81.5% vs. 73.6%; p=0.01). The analysis investigated several metrics, including area under the curve less than 60 mg/dl and area under the curve less than 70 mg/dl.

Metric	Median
	MDLP	SAP	P-value
Time <60 mg/dl (%)	1%	1.8%	0.02
Number of Events <60 mg/dl	0	0	0.1
Area <70 mg/dl	0.3	0.4	0.03
Area <60 mg/dl	0	0.13	0.03
Low Blood Glucose Index	0.7	1.2	0.01
Time 70-140 mg/dl (%)	52.6%	43.5%	0.04
Mean Glucose 90-140 mg/dl (% of nights)	67%	50%	0.33
Time >180 mg/dl (%)	13.7%	20%	0.39

• Restricting the analysis to children and adolescents (n=30), MDLAP (n=109 nights) showed a reduction in nighttime hypoglycemia by numerous metrics when compared to SAP control (n=104 nights).

Metric	Median (Inter-quartile Range)
	MDLAP	SAP	P-value
Number of Events <60 mg/dl	0 (0, 0.25)	0 (0,0.4)	0.02
Time <60 mg/dl (%)	0.5%	1.8%	0.01
Area below 60 mg/dl	0	0.1	0.02
Low Blood Glucose Index	0.6	1.1	<0.01
Time in 70-180 mg/dl (%)	81%	71%	0.02

• Dr. Nimri’s presentation built on interim DREAM 4 results, which she presented at the 6th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD). See our ATTD full report for detail.

 

Questions and Answers

Q: Someone was monitoring remotely? They were able to talk to the subject? A: The study itself was a home study with remote monitoring. The data went to a physician. Q: What information does the algorithm require at initialization?

A: We do a profile according to the patient’s insulin requirements. It’s a special profile that is built for the closed-loop.

Q: It is individualized to patients?

A: Yes, it is individualized.

Q: How many times was the remote monitor contacting the patient?

A: I don’t have all data but for the first 15 patients, we had two interventions in the closed loop group for hypoglycemia, which happened at the time the patient connected to the closed loop.

 

A Robustly Adaptive Bi-Hormonal Bionic Pancreas for Automated Glucose Control in Children and Adults (15-OR)

Steven Russell, MD, PhD (Harvard Medical School, Boston, MA)

Dr. Steven Russell discussed the performance of an adaptive bihormonal bionic pancreas with and without an adaptive meal priming bolus in pediatric and adult patients with type 1 diabetes; pediatric patients were aged 12-20 years old. This randomized, parallel design trial (six pediatric and six adult patients in each group) was conducted in the inpatient setting and tested closed-loop control over ~two day periods. The closed loop was CGM driven, using Abbott Navigator readings as an input to the algorithm. Adults achieved good glycemic control without adaptive meal priming boluses and blood glucose significantly improved with the bolus (mean blood glucose [BG]: 148 vs. 132 mg/dl; p=0.03). Children showed a similar improvement in average BG with the addition of a meal bolus; however, the improvement was from a higher baseline (mean BG: 178 vs. 167 mg/dl; p=0.01). Time in range (70-180 mg/dl) also improved with adaptive meal priming boluses. In adults, time spent in range increased from 70% to 80% (p=0.04). In children, time spent in range increased from 60 to 68% (p=0.05). There was no significant difference in time spent in hypoglycemia (BG <70 mg/dl).

• Notably, Dr. Russell identified continuous glucose monitor (CGM) calibration error as a source of poor closed-loop performance in the trial. In what he called “my absolute worst experiment,” CGM calibration was done when blood glucose was rising sharply, resulting in CGM readings ~50 mg/dl higher than actual blood glucose and requiring the patient to take multiple carbohydrate interventions. Consequently, Dr. Russell and his team have implemented rules about good times to calibrate CGM as they continue to develop their bionic pancreas. Currently, the next-generation system is being tested in the ongoing Beacon Hill study (n=12 of 20 experiments completed). For detail on Beacon Hill study design and preliminary results, see page six of our GTCBio Diabetes Summit report: http://www.closeconcerns.com/knowledgebase/r/ec2eec39 for a patient perspective on being part of the trial, see http://www.diatribe.org (issue #55).

 

Automated Bi-Hormonal Closed-Loop Treatment of Type 1 Diabetes Using an Adaptive Alogrithm (14-OR)

Kenneth Ward, MD (Oregon Health & Science University, Portland, OR)

In a wide-ranging talk full of unpublished data, Dr. W. Kenneth Ward shared his group’s latest findings on closed-loop glucose control with both insulin and glucagon. Dr. Ward reminded the audience that regular glucagon is too unstable to be used for multiple days in a pump. However, his team has performed animal studies with curcumin-stabilized glucagon; this formulation has gone at least seven days without its kinetics slowing down. Turning to clinical data, Dr. Ward briefly discussed encouraging data from small trials (n=15, n=13) of a bihormonal system that uses an adaptive algorithm for insulin delivery. He noted that inter-device communication has been problematic, but he added that the Dexcom G4 is much more reliable than the Seven Plus, which the researchers were previously using. The G4 was also found to be more accurate than the Seven Plus (mean ARD of 10.1% vs. 15.6%) – an advantage that seems to have helped avoid hypoglycemia (no hypoglycemia at all with the G4 vs. 1.3% daytime and 0.4% nighttime hypoglycemia with the Seven Plus). Dr. Ward closed with a preview of an outpatient, hotel-based study in which patients will use two sensor, two pumps, and a Motorola phone as the controller – excitingly, a pilot study was carried out “earlier this week.”

• Dr. Ward discussed his group’s current bihormonal closed-loop system, which he emphasized is not entirely closed-loop. The trials do include meal announcement (based on carbohydrate content estimated to the nearest 20 g) and a mealtime bolus (usually about half the size of a patient’s typical bolus). Insulin and glucagon are both delivered using OmniPods. In the past, the Oregon group has used two Dexcom Seven Plus sensors, but in the latest studies they use two Dexcom G4 sensors. The control algorithms use the average of the two sensors; if the sensorsdeviate by 65% or more, the patient is prompted to perform a fingerstick test for calibration. The control algorithm for glucagon uses proportional-derivative (PD) control. The algorithm for insulin uses a variant of proportional-integrative-derivative (PID) control, with a modulating layer that periodically updates its estimate of the patient’s insulin sensitivity. (Every 30 minutes, the algorithm re-analyzes the insulin and glucose data from the prior 90 minutes.) In subjects with insulin resistance due to corticosteroid treatment, the addition of this adaptive layer has been shown to reduce hyperglycemia without increasing hypoglycemia (El Yousseff et al., J Diabetes Sci Technol 2011).
  • The system’s algorithms include several features to avoid “bihormonal instability.” For example, when glucagon is given, insulin delivery is reduced. On the other hand, glucagon delivery is avoided after meals, and it is reduced when there is little insulin on board (IOB). The system also must wait at least 50 minutes between doses of glucagon.
• Commercially available glucagon is too unstable to be used in pumps, but a curcumin-stabilized formulation holds promise. Dr. Ward reviewed that when glucagon is in solution, it rapidly forms fibril aggregates. He said that these aggregates can be cytotoxic and that they can affect the pharmacokinetics of glucagon (Caputo et al., Peptides 2013). Several groups are working to stabilize glucagon in various ways; the Oregon researchers are experimenting with the polyphenolic compound curcumin. Curcumin-stabilized glucagon has shown much lower levels of fibrillation than regular glucagon at both three and seven days. Also, seven-day-old samples still have the same rapid kinetics as fresh glucagon.
• Dr. Ward shared encouraging data from a small (n=15), 30-hour, inpatient clinical trial of bihormonal closed-loop control with meal announcement. Glucagon was given in 78 cases, and reference blood glucose measurements fell below 60 mg/dl eight times (10% failure rate). A major goal of the study was to assess postprandial glucose control after large mealsindeed, carbohydrate consumption was unrestricted. Patients’ estimates of mealtimecarbohydrate content were often inaccurate: underestimation was more common than overestimation, and the average amount of underestimate was 20 g. Nonetheless, postprandial control was quite good: the average excursion was 55 mg/dl. The postprandial rise was biggest at breakfast (>80 mg/dl), smallest at lunch (<20 mg/dl), and of intermediate size at dinner (>60 mg/dl). Remarking on the between-meal differences, Dr. Ward noted that lunch came only four hours after breakfast, whereas dinner came five hours after lunch; perhaps patients already had significantly more insulin active at lunchtime. Dr. Ward said that patients did not perceive meal announcements to be extra work (“no problem whatsoever”), but he acknowledged that safety concerns could arise if patients announce a meal and then do not actually eat anything. Therefore he and his colleagues encourage patients not to announce a meal until they actually see it.
  • One goal of this trial was to compare the performances of the Dexcom Seven Plus and the Dexcom G4. The trial included roughly 500 sensor-hours of data for the Seven Plus and roughly 224 sensor-hours for the G4. By every metric shown, the Seven Plus was not as good as the G4. These metrics included mean absolute relative difference (15.6% vs. 10.1%), Clarke Error Grid A score (91.4% vs. 75.6%), and the percentage of errors that were “egregious” – i.e., more than 50% away from reference blood glucose (3.2% vs. 0.0%).
• Dr. Ward briefly discussed top-line data from the Oregon group’s most recent inpatient closed-loop study (n=13). Excluding the first five hours, glucose control appearedexcellent as defined by mean and standard deviation: 12937 mg/dl at night and 15155 during the day.
  • Reminding us that the average data don’t tell the entire story, Dr. Ward showed a tracing from one patient for whom the system malfunctioned at several levels. One sensor was over-reading, the other was reading correctly, and both dropped out at least once during a period of a few hours. Despite these sensor errors, the system correctly called for glucagon to address falling blood glucose levels – but the OmniPod containing glucagon failed. The researchers gave an open-loop administration of carbohydrates, and the patient’s glucose levels spiked. Dr. Ward said that inter-device connectivity was “much better” with the Dexcom G4 than the Seven Plus, but he nonetheless looked forward to a future when all the system components are more integrated.
• Dr. Ward showed unpublished data on whether repeated administration of glucagon might deplete patients’ stores of glycogen – one of the safety concerns we hear with regard to glucagon delivery in the artificial pancreas. Under fasting conditions, patients with type 1 diabetes (n=7) were given eight small doses of glucagon (2 ug/kg) in succession. Even after the eighth dose, patients still seemed to have a good supply of glycogen, and their blood glucose still rose in response to glucagon delivery. This finding is certainly encouraging, but we are still curious to see research on glucagon delivery when glycogen is dramatically depleted – e.g., in extreme exercise conditions.
• The Oregon researchers have just conducted a pilot trial to prepare for a hotel- based study of a new closed-loop system that runs on a Motorola phone. Dr. Ward explained that this portable system would also include two sensors and two pumps. The cell- phone controller enables remote monitoring; Dr. Ward and one of his colleagues keep an eye on the system’s performance from a separate room in the same hotel. We did not learn many specifics on study design, but Dr. Ward noted that challenges would include restaurant meals and exercise.

Questions and Answers

Q: Are you still switching between sensors, or now that you use the G4, are you sticking with just one?

A: In the first study I showed, we used the sensor with the best accuracy at time of calibration. In a pilot study, we found that using the average of the two sensors worked as well or better. In the most recent trial we used the average. If the sensors disagree by 65% or more, the system forces a calibration.

Q: Are you using the curcumin-stabilized glucagon in the clinical studies?

A: Curcumin-stabilized glucagon is not FDA approved, and we are not using it. We are using commercially available glucagon, which requires frequent reconstituting. We just initiated discussion with FDA on this.

 

Overnight Control Performance of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System (10-OR)

Daniel Finan, PhD (Animas Corporation, Westchester, PA)

Animas’ Dr. Daniel Finan presented results from a 24-hour study (n=20 type 1 pumpers) of the company’s hypoglycemia-hyperglycemia minimizer (HHM) system, consisting of a Dexcom Seven Plus CGM, OneTouch Ping insulin pump, and an MPC controller with a safety module. He presented overnight statistics (9 pm- 7 am) for the system, which were strong: a mean YSI glucose of 129 mg/dl (135 mg/dl on CGM), with a low standard deviation of 32 mg/dl (28 mg/dl on CGM). The median percentage of time in range (70-180 mg/dl) overnight was 91% (measured by YSI), with a median of 0% of the time spent >180 mg/dl (n=9 patients), 0% of the time spent <70 mg/dl (n=5), and 0% of the time spent <55 mg/dl (n=3). We wish Dr. Finan had showed mean values as well (some say median values are more likely to positively portray the performance of the system). On a separate note, this also speaks to the need for consensus on appropriate closed-loop study metrics. Dr. Finan explained that the controller took hypoglycemia mitigation “action” at a median glucose of 110 mg/dl and hyperglycemia mitigation “action” at a median glucose of 190 mg/dl (definitions below) – the hypoglycemia value was higher than we would have expected and this may be a safety issue on which the company has agreed with FDA. Overall, the study demonstrated good feasibility of the HHM in the overnight period, certainly a positive first step – in the future, we hope to see studies that test the system in more challenging conditions (meals, incorrect boluses, and exercise), along with incorporation of Dexcom’s Gen 4 CGM.

• This non-randomized, uncontrolled, clinical research study tested the feasibility and insulin delivery characteristics of Animas’ hyperglycemia-hypoglycemia minimizer (HHM) system in 20 adult type 1 pumpers. The system uses the Animas One Touch Ping insulin pump, the Dexcom Seven Plus CGM, a control system that doses insulin automatically to minimize hyperglycemia and hypoglycemia (MPC controller with a safety module), and the Artificial Pancreas System (APS) framework.
• The HHM algorithm was based on CGM readings every five minutes, and YSI reference values were obtained regularly (though less frequently than every five minutes). HHM closed loop started just before dinner at 6 pm (accompanied by a meal and bolus), ran through the overnight period (9 pm-7 am), breakfast at 7 am, lunch at 12 pm, and wrapped up around 6 pm the following day (~24 hours of closed-loop). Dr. Finan did not show data or statistics outside of the overnight period.
• The HHM takes “action” to mitigate hypoglycemia and hyperglycemia. For hypoglycemia, “action” was defined as three consecutive deliveries <50% of the normal basal amount. For hyperglycemia, action was defined as three consecutive deliveries of >150% of the normal basal amount. Using three consecutive deliveries was intended to isolate when the controller takes sustained action vs. one-off occurrences.

Questions and Answers

Q: You did not tell us the slope of glucose change when the controller took action…

A: On the insulin delivery metrics, that’s why we showed a distribution. Due to the system’s predictive nature and the model in the algorithm, there will be different action points based on different slopes. To your point, that’s exactly why we showed a distribution of concentration. You could quantify the slope in a variety of different ways; this is just the method we chose for this small feasibility study. We just wanted to get a ballpark.

Q: For the patients with any glucose values <55 mg/dl – was there any reason why? Were they not compliant?

A: These patients were within the confines of a clinical research center; they really didn’t have any say in terms of being cooperative or not. This was just naturally reflective of variable glucose levels.

Q: When did you turn on the controller relative to the overnight period?

A: We started closed-loop control shortly before dinner at 6 pm on the first night. So the closed-loop controller was running for at least four or five hours by the time 9 pm rolled around.

Q: How did the glycemic start point at the beginning of the overnight period affect the controller’s performance?

A: Good question. We did no analysis of subject-by-subject. The nice thing about overnight control is there are fewer disturbances happening. If the patient was high or low, the controller brought them into range.

Dr. John Mastrototaro (Medtronic Diabetes, Northridge, CA): Why didn't you show overall percent of time in each glucose range, as opposed to median?

A: We quantified the median of 20 numbers. Each of those 20 numbers, one per patient, was a mean. We took the median across patients.

Dr. Mastrototaro: You didn’t just look at the overall percent of time in range?

A: We have that. But we just decided to go with the median.

Dr. Bruce Buckingham (Stanford University, Stanford, CA): That brings up an interesting question. We need common metrics. Later presentations use 70-150 mg/dl as their range. Do you know what your data showed in that range?

A: Not off the top of my head. To your point, it would be nice if we could all use the same ranges and common metrics.

 

Clinical Results of Artificial Pancreas Using Intraperitoneal Insulin Delivery (16-OR)

Howard Zisser, MD (Sansum Diabetes Research Institute, Santa Barbara, CA)

Dr. Howard Zisser discussed a clinical study of closed-loop control comparing two modes of insulin delivery: subcutaneous (with a pump) and intraperitoneal (with a pump connected to Roche’s transcutaneous DiaPort 2). Ten patients with type 1 diabetes were studied in 24-hour inpatient admissions that included three meals with carbohydrate counts of 70 g, 40 g, and 70 g. Compared to subcutaneous insulin, intraperitoneal insulin made the postmeal excursions significantly smaller after lunch (difference of 105 mg/dl) and after dinner (difference of 54 mg/dl). The post-breakfast excursion was smaller as well, but by a non-statistically significant margin of 15 mg/dl. Despite these improvements, Dr. Zisser concluded that intraperitoneal insulin delivery would be better able to incorporate large meals if those meals were announced. Therefore he said that future trials would use meal announcement. He also looked forward to better results with newer CGM sensors and said that further work was needed on the pharmacokinetics and pharmacodynamics of intraperitoneal insulin…and glucagon!

• Dr. Zisser noted that this preliminary study had some limitations. It enrolled only patients who had historical problems with subcutaneous insulin infusion, because this is a criterion for use of the DiaPort. Also, the subcutaneous experiments were all performed before the intraperitoneal ones, because all insulin delivery was intraperitoneal after the surgery to implant the DiaPort 2. More insulin was given with intraperitoneal delivery, but Dr. Zisser said that the majority of the difference was seen at breakfast.

Questions and Answers

Q: When you showed subcutaneous vs. intraperitoneal, it’s been discussed that you often see the lowest peak at lunch, because you get that carry-over from breakfast. With the faster off-profile of IP, you might expect that benefit to be mitigated, but this isn’t what you see. If it isn’t a carry-over from the breakfast-time insulin, what do you think causes the improvement at lunch?

A: In the 10 subjects, intraperitoneal insulin looks like it’s kicking in earlier than subcutaneous. We think that it has to do with how the controller tuned – it’s not that control is more aggressive, but the controller gives more insulin because the clearance is faster, as well.

Q: How are you sensing? Subcutaneously? Isn’t there a 20-minute delay?

A: We used the Dexcom Seven Plus, which has a delay of five to 10 minutes. We are looking at intraperitoneal sensing as well to see if that can give a smaller delay. We are also looking at inhalable insulin for faster delivery at meals.

Q: Was there any problem with cellulitis at the skin site, or peritonitis?

A: No. There were some local wound infections with the first-generation DiaPort because its port was not really fixated well. With the dacron cuff on the new version, the skin tends to grow stably around the site.

Q: Is it difficult to remove?

A: I don’t think so. Also, you can change out the catheter over a wire if it occludes.

Q: What is the life expectancy of the port?

A: It is kept in until catheter occlusion, then the catheter is changed over a wire.

Q: What’s happened with amylin in this mix?

A: There have been a number of studies, including from UVA and Yale. Preliminary studies showed promising results, but pramlintide almost seemed to be shifting the meal’s glucose curve in time rather than compressing the rise.

Comment (Yale researcher): Come to our poster, late-breaker 49!

 

Adding Hyperglycemia Mitigation to Predictive Low Glucose Suspension (9-OR)

Fraser Cameron, PhD (University of Stanford, Stanford, CA)

Dr. Fraser Cameron described an in-silico experiment, which explored how adding hyperglycemia mitigation to his team’s predictive low glucose suspend (LGS) system affected nocturnal glucose control. The LGS system was previously tested in an outpatient pilot study, which found a 50% reduction in blood glucose less than 70 mg/dl at the expense of an 11 mg/dl increase in mean morning glucose (n=77 intervention nights; 38 control nights). Seeking to lower mean morning glucose and increase time spent in euglycemia, Dr. Cameron and his team tested an approach that added insulin delivery to the existing LGS algorithm. The hyperglycemia mitigation approach estimated insulin sensitivity from the current basal rate and used a Kalman filter to extrapolate the glucose trend. Dr. Cameron showed data suggesting that the addition of hyperglycemia mitigation reduced morning mean glucose and increased time spent in euglycemia (70-180 mg/dl) compared to LGS alone and to control nights when tested via the UVA/Padova simulator; significance not provided.

• Low glucose suspend (LGS) plus hyperglycemia mitigation decreased mean morning glucose and increased overnight time spent in range (70-180 mg/dl) compared to LGS alone and to control nights. The simulation was run on the UVA/Padova simulator. Importantly, the simulation of the LGS with hyperglycemia mitigation approach did not allow for nighttime patient boluses.

Method	BG <70 mg/dl (%)	70<BG<180 mg/dl (%)	BG >180 mg/dl (%)	Morning BG (mg/dl)	Boluses/night	Carbs/night
Control Nights	1.59%	73%	25%	158 mg/dl	0.15	0.09
LGS	1.02%	72%	27%	160 mg/dl	0.15	0.09
LGS + Hyperglycemia Mitigation	1.14%	89%	10%	138 mg/dl	0	0.07

• Dr. Bruce Buckingham (Stanford University, Stanford, CA) presented the results of the pilot outpatient study at the Diabetes Technology Meeting 2012. For study result details, please see our discussion on page 19 at: http://www.closeconcerns.com/knowledgebase/r/c81dc4ef. For background, the team advanced the third iteration of the LGS algorithm tested in the pilot study to a larger outpatient study; this is the version of the LGS algorithm discussed above.
• Dr. Buckingham presented initial safety data from the larger outpatient study of the LGS algorithm at the FDA-JDRF-NIH Workshop on Innovation towards an Artificial Pancreas. To see our discussion of the results, see page four at: http://www.closeconcerns.com/knowledgebase/r/7e98f344. Dr. Cameron commented that the study’s full results are currently being analyzed.

Oral Sessions: Emerging Evidence in Pediatric Type 1 Diabetes

Feasibility Data of the Predictive Low Glucose Management Algorithm - The Pilgrim Study (357-OR)

Thomas Danne, MD (Kinderkrankenhaus, Hannover, Germany)

Dr. Thomas Danne presented encouraging feasibility data on Medtronic’s predictive low glucose management (PLGM) algorithm from the PILGRIM study. The study used a 30-minute predictive horizon with a sensor threshold of 70 or 80 mg/dl. Twenty-two adolescents (14-20 years of age) exercised with the PLGM system until either the system suspended insulin delivery or until the reference blood glucose value (HemoCue) reached the predictive suspension threshold setting. Of the 16 patients who reached the hypoglycemic threshold for PLGM activation, PLGM was successfully activated in 15 of the experiments and prevented hypoglycemia (reference blood glucose ≤63 mg/dl) in 12 of the 15 experiments. Dr. Danne commented that in one of the cases of hypoglycemia, the patient had restarted basal delivery manually. Notably, suspension time averaged 90 minutes. Dr. Danne underscored that this was 30 minutes less than the fixed two-hour suspension time of the Veo (Medtronic’s low glucose suspend [LGS] system). He attributed the lower post-suspension nadir (HemoCue) of 77 mg/dl (vs. 91.4 mg/dl in ASPIRE in-Clinic, which tested LGS [now called threshold suspend]) to the more flexible length of suspension provided by the PLGM algorithm. Dr. Danne reminded the audience of an in-silico modeling experiment of the PLGM system (Roy et al., Diabetologia 2012), which had similar results to the PILGRIM study. He suggested, therefore, that it was reasonable to extrapolate clinical outcomes from the in-silico experiment. Roy et al. found that the number of hypoglycemic events with PLGM was less than on CSII and that the time spent in hypoglycemia with PLGM was significantly less than the time spent in hypoglycemia with LGS (p<0.001). Dr. Danne concluded his talk with his perspective on where the field stands on the path towards an artificial pancreas. “I think we’re just a step away from treatment with PLGM in the day and closed-loop control during the night,” said Dr. Danne.

Oral Sessions: Inpatient Management of Diabetes

Safety and Efficacy of Automated Closed-Loop Glucose Control in the Critical Care Unit (313-OR)

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

Dr. Lalantha Leelarathna presented a compelling feasibility study on the safety and efficacy of automated closed-loop control in critical care patients. Twenty-four patients were randomized to 48- hours of automated closed-loop control (n=12) or local protocol (intravenous sliding scale insulin; n=12). The closed-loop system was comprised of a bedside laptop running an MPC algorithm, FreeStyle Navigator sensor and receiver, and both insulin and dextrose (20%) pumps. Both groups had hourly arterial blood gas (ABG) taken and sensors were calibrated with ABG every one to six hours. Based on reference glucose values, closed-loop control led to a significantly greater time in range (6.0-8.0 mM [108-144 mg/dl]; 54.3% vs. 18.5%; p=0.001) and significantly decreased time above range (39.0% vs. 78.4%; p=0.001). Neither group recorded any reference glucose readings below 4.0 mM (72 mg/dl). Dr. Leelarathna concluded that the findings support conducting a larger closed-loop trial in a more diverse patient population.

• “Although there is no agreement about the correct [blood glucose] target, there is general consensus that we need better methods for glucose control,” said Dr. Leelarathna. As such, his group set forth to investigate whether closed-loop control could provide superior glycemic control to standard protocols.
• The study was conducted in the neurosciences critical care unit at Addenbrooke’s Hospital and enrolled 48 patients. Inclusion criteria included having blood glucose ≥10 mmol/l (180 mg/dl) or being on insulin therapy. Exclusion criteria included irreversible organ failure, therapeutic hypothermia, or major blood clotting abnormalities. Patients had similar baseline characteristics, including age (standard arm vs. closed-loop arm: 58.3 vs. 62.8 years), BMI (27.8 vs. 27.1 kg/m2), Apache II score (a measure of illness severity; 11.2 vs. 12.9); prior diabetes (n=6 vs. n=5); and insulin infusion at baseline (n=10 vs. n=10).
• The components of the closed-loop system included the FreeStyle Navigator sensor and receiver, a bedside computer running an MPC algorithm, an insulin pump, and a dextrose (20%) pump. The system was initiated with approximate body weight; no nutritional information was provided.
• Impressively, closed-loop control resulted in 54.3% of time 6.0-8.0 mM (108-144 mg/dl) and 92.2% of the time 5.6-10.0 mM (100.8-180 mg/dl). By both metrics, time in range was significantly greater with closed-loop control than local protocol (p=0.001); however, as pointed out in Q&A, sliding scale insulin therapy is not the best available care. Looking forward, Dr. Leelarathna hopes to test the system against more dynamic care protocols.
  • Blood glucose less than 4.0 mM (72 mg/dl) were not recorded in either group; seven patients required dextrose intervention in the closed-loop group (six of those seven required less than 10 g per 24 hours). During Q&A, Dr. Leelarathnaattributed at least part of the dextrose intervention to care changes. For example, the algorithm wasn’t aware when nutrition stopped, he said.

Metric	Results Based on Reference Glucose
	Standard Care	Closed Loop	p-value
Starting Glucose	10.8 (194.4)	10 (180)	0.219
Time in Target (6.0-8.0 mM [108-144 mg/dl])	18.5%	54.3%	0.001
Time Above 8.0 mM (144 mg/dl)	78.4%	39.0%	0.001
Time Below 4 mM (72 mg/dl)	0	0	NA
Time in Target (5.6-10.0 mM [100.8-180 mg/dl])	73.2%	92.2%	0.001
Mean Glucose (mM [md/dl])	9.1 (163.8)	7.9 (142.2)	0.001
SD of Glucose (mM [mg/dl])	1.8 (32.4)	1.3 (23.4)	0.089
Total Insulin (24 hours; U)	40.9	57.4	0.478

• The sensor recorded a median absolute relative deviation of 7.0% and an absolute deviation of 0.5 mmol/l (9 mg/dl). Median bias was -0.1 mmol/l (-1.8 mg/dl). Eighty-eight percent of readings fell in Clark Error Grid Zone A. During the first 24 hours, the sensor was calibrated at a median interval of 152 minutes; during the second 24 hours, the sensor was calibrated at a median interval of 205 minutes. Sensor accuracy was a point of discussion during Q&A; Dr. Leelarathna commented that his group was able to achieve the accuracy by calibrating more than recommended by the manufacturer. When the algorithm detected that sensor values were deviating too far from reference values, it would calibrate more frequently.

Questions and Answers

Q: How long did you treat the patients for?

A: The study duration was 48 hours. At 48 hours, they went back to usual treatment. If they left the critical care unit, the experiment was terminated. At least 24 hours was required to be included in the analysis.

Q: Many will argue that in critical care populations, sensors cannot capture hypoglycemia. How did you achieve the accuracy?

A: The majority of previous studies looking at the accuracy of sensors have looked at Medtronic. And out of the three sensors Medtronic has the highest MARD. The science behind the Navigator is less susceptible to medication and less dependent on tissue oxidation. We also calibrated the sensor much more frequently than recommended by the manufacturer. The algorithm detected when sensor glucose started to deviate.

Q: How do you view hypoglycemia with the dextrose infusions – 58% received dextrose?

A: The infusion was to prevent hypoglycemia. For example, the algorithm wasn’t aware when nutrition was stopped.

Q: Were any of those patients close to sepsis. Did you see worse performance there?

A: We haven’t analyzed sensor performance in that way. We need a bigger study with a more diverse population, and we need to assess whether the sensor varies with diagnosis.

Q: The exclusion criteria said permanent organ failure was excluded. What about acute organ failure?

A: Yes, we had patients on vasopressors included in the study.

Q: Most people do use a dynamic protocol. Moving forward, you need to compare closed- loop control to a dynamic protocol [vs. sliding scale].

A: That is a fair point. It will be more informative to compare it to a more advanced insulin infusion protocol.

Posters

Reduction in Hypoglycemia and No Increase in A1C with Threshold-Based Sensor-Augmented Pump (SAP) Insulin Suspension: Aspire In-Home (48-LB)

Richard Bergenstal, David Klonoff, Bruce Bode, Satish Garg, Andrew Ahmann, Robert Slover, Melissa Meredith, and Francine Kaufman

This poster detailed the results from the ASPIRE In-Home study of low glucose suspend (called “threshold suspend” in this poster), simultaneously published in the New England Journal of Medicine by Dr. Richard Bergenstal et al. The three-month in-home study was a randomized, controlled trial of 247 patients comparing sensor-augmented pump (SAP) therapy alone to SAP plus “threshold suspend” (i.e., low glucose suspend set at 60-90 mg/dl). Results showed that nocturnal hypoglycemic events per patient week occurred 32% less frequently in the threshold suspend group (p <0.001). Moreover, mean area under the curve (magnitude plus duration) of nocturnal hypoglycemia events decreased 38% in the threshold suspend group compared to control (p <0.001). Strikingly, four severe hypoglycemia events were observed in the three-month study, with ALL occurring in the control group – this really resonated with us, and we hope payers will also appreciate this technology’s power to improve healthcare costs over the long term. Significantly, there was no change in A1c levels between the groups, both maintaining baseline A1cs of ~7.2% after three months in the study – that’s a huge win given the improvements in hypoglycemia. We think this data will build an even stronger case for the FDA to approved this device. (Approval is expected “this calendar year” per the last update in Medtronic F4Q13.)

• ASPIRE in-home was a 19-site, open label, randomized controlled study comparing sensor-augmented pump therapy with the threshold suspend feature (n=121) to sensor-augmented pump therapy alone (n=126). The study had a two-week run-in phase for all patients, followed by randomization and a three-month study phase. The run-in established eligibility for the study, as subjects were required to have experienced two or more episodes of nocturnal hypoglycemia during the run-in phase. An episode of nocturnal hypoglycemia was defined as a sequence of sensor glucose values ≤65 mg/dl, lasting >20 min, between 10:00 pm and 8:00 am, and with no evidence of user-pump interaction. For those in the intervention group, the threshold to suspend insulin was set at 60-90 mg/dl. Runs of sensor glucose levels <65 mg/dlad < 20 minutes were not included in the primary analysis. Full study details and methods were published on June 23, 2013 in the Journal of Diabetes Science and Technology.
• The study randomized 247 patients with a mean A1c of 7.2%, a mean age of 42-45 years, a mean diabetes duration of 27 years, and a mean BMI of 27-28 kg/m2. Patients in the two groups were 38-40% male.
• Mean area under the curve (magnitude plus duration) of nocturnal hypoglycemia events decreased 38% in the threshold suspend group compared to control (p<0.001). Mean AUC declined from 1,547 mg/dl x min to 980 mg/dl x min in the thresholdsuspend group vs. 1,406 to 1,568 mg/dl x min (an increase) in the control group.
• Nocturnal hypoglycemic events per patient-week occurred 32% less frequently in the threshold suspend group (p <0.001) – these declined from 2.4 to 1.5 events per week in the threshold suspend group vs. 2.5 to 2.2 events per week in the control group. Since the pump is not predictively suspending, we would guess this reduced number of events per week could be attributed to a couple factors: 1) the previous finding that hypoglycemia begets hypoglycemia (i.e., reducing the magnitude and duration of nocturnal hypoglycemia reduced the susceptibility and occurrence of subsequent events); or 2) since the threshold can be set from 60-90 mg/dl, those using the higher end of the threshold suspend range would see a reduction in events, particularly if the sensor was biased to read low).
  • Combined day and night hypoglycemia events per patient week happened 30% less often in the threshold suspend group (p<0.001). Day-only data was not shown, which we suspect means it was not significant. Of course, since the vast majority of two-hour suspends occurred at night, this is not very surprising.

• The percentage of sensor glucose values <70 mg/dl decreased 40% overnight in the threshold suspend group compared to the control group – in absolute terms, this represented a decline in values <70 mg/dl values from 10% of the time in the control group to 6% of the time in the threshold suspend group. As might be expected (given the nature of threshold suspend at 60-90 mg/dl), the biggest boost came in the <50 mg/dl range – a 57% reduction with threshold suspend vs. the control group.
• Following two-hour suspend events, insulin delivery resumed at an average of 93 mg/dl, rising to an average of 169 mg/dl two hours later (and then leveling out). We expect predictive low glucose management will markedly improve that two-hour average of 169 mg/dl. Two hours after insulin delivery resumed, 26% of values were >200 mg/dl, 70% of values were 70-200 mg/dl, and only 4% of values were <70 mg/dl. Consistent with previous data, most (77%) of the 1,873 two-hour suspends occurred at night.
• In a huge win, there was no significant change in A1c in either group. To us, this is a testament to the power of preventing hypoglycemia – we’d speculate it had positive ripple effects on hyperglycemia as well (e.g., less hypoglycemia meant less tendency to overcorrect and go high).

	A1c at Baseline	A1c at three months
Threshold suspend group	7.26	7.24
Control group	7.21	7.14

• There were four occurrences of severe hypoglycemia in the control group vs. zero in the threshold suspend group. Previous data on blinded CGM has shown that seizures typically occur overnight after several hours of hypoglycemia – though threshold suspend is not preventative in nature, it does reduce the number of hours a sleeping patient would spend hypoglycemic. It was welcoming to see the positive severe hypoglycemia results in this study. From a cost-effectiveness perspective, this certainly seemed attractive.
  • A six-month study presented by Dr. Trang Ly was also presented at ADA, which tested use of the Medtronic Veo over a six-month period in patients with hypoglycemia unawareness. That study also demonstrated an impressive reduction in severe hypoglycemic events. Please see our write-up of abstract 228-OR in our ADA Day #3 coverage.

• Severe hyperglycemia (blood glucose>300 mg/dl with ketones >0.6 mmol/l) occurred three times in the threshold suspend group vs. zero times in the control group. These were labeled “infusion set related,” so we would guess they were caused by infusion set failure and were not linked to pump suspension. DKA was of course a worry of the FDA, so this was encouraging to see.
• ASPIRE In-Home only included patients prone to hypoglycemia (see eligibility criteria above), meaning that these results may not be generalizable to all populations. Still, we think this was a rationale study design choice, as this group stands to benefit the most from this device.
• This study was published in the New England Journal of Medicine on June 22, 2013. More information is at http://www.nejm.org/doi/full/10.1056/NEJMoa1303576?query=featured_home

Symposium: ADA Diabetes Care Symposium

Feasibility of Outpatient Fully Integrated Closed-Loop Control - First Studies of Wearable Artificial Pancreas

Boris Kovatchev, PhD (University of Virginia, Charlottesville, VA)

Dr. Boris Kovatchev reviewed his group’s progress toward outpatient closed-loop control and shared data from two recent multi-site, outpatient trials (n=20 each) – an early feasibility study and an efficacy study with randomized crossover design. Each study compared open-loop and closed-loop control during roughly 40-hour sessions that included unrestricted, announced meals and used the University of Virginia’s smartphone-based platform (DiAs). The first study’s primary endpoint was simply the percentage of time that inter-device communication was maintained (98.9% for closed-loop and 97.1% for open-loop). In this study, closed-loop control gave slightly worse time in target at night (72% vs. 80%), but it reduced exposure to overnight hypoglycemia (0.27 vs. 0.53 events per 24 hours). The second study used a more advanced system and had a primary endpoint of hypoglycemia reduction, as measured by low blood glucose index. By this metric, closed-loop control outperformed open-loop control with a highly statistically significant effect size of 0.64 – better than the 0.4 effect size that the researchers had anticipated.

• Between November 2011 and July 2012, Dr. Kovatchev and collaborators conducted a multicenter feasibility study of outpatient closed-loop control, the results of which will be published in Diabetes Care in July. The study was conducted at the University ofVirginia, the University of Padova, the University of Montpellier, and Sansum Diabetes Research Institute (n=5 patients per site). Each study visit was 42 hours long.
  • The system included a DiAs-enabled smartphone, a Dexcom Seven Plus CGM, an OmniPod, and a separate handheld device to link the CGM and pump with each other and the smartphone. The algorithm architecture included a range- correction module, a meal bolus calculator, and a safety system.
  • In Europe, once closed-loop control was begun, it continued for the rest of the trial; in the US, the system was placed in an overnight safety mode (i.e., basal insulin delivery rate, plus the safety module). As the time that the study designs were submitted to the FDA, the agency would have allowed nocturnal closed-loop control only if blood glucose measurements were taken throughout the night. A benefit of the Europe-US difference was that to allow rough comparison between closed-loop and quasi-open-loop control in the overnight period. Another difference was that in Europe, closed-loop control was initiated in the hospital, whereas in the US, control was entirely outpatient (e.g., at a hotel or guesthouse).
  • The researchers succeeded with regard to their prespecified primary endpoint – to maintain inter-device communication for at least 80% of the time. Altogether, the study included 830 hours of data (277 hours of open-loop control and 550 hours of closed-loop control). Inter-device communication was maintained for 98.9% of the time in open-loop mode and 97.1% of the time in closed-loop mode. Dr. Kovatchev showed how often each system component malfunctioned and caused data loss. Measured in events per 24 hours, malfunction frequency was as follows for: sensor components (0.0 in open-loop, 0.04 for closed-loop), pump components (0.17 in open- loop, 0.09 in closed-loop), and DiAs (0.17 in open-loop, 0.04 in closed-loop). The control algorithm performed as intended, producing a dose recommendation 100% of the time.
  • Closed-loop control and open-loop control were roughly comparable in terms of time in target range, but closed-loop control did reduce the number of overnight hypoglycemic events. In the overnight period (11:00 pm to 7:00 am), time in target was slightly better with open-loop control (80% vs. 72%) – Dr. Kovatchev noted that the patients in the study were good at managing their diabetes. However, open-loop control led to more nocturnal hypoglycemic events below 70 mg/dl (0.53 vs. 0.27 events per 24 hours). Dr. Kovatchev also noted that the outpatient closed-loop results compared favorably to inpatient closed-loop data from a previous study with a similar system (Breton et al., Diabetes 2012). The studies had differences in design that prevent a straightforward comparison. Still, we thought it generally encouraging to see such similarities with inpatient and outpatient results, respectively, for overall time in range (74% vs. 70%), overnight time in range (74% vs. 72%), and the frequency of overnight hypoglycemic episodes (0.24 vs. 0.27).
  • Dr. Kovatchev highlighted the system’s ability to allow remote monitoring. The remote monitoring function was tested most thoroughly at the Sansum Diabetes Research Institute, where all five patients performed their study visits simultaneously. Their data could be viewed in real-time via computer and even on Dr. Howard Zisser’s iPad. The main screen included each patient’s top-line glucose data and a traffic light to indicate whether the system was functioning properly (e.g., no pump occlusions). Researchers could also click on each patient’s icon to get a more in-depth look at the study data.
• Following up on interim results shown at ATTD 2013, Dr. Kovatchev shared top-line data from his consortium’s most recent outpatient closed-loop trial. (He noted that full results could be seen in the poster hall, at poster 993-P.) The study enrolled five patients at each of the same four clinical centers from the feasibility study (total n=20). Patients took part in two 40-hour sessions: once using a closed-loop system, and once using the same system in open- loop mode. Patients had dinners and restaurants and had no restrictions on meal size, as long as they announced meals to the system and estimated carbohydrate content. The other main challenge to the system was 45 minutes of walking.
  • Patients used a DiAs-enabled smartphone that was linked to a Dexcom G4 receiver by USB cable and linked to a Tandem t:slim pump by Bluetooth low energy. Remote monitoring was possible via WiFi and the cellular network. The algorithm featured an enhanced version of the feasibility study’s range-correction module, as well as insulin-on-board constraints.
  • During the study, roughly 1,400 hours of data were collected: 700 hours each for open- and closed-loop control. Dr. Kovatchev said that approximately 96.5% of the data were valid, with 3.5% lost to pump occlusions and other technical problems.
  • Closed-loop control was a success according to the study’s primary efficacy endpoint: reduction in the risk of hypoglycemia, as defined by the low blood glucose index (LBGI). By this metric, closed-loop control had an effect size of 0.64 – a highly statistically significant result, and better than the 0.4 effect size that the researchers had anticipated.
  • The Dexcom G4 performed with a mean absolute relative difference of 11.5 mg/dl, as compared to self-monitoring of blood glucose (SMBG) tests taken during the study. The correlation between the CGM and SMBG was r=0.95. Dr. Kovatchev noted that these results were consistent with a paper on the G4’s accuracy that was written by Christiansen and colleagues and published online just a few days before ADA (Diabetes Technol Ther 2013).

Symposium: Confronting Hypoglycemia with Diabetes Technology: The Arc of Progress (Supported by Medtronic and Bayer)

Introduction

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

Dr. Irl Hirsch opened the session with a conclusive statement: “The theme tonight is hypoglycemia. This is not just a nuisance. This is clearly one of the most important, rate limiting aspects of insulin therapy.” He then outlined the session’s content and introduced the first speaker, his brother James Hirsch. He then shared what fellow type 1 Mr. Paul Madden told him earlier in the day – “In the ADA’s 73-year history, I don’t know if there was any moderator that ever introduced his brother. This may be a first at an ADA!” Before leaving the podium, Dr. Hirsch humorously quipped, “ I can tell you many things about my little brother, [laughter] but I will refrain due to lack of time.”

 

Facing Insulin's Demon

James Hirsch (Author, Cheating Destiny, Needham, MA)

Writer extraordinaire Mr. James Hirsch gave a moving speech on “insulin’s demon,” beginning with a bit of humor: “It’s a true honor to be on such a distinguished panel. At least that’s what Irl told me to say.” He shared a number of poignant stories, including some perspective on the history of insulin, stories he learned from patients while writing Cheating Destiny, his personal experience of a car accident stemming from hypoglycemia, and his son’s experience with hypoglycemia at a baseball game just a few weeks ago. Below, we’ve included some of our favorite quotes below from Mr. Hirsch’s talk.

• “Early patients understood the power and paradox of insulin – it can keep you alive, but also induce harm…Doctors call it the rate-limiting factor of insulin. I call it living on the damn precipice.”
• “When writing my book, I put an ad in the back of Diabetes Forecast. The ad said I was writing a book and to please send me your stories of having diabetes. I received hundreds of letters and emails. What struck me was how many had something to do with hypoglycemia. For one woman, her blood glucose falls every time she shops at Marshalls. Another man was on vacation with his wife, and he passed out in a Jacuzzi. The wife had to call 911 and the paramedics had to pull him out. What was a surprise was that he weighed 400 lbs and they were at a nudist camp. What I didn’t fully appreciate is that hypoglycemic patients can exist in this Netherworld – neither conscious nor unconscious, are half dead and half alive.”
• “I have a personal hypoglycemia index that everyone should follow. Clarity of thought is paramount. Never drive when you’re under 80 mg/dl. Never play poker when you’re under 70 mg/dl. And never get married when you’re under 60 mg/dl.”
• “That is what makes hypoglycemia is diabolical. It impairs the one organ you need to fix the problem – the brain.”
• “From low blood glucose, Garret couldn’t catch the ball and couldn’t hit the ball. He doesn’t like to carry candy in his pocket or keep a juice box on the bench…Garret also doesn’t come over and get it from me. The last thing a 12 year old with diabetes wants to do is draw attention to himself…I told him that in baseball, there is this little known rule that if you strike out with a blood sugar of under 70 mg/dl, it doesn’t count against your batting average.” [Laughter]
• “I’m reminded of what Nietzsche said, ‘That which does not kill me makes me stronger.’ With insulin, the inverse is true – that which makes me stronger can also kill me. If improved technology can indeed reduce these threats, then we will need fewer guardian angels. Thank you.”

 

Overcoming the "Rate-Limiting Factor" in Diabetes Management with Insulin

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

Dr. Irl Hirsch provided a comprehensive overview of the history of hypoglycemia and the severity of its associated complications. He emphasized the need to stop hypoglycemia, as the body can adapt to hypoglycemic symptoms after even just one exposure to a hypoglycemic event (Keller and Cryer, 1991). However, strict avoidance of hypoglycemia can reverse defective glucose counterregulation in type 1 diabetes (Fanelli et al., Diabetes 1993). Dr. Hirsch also gave definitions of various types of hypoglycemia, particularly emphasizing the new definition of “pseudohypoglycemia” – when a person reports typical symptoms of hypoglycemia without a plasma glucose concentration of <70 mg/dl. Using T1D Exchange data, he emphasized that severe hypoglycemia increases with age, and by the age of 50,there is a 14% per year risk of seizure or coma. Further, he highlighted the T1D Exchange data that demonstrates roughly equivalent rates of severe hypoglycemia at all levels of A1c – a clear reminder of what an important issue hypoglycemia is for all people with diabetes. Dr. Hirsch also discussed glucose control metrics and outlined how to fight hypoglycemia (the audience appreciated the size 72-font “CGM” bullet point that punctuated the slide). Although CGM has been shown to significantly decrease the amount of time a patient spends in hypoglycemia, Dr. Hirsch wondered why the technology has not been more widely adopted in the diabetes community. He concluded his talk by explaining that better basal insulin, faster prandial insulin and pumps, and more comfortable and accessible low glucose suspend (LGS) will lead to improved control of hypoglycemia. As Dr. Hirsch said (and we agree), it will be fun to watch the evolution of diabetes technology.

• Dr. Hirsch discussed glucose control metrics as outline by Bergenstal et al. (Diabetes Technology & Therapeutics 2013). In addition to outlining these metrics, he explained that standard deviation is most likely going to be used in the future – in Q&A, he underscored its simplicity. However, Dr. Hirsch mentioned that there are benefits to coefficient of variation as well, which incorporates both standard deviation and mean. Additionally, he noted that if patients spend 50% of their time in the designated range, their A1c should be around 7%.
• Dr. Hirsch provided a list of strategies to minimize hypoglycemia. Specifically, he called for HCPs to fight payers on the potential for restricting use of insulin analogs – it was disconcerting for us to hear that this is a problem he is now facing (along with the recent discussion of limiting strips in Washington). With this, Dr. Hirsch noted that there is never a reason to “ever use NPH ever again.” He also called for more conservative glucose targets, strict avoidance of hypoglycemia, use of insulin pump therapy, and the appropriate use of bolus calculators. Last, he strongly voiced support for the use of CGM.

 

Automatically Stopping Insulin Delivery to Reduce Hypoglycemia: An Interim Goal or an End in Itself?

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

Dr. Satish Garg provided a broad overview of pump therapy and closed-loop development, bringing attendees up to speed on sensor-augmented pump therapy (STAR-3), low glucose suspend (the ASPIRE in-clinic and in-home studies), and the path towards an artificial pancreas. Most of his presentation focused on already-published studies, though he briefly discussed the ASPIRE in-home study of the MiniMed 530G (now dubbed “threshold suspend”) – this will be presented Saturday morning by first author Dr. Richard Bergenstal as late-breaking poster #48. Notably, the study is being published in the New England Journal of Medicine, a huge win and a nice follow-up to STAR-3’s 2010 publication in the prestigious journal (Bergenstal et al.). Dr. Garg could note share any study results – it is embargoed until 10 am tomorrow – though he discussed the study design as published in the Journal of Diabetes Science and Technology (Klonoff et al., 2013). He concluded with a brief overview of predictive low glucose management (PLGM), Medtronic’s next-gen algorithm (i.e., the MiniMed 640G) that suspends basal insulin when glucose <120 mg/dl and predicted to be <70 mg/dl in the next 30 minutes. We certainly look forward to seeing clinical data on PLGM later this ADA from Dr. Thomas Danne. Dr. Garg closed with an important statement, “What we are not keeping pace with is finding ways to use the technology appropriately in the clinical setting. We need better ways to implement the technology in real life.” Given that only ~30% of US type 1 patients are on pump therapy and only ~5-10% are on CGM, we agree.

 

Overnight Closed-Loop Blood Glucose Control Under Real-Life Conditions: Initial Experiences

Moshe Phillip, MD (Medical Tel Aviv University, Tel Aviv, Israel)

Dr. Moshe Phillip explained the benefits of the MD-Logic artificial pancreas (MD-LAP), citing results from his recent slew of DREAM studies. Dr. Phillip shared initial pilot study results from the DREAM 4 closed-loop trial, which compares overnight glycemic control with the MD-Logic AP (MDLAP) system to sensor-augmented pump (SAP) control in patients' homes. These were presented earlier in the day in the artificial pancreas oral session by Dr. Revital Nimri. When considering the entire intent-to-treat study population (n=44), MDLAP significantly decreased the time spent in hypoglycemia (blood glucose [BG] <70 mg/dl; 2.9% vs. 5.6%; p=0.02) and increased the time spent in euglycemia (BG 70-180 mg/dl; 81.5% vs. 73.6%; p=0.01). In addition, Dr. Phillip presented the blood glucose control from five patients in DREAM 4's second pilot, which is an impressive six-week long study at patients' homes. He showed that the device prevents hypoglycemia and reduce the mean blood glucose to the desired range. And as a reminder, DREAM 5 (pump advisor during the day) and DREAM 6 (24-hour closed-loop control) are on the docket as well – very exciting! Dr. Phillip also reviewed data from the STAR-3 and ASPIRE studies, which both demonstrated the abilities of a pump and sensor to reduce hypoglycemia and improve time in-range. He noted that these results are especially significant because HCPs are not achieving their goal of maintaining the balance between hypoglycemia and complications. The MD-LAP’s unique “fuzzy logic” algorithm was a point of emphasis, as the system has the ability to learn from the patient and adjust to their lifestyle. Dr. Phillip was also quite positive on remote monitoring, which allows researchers to ensure safety during more ambitious outpatient trials.

• Dr. Phillip provided an example of daytime success with the MD-LAP. Although the group’s studies focus on overnight use of the MD-LAP, Dr. Phillip showed the glucose levels of an individual on the system during the day, which achieved a mean glucose level of 139 mg/dl.
• “I don’t want to give the system back… even if it is unfinished, I don’t care”. Dr. Phillip emphasized that patients have been very pleased with the system. True to form from ATTD’s past, he presented a video clip of a father and son that loved the system. Indeed, the father said he “didn’t care about regulatory,” he just wanted the system.

Panel Discussion

Irl Hirsch, MD (University of Washington School of Medicine, Seattle, WA); Satish Garg, MD (University of Colorado Denver, Aurora, CO); James S. Hirsch (Author, Cheating Destiny, Needham, MA); Moshe Phillip, MD (Schneider Children’s Medical Center of Israel, Petah Tikva, Israel)

Q: How does the predictive suspend work?

Dr. Garg: It is preset for 120 mg/dl. If you don’t do any intervention, it will stop basal insulin at 120 mg/dl for two hours.

Q: Where is the FDA with approving the threshold suspend pump? [Laughter]

Dr. Garg: I don’t know if I want to answer that question.

Q: Regarding closed loop, what are built in safeguards if the system fails to respond correctly?

Dr. Phillip: The program is based on the sensor readings, but also recognizes problems with the sensor. The system also recognizes amount of insulin delivered, and of course, the future closed-loop system will be built on a future sensor. The future will be based on two sensors at least, maybe more. They will be talking to each other and verifying if the reading is correct or not, duplicating the safety. This is the future. We are getting there with the help of talented people.

Q: Will the ADA be using a different severe hypoglycemia definition for children under six years of age?

Dr. Garg: I don’t know the answer to that. Maybe someone else can answer this question. But I want you to remember that you can always change the threshold, so HCPs are welcome to change it to whatever the patient feels at ease at. If a patient is nine years old, maybe you don’t want to start the threshold suspend at 70 mg/dl, but you want to start at 100 mg/dl. Maybe Medtronic can answer the question on the regulatory approval by FDA.

Dr. Phillip: In Europe, it’s allowed to use suspend, and yes, you treat children and adolescent differently. You are building experience.

Q: Dr. Bob Vigersky performed a study looking at CGM and type 2 diabetes, and this showed great improvement with A1c levels. Do you have experience with type 2 diabetes patients using CGM?

Dr. Garg: I have many patients with type 2 diabetes using insulin, either using basal or prandial. I think the CGM is very beneficial for type 2 diabetes patients. It is amazing how much of a reduction you can see in A1c levels for those with type 2 diabetes who use CGM. Of course, we focused on the artificial pancreas here, so we did not really talk about CGM for type 2s.

Q: It’s interesting how many questions came up on the topic of our metrics. This is sort of one we talk about a lot – the metric for glycemic variability. From the white paper I showed you, standard deviation was picked. Someone is pointing out that interquartile range would overcome the problem of non-Gaussian distribution – they are asking why interquartile range was not chosen.

Dr. Hirsch: In the clinical situation, we don’t want to do too much differentiating between clinics and research. Interquartile range goes so far into the complex math of what we are able to do and what people understand. They are taking us to learning something that is just difficult to rationalize. It’s hard to rationalize such a complex metric. It may be that if we do show variability is important, we can look at the metrics and compare them to outcomes. People are familiar with standard deviation and it does an okay job.

Dr. Garg: This was discussed at length. Everyone agreed that we need to make it easy for people to understand. With standard deviation, you can explain what it is to patients in less than a minute. Remember, math skills in the United States are not that good. That’s all I will say. We wanted to make it simple and easy.

Q: How did the non-LGS arm of the in-clinic ASPIRE study manage hypoglycemia?

Dr. Garg: Remember that at any time, if the patients’ blood glucose went less than 50 mg/dl, patients had to repeat that experiment. Many of the sessions had to be done again just because of that reason. Trust me when I said it was the hardest study, it was. Lots of patients had headaches and they wanted x, y, and z. The protocol wouldn’t allow it. We didn't put patients’ health in jeopardy, but many would have normally had juice. The study protocol was written so strictly, thanks to Fran and Katy.

Q: What advice would you give Garret when he drives?

Jim Hirsch: I haven’t really thought about that question. Certainly, when he gets old enough to drive, he’ll understand that if he’s not on CGM, he needs to test his blood glucose each time before he gets behind the wheel. That is more or less what I do now. He has vague recollections of the car accident, so that should help.

Q: What kind of hardware is used in the MD-LAP?

A: We have been using the Medtronic Veo pump and Enlite 2 sensor.

Q: What do you think of the new competitive bidding process through Medicare?

Dr. Irl Hirsch: I’m very concerned about it because some of the offshore strips vary from lot to lot. For checking blood glucose and calibrating the CGM device, I’m afraid we are going to get levels that are way off. We’ve rushed into this much too quickly, I don’t feel we’re moving into an area of more safety. I think it’s going to be less.

Dr. Garg: I think we became the easy target. I think if you look at the way they are going to enforce this, it is unbelievable. Doctors are not well represented in the Congress, so they are taking out billions just to balance the sheet. You are going to have meters with no quality. We do not see a patient if the pump cannot be downloaded, and none of these pumps can be downloaded. It is very unfortunate how the healthcare is going.

Q: What do your patients do with this system in the water?

Dr. Phillip: They take the pump off and continue with the senor

Dr. Garg: Any time patients go diving or what have you, they continue to use the sensor but they remove the pump.

Symposium: Moving Toward a Cure in Type 1 Diabetes

Closed-Loop Therapy Update

Andrew Bremer, MD, PhD (Vanderbilt University School of Medicine, Nashville, TN)

Dr. Andrew Bremer provided a basic background presentation on recent closed-loop research and approaches. His talk did not share any highly novel opinions or new data, but did remind us of the sheer breadth of approaches in development by various groups (similar to Dr. Aaron Kowalski’s opening remarks at the JDRF/NIH Closed-Loop Research Meeting on Day #3 of ADA). He also briefly discussed future directions for closed-loop research. We appreciated his view that we should avoid thinking of these devices in isolation – for him, improvements will not depend on small differences between devices, but on real life interactions between people and the technology. We believe this area of human factors – especially as systems become more autonomous – will become increasingly important as systems move from feasibility to commercialization.

• Dr. Bremer summarized some of the key future directions for closed-loop research: 1) expanding identifiable virtual patient models and in silico studies; 2) combine insulin delivery and CGM into a single device (interestingly, he called two sites a “big detriment” of current systems”); 3) combining insulin delivery with other agents; 4) developing new insulin formulations and routes of delivery; and 5) moving to the outpatient setting (“the big one”).

Questions and Answers

Q: How did families deal with getting a pump and sensor at diagnosis? [In reference to the DirecNet/TrialNet metabolic control study]

A: In our experience at Vanderbilt, the families in these studies were extraordinarily high functioning. Where we felt most scared was when they went home. They went from knowing nothing to being taught how to use the sensor and pump. That was often 48 hours after beginning to cope with a son or daughter’s new disease. We offered 24-hour patient support.

Q: Have you looked at changes over time in these patients?

A: That data is being processed. These patients are being followed. I did not put it on the slide, but our primary endpoint is changes in C-peptide at one year. The hope was that early intervention would preserve beta cell function. Two-year data will be forthcoming. [Editor’s Note: Dr. Bremer did not share the disappointing one-year results of this study shared by Dr. Bruce Buckingham at ATTD 2013; see page 10 of our report at http://close.cx/ATTD2013]

Q: Moving into commercial availability, what is the FDA pathway?

A: The FDA has accelerated the development of diabetes devices, especially with LGS currently pending FDA approval for use in the commercial setting. [Editor’s Note: Given the Veo’s three-plus-year delay in the US, we thought this was an odd example of the FDA accelerating device approvals.]

Q: Are any closed-loop devices available commercially?

A: To my knowledge there is not one that is FDA approved and available here in the US.

Symposium: Continuous Glucose Monitoring (CGM) in the Management of Diabetes in Pregnancy

Closed-Loop in Type 1 Diabetes in Pregnancy

Helen Murphy, MD (University of Cambridge, Cambridge, UK)

“What do we do in pregnancy?” asked Dr. Helen Murphy in her discussion of closed-loop systems. “Do we accept the limitations of current technologies and work with the sensor and pump manufacturers and algorithm engineers to improve them?” Dr. Murphy and her clinic believe they should have this dialogue and have begun to explore how closed-loop systems can best be applied to patients with type 1 diabetes in pregnancy. “As with any new field,” said Dr. Murphy, “it is best to start with something smaller and more achievable.” As such, her team has focused their closed-loop investigation on reducing nocturnal hypoglycemia. Dr. Murphy and colleagues are about to embark on the first outpatient feasibility study investigating overnight closed-loop control in pregnant women with type 1 diabetes (n=16). Patients will be randomly assigned to either four weeks of overnight closed-loop control or sensor-augmented pump therapy. Previous pilot studies in the clinical research facility suggested that nighttime closed-loop control could indeed adjust insulin delivery safely and effectively in both early- and late-stage pregnancy.

JDRF/NIH Closed-Loop Research Meeting

Progress Report - Brief Overview of AP Highlights from the Last Year

Aaron Kowalski, PhD (JDRF, New York, NY)

Dr. Aaron Kowalski opened the meeting with a nice mix of optimism and realism: “We’ve made tremendous progress, but the proof is in the pudding and we need to deliver…Here we are. We’re on the cusp. It’s go-time right now.” He provided a nice review of the major artificial pancreas (AP) achievements since ADA 2012, headlined by significant outpatient trial experience in the past year. Dr. Kowalski then ran through each area of his six-panel roadmap to an AP – he detailed all the specific examples research groups are doing in each area. At next year’s ADA, Dr. Kowalski expects to see significant new data as a number of trials report out. He concluded with optimistic gratitude, “ As someone with diabetes and the brother of someone who battles severe hypoglycemia, I really thank everybody. Everybody in this room is playing an integral part in moving this field forward. We’re not there yet. We have a low glucose suspend system around the world, hopefully in the US soon.”

• “We still need better tools to treat people with diabetes.” Dr. Kowalski showed A1c, severe hypoglycemia, and DKA data from the T1D Exchange – all emphasized his point quite well that people with type 1 diabetes are not doing that well. We’ve become very familiar with these slides given their growing presence in talks in the past year, though they are still jarring every time we see them. Whenever we see those average A1c numbers at top clinical centers (well above 8%) and rates of severe hypoglycemia as high as 14%, we're reminded that the needle still has plenty of room to move.
• Dr. Kowalski’s six-panel AP roadmap has guided JDRF’s strategy in the past few years. He emphasized that it was not designed to say, “This is the only way to develop an AP.” Rather, it was Dr. Kowalski’s attempt at Voltaire’s maxim: “Don’t let perfection be the enemy of the good” – the roadmap attempts to bring incremental devices to market that bring meaningful clinical outcomes.
• The “number one major advance” in AP research since ADA 2012 has been the dramatic increase in outpatient experience with prototype systems (“absolutely incredible”). Later on in his talk, he called Dr. Roman Hovorka’s ongoing three-week, unsupervised home study “absolutely amazing and “data that is going to be game changing.” Other important 2012-2013 achievements include(d) the release of the FDA’s comprehensive AP guidance, the recent FDA/NIH/JDRF workshop, 16+ approvals of new or significantly modified studies by FDA, MHRA, and other regulatory bodies, and 32+ peer reviewed manuscripts and abstracts within the JDRF consortium.
• Threshold low glucose suspend: “This is so incredibly important as a first step.” Dr. Kowalski highlighted the ASPIRE in-home data just published in the NEJM. Even just based on a threshold, the study showed a significant 38% reduction in nocturnal hypoglycemia (“fantastic”). “Importantly,” he noted, “A1c was similar in the two groups.” He concluded the section emphatically: “We can do this. This is real, and the risk is low.”
• Predictive suspend/attenuation: Several studies are going on around the world, including work at Medtronic, studies in Australia from Dr. Tim Jones’ group, and joint work at Stanford/Colorado/Western Ontario/Jaeb. These include several home studies.
• Treat to range: Dr. Kowalski covered an broad array of ongoing work in this area, including studies at UVA/Montpellier/Padova/UCSB/Sansum, AP@home, and Stanford/Colorado/Yale. Exciting upcoming studies include a Helmsley Charitable Trusted funded camp study this summer, multi-center treat-to-range studies, and trials at Yale testing missed meal boluses and exercise.
• Speeding insulin: Yale has upcoming closed-loop studies on the InsuPatch and hyaluronidase. UCSB/Sansum are testing Roche’s DiaPort and MannKind’s Technosphere insulin.
• New algorithms: UCSB and the Illinois Institute of Technology are working on new algorithms, especially those that deal with exercise and reduce the burden of meal announcements.
• Multi-hormone: A number of investigators are in this space, including Dr. Ken Ward’s group in Oregon (glucagon), the BU/MGH team’s bihormonal work (the ongoing insulin/glucagon Beacon Hill study and upcoming Summer 2013 camp studies; “lots of buzz”), and Yale’s work on pramlintide and upcoming work with liraglutide.

Remote Monitoring - Should There Be More or Less Real-Time Oversight?

Roman Hovorka, PhD (University of Cambridge, Cambridge, UK) and Boris Kovatchev, PhD (University of Virginia, Charlottesville, VA)

Dr. Roman Hovorka and Dr. Boris Kovatchev exchanged competing views about the role of remote monitoring for artificial pancreas devices. Dr. Hovorka contended that remote monitoring is useful for data collection but not worthwhile as an added safety feature. In his group’s ongoing outpatient trial of overnight control, the only safety measure is reversion to open-loop dosing; he noted that this feature has been used safely in 10-15% of ~450 nights. In his rebuttal, Dr. Kovatchev said that the main value of remote monitoring would be to diagnose problems proactively, while several other attendees spoke in favor of reactive, real-time monitoring as well. These speakers argued that the costs of remote monitoring are low and that the additional safety could be important, even if it doesn’t make the system “bulletproof.”

• Dr. Hovorka considers remote monitoring a “wonderful tool for data capture” during clinical studies, but he is wary of using it for safety mitigation during clinical research. He believes that the technology is not reliable enough to be a core safety feature. Also, once remote monitoring has been introduced, researchers may have a hard time ever proving that the system is safe enough to remove the monitors. He proposed that the failsafe for closed-loop products should be a reversion to open-loop diabetes management – a strategy that has worked safely in roughly 450 nights of home use in his group’s ongoing clinical study. (He indicated that reversion to open loop has occurred on 10% to 15% of nights.) Looking to commercialized closed-loop products, Dr. Hovorka said that he can see the utility of real-time remote monitoring for a patient’s loved ones, but he is more skeptical of the value for clinicians, given that healthcare providers already have so little time to interact with patients.
• Taking the engineering perspective, Dr. Kovatchev argued that the chief benefit of remote monitoring is not to respond to emergencies, but to diagnose issues in advance. (We were unsure whether he meant diabetes problems, technical problems, or both.) He added that remote monitoring would reduce the burden on local clinical staff and would allow centralized teams of experts to address complex problems. Dr. Bruce Buckingham has shown that remote monitoring can reduce hypoglycemia in a diabetes camp setting, Dr. Kovatchev noted. The system operated 97% of the time in this study; given that it was “the first trial ever done,” Dr. Kovatchev suggested that the technology would improve from here. He envisions a gradual progression toward a system that has vertical integration of the pump and sensor, a smart-phone controller, and a cloud-based platform for data management. He explained that this design would enable distributed computing (e.g., basic safety features on the pump, with increasingly complex calculations on the smartphone and in the cloud).

Quotes from discussion

Dr. Roman Hovorka: Our experience is that the educational barrier is not about closed-loop control; it’s about learning about pumps and CGM.

Dr. Aaron Kowalski: This is not an artificial-pancreas-specific issue…I think the idea that you can remotely monitor and identify diabetes issues is a broad problem, and we don’t do it with the technologies that we have now.

Dr. Stuart Weinzimer (Yale University, New Haven, CT): It’s a totally different scale once you release these systems to the public. I will use John Mastrototaro’s example: if 300,000 people use a system every night for a year, that is 110 million nights.

Dr. Howard Zisser (Sansum Diabetes Research Institute, Santa Barbara, CA): I think that we get lost in the idea that the system has to be bulletproof. I think that the goal is simply to make the risk as low as possible.

Dr. Jonathan Javitt (Telcare, Bethesda, MD): An artificial pancreas is probably $500 to $1,000 of electronics, at the end of the day. The cost of adding remote monitoring is under $50. The bandwidth cost of transmitting that data at Telcare’s connectivity rates is less than $5 per month, so I think that the economic argument is challenging.

Dr. Jonathan Javitt: An artificial pancreas is probably $500 to $1,000 of electronics, at the end of the day. The cost of adding remote monitoring is under $50. The bandwidth cost of transmitting that data at Telcare’s connectivity rates is less than $5 per month, so I think that the economic argument is challenging.

Dr. Jonathan Javitt: [Remote monitoring] is today’s technology, and it’s easily achievable. If it only saves one life per 10,000, that’s probably all it needs to do.

Bryan Mazlish (New York City, NY): My wife and I have a remote-monitoring tool for our seven- year-old son. I think that we can all agree that there is a benefit to remote monitoring in general. With regard to the artificial pancreas, at the end of the day, if you are okay with open-loop therapy and that is the failsafe, I don’t see why you need remote monitoring specifically to monitor the artificial pancreas.

Dr. Steven Russell (Massachusetts General Hospital, Boston, MA): I think that we will probably move to automated monitoring. A system that sends alerts might someday be part of the business model: what you are actually paying for is the remote-monitoring service, and the bionic pancreas is sold for free…I am not sure what the right business model is, but I would not be comfortable without some kind of remote monitoring.

Dr. Boris Kovatchev: Similar to Dr. Kowalski’s progression toward the artificial pancreas, I suggest that we view the current version of remote monitoring as a first step toward the final goal: remote diagnostics, vertical integration, and distributed computing.

 

Perspectives on Initial Closed-Loop Products

Bruce Buckingham, MD (Stanford University, CA) and Moshe Phillip, MD (Tel Aviv University, Petah Tikva, Israel)

The debate led by Drs. Bruce Buckingham and Moshe Phillip asked whether treat-to-range techniques (such as predictive low glucose suspend) worked better than full closed-loop control (either at day or at night). Both researchers had conducted closed-loop studies, and they unsurprisingly came out strongly in favor of the closed loop. They admitted that closed-loop control still has some safety issues to be mitigated, but they were clear that it gave superior results. As Dr. Phillip put it: “Some people say ‘perfection is the enemy of the good,’ but sometimes it’s okay to be the best.” In the discussion, Dr. Hovorka maintained that “the argument that treat-to-range is better overnight [than closed loop] doesn’t hold in any studies I have seen.”

• Drs. Buckingham and Phillip considered four approaches to diabetes management – two during the night and two at daytime. Predictive low glucose suspend (PLGS) anticipates nocturnal hypoglycemia and avoids it by switching off insulin. Nocturnal closed-loop control completely manages insulin administration with no input from the patient during sleep. (The chief risk of nocturnal closed loop is that the sensor could fail or become impaired by drug interference.) Treat to range (TTR) during the day is similar to open loop, except that when the system detects hypoglycemia or hyperglycemia, it takes action to keep the patient in the safe range. A daytime closed-loop system would take total control of glucose levels during the day, without a pre-meal bolus.
• Dr. Buckingham took the view that PLGS is highly effective - the sensor doesn’t trigger insulin dosing (so it’s safer), severe hypoglycemia is avoided, and data clearly that there is no increased risk of diabetic ketoacidosis (DKA). When comparing closed-loop control to TTR in the daytime, he took the view that TTR really just creates a buffer zone for inaccurate sensors – when sensors are more accurate, then the closed-loop system would do a better job. However, he acknowledged that many safety aspects still need mitigating, such as sensor failure, set failure, communication failure, and unrealistic insulin doses.
• Dr. Phillip took the view that nocturnal closed loop gives many benefits (reduces variability, reduces mean glucose, improves time in range, reduces morning glucose, reduces patient mistakes, improves quality of life, decreases the burden of alarms). In short, it has all the benefits of PLGS. Dr. Phillip quipped, “Some people say ‘perfection is the enemy of the good’, but sometimes it’s OK to be the best.”

Quotes from discussion

Dr. Aaron Kowalski: I used to think it would be hard to get approval for an overnight closed loop controller … but I have flipped my opinion. We are significantly reducing risk versus open loop.

Dr. Roman Hovorka: Our experience is that closed loop works by reducing hypoglycemia risk. The argument that treat to range [meaning PLGS] is better overnight [than closed loop] doesn’t hold in any studies I have seen.

Dr. Moshe Phillip: Almost any controller does very well at night. But if someone takes a big bolus before they went to bed, then nobody’s controller can’t take the insulin away.

Dr. Steven Russell: I think the thing you are looking for is glucagon.

Dr. Tom Peyser (Dexcom, San Diego, CA): We have a membrane [that reduces acetaminophen interference] that is being used in partnership with Edwards, and that will be part of a future product.

Dr. John Mastrototaro (Medtronic Diabetes, Northridge, CA): Our approach [to eliminate interference from acetaminophen] has been to look at an orthogonally redundant sensor. One sensor is a check for the other, and both don’t behave similarly in the presence of interference. Which sensor do you believe? We have some diagnostics, but you can always ask the person to double-check their blood glucose.

 

What are the Challenges and Benefits Associated with More or Less Standardization in Outcome Reporting?

Steven Russell, MD, PhD (Massachusetts General Hospital, Boston, MA) and Rick Mauseth, MD (Benaroya Research Institute at Virginia Mason, Seattle, WA)

Drs. Steven Russell and Rick Mauseth provided short lists of closed-loop metrics that they believe should be standardized – there was a refreshing amount of overlap between their five-minute talks. In our view, there is clear need for this in the AP research field, so we’re glad to finally see some very tangible and visible discussion of it.

• Dr. Russell emphasized that there is not a one-size fits all approach to standardization, but it would be useful to have some common metrics. He proposed a nice list of minimum standards for bionic pancreas study reporting: mean blood glucose and mean CGM glucose over the study period, time in the specific ranges of <70 mg/dl and 70-180 mg/dl, use of means (“we’re not treating median people”), carbs per meal, carb interventions (timing and size), exercise (type and intensity), insulin and glucagon dosing, when CGM calibrations occurred (what was the criteria for doing them?), accuracy of CGM (MARD) and reporting percentage, any open loop interventions that are done, any system down-time and the causes of that down time, and data excluded from analysis (was it pre-specified criteria?). 
  • “There is no reason not to show subject level data from all of these experiments. You can learn a lot.” Dr. Russell enthusiastically called for reporting subject-level data, which he believes can be done in online supplements. The MGH/BU team always does this in their publication (one page per patient showing CGM, blood glucose, insulin data, glucagon data, meal information, and more). Dr. Russell’s team has gotten a lot of useful insights to improve their controller, and he believes publishing such granular data could help others too.
• Dr. Rick Mauseth also discussed closed-loop metrics standardization, focusing on controllers and system-level performance. He believes a comparison of controller efficacy (e.g., AP@home’s CAT trial) is possible, though it’s highly dependent on study design. Additionally, since controllers evolve incrementally and quite quickly, it’s challenging to run head-to-head studies before the controllers have already moved to the next version. Dr. Mauseth argued for comparison metrics on basic system performance (e.g., how much of the time did itwork) and a focus on reliability, robustness, and safety. He noted that JDRF consortium members have drafted a white paper on this topic (Bequette, Doyle, Hovorka, Lum, Weinzimer, Zisser). In terms of system failure, he questioned how groups are defining it and for how long. Other key areas for standardized reporting include how a controller was initialized, when a missed reading or dose occurred and why, whether there was interference (cable loose vs. restart), and details on the frequency of user action (including calibration).

Quotes from Discussion

Dr. Steven Russell: JDRF has recently funded a project to develop a continuous insulin monitor. It will be measuring insulin in close to real time. We will be able to measure pharmacokinetics and how long until insulin levels in the interstitial fluid rise. That feedback can then be given to the closed loop controller.

Dr. Roy Beck: There’s an awful lot to learn on a subject level. I agree with what you said about median. But the same is true of the mean. You can lose a lot of the information when looking at the central tendency. In a lot of these cases, we’re interested in the extremes.

Dr. Roman Hovorka: I wanted to comment on the reporting a mean of population results. If I look at it from the outside, closed loop is another therapeutic intervention. Any other study reports population means. It’s compare a standard treatment in a population. The AP is nothing different than that. Immunosuppressive therapy treats an individual, just like an AP. But you always see population level mean plus errors.

Dr. Steven Russell: As we move to pharmacogenomics, there are drugs that have failed that will probably be resurrected. Something that worked in 20% of people and did not in 80% of the people is going to fail a phase 3 trial. That could still be a good drug.

Dr. Aaron Kowalski: It is astounding how far this field has come. This year is the tipping point. We’re going to have a tremendous amount of outpatient data for the next 12 months. It’s going to be transformative.

Biodel Luncheon

Glucagon Rescue Delivery Device Demonstration

Gerard Michel (CFO and VP Business of Development, Biodel, Danbury, CT)

We had the privilege of attending a small luncheon hosted by Biodel to demonstrate its new prototype glucagon rescue delivery device. As we noted in our recent Closer Look (http://close.cx/BiodelGlucagonDevice), the company’s new glucagon rescue product will use a dual chamber, automatic reconstitution device – pictures of the device can be found at http://www.biodel.com/glucagondevicee/. The device contains a lyophilized cake of glucagon, which can be delivered in three steps: 1) remove a cover and twist (reconstitutes the glucagon and unlocks the front needle cover; 2) remove the needle shield; 3) push plunger to give dose (the needle automatically retracts into the barrel following completion of a full dose). The device is expected to have two-year dating and come in 1 mg and 0.5 mg (children) doses. As of the update in early June, the goal was an NDA filing in 2015 under the 505(b)(2) regulatory pathway. After getting a demo and seeing the device in person, we believe it offers strong ease-of-use improvements over the current Lilly and Novo Nordisk glucagon kits, though Biodel’s solution is not yet as simple as an EpiPen-like auto-injector. Still, we thought it did a good job of guiding a new user with arrows and number marked on the device itself. CFO Gerard Michel described the design goal as making it “intuitive and panic proof.” We note that will neither yet knocks either criterion out of the park (it’s first generation!), it does make important strides forward on the simplicity and training fronts – standard of care right now has major shortcomings on these fronts. As expected, we understand that Biodel is going to make further modifications to simplify the device based on feedback from the diabetes community. Mr. Michel said the feedback has been “very positive” on the device and its form factor (about 60 people total have attended over three days), though some have not been fans of the twist (step one above). We particularly enjoyed the questions this session stimulated, summarized below. As a reminder, Biodel is separately developing stable liquid glucagon presentation for pump usage (and potential emergency usage); as of the last update, formulation work was close to being finalized.

• What is the biggest barrier to more widespread glucagon rescue device penetration? The training required? Unawareness that severe hypoglycemia is an important issue? Cost? Patient perceptions that there is no reason to have a glucagon kit, as no one would no how to use it? An unwillingness to train loved ones/friends on how to use the glucagon device, perhaps because it self-identifies diabetes or suggests one is not managing diabetes well?
• Is there a market for a glucagon mini-dose indication for moderate hypoglycemia? How do patients feel about this vs. just eating food? What is the optimal delivery presentation – pen, patch, sublingual, intranasal?
• Have Lilly and Novo Nordisk contributed to the under-penetration of their glucagon kits? Since both companies make insulin, is it really in their best interests to emphasize the dangers of insulin and severe hypoglycemia?

• Who is the target market for a glucagon rescue product? Does it make more sense to market to HCPs or direct to patients? We would argue for the latter, since a pull strategy from patients has more potential to drive demand than a push strategy through already-busy HCPs.
• What role could a public awareness campaign play in expanding glucagon penetration? Does it make sense to do this on a broad, societal scale, or focused on people with diabetes?
• What color should a rescue device be? Should it be red to identify it easily during an emergency, or perhaps a color/skin of one’s choice to de-medicalize it?
• What are unique ways to train users on how to use a glucagon delivery device? Could a training app be created for a smartphone, perhaps downloadable via a QR code on the outside of the device?
• How have other rescue products, such as defibrillators and EpiPens, been widely adopted? What lessons can be learned from the adoption of these technologies?

CGM and SMBG

Oral Sessions: Updates and Applications of Continuous Glucose Monitoring

A Comparative Effectiveness Analysis of Three Continuous Glucose Monitors (171-OR)

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

Dr. Steven Russell presented a head-to-head-to-head accuracy comparison of the Abbott FreeStyle Navigator, Dexcom G4 Platinum, and Medtronic Enlite CGMs in 24 patients simultaneously wearing all three sensors in 48-hour closed loop experiments – this expanded on some of the preliminary data shared at ADA 2012 in fewer patients. Dexcom’s G4 Platinum was the most accurate sensor (MARD: 10.8%, 85% in Zone A of the Clarke Error Grid), followed closely by Abbott’s FreeStyle Navigator (12.3%, 84% in Zone A); both were much more accurate than Medtronic’s Enlite with the Veo algorithm (17.9%, 68% in Zone A). All three devices had similar rate accuracy. Concluding, Dr. Russell discussed CGM calibration errors, noting that they are still a problem and providing some rules for optimal calibration. We salute the team’s strong data-driven approach to closed-loop control – it certainly shows in their rapid progress, and we’re guessing it helps quite a bit when talking with the FDA.

• Dr. Steven Russell shared comparative CGM accuracy data from 48-hour, inpatient closed-loop experiments in 24 patients (12 adults, 12 children). Patients simultaneously wore the Abbott FreeStyle Navigator, Dexcom G4 Platinum, and Medtronic Enlite (Veo algorithm). Calibration occurred per the manufacturer’s schedule using reference blood glucose values (GlucoScout) – before breakfast and dinner for the G4 Platinum and Enlite, and as prompted by the Navigator (i.e., it asks for calibrations at specific intervals after the sensor starts). A 6 am “sanity check” was also performed for the Navigator, and if the sensor value was far enough off, a calibration would be forced. Reference blood glucose values were taken every 15 minutes with the GlucoScout (n=4,657) and YSI was taken approximately every hour. Relative to YSI, the GlucoScout had a MARD of 6% and a slope of 1.05 (“This is a very accurate device”). Some of the inaccuracy of the GlucoScout was likely due to sampling error.
  • Dexcom’s G4 Platinum was the most accurate sensor (MARD: 10.8%, 85% of points in Zone A of the Clarke Error Grid), followed by Abbott’s FreeStyle Navigator(12.3%, 84% in Zone A) and Medtronic’s Enlite sensor (17.9%, 68% in Zone A). While the CGM vs. YSI slopes of the Enlite and G4 Platinum were near one, the Navigator had a slope of– this implies it tends to underestimate high blood glucose values. In the team’s previous study presented at ADA 2012, MARDs were 11.8% for the FreeStyle Navigator, 16.5% for the Dexcom Seven Plus, and 20.3% for the Guardian.

• Dr. Russell noted that both the FreeStyle Navigator and G4 Platinum have lower MARDs and narrower standard deviations relative to the Medtronic Enlite. We see this narrowing of the spread in CGM accuracy as a very important advance, since it reduces the types of large errors that frustrate patients. All three devices had similar rate accuracy (CGM rate of change vs. plasma glucose rate of change).
• “CGM calibration errors are still a problem.” Dr. Russell showed a study example from a FreeStyle Navigator calibration when the blood glucose was rising sharply – the calibration shifted the CGM curve right (relative to YSI) and made the Navigator inaccurate for the rest of the day. Dr. Russell also candidly explained that calibration can also rescue poor accuracy – he showed another example where an inaccurately reading sensor was quickly corrected after a calibration.
• The MGH/BU team has developed calibration rules for the ongoing outpatient Beacon Hill study. Good times to calibrate are when the CGM rate of change is <1 mg/dl/min, it’s been 15 minutes since the last glucagon dose, and when at least 30 minutes have passed since the last meal. If it’s not a good time to calibrate, the team waits until the aforementioned conditions are met.

Questions and Answers

Q: With this technology being so young, it’s amazing how quickly these devices have developed to become so accurate. They are pretty close to traditional meters.

A: If you give them the right calibration. That’s still the Achilles of this technology. We had times when we calibrated poorly because we didn’t have these rules in place. Someone in the real world could fall prey to those, and it could lead to a dangerous conditions. We need a way to translate what we do in highly supervised settings to the real world.

Q: Which improvement is more important?

A: Accuracy is important, but it’s also important to reduce the variability of this accuracy. Ken Ward and others have looked at the occurrence of large CGM errors. Reducing the incidence of very large errors is really important as well. Those are the ones that can lead to inappropriate performance of the closed loop.

Q: In all of these studies, I would encourage comparison of the bihormonal approach with the insulin-only approach. Only you can do that. The field needs that to establish the necessity of glucagon. I would guess the average glucose would be substantially better with glucagon than without, but we need that study.

A: Thanks for the comment. We have decided to go with the bihormonal approach given the limited amount of effort we can spend and limited money. We’re fairly convinced that there are circumstances where only glucagon can prevent hypoglycemia – specifically, exercise. If you go into exercise with a normal blood glucose and see an average rate of change of -2 mg/dl/min, you’re hypoglycemic in 15 minutes. There’s no way that adjusting insulin can prevent that hypoglycemia. If you want truly closed loop with no carb interventions, the only way to do that is with a counterregulatory hormone.

Q: Has your closed-loop work established an accuracy threshold for CGM?

A: I presented data on our closed-loop results on Friday. We’ve only used the FreeStyle Navigator and G4 Platinum to drive our algorithm. Our control is very good with those sensors. The Navigator has had a MARD of ~12% in our studies. I can say that that’s good enough. To the extent that you can get better than that with G4 is great. What I cannot say is the upper threshold – what is the maximum you can have to still drive closed-loop. I don’t have clinical data on that.

 

A Comparison of Utilizing SMBG vs. CGM Data to Optimize Glycemic Profiles and Glucose Control in Patients with Type 2 Diabetes (170-OR)

Richard Bergenstal, MD (Park Nicollet International Diabetes Center, St. Louis Park, MN)

Dr. Richard Bergenstal and colleagues compared the impact of continuous glucose monitoring (CGM) vs. structured self-monitoring of blood glucose (SMBG) on glycemic control in patients with uncontrolled type 2 diabetes (n=104). The trial assessed change in A1c, time in range (70-180 mg/dl), and percent of readings in the hypoglycemic range. After four months, patients in both the CGM group and the SMBG group showed a statistically significant improvement in A1c and time in range from baseline; no between group difference was observed. However, when considering the percent of readings in the hypoglycemic range (blood glucose <70 mg/dl, <60 mg/dl, or <50 mg/dl) CGM utilization conferred a more significant benefit. By this metric, in fact, SMBG did not result in any significant changes from baseline. Dr. Bergenstal concluded that effective utilization of either SMBG or CGM data can improve A1c, but that CGM may be more effective than SMBG in reducing hypoglycemia whilst improving A1c in patients with type 2 diabetes.

• “No study to date has used CGM to compare the degree of glucose control achieved using SMBG.” We were certainly excited to see Dr. Bergenstal and his team be the first and hope that his study will further fuel the conversation on CGM use and reimbursement in type 2 diabetes.
• Patients with uncontrolled type 2 diabetes (A1c >7%) were randomized to either structured SMBG or real-time CGM (n=55 and 59, respectively); baseline therapy varied. Patients were seen every two to four weeks for therapy adjustments, which were made by reviewing the Roche Accu-Check 360 View for SMBG patients or the ambulatory glucose profile (AGP) for CGM patients. Patients’ baseline characteristics are outlined below.

Characteristic	CGM Group	SMBG Group
n	59	55
Number of Females	29	32
Age (years)	59.3	58.8
Age of Onset (years)	11.8	12.7
Height (in)	67.6	67.2
Weight (lbs)	233.9	239.6
BMI (kg/m2)	34.5	37.1
Systolic Blood Pressure (mmHg)	132.3	128.4
Diastolic Blood Pressure (mmHg)	74.9	72.2

• Primary outcomes included change in A1c, time in range (70-180 mg/dl), and percent of readings in the hypoglycemic range, when defined as blood glucose <70 mg/dl,<60 mg/dl, or <50 mg/dl.

• After 16 weeks, patients utilizing both CGM and SMBG data saw a significant decrease in A1c from baseline (p <0.001). Area under the curve (mg/dl x 24 hours) also improved significantly (p <0.001). However, there was no significant between group difference for either metric.

Study Arm	Baseline A1c	16-week A1c	P-value
CGM	8.19%	7.07%	<0.001
SMBG	7.85%	7.03%	<0.001

• Time in range (70-180 mg/dl) significantly improved in both groups (p <0.001); however, only CGM utilization resulted in a significant reduction in the percent of readings in hypoglycemia. Between group difference was significant for the percent of readings <70 mg/dl (p <0.01), <60 mg/dl (p <0.01), and <50 mg/dl (p <0.05). Importantly, when segmenting the data by medical therapy (insulin, sulfonylureas [SFU], or non-hypoglycemic agents) CGM appeared to have a more pronounced benefit compared to SMBG in insulin- and SFU- treated patients. In patients on non-hypoglycemic agents, this between group difference seemed to disappear.

Questions and Answers

Dr. Robert Vigersky (Walter Reed National Military Medical Center, Bethesda, MD): In your insulin-using patients, did you break out those who were on prandial insulin vs. basal insulin and were there any differences in the results?

A: Excellent question. We have a lot of breaking out to do with the data. You saw there were 59 patients in the CGM group and 55 patients in the SMBG group. When you break out these numbers among the various therapies it is getting very hard; the numbers are too small. It does appear that the benefits continue to expand the more aggressive the insulin therapy you are on, but again there’s a handful of patients in each group so I would be reticent to make conclusions.

Dr. Vigersky: Did you look at the use of CGM from a behavior modification approach? Did you give any questionnaires to patients on quality of life or to see how they actually use the CGM?

A: We’re just starting to look at that. We’ll be reporting on that, but the top level is that they say, ‘This is really interesting. No one has really showed me my data before.’ It wasn’t that we made dramatic medication changes, but that people saw their data and took some of their own action.

Dr. David Price (Dexcom, San Diego, CA): I was surprised that time spent in hyperglycemia didn’t change. Why do you think this was the case and what instructions did you give people about how to modify their lifestyle? What did you tell people to do?

A: There wasn’t extensive real-time education; it was more that we explained the therapy and said, here’s how to look at numbers, but we didn’t give them structured guidance. It was more look at your numbers and understand what’s happening. Then we did structured reviews each month.

 

High Performance of a Novel Sensor for Continuous Glucose Monitoring in the Hypoglycemic Range (172-OR)

Guido Freckmann, MD (Institute for Diabetes Technology at the University of Ulm, Ulm, Germany)

Dr. Guido Freckmann presented data on the accuracy and precision of Roche’s investigational CGM sensor. People with type 1 diabetes (n=30) each wore two sensors for seven days, calibrating each sensor twice daily. Every day, subjects performed self-monitoring of blood glucose (SMBG) tests at each of several time points: bedtime, 3 am, before each meal, and one, two, and three hours after each meal. Also, on days two and three, patients ate a high-glycemic-index breakfast with a delayed insulin bolus in order to cause large glucose excursions; reference measurements were taken every hour for five hours after this meal. Sensor accuracy was measured in mean absolute relative deviation (MARD) between CGM and SMBG values. The aggregate mean MARD was 9.2% overall (n=6,801 paired measurements), 12.3% in hypoglycemia (≤70 mg/dl), 9.1% in euglycemia (71-180 mg/dl), and 8.5% in hyperglycemia (>180 mg/dl). Researchers also measured the agreement between sensors, as described by precision absolute relative difference (PARD). The aggregate mean PARD was 7.5% overall (n=281,394 paired measurements), 12.4% in hypoglycemia, 7.4% in euglycemia, and 6.4% in hyperglycemia. We agree with Dr. Freckmann that the data suggest promise, but we do not think that the study design permitted a direct comparison to pivotal trials of available CGM systems (because, e.g., the Roche sensor’s accuracy was compared to measurements with the same meter used for calibration, rather than a separate reference method).

• In this seven-day study of Roche’s investigational CGM system, 30 people with type 1 diabetes each wore two Roche sensors concurrently. Patients had mean age of 47 years old, mean BMI of 27 kg/m2, mean A1c of 7.7%, and mean duration of diabetes of 23 years. Half were male, and 22 of the 30 used insulin pumps.
• To obtain reference measurements, patients tested their capillary blood glucose with Accu-Chek Aviva meters roughly 15 times per day: before each meal; one, two, and three hours after each meal; before bed; and at 3 am. Dr. Freckmann explained that at each time point, two fingerstick tests were performed. The first fingerstick was used for calibrations and reference measurements, but it was considered “valid” only if the second fingerstick was within 10% of the first one – if not, testing were repeated until 10% agreement occurred. This certainly is a deviation from real-life conditions (albeit not as much as if the two fingersticks had been averaged), and we are not sure how much the performance data were inflated by this choice of study design.
• The sensors were calibrated two hours after insertion and twice every day thereafter. The prototype sensors did not have the capacity for real-time calibration, so calibrations were performed retrospectively. These retrospective calibrations were prespecified in a partial effort to simulate prospective conditions: calibration was always performed with the same two tests every day (the pre-meal test in the morning and the pre-meal test in the evening). However, as noted above, a fingerstick was considered “valid” for calibration only if it agreed within 10% to another fingerstick taken concurrently.
• On two study days, patients were fed high-glycemic-index breakfasts with a delayed insulin bolus in order to produce a wide range of glucose data. Fingerstick tests were performed every 15 minutes for the five hours following this meal. However, only one test per hour was included in the analysis as a reference measurement. (According to Dr. Freckmann, including more-frequent reference measurements would have made the sensor appear less accurate. As we understand it, MARD would have been roughly 1.5% higher if the reference measurements included the post-meal tests every 15 minutes rather than just the ones that occurred on the hour.) Evaluating the accuracy of the CGM compared to the Accu-Chek Aviva blood glucose meter, the researchers found that mean absolute relative deviation (MARD) was below 10% overall and below 14% in the hypoglycemic range. Data were presented in two ways: aggregate MARD (with all of the paired data points pooled together) and average MARD (the average of the MARDs for each individual sensor). Dr. Freckmann cited the user guides of the Dexcom G4 Platinum, the Abbott FreeStyle Navigator, and the Medtronic Guardian REAL-Time to say that currently marketed CGM systems have MARDs ranging between 12.8% and 19.7%. However, we caution that a direct comparison of those numbers to the Roche sensor’s MARD could be misleading, due to the unusual protocol for calibration and reference measurements described above.

	# CGM-SMBG data pairs	Aggregate MARD	# CGM sensors	Average MARD
Overall	6,801	9.2%	59	9.2%
≤70 mg/dl	594	12.3%	55	13.6%
71-180 mg/dl	4,370	9.1%	59	9.2%
≥180 mg/dl	1,837	8.5%	59	8.3%

• To measure sensor precision, the researchers compared each patient’s two sensors to each other; they found that precision absolute relative deviation (PARD) was below 8% overall and below 12.5% in the hypoglycemic range. Data were presented in two ways: aggregate mean PARD (the mean of all the paired data points pooled together) and average mean PARD (the mean of the PARDs for each pair of sensors). To suggest a rough benchmark, Dr. Freckmann noted that PARD for currently marketed sensors have been found to range from 15.3% to 16.0% (Bailey et al., Diab Technol Ther 2009; Zisser et al., J Diabetes Sci Technol 2009) – of course, differences in study design make a direct comparison difficult.

	# CGM-CGM data pairs	Aggregated mean PARD	# sensor pairs	Average mean PARD
Overall	281,394	7.5%	29	7.6%
≤70 mg/dl	20,236	12.4%	27	12.1%
71-180 mg/dl	200,982	7.4%	29	7.5%
≥180 mg/dl	60,176	6.4%	29	6.4%

• Dr. Freckmann presented a simple simulation of the sensor’s ability to detect hypoglycemia below 55 mg/dl. To perform this analysis, the researchers looked at all of the CGM values taken when the reference measurements was under 55 mg/dl. Of these CGM values, 79% were below 60 mg/dl, 88% were below 65 mg/dl, and 96% were below 70 mg/dl. These percentages roughly correspond to how often a glucose value of 55 mg/dl or lower would be detected by the CGM if its alarm threshold were set at 60, 65, or 70 mg/dl. However, the actual rates of hypoglycemia detection and prediction would bedifferent if the CGM could use trend information instead of just point estimates, so Dr. Freckmann looked forward to a that a true study of hypoglycemia prediction/detection with the Roche CGM.

Questions and Answers

Q: With accuracy improving in the hypoglycemic range, can you think of other applications for this sensor besides diabetes? We also monitor hypoglycemia in glycogen storage diseases and other metabolic disorders.

A: Yes.

Q: It seems currently that MARD is widely used as a measure for CGM accuracy. What is the regulatory requirement for accuracy, in order for a system to become commercially available? I do not see such a clear-cut requirement as the ISO standards for blood glucose meters.

A: I am not aware of such a clear-cut threshold. Currently CGM devices are approved only as adjuncts to blood glucose meters.

 

Insertion Site Performance Differences for a Fluorescence-Based Continuous Glucose Monitor (176-OR)

Xiaoxiao Chen, PhD (Senseonics, Germantown, MD)

Dr. Xiaoxiao Chen presented data from a recent 29-day clinical trial of Senseonics’ implantable continuous glucose monitor (n=24 patients, each with one-to-two sensors implanted in the wrist, upper arm, or abdomen). He showed that the fluorescence-based sensor appears to be more accurate when placed in the upper arm or abdomen (MARD <12%, Clarke A >80%) rather than the wrist (MARD 13.1%). Dr. Chen attributed the between-site differences to the wrist’s greater temperature fluctuations. These temperature fluctuations affect the sensor’s fluorescence, which seems to have led to worse accuracy (even though the system is designed to account for temperature changes). Dr. Chen concluded that Senseonics is now focused on developing sensors for implantation in the abdomen or upper arm.

• Dr. Chen described Senseonics’ current plans for the design of its CGM. The device would consist of four elements: a sensor, a transmitter, a smartphone for data display, and a Web-based data management system.
  • Senseonics’ subcutaneously implanted sensor uses a glucose-binding, fluorescent polymer hydrogel inside a rigid PMMA encasement. Also inside the encasement is a light-emitting diode (LED) to excite the fluorescence, as well as two photodiodes to filter the light and detect fluorescence. When glucose reversibly binds to the hydrogel, the hydrogel fluoresces more strongly. As for shape and size, the sensor is a rounded cylinder with a length roughly equal to the diameter of an M&M.
  • Fluorescence data are wirelessly sent from the sensor to a transmitter that is worn on the body, near where the sensor is implanted. The transmitter converts the raw sensor data into glucose values and trends. Depending on the glucose signal, the transmitter can issue its own alarms and alerts by vibration and/or LED lights. Additionally, the transmitter wirelessly sends power to the sensor via near-field communication. The transmitter sendsglucose data via Bluetooth low energy to a smartphone, where they are displayed for patients. The data will also be stored online.
  • The transmitter looks fairly similar to the transmitters worn with current CGM, albeit perhaps a bit larger. Based on the pictures that Dr. Chen showed, the transmitter appears to be thicker than a typical smartphone, with a ‘footprint’ roughly half the size of a playing card. Relative to Senseonics’ previous plans of using a transmitter embedded in a wristwatch, we think that the new design offers less of a convenience advantage compared to current CGM. That said, we think that many patients would be excited for a CGM that does not require frequent sensor insertions and that eliminates concerns about the sensor itself falling out.

• Dr. Chen described the results of a 29-day study that included 24 patients wearing one-to-two sensors in various sites: the wrist, the upper arm, or the abdomen. The study included six clinical visits, each at least eight hours long, during which sensor accuracy was compared against YSI reference values. Dr. Chen mentioned during Q&A that calibration was required twice daily, but we are unsure of the details.
• Sensors implanted in the wrist had a mean absolute relative deviation (MARD) of 13.1% – slightly less accurate than sensors in the upper arm (MARD 11.5%) or abdomen (9.4%). A similar pattern was seen for performance in the Clarke Error Grid zone A (77% wrist, 83% upper arm, 91% abdomen) and in the Continuous Glucose Error Grid zone A (78%, 84%, 85%). This pattern of relative accuracy was consistent for measurements in hypoglycemia, euglycemia, and hyperglycemia.
• The discrepancy between sensor sites was attributed to greater temperature fluctuations in the wrist than in other sites. The average difference between minimum and maximum temperatures in the wrist was found to be 6.4C, as compared to 4.4°C in the upper arm and 4.0°C in the abdomen. The wrist also had a faster average rate of change: 2.5°C per hour, as compared to 1.0C per hour in both the upper arm and abdomen. Someone during Q&A noted that the wrist is also exposed to more ambient light, but Dr. Chen indicated that the sensor is designed to block external light sources.

Questions and Answers

Q: Is this system self-calibrating?

A: It requires two calibrations per day.

Q: Your system is photodynamic. Can it be affected by external light sources?

A: Yes, it could be affected by ambient lights. But our sensor is designed to block light from other sources.

Q: So sunlight would not affect it.

A: It could, but we do have a design to block the ambient light.

 

A New Percutaneous Optical Fiber Sensor with Longer Life Time (177-OR)

Achim Muller, MD (EyeSense, Grossostheim, Germany)

Dr. Achim Muller presented a small study of 10 people with diabetes wearing EyeSense’s percutaneous optical fiber CGM (FiberSense). The fiber is placed 5 mm under the skin (abdomen or upper arm) and connects to a base plate on the surface of the skin that fixes the sensor; a rather clunky detector is then worn on the body and reads the sensor data via a small cable. MARDs ranged from 7.8-8.8%, and 93- 94% of/ points were in Zone A of the consensus error grid (Zone B was the remaining 6-7%). For hypoglycemic, euglycemic, and hyperglycemic ranges, MARDs consistently averaged below 10%. The device was worn by patients for up to four weeks, and error over time only deteriorated in the last few days. Dr. Muller attributed this to the adhesive between the base plate and the skin, not the sensor itself. The accuracy data is encouraging, though the study was quite small. A major limitation we see is that the device does not look user friendly to wear – this will be key for the company to improve if it seeks to commercialize this CGM. It was also unclear whether the data was displayed real-time during the study, or whether it was retrospectively calculated. We would assume the latter since we did not see a receiver screen in the picture of the device.

• The accuracy and acceptability of EyeSense’s FiberSense CGM was tested in 10 people with diabetes for up to four weeks of wear time. The study included six in-clinic measurement sessions with glucose challenges (3.5-4.5 hours each) and laboratory blood glucose taken every 10 minutes. Five off-clinic measurement sessions (up to five hours each) were also performed, with SMBG taken every hour. There were 947 reference-CGM pairs for the upper arm placement and 857 reference-CGM pairs for the abdominal placement. To measure clinical acceptance, patients were asked to compare the EyeSense device to the Dexcom Seven Plus on comfort.
• Patients rated the FiberSense a 4.0/5 for overall upper arm comfort and a 2.8/5 for abdomen comfort; this compared to 4.2/5 for the Dexcom Seven Plus worn on the abdomen. The slide noted that comfort was “comparable” to the Dexcom, though we would note that the abdomen rating for overall comfort was substantially lower. No p-values were presented. There were “no or only very mild skin effects” after four weeks of wearing FiberSense.

 

Increased Time in Near-Normoglycemia and Reduced Time in Hypoglycemia in Patients with Type 1 Diabetes Using a Personal Glucose Predictive Diabetes Advisor: A Randomized Control Trial (173-OR)

Eric Renard, MD, PhD (Montpellier University Hospital, Montpellier, France)

Dr. Eric Renard detailed a phase 2a, randomized, controlled, crossover study of the DIAdvisor. The small tablet device/software takes CGM, food, and insulin data, predicts blood glucose on a 20-minute time horizon, and offers therapy recommendations (e.g., “take four units of insulin”). This study compared the DIAdvisor system to SMBG alone (with blinded CGM) over three-day in-clinic admissions in 56 patients with type 1 diabetes. Use of the DIAdvisor system significantly reduced time spent in hypoglycemia (<70 mg/dl) by 38% (p=0.02) and significantly increased time in target (70-180 mg/dl) by 8% (p=0.03). Encouragingly, the reduction in hypoglycemia did not correspondingly increase mean blood glucose. We wish the control group had been able to use real-time CGM, as SMBG alone plus blinded CGM vs. the DIAdvisor plus real-time CGM is not quite a fair comparison in our view (i.e., the results of this study do not make it clear if the glycemic benefits are due to DIAdvisor’s advice or simply the addition of CGM). Still, we are big fans of the DIAdvisor approach, as we believe there is so much room to improve the utility of CGM and make open-loop therapy easier for patients. Such advances could have real clinical impact, but should not be subject to the higher regulatory burden of closed-loop systems. Our fingers are crossed for much more movement on this front in the coming years. The current system is still somewhat early stage in our view, so we look forward to further work on DIAdvisor: at-home trials, algorithm improvements to increase the prediction horizon to 30-40 minutes, and integration with CGM (the current version connects the Seven Plus receiver to a tablet PC).

• The DIAdvisor gives patients advice on therapy adjustments (e.g., “Take four units of insulin”) based on real-time CGM data and patients’ inputted information on insulin and food. The algorithm predicts glucose on a 20-minute time horizon. The DIAdvisor2 platform includes a small tablet containing the DIAdvisor 2 patient software and a display, a Dexcom Seven Plus CGM, a Hemocue glucometer, and a Window 7 laptop with DIAdvisor 2 clinician software. The Seven Plus receiver connects to the UMPC with a cable, meaning patients have to carry both devices. It will be key to make this wireless in the future, and we would expect this to happen should the project continue.
• The DIAdvisor 2 Study occurred at three centers in Europe and included 56 patients with type 1 diabetes. Patients had a mean age of 39 years and were 77% male. Fifty-three percent were on an insulin pump and 47% used MDI. Baseline A1c was not provided.
• Study participants underwent a four-week run-in period with CGM, followed by a meal test and randomization to use of the DIAdvisor or a control condition. The control group used SMBG alone (no limit to number of tests) plus blinded CGM. The DIAdvisor group used real-time CGM along with advice from the DIAdvisor. After randomization, patients had a three-day hospital admission and subsequently crossed over to the other arm of the study. The 60-hour study period had seven meals, including one large lunch meal and one meal with a delayed insulin bolus (two hours after meal start). The study also incorporated 30 minutes of exercise.
• Relative to the control group, use of the DIAdvisor reduced time spent in hypoglycemia (<70 mg/dl) by 38% (p=0.02) and increased time in target (70-180 mg/dl) by 8% (p=0.03). Encouragingly, the reduction in hypoglycemia did not correspondingly increase mean blood glucose – 153 mg/dl in the DIAdvisor group vs. 154 mg/dl in the control group (p=0.66). The percentage of time spent in hyperglycemia decreased slightly in the DIAdvisor group (28% vs. 31%), but the result was not statistically significant (p=0.14). We note that only 45 of the 56 patients were included in this analysis, as nine patients did not follow at least 50% of DIAdvisor advice and two patients had algorithm dysfunction (e.g., no correction bolus was recommended in cases of sustained hyperglycemia).
• Compliance with CGM and the DIAdvisor’s advice varied but was generally good – 44% of patients (n=25) had >70% CGM data availability and compliance to DIAdvisor advice, and 36% (n=20) had >70% CGM data availability and 50-70% DIAdvisor compliance. Sixteen percent (n=9) had compliance <50%, and 4% (n=2) had algorithm dysfunction.

Questions and Answers

Q: Does your algorithm learn from its predictions – is it adaptive?

A: I’m not aware of that. The prediction itself is based on CGM data. As it stands, there is no adaptive mode, but it could be added in further developments.

Dr. Robert Vigersky (Walter Reed National Military Medical Center, Bethesda, MD): You gave three challenges to patients that are common in daily life – late insulin, exercise, and a high carb meal. I’m wonder if you could tell us if the algorithm performed equally in all those three types of challenges. Was it better in one?

A: There was no significant difference according to meals or exercise. The system was giving quite faithful advice related to meal size or exercise, and there was no specific condition where it appeared to be better.

 

Oral Sessions: ADA President’s Oral Session II

A Definitive Multicenter RCT to Restore Hypoglycemia Awareness and Prevent Recurrent Severe Hypoglycemia in Adults with Long-Standing Type 1 Diabetes: Results from the Hypocompass Trial (387-OR)

Stuart Little, MBBS (Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom)

Mr. Stuart Little presented the intriguing results of the HypoCOMPaSS trial, a 24-week 2x2 factorial randomized control trial (RCT) conducted in adults with type 1 diabetes and impaired awareness of hypoglycemia (IAH). The objective was to compare analog multiple daily injections (MDI) vs. insulin pump therapy (CSII), as well as to compare self-monitoring of blood glucose (SMBG) vs. real-time continuous glucose monitoring (RT). The primary endpoint was the difference in validated IAH Gold score at 24 weeks. The study had monitoring arms with and without real-time continuous glucose monitoring (RT) and insulin delivery arms of multiple daily injections (MDI) and insulin pump therapy (CSII). Ninety-six subjects were included in the study with the inclusion criteria of aged 18-74 years, C- peptide negative type 1 diabetes, and a gold score ≥4. All participants were provided with standardized 2 hour HypoCOMPaSS education focused on hypoglycemia avoidance. All groups were provided with equivalent input and follow up visits, with the goal being rigorous avoidance of biochemical hypoglycemia without relaxation of overall HbA1c. At the end of 24 weeks, the study population had an overall significant improvement in Gold score as well as a significant reduction in episodes of severe hypoglycemia. Both MDI and CSII had very similar biomedical outcomes. In patient-reported outcomes, both MDI and CSII had similar levels of fear of hypoglycemia, though CSII had significantly higher treatment satisfaction than MDI. Equivalent benefits were observed between SMBG and RT in both biomedical outcomes and patient-reported outcomes. Dr. Little concluded through these results that IAH can indeed be improved and that recurrent severe hypoglycemia can be prevented through strategies targeted at avoiding biochemical hypoglycemia without relaxation of overall glycemic control. Other than satisfaction being higher in the CSII group compared to the MDI group, no significant differences were observed between MDI and CSII as well as between SMBG and RT.

• Dr. Little stated that IAH affects approximately 25% of those with type 1 diabetes and that severe hypoglycemia is six times more likely to occur in those with IAH.
• While 92% of participants were affected by severe hypoglycemia in the year before the trial, only 19% were affected during the trial. Seventy-seven percent were affected in six months before the trial. The number of severe hypoglycemia episodes per participant per year was reduced from 913 to 12, with the mean HbA1c remaining unchanged.

Questions and Answers

Q: Can you comment on the different adjunct treatments in terms of education and follow- up that allowed them to reduce overall episodes of hypoglycemia?

A: All patients received standardized education about how to avoid hypoglycemia. Further analysis will look at the differences between the responders and non-responders.

Posters

Improved Hypoglycemic Accuracy, Alerts, and Detection with the G4 Platinum CGM System (391-P)

Thomas Peyser, Lucas Bohnett, and Katherine Nakamura

This poster presented new methods, referred to as “severe hypoglycemic detection rate” and “true severe hypoglycemic alarm rate,” to gauge the accuracy of Dexcom’s G4 Platinum CGM in the hypoglycemic range. Data for this poster comes from Dexcom’s G4 Platinum pivotal study in 72 patients (13,538 paired CGM-YSI points. The severe hypoglycemia rate is defined as the percentage of CGM measurements that show any hypoglycemia (≤70 mg/dl) when the YSI value indicates severe hypoglycemia (≤55 mg/dl). The true severe hypoglycemic alarm rate is essentially the reverse of the first metric: it tracks the percentage of YSI values that show any hypoglycemia (≤70 mg/dl) when the CGM shows severe hypoglycemia (≤55 mg/dl). The G4 Platinum’s severe hypoglycemic detection rate was a strong 90%, and its true severe hypoglycemic alarm rate was 79%. This represents a solid improvement over the respective 76% and 67% rates for the Dexcom Seven Plus. The poster concludes that this improved performance in hypoglycemia detection may increase patient confidence in CGM, which in turn could boost utilization rates and ultimately patient outcomes. Dr. Irl Hirsch expressed sincere enthusiasm for this improvement in his talk on CGM and hypoglycemia. We’re glad to see new metrics to evaluate the accuracy of CGM, especially in the hypoglycemic range. It’s also great to see metrics that mimic the more real-world experiences of patients. We salute Dexcom for really raising the visibility and level of conversation related to CGM accuracy. In the past year, it seems that presentations from companies are being held to a higher standard and viewed with a more discerning eye. This is a major plus in our view, since CGM data is subject to fiddling, manipulation, and misrepresentation.

• Both of the study’s two new hypoglycemic tracking methods were devised to better match the patient experience, in which hypoglycemia is typically treated the moment blood glucose drops to 70 mg/dl or below. Both methods are consistent with the 2013 ISO 15197 guidelines, which call for required hypoglycemic accuracy of 15 mg/dl.
• The multicenter study collected nearly 15,000 paired data points between the G4 Platinum and YSI values from 72 subjects. Of these, 13,538 paired points indicated a YSI blood glucose value of 40-400 mg/dl, while 1,373 paired points showed YSI values below 80 mg/dl. For more on Dexcom’s pivotal study of the G4 Platinum, see our ADA 2012 report at http://www.closeconcerns.com/knowledgebase/r/11c4fcda.

 

Differences Between Continuous Glucose Monitoring (CGM) Systems May Influence Frequency of CGM Usage: Persistence of Continuous Glucose Monitoring (CGM) Use in a Community Setting One Year After Purchase (886-P)

James Chamberlain, Dana Dopita, Emily Gilgen

This comparison study explored whether the differences in performance and usability between different CGM devices may help explain why patients continue or discontinue CGM use. As the authors noted, patients often reduce or end their use of CGM soon after the start of use, despite CGM’s documented clinical benefits. This could be seen to reflect a general flaw with present CGM technology; however, the authors presented survey data suggesting clear differences in patient satisfaction with the Medtronic MiniLink vs. the Dexcom Seven Plus. According to the survey, 76% of Seven Plus users wore their CGM daily or almost daily a year after purchase, compared to just 19% of MiniLink users; this seemed to corroborate differences in user satisfaction between the two CGMs. The authors argued that patient perception of the performance and usability of various CGM devices can potentially have a strong influence on the frequency of real-world use, which in turn can affect patient satisfaction and, ultimately, clinical outcomes. As such, the authors suggested that clinicians, payers, and patients themselves would be well-served to incorporate these perceptions of specific CGM devices into their overall assessment of the utility of CGM in general. In our view, this study, despite working with a fairly small sample size, does provide an effective reminder of a basic but overlooked point in any discussion of CGM’s place in patient care; patients do not respond positively or negatively to CGM in the abstract, but rather to their specific experiences with specific systems. We encourage caution in interpretation of the results, however, as the next-generation systems are considerably easier to use and more accurate than the systems used in this study. While it is helpful to see the data, we believe that doctors won’t be able to use it too actively since next-generation systems are now available for one of the products and the other is hoped to be approved by the FDA by year end (it has been available globally except in the US since 2009).

• When asked if they would purchase the same system again, 44% of MiniLink users and a whopping 92% of Seven Plus users said they would; 44% of MiniLink users said they would be open to trying another system, compared to just 16% of Seven Plus users. Among the 28% of MiniLink users who wore their CGM less than 1 week per month, 50% reported accuracy and reliability concerns (including sensor and signal problems) as key reasons for infrequent use; only one Seven Plus user fell into this usage category, with the reason being that more frequent use was “too expensive.”
• For those respondents who continued to use the CGM almost daily, the majority (52%) of Seven Plus users attributed their satisfaction to being able to know where their glucose was at all times. Most MiniLink (50%) users in this category attributed their satisfaction to feeling it improved their glucose.

	Felt It Improved My Glucose	Helped Prevent Very Low/High Glucoses	Helped Make Better Decisions to Manage Diabetes	Like Knowing Where Glucose Was At All Times	Other
Medtronic MiniLink (n=8)	4 (50%)	3 (38%)	0 (0%)	1 (13%)	0 (0%)
Dexcom Seven Plus (n=29)	4 (14%)	10 (35%)	0 (0%)	15 (52%)	0 (0%)

• For those who used their CGM less often than almost daily, the biggest reason among MiniLink users was that the CGM did not appear accurate enough (31%); cost (20%) and sensor site irritation (17%) were also mentioned frequently. Seven Plus users who used their CGM less often than almost data had reactions that were fairly evenly split between cost (22%), being tired of having two insertion sites (22%), pain or irritation at sensor sites (22%), and other (22%).

 

	Glucoses Improved So I No Longer Needed It Continuously	Cost Was Too Expensive For Me	CGM Did Not Seem Accurate Enough	Frustrated with "Bad Sensors" or Loss of Signal Alarms	Frustrated with How Frequently It Alarmed
Medtronic MiniLink (n=35)	1 (3%)	7 (20%)	11 (31%)	4 (11%)	2 (6%)
Dexcom Seven Plus (n=9)	0 (0%)	2 (22%)	0 (0%)	1 (11%)	0 (0%)
	Tired of Having Two Insertion Sites	Sensor Sites Painful Or Irritating	System Stopped Working and I Chose Not to Reorder	Other
Medtronic MiniLink (n=35)	3 (9%)	6 (17%)	0 (0%)	1 (3%)
Dexcom Seven Plus (n=9)	2 (22%)	2 (22%)	0 (0%)	2 (22%)

 

• Participants were also asked how much training they had received in CGM use. Almost half (47%) of MiniLink respondents said they had received initial training and one or two follow-up sessions, which was also the most common response (39%) for Seven Plus users.

	Multiple Training/Follow -Up Sessions	Initial Training and 1-2 Follow-Up Sessions	Initial Training Only	Used Online Resources/ Training	None
Medtronic MiniLink (n=43)	5 (12%)	20 (47%)	16 (37%)	1 (2%)	1 (2%)
Dexcom Seven Plus (n=38)	4 (11%)	15 (39%)	12 (32%)	6 (16%)	1 (3%)

 

Comparative Accuracy Evaluation of Six Blood Glucose Monitoring Systems (BGMSS) (873-P)

Leslie Klaff, Ronald Brazg, Kristen Hughes, Ann Tideman, Holly Schachner, Patricia Stenger, Scott Pardo, Nancy Dunne, Joan Parkes

This Bayer-supported study compared the accuracy of six blood glucose monitoring systems (BGMS): Bayer Contour Next, Roche Accu-Chek Aviva Nano, Abbott FreeStyle Lite, J&J OneTouch Ultra 2, J&J OneTouch Verio Pro, and Truetrack. Across the entire blood glucose range (21-496 mg/dl) and including modified blood samples (i.e., by glycolysis or the addition of glucose solution in vitro in order to obtain extreme glucose levels), Contour Next had a significantly lower mean absolute relative difference (MARD) than the other systems tested. Across the entire blood glucose range excluding modified samples, Contour Next’s MARD remained significantly lower than the other systems tested. When restricting the analysis to blood glucose <70 mg/dl, Contour Next had a significantly lower MARD than all systems when modified samples were included and had a significantly lower MARD than all systems but FreeStyle Lite when modified samples were excluded. Blood glucose monitoring system accuracy continues to be at the forefront of BGM discussion and we look to the FDA to see when it will revise its pre- or post-market accuracy requirements for meters; we had expected this some time ago in the aftermath of the FDA public hearing on SMBG accuracy in 2010 (read our Day #1 and #2 reports from the meeting at http://close.cx/FDAmeetingSMBGday1 and http://close.cx/FDAmeetingSMBGday2, respectively. Of note, the poster’s authors write that ISO standards are often used to evaluate the performance of individual blood glucose monitoring systems; however, they posit that “other analyses may be better suited for evaluating comparative accuracy of multiple meters.” To this end, the study focused on MARD (using YSI for reference) to evaluate the meters included in the study.

• Study staff collected blood samples from 146 individuals with type 1 or 2 diabetes; the same sample was used to test each BGMS and to obtain YSI results. In order to obtain extreme glucose values, blood glucose was raised in vivo through carbohydrate (CHO) ingestion or CHO ingestion with delayed insulin administration and blood glucose was lowered through a delayed meal, insulin administration and a delayed meal, or short periods of exercise. If extreme blood glucose levels were not met in this fashion, samples were modified in vitro by either glycolysis or the addition of glucose solution.
• Five hundred thirty eight blood samples were evaluated in the study; 438 were unmodified, 50 were modified by glycolysis, and 50 were modified by the addition of glucose solution. Blood glucose ranged from 21-496 mg/dl by YSI measurement, a range that all six systems were designed to accommodate. In the case that a meter displayed a “low” reading as opposed to a numerical value, blood glucose of 20 mg/dl was assigned. This occurred two, four, and 31 times for the Free Style Lite, OneTouch Ultra 2, and Truetrack meter, respectively.
• Across all samples, including modified ones, the Contour Next had the lowest mean absolute relative difference (MARD) of 3.09%, followed by the Accu-Chek Aviva Nano at 4.17%. Across all samples, excluding modified ones, the Contour Next had the lowest MARD of 3.07%, followed by the Accu-Chek Aviva Nano at 4.02%.

Sample Type	Meter System	Blood Glucose Range
		YSI <70 mg/dl		YSI 70-180 mg/dl		YSI > 180 mg/dl		YSI 21-496 mg/dl
		N	MARD	N	MARD	N	MARD	N	MARD
All Samples	Contour Next	135	4.28%	172	3.26%	231	2.58%	538	3.09%
	Accu-Chek Aviva Nano	135	7.24%*	172	3.61%	231	3.05%	538	4.17%*
	OneTouch Ultra 2	135	20.91%*	172	9.01%*	230	6.62%*	537	10.84%*
	FreeStyle Lite	135	6.08%*	172	8.36%*	231	12.86%*	538	9.60%*
	OneTouch Verio Pro	134	9.47%*	172	4.11%	230	3.92%*	536	5.23%*
	Truetrack	135	24.24%*	172	7.60%*	231	9.17%*	538	12.32%*
Unmodified Samples Only	Contour Next	85	3.51%	172	3.26%	181	2.16%	438	3.07%
	Accu-Chek Aviva Nano	85	5.99%*	172	3.61%	181	2.91%*	438	4.02%*
	OneTouch Ultra 2	85	15.96%	172	9.01%	180	6.71%*	437	9.61%*
	FreeStyle Lite	85	4.05%	172	8.36%*	181	12.12%*	438	9.31%*
	OneTouch Verio Pro	84	7.83%*	172	4.11%	180	3.53%*	436	4.80%*
	Truetrack	85	13.56%*	172	7.60%*	181	9.29%*	438	9.70%*

* Denotes that the MARD for Contour Next was significantly lower (p <0.05)

 

Clinical Evaluation of a Prototype 7-Day CGM System (865-P)

Bradley Liang, Taly Engel, Stracie Haller-Wich, Xiaolong Li, Megan Little, Keith Nogueira, Cyrus Roushan, Ashley Sullivan, John Welsh, Jerome Fischer, Rajiv Shah, Francine Kaufman, Scott Lee  

This retrospective study evaluated the accuracy, consistency, and lifetime of a prototype seven-day subcutaneous CGM sensor with a nine-millimeter implant. Blood glucose and sensor data were simulated through the commercially available six-day, real-time Paradigm Veo calibration algorithm and a novel CGM algorithm. Twenty patients with either type 1 or type 2 diabetes participated in the study over two seven-day wear periods. The study found the novel sensor to be accurate (11% MARD and 99% within Clarke A+B) and consistent (97% of sensors having a MARD <20%). The mean functioning life of the prototype sensor was 6.4 days, and 86% of the readings met the International Organization for Standardization (IOS) requirements for blood glucose monitoring self-testing systems. Using one calibration per day instead of two only reduced the sensor accuracy slightly (86% vs. 83%, according to IOS values), although it did increase the MARD slightly (11% for one per day vs. 12% for two per day). Additionally, the sensor slightly reduced blood glucose-sensor glucose delay, displayed a bit more data, and had a longer life than the reference sensor (4% increase for both). The poster also emphasized the importance of mechanical deformation on the noise level, noting that proper adhesion is necessary for the best readings. We think these accuracy and reliability improvements are encouraging, though await a larger, prospective, real-time study of the calibration algorithm.

• Twenty patients with either type 1 or type 2 diabetes participated in the study over two seven-day wear periods. Patients had either one or two glucose sensors transmitting to a Medtronic iPro2 recording device and MiniLink transmitter. Most of the sensors were worn between 144 and 168 hours (full range not given). Typical testing involved four to six blood glucose entries per day, with three periods of frequent sampling tests (days one, three to four, and six to seven) during which 12 or more blood glucose samples were taken over three hours. All blood glucose concentrations were measured with Bayer’s Contour Next Link meter.
• There is certainly room to improve CGM accuracy with better algorithms, an approach Dexcom recently committed to in its 1Q13 call – as a reminder, a new G4 Platinum algorithm is expected to improve MARD by a full two percentage points, meaning the G4 should have some days during the week with a sub-10% MARD. FDA filing is expected in late 2013/early 2014. We expect the new algorithm to be a software upgrade, which would be a huge plus for patients who will not need to obtain a new receiver. We will be interested to see if Medtronic pursues a similar approach with its CGM pipeline.

 

Feasibility Assessment of the Minimed Duo Device: Combined Insulin Delivery and Glucose Sensing (970-P)

Kirsten Nørgaard, Gayane Voskanyan, Sumona Adhya, Henrik Egesborg, Julie Theander, Pratik Agrawal, and Rajiv Shah

This feasibility study evaluated the accuracy and time spent in-range (70-180 mg/dl) of the MiniMed Duo, a device that combines the Enlite CGM sensor and an insulin infusion catheter under a single patch (see our page 95 of our ADA 2012 report at http://close.cx/ADA2012FullReport on the feasibility of adjacent insulin infusion and glucose sensing). The three-day study of 20 type 1 patients included two, 12-hour inpatient sessions (days one and three) with three meals on each day. Participants were required to wear both the MiniMed Duo and the Enlite sensor alone. Accuracy was slightly worse for the MiniMed Duo, which had a MARD of 19% vs. 16% for the Enlite alone. We note that most of the inpatient time was spent in range (90% on day one, 100% on day two), which might limit the real-world applicability of the study’s accuracy findings. From these results, the researchers concluded that the integrated sensor and infusion device can be used successfully and may reduce the burden for patients with diabetes. While a combined CGM and insulin infusion is a desire of many patients, we believe the discordant wear times present a problem not just for manufacturers but also for patients – CGMs last longer than infusion sets and generally get more accurate over time. The key question on our mind is how much patients and HCPs would be willing to trade off potentially worse overall accuracy over three days vs. a combined system. We look forward to hearing Medtronic’s commercialization plans for the MiniMed Duo, as this has not been mentioned in the company’s earnings call or analyst day discussion of its pipeline. We note that this device was on display in Medtronic’s exhibit at ATTD 2013; see pages 118-119 at http://close.cx/ATTD2013.

• The study assessed patients during two, 12-hour inpatient observations on day one and day three (7 am- 7 pm). During these 12 hours, participants ate three meals and took insulin boluses and “frequent” (not defined) blood glucose tests. Participants wore an Enlite sensor alone on the opposite side of the abdomen.
• Twenty adults with type 1 diabetes were recruited for the study. The mean age was 47 years, 45% were male, and the mean duration of diabetes was 23 years. All participants were accustomed to using sensors and Medtronic pumps (17 regularly wore sensors while three only occasionally wore their sensors). The two-hour postprandial blood glucose values (19 subjects, 114 meals) were 138 mg/dl on both days, with a mean time of 138 minutes to return to preprandial levels. The average maximum blood glucose observed in the study was 199 mg/dl, which suggests to us that the system was not fully tested for a wide range of glycemic values.
• The MiniMed Duo had a accuracy comparable to, though slightly worse than, the Enlite sensor alone.

	Integrated set (N=18)	Enlite (N=19)
Mean ARD (%)	19	16
Median ARD (%)	14	12
Clarke Grid A+B	96%	97%

• MiniMed Duo had a high mean satisfaction score of 6.6 on a Likert scale (with 7 being the best) and a low pain level at insertion score of 0.45. Results were not given for the Enlite alone, making a comparison impossible. While we were happy to see high satisfactionand low pain, it’s hard to understand the implications without a comparison to the Enlite-alone insertion and satisfaction.

Symposium: Intensifying Therapy Using Technology and Teams

Promoting Patients' Enthusiasm about Blood Glucose Monitoring

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

Dr. William Polonsky gave a stirring talk on the motivation underlying self-monitoring of blood glucose, framing the problem realistically: “If you’re asked to collect numbers for something having to do with your health, and you feel like you cannot do much with those numbers, and it makes you feel bad about yourself, would you keep collecting them?” He then provided a valuable literature review to highlight some of the most common feelings about SMBG that patients endorse. Dr. Polonsky also offered four tips to motivate patients to perform SMBG, followed by a short discussion of gamification/ apps and CGM. He concluded that “blood glucose monitoring is just a tool. It’s not a therapy.”

• Dr. Polonsky reviewed some of the most commonly endorsed (and fairly discouraging) patient beliefs about SMBG. In his own 2009 study of 483 poorly controlled insulin naïve type 2s, a striking 81% of patient said they blame themselves when a reading is high. Additionally, 32% said SMBG results makes them feel bad, 34% find the results discouraging, 21% believed there was nothing they could do with the result, and 22% felt there was no rhyme or reason to the numbers.
  • In a survey of ~1,000 type 2 patients (in press), there were three major contributors to the belief that SMBG is not worth doing: 1) burdensome (expensive and painful); 2) pointlessness (“I can’t do anything about the number anyway” and “my doctor’s comments aren’t helpful”); and 3) discouraging (“I often know it will be high, and I’d rather not have to see it,” “the result often makes me feel bad, so I’d rather not check,” and “it makes me think about diabetes more than I want”). Measures of avoidance and pointlessness were most strongly predictive of how often patients tested.
• Dr. Polonsky shared four tips that can promote patients’ enthusiasm to test:
  • First, SMBG must address a perceived patient need (e.g., elevated A1c, worries about hypoglycemia, making sure the right medications are being used).
  • Second, it’s key to use structured SMBG testing so that actionable data patterns may be observed. Dr. Polonsky supports using very simple paired testing (e.g., pre- and post-meal), which promotes behavior changes that can make a huge difference. He shared a case study of an obese man with type 2 diabetes that refused to exercise. The man’s one week experiment of paired testing before and after walking made him realize something “really shocking”: exercise lower blood glucose! Dr. Polonsky recommends that this type of discovery learning approach be practiced in seven-day experiments (i.e., there is “too much noise” in blood glucose meters to do it just once).
  • Third, help patients see how SMBG data is actionable – not chaos, not blame.​ Dr. Polonsky supported the use of problem-focused coping: “Not ‘what did you do wrong?’ but ‘what do you/we do next.’”
  • Last, somebody has to do something useful with the data (“This is what drives me nuts about recent review articles that say blood glucose monitoring isn’t useful innon-insulin-using type 2s”; he just wrote an editorial on this topic in Diabetes Care). Dr. Polonsky showed the Accu-Chek 360 view blood glucose paper tool, noting that the graphing “makes it pop” for patients and physicians (i.e., seeing the results on a chart helps make things visible and guide changes in therapy). In the STeP study, this tool made the biggest difference for physicians, who were much more likely to make aggressive, targeted changes in therapy. Dr. Polonsky also reviewed the St. Carlos study (Duran et al., 2010) of structured testing, calling it “really amazing data.” What about apps and gamification? Dr. Polonsky expressed cautious optimism in this area, though noted that most apps out there are logbooks. Still, he “hope(s) this is going to turn into something wonderful.” We listened carefully for any mention of his favorite app(s), though he didn’t comment on this front.
• “CGM changes everything…we’re turning blood glucose into a worthwhile activity.” Dr. Polonsky enthusiastically highlighted the benefits of CGM, especially trend arrows. He reviewed his 2011 study of users and ex-users, highlighting that CGM users feel more confident they can avoid hypoglycemia, feel safe when they exercise, feel safe when they are sleeping, are more confident they can control their diabetes.

Questions and Answers

Q: Would you like to come to Little Rock, Arkansas?

A: Yes!

Q: Often what I see is like your case study – when patients quit testing because they don’t perceive any value from it. They stop testing and then they are labeled as non-compliant.

A: Thank you so much. When people stop testing, we have certain labels – they are non-compliant, stupid, etc. Instead, we should remember that our patients are acting rationally. Just like us, if you’re doing an activity that seems like a waste a time, you will give it up. We need to refrain from using those labels.

Q: What’s your philosophy behind paired testing? Why is it so powerful when patients can see the before and after?

A: The major reason is because we want people to see that their actions make a difference. One of the biggest problems in diabetes is that it is a relatively invisible disease. It’s easy to ignore. This is the ultimate tool – it’s a very simple technique and can be done with very little testing. We find that it’s really empowering for patients.

 

Using Paired Testing - Educating Patients on Self-Monitoring of Blood Glucose

Deborah Greenwood, MEd, CNS, BC-ADM, CDE (Sutter Medical Foundation, Sacramento, CA)

Ms. Deborah Greenwood, the “president elect elect” of the American Association of Diabetes Educators, described her group’s study of self-monitoring of blood glucose (SMBG) in type 2 diabetes patients not using insulin. The 12-week randomized, controlled trial enrolled people who had been involved in a telehealth diabetes program for at least one year, but still had A1c between 7.0% and 10.9% (n~1,000). Patients were randomized to receive standard care or an intervention that included education, automated and personalized feedback via an electronic tablet, and structured SMBG (pre-and-postmeal tests every day, switching to different meals in different weeks). Ms. Greenwood did not reveal top-line data but did share the story of one study participant named Maria. Maria was initially intimidated by her blood glucose data, but then experienced a “transformation” midway through the trial. By eating differently and exercising more (even running a 5k race at week 10), Maria dropped her A1c from 7.3% to 6.6% during 12 weeks. “Not till I tested in pairs did I REALLY see how important my eating and activity was,” Maria wrote in her journal. “In many ways this study has saved my life, it has given me the tools to give years to my life living with diabetes.” These quotes certainly attest to the power of structured SMBG, and we hope that many other patients in the study have had similar epiphanies.

Symposium: Hypoglycemia in Clinical Practice (Supported by Merck)

Impact of Continuous Glucose Monitoring on Risk of Hypoglycemia - An Update

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

Before beginning, Dr. Irl Hirsch dedicated his presentation to Dr. Richard Rubin: “I have nothing but amazing memories over the years. He was a true inspiration to many of us. I miss him greatly.” Dr. Hirsch then proceeded with a stellar review of hypoglycemia and CGM, emphasizing a few key points: 1) hypoglycemia remains a major barrier to glycemic control (and is linked to a 4-10% lifetime risk of death); 2) CGM has the potential to reduce this burden; 3) CGM accuracy is less than perfect, especially in hypoglycemia, but it’s improved from earlier generations (he was particularly psyched about the 90% “severe hypoglycemia detection rate” with Dexcom’s Gen 4); 4) CGM accuracy is lower on day one; 5) like many aspects of diabetes therapy, patients need to be active participants with use of CGM; and 6) as CGM continues to evolve, it appears possible that there could be major improvements in the burden of hypoglycemia in type 1 diabetes. In the immediate future, Dr. Hirsch believes benefits on hypoglycemia should be most obvious with CGM activated insulin interruption (i.e., low glucose suspend). However, “the major” unanswered question for him is whether this technology will be available to the people who need it most.

• Hypoglycemia is not a benign problem – 4-10% of the population of pediatric and young adults with type 1 diabetes actually die from hypoglycemia. This point was disputed in Q&A, though Dr. Hirsch clarified that this represents lifetime risk of death (he went back to the original studies to double check). He also showed T1D Exchange data on the 12-month frequency of severe hypoglycemia, which ranges from 6% in the youngest age group up to 14% in those over 50 years old. “This is absolutely huge,” he noted.
• Although the DCCT established a clear inverse relationship between severe hypoglycemia and A1c, the link has not been confirmed in the T1D Exchange. Patients in the Exchange have a rate of severe hypoglycemia ranging from 6-9%, quite consistent at all A1c levels. This was puzzling to Dr. Hirsch, and after offering a few unlikely explanations, he admitted the real answer to this question – “We don’t know, but these numbers are too high.” Dr. Hirsch highlighted two things that are required to reduce or eliminate the risk of severe hypoglycemia: 1) an accurate CGM device and 2) a knowledgeable patient that is proactive and makes adjustments for food and insulin. He emphasized that a “knowledgeable” patient is NOT synonymous with “compliant” or “adherent.” Current CGM technology has improved over less accurate early generation devices, though there are still a few key areas for improvement: accuracy in hypoglycemia is still lower than that at more in range values, day one accuracy is worse (an “Achilles heel”) than accuracy on subsequent days, and accuracy is lowest during times of rapid glucose change. Dr. Hirsch emphasized that the inaccuracy of initial CGM devices led to lots of initial frustration with the technology (overall MARDs of 20-26% with the Guardian RT, GlucoWatch, and Dexcom STS);
• Dr. Hirsch reviewed the accuracy data on Dexcom’s Gen 4 CGM; he was quite positive on the device’s 90% “severe hypoglycemia detection rate.” The severe hypoglycemia detection rate is defined as the percentage of CGM values that are <70 mg/dl when the YSI reads <55 mg/dl. This is poster #391 (Peyser et al.) at ADA 2013. He also highlighted the~80% of values in Zone A of the Clarke Error Grid. Still, he noted that Dexcom’s accuracy can improve in hypoglycemia and on day one.
• Dr. Hirsch also summarized the accuracy of Medtronic’s Enlite sensor from an ADA poster last year (“not in the US, but available in most countries”). He showed a display of the accuracy data based on rate of glucose change (a “smart way to present”; Bailey et al., Poster 30LB at ADA 2012). As would be expected, lower rates of change had better accuracy: MARD was 13.6% at <1 mg/dl/min and 12.9% at 1-2 mg/dl/min, much better than 16.3% when glucose changed rapidly at >2 mg/dl/min. He reiterated the same limitations as those covered on the Dexcom slides: worse accuracy in hypoglycemia (a 17% MARD for <75 mg/dl vs. 12.6% for 70-180 mg/dl) and worse accuracy on day one (15.9% vs. 11.8% on day three and 13.6% overall).
• “Even the best CGM won’t help if the patient is not able to or interested.” Dr. Hirsch reviewed the now commonly accepted belief that level of CGM use predicts A1c decline – this finding has been demonstrated in STAR-1, STAR-3, the JDRF CGM trial, and in T1D Exchange data.
• Per JDRF CGM trial data, the technology is beneficial for patients who have already achieved excellent glycemic control (“A very important trial that I must continually point out to payers in Washington”). At baseline, those in the JDRF CGM trial with an A1c<7% spent a median of 91 minutes per day in hypoglycemia; after 26 weeks on CGM, this droppedto just 54 minutes, while the control group had no improvement (JDRF Study Group, Diabetes Care 2009).

Questions and Answers

Q: Can you discuss the problems of CGM detecting hypoglycemia at night vs. during the day?

A: The big issue with nocturnal hypoglycemia is that even when everything is working perfectly, the patient is in too deep a sleep. The CGM doesn't wake the patient up and do its job. It’s often a spouse or a parent that wakes up. The other thing is that patients don’t want to be bothered with it, so they turn the alarms off during the day and forget to turn them back on at night. Regarding the problem of accuracy if the device is slept on, I don’t think that’s as big of a problem now as it was with previous generations. The big issue is people sleep through the alarms.

Dr. Robert Engler (GlySens, San Diego, CA): With implantable CGM, we find that with falling glucose, the implanted sensor actually leads the blood YSI, whereas with a rise in glucose it trails it. Timing is very important for hypoglycemia detection. Can you say whether the MARD and statistical data you showed is different for a rising vs. falling glucose?

A: You’d have to ask the engineers. I showed rate of change for Enlite. I did not have access to data for Dexcom. Data anecdotally shows that this is not as big of a problem. I want to be clear – there is still a lag time for interstitial fluid. Given the physiology of interstitial fluid, I’m not sure that’s a problem that can go away with this technology.

Q: I’m a little confused with the mortality rate from hypoglycemia – 4-10% of patient die? I’m certainly not losing 4-10% of my patients to hypoglycemia.

A: It’s not per year. It’s risk of death in lifetime. I went back to each of these studies and made sure I was reading it right. If I’m recalling it correctly, I got this from Phil Cryer’s review article. It wasn’t every year it was in someone’s lifetime. And these are relatively recent data. Phil did I get that right? Dr. Phil Cryer (Washington University School of Medicine, St Louis, MO): Yes.

Q: Is it known whether lower accuracy on the first day is a function of the sensor chemical reaction itself? Is it calibration?

A: I’m going to leave that for the engineers at Medtronic and Dexcom. My understanding is that it has to do with the wound produced when the catheter goes in. There is inflammation around that wound, and that happens until that wound heals up. That’s why it may be difficult to get that first day MARD a lot better. In our institution, we are thinking about using CGM in non-ICU settings. What’s really hurting us is the fact that the first day is not as accurate as we would like it to be. That’s a big reason why it has been decided not to use CGM for surgical procedures and short span stays in the hospitals.

Q: I had an educator tell me to put on a new sensor the day before or 12 hours before taking off the previous one. This allows time to warm up. Do you recommend that?

A: I haven’t been recommending it. However, if the issue is the wound and inflammation causing the inaccuracy, maybe that would be nice. That would be a very nice and easy study to do – it would be great to do that study. It makes a lot of common sense.

 

Nocturnal Hypoglycemia in Diabetes

Stephanie Amiel, MD (Kings College London, London, United Kingdom, London, UK)

Dr. Stephanie Amiel gave a whirlwind review of nocturnal hypoglycemia studies tracking all the way back to 1936 and the Somogyi phenomenon, which suggests that nocturnal hypoglycemia precipitates hyperglycemia. Dr. Amiel undermined this hypothesis by demonstrating the lack of a counterregulatory stress response to hypoglycemia in deep sleep (Jones et al., NEJM 1998). Instead, she argued that nocturnal hypoglycemia is associated with morning hypoglycemia and often precedes diurnal hypoglycemia by blunting patients’ stress response and symptom response to daytime lows. Her historical overview showed how the field’s understanding of this topic has improved and also how the insulins used to manage hypoglycemia have improved. However, as Dr. Amiel said in her introduction, nocturnal hypoglycemia is still common. The nighttime poses a particular challenge to patients because it represents the longest interval between meals, the longest interval between self-monitoring, and the time of maximal insulin sensitivity. She pressed that nocturnal hypoglycemia needs to be actively sought – we still don’t routinely ask our patients about their hypoglycemia experience, said Dr. Amiel. “[Nocturnal hypoglycemia] can be diminished,” she concluded, “even pending the availability of new technologies.”

Questions and Answers

Q: With kids it is important for them to get frequent exercise. One of the things we found helpful was reducing insulin during exercise. We also found that if blood sugar was above 180 mg/dl at bedtime, there was low risk for hypoglycemia. And above 130 mg/dl greatly reduced the risk.

A: We need to understand how to use insulin and teach our patients that knowledge.

Q: Is there data to suggest that giving Levemir (insulin detemir) in the morning instead of at bedtime works?

A: That is a common response for people who experience hypoglycemia at night. I feel strongly that once- daily background insulin is not ideal. My preference is to use Levemir in the morning and at bedtime. It also gives you the ability to adjust the dose.

Q: You implied that the Somogyi rebound isn’t so. Could you elaborate?

A: What he was describing was in a very different era. With current studies, there is very little evidence that hypoglycemia in the night is a major driver of hyperglycemia in the morning. My own interpretation comes from perhaps the fact that we know in deep sleep hypoglycemia doesn’t have a counterregulatory response.

Q: Do you think “dead in bed” occurs in patients with type 2 diabetes?

A: I have never been asked that before. I can’t see why it would be different once a patient with type 2 diabetes has the same kind of insulin deficiency as in type 1 diabetes. Hypoglycemia-related death can certainly occur in patients with type 2 diabetes on SFUs, but whether it’s the same mechanism, I don’t know. But death certainly can occur.

Q: Nocturnal hypoglycemia has become a major focus for new basal insulins. Do you think there is a real pathophysiological difference between hypoglycemia in the day and nocturnal hypoglycemia?

A: The most important difference is the lack of a counterregulatory stress response. One of the nice things about new agents intended to cause less hypoglycemia is that they raised the awareness among health care professionals.

 

Hypoglycemia in Hospitalized Patients - Etiology and Prevention

Joel Zonszein, MD (Montefiore Medical Center, Bronx, NY)

Dr. Joel Zonszein provided a comprehensive review of outpatient and inpatient studies investigating intensive insulin therapy (IIT), hypoglycemia, and mortality. The collection of inpatient trials to date has not presented a uniform message as to the relationship between IIT and mortality; however, he stressed that the association between hypoglycemia and increased mortality is clear. Further, Dr. Zonszein said that hypoglycemia has been associated with greater hospital cost, length of stay, and morbidity. He reminded the audience that blood glucose monitoring is an important component to preventing hypoglycemia. We were struck by the statistic that fingerstick testing costs ~$2.5 million dollars per year in a large institution like Montefiore (Bronx, New York). This doesn’t mean we should test less, said Dr. Zonszein, it means we should make sure we test in a meaningful way. Dr. Zonszein spent the remainder of his presentation comparing iatrogenic to spontaneous hypoglycemia and suggested that iatrogenic hypoglycemia is associated with lower mortality (Boucal, Am J Med 2011).

Symposium: Continuous Glucose Monitoring (CGM) in the Management of Diabetes in Pregnancy

Potential Use of CGM in Newborns with Hypoglycemia

Jane Harding, DPhil (University of Auckland, Auckland, New Zealand)

Dr. Jane Harding discussed the possibility of using continuous glucose monitoring (CGM) to detect hypoglycemia in newborns. To set the stage for her presentation, she explained that hypoglycemia is the commonest metabolic disorder in newborns and the only common preventable cause of brain damage in newborns. Hypoglycemia (blood glucose <47 mg/dl) is estimated to occur in 5-15% of all births. Dr. Harding believes that CGM can potentially uncover the duration, frequency, and severity of hypoglycemia and can be used to better understand the effects of interventions. Currently, said Dr. Harding, we do lots of blood tests and treatments without being sure they are changing the outcome. However, the inaccuracies of today’s CGM technology are particularly un-ideal in newborns. CGM tends to be least accurate in the first 24 hours and most problems in newborns occur during this time period, especially in the first two to four hours after birth. Dr. Harding concluded that CGM is a “wonderful research tool” in newborns, but it is not yet a “day-to-day tool.”

• “Either we are missing a whole lot of hypoglycemia or CGM is detecting a whole lot of hypoglycemia that doesn’t matter. It is very important to know which one of these is true before we start using CGM clinically.” In the BABIES study cohort (n=102 babies ≥ 32 week old and at risk of neonatal hypoglycemia), newborns received routine clinical care (intermittent blood glucose measurement) plus blinded continuous interstitial glucose monitoring. CGM detected 266 episodes of low blood glucose lasting five to 475 minutes and only 19% of these episodes were detected by intermittent blood glucose measurement. Further, of the 107 hypoglycemic episodes lasting >30 minutes (an arbitrary threshold, she said), only 27% were detected by intermittent blood glucose measurement (Harris et al., J Pediatr 2010).
• Dr. Harding highlighted three CGM inaccuracies that make it less valuable to newborn patients: 1) CGM takes one to two hours to initialize and tends to be least accurate in the first 24 hours; however, most problems in newborns occur in the first 24 hours; 2) CGM has a delayed response to rapid changes in blood glucose, but babies often have rapid changes in glucose concentration; and 3) CGM needs calibration at extremely low glucose concentrations (blood glucose <2.2 mM [40 mg/dl]) and this is where glucose concentration is of most concern in newborns. In one of Dr. Harding’s studies, she applied a modified retrospective calibration algorithm to the CGM, such that the CGM trace had to go through each individual blood glucose value taken by intermittent monitoring.
• “It would be nice to know if [47 mg/dl] is the right number.” Dr. Harding explained that she defined hypoglycemia as 47 mg/dl because it is widely regarded as the threshold for considering treatment, “not because I can defend it strongly.” An underlying theme to Dr. Harding’s presentation was the need for greater understanding of the clinical significance of low blood sugar in newborns. We were also impressed by the novelty of CGM research in newborns and were excited to see CGM being applied to this patient group. Neonatologist Dr. Harding began working with CGM just five years ago.

Questions and Answers

Dr. Ken Ward (Oregon Health and Science University, Portland, OR): I would like to comment that we’ve been doing some work with newest Dexcom gen 4 sensor and it has a low delay time and I know the new Enlite sensor [editor’s note - Medtronic; CE marked, but not yet FDA approved] has a low delay too. New-generation sensors might make the delay less. My question was about the recalibrated data. Can you only do that retrospectively or can you apply it real time?

A: At the moment, we have only done it retrospectively. I am sure it could be built into a real-time algorithm but the question is how many points do you want to draw in? With regard to the delay, perhaps it would be better to wait a little bit to respond with treatment, so perhaps it might work in our favor.

 

CGM in Normal and Obese Women Insights from Clinical Studies

Teri Hernandez, PhD (University of Colorado Anschutz Medical Campus, Aurora, Colorado)

Dr. Teri Hernandez discussed the goals of using CGM in pregnant women without diabetes and the limitations of research to date. Throughout her talk she emphasized the value of CGM as a clinical and research tool that gives a wealth of granular data, as well as a “Gestalt” picture of overall glycemic control. However, she pointed out that CGM-based metrics can be tricky to define (e.g., what exactly is “fasting glucose”?), and she called for her colleagues to develop standardized metrics. She also lamented the dearth of good glycemic data in pregnant women without diabetes – as of 2011, only 12 such studies had ever been conducted (total n~250), only six had used CGM (total n~150), and hardly any of the patients studied had been obese. Thus healthcare providers have a hard time knowing what the “normoglycemic” targets should be for women with diabetes. However, even based on published data, Dr. Hernandez made a strong case that normoglycemic glucose excursions tend to be much smaller than the targets that are currently recommended for one-hour and two-hour postprandial glucose values (120 and 140 mg/dl, respectively). Another notable finding has been that obese women tend to have higher glucose values than non-obese women, both early and late in pregnancy (Harmon et al., 2011 Diabetes Care).

Questions and Answers

Q: It is important to take into consideration the type of CGM. The algorithms for retrospective CGM may not be the same as real-time systems.

A: This is how geeky I am: I have learned the physiology of the sensors we are using, and the engineers for that company are sick of talking to me. Yes, that is a great point – retrospective vs. real-time matters.

 

Use of CGM in Women with Type 1 Diabetes Mellitus in Pregnancy - A Randomized Trial - Lessons Learned

Elizabeth Mathieson, MD (University of Copenhagen, Copenhagen, Denmark)

Dr. Elizabeth Mathieson reviewed a randomized trial of intermittent use of Medtronic’s Guardian REAL-Time CGM in pregnant women with type 1 or type 2 diabetes (n=154; Secher et al., Diabetes Care 2013). Women in both groups performed rigorous glycemic management using seven daily tests of plasma glucose; indeed, Dr. Mathieson remarked several times that she was proud of the control group’s results. Unfortunately, more women in the CGM group had newborns that were large for gestational age (45% vs. 34%), even in the per-protocol analysis (49% vs. 34%). The incidence of severe neonatal hypoglycemia was similar in the CGM group and control group (13% vs. 14%), though CGM use showed a tendency toward improvement in the per-protocol analysis (11% vs. 19%). Dr. Mathieson concluded that the results do not support routine, intermittent use of real-time CGM in unselected pregnant women with diabetes. She instead said that the study highlights the importance of improving CGM sensor accuracy and patient friendliness. She noted that sensors have improved beyond the Sof- Sensor used in this study, and during Q&A she said that she still even prescribes the Sof-Sensor for women who are at especially high risk of severe hypoglycemia during pregnancy.

Questions and Answers

Comment: I think that the study shows the remarkably good care you have given to these patients in the control group. It is interesting that there was no improvement compared to the control group. Maybe the problem was data overload, similarly to when a patient gives you 15-to-20 fingerstick tests a day – you don’t know what to do with them. Maybe data interpretation is a problem as well.

A: I had been working with CGM for 5-to-10 years prior to initiating this study. I thought that I knew what I was doing.

Q: The performance of the sensors was considerably worse than what we usually see in a real-time setting with non-pregnant people. Might this be due to using them in a pregnant population?

A: In this lecture I gave you some accuracy data from a non-pregnant published study. I have seen similar results in non-pregnant studies – that the accuracy is around 20 mg/dl different. Not only is the average sensor different from the meter, but this bias is not consistent. A recent paper looked at precision in the hypoglycemic range and found it not useful. In this study we used the Sof-Sensor. Since then, other, more precise sensors have come out. Even though I have these results, I still use these devices in selected women with high risk of severe hypoglycemia, and I do find them useful.

Q: Despite equivalent values with A1c, you have 45% incidence of LGA in the CGM group and 34% in the self-monitoring group. I wonder – is it possible that we are looking at the wrong analyte with regard to fetal growth? I recall research showing a stronger relationship of gestational outcomes with maternal triglycerides than with glucose. I think Dr. Harmon’s study showed the same thing. Did you measure triglycerides?

A: You are right – we have to look at other things apart from glucose. We also have to look at different time periods of glucose – before pregnancy, during pregnancy, and right before delivery. We are going to publish that we saw a correlation between the SED score and plasma glucose of newborn, as well as mean glucose in the eight hours before delivery and neonatal hypoglycemia. I didn’t bring those data. Within the sensor group, we looked at the percentage of time when women had glucose above 7 mmol/l (126 mg/dl). Among those who had values above 7 mmol/l within eight hours of delivery, the duration of time above 7 mmol/ correlated with the neonatal glucose of the newborn.

Q: CGM is not a therapy, it is a tool. The benefits depend on how CGM used. Given the fact that the CGM in this study was inaccurate, my assumption is that people didn’t trust it. What instructions did you give patients about how to use CGM to manage diabetes? You showed data about how it’s a tough decision whether to give an extra bolus, for example. Did you measure compliance?

A: CGM was not approved to use without testing plasma glucose, and in this study we had both women test their plasma glucose seven times a day, every day. My patients are excellent in using plasma glucose to obtain strict metabolic control. With CGM, I asked patients mainly to focus on getting rid of hypoglycemia at night, and to look at the glucose curves in the morning to see how the night worked.

Q: So patients were not using the direction or rate of change of glucose?

A: Not in any systematic way.

 

Symposium: Closing the Communication Loop – Technology Update in Pediatric Diabetes

Making Sense of the Data at Home and In Clinic

Stephen Ponder, MD (Scott and White Healthcare, Temple, TX)

Dr. Stephen Ponder stressed the role of the patient within his or her own diabetes care, focusing on results with the Advanced Diabetes Management System (ADMS), a new technology that automates blood glucose monitoring data retrieval, analysis, and reporting. In the trial, children in his practice (age <12) were randomized to receive either standard care (n=24) or supplementation with the ADMS device (n=24) for one year. Patients with ADMS were allowed access to two features, with no further physician intervention: 1) A real-time alert sent out to a family member whenever a recent blood sugar was out of range and 2) a daily email of a day-over-day plot that displayed data from the last 21 days. At the end of the trial, patients using the device 1-3 times/week showed significantly greater declines in A1c (7.8% to 7.1%; p=0.01) versus patients using the device <1 time/week (8.0% to 7.8%) and controls (8.1% to 8.3%). Dr. Ponder suggested these results support the notion that anyone can be taught pattern management without the aid of a provider in the middle of the data stream. We are encouraged by the potential of this device and hope for more data from a wider range of individuals outside one practice; we also are interested to see what qualities define those patients that used the device more frequently.

• Dr. Ponder stressed the role of the patient within his or her own diabetes care. With regard to the patient-provider relationship, he suggested the patient is the only person with full access to the details of their care, with the provider often making decisions based off incomplete data. Referencing a recent psychological study that indicated the average person makes 200-300 decisions per day about food alone, he posited that the patient’s numerous daily decisions about care likely play a strong role and asked how providers might empower those decisions.
• The ADMS is a new technology that automates blood glucose monitoring data retrieval, analysis, and reporting, with the hopes of informing patient decision- making. Dr. Ponder emphasized the simplicity of the device, which only requires a single plugging into one’s blood glucose monitor; data is then transferred wirelessly to a server for collection and analysis.
• Dr. Ponder spent the remainder of this talk discussing a study (Toscos et al., Diabetes Care 2012) evaluating the use of the ADMS in pediatric patients. In the trial, children in his practice (age <12) were randomized to receive either standard care (n=24) or supplementation with the ADMS device (n=24) for one year. Patients with ADMS were allowed access to two features: 1) A real-time alert sent out to a family member whenever a recent blood sugar was out of range and 2) a daily nighttime email of a day-over-day plot that displayed data from the last 21 days in a simple color-coded format to highlight values out of range. Importantly, following initial education in pattern management, families received no additional input or education from the provider and were left to use the data independently.
• Results indicated improvements in both diabetes and psychosocial outcomes in patients demonstrating high frequency of usage of the ADMS. Patients naturally divided into two groups of varying frequency, with 13 using the device <1 time/week and 11 using the device 1-3 times/week. At the end of one year, patients using the device 1-3 times/week showed significantly greater declines in A1c (7.8% to 7.1%; p=0.01) versus patients using the device <1 time/week (8.0% to 7.8%) and controls (8.1% to 8.3%). Parents of more frequent users showed improvements on the Blood Glucose Monitoring Communication scale (a validated survey to gauge emotional response to BGM; lower score is better) as well (13.5 to 11.3 vs. 13.6 to 14.3 inless frequent users and 13.5 to 14.5 in controls; p=0.03), though there was no difference in the children’s scores on the survey. Finally, more frequent users showed improvements in the Diabetes Self-Management Profile (higher score better; 63.8 to 71.2 vs. 62.0 to 61.3 and 59.5 to 61.8; p=0.04), indicative of more rigorous diabetes self-management.

Questions and Answers

Q: Do you think there’s a possibility that comprehension, education, and confidence may have been driving the frequency of docking? You said the frequency was associated with change.

A: I think that’s a very valid point. We wanted consistency across teaching style so stayed in our own clinic and to avoid the challenges of adolescence, patients who were under 12. We eliminated patients with A1c over 12 or psychoaffective disorders. But you’re absolutely right; we can look at people not docking frequently and pull them in. I’m asking what can we do with the device itself and how to leverage the technology.

Q: I like the idea of letting patients take care of themselves, and we started a social network. Is there research on that?

A: We did a study in 2005 not published with 75 people. They could designate a loved one to receive data. We found significant improvements in measurements of control over six months. They were sending that data to someone socially they thought cared about them. So that social implication is important. If we can tap into that, it has tremendous power.

Q: Do you have any stories to tell about what actions they could take with the data?

A: All were taught to look for trends and patterns. It was up to them to see what they did with that information.

 

Pediatric Continuous Glucose Monitoring Update - Why Is It So Often Discontinued?

Michael Tansey, MD (University of Iowa, Iowa City, IA)

Dr. Michael Tansey explored CGM use in pediatric populations. Setting the stage for his presentation, Dr. Tansey reviewed findings from the landmark JDRF-CGM trial to demonstrate that the benefits of CGM were closely related to frequency of use across all ages; as a reminder, the study included patients with type 1 diabetes ≥ eight years of age. He next explored CGM use in younger children (ages four to 10 years) in his discussion of the DirecNet CGM efficacy and safety study (Mauras et al., Diabetes Care 2012). He noted that parents of children with type 1 diabetes on CGM were highly satisfied with the technology, despite no significant improvement in A1c at six months. Dr. Tansey then provided myriad cuts of data from the T1D Exchange clinical registry according to patient age. Interestingly, of the patients using CGM at the time of enrollment into the registry, adults (age ≥26 years) were more likely than any other age group to have discontinued CGM use one year later. Dr. Tansey underscored that CGM discontinuation was a challenging topic to study and that further research was needed to understand the multiple factors at play.

Questions and Answers

Q: My impression of the JDRF study and the DirecNet study was that the overall satisfaction was very high yet use was low. How does one understand that paradox?

A: It speaks to the heart of this matter. I don’t have a real clear answer. There is not always a direct correlation between the degree of benefit and degree of use. There may be a disconnect there.

Q: I don't have many patients on CGM, but I do have patients who love the device because they know every time they want to use it they can.

A: An additional comment. I think more education is going to be needed about the devices so people can understand what they are getting into.

Q: Do you have any thoughts on what drives the discontinuation of CGM? There seems to be a conflict between the two studies. The JDRF study indicated that CGM use did not improve A1c and the T1D exchange indicated there was a significant improvement in A1c.

A: First, as to the reason why people might discontinue use, I think the reasons will be varied. There are barriers to use: alarms, some patients were bothered by pain at insertion. More data is needed. To answer your second question, again in the JDRF study there was no significant difference in patients aged 9-15 and 15-24 years old, but look at use. There was a significant drop in those using it ≥6 days per week. Remember the T1D data was not a randomized data set.

Dr. David Price (Dexcom, San Diego, CA): I have a comment and a question. First, a comment: the discontinuation rate in the T1D Exchange was different between the sensors being used. There’s another poster, 886-P, that went into discontinuation rates and the difference between sensors. Dr. William Polonsky did a survey in adults and found that greatest reason for discontinuation and the greatest benefit was related to trust in the data – people that trusted the data used it and did not discontinue it. Do you think that’s true in kids?

A: Anecdotally, the earlier CGM devices, which some of this data does cover, were not as good. When you look at some of the data on testing frequency, subjects who are testing less are inherently trusting more.

Q: I wonder about CGM marketing strategy. Is there a role for short-term CGM use? For example, just during sick days.

A: I don’t think that has been addressed very clearly. I think there could be potential for specific situational, short-term use.

Dr. Stuart Weinzimer (Yale University, New Haven, CT): There is something I don’t think the JDRF study captured. Although people saw a benefit, many of our children don’t want to be thinking about diabetes more than they have to. There are a small percentage of people who want to think about diabetes every minute, every day but the rest of us may not want to and we didn’t really capture that in our data.

 

Insulin Pumps

Oral Sessions: Hypoglycemia – Novel Concepts

Reduction of Severe Hypoglycemia with Sensor-Augmented Insulin Pump Therapy and Automated Insulin Suspension in Patients with Type 1 Diabetes (228-OR)

Trang Ly, MBBS, DCH (Princess Margaret Hospital, Perth, Australia)

Dr. Trang Ly presented what Dr. Hans DeVries called “the most important study at this whole meeting” a randomized controlled trial comparing low glucose suspend (LGS; n=46) to pump-only therapy(n=49) over a six-month period in patients with hypoglycemia unawareness. In the six months prior to baseline, the number of severe hypoglycemia events was comparable between the groups: six in the low glucose suspend group and five in the insulin pump-only group. Notably, after six months on low glucose suspend, the number of severe hypoglycemia events dropped from six to zero (!) in the low glucose suspend group, compared to an increase from five events to six events in the group on a pump only. This was highly compelling data in our view, especially because the definition of severe hypoglycemia was very strict in this study: seizure or coma (i.e., it was not the more common “needing assistance definition). Those using LGS also experienced less average time spent <70 mg/dl. Most importantly, these benefits occurred without a deterioration in A1c (baseline: 7.4%). We commend the authors for deliberately selecting hypoglycemia unaware patients, as this group stands to benefit the most from low glucose suspend technology.

• This study randomized 95 hypoglycemic unaware patients to use of low glucose suspend or pump therapy-only for six months. Patients had a mean age of 19 years (4-50 year olds were included, with a fairly even distribution), a mean duration of diabetes of 11 years, at least six months on a pump, and a Clarke’s questionnaire score of >4. Hypoglycemia clamps were done in a subset of patients to assess counterregulatory responses. Severe hypoglycemia events were strictly defined as “seizure or coma.”
• Low glucose suspend dramatically reduced the number of severe hypoglycemic events vs. those on pump therapy only. We found this data quite striking in just a six- month period – to us, it truly shows the power of even very simple automated insulin delivery to improve upon the challenges of dosing insulin manually.

	Insulin Pump Only (n=49)	Low glucose suspend (n=46)
Severe hypoglycemia in six months preceding baseline	5 events 25.5/100 patient years	6 events 22.0/100 patient years
Severe hypoglycemia at study end (six months)	6 events 26.7/100 patient years	0 events 0/100 patient years
Incidence rate difference from baseline to endpoint	17.8 (p=0.019)

• Importantly, there was no change in A1c in either group from a baseline of 7.4%. There was no difference at baseline in the percentage of time spent under 70 mg/dl. However, the average percentage of time spent under 70 mg/dl and 60 mg/dl during the overnight period improved in the LGS group during the study (p=0.006 and p=0.009).
• As might be expected, sensor usage was not particularly high in the 12-18 year-old group using sensor-augmented pumps with low glucose suspend. By age group sensor use was 71% (4-12 years), 54% (12-18 years), and 81% (12-18 years). We would have been interested to see the data cut by age, as we would guess the improvement in hypoglycemia were even stronger in the youngest and oldest groups.
• Epinephrine responses to hypoglycemia were unchanged in both groups post intervention. Parents and patients reported reduced fear of hypoglycemia in both groups. Hypoglycemia unawareness score improved in both groups.
• As pump therapy-only was the control group in this study, we wonder how pump + CGM would have fared in a similar study of hypoglycemia unaware patients. Withoutthis third arm, it’s possible that the severe hypoglycemia benefits could be attributed to the addition of CGM alone (rather than LGS). Of course, the results closely parallel the severe hypoglycemia findings in the ASPIRE in-home study (four events in the control group vs. zero in the intervention group), which compared sensor-augmented-pump alone to threshold suspend.

Questions and Answers

Dr. Hans DeVries (University of Amsterdam, Netherlands): I must compliment you. I think this is the most important study presented at this whole meeting. Did you administer the Clarke questionnaire at the end?

A: Yes. There was improvement, in both groups. In the interest of time, I have not presented all the data. We also measured fear of hypoglycemia and quality of life. There was a significant improvement in both groups.

Q: How did the LGS work – was it patients waking up to alarms and taking glucose, or was it mostly automated?

A: In about half of the cases, there was patient intervention – they would wake up and either resume insulin delivery or eat. In about half of the cases, you would continue to the full two-hour insulin suspension.

Q: Were there problems with false alarms?

A: It’s an important question; we found that early morning glucose values did correspond to patients being low overnight. That did suggest that these were true events being treated rather than false positives.

Q: You had limited data for clamps. Did symptom scores or anything change?

A: We did not show a change in the counter-regulation hormone response. It comes down to the fact that patients did not wear it enough. Perhaps they did not avoid hypoglycemia enough. As you know, the pilot data was in a smaller group.

Q: Did cognitive function and symptom scores change?

A: We did not do a cognitive assessment. Symptom scores did not change convincingly.

Q: Were all the severe hypoglycemia episodes at night?

A: In the control group, there were six events, and five of the six were at night. So severe hypoglycemia that was avoided was nocturnal hypoglycemia.

Posters

Use of PaQ, A Simple 3-Day Basal/Bolus Insulin Delverine Device, Reduces Barriers to Insulin Therapies in Patients with Type 2 Diabetes (812-P)

Norbert Hermanns, Leslie Lilly, Julia Mader, Felix Aberer, Joerg Paschatz, Stefan Korsatko, Jay Warner, and Thomas Pieber

This 19-person study assessed how the CeQur PaQ insulin delivery device addressed known barriers to insulin therapy for patients with type 2 diabetes. PaQ provides set basal and bolus insulin and requires only one injection every three days to reposition the cannula; this represents a significant reduction from the four to eight daily insulin injections study participants received on MDI. This study primarily tracked participants’ attitudes toward insulin therapy before and after PaQ therapy using the Barriers to Insulin Treatment (BIT) questionnaire, the Problem Areas in Diabetes Scale (PAID) and the Insulin Treatment Appraisal Scale (ITAS). Based on 19 respondents, the study demonstrated significant reductions in psychological concerns about insulin therapy, as measured by the BIT questionnaire. The authors acknowledge that the study was limited by its uncontrolled nature and small sample size; still, the moderate to large positive effects observed in the study seem to suggest PaQ’s ability to improve patient attitudes toward insulin therapy. Of course, the more crucial question is whether that psychological improvement translates to physiological benefits like improved glycemic control or reduced A1c values; that is an issue the authors left to future studies.

• The study was divided into three two-week periods. Participants began with their baseline MDI treatment, then transitioned from MDI to PaQ, and finally used PaQ exclusively. Participants were on average 59 years old, had had type 2 diabetes for 15 years, and had an A1c of 7.7%; 21% were female. Participants completed the study’s three questionnaires at the end of baseline and at the end of PaQ treatment. The Barriers to Insulin Treatment (BIT) survey asked patients to assess attitudes such as “Expected hardship from insulin therapy,” “Fear of hypoglycemia,” and “Fear of injections and self-testing” on a scale of one to 10; a lower score indicated fewer barriers. The Insulin Treatment Appraisal Scale (ITAS) asked patients to assess 16 negative and four positive statements about the PaQ, with a final score ranging from 0 to 100; a lower score indicated a more positive appraisal. The Problem Areas in Diabetes Scale (PAID) tracked diabetes-related distress on a scale from 0 to 100; a lower score indicated less distress.
• Comparing baseline and PaQ questionnaire data, the mean BIT total score decreased significantly (2.5 to 2.1; difference (D) = 0.4), with non-significant reductions in ITAS (21.7 to 21.0, D = 0.7) and PAID (42.8 to 40.8, D=2.0) scores. Analysis of the BIT subscales indicated that the biggest effects were in “hardship of insulin therapy,” “less feelings of stigmatization,” and “less fear about hypoglycemia.” The authors suggested that these first two patient reactions may be due to the fact that PaQ replaced an average of 5.2 daily insulin injections. The reduced fear about hypoglycemia does not have as immediately obvious an explanation, although the authors reported that in post-study interviews, participants mentioned that they believed the PaQ’s constant insulin delivery had provided improved glycemic control.

 

High Sensitivity Occlusion Detection Using Fluid Pressure Monitoring During Basal Insulin Infusion (975-P)

Steven Keith, Elaine McVey, and Ronald Pettis

Dr. Ronald Pettis and colleagues retrospectively developed an algorithm to detect pressure variation during basal infusion and correlated this to insulin flow during subcutaneous and intradermal (i.e., BD’s microneedles) insulin delivery. In a conversation we had with Dr. Pettis, he described the importance of quantifying interrupted insulin flow, especially in ongoing closed-loop developments. He suggested that such an algorithm if applied in a real-time fashion could 1) alert patients to insulin delivery problems and precipitate corrective action; 2) grant insight on an individual patient level into inconsistent or unexpected closed-loop performance (i.e., if problems were due to undetected occlusions); and 3) provide a method for evaluating overall system mechanical performance. The algorithm detected rising infusion pressures that occurred below 10 psi, which is the lowest pressure that triggers an alarm in commercial insulin pumps. We note that understanding the clinical significance of these low-pressure disruptions will be necessary to demonstrate the possible benefit of a pressure algorithm, and we hope to see more data on this front from BD.

• The  poster built on a previously conducted study comparing the pharmacokinetics (PK) of basal insulin infusion between subcutaneous and intradermal insulin infusion. During the prior study investigating the PK profile and feasibility of intradermal infusion (Keith et al., ATTD Poster 2013), the researchers noted transient deviations in several expected PK profiles that corresponded to increased pressure signatures in measured infusion pressure. The subcutaneous set used was the Roche Accu-Chek Rapid-D (6 mm x 28 gauge) and the intradermal set used was BD investigational microneedle catheter (1.5 mm x 34 gauge). Insulin lispro was delivered over two six-hour periods with a Harvard syringe pump and a placebo was administered using the Animas One Touch Ping over a 16-hour period.
• Using computational PK modeling, Dr. Pettis and colleagues determined that rapidly increasing and/or high (>3 psi) infusion pressure was associated with delayed and/or decreased insulin infusion. For comparison, many normal insulin infusions with regular PK profiles were associated with stable and low (≤1 psi) infusion pressure. These increased pressure signatures corresponded to irregular insulin delivery in both subcutaneous and intradermal insulin delivery.
• The researchers developed an occlusion detection algorithm based on infusion fluid pressure signatures. The authors noted that the detected occlusion events were well correlated to the PK variability observed in the trial. We look forward to learning how this algorithm may be applied to BD’s infusion sets and on what timeline; certainly, it seems that the algorithm could be used in both BD’s subcutaneous and intradermal sets. As of the JP Morgan Healthcare conference in January, BD is expected to enter the insulin infusion set market with its own product line in 2014. As we understood it, the first-generation infusion set would not incorporate BD’s microneedle technology, but a later-generation set could. (See page 48 of our JP Morgan full report for detail: http://www.closeconcerns.com/knowledgebase/r/07e26d4f.)

 

Effectiveness of V-Go for Patients with Diabetes in a Real-World Setting (Simple) (985-P)

George Grunberger, Bruce Bode, Kenneth Hershon, Cheryl Rosenfeld, Poul Strange

This poster reported positive interim results from a prospective, observational, multi-center study (SIMPLE) that assessed changes in glycemic control after V-Go therapy (n=89). Patients entered the trial on their current diabetes therapy, which ranged from oral agents only to multiple daily injections. After three months of V-Go insulin delivery, mean A1c improved from 8.8% at baseline to 8.1% (p<0.0001). During the V-Go intervention, seven patients reported documented hypoglycemia (<70mg/dl) and two patients reported severe hypoglycemia (requiring third-party assistance). Compared to baseline, patients saw a statistically significant reduction in total daily insulin dose (11 units; p<0.0001) and body weight (0.71 lbs; p <0.0001). Certainly, there is need for improved insulin delivery options for patients with type 2 diabetes and we are encouraged to see initial positive findings with V- Go in the clinic. We look to six-month results to corroborate the interim analysis and believe that extended V-Go use should lead to even greater improvements in glycemic control.

• SIMPLE included 89 patients with type 2 diabetes who were not at glycemic control (A1c <7.0%). Patients entered the study on their current diabetes care regimen, which the investigators divided into five categories: 1) oral agents only (OAD); 2) oral agents in combination with exenatide, pramlintide, or liraglutide (OAD +/- incretin); 3) once or twice daily injection of intermediate or long acting insulin with or without oral agents and/or incretin therapy (basal); 4) one to three injections of pre-mix insulin with or without oral agents and/or incretin therapy(premix); and 5) multiple daily injections with or without oral agents and/or incretin therapy (MDI). Average diabetes duration was 14 years and A1c at study screening was 9.0%.
• Patients were followed for four to six weeks on their current diabetes therapy and then were switched to V-Go insulin delivery with no restrictions on concomitant therapy. After the four to six week run-in period, A1c decreased by 0.2% to 8.8% from A1c of 9.0% at the initial screening (p-value not provided). Baseline A1c was considered to be the value post-run-in period (i.e., 8.8%). The full study will assess six-month V-Go utilization; however, the current analysis provided three-month interim results.
• After three months of V-Go therapy, mean A1c decreased from 8.8% to 8.1% (p < 0.0001). A1c changes according to therapy type at enrollment are outlined below; specifics on the group entering the trial on oral agents only and oral agents in combination with exenatide, pramlintide, or liraglutide were not provided due to an insufficient number of patients enrolled with these starting therapies at the time of analysis.

Three-month A1c Change from Baseline
Starting Therapy	n	Mean A1c Change	p-value
Basal	25	-0.76%	0.0003
Pre-mix	12	-0.66%	0.0306
MDI	47	-0.66%	0.0002
Overall	87	-0.71%	<0.0001

• The A1c reduction occurred alongside an 11.4-unit decrease in mean total daily insulin dose (TDD; p <0.0001). The TDD reduction occurred in the group that began on pre- mix (23 unit decrease) and MDI (18.1 unit decrease); p-values not provided.

Three-month Total Daily Insulin Dose Change from Baseline
Starting Therapy	n	Mean TDD Change
Basal	26	1.8
Pre-mix	13	-23
MDI	47	-18.1
OAD	2	34
OAD +/- Incretin	1	20
Overall	89	-11.4

• The investigators noted a small, but significant reduction in body weight after three months of V-Go use (0.71 lb; p <0.0001). A body weight reduction was even seen in the group entering the trial on basal therapy (-0.76 lb; p=0.0003), despite no reduction in TDD; presumably, this is a trial effect. Group results are outlined below. As with A1c, specifics on the group entering the trial on oral agents only and oral agents in combination with exenatide, pramlintide, or liraglutide were not provided due to an insufficient number of patients enrolled with these starting therapies at the time of analysis.

Three-month Weight Change from Baseline
Starting Therapy	n	Mean Weight Change (lb)	p-value
Basal	25	-0.76	0.0003
Pre-mix	12	-0.66	0.0306
MDI	47	-0.66	0.0002
Overall	87	-0.71	<0.0001

Symposium: Current State of Insulin Pump Therapy

Use in Type 2 Diabetes

Phillip Raskin, MD (The University of Texas Southwestern Medical Center, Dallas, TX)  

Dr. Phillip Raskin led off this completely packed session (competing with the ADA President’s session) with a review of insulin pumps in type 2 diabetes (though we would note that he spent a decent portion of the talk on type 1s). He noted right at the start that “there isn’t much good clinical trial data about use of pumps in type 2 diabetes.” Indeed, he only covered three prospective, randomized controlled trials comparing MDI to pumps in type 2 diabetes (Saudek et al., JAMA 1996; Raskin et al., Diabetes Care 2003; and Herman et al., Diabetes Care 2005). A1c declines were comparable between MDI and pumps, though patients preferred pump therapy (“patients love this insulin pump therapy more than injections”). He last reviewed Medicare’s extensive criteria for reimbursing pumps in type 2 diabetes – interestingly, these were based on his group’s 2005 study (“I don’t like it myself”). Overall, Dr. Raskin concluded that a pump is probably a reasonable treatment option in type 2 patients who are NOT on Medicare; it has better patient acceptability than MDI; and “this is the truth: it is way more expensive than injections” (~$2,000 a year more to use a pump than MDI). We were struck by the dearth of good data on pumps in type 2, much of it old – we look forward to the reporting of Medtronic’s ongoing, very large OpT2Mise trial of pumps in type 2 diabetes (ClinicalTrials.gov Identifier: NCT01182493).

• Dr. Raskin covered three randomized controlled trials comparing pumps to MDI in type 2 diabetes (“When you evaluate data, nothing beats the randomized controlled trial”).
  • The first was Saudek et al.’s (JAMA 1996) comparison of the MiniMed implantable pump to MDI in 121 type 2 patients. Baseline A1c of 8.8% declined by 1.5% for those on the pump, comparable to the 1.4% decline (baseline: 8.9%) in those on MDI. The pump group used less insulin and saw less weight gain over the course of the 12-month study.
  • Dr. Raskin also covered his own 2003 study published in Diabetes Care. He studied 127 type 2s, randomizing them to the implantable MiniMed 507C pump or MDI (aspart plus NPH). Declines in A1c were similar: -0.6 in the pump group (baseline: 8.2%) and -0.5% in the MDI group (baseline 8.0%). There was no difference in the frequency of hypoglycemia, though quality of life was higher in the pump group.
  • Last was Herman et al.’s influential 2005 Diabetes Care study in 107 patients with type 2 diabetes. Patients were 60 years or older in this two center, randomized, 12-month study comparing pumps (lispro in MiniMed 508 pump) to MDI (preprandial lispro vs. glargine). There was no difference in A1c between the groups. Disappointingly, he noted, “As a result of our study, Medicare will not pay for insulin pumps or pump supplies for individuals with type 2 diabetes. Thank you very much, I don’t like it myself.”
• Dr. Raskin also covered a few uncontrolled trials comparing pump therapy to MDI in type 2 diabetes (“not the trial we want to use”). Lenhard and Maser (ADA 2001) saw no decline in A1c (baseline 7.6%) in 12 MDI patients switched to a pump. On the other hand, Frias et al. (JDST 2011) observed a 1.2% decline in A1c (baseline: 8.3%) in 21 MDI patients (100 units of insulin per day) switched to a once-daily basal rate in a pump and boluses at each meal over 16 weeks. Leinung et al. (Endocrine Practice 2013) studied 57 type 2s via retrospective chart review, finding a 1% decline in A1c over 16 weeks (baseline 8.7%). Insulin dose also declined.
• A slide displayed Medicare’s long-list of criteria for pump therapy in type 2 diabetes. The first and arguably most important one is fasting C-peptide <110% of the lab’s lower limit of normal. The full criteria can be found at www.cms.gov/medicare-coverage.
• Dr. Raskin reviewed criteria for selecting type 2 patients for insulin pumps, noting that it’s “pretty much the same as type 1 diabetes.” Factors include suboptimal glycemic control, motivation to pursue intensive therapy, willingness and ability to perform frequent SMBG, sufficient education and ability, adequate psychological stability, appropriate financial resources, and skilled medical staff available.
  • Said Dr. Raskin, “A pump is not magical. It takes work. People need to be motivated to do this. They need to have a brain, to be frank.” Contraindications include hypoglycemia unawareness, counterregulatory unresponsiveness (neither common in type 2 diabetes), age (older, complications), and medical reasons (short life expectancy, malignancy, etc.).

Questions and Answers

Q: The pumps that you used, the MiniMed 507 and 508, did not have a bolus calculator, a correction factor, or active IOB. They were just sophisticated basal machines. Can newer insulin pumps provide better control?

A: Everything changes. We had to use NPH insulin in those studies. Things move ahead and the world goes forward. A person with a brain can do what the bolus wizard can do. Personally, I think it’s better if the person does it himself and does not use the calculator. People have to tell the pump what to do. This is not simple stuff. Calculations are better on that machine than I can make in my head, for sure. But I don’t think a newer pump or fancier pump would make a difference.

[Editor’s Note: We found this comment surprising. We believe many patients receive tremendous value out of bolus calculators, as the math is often quite challenging (a blood glucose of 186 mg/dl and a correction factor of 35 mg/dl is not easy math for many patients!). It’s easy to say that bolus calculators are worthless in the long-term because they remove the need for patients to think, but the exact same could be said for any new technology (GPS systems make driving much easier, though no one is arguing that they should be avoided and we should all go back to paper maps. This also reminds us of what people used to say in the 1970s, when some HCPs questioned whether patients should even have SMBG). We believe making things easy for patients is absolutely critical, as things that are challenging are just not used (e.g., early-generation CGM).]

Q: Do you have any experience with very insulin resistant patients using U-500 insulin?

A: Not really.

Low Glucose Suspend

Timothy Bailey, MD (UCSD School of Medicine, San Diego, CA)

Dr. Timothy Bailey gave a comprehensive presentation on low glucose suspend (note: the term has now changed to “threshold suspend”), highlighting the data collected to date on the Medtronic Veo/MiniMed 530G. He covered CareLink data, UK evaluation data (Choudhary et al., Diabetes Care 2011), data from Europe and Australia (Danne DT&T 2011 and Ly et al. at ADA 2013; see our Day #3 report at http://close.cx/ADA2013Day3), ASPIRE in-clinic (Garg et al., DT&T 2012), and ASPIRE in-home (Bergenstal et al., NEJM 2013; poster 48-LB at this meeting, covered in detail in our Day #4 report at http://close.cx/ADA2013Day4). All the trials have demonstrated consistent results: improvements in hypoglycemia, no decrement in A1c, and no risk of DKA. Dr. Bailey concluded that “threshold glucose suspend is safe and effective in reducing hypoglycemia in vulnerable patients with type 1 diabetes.”

• “We want to get our patients down to near normal glycemia without hypoglycemia. That’s an A1c of 6%. With this, you need technology.” He was quite frank in noting that bringing down the A1c safely in type 1s is “difficult.” For someone with an A1c of 8-9% with lots of glycemic variability, shifting the entire glucose curve down will eventually result in lots of hypoglycemia. As a result, it’s key to both drop the A1c and reduce the peaks and valleys.

 

Patch Pumps

Howard Zisser, MD (Sansum Diabetes Research Institute, Santa Barbara, CA)

In an insightful and entertaining discussion of insulin patch pumps, Dr. Howard Zisser described devices in two broad categories. Among “more complex, more customizable” pumps, the only available option is Insulet’s OmniPod; investigational devices Roche’s Solo MicroPump, Cellnovo’s diabetes management system, and Debiotech’s JewelPUMP. These pumps have complex features like programmable basal rates, data recording, and integrated blood glucose monitoring. Other devices are designed to be “simpler and less adjustable”; Dr. Zisser suggested that these simple options could encourage engagement among people who have trouble taking insulin currently. He reviewed CeQur’s PaQ (three days of basal rate, plus bolus dosing), Valeritas’ V-Go (one day of basal rate, plus bolus dosing), and J&J Diabetes Care’s Finesse (a “wearable pen” with bolus dosing only). Dr. Zisser also noted that patch pumps might offer a glycemic advantage compared to conventional pumps, because the latter require frequent, short-term disconnection of the infusion set (e.g., to take showers); blood glucose levels have been shown to rise ~1 mg/dl for each minute that insulin delivery is interrupted (Zisser, Diabetes Care 2008). Looking ahead, he noted that these pumps can be used for other drugs besides insulin; he specifically raised the possibility of patch pumps for glucagon.

 

Combination Infusions

Rubina Heptulla, MD (The Children’s Hospital at Montefiore, Bronx, NY)

“We have made much advancement in blood glucose monitoring, the varieties of insulin…and the very cool delivery devices. However, at the end of the day we have used only insulin,” said Dr. Rubina Heptulla. She delved into the motivation behind dual-hormonal control and explored the potential for pramlintide, exenatide, and glucagon to confer benefits beyond insulin monotherapy in patients with type 1 diabetes. For background, she reminded the audience that type 1 diabetes is a bihormonal disease with insulin deficiency and glucagon dysregulation (characterized by a lack of glucagon suppression following meals and a loss of glucagon response during hypoglycemia). Dr. Heptulla investigated pre- meal bolus injections and subcutaneous infusion of pramlintide (a synthetic amylin analog). “Pramlintide is good,” she said, “but it is not ideal.” She showed that it reduced glycemic excursions, but did not normalize glucose excursions for all meals consistently. Moving forward, Dr. Heptulla explored glycemic control with combination insulin and exenatide (GLP-1 agonist) therapy and demonstrated its effectiveness at lowering postprandial glucose excursions. In a direct comparison of pramlintide injection pre-lunch and dinner (30 mcg) to exenatide injection pre- lunch and dinner (2.5 mcg) in closed- loop insulin therapy, Dr. Heptulla suggested that exenatide was indeed superior to pramlintide in reducing blood glucose excursions in the context of closed-loop control. To round out her review, Dr. Heptulla discussed ongoing investigations of bi-hormonal closed-loop systems, highlighting efforts by Drs. Edward Damiano (Boston University, Boston, MA) and Steven Russell (Harvard Medical School, Boston, MA) and by Dr. Ken Ward (Oregon Health and Science University, Portland, OR).

 

Product Theater: Simple Insulin Infusion for People with Type 2 Diabetes: Clinical Results Released (Sponsored by CeQur)

Simple Insulin Infusion in Type 2

Juan Frias, MD (University of California San Diego, San Diego, CA)

Dr. Juan Frias framed his presentation with his view on why there has not been a recent improvement in type 2 diabetes control: current treatments are not targeting the correct physiology. He noted that many patients require insulin, but even when patients are put on basal and bolus insulin, many are suboptimally controlled. Dr. Frias asserted that this may be due to non-adherence to insulin pen injections, a problem that could be solved with continuous subcutaneous insulin therapy. In one study, patients saw a 1.6% drop in A1c levels (baseline: 8.4%), along with a 20% reduction in insulin dose at 16 weeks. However, the majority of patients (80%) were using only one basal rate setting on a multi- setting pump. Dr. Frias highlighted that Valeritas’ V-Go, targeted at patients with type 2 diabetes, has only a few preset basal doses, which simplifies the device, reduces A1c with a similar level of insulin use, and can drive down costs of therapy. To conclude, Dr. Frias briefly introduced the CeQur’s new, three- day device, PaQ.

• One-third of patients missed an average of three days of insulin therapy or omitted insulin therapy three times a month (Peyrot et al., Diabetes Medication 2012). Dr. Frias gave examples of why patients said they skipped insulin, which included being too busy or embarrassed by the injection.
• Valeritas’ V-Go is a daily, disposable insulin delivery device with predefined basal doses of 20, 30, and 40 units of insulin over 24 hours. The pump can also easily deliver a bolus, and Dr. Frias presented data showing an average reduction in A1c levels of 1.2% (baseline: 8.8%) after pump use. CGM data showed improved trends for glycemic control.
• CeQur’s PaQ is a disposable, wearable insulin delivery device built for three-day use. It has seven preset basal doses ranging from 16-60 units per 24 hours. Patients also have the ability to self-administer two-unit boluses until the 330-unit reservoir is empty. For more on CeQur’s PaQ, please see our report on the CE Mark at http://www.closeconcerns.com/knowledgebase/r/a314eba7.

 

PaQ Study Results

Julia Mader, MD (Medical University Graz, Graz, Austria)

Dr. Julia Mader detailed the study design and results of a two-week feasibility study evaluating the use and performance of CeQur’s PaQ insulin delivery device in 18 patients with type 2 diabetes currently on basal-bolus insulin therapy. The study was patient controlled and was not a dose optimization or treat to range study. None of the patients had difficulty using PaQ, and there was 83% satisfaction with the time it took to learn how to use the pump as well as the time it took to administer the bolus. Dr. Mader expressed excitement that no patients reported missing insulin doses because of embarrassment. She also noted that the mean total dose of insulin after starting PaQ was the same or slightly lower than before the study (from 60 units per day to 57 units per day). There were also trends toward better glycemic control in most patients, with no hypoglycemic events. Notably, patients were only trained on the device for one hour. The results are encouraging, though the absence of a control group makes it difficult to interpret the findings. We’d love to see a randomized, controlled study comparing use of the PaQ to the V-Go to a full-featured insulin pump to MDI in patients starting on basal-bolus therapy, though such a study would of course be very expensive to run in practice. Still, we think there are fundamental differences between each approach, and a controlled study would be a great way to establish which patients stand to benefit the most from which type of therapy. The greater point is that so many patients who should be on insulin are not – we hope they are able to benefit from the more progressive and more discreet insulin delivery alternatives. While they are absolutely more expensive, so is non-adherence to insulin that is more and more commonly seen.

• At baseline, the 18 patients had an A1c of 7.7%, 21% of participants were females, and the mean BMI was 32 kg/m2. The mean duration of diabetes was 15 years, and the mean number of insulin injections that people took was five (ranging from four to eight).
• Patients did not start using PaQ until at least three weeks into the study. Patients first spent two weeks on MDI, followed by a 24-hour safety visit, followed by a period to select basal rates that lasted between six days and two weeks. During the 24-hour safety visit, patients received only one hour of training on how to use the pump. After that, the two-week period of blood glucose control on PaQ began, during which patients called every three days.

 

Patient Reported Outcomes Following Use of PaQ

Norbert Hermanns, PhD (Diabetes Centre Mergentheim, Mergentheim, Germany)

Dr. Norbert Hermanns focused his presentation on the psychological aspects of insulin resistance, specifically highlighting emotional and practical components like guilt and fear of injection. Dr. Hermann’s emphasized that CeQur’s PaQ can reduce some of these psychological challenges. The psychological evaluations from the aforementioned study were based on barriers to insulin therapy (BIT), problem areas in diabetes (PAID), and insulin treatment appraisal scale (ITAS). After use of PaQ, BIT decreased in fear, pain, and hardship. Those using PaQ also had less stigmatization and less hypoglycemia. All changes in insulin resistance were clinically meaningful (i.e., they had an effect size values >0.1; the difference between baseline and end of study divided by standard deviation). Additionally, Dr. Hermanns presented data demonstrating a reduction in ITAS and PAID, specifically in negative attitudes toward insulin treatment (with effect size values >0.1). Dr. Hermanns concluded by underscoring that PaQ has the ability to reduce barriers to insulin treatment without increasing other diabetes related stress. He also commented that he hoped further studies will show that addressing psychological barriers to insulin treatment will result in improved long-term glycemic control.

 

CGM Data

Ellie Strock, ANP-BC, CDE (International Diabetes Center, Minneapolis, MN)

Patients using continuous glucose monitoring (CGM) in the aforementioned feasibility study showed improved glucose control when using the pump, although the trend was not significant (p=0.18). Given the short two-week duration of the study and small population, this was not terribly surprising. Based on classic measures not using considering CGM values, only six of the patients saw improved glycemic control; when CGM was added, it revealed much richer glycemic data in many patients. For example, one patient had ‘similar’ results pre- and post-PaQ use based on blood glucose values; however, based on in-range values and the patterns of CGM, he experienced reduced variability in his overnight glucose levels while on PaQ. Dr. Ellie Strock concluded by emphasizing that CGM analysis reveals that more stabilized glycemic control is possible in patients using PaQ. We think the use of CGM in clinical trials will expand in the coming years, especially as the technology gets easier to use and time-in-range becomes a more accepted standard. For much more on this topic, see our recent report at (https://closeconcerns.box.com/s/9cfx31hmaz6799cvyvnn.

 

Panel Discussion

Moderator: David Harlan, MD (University of Massachusetts Memorial Medical Center, Worcester, MA)

Panelists: Juan Frias, MD (University of California San Diego, San Diego, CA); Julia Mader, MD (Medical University Graz, Graz, Austria); Norbert Hermanns, PhD (Diabetes Centre Mergentheim, Mergentheim, Germany); Ellie Strock, ANP-BC, CDE (International Diabetes Center, Minneapolis, MN)

Q: What is the insulin capacity, in units, of the PaQ? How much can it hold?

Dr. Frias: Patients can use up to 110 units per day, and PaQ lasts for three days, so it can hold 330 units. You can load all of that 330 units at once and be all set for three days.

Q: If PaQ does not use a battery or motor, what is the pumping mechanism?

Dr. Mader: It uses an elastomer bladder that is filled with the insulin. The tension inside the elastomer bladder presses the insulin to move to the capillaries, and the rate of uptake is limited by the diameter of the capillaries.

Q: Do patients have to put in 330 units? Have you considered using U-500 insulin?

Mr. Jay Warner (CeQur): You can fill up PaQ to any level from 170-330 units. We are not pursuing U-500 at this point, but that is a potential future option.

Q: You mentioned that you only trained the patients for one hour, but they stayed in your center for 24 hours. What were they doing for those other hours?

Dr. Mader: This was the first time the device was used in patients, so we wanted to make sure the patients could use it correctly. We observed them during breakfast, lunch, and dinner; however, we only trained them for one hour with no dietary counseling.

Q: How do you achieve the different basal rates?

Dr. Mader: The basal rate depends on the reservoir. You could change the basal rate by exchanging the reservoir.

Q: Of your 20 patients enrolled in the study, why did two patient discontinue the study?

Dr. Mader: One patient discontinued because he stopped taking his basal insulin, and we could not include him. The other one said that the study was too time-consuming, and he could not participate and also go to work.

Mr. Warner: The patient not taking his basal insulin discontinued during the run-in period – he had stopped his MDI.

Q: I just want to mention, that all the insulin administered in PaQ is short-acting. That means that there is only one insulin in the PaQ, so patients in the United States with a co- pay will pay the same price they currently pay for insulin. I was most intrigued by the psychological barriers you discussed, Dr. Hermann. I think the number one barrier to patient treatment is getting them engaged in their own care. Do you think there any ways to measure patient engagement?

Dr. Hermann: I think this technology provides a nice way to get patients engaged in their care. I think we also need to think of other applications. I would be great to have patient engagement, and that is something to address in further studies.

 

Incretins

Oral Sessions: GLP-1 Agonists in Practice

Ideglira, a Novel Fixed Ratio Combination of Insulin Degludec and Liraglutide, is Efficacious and Safe in Subjects with Type 2 Diabetes: A Large, Randomized Phase 3 Trial (65-OR)

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

Dr. John Buse presented an excellent summary of the phase 3 DUAL-1 trial, which compared a fixed dose combination of insulin degludec and liraglutide (known as IDegLira) with insulin degludec (Tresiba) and liraglutide (Victoza) alone (all from Novo Nordisk). The concept of IDegLira is to combine the benefits of both therapies, with each agent mitigating the side effects of the other. The trial showed this approach was valid, yielding excellent results. In fact, the (nearly a third) lower hypoglycemia of the combination allowed participants to achieve 0.5% lower A1c levels with a lower total daily dose of insulin. GI side effects of the combination were also lower than with liraglutide alone and the combination yielded a small but sustained weight loss (of,0.5kg [1.1 lbs]) compared to a weight gain with insulin. The trial studied over 1,600 participants for a 26 week period titrated to target in the case of the IDegLira and insulin degludec arms, and titrated in the usual ramp-up fashion for liraglutide. After 26 weeks, the IDegLira group was taking 38U/day of insulin, compared to 53U/day in the degludec group. However, A1c reduction from a baseline of ~8.3% was 1.9% for IDegLira, compared to a reduction of 1.4% with degludec and 1.3% with liraglutide. The IDegLira group achieved and maintained a very low A1c of 6.4%. Even better was the result that 81% of the IDegLira group reached the target of a7.0% A1c (compared to 65% of the degludec group). IDegLira used a ratio of 50U of insulin degludec to 1.8 mg of liraglutide. Dr. Buse concluded that IDegLira resulted in better glycemic control with a lower risk of hypoglycemia and weight gain than either of its components alone.

• The combination of a basal insulin and a GLP-1 agonist has the potential to combine their benefits while also mitigating the adverse effects of the two agents used individually. The post-prandial effects of GLP-1 can be combined with the fasting glycemic control of basal insulin. The weight loss of GLP-1 can mitigate any weight gain with insulin. Theobserved low hypoglycemia with GLP-1 can mitigate the potential for hypoglycemia with insulin and therefore allow patients to achieve lower A1c levels.
• The open label DUAL-1 trial studied 1,663 people with type 2 diabetes randomized to IDegLira (Novo Nordisk), insulin degludec (Tresiba, Novo Nordisk) and liraglutide (Victoza, Novo Nordisk). IDegLira is Novo Nordisk’s once-daily injectable combination of insulin degludec and liraglutide. The IDegLira used in this study was fixed at a ratio of 50U degludec to 1.8 mg of liraglutide. Participants at baseline were inadequately controlled on metformin with or without pioglitazone (Takeda’s Actos). Liraglutide was titrated up from an initial 0.6 mg dose to a maximum dose of 1.8 mg as per the label, although the average dose at the end of the trial was 1.4 mg. IDegLira and degludec were titrated twice a week in two unit steps to a target fasting plasma glucose of 72-90 mg/dl (4.0-5.0 mmol/l). At baseline, BMI was roughly 31 kg/m2 and duration of diabetes was seven years. Eighty-three percent of the participants were taking metformin and 17% were taking metformin plus pioglitazone.
• After 26 weeks, A1c reduction was 1.9% for IDegLira, 1.4% for degludec, and 1.3% for liraglutide from a baseline of 8.3%. Remarkably, IDegLira participants had an average A1c of 6.4%. In the IDegLira group, 81% of participants reached a target A1c of ≤7%, and 70% reached a target of ≤6.5%. While IDegLira and degludec provided an equivalent reduction in fasting plasma glucose (65 mg/dl) at 26 weeks, those in the IDegLira group were taking much less insulin (38 units/day, vs. 53 units/day for degludec). Nine point blood glucose profiles established that the post-prandial control of IDegLira was similar to that of liraglutide alone.
• The rate of hypoglycemia in the IDegLira group was only 68% of the rate in the degludec group. Rates in the IDegLira group were around two events per patient year of exposure. The rates of hypoglycemia in the liraglutide group were ten times lower (around 0.2 events per patient year). Since hypoglycemia is usually the limiting factor in treating to target, Dr. Buse presented a model of the relationship between A1c and hypoglycemia rate for IDegLira and degludec, which supported the argument that a patient taking IDegLira can achieve a lower A1c at the same risk of hypoglycemia, (or that patients with the same A1c have a lower risk of hypoglycemia with IDegLira).
• Participants taking liraglutide exhibited a weight loss of 6.6 lb (3 kg) after 26 weeks, compared to those taking degludec, who gained 1.5 kg (3.3 lb). Those taking IDegLira exhibited a slight weight loss of 1.1 lb (0.5 kg). This small weight loss was sustained to the end of the trial (and even out to 52 weeks in the DUAL-1 extension, according to Novo Nordisk).
• Nausea for IDegLira was much better than liraglutide alone. For the first four weeks, nausea peaked at over 10% of subjects in the liraglutide group, but resolved to a sustained level of 3% after 13 weeks. In comparison, nausea for IDegLira declined steadily from roughly 3% of participants initially to 1% by the end of the trial. Dr. Buse commented that this reduction is probably related more to the titration program and the lower starting dose than to the total amount of exposure to liraglutide.
• Compared to insulin degludec, IDegLira exhibited reduced hypoglycemia, leading to a lower A1c with better post-prandial control and no weight gain. Compared to liraglutide, IDegLira had a better reduction in A1c and fasting glucose, with a reduced amount of nausea.

Questions and Answers

Q [Dr. Julio Rosenstock]: The trial would have been nicer if it had been blinded…

A: Later this year, we plan to present the results from the DUAL-2 study, which is blinded. (Note that Novo Nordisk has already announced some top level results for DUAL-2, which compares IDegLira and degludec. In DUAL-2, IDegLira also achieved a 1.9% A1c reduction over baseline, which was reported to be statistically significantly better than degludec (A1c reduction not yet disclosed)..

Q: Do you have any data regarding nocturnal hypoglycemia?

A: I can’t remember it all off the top of my head, but it’s more or less equal across degludec and IDegLira.

Q: What was the washout protocol?

A: I don’t know what you are referring to. There was no washout period. I suspect that you were referring to the concern raised at the FDA Advisory panel on degludec on CVD events during the washout period. There was one adjudicated cardiovascular event in each of the three arms, but no issues with washout protocol.

Q: Can we combine U500 insulin with GLP-1?

A: I think that Wendy Lane from Asheville NC will be presenting on the combination with severely insulin resistant patients.

Q: There is an impressive reduction in hypoglycemia – what is the reason?

A: It’s related perhaps to the lower amount of insulin, but I remember the GWCO trial adding exenatide to glargine where we saw a similar thing. I suspect that it is also related to an improvement in alpha cell function.

 

Efficacy and Safety of Dulaglutide Versus Placebo and Exenatide in Type 2 Diabetes (Award-1) (66-OR)

Carol Wysham, MD (University of Washington, Spokane, WA)

Dr. Carol Wysham presented the results of the phase 3 AWARD-1 study, which compared dulaglutide at two doses (1.5 mg and 0.7 5mg) to exenatide twice daily (BMS/AZ’s Byetta) and placebo on a background of maximal doses of metformin and pioglitazone (Takeda’s Actos). After 26 weeks, dulaglutide 1.5 mg provided an A1c reduction of 1.5% from baseline (compared to a reduction of 1.0% with exenatide), which was roughly 1% lower than the placebo group. Nearly 80% of the patients in the high dose dulaglutide group reached an A1c target of 7%, compared to 54% in the exenatide group and 45% in the control group. Weight loss for dulaglutide 1.5 mg was similar to exenatide, while dulaglutide mg was weight neutral.

• Dulaglutide (Lilly) is a once-weekly GLP-1 agonist that links recombinant human GLP-1 to a human IgG4 Fc fragment in order to obtain longer plasma half-life (~5 days) in an injectable solution. This study, AWARD-1, is part of a series of five phase 3 trials, leading up to Lilly’s FDA submission, scheduled for 2013. Lilly has previously announced top-line results for the AWARD trials that are covered in this session (see our report at http://www.closeconcerns.com/knowledgebase/r/bd49e680).
• The primary objective of the AWARD-1 trial is to prove superiority of once-weekly dulaglutide versus placebo in people with type 2 diabetes treated with metformin and pioglitazone (Takeda’s Actos). Metformin and pioglitazone were optimized to maximum doses before the start of the trial, and dulaglutide was investigated at two doses – 1.5 mg and 0.75 mg. The AWARD-1 trial also includes twice daily exenatide (BMS/AZ’s Byetta) as a comparator. The trial randomized 978 type 2 diabetes patients to dulaglutide 0.75 mg or 1.5 mg for 52 weeks,to exenatide for 52 weeks, or to placebo for 26 weeks followed by dulaglutide (0.75 mg or 1.5 mg) for 26 weeks. A1c at baseline was roughly 8.1% and participants had an average BMI of 33 kg/m2.
• Dulaglutide 1.5 mg achieved an A1c reduction of 1.05% compared to placebo, demonstrating superiority. A1c reduction from baseline was 1.5% for dulaglutide 1.5 mg, 1.3% for dulaglutide 0.75 mg, 1.0% for exenatide and 0.4% for the placebo. In addition, 79% of participants taking dulaglutide 1.5 mg reached a target A1c of ≤7%, compared with 67% for the0.75 mg dose and 54% for exenatide. Both doses of dulaglutide were better at lowering fasting serum glucose than either placebo or exenatide.
• Participants in the dulaglutide 1.5 mg group achieved a weight loss of 3.3 lb (1.5 kg) after 26 weeks, which was similar to exenatide. The weight loss was achieved after four weeks and was then maintained throughout the study. Dulaglutide 0.75 mg was weight neutral and the placebo group gained 3.1 lb (1.4 kg).
• The majority of adverse events were GI related but reduced over time for dulaglutide. Dulaglutide 1.5 mg had the most nausea, at 28% of participants, dulaglutide 0.75 mg had a nausea rate of 16%, and exenatide was in the middle at 26%. There was one case of pancreatitis and one case of pancreatic cancer in the pooled dulaglutide group (five months of exposure).
• The A1c reductions in all three GLP-1 groups were sustained out to 52 weeks.

​​Questions and Answers

Q: Longer acting GLP-1 agonists tend to be associated with lower GI side effects, but that’s not the case here. Can you explain?

A: The answer is probably in the chemistry of the compound. We see a reduction in glucose earlier, this shows earlier efficacy, maybe just after first injected.

 

Efficacy and Safety of Dulaglutide vs. Metformin in Type 2 Diabetes - Award-3 (69-OR)

Guillermo E. Umpierrez, MD (Emory University, Atlanta, GA)

Dr. Guillermo Umpierrez treated us to the full results of the AWARD-3 trial, part of the phase 3 package for dulaglutide (Lilly) that was presented at this session. AWARD-3 compares dulaglutide to metformin in monotherapy, for patients with an average duration of 2.6 years of diabetes, and an A1c of 7.6%. Results indicate that dulaglutide has a superior A1c reduction at 26 weeks (around 0.2% better than metformin, but statistically significant), together with a higher percentage of patients reaching target. The dulaglutide 1.5mg dose had an identical effect on weight as metformin at 26 weeks, although the data suggested that it weight was trending less favorably than metformin at 52 weeks. Adverse events were comparable (mainly GI in nature), so Dr. Umpierrez was able to conclude that dulaglutide was superior to metformin in monotherapy. However, in the Q&A Dr. Rosenstock (chairing the session) admitted that while dulaglutide was statistically superior, he didn’t think it was clinically superior. Dr. Umpierrez countered that since a significantly greater percentage of people reach target, then by definition it has to be clinically superior.

• This study, AWARD-3, is part of a series of five phase 3 trials, leading up to Lilly’s FDA submission for dulaglutide (a once-weekly injectable GLP-1 agonist) scheduled for 2013. Lilly has previously announced top-line results for the AWARD trials that are covered in this session (see our report at http://www.closeconcerns.com/knowledgebase/r/bd49e680).

• This double blind randomized controlled study is designed to compare dulaglutide at two doses (1.5 mg and 0.75 mg once weekly) with metformin in monotherapy over 52 weeks. Participants were randomized to dulaglutide plus oral placebo (two doses) or metformin (1,500 or 2,000 mg/day according to tolerability) plus injectable placebo. The 807 treatment naïve participants were randomized, and at baseline, A1c was low at 7.6%, BMI was 33 kg/m2, average age was 56 years, and duration of diabetes a relatively short 2.6 years, since it was an early trial.
• At 26 weeks, patients taking dulaglutide 1.5 mg experienced a mea 0.8% A1c reduction from baseline, which was 0.22% better than metformin (p<0.025). Dulaglutide was also superior at 52 weeks vs. metformin (A1c reduction for dulaglutide 1.5 mg was 0.7% lower than baseline and 0.17% lower than metformin). However, the lower dose dulaglutide was non-inferior, but not superior to metformin at 52 weeks. For the high dose dulaglutide, A1c showed a rapid reduction over the first 13 weeks, with a slight rise from week 26 to week 52. At 26 weeks, 62% of patients achieved a target of ≤7% A1c in the dulaglutide 1.5 mg arm, compared with 54% of metformin patients.
• At 26 weeks, both the metformin and dulaglutide 1.5 mg arms showed the same weight loss, around ~4.4 lb (2 kg). By 52 weeks, dulaglutide patients appeared to be gaining weight compared to metformin patients, but the difference was non-significant. Overall adverse events were similar in all three arms – GI side effects were the largest component.

Questions and Answers

Q [Dr. Julio Rosenstock]: Dulaglutide maybe be statistically superior, but maybe not clinically superior – I would conclude that dulaglutide is effective, but you would never use it in monotherapy instead of metformin.

A: The number of people achieving A1c targets was significantly greater than metformin so it is clinically better. On A1c, dulaglutide is up to 0.4% better, and we think that this is clinically significant.

Q: Do you have any blood pressure data?

A: I didn’t show blood pressure data but there is 2mmHg difference in favor of dulaglutide - but it’s not significant.

Q: Did you ever consider the cost efficacy of this drug? Because metformin is very cheap…

A [Dr. Rosenstock]: Obviously!

 

Efficacy and Safety of Dulaglutide vs. Sitagliptin After 52 Weeks in Type 2 Diabetes: AWARD-5 (71-OR)

Michael Nauck, MD, PhD (Diabeteszentrum Bad Lauterberg, Harz, Germany)

Dr. Michael Nauck presented the results of Lilly’s AWARD-5 study, which compared dulaglutide 1.5 mg and 0.75 mg to sitagliptin and placebo in 1,098 type 2 patients. At 52 weeks, both dulaglutide doses provided superior A1c reductions compared to sitagliptin (1.11%, 0.86%, and 0.39%, respectively), as well as larger improvements in body weight (-3.22 kg [-7.1 lbs], -2.7 kg [-6.0 lbs], and -1.63 kg [-3.6], respectively). Furthermore, a greater proportion of people in the dulaglutide 1.5 mg and 0.75 mg arms achieved the A1c goal of <7% (58% and 49%, respectively, vs. 33% for sitagliptin), as well as the A1c goal of ≤6.5% (42% and 29%, respectively, vs. 19% for sitagliptin). While dulaglutide was associated with a higher rate of GI side effects, Dr. Nauck noted that the rates fell within the range typically observed for long-acting GLP-1 agonists.

• The doubled blind RCT enrolled type 2 patients who had been on either monotherapy (metformin or an oral anti-diabetic medication [OAM]) or combination therapy (metformin plus one OAM) with an A1c between 7.0% and 9.5%. Participants were first placed on metformin during the 11-week run-in period and then randomized to multiple doses of dulaglutide (ranging from 0.25 mg to 3.0 mg) for 13 weeks. Dr. Nauck explained that this phase mimicked a dose-ranging phase 2 trial and allowed Lilly to select the optimal doses for the subsequent phase 3 trial (AWARD-5). Patients not assigned to the selected doses were exclude from the phase 3 study. Lilly chose the 0.75 mg and 1.5 mg doses using a Clinical Utility Index, which takes into account efficacy and safety measures, with a focus on blood pressure and pulse rate effects – Dr. Nauck remarked that the full details of this process are being presented in poster 1045-P.
• The phase 3 trial compared dulaglutide 0.75 mg and 1.5 mg (n=302 and 304, respectively) to sitagliptin (100 mg; n=315) and placebo (n=177). Patients randomized to placebo took the placebo capsule for 26 weeks (“for ethical reasons”) before switching to sitagliptin. At baseline, the participants had an average age of 55 years, weight of 86-87 kg (190- 192 lbs), and A1c of 8.1-8.2%. Dr. Nauck noted that while Lilly obtained both 52-week and 104- week data, his presentation would focus on the former. The 104-week data is detailed in poster 1004-P.
• At 26 weeks, greater improvements in A1c were observed with dulaglutide 1.5 mg (- 1.22%) and 0.75 mg (-1.01%) compared to sitagliptin (-0.61%) and placebo (+0.04%). This pattern was also observed at 52 weeks, with larger A1c reductions observed for dulaglutide1.5 mg (-1.11%) and 0.75 mg (-0.86%) vs. sitagliptin (-0.39%; p<0.001 for both comparisons). At52 weeks, the three treatment groups had ending mean A1c values of 6.83%, 7.08%, and 7.57%, respectively. Both dulaglutide doses also provided greater reductions in fasting plasma glucose (- 43 mg/dl for 1.5 mg and -29 mg/dl for 0.75 mg) compared to sitagliptin (-16 mg/dl).
• Not surprisingly, greater weight loss was observed with dulaglutide 1.5 mg (-3.22 kg [-7.1 lbs]) and dulaglutide 0.75 mg (-2.7 kg [-6.0 lbs]) compared to sitagliptin (-1.63 kg [-3.6]).
• On the safety front, dulaglutide 1.5 mg and 0.75 mg were associated with slightly higher rates of adverse events (77% for both groups) compared to sitagliptin (70%). Dr. Nauck commented that while dulaglutide was also associated with higher rates of GI side effects, the numbers fall within the range typically observed with long-acting GLP-1 agonists. All three groups experienced a similarly low rate of injection site reactions (1%), as well as comparable rates of hypoglycemia (1.6% for dulaglutide 1.5 mg; 2.6% for dulaglutide 0.75 mg, and 1.1% for sitagliptin). No cases of severe hypoglycemia were reported in the study.
  • The trial also tracked cases of pancreatitis and pancreatic cancer over 104 weeks. The rate of pancreatitis was zero cases for the dulaglutide arms and 3 cases/1,000 patient-years in the sitagliptin arm. No cases of pancreatic cancer were reported.

Questions and Answers

Q: Can you tell us something about the biodistribution of the compound? It might be more concentrated in certain compartments such as the lymph nodes. We know that GLP-1 can act on T cells and B cells.

A: I can’t give you a detailed account of how this molecule is distributed in circulation. I can tell you that the half life is 5 days, so it’s in the circulation for quite a period of time. It’s thought to be degraded within the circulation. So I think the question you ask is very justified and it would be interesting to know such details – i.e., what is the concentration of dulaglutide in lymph vessels. I think we need to ask the company to do such details.

Q: Is it degraded in kidney?

A: It is not degraded in the kidney. All the large, once-weekly agonists are somewhat coupled to a big protein. The purpose is to circumvent the fact that the original peptide related to GLP-1 is cleared in the kidney. This is also another compound that could be tested in renally-impaired patients.

Q: Regarding the adaptive design, it was very interesting, very nice. I’m guessing that because of the short timeframe, the reason for choosing the doses of the drug was the side effect profile. You have 17% nausea, 13% vomiting. What happened when you tested the higher doses?

A: I think we should go to poster 1045-P for these details. The major reason for not selecting the 3 mg dose was cardiovascular parameters – e.g., pulse rate and blood pressure. It wasn’t because of GI side effects.

Q: Was there a change in pulse rate in this trial?

A: We saw minor changes in pulse rate that are typical for most long-acting GLP-1 agonists.

 

Exenatide Once Weekly: Sustained Glycemic and Weight Control Through 3 Years Compared with Insulin Glargine (67-OR)

Michael Trautman, MD (Lilly, Indianapolis, IN)

Dr. Michael Trautman presented three-year results from the DURATION 3 study, which compared the safety and efficacy of exenatide once weekly and insulin glargine in people with type 2 diabetes. Overall, treatment with exenatide once weekly was associated with greater reductions in A1c (7.3% vs. 7.5%; p=0.033) and weight (-2.49 kg vs. +2.0 kg; p<0.001) than with insulin glargine in the ITT groups. However, insulin glargine provided greater improvements in fasting glucose than exenatide once weekly (-47.4 mg/dl vs. -31.2 mg/dl; p<0.001). Expectedly, incidence of minor hypoglycemia was significantly lower with exenatide once weekly compared to insulin glargine throughout the three-year period. No new safety signals were detected, with one incidence of pancreatitis in both treatment groups. Finally, exenatide antibodies were found to decrease over time, with 80% of individuals anti- body negative at three years. There was no association between exenatide anti-body status and degree of clinical effect.

• DURATION 3 was a three-year open label, randomized controlled study that compared the safety and efficacy of exenatide once weekly with titrated insulin glargine. Insulin glargine was continuously titrated following a treat to target algorithm. Exenatide once weekly was administered as a fixed 2.0 mg dose. The study randomized 223 individuals to receive insulin glargine and 233 individuals to receive exenatide once weekly. At three years, 140 individuals remained in the exenatide once weekly arm and 147 individuals remained in the insulin glargine arm. Both arms were balanced at baseline, with an average A1c of 8.3%, body weight of 90 kg, and average duration of diabetes of eight years. 70% of patients in both arms were inadequately controlled on metformin alone, whereas the other 30% were inadequately controlled on metformin and a sulfonylurea. These baseline drugs were continuedthroughout the trial. Importantly, there were no differences in baseline characteristics between the intent to treat and the completer populations.
• Exenatide once weekly provided a greater average reduction in A1c over three years vs. insulin glargine. Rapid reductions in A1c through week 36 were observed in both arms. Following week 36, A1c steadily increased with both treatments at approximately the same rate. At three years, however, average A1c remained statistically significantly lower in the ITT group with exenatide once weekly treatment (7.3%) vs. insulin glargine (7.5%; p=0.033). The average insulin glargine dose at three years was 39 IU/day. In the completer population, a similar result was observed with an average A1c of 7.1% in the exenatide once weekly arm vs. 7.4% in the insulin glargine arm (p=0.022). A greater percentage of patients were found to achieve the A1c goals of 7.0% and 6.5% with exenatide once weekly than insulin glargine at three years. In the ITT population, this result was only significant for the 6.5% target (24% v 15%; p=0.02). The 7.0% target was achieved by 40% of patients in the exenatide once weekly arm and 33% of patients in the insulin glargine arm (p=0.12).
• Insulin glargine was associated with greater reductions in fasting plasma glucose. At three years, there was a -47.7 mg/dl reduction in FPG in the insulin glargine arm vs. a -31.16 mg/dl reduction in the exenatide once weekly arm. Dr. Trautman noted that this result was not surprising and suggested that the much of the glycemic benefit associated with exenatide once weekly is due to its impact on post-prandial control.
• Exenatide once weekly provided significantly greater weight loss at three years vs. insulin glargine. In the ITT population, there was a weight gain of 2.0 kg with insulin glargine vs. a weight loss of 2.5 kg with exenatide once weekly (p<0.001). The weight loss achieved by exenatide was rapid, with maintenance beginning around week 36 through the end of the trial.
• No surprising new tolerability and safety findings were reported. Minor hypoglycemia was reported less frequently with exenatide once weekly treatment than with insulin glargine. In the metformin subgroup, 12% of patients treated with exenatide once weekly vs. 40% of patients treated with insulin glargine reported one or more minor hypoglycemic episodes over three years. There was one event of pancreatitis in both groups. Anti-exenatide antibodies were found to decrease overtime, with 80% of the exenatide once weekly treatment group anti-body negative at three years. Further, there was no association between anti-body status and degree of glycemic effect.

 

Harmony 8: Once Weekly (QW) GLP1 Agonist Albiglutide (ALBI) vs. Sitagliptin (SITA) in Type 2 Diabetes (T2D) PTS with Renal Impairment (RI): Week 26 Results (68-OR)

Lawrence Leiter, MD (University of Toronto, Toronto, Canada)

Dr. Lawrence Leiter presented 26-week results from the HARMONY 8 trial, which evaluated the efficacy, safety, and tolerability of albiglutide (n=246) compared to sitagliptin (n=240) in people with type 2 diabetes and renal impairment inadequately controlled on lifestyle modifications or other oral diabetes agents. Through week 26, albiglutide (-0.8%) provided significantly greater reductions in A1c than sitagliptin (-0.5%; p=0.0003). Further, numerically greater reductions in A1c were observed with albiglutide regardless of renal impairment severity, although the result only reached significance for the moderate renal impairment group. However, Dr. Leiter noted that the trial was not adequately powered to detect these differences. Greater weight loss was also achieved with albiglutide than with sitagliptin (-0.8 kg vs. -0.2 kg; p=0.028). Both treatments were generally well tolerated and safe. The most common adverse side effects included diarrhea (8.8% with albiglutide vs. 6.1% with sitagliptin), constipation (6% vs. 2%), and nasopharyngitis (5.2% vs. 6.5%). Notably, nausea (4.8% vs. 2.8%) and vomiting (1.6% vs. 0.8%) rates were low in both groups. Although a greater proportion of people experienced hypoglycemia with albiglutide than with sitagliptin (20.5% vs. 13.4%), the majority (>90%) of cases in both arms occurred in participants who were also using an SFU. Antibodies to albiglutide were found in 2.8% of participants, but none of the antibodies were determined to be neutralizing. There was one incidence of pancreatitis in the albiglutide group, but it was determined to be non-related to treatment.

• HARMONY 8 is a 52-week randomized, double blind, active controlled phase 3 study that will evaluate the efficacy, safety, and tolerability of albiglutide compared to sitagliptin in people with type 2 diabetes and renal impairment (eGFR <90 and>15 ml/min/1.73 m2) inadequately controlled on lifestyle modifications (11%) or oral diabetes agents (metformin, TZD, and/or SFU). After a four-week run in period, participants were randomized to 30 mg albiglutide once weekly (n=246) or sitagliptin (n=240). Albiglutide was uptitrated to 50 mg beginning at week four if glycemic targets were not met. Sitagliptin was dosed by degree of renal impairment according to product label. The treatment arms were balanced at baseline, with an average age of 63 years, BMI of 30 mg/kg2, A1c of 8.1%, and duration of diabetes of 11 years. Further, 8% of each group had a prior MI, 51% in each group were determined to have mild renal impairment (eGFR of 60-89 ml/min/1.73 m2), 40% moderate renal impairment (eGFR 30-60 ml/min/1.73 m2), and 9% severe renal impairment (eGFR <30 ml/min/1.73 m2).
• Through week 26, albiglutide (-0.8%) provided significantly greater reductions in A1c than sitagliptin (-0.5%; p=0.0003). Further, numerically greater reductions in A1c were observed with albiglutide regardless of renal impairment severity, although the result only reached significance for the moderate renal impairment group. Dr. Leiter noted that the trial was not powered to detect statistically significant differences between these subgroups. In the mild renal impairment group, albiglutide provided an average A1c reduction of -0.72% vs. a -0.66% reduction with sitagliptin. In the moderate renal impairment group, a -0.88% reduction with albiglutide was observed vs. a -0.37% reduction with sitagliptin. In the severe renal impairment group, a -1.08% reduction in A1c was observed vs. a -0.65% reduction with sitagliptin. With regards to FPG, albiglutide provided statistically significantly greater reductions than sitagliptin(-26 mg/dl vs. 4 mg/dl; p<0.0001). A greater proportion of participants were also able to achieve a target A1c of 7.0% with albiglutide (24.5%) than with sitagliptin (19.2%). 34% of participants in the albiglutide arm required uptitration to the 50 mg dose during the trial.
• Greater weight loss was achieved with albiglutide than with sitagliptin. The albiglutide arm achieved an average weight loss of 0.8 kg vs. an average weight loss of 0.2 kg in the sitagliptin arm (p=0.028).
• Both treatments were demonstrated to be generally well tolerated and safe. Drug related adverse effects were slightly more frequent in the albiglutide arm than the sitagliptin arm (19.7% vs. 13.0%). The most common adverse side effects included diarrhea (8.8% with albiglutide vs. 6.1% with sitagliptin), constipation (6% vs. 2%), and nasopharyngitis (5.2% vs. 6.5%). Notably, nausea (4.8% vs. 2.8%) and vomiting (1.6% vs. 0.8%) rates were low in both groups. Dr. Leiter attributed albiglutide’s low rate of nausea and vomiting to its long half-life, slow ramp up to therapeutic blood levels, and limited access to the CNS given its large size. With regards to hypoglycemia, there were no severe cases of hypoglycemia with albiglutide vs. two severe cases with sitagliptin. 20.5% of people in the albiglutide arm experienced hypoglycemia (94% of whom were also using an SFU) vs. 13.4% with sitagliptin (91% of whom were also using an SFU). Antibodies to albiglutide were found in 2.8% of participants, but none of the antibodies were determined to be neutralizing. There was one incidence of pancreatitis in the albiglutide group, but it was determined to be non-related to treatment. There were no incidences of pancreatitis in the sitagliptin group.

Questions and Answers

Q: How is albiglutide metabolized?

A: It is cleared by the reticuloendothelial system.

Q: What are your thoughts regarding the minimal GI side effects observed?

A: These results are consistent with previous data for albiglutide which showed a low incidence of nausea and vomiting. It is related in part to its once weekly injection, the gradual increased in blood drug levels, and the molecules large size, which possibly prevents CNS entry.

Q: Was there any difference in nausea rates with changing levels of renal impairment?

A: No, there was not.

Exenatide BID vs. Insulin Lispro TIDM Added to Titrated Insulin Glargine QD in Metformin-Treated T2DM Patients Resulted in Similar Glycemic Control but Weight Loss and Less Hypoglycemia: The 4B Study (70-OR)

Michaela Diamant, MD, PhD (VU University Medical Center, Amsterdam, Netherlands)

Dr. Michaela Diamant presented the results of a study directly comparing a prandial GLP-1 receptor agonist (exenatide BID) with a standard prandial insulin (insulin lispro T1DM). Following 12 weeks of basal insulin titration, participants who did not achieve A1c levels below 7.0% were randomized 1:1 to receive exenatide BID (5 g, titrated up to 10 g after four weeks, n=315) or insulin lispro treatment (n=312) for 30 weeks. This phase-3b 44-week open-label randomized, comparator-controlled interventional study found that both treatment arms achieved similar A1c reductions, but with lower fasting glucose, greater weight loss, less daytime hypoglycemia, and reduced systolic blood pressure observed among patients receiving exenatide BID.

• In addition to establishing non-inferiority in A1c reduction (baseline after basal titration A1c 8.5%; exenatide 7.0% vs. 7.1% with insulin lispro at 44 weeks), while individuals receiving insulin lispro gained 2.1 kg (4.6 lbs), individuals receiving exenatide BID lost an average of 2.5 kg (5.5 lbs) after 30 weeks into treatment intensification. The change in fasting glucose was also 0.64 mmol/L (11 mg/dl) lower among patients receiving exenatide BID compared to patients receiving insulin lispro. Self-monitored blood glucose was also lower, except at lunch due to insulin lispro delivery at each meal, versus exenatide’s twice-daily delivery. At this time, approximately 15 more units of basal insulin were required daily by participants receiving exenatide BID (57 vs. 42).
• Daytime hypoglycemia was found to be less frequent among individuals receiving exenatide BID than insulin lispro (15.2% vs. 33.7% incidence; p<0.001). Minor hypoglycemia was also less common; 40.7% incidence was reported among patients receiving insulin lispro, whereas only 29.5% incidence was observed among patients receiving exenatide BID (p<0.004). Given these results, Dr. Diamant believes that a short-acting GLP-1 receptoragonist may be a novel and efficacious treatment strategy for patients who fail on insulin and further intensification.

Oral Sessions: New Information on DPP-4 Inhibition

Cardiovascular (CV) Safety of Linagliptin in Patients with Type 2 Diabetes (T2D): A Pooled Comprehensive Analysis of Prospectively Adjudicated CV Events in Phase 3 Studies (376-OR)

Odd Erik Johansen, MD, PhD (Boehringer-Ingelheim, Ingelheim, Germany)

Dr. Odd Erik Johansen discussed a study that compared the incidence of CV events for linagliptin (Tradjenta) vs. comparator drugs in ~9,500 patients with type 2 diabetes enrolled in 19 double-blind RCTS (“the most comprehensive trial database available”). He dedicated nearly half of his presentation time to background information, ending with a swift review of the data. The study used a prospectively defined adjudication process and measured time to first event for a composite primary endpoint of CV death, non-fatal stroke, non-fatal myocardial infarction, and hospitalization for unstable angina pectoris (4P-MACE). The primary analysis indicated that linagliptin has a favorable effect on CV risk (HR: 0.78; 95% CI: 0.55-1.12). Further analysis shows that this result was mediated mostly by linagliptin’s effect on non-fatal stroke (HR: 0.34; 95% CI: 0.15-0.75) – data for CV death (HR: 1.04; 95% CI: 0.42-2.60) and non-fatal myocardial infarction (HR: 0.86; 95% CI: 0.47-1.56) were not as compelling. For these three endpoints combined (strict MACE composite), the hazard ratio was positive (0.74) and with the upper bound of the 95% confidence interval over one (95% CI: 0.49-1.13). Dr. Johansen ended his presentation by highlighting that further CV data will emerge from linagliptin’s cardiovascular outcomes trial (CVOT) CAROLINA (comparing the drug to glimepiride; n=6,103), as well as from the CARMELINA study (a placebo-controlled CV and renal outcomes study; n=8,300). As noted previously, BMS/AZ just reported topline results from Onglyza’s CVOT SAVOR-TIMI showing that the drug was non-inferior but not superior to placebo in reducing CV risk (full details are in our report at http://close.cx/SAVORtopline).

• Dr. Johansen explained the rationale for this analysis of CV risk. He began by reminding the audience that type 2 diabetes confers a roughly two-fold excess risk for a wide range of vascular disease, independent of other cardiovascular (CV) factors. Studies show that people with type 2 diabetes exhibit characteristics of CV biopathology distinct from those without the disease. Dr. Johansen cited an independent CV assessment of the DPP-4 inhibitor class vs. other drugs that included 70 trials (n= ~42,000) and found no CV harm associated with DPP-4 inhibitors (Monami et al., Dibaetes Obes Metab 2013). In addition, mechanistic trials have indicated that DPP-4 inhibitors have several potential CV benefits independent of their effects on glycemia (e.g., decreasing blood pressure and triglyceride levels, increasing left-ventricle function, decreasing inflammation and oxidative stress, increasing endothelial function, decreasing myocardial infarct size).
• The pooled analysis included 5,847 patients on linagliptin (5 mg and 10 mg; 4,421 patient years in total) and 3,612 patients on the comparators (placebo, glimepiride, voglibose; 3,255 patient years of exposure). As baseline, the participants had an average age of 59 years, A1c of 8.1%, BMI of 29-30 kg/m2, and Framingham 10-year CV risk score of 9.7- 10.2.
• Hazard ratios for secondary and tertiary endpoints:

Secondary Endpoints						Tertiary Endpoints
Strict MACE		HR: 0.74 (95% CI: 0.49-1.13)				CV death		HR: 1.04 (95% CI: 0.42-2.60)
All adjudicated CV events		HR: 0.82 (95% CI: 0.61-1.09)				Non-fatal MI		HR: 0.86 (95% CI: 0.47-1.56)
FDA-custom MACE (not defined)		HR: 0.74 (95% CI: 0.45-1.08)				Non-fatal stroke		HR: 0.34 (95% CI: 0.15-0.75)
						Transient ischemic attack		HR: 0.09 (95% CI: 0.01-1.75)
						Unstable angina pectoris w/ hospital.		HR: 1.08 (95% CI: 0.56-2.06)
						Total Mortality		HR: 0.89 (95% CI: 0.45-1.75)

 

The Effects of Linagliptin, a DPP-4 Inhibitor, in Diabetic Hemodialysis Patients Assessed by CGM (374-OR)

Satoshi Funakoshi, MD, PhD (Sakuramachi Clinic, Nagasaki, Japan)

Dr. Satoshi Funakoshi presented evidence that replacing insulin with the DPP-4 inhibitor linagliptin (Lilly’s Tradjenta) in people undergoing hemodialysis can reduce the glycemic variability associated with hemodialysis. In his study, six relatively well-controlled patients with diabetes (A1c <7%) who were on insulin were monitored by CGM on one hemodialysis-free (HD-free) day and one hemodialysis (HD) day. Subsequently, they were switched off of insulin onto linagliptin 5 mg and again monitored by CGM on one HD-free and one HD day. Dr. Funakoshi did not specify how long the patients underwent linagliptin treatment prior to the second round of HD and CGM monitoring. He presented two case studies where mean amplitude of glucose excursions (MAGE) was markedly decreased on HD days after switching to li/nagliptin (~20 mg/dl decrease in one case and ~100 mg/dl decrease in the other). Overall, when he showed the composite CGM traces for all six patients, it was very clear that switching to linagliptin flattened out the glycemic variability at the time of HD. As such, MAGE on the HD days significantly decreased after switching from insulin to linagliptin (mean value not provided; p<0.05). No changes in hypoglycemia or A1c were observed after switching to linagliptin. We found it quite striking that patients needing insulin could achieve adequate control after switching to linagliptin – it was unclear whether Dr. Funakoshi was suggesting that patients should only switch to linagliptin treatment on days when they need HD or if they should switch to linagliptin treatment long-term. This seems like a creative approach to solve a key problem for this very sick population.

• Hemodialysis (HD) is often associated with glycemic variability. Dr. Funakoshi proposed a number of potential reasons: 1) glucose transport into erythrocytes may change due to cytoplasmic pH changes during HD; 2) the HD solution contains glucose to buffer against acute hypoglycemia; 3) removal of insulin from the blood during HD could result in an insulin shortage afterwards; 4) clearance of uremic toxins from the blood improves insulin resistance.
• Dr. Funakoshi hypothesized that, with its glucose-dependent mechanism of action, linagliptin could reduce glycemic variability during and after HD. Linagliptin is theonly DPP-4 inhibitor cleared hepatically rather than renally, so is the prime incretin option for patients with renal impairment. Additionally, it has a high protein binding rate (>90%), so it is minimally removed by HD therapy.
• The study enrolled six relatively well-controlled patients with diabetes on hemodialysis (A1c <7%) and on background insulin therapy. Patients monitored by CGM on one hemodialysis-free (HD-free) day and one hemodialysis (HD) day. Subsequently, they were switched off of insulin onto linagliptin 5 mg and again monitored by CGM on one HD-free and one HD day. Dr. Funakoshi did not specify how long the patients underwent linagliptin treatment prior to the second round of CGM monitoring.
• Switching from insulin to linagliptin significantly reduced mean amplitude of glucose excursions (MAGE) on HD days (mean value not specified; p<0.05). A composite graph of all six CGM tracings visually supported this finding – tracings on the HD day were much flatter and much more resembled the HD-free day after switching to linagliptin.

 

A Local GLP-1 System in Human Pancreatic Islets: The Role of DPP-IV Inhibition by MK-0626 (378-OR)

Marco Bugliani, PhD (University of Pisa, Pisa, Italy)

Dr. Marco Bugliani presented in vitro evidence that 1) the DPP-4 enzyme is present in human islet cells (particularly in the alpha cells); 2) that DPP-4 inhibitors exert a direct protective effect against gluco- and lipotoxicity; and 3) that these effects may be mediated by actions of a local GLP-1 system in the islet cells. Using immunohistochemistry, Dr. Bugliani’s group found that human pancreatic tissue stained positively for DPP-4; using immunofluorescence, they co-localized the DPP-4 expression with glucagon but not with insulin or GLP-1, thus concluding that DPP-4 was present primarily in alpha cells. They then treated islet cells with the DPP-4 inhibitor MK-0626 and found that MK-0626 treatment increased active GLP-1 concentration in islets; induced insulin release; preserved beta cell glucose sensitivity after 24-hour exposure to 11 mM glucose or 0.5 mM palmitate; and conferred a partial preservation of insulin granules. MK-0626 produced a significant increase in DPP-4 gene expression in islets cells, but no major changes were observed for insulin or GLP-1 gene expression.

 

Change in Body Weight after 24 Weeks of Vildagliptin Therapy as a Fuction of Baseline Fasting Glucose Levels in Patients with Type 2 Diabetes (372-OR)

James Foley, PhD (Novartis Pharmaceuticals Corporation, East Hanover, NJ)

Dr. James Foley presented evidence that baseline fasting plasma glucose predicts the weight change a patient experiences on vildagliptin (Novartis’ Galvus). Type 2 diabetes patients frequently have plasma glucose levels above the renal threshold for glucose reabsorption (approximately 10 mmol/L or 180 mg/dl), resulting in glucose loss through urine. Thus, any anti-hyperglycemic agent may reduce glycosuria, thereby increasing caloric retention. Dr. James Foley’s group hypothesized that weight change experienced by patients on the DPP-4 inhibitor vildagliptin is dependent on baseline fasting plasma glucose (FPG) levels, with high initial FPG corresponding to a higher potential for accruing a positive caloric balance due to increased caloric retention. They compiled data on 2,863 type 2 diabetes patients drawn from a database of vildagliptin clinical monotherapy studies. The average age was 54 years, and the mean duration of their diabetes was two years. The results showed that patients’ change in body weight was positively and significantly correlated with their baseline FPG levels, with a coefficient of approximately 8.5 g/(mg/dl). A FPG level of 230 mg/dl was the break-even point in terms of weight gain or loss. The data demonstrated that, when glycosuria is taken into account, vildagliptin treatment results in negative caloric balance. To explain the phenomenon, Dr. Foley noted that DPP-4 inhibitors likely reduce intestinal triglyceride absorption, increase lipolysis in adipose tissue, and increase fat oxidation in muscles. Despite the limitations of database studies, we believe these results provide valuable information on which patients can expect to see weight loss or gain on vildagliptin therapy.

Questions and Answers

Q: Have you been able to do any studies on thermogenesis?

A: No, we haven’t yet done a formal thermogenic study.

Q: I assume the same would hold true for measurements of glycosuria?

A: No weren’t able to obtain those data either.

 

Sitagliptin, a Dipeptidyl Peptidase-4 Inhibitor, Improves the Unfavorable M1/M2-Like Phenotypes of Peripheral Blood Monocytes in Japaense Type 2 Diabetic Patients (377-OR)

Noriko Satoh-Asahara, MD (Kyoto Medical Center, Kyoto, Japan)

Dr. Noriko Satoh-Asahara discussed a study evaluating the effect of sitagliptin on M1 and M2-like phenotypes, an indication of the drug’s cardiovascular (CV) effects. To begin, Dr. Satoh-Asahara noted that GLP-1 has pleiotropic effects that could favorably impact CV risk (e.g., decreasing body weight, glucose levels, and silent inflammation, oxidative stress, endothelial dysfunction, and blood pressure). She further explained that obesity induces a phenotypic switch in the macrophages of adipose tissue – from an anti-inflammatory (M2) to a pro-inflammatory (M1) polarization – which contributes to inflammation and insulin resistance. This switch is detected by measuring M1 and M2 markers in peripheral blood monocytes (a type of white blood cell). This open-label trial randomized 52 Japanese type 2 patients (at baseline, mean age of 60 years, BMI of 26 kg/m2, A1c of 8.2%) to either diet alone (n=26) or to diet plus sitagliptin (50 mg daily; n=26) for three months. As expected, sitagliptin therapy decreased fasting plasma glucose, A1c and plasma GLP-1 levels. In addition, sitagliptin decreased levels of the M1 pro-inflammatory marker TNFα in both serum and monocytes (p<0.01 and p<0.05, respectively) and increased levels of the M2 an-inflammatory marker IL-10 in both serum and monocytes (p<0.01 and p<0.05, respectively). Dr. Satoh-Asahara noted that multivariate regression analysis revealed that sitagliptin was the only factor independently associated with an increase in monocyte IL-10. The therapy also decreased serum levels of two other inflammatory markers – oxidized LDL and C-reactive protein – further suggesting its positive effect on the CV system.

Oral Sessions: Novel Therapeutics

TTP054, A Novel, Orally-Available Glucagon-Like Peptide-1 (GLP-1) Agonist: Results from a 28 Day Study in Subjects with Type 2 Diabetes Mellitus (T2DM) (115-OR)

Stephanie Gustavson, PhD (TransTech Pharma, High Point, NC)

Dr. Stephanie Gustavson presented the results of a four-week trial examining the safety and efficacy of TTP054, a phase 2 oral GLP-1 agonist currently in development by TransTech Pharma. In the trial, patients with type 2 diabetes on background metformin were randomized to receive either TTP054 400 mg QD (n=9), TTP054 200 mg BID (n=10), TTP054 200 mg QD (n=11), or placebo (n=18). TTP054-treated patients demonstrated consistently greater declines in fasting plasma glucose (-15 mg/dl with 200 mg QD, -18 mg/dl with 200 mg BID, and -20 mg/dl with 400 mg QD vs. -5 mg/dl with placebo) and two-hour postprandial glucose (-30 mg/dl, -32 mg/dl, and -35 mg/dl vs. no change with placebo) versus placebo-treated patients; while not long enough in duration to produce change in A1c, predictive models estimated A1c change at three months at about 0.6-1.0% across doses, which Dr. Gustavson suggested was a conservative estimate given the study design. Notably, TTP054-treated patients demonstrated no hypoglycemic events, with incidence of GI adverse events similar to those observed in the placebo arm. Dr. Gustavson noted that all phase 1 studies with TTP054 have completed, with one 12- week phase 2 study just recently completed. Given the reduction in GI adverse events and convenience, we will be interested to follow TTP054 as further clinical data is released. The company currently has another oral GLP-1 candidate, TTP273, in development; other companies known to have oral GLP-1 candidates include Zydus Cadila (ZYOG1; phase 1), Arisgen (preclinical), Heptares (preclinical), and Novo Nordisk (NN9924, NN9926, NN9927; all phase 1).

• TTP054 is a phase 2 oral GLP-1 agonist currently in development by TransTech Pharma. In addition to the convenience of oral administration, given administration is through the intestine, it is expected the drug will have a lower incidence of GI adverse events versus injectable GLP-1; additionally, the oral drug is a small molecule agonist rather than a peptide, so it is believed it will not incur antibody formation. Dr. Gustavson noted that TTP054 has completed phase 1 studies, with one 12-week phase 2 study recently completed. An additional candidate TTP273 is in development with proof of mechanism demonstrated and phase 2b trials to begin in early 2014.
• In this trial, patients with type 2 diabetes on background metformin were randomized to receive either TTP054 400 mg QD (n=9), TTP054 200 mg BID (n=10), TTP054 200 mg QD (n=11), or placebo (n=18) for four weeks. Evidencing proper absorption, TTP054 had a median Tmax of ~3 hours and mean terminal half-life of ~6 hours.
• TTP054-treated patients demonstrated consistently greater declines in fasting plasma glucose (-15 mg/dl with 200 mg QD, -18 mg/dl with 200 mg BID, and -20 mg/dl with 400 mg QD vs. -5 mg/dl with placebo) and two-hour postprandial glucose (-30 mg/dl, -32 mg/dl, and -35 mg/dl vs. no change with placebo) versus placebo-treated patients. As the trial was too short in duration to show A1c change, Dr. Gustavson relied on various models to estimate A1c change at three months using these effects on glucose variables. Across four models of predicted change, declines in A1c were roughly -0.7% with 200 mg QD, -0.6% with 200 mg BID, and -1.0% with 400 mg QD versus -0.2% versusplacebo (baseline A1cs 8.1%, 8.1%, 8.3%, and 7.7%, respectively). Additionally, she suggested these estimates were conservative, given the meal challenge in this trial was small with no stabilization of diet prior to assessment and glucose lowering was continuing to decline with treatment at the time of assessment.
• TTP054-treated patients demonstrated no hypoglycemic events, with incidence of GI adverse events similar to those observed in the placebo arm (2/11 patients [18%] with 200 mg QD, 0/10 patients [0%] with 200 mg BID, and 1/9 patients [11%] with 400 mg QD vs. 1/18 [6%] with placebo). Dr. Gustavson suggested all GI adverse events weremild in severity with investigators indicating adverse events were due to diet rather than the study treatment. While not powered to assess changes in lipids, Dr. Gustavson suggested that the maximum change in triglycerides with TTP054 was -50 mg/dl versus -10 mg/dl with placebo and that the maximum change in total cholesterol was -15 mg/dl versus +5 mg/dl with placebo, indicating a potential benefit.

Questions and Answers

Q: Do you have evidence of effect on GI motility?

A: We did look at GI motility in other studies and saw reduction. We also saw reduction in gastric emptying, so we think the drug is acting in the same way as the mimetics.

Oral Sessions: Diabetic Dyslipidemia

Treatment with Linagliptin Can Attenuate Postprandial Hyperlipidemia (299-OR)

Daisaku Masuda, MD, PhD (Osaka University, Osaka, Japan)

Presenting a study of linagliptin in mice, Dr. Daisaku Masuda explained how the drug reduces post- meal triglyceride levels. He opened by reminding the audience that recent studies have demonstrated postprandial hypertriglyceridemia (PHTG) as a risk factor for atherosclerotic cardiovascular diseases. The cause of PHTG is accumulation of chylomicrons, including triglycerides, Oral fat loading (OFL) tests have shown that people with type 2 diabetes have high triglyceride peaks, and reports have shown that DPP-4 inhibitors may improve PHTG in these individuals. Using eight-week old mice (n=20), Dr. Masuda’s group investigated the possible mechanisms of this effect. After four weeks on chow alone or chow plus linagliptin, the mice underwent an overnight fast and then an OFL test. The linagliptin treatment group showed significantly reduced postprandial triglyceride levels four hours after the OFL compared to the control group (320 mg/dl vs. 464 mg/dl, p<0.01), and significantly reduced free fatty acid levels six hours post-OFL. Treatment with linagliptin also decreased the postprandial mRNA expression of apoB and FATP-4, which are involved in the intestinal formation of chylomicrons. Dr. Masuda concluded that oral administration of linagliptin improves postprandial triglyceride and triglyceride-rich lipoprotein metabolism and decreases the intestinal production of/ chylomicrons. In other words, the research demonstrated a possible pathway by which DPP-4 inhibitor therapy might improve PHTG in people with type 2 diabetes.

Posters

Liraglutide as Adjunct to Insulin in Type 1 Diabetes: Effects on Glycemic Control and Safety in a Randomized, Double-Blind, Crossover Trial (1007-P)

Simon Heller, Stefan Korsatko, Jamala Gurban, Lene Jensen, Erik Christiansen, Fumiaki Kiyomi, and Thomas Pieber

This randomized, double-blind, crossover study investigated the effects of liraglutide treatment as an adjunct to insulin in patients with type 1 diabetes (n=45). Patients were randomized to one of three doses of liraglutide – 0.6 mg (n=15), 1.2 mg (n=14), or 1.8 mg (n=16) – and to one of two sequences (placebo/liraglutide or liraglutide/placebo) for a four-week treatment period, two-to-three-week washout period, and four-week crossover treatment. At baseline, patients across groups had comparable age (30-39 years), A1c (7.5-7.8%), body weight (mean 72-75 kg [158-165 lbs]), duration of diabetes (14-18 years), and daily insulin dose (42-49 U). Daily insulin dose was significantly reduced by 27% in the 1.2 mg and by 24% 1.8 mg liraglutide group versus placebo (p<0.001), but there were no significant reductions in the 0.6 mg liraglutide group. This indicates a pharmacologic effect that may contribute to glycemic control when liraglutide is added as an adjunct to insulin to treat type 1 diabetes. All liraglutide groups significantly reduced weight (p<0.001). Adverse events were higher during liraglutide treatment compared to placebo, with the highest number due to gastrointestinal adverse events. No differences in the number of hypoglycemic episodes or patients with hypoglycemic episodes were observed across treatments, and there were no severe hypoglycemic episodes reported.

• After four weeks, liraglutide 0.6 mg, 1.2 mg, and 1.8 mg brought about significant weight reductions versus placebo. Patients lost 2.0 kg (4.4 lbs), 3.7 kg (8.1 lbs), and 3.3 kg(7.3 lbs) versus placebo (p<0.001) in the 0.6 mg, 1.2 mg, and 1.8 mg arms, respectively. There were no significant changes in blood pressure, pulse, or body temperature for liraglutide versus placebo.
• Adverse effects were common: 67%, 86%, and 93% of patients in the 0.6 mg, 1.2 mg, and 1.8 mg liraglutide groups experiencing GI adverse events. Nausea occurred in 53%, 78%, and 78% of patients in the 0.6 mg, 1.2 mg, and 1.8 mg liraglutide groups, respectively. At least 86% of patients among all groups experienced documented symptomatic hypoglycemic episodes, and at least 93% of patients among all groups experienced an ADA-classified hypoglycemic event during treatment. However, no severe hypoglycemic episodes occurred.

 

Safety and Efficacy of Dulaglutide Versus Sitagliptin After 104 Weeks in Type 2 Diabetes (AWARD-5) (1004-P)

Ruth Weinstock, Guillermo Umpierrez, Bruno Guerci, Michael Nauck, Karen Boleyn, Zachary Skrivanek, and Zvonko Milicevi

This 104-week, randomized, multi-center, double-blind trial examined the efficacy and safety of dulaglutide (DU) versus sitagliptin (SITA) in patients with type 2 diabetes treated with metformin. Patients were randomized to 1.5 mg DU (n=304), 0.75 mg DU (n=302), 100 mg SITA (n=315), or placebo (n=177) (to 26 weeks). At baseline, subjects had average BMI of 31 kg/m2, age of 54 years, A1c 0f 8.1-8.2%, and weight of 87 kg (191 lbs). At the end of 104 weeks, DU provided significantly greater A1c reductions than SITA (p<0.001). In the 1.5 mg DU, 0.75 mg DU, and SITA arms, A1c decreased an average 1.0%, 0.7%, and 0.3%, respectively, over 104 weeks. The 1.5 mg DU group was associated with greater body weight loss than SITA (1.1 kg [2.4 lbs] beyond placebo) (p<0.001) at 104 weeks. There was a higher rate of GI adverse events in the DU arms compared to SITA, including nausea, vomiting, diarrhea, constipation, and abdominal distension. The incidence and rates of hypoglycemia were similar across treatment groups and there were no reports of severe hypoglycemia episodes.

• At the end of 104 weeks, A1c decreased in the 1.5 mg DU, 0.75 mg DU, and 100 mg sitagliptin groups by an average 1.0%, 0.7%, and 0.3%, respectively. The percentage of patients who achieved an A1c <7.0% at the end of the study was 54%, 45%, and 31%, with a weight reduction of 2.9 kg (6.4 lbs), 2.4 kg (5.3 lbs), and 1.7 kg (3.8 lbs), in the 1.5 mg DU, 0.75 mg DU, and 100 mg SITA arms, respectively. We note that a high number of people dropped out in each treatment arm: 37% in the 1.5 mg DU, 39% in the 0.75 mg DU, 41% in the SITA, and 46% in the placebo arm, with the primary cause due to adverse events. We also note that serious adverse events were reported in 12% of patients in the 1.5 mg DU arm, 8% of patients in the 0.75 mg DUarm, and 10% of patients in the 100 mg SITA arm. No difference across treatment groups in systolic or diastolic pressure was observed.
• There were no reports of severe hypoglycemic episodes at any point during the study. The incidence of hypoglycemia was, however, prevalent and similar across treatment groups: 13% in the 1.5 mg DU arm, 9% in the 0.75 mg DU arm, and 9% in the 100 mg SITA arm. The authors concluded that the safety profile and superior A1c-lowering efficacy of DU vs. SITA after 104 weeks indicate an acceptable benefit/risk profile; however, it is important to note that significantly more patients in the DU arms experienced adverse events during the first 24 weeks of the study, with the highest incidence among the 1.5 mg DU group.

 

Dipeptidyl Peptidase 4 Inhibitors and Comparative Pancreatic Cancer Risk (111-LB)

Mugdha Gokhale, John Buse, Virginia Pate, Christine Gray, Alison Marquis, and Til Stürmer

Recent evidence has suggested a potential association between pancreatic cancer and DPP-4 inhibitors, but to date, the human populations studied have been limited. This poster explored the incidence of pancreatic and all other cancers among new DPP-4 inhibitor users compared to those initiating sulfonylureas or TZDs. The source of data was a 20% random sample of all available Medicare claims from 2007-2010. All patients were >65 years of age and inclusion criteria required at least 6 months enrollment prior to drug initiation. Patients with any cancer diagnosis prior to initiation or between the first and second prescription were excluded. The study found no increased risk of pancreatic cancer or any cancer post initiation of DPP-4 inhibitors as compared to sulfonylureas or TZDs. For pancreatic cancer, the adjusted hazard ratio (HR) for the as-treated patient group was 0.52 (95% CI=0.28-1.00) for DPP-4 inhibitors vs. SFU and 1.11 (95% CI=0.67-1.83) for DPP-4 inhibitors vs. TZDs. The results for crude HRs and the intent-to-treat population followed the same pattern whereby the point estimate for DPP-4 vs. SFU was below 1.0 but the 95% CI did not exclude 1.0, and the point estimate for DPP-4 vs. TZD was slightly over 1.0 but with the 95% CI crossing 1.0. To exclude the possibility of reverse causality, the authors repeated the analysis excluding first 6 months of the follow-up. However, results remained the same. To address concerns about detection bias, the authors noted that there was little evidence for differential diagnostic work-up in DPP-4 inhibitor initiators. There was also no difference in any-cancer incidence when comparing DPP-4 inhibitor to SFU or TZD initiators.

• The comparison between DPP-4 inhibitors and sulfonylurea initiators compared 11,602 and 48,746 individuals, respectively. The comparison between DPP-4 inhibitors and TZDs followed 18,991 DPP-4 inhibitor initiators and 22,441 TZD initiators. Populations were weighted to balance distribution of baseline covariates. The mean age of DPP-4 initiators was 75 years with 35% men and 65% women. Insulin use 6 months prior to index date was 15.2-19.5% among all groups.
• Limitations in this study include the short treatment from actual treatment (as treated analysis), homogeneity of age (all subjects over 65), and data availability (intent-to-treat analysis).

 

Lower Risk of Hypoglycemia in Elderly Type 2 Diabetes Patients When Linagiptin is Added to Basal Insulin: An Exploratory Analysis (2-LB)

Silvio Inzucchi, Michael Nauck, Maximilian von Eynatten, Uwe Hehnke, Hans-Juergen Woerle, and Robert Henry

Hypoglycemia remains a prominent concern for elderly insulin-requiring patients with type 2 diabetes. This study demonstrated that adding linagliptin to basal insulin could both improve glycemic control and improve hypoglycemia compared to placebo. Linagliptin (Lilly/BI’s Tradjenta), is one of few oral agents that is not renally excreted, and thus does not require dose adjustments for people with impaired renal function. This study was a pooled analysis of data from two phase 3, double-blind, randomized, placebo-controlled trials that investigated hypoglycemia risk with linagliptin in patients above the age of 70 receiving basal insulin. Baseline characteristics of subjects included a mean age of 74 years, A1c of 8.2%, BMI of 30 kg/m2, and basal insulin dose of 36 U/day. Treatment with linagliptin resulted in significantly reduced A1c levels from baseline (-0.77%) and no relevant changes in daily basal insulin requirements. Despite this improvement in glycemic control, adding linagliptin substantially decreased overall (-37%; OR 0.63) and confirmed symptomatic (-34%; OR 0.66) hypoglycemia risk compared to placebo. We think that now the even more interesting follow-up study would be to compare linagliptin to prandial insulin or a GLP-1 agonist as a basal-insulin add-on.

• In two phase 3 trials of linagliptin, patients with type 2 diabetes were randomized to receive either 5 mg once-daily linagliptin (n=126) or placebo (n=121), in addition to stable doses of basal insulin. Baseline characteristics of subjects included a mean age of 74 years, A1c of 8.2%, BMI of 30.3 kg/m2, and basal insulin dose of 36 U/day. One study was ≥ 52- weeks comprised of patients ≥ 18 years taking basal insulin alone or in combination with metformin and/or pioglitazone. The other study was a 24-week study consisting of patients ≥ 70 years of age receiving metformin and/or sulfonylurea and/or basal insulin. The glycemic efficacy and tolerability of linagliptin relative to placebo was assessed using data pooled from the subgroups of patients ≥ than 70 years of age in each study. Basal insulin doses did not change notably during the study.
• A significant reduction in incidence of overall and confirmed (blood glucose ≤ 70 mg/dl) hypoglycemia was found in patients treated with linagliptin. A downward trend in incidences of hypoglycemia with linagliptin vs. placebo was observed for patients with mild- moderate baseline hyperglycemia (A1c 7.5-<9.0%; OR 0.41), with baseline A1c <7.5% (overall OR 0.77), and subgroups receiving insulin glargine, insulin detemir, or NPH insulin (overall OR 0.74, 0.59, 0.49, respectively). The mechanism for reduced risk of hypoglycemia with linagliptin was speculated to involve the glucagon counterregulatory response, though this hypothesis requires further study to confirm.

 

Comparison of Treatment with Sitagliptin (SITA) or Sulfonylurea (SU) in Patients with Type 2 Diabetes Mellitus (T2DM) and Mild Renal Insufficiency (549-P)

Samuel Engel, Lei Xu, Gregory Golm, Edward O’Neill, Keith Kaufman, and Barry Goldstein

This post-hoc analysis of pooled data from three double-blind studies compared the efficacy and safety of sitagliptin (Merck’s Januvia) to sulfonylureas in patients with type 2 diabetes (n=1,180) and mild renal impairment (eGFR=60-89 ml/min/1.73 m2). The primary endpoint was change from baseline A1c, and special focus was placed on differences in rates of hypoglycemia between the two treatment arms. From a baseline A1c of 7.6%, sitagliptin and sulfonylurea provided similar A1c reductions (0.62% and 0.68%, respectively). However, treatment with sitagliptin provided less symptomatic hypoglycemia (7% vs. 26% of patients on sitagliptin vs. sulfonylurea, respectively, reported at least one event; p<0.001), and loss in body weight compared to SU (0.9 kg [2 lb] weight loss on sitagliptin vs. 1.4 kg [3 lb] weight gain on sulfonylurea; p<0.0001). Most notably, 40.6% of patients on sitagliptin’s met the composite endpoint of an A1c decrease of greater than 0.5% with no symptomatic hypoglycemia and no weight gain vs. only 16.8% of patients treated with SU.

• Data from 1,180 patients with type 2 diabetes and mild renal insufficiency were pooled from 3 randomized, double-blind studies. Subjects were administered either 100 mg/day of sitagliptin (SITA n=584) or sulfonylurea (SU n=596) in titrated doses over a 25-30 week study period. In both treatment arms, subjects were similar in terms of baseline characteristics. The mean age was 58 years, and mean BMI was 30-31 kg/m2. Baseline A1c levels were 7.6% in both groups, while baseline fasting plasma glucose (FPG) levels were 154 mg/dl in the SITA group and 156 mg/dl in the SU group.
• SITA and SU demonstrated comparable improvements in glycemic control. In terms of change in A1c from baseline, SITA achieved a 0.62% reduction vs. 0.68% reduction in SU. Furthermore, 60% of patients treated with SITA reached A1c levels of less than 7%, compared to 64% of patients on SU. The percentage of patients with greater than a 0.5% reduction in A1c in SITA vs. SU were 56.7% and 56.5%, respectively. Change in FPG levels from baseline were also similar, with a decrease of 16.6 mg/dl in SITA and 18.0 mg/dl in SU. None of these small differences were statistically significant, suggesting equivalent glycemic efficacy in both drugs.
• SITA was associated with a lower incidence of hypoglycemia. Compared to 26.2% of patients on SU, only 6.8% of patients on SITA experienced at least one symptomatic hypoglycemic event. None of the patients treated with SITA reported an episode of severe hypoglycemia (defined as requiring assistance), while one patient receiving SU reported at least one event.
• Treatment with SITA resulted in greater weight loss compared to SU. In addition to the lower prevalence of adverse events of symptomatic hypoglycemia, patients treated with SU experienced weight gain from baseline (+1.4 kg [3 lb]), whereas those on SITA observed a 0.9 kg (2 lb) loss in body weight.

Symposium: Non-Glycemic Effects of Incretin-Based Therapy – Glucagon-Like Peptide-1 (GLP-1) and Dipeptidyl Peptidase-4 (DPP-4) (Supported by Boehringer Ingelheim and Eli Lilly)

Update on Safety of Short- And Long-Acting Incretin Therapy (GLP-1 and DPP-4)

Vanita Aroda, MD (MedStar Health Research Institute, Hyattsville, MD)

Upwards of 1,000 people flocked to hear Dr. Vanita Aroda present on incretin safety. Dr. Aroda attended the June 5-6 NIDDK/NCI workshop on pancreatitis, diabetes, and pancreatic cancer, and her presentation largely recapitulated highlights from the discussion that took place there (for our coverage of the meeting, please see our reports at https://close.cx/NIDDKpancreatitisDay1 and https://close.cx/NIDDKpancreatitisDay2). The conclusions she drew, in her fast-paced and well- organized presentation, resonated largely with prevailing sentiments on the subject: 1) potential mechanisms have been identified in animal models to suggest a potential pancreatitis risk; in her view, the real debate is over which of these models is relevant and representative since different models have produced differing results; 2) current clinical evidence is insufficient to support altering the risk/benefit profile for incretin-based therapies; 3) recent studies (e.g., Singh et al., JAMA Int Med 2013 and Butler et al., Diabetes 2013) have not prompted substantial changes in clinical recommendations; and 4) there is a great need to pool together the appropriate expertise and data based on rigorous methodologies to address these questions.

• Dr. Aroda reviewed the many factors that complicate the investigations into pancreatitis risk: type 2 diabetes increases risk of pancreatitis and pancreatic cancer by 82% (Huxley et al., British Journal of Cancer 2005). Additionally, there is the chicken and the egg problem of reverse causality – individuals recently diagnosed with diabetes (<4 years) have a 50% greater risk of pancreatic cancer compared to those with longer diabetes duration (odds ratio of2.1 vs. 1.5; Huxley et al., British Journal of Cancer 2005).
• Mechanistically, animal models have provided conflicting results on the effects of the exocrine pancreas. Dr. Aroda emphasized that the real question is discerning which models were appropriate and representative. Liraglutide did not induce pancreatitis in mice, rats, or monkeys at exposure levels greater than 60 times the levels used in humans (Nyborg et al., Diabetes 2012). Meanwhile, in the Pdx-1 Kras mouse (a model primed to develop chronic pancreatitis and pancreatic cancer), 12 weeks of exendin-4 treatment produced expansion of pancreatic duct glands; additionally, premalignant pancreatic intraepithelial lesions (PanINs) were identified in these animals. The FDA’s re-examination of toxicology and carcinogenicity studies has not provided any additional clarity.
• Recent clinical evidence of elevated pancreatitis risk for incretin-based therapies comes from two largely flawed studies. Dr. Aroda relayed criticisms of Dr. Butler’s “cadaver study” (Diabetes 2013) and Dr. Singh’s retrospective insurance claims cohort analysis (JAMA Int Med 2013) that were discussed at the NIH pancreatitis meeting. The three groups from which Dr. Butler collected pancreata (nondiabetic, type 2 diabetes + incretin therapy, and type 2 diabetes without incretin therapy) were very small and poorly matched for background treatment, diabetes duration, age, gender, and BMI. Dr. Aroda referenced Dr. Steven Kahn’s commentary stating that the study also did not control for the effects of prolonged life support. In the Singh et al., study, the treatment group had higher background rates of risk factors for pancreatitis (e.g., obesity, alcohol use, hypertriglyceridemia, tobacco abuse, etc.). She reviewed methodological limitations of observational studies, including inability to control for covariates/confounders or a number of biases (e.g., reporting bias, notoriety bias, channeling bias [preferential prescribing to specific patient populations], selection bias, and reverse causation). For our previous coverage of the diabetes community’s reactions to these studies when they were initially published, please see our Closer Looks at http://www.closeconcerns.com/knowledgebase/r/b03ed6a2 and http://www.closeconcerns.com/knowledgebase/r/70054620.
• The NIH workshop also discussed the FDA’s Adverse Events Reporting System (AERS) database, concluding that additional data mining of AERS is unlikely to shed more light on these safety signals. Given the nature of the voluntary, spontaneous reporting, these data can only be hypothesis generating rather than hypothesis confirming.
• So far, randomized controlled trials (RCTs) of incretin-based therapies have not detected a pancreatitis signal. For liraglutide, Dr. Aroda stated that the rate of pancreatitis has been 1.8 cases/1,000 patient-years of exposure, which is comparable to the background rate in diabetes. Three cases of pancreatic cancer have been reported on liraglutide – one in a patient treated on liraglutide for 152 days, one in a patient treated for seven days and was then diagnosed at stage 4 (suggesting it was not related to the seven days of liraglutide treatment); and one was reported prior to randomization. Similarly, she stated that no significant difference has beenobserved between exenatide and control groups in RCTs for exenatide. In RCTs for sitagliptin, the rate of pancreatitis was found to be 0.05 events/100 patient-years and 0.06 events/100 patient- years in the sitagliptin and comparator groups, respectively.
• Looking forward, Dr. Aroda expressed optimism that the EMA’s Safety Evaluation of Adverse Reactions in Diabetes (SAFEGUARD) to assess the CV, cerebrovascular, renal, and pancreatic safety of currently marketed non-insulin glucose lowering agents will provide better quality clinical data – it will provide up to 240 million patient years of exposure.
• During this presentation, Dr. Aroda also briefly addressed hypoglycemia, CV safety, and thyroid concerns for incretin-based therapies. As we have heard before, she stated that thyroid safety concerns stem from rodent models that do not match humans’ thyroid c-cell response to GLP-1 receptor agonism.

Questions and Answers

Q: That was a beautiful description done in a scholarly way. However if tomorrow morning you have a patient in front of you, and the patient is currently on an incretin and says, Doctor, I have heard a lot of things on television. What is your advice going to be with regard to that patient?

A: I guess I would flip that question to the audience and ask how many would say to stop the drug?

[No one raises hand]

A: How many would try to educate the patient on balancing risk and continue?

[Scattered hands go up]

A: I am overwhelmingly surrounded by scholars much more scholarly than I. I think we need to spend more time with patients educating them on what the comparative benefits and risks are.

Q: I agree with that answer

[Applause]

Q: Can you speak about levels of GLP-1 induced by surgery?

A: GLP-1 levels increase after bariatric surgery, and we have not seen an increase in pancreatic cancer. Obesity surgery is an area where you see decrease in these cancers.

Q: We can capture numbers of acute pancreatitis. My bigger question is, what about numbers of people with asymptomatic pancreatitis that we won’t know about for a long time? Studies may not show us that we have increased risk of pancreatic cancer until we have a whole lot of people already at greater risk. That’s my concern with short-term risk.

A: Do you mean chronic pancreatitis?

Q: Yes.

A: Some have asked whether it is worth it to look at enzyme fluctuations. There hasn’t been good correlation. All studies have been monitored by Data Safety Monitoring Boards, so if there were signals we would be alerted.

Corporate Symposium: Impacting Type 2 Diabetes and Optimizing Patient Outcomes with GLP-1 Agonists (Sponsored by Novo Nordisk)

GLP-1 Receptor Agonists in Patients with More Established Diabetes

Lawrence Blonde, MD (Ochsner Medical Center, New Orleans, LA)

Dr. Lawrence Blonde, co-author of the new AACE guidelines, maintained that minimizing the risk and magnitude of hypoglycemia and/or weight gain should be a high priority for patients with type 2 diabetes. He presented data demonstrating that hypoglycemia may be more common and have a more profound impact on patient outcomes than providers might expect: a patient survey found that at least 60% of type 2 diabetes patients experience at least one episode of non-severe hypoglycemia/month, with 35% experiencing episodes once-daily to once-weekly (Brod M et al., Value Health 2011); in addition, hypoglycemia is associated with increasing healthcare costs and reduced long-term survival in type 2 diabetes (Williams SA, et al., J Diab Complications 2012; Hsu et al., Diabetes Care 2013). Dr. Blonde presented GLP-1 agonists and DPP-4 inhibitors as effective options for avoiding hypoglycemia and weight gain, highlighting their prominence in recent algorithms (e.g., ADA/EASD 2012 and AACE 2013). He presented data demonstrating that adding a GLP-1 agonist to metformin provides better glucose control and greater weight loss than adding a DPP-4 inhibitor. In addition, he argued that GLP- 1 agonists may “prime” a patient for eventual progression to insulin (he noted that most patients with type 2 diabetes will end up needing insulin). In DeVries et al. (Diabetes Care 2012), adding basal insulin detemir when people failed to achieve A1c goals on metformin plus liraglutide provided a further 0.5% improvement in A1c without causing patients to regain the weight they had lost by adding liraglutide to metformin. He interpreted this to mean that optimizing one’s own endogenous glucose excretion prior to adding exogenous insulin may have a benefit.

• Notably, Dr. Blonde referenced results from a poster he presented earlier today suggesting that A1c reduction derived from Bydureon was independent of weight lost. He emphasized that even if patients do not lose weight on GLP-1 agonist therapy, they can still experience tremendous glycemic benefits.

 

Using GLP-1 Receptor Agonists in Early-Stage Diabetes and Special Circumstances

Michael Nauck, MD, PhD (Diabeteszentrum Bad Lauterberg, Harz, Germany)

To begin the symposium, Dr. Michael Nauck discussed the use of GLP-1 agonists in the treatment of early-stage type 2 diabetes. After discussing incretin mimetics’ mechanism of action, he began discussing the efficacy of GLP-1 agonists compared to placebo, metformin, and DPP-4 inhibitors. Compared to (or in addition to) the three alternatives, GLP-1 agonists led to equal or better glycemic control and weight loss. He added that some studies even suggest that GLP-1 agonists are slightly more effective than basal insulins and have better effects on weight. Dr. Nauck noted that the most recent ADA/EASD treatment algorithm facilitates shared decision-making, allowing healthcare providers narrow down the list of therapy options depending on patients’ preferences and limitations. He used a case study to illustrate that GLP-1 agonists are optimal for those seeking to avoid weight gain or hypoglycemia. Dr. Nauck confronted concerns over GLP-1 agonists and thyroid tumors, noting that concerns arose from data from rodent models, which process GLP-1 differently in the thyroid. He cited a year-long human trial that showed that high doses of liraglutide did not lead to a significant increase in thyroid tumors. Dr. Nauck also allayed concerns over incretins and pancreatitis. He ended by proposing that GLP-1 agonists may have potential in individuals with only impaired glucose tolerance (IGT), citing data that demonstrated that GLP-1 administration improved insulin response in patients with IGT.

Questions and Answers

Q: Given the concerns you mentioned, is it still appropriate to use GLP-1 agonists and DPP- 4 inhibitors earlier in the course of diabetes?

A: Those familiar with these studies say that there is no need to change the clinical practice regarding these drugs, and that they should be used as they have been. Psychologically speaking, the only reason to stop therapy is if the patient feels uncomfortable about using them. Otherwise, I would not be comfortable recommending that patients go off these medications.

Dr. Vivian Fonseca: I’d like to remind audience that these agents are not approved for use in prediabetes.

 

Treatment Intensification to Improve Glycemic Control While Minimizing Weight Gain and Hypoglycemia: Use of GLP-1 Receptor Agonists

Vivian Fonseca, MD (Tulane University Health Sciences Center, New Orleans, LA)

In his presentation, Dr. Vivian Fonseca discussed the treatment of well-established type 2 diabetes patients. He noted that many such patients are already on basal insulin, and that if more intensive therapy is needed, providers generally need to choose between initiating the patient on a prandial insulin or adding another agent. Dr. Fonseca noted that prandial insulins require more careful management, are less convenient, and come with an increased risk of hypoglycemia. In contrast, he suggested that incretin mimetics can be effective and safe when added to basal insulins. He argued that basal insulin and GLP-1 agonists have complementary actions, with the former contributing more to fasting and nocturnal control and the latter acting more postprandially. He discussed experimental findings showing that combined therapy with a basal insulin and GLP-1 agonist results in better glycemic control (especially postprandially) and more weight loss. Notably, some companies are starting to explore head to head comparisons between adding a GLP-1 agonist or prandial insulin on top of basal insulin (Dr. Fonseca mentioned GSK’s albiglutide). He added that DPP-4 inhibitor/basal insulin combination therapy also resulted in improvements, albeit with less efficacy and weight loss than the GLP-1/basal insulin combination. Dr. Fonseca concluded that the joint use of incretin-based therapy and basal insulin is a potent combination, able to significantly and safely improve glycemic control. Looking to the future, he expressed optimism about co-formulations of GLP-1 agonists and basal insulins. He cited an early study demonstrating that IDegLira (a combination of insulin degludec and liraglutide) reduced A1c and provided 2 kg more weight loss than the insulin component alone, and expressed hope that future studies will confirm these promising findings.

 

Panel Discussion

Michael Nauck, MD, PhD (Diabetes Zentrum bad Lauterberg, Harz, Germany) Lawrence Blonde, MD (Ochsner Medical Center, New Orleans, LA), Vivian Fonseca, MD (Tulane University, New Orleans, LA)

Dr. Fonseca: Monitoring lipase and amylase – do either of you recommend that in clinical practice?

Dr. Nauck: Not at all. We now know a significant proportion of patients with type 2 diabetes have elevated lipase if you just do a random blood sampling. That does not predict pancreatitis. One must know that the positive predictive value of an elevated lipase concentration is really restricted to the ER setting when severe abdominal pain appears. In that situation you can count on a sever lipase concentration to diagnose. In the asymptomatic patient, it doesn’t tell you anything

Q: Is GLP-1 expressed in other tissues in humans?

Dr. Nauck: It certainly is. Mainly the brain and nervous system are equipped with GLP-1 receptors. Some important metabolic organs apparently are not, such as the liver, muscle, and adipose tissue. There have been occasional reports of binding sites if you look at radioactively labeled material, but no GLP-1 receptor has ever been identified in this tissue.

Dr. Fonseca: Larry, there are a lot of outcomes trials going on, all looking at the same composite endpoint for MACE, etc. Should we be a bit more adventurous in this area? Are there novel endpoints we could explore? What else would you like to see, especially now that SAVOR TIMI reported a neutral outcome?

Dr. Blonde: I’m hopeful these trials will in fact show whether or not there is an increased risk of pancreatitis. Other safety endpoints including pancreatitis are being adjudicated or collected. As SAVOR is reported, we may learn about these data. If individual trials are not enough, then we’ll pool data. I think we will learn more about safety in a rapid period of time than we have before.

Dr. Fonseca: The ADA has asked for patient level data for all of these trials to be pooled, or else individual trials will never show a signal because it is so rare. If there is an increase, what will the level be? From the data we have now, we can’t tell.

Dr. Blonde: I agree completely. In these discussions we lose any discussion of benefits vs. risk. There is a risk to not treating a patient. The absolute risk in the Singh study was relatively low, whether or not it was done correctly.

Q: In clinical trials comparing GLP-1 receptor agonists and insulin, what was the mean insulin dose?

Dr. Fonseca: In most studies I recall around 25-30 units.

Q: Dr. Nauck do you know what predisposes somebody to respond well to treatment? Are there genetic markers to look at?

Dr. Nauck: There is a publication on a variant of the GLP-1 receptor that at least determines response to an experimental administration of GLP-1. It could well be that such polymorphisms exist, but there’s no test you can order so it’s something for the future. It’s not applicable today.

Dr. Blonde: And as I showed before, there’s more consistency for A1c reduction than weight loss, though in that study most people had a reduction in both A1c and weight.

Q: How should you consider these agents for children vs. adults?

Dr. Nauck: Small trials have reported, and it works. But safety considerations are different from treating adult populations, so you need a large database to judge safety. The interference of growth in adolescents means we don’t have the data right now.

Q: A point of clarification on the thyroid issue – have there been primate studies of C-cell proliferation?

Dr. Nauck: There is some discussion. Some groups use antibodies or ligands for GLP-1 receptors that have been found to be not specific by other groups. There may be erroneous data out there claiming the existence of GLP-1 receptors where there are none. We’ll have to see with better methods. Regarding thyroid and primates, we know Novo Nordisk has published a large study looking at the pancreas. I would be surprised if they have not looked at the thyroid as well.

Dr. Fonseca: You did mention prediabetes, though it is obviously not an approved use right now. Is a trial being done? What kind of endpoints would be used in such a trial?

Dr. Nauck: The kind of endpoint is under discussion. Is it just the prevention of diabetes? Is that sufficient to support clinical use of these agents? The answer is probably no. So you want so need long-term benefit to be proven. Also the bar is raised pretty high with regards to safety because basically then you would be treating “healthy” people.

Dr. Fonseca: But of course many of these people are obese, and you have an obesity trial ongoing.

Dr. Nauck: In the end it’s about the prevention of diseases that are associated with high glucose. So diabetic complications. You would have to do extremely large trials and observe patients from early stages of prediabetes to stages where you expect diabetes complications to draw conclusions that the event rate would certainly be much lower.

 

Corporate Symposium: GLP-1 in Focus: The Knowledge Challenge (Sponsored by Novo Nordisk)

Charles Reasner II, MD (University of Texas Health Science Center, San Antonio, TX); Melissa Magwire, RN, CDE (Shawnee Mission Medical Center, Shawnee, KS)

In this highly interactive corporate symposium, Dr. Charles Reasner and Ms. Melissa Magwire discussed the use of GLP-1 agonists for the treatment of type 2 diabetes in a game-show format, complete with Jeopardy music and the occasional audience spotlight. They focused on issues such as the incretin effect, differences between GLP-1 agonists and DPP-4 inhibitors, and the role of glucagon in the pathophysiology of type 2 diabetes. In the discussion of Novo Nordisk’s Victoza, they discussed clinical trials showing that Victoza was more effective than sitagliptin at reducing patient A1c levels and improving the proinsulin/insulin ratio, while also helping patients achieve greater weight loss compared to sitagliptin. They noted that nausea, Victoza’s most common side effect, generally decreases over time. Dr. Reasner attempted to alleviate certain safety concerns about Victoza therapy, such as the thyroid cancer tumor increase seen in rodent studies. He acknowledged that other concerns, such as the potential association between GLP-1 agonists and pancreatitis highlighted at the recent NIDDK summit, are still being studied. During the symposium, the audience of ~400 was divided into four teams that competed with each other – though our team (Team 4) had the initial lead, Team 1 ultimately won the day’s event. Given the changes in pharma marketing guidelines in recent years, Dr. Reasner congratulated the victors jokingly, “You win absolutely nothing!”

 

Diabetes Mixed Bag

Melissa Magwire, RN, CDE (Shawnee Mission Medical Center, Shawnee, KS)

Ms. Melissa Magwire reviewed the incretin effect and the key defects associated with type 2 diabetes. She explained the incretin effect, noting that the insulin response is enhanced after an oral glucose load versus intravenous glucose (Nauck et al., Diabetologia 1985). Subsequently, Ms. Magwire highlighted eight core defects in type 2 diabetes – the infamous “ominous octet” – decreased insulin secretion, decreased incretin effect, increased glucagon, increased glucose absorption and production, decreased glucose uptake, neurotransmitter dysfunction, increased lipolysis and inflammation, and increased hepatic glucose production (DeFronzo et al., Diabetes 2009). In addition, Ms. Magwire emphasized that the brain is essential in glucose metabolism, as it processes information from neural, hormonal, and nutrient signals, and modulates glucose output in the liver and glucose uptake in peripheral tissues. Finally, she noted that excess weight contributes to the development of type 2 diabetes: 1) insulin resistance is related to abnormal partitioning of fat among adipose, hepatic, muscle, and pancreatic tissues; 2) secretion of excess free fatty acids and proinflammatory cytokines may damage beta cells; and 3) increased levels of free fatty acids decrease glucose uptake and tolerance.

 

What's the Buzz on Glucagon?

Charles Reasner II, MD (University of Texas Health Sciences Center, San Antonio, TX); Melissa Magwire, RN, CDE (Shawnee Mission Medical Center, Shawnee, KS)

Dr. Charles Reasner and Ms. Melissa Magwire reviewed the role of glucagon in type 2 diabetes and the impact of GLP-1 on decreasing glucagon secretion. Whereas people with type 2 diabetes have a ~25-50% deficiency in beta cell mass compared to individuals without diabetes (we’d love to see an end, by the way to the “healthy individuals” moniker, since so many with diabetes are very healthy), they have the same alpha cell (glucagon-producing cells) mass as healthy individuals. Thus, people with type 2 diabetes have a relative surplus of glucagon. Dr. Reasner and Ms. Maguire presented data suggesting that this hyperglucagonemia in type 2 diabetes is responsible for about 50% of the excess postprandial increase in glucose levels (with lack of sufficient insulin accounting for the other 50%; Shah et al., J Clin Endocrin Metab 2000). Thus, they portrayed hyperglucagonemia as a significant underlying defect of type 2 diabetes. Finally, they discussed how GLP-1 addresses hyperglucagonemia by inhibiting glucagon secretion in a glucose-dependent manner: over a six-hour post-meal GLP-1 infusion, GLP-1 caused insulin to increase and glucagon to decrease until blood glucose levels reached about 100 mg/dl, at which point the insulin and glucagon secretion levels returned to baseline despite continued GLP-1 infusion (Nauck et al., Diabetologia 1993).

 

GLP-1 and DPP-4: What's the Difference?

Melissa Magwire, RN, CDE (Shawnee Mission Medical Center, Shawnee, KS)

Ms. Melissa Magwire discussed the differences between GLP-1 and DPP-4, emphasizing the therapeutic effects GLP-1 agonism provides beyond DPP-4 inhibition. To start, Ms. Magwire highlighted that GLP-1 is a hormone that directly stimulates insulin release from the beta cell in a glucose-dependent manner, while DPP-4 is an enzyme responsible for degrading native GLP-1 in the body. She noted that both GLP- 1 and GIP are nutrient-stimulated intestinal incretin hormone, with GLP-1 having a much more robust effect on glucagon suppression than GIP (Vilsbøll et al., JCEM 2003; Nauck et al., JCEM 1993). She then emphasized that though the insulin response to physiologic levels of native GLP-1 is severely impaired in type 2 diabetes, pharmacologic levels can elicit a much more robust and improved response. To conclude, Ms. Magwire commented that though both physiologic GLP-1 levels for people without diabetes, and DPP-4 inhibition can bring about increased insulin secretion and decreased glucagon secretion, only GLP-1 receptor activation confers the full pharmacologic effects of delayed gastric emptying, increased satiety, decreased energy intake, decreased weight, and increased nausea.

 

Introduction to Victoza and Safety

Charles Reasner II, MD (University of Texas Health Science Center, San Antonio, TX)

In this section, the presenters focused on Novo Nordisk’s GLP-1 agonist Victoza (liraglutide). Dr. Reasner attempted to allay concerns about potentially increased thyroid tumor risk by noting that the association was only found in rodent studies. A number of differences between mice and humans exist that led Dr. Reasner to suggest that thyroid tumor risk may not be a concern for humans: the target for GLP-1 is the thyroid, not the pancreas, in mice; GLP-1 receptor stimulation causes release of calcitonin, which rodents need for calcium absorption and without which they will die; humans do not need calcitonin to absorb calcium, and don’t exhibit an increase in calcitonin when administered GLP-1 agonists. Dr. Reasner did acknowledge that the potential connection between GLP-1 agonists and pancreatitis will likely come up frequently during the conference. He echoed the sentiment that no causal relationship has been established. The presenters then discussed the positive results of Victoza clinical trials, namely that Victoza is twice as effective as sitagliptin (100 mg) in helping type 2 diabetes patients achieve A1c levels under 7% (the ADA recommendation), and that it leads to weight loss (approximately 6 lbs lost compared to 2 lbs lost on sitagliptin). Of note, Victoza was able to reduce glucose with a concomitant decrease in the ratio of proinsulin to insulin. A high proinsulin/insulin ratio is indicative of pancreatic stress and suggests that the pancreas is producing more proinsulin than it has the capacity to appropriately cleave. Thus, a decrease in the proinsulin/insulin ratio suggests that Victoza’s glycemic effects are not solely attributable to increasing insulin production. Finally, the presenters also noted that the nausea associated with Victoza use decreases with time; we would also point out that it is easier for patients to manage, albeit off-label, since there are many doses one can take outside the recommended three.

 

Getting Started with Victoza

Melissa Magwire, RN, CDE (Shawnee Mission Medical Center, Shawnee, KS)

During this portion of the symposium, Ms. Melissa Magwire encouraged audience members to change the injection conversation with their patients (highlighting Victoza’s user-friendly pen), emphasized that there are generally no dose adjustment requirements for special populations, and stressed that Victoza can be taken any time of day. Subsequently, she provided a brief overview of the ADA/EASD position statement and AACE diabetes management algorithm, noting that incretin-based therapies, in particular GLP-1 agonists, are the preferred second-line option following metformin in the AACE algorithm.

• Ms. Magwire encouraged audience members to change the injection conversation, noting that most patients surveyed are open to self-injection when provided an explanation of the clinical benefits. In an online market research survey of US adults with type 2 diabetes (n=797), 80% were open to self-injection (data on file at Novo Nordisk, 2011). In a randomized, multicenter, open-label single visit study, 90% of participants (n=90) considered the Victoza pen easy to learn how to use (data on file at Novo Nordisk). In addition, Ms. Magwire emphasized that most experience little or no pain when using a 32-gauge, 6 mm needle for Victoza injections. She noted that the Victoza pen can be used with NovoTwist needles, which are designed with user-friendly features: a “Just Twist” injection needle, quick and easy attachment, and an audible and tactile confirmation click to signal correct attachment.

 

Panel Discussion

Charles Reasner II, MD (University of Texas Health Sciences Center, San Antonio, TX); Melissa Magwire, RN, CDE (Shawnee Mission Medical Center, Shawnee, KS)

Q: Have you observed decreased nausea when taken right before bedtime?

Ms. Magwire: Studies didn’t look at timing, but if you think about the mechanism of action, overeating with delayed gastric emptying may exacerbate nausea, so we tend in our practice to dose at bedtime to minimize that. You can educate patients, if they dose first thing in morning, to listen to their bodies and not overeat. That will help with nausea. If you know someone has a tendency to overeat, potentially bedtime is a better choice.

Q: Are there GLP-1 receptors on alpha cells, or is glucagon suppression an indirect effect?

Dr. Reasner: You can find both schools of thought. Some feel that there are indirect effects, while others think there are receptors on the alpha cell.

Q: Can you use DPP-4 and GLP-1 together?

Ms. Magwire: If you think about the mechanism of action, it doesn’t make a lot of sense. You don’t get that much additional response from adding a DPP-4 inhibitor. If you add on the GLP-1 agonist, there’s other things like appetite suppression, potential decrease in weight, and a satiety effect – you’ll get more of that with a GLP-1 so it doesn’t make a lot of sense to use the two in combination.

Q: Can you talk about the latest safety issues with incretins and DPP-4 inhibitors?

Dr. Reasner: My understanding of the safety issues revolves mainly around pancreatitis and pancreatic cancer. If you look at clinical trials with Victoza, there are more patients that develop pancreatitis with Victoza than with placebo. However, the number of cases did not exceed what you would have predicted with historical controls. The increase is small. I think the big concern recently has been over a study done by Dr. Peter Butler. He is a pathologist who specializes in studying pancreases. He looked at 34 individuals who died of various causes, and of these, 20 had diabetes. Of the 20 who had diabetes, seven were on sitagliptin, and one was on Byetta. When he looked at the histology of patients with diabetes on incretin-based therapies, he found evidence of increased inflammation. It is not clear to me how extensive the inflammation was, and we don’t have any data on the patients while they were alive complaining about pain. I don’t know if they had other causes of pancreatitis. Also, there was evidence of hyperplasia in some of the samples. Some suggest that it is a good thing, whereas others feel that it may be a precursor to cancer. There is also evidence of ductal metaplasia. Some think it was a common finding, and some think it could be a precursor of cancer. There was no cancer detected, but there were suggestions that hyperplasia and metaplasia may be precursors. This has gotten a lot of interest. The FDA has become interested, and there was an NIDDK symposium last week (editor’s note – see our coverage at http://www.closeconcerns.com/knowledgebase/r/8326d0dd and http://closeconcerns.com/knowledgebase/r/e90a832d). I’m sure we’ll hear more about this in the future.

Q: What about the combination of premixed insulin and Victoza?

Ms. Magwire: Currently the package insert is for combination with basal insulin only. At this point it’s not an indication. We can’t comment more than that.

Q: Do sulfonylureas increase glucagon levels?

Dr. Reasner: Not to my knowledge. If anything it should suppress glucagon. Anything that increases insulin should decrease glucagon levels.

Q: What is the main defect in type 2 diabetes – a decrease in production of GLP-1, or GLP-1 resistance?

Dr. Reasner: In an early study, it was shown that those with type 2 diabetes had a lower post-meal GLP-1 response than those without diabetes. Initially the thinking was that once you have diabetes, you have a decrease in GLP-1 secretion, which was likely to track with beta cell function (just like we see a decrease in insulin secretion). In more recent studies, it has been shown that most people with type 2 diabetes have normal levels of GLP-1. If you measure production and it’s normal, and it’s binding to its receptors normally, but the insulin response is abnormal, then there is a post-receptor defect in the pancreatic beta cell. You don’t get the insulin secretion you would expect. People with type 2 diabetes are resistant to GLP-1 the same way they are resistant to insulin.

Q: Comparative difference between GLP-1 and DPP-4 inhibitors on glucagon?

Dr. Reasner: I think the best study that was done was by Michael Nauck. Glucagon suppression at physiologic levels of GLP-1 it was about 38% and at a pharmacologic level it was about 55%. So that’s pretty similar to DPP-4 inhibitors vs. GLP-1 agonists.

Product Theater

Choosing a Therapy for the Individual Patient - Takeda's Powerful Portfolio of Treatments for Type 2 Diabetes (Sponsored by Takeda)

Eugenio Cersosimo, MD, PhD (University of Texas Health Science Center, San Antonio, TX)

In this lightly attended product theater, Dr. Eugenio Cersosimo reviewed the efficacy, safety, dosing, and administration of Nesina (alogliptin), Kazano (alogliptin/metformin), and Oseni (alogliptin/pioglitazone). He emphasized that alogliptin is effective as monotherapy, in dual and triple therapy, and in fixed-dose combinations, and that Nesina, Kazano, and Oseni all address multiple defects of type 2 diabetes. In addition, he highlighted that the Takeda Diabetes Advantage Program offers support programs for patients; eligible patients who are enrolled in the program pay no more than $4 per month for up to 12 months for Nesina, Kazano, or Oseni.

SGLT-2 Inhibitors

Oral Sessions: On the Horizon – Selective Sodium Glucose Co-Transporter Inhibition

LX2761, an SGLT-1 Inhibitor Restricted to the Intestine, Improves Glycemic Control in Mice (240-OR)

David Powell, MD (Lexicon Pharmaceuticals, The Woodlands, TX)

Dr. David Powell presented the results from a series of mouse studies characterizing the effects of the SGLT-1 inhibitor LX2761. In these mouse studies, Lexicon demonstrated: 1) LX2761 has poor systemic exposure, and causes little, if any, increase in urinary glucose excretion (as assessed by oral gavage to adult C57 mice); 2) LX2761 delays intestinal glucose absorption and increases intestinal GLP-1 release; 3) LX2761 acts synergistically with a DPP-4 inhibitor (sitagliptin) to increase postprandial levels of GLP-1; 4) LX2761 decreases postprandial excursions 15 hours after delivery to healthy mice fed ad libitum; and 5) LX2761 improves glycemic control in the KKAy mouse model of type 2 diabetes and in adult male C57 mice with STZ-induced diabetes. In conclusion, Dr. Powell stated that selective inhibition of SGLT-1 can improve glycemic control in mice, and as such, further studies are warranted to test whether SGLT-1 inhibition can improve glycemic control in people with diabetes.

Questions and Answers

Q: How do you speculate this drug would compare to alpha-glucosidase inhibitors?

A: We’re averaging once a day, and don’t have to give the drug with meals. I think with those drugs you have to give them with meals. Those don’t block absorption of glucose, but rather, disaccharides. The gut is fairly good at absorbing glucose fermented to short-chain fatty acids, but complex carb metabolites are produced with alpha-glucosidase inhibitors, so I wonder whether that is associated with GI side effects.

Q: Do you expect to dose this drug once daily in humans?

A: Based on what we’re seeing in mice, once-daily dosing improved glycemic control. That’s what we’re hoping for.

Dr. Ralph DeFronzo (University of Texas Health Science Center, San Antonio, TX): This is a very important study. Although people have shied away from combining SGLT-1 and SGLT-2, I actually strongly advocated that pharmaceutical companies think about this. As long as you do not cause GI symptoms, this could be an added benefit. Having said that, you didn’t tell us if the rats had GI side effects.

A: We got interested in this because LX4211 inhibits SGLT-1, yet there were no GI side effects seen relative to placebo. So, it made us realize there’s a therapeutic window. That’s why LX2761 was developed. Are there more side effects when you target the gut? The bottom line is, yes, if you give enough, you will cause loose stools. It’s dependent on the amount. We do see a therapeutic window of maybe ten fold between the dose that gives a 50% decrease in glucose excursion and the dose that gives you loose stool.

 

The Impact of LX4211, a Dual Inhibitor of Sodium Glucose Transporters SGLT-1 and SGLT-2, on Blood Pressure in Patients with Type 2 Diabetes (241-OR)

Pablo Lapuerta, MD (Lexicon Pharmaceuticals, The Woodlands, TX)

In this 12-week dose-ranging study (n=299), patients with inadequately controlled type 2 diabetes taking metformin were assigned to receive placebo or one of four doses of LX4211 – 75 mg QD, 200 mg QD, 200 mg BID, or 400 mg QD. Patients in the study were 18-75 years of age, had BMI ≤45 kg/m2, and A1c between 7.0-10.5%; at baseline, patients were on average 56 years old, with A1c of 8.1%, BMI of 33 kg/m2, and blood pressure of 125/79 mmHg. Both patients with and without hypertension were allowed in the study, and there were no restrictions on antihypertensive medication use. LX4211 reduced systolic blood pressure (SBP) in a dose-dependent manner (estimating from the chart presented, the 75 mg QD, 200 mg QD, 200 mg BID, and 400 mg QD doses reduced SBP approximately 0.1, 4.0, 4.5, and 6.0 mmHg, respectively, while SBP lowered approximately 0.3 mmHg with placebo). For the 400 mg dose, patients with elevated SBP (≥130 mmHg) experienced an average reduction of 14 mmHg versus placebo (p=0.002), while those with normal SBP (<130 mmHg) experienced minimal change in SBP – Dr. Pablo Lapuerta noted that these decreases were consistent with approved antihypertensive therapies. In contrast, there were no dose-dependent changes in diastolic blood pressure (DBP). For the trial’s A1c- lowering efficacy results, please see our AHA 2012 coverage at http://close.cx/AHA2012Day4.

• Dr. Lapuerta noted several strengths and limitations of the study. In terms of strengths, the study had precise blood pressure data, demonstrated a clear dose response, hadclear separation between treatment and placebo, used trough measures of blood pressure, and demonstrated systolic-blood-pressure-lowering efficacy consistent with that of approved antihypertensive agents. As for limitations, the study had a limited sample size, many patients already had good blood pressure control at baseline, the study duration was short, there were no blood pressure measures taken at the peak, and GLP-1 was not measured.

 

Plasma Glucose Reduction with the SGLT-2 Inhibitor, Dapagliflozin, Improves Insulin Sensitivity and Insulin Secretion in T2DM (242-OR)

Muhammad Abdul-Ghani, MD, PhD (University of Texas Health Science Center, San Antonio, TX)

Dr. Muhammad Abdul-Ghani presented results from a study examining the effects lowering plasma glucose concentrations with dapagliflozin has on insulin sensitivity and insulin secretion in patients with type 2 diabetes. In the study, patients (n=18; baseline BMI 31.1 kg/m2, baseline A1c 8.2%) were randomized to dapagliflozin or placebo in a 2:1 fashion. Five days and three days prior to randomization (Days -5 and -3), respectively, subjects received a 75-gram OGTT and an insulin clamp 3H-glucose infusion. Following randomization (Day 0), subjects were admitted to the clinical research center (on Day 1) for measurement of basal hepatic glucose production with 3H-glucose on Days 1-3. Subjects were administered dapagliflozin from Day 2 through Day 15 of the study, and were given a repeat OGTT on Day 14 and insulin clamp 3H-glucose infusion on Day 15. In the study, lowering the plasma glucose concentration with dapagliflozin improved insulin-stimulated tissue glucose uptake (insulin sensitivity) and glucose-stimulated insulin secretion. However, the glucosuria produced by the inhibition of SGLT-2 stimulated a compensatory increase in (hepatic) glucose production, which attenuated the clinical efficacy of dapagliflozin.

Questions and Answers

Q: What do you suppose the signal for glucagon secretion in your study was?

A: We don’t know. We’re searching for that now.

Q: Did you do any follow-up studies where you gave a DPP-4 inhibitor?

A: We don’t have results yet, but that’s the obvious thing to look into.

Q: Are there other drug candidates you may consider combining with SGLT-2 to knock down hepatic glucose production?

A: DPP-4 inhibitors are the obvious option, but I am a little bit skeptical about their efficacy. GLP-1 agonists may be more efficacious, but I am still skeptical. Maybe glucagon antagonists, but I’m not sure if they’d be good for clinical use.

Q: What about metformin?

A: Subjects were already receiving metformin. Also, metformin doesn’t affect glucagon. This study exposes the interaction between the liver and kidney, and their crosstalk in glucose homeostasis. I personally think they are related through the central nervous system. When we are giving dapagliflozin, we are forcing the kidney to excrete glucose, and producing a difference in glucose between the renal artery and the renal vein. It could possibly be that the brain is detecting the difference, and acting directly on the liver to compensate. For example, someone with normal glucose tolerance would be at the immediate risk of hypoglycemia. The brain has to react in order to prevent hypoglycemia. This compensatory response is problematic in people with type 2 diabetes, as they are already hyperglycemic.

 

LX4211, A Dual SGLT-1/SGLT-2 Inhibitor, Decreases Body Weight and Triglycerides in Patients with Type 2 Diabetes Mellitus and Elevated Baseline Values (243-OR)

Brian Zambrowicz, PhD (Lexicon Pharmaceuticals, The Woodlands, TX)

In this 12-week dose-ranging study (n=299), patients with inadequately controlled type 2 diabetes taking metformin were assigned to receive placebo or one of four doses of LX4211 – 75 mg QD, 200 mg QD, 200 mg BID, or 400 mg QD. Patients in the study were 18-75 years of age, had BMI ≤45 kg/m2, and A1c between 7.0-10.5%; at baseline, patients were on average 56 years old, with A1c of 8.1%, BMI of 33 kg/m2, and blood pressure of 125/79 mmHg. In the subgroup of patients with BMI ≥30 kg/m2 at baseline, patients in the 75 mg QD, 200 mg QD, 200 mg QID, and 400 mg QD LX4211 lost an average 0.9 kg (2.0 lb), 1.8 kg (4.1 lbs), 2.9 kg (6.4 lbs), and 2.0 kg (4.3 lbs), while patients on placebo lost an average 0.4 kg (1.0 lb) (the 200 mg QD, 200 mg BID, and 400 mg QD were statistically significant versus placebo [p<0.001]). In the subgroup of patients with elevated triglycerides (200-500 mg/dl) at baseline, LX4211 treatment resulted in significant reductions from baseline in the 75 mg QD, 200 mg QD, and 400 mg QD arms (67.6 mg/dl, 49.0 mg/dl, and 81.8 mg/dl, respectively; p<0.05). Side effects appeared balanced with placebo; there were no major increases in GI side effects such as diarrhea, nausea, vomiting, and constipation beyond placebo. For the trial’s A1c-lowering efficacy results, please see our AHA coverage at http://close.cx/AHA2012Day4.

Questions and Answers

Q: In your study, there were relatively fewer mycotic infections with your dual SGLT- 1/SGLT-2 inhibitor than with SGLT-2 alone. Can you explain the reason behind that?

A: One of the things I did mention was that LX4211 had relatively low urinary glucose excretion. I think there is a mechanistic reason for that. The amount of glucose you’re going to spill is dependent on how well you inhibit SGLT-2, but you also have to look at how much blood glucose can be filtered. SGLT-1 addresses mainly postprandial glucose. If you lower postprandial glucose all meals of the day, there is less glucose to filter in the blood. The maximum glucose excretion we’ve seen in trials is 45 grams in a day with LX4211, whereas [other SGLT-2 inhibitors had maximum glucose excretion of 60-70 grams] over 24 hours.

Q: Can you elaborate on what happened to LDL? Triglycerides went down, but what happened to LDL, and how does it compare to SGLT-2 inhibitors?

A: There was no significant increase from baseline; however, I would say there’s probably a trend there. The increase is quite small. It may become significant in larger studies. I would say that the effect is clearly SGLT-2 dependent. Though we don’t understand why, we think it may be related to the level of urinary glucose excretion as well.

Q: Did you look at leptin levels in the study?

A: Not in this study. But, we are planning to look at that. There’s a cascade, because short-chain fatty acids are absorbed fermentation of glucose in the colon, and when they reach the bloodstream they can trigger the release of leptin.

 

Combined HbaA1c and Weight Reduction is Achieved More Frequently with Add-On Dapagliflozin Than Add-On Glipizide in Patients with Type 2 Diabetes Inadequately Controlled on Metformin (236-OR)

Katja Rohwedder, MD (AstraZeneca, Cambridge, UK)

Dr. Katja Rohwedder discussed a post-hoc analysis of a 52-week non-inferiority trial comparing dapagliflozin (2.5-10 mg/day) to glipizide (5-20 mg/day) as adjuncts to metformin (≥1,500 mg/day) in ~800 type 2 patients (at baseline, mean age of 58-59 years, diabetes duration of 6-7 years, weight of 88 kg [194 lbs], and A1c of 7.7%). At 52 weeks, 78% of participants remained in the study and drop-out rates were comparable between the two treatment groups. The two drugs provided the same A1c reduction (0.52%); furthermore, a similar percentage of participants in each arm experienced an improvement in A1c (75% with dapagliflozin vs. 74% with glipizide). Not surprisingly, more patients on dapagliflozin exhibited weight loss (83%) than those on glipizide (27%). An A1c reduction coupled with weight loss was observed in 67% of the dapagliflozin group vs. 21% of the glipizide group (difference of 46 percentage points; 95% CI: 39-52); furthermore, 31% of those on dapagliflozin achieved an A1c reduction ≥0.5% with a weight loss ≥3 kg (7 lbs), compared to only 4% of those on glipizide (difference of 27 percentage points; 95% CI: 22-32). While response to drug therapy did not differ by baseline weight, disease duration, or gender, the patients who achieved A1c reductions ≥0.5% had higher A1c levels at baseline (this is consistent with previous studies). Regarding safety data, dapagliflozin was associated with less hypoglycemia compared to glipizide; however, both urinary tract infections and genital infections were more commonly observed with dapagliflozin, though Dr. Rohwedder commented that these events rarely led to study discontinuation.

Efficacy Data
Drug	A1c Reduction	Weight Reduction	Both A1c & Weight Reduction	A1c Reduction >0.5% & Weight Reduction >3 kg (7 lbs)
Dapagliflozin	74.7%	83.4%	66.9%	30.8%
Glipizide	73.8%	26.8%	21.3%	3.8%
Safety Data
Drug	No Hypoglycemia	Genital Infections	Urinary Tract Infections
Dapagliflozin	96.4%	10.8%	8.6%
Glipizide	59.6%	1.7%	5.4%

Questions and Answers

All questions were asked by the session moderator, Dr. Ralph DeFronzo.

Q: Is it time to get rid of SFUs and go to drugs that really work without causing safety issues?

A: I hope so.

Q: Did you look at glucose excretion in the urine and was it related to weight loss in either group?

A: No, we haven’t looked at that specific analysis.

Q: Did you measure insulin levels, since insulin is related to weight gain and hypoglycemia?

A: We have the information on fasting insulin levels and we have done OGTTs in a subgroup of patients, but we haven’t looked at the specific correlation. It’s a good idea.

Q: What was the definition of hypoglycemia and severe hypoglycemia?

A: Major hypoglycemia episodes were defined as less than 3 mmol/l (54 mg/dl) or if the investigator saw symptoms. It was only reported in the glipizide arm, in three patients. In general, every blood glucose measurement below 3.5 mmol (63 mg/dl) was considered hypoglycemia. Investigators could also report cases of hypoglycemia if they saw symptoms.

Comment: I would say this is a pretty clear-cut distinction between oral agents in terms of weight gain, an important side effect that we’re all concerned with in diabetes.

 

Canagliflozin (CANA) Compared with Sitagliptin (SITA) in Subjectswith Type 2 Diabetes Mellitus (T2DM) On Metformin (MET) Over 52 Weeks (238-OR)

Fernando Lavalle González, MD (Universidad Autonoma de Nuevo Leon, Nuevo Leon, Mexico)

Dr. Fernando Lavalle González presented data showing that canagliflozin provided greater reductions in A1c and in weight compared to sitagliptin. The 52-week study randomized 1,294 type 2 patients to canagliflozin 300 mg, canagliflozin 100 mg, sitagliptin 100 mg, or placebo (2:2:2:1 ratio). After 26 weeks, those assigned to placebo were switched to sitagliptin 100 mg (PBO/sita group). The modified intent-to-treat analysis showed that at 52 weeks, canagliflozin 300 mg provided a larger A1c reduction (-0.88%) than canagliflozin 100 mg and sitagliptin (-0.73% for both). Greater improvements in fasting plasma glucose were also observed with canagliflozin 300 mg (-35 mg/dl) and 100 mg (-26 mg/dl) compared to sitagliptin (-18 mg/dl; p<0.001 for both comparisons). As expected, the weight loss observed with canagliflozin 300 mg (4.2%; 3.7 kg [8.1 lbs]) and 100 mg (3.8%; 3.3 kg [7.3 lbs]) was significantly more than that observed with sitagliptin (1.3%; 1.2 kg [2.6 lbs]; p<0.001 for both comparisons). Canagliflozin was associated with reductions in blood pressure, as well as elevations in LDL and HDL cholesterol. On the safety front, canagliflozin was associated with a higher rate of genetic mycotic infections compared to sitagliptin and PBO/sita, as well as a higher rate of osmotic diuresis, though Dr. González noted that these events led to few study discontinuations. Interestingly, the incidence of documented hypoglycemia was higher with both doses of canagliflozin (6.8%) compared to sitagliptin (4.1%) and PBO/sita (2.7%).

• Canagliflozin was associated with reductions in blood pressure, as well as elevations in HDL and LDL cholesterol (data in table below). Dr. González mentioned during Q&A that the rise in HDL-C was unexpected.

	Δ Systolic Blood Pressure	Comparison to SITA	Diastolic Blood Pressure	Comparison to SITA
CANA 300 mg	-4.7 mmHg	-4.0 mmHg p<0.001	-0.3 mmHg	------
CANA 100 mg	-3.5 mmHg	-2.9 mmHg p<0.001	-1.8 mmHg	------
SITA 100 mg	-0.7 mmHg	------	-1.8 mmHg	------

• While the rates of adverse events (AE) were comparable between the groups, canagliflozin was associated with higher rates of genital infections and hypoglycemia:

	PBO/SITA	SITA 100 mg	CANA 100 mg	CANA 300 mg
Any adverse event	66.7%	64.5%	72.3%	62.7%
Serious adverse event	3.8%	4.9%	4.1%	3.3%
Urinary tract infection	6.6%	6.3%	7.9%	4.9%
Genital infection (M)	1.1%	1.2%	5.2%	2.4%
Genital infection (F)	1.1%	2.6%	11.3%	9.9%
Osmotic  diuresis-related adverse events	0.5%	1.9%	8.2%	4.4%
Hypoglycemia	2.7%	4.1%	6.8%	6.8%
Severe  hypoglycemia	0%	0.3%	0.3%	0%

Q: Have you found an explanation for why the higher canagliflozin dose had less of an effect than the lower dose?

A: No, not really.

Q: Are there any particular side effects of canagliflozin that you found to be part distributing?

A: No, you usually see genital mycotic infections, which are treated rapidly and resolve in patients. We didn’t see any safety concern

Comment: It’s very commonly stated that this class is associated with an increase in  urinary tract infections. If you look at the data rather than what’s said, this opinion doesn’t hold up. And you saw this today. At the lower dose, there was a little rise in UTIs, but it was not statistically significant. At the higher dose, there was no increase. I bet that if you combine the two doses, you wont’ see a statistically significant increase.

Q: I was surprised by the LDL cholesterol levels with the sitagliptin group.

A: This was unexpected but you can see that it’s a small rise, just 6%. This is what we obtained in the study.

Comment: Across studies with canagliflozin, the rise in LDL with canagliflozin 100 mg is 4 mg/dl. It’s 8 mg/dl with canagliflozin 300 mg. If you’re truly treating you patient to goal and their LDL level is 70 mg/dl, the worst case scenario is that you’re going from 70 to 78 mg/dl and most HCPs wouldn’t even increase the dose of statin.

A: Yes, the increase is very small. It’s no more than four to six mg/dl.

 

Posters

Exploring the Potential of Dapagliflozin in Type 1 Diabetes: Phase 2A Pilot Study (70-LB)

Robert Henry, Julio Rosenstock, Alexandros-Georgios Chalamandaris, Sreeneeranj Kasichayanula, Allyson Bogle, and Seven Griffen

This double-blind phase 2a study evaluated the short-term safety, tolerability, and pharmacokinetics  and pharmacodynamics (PK/PD) of dapagliflozin (BMS/AZ’s Forxiga) after two-weeks as an add-on to insulin in 70 patients with type 1 diabetes. These are the first clinical results we have seen for an SGLT-2 inhibitor in type 1 diabetes. Patients on insulin (mean baseline A1c 8.5%) were randomized to receive dapagliflozin (1 mg, 2.5 mg, 5 mg, or 10 mg) or placebo once-daily for two weeks. Treatment resulted in  a reduction in total daily insulin requirement (-19% on the 5 mg and -16% on the 10 mg doses) while also producing a trend toward reduced glucose levels and reduced glycemic variability (both measured by CGM): mean 24-hour glucose decreased by ~10-20 mg/dl relative to placebo on dapagliflozin 5 mg and 10 mg (no decrease was observed relative to placebo for the lower doses), and placebo-adjusted MAGE decreased by ~50-60 mg/dl on those same doses. Hypoglycemia appeared slightly elevated in the treatment groups compared to placebo, though there was no dose-dependent effect (i.e., the highest rate was observed in the 1 mg group; in contrast, there was a clear dose-dependent urinary glucose   excretion effect). As elated conference attendees with type 1 diabetes expressed during the poster session, these findings (though preliminary) are exciting because of the possibility of increasing treatment  options for a population challenged by a deficiency of therapies. However, the findings of this study, while encouraging, are very limited due to sample size and would be considered pilot data for larger and longer trials testing dapagliflozin (which Dr. Henry was confident will be underway in the near future).

• Seventy patients (62 completed trial) with inadequately controlled type 1 diabetes were randomized to receive daily doses for 14 days of placebo or dapagliflozin (1, 2.5, 5, or 10 mg) in addition to their insulin regimen. Baseline characteristics across all treatment arms were comparable and included a mean A1c of 8.5%, BMI of 25 kg/m2, and age of 35. The mean duration of type 1 diabetes ranged from 16-22 years across all groups. The primary objective was to assess the safety and tolerability of dapagliflozin after 14 days. Secondary objectives included change from baseline to day seven in 7-point monitoring profiles and pharmacokinetics. Exploratory objectives consisted of change from baseline at seven days in 24- hour CGM profiles, total daily insulin dosing (%), fasting plasma glucose (FPG), and 24-hour urine glucose output.
• Dapagliflozin appeared to improve glycemic control and diminish glycemic variability while also decreasing total daily insulin requirement. FPG levels were significantly lowered in the treatment arms compared to placebo (-26, -20, -42, -36 vs. -8 mg/dl for the 1, 2.5, 5, and 10 mg doses vs. placebo, respectively). CGM data suggested a dose-dependent improvement in glycemic control through a decrease in 24-hour average glucose levels relative to baseline (-16, -14, -30, and -41 mg/dl for the 1, 2.5, 5, and 10 mg doses of dapagliflozin, respectively, compared to -20 mg/dl in placebo). There was also a significant reduction in total daily insulin needs (-16, -11, -19, and -16 units for the 1, 2.5, 5, and 10 mg doses, respectively, compared to +2 units in placebo).
• Despite a dose-dependent increase in urine glucose from baseline to day seven (41.9, 48.5, 72.4 and 88.8 g/24 h for the 1, 2.5, 5 and 10 mg doses, respectively and-​ 21.6 g/24 h on placebo), there was no dose-dependent effect on hypoglycemia. The percentage of patients that experienced hypoglycemia was 62% for the placebo group and 92%, 60%, 79% and 67% for the dapagliflozin 1, 2.5, 5 and 10 mg groups, respectively. There were no apparent effects of dapagliflozin on urine output, total fluid output, or fluid intake.
• Genitourinary events, such as genital and urinary tract infections, were rare. One such event was recorded in the placebo arm, one each in the dapagliflozin 1 mg, 2.5 mg, and 5 mg arms, and zero in the 10 mg arm. Dr. Henry mentioned that, in his opinion, the rate of hypoglycemia was the greatest potential safety concern in the use of dapagliflozin.
• Treatment with dapagliflozin appears to have a neutral to slightly positive effect on body weight, though the short duration of the study may be the cause of this meager outcome.

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The Sodium Glucose Co-Transporter-2 (SGLT2) Inhibitor Empagliflozin Improves Glycemic Control in Patients with Type 1 Diabetes: A Single-Arm Clinical Trial (1074-P)