American Diabetes Association 77th Scientific Sessions

June 9-13, 2017; San Diego, CA; Full Report – Glucose Monitoring – Draft

Executive Highlights

This document contains our coverage of glucose monitoring at ADA 2017. Immediately below, we enclose relevant themes from this category, followed by detailed discussion and commentary. Talk titles highlighted in yellow were among our favorites from ADA 2017; those highlighted in blue are new full report additions from our daily highlights coverage.

Note that there is definitely some subjective overlap with digital health (where we’ve placed insulin dose titration and apps) and closing the loop, and some talks may appear in only one or multiple reports.

Table of Contents 


CGM Outcomes Standardization and Outcomes Beyond A1c

  • Many in attendance felt that key stakeholders finally arrived at a consensus on standardizing CGM outcomes metrics during a Friday session chaired by Stanford’s Dr. Bruce Buckingham and our very own Ms. Kelly Close. Dr. Bruce Buckingham proudly proclaimed, “You were all in the room where it happened,” stealing a line from Hamilton. See the table below for the proposed metrics. During the session, a range of experts (Drs. Rich Bergenstal, Anne Peters, Thomas Danne, Simon Heller, George Grunberger, and Aaron Kowalski) all emphasized that A1c alone is not sufficient to guide next-gen treatment development, optimized care, regulatory approvals, reimbursement, and other important decisions. There have been nine CGM outcomes consensus statements since 2013, and Dr. Bergenstal thinks that we probably don’t need any more – it’s time to settle on the core metrics and move forward. Now that sensor technology has caught up to enable accurate tracking of time-in-range, hypoglycemia, hyperglycemia, and variability, implementation will be easier than ever before. We are elated with this progress, though we realize there much still needs to happen to make ensure that outcomes beyond A1c are reported in clinical trials, used as a basis for approval, and are included in product labels to drive better reimbursement. There has been so much discussion and so many stakeholders involved, and we’re glad to see meaningful progress on consensus. The terminology remains a point of debate (e.g., Level 1 hypoglycemia”), and we’ll hear much more at the July 21 gathering in Bethesda, Maryland on this topic (register here for free).  

Standardizing 14 Core CGM Metrics

Time in Range

70-180 mg/dl


Level 1

<70 mg/dl

Level 2

<54 mg/dl


Clinical diagnosis: Event requiring assistance


Level 1

>180 mg/dl

Level 2

>250 mg/dl


Clinical diagnosis: ketones, acidosis (hyperglycemia)

Glycemic Variability

Coefficient of Variation

Standard Deviation (?)

Mean glucose


Estimated A1c


CGM Visualization


Episode of Hyperglycemia/Hypoglycemia

15 minutes

Sleep-Wake Time Blocks

12am-6am, 6am-12am

Data Sufficiency Recommended

Two weeks of collection

70-80% of CGM readings (minimum)

  • In a separate talk’s Q&A, Dr. Peters remarked with a hint of cynicism: “I don’t know if I can get away with saying that we are following the wrong marker in clinical practice, but we need to consider other outcomes beyond A1c.” For providers, titrating therapies based on A1c alone can be misleading in a sizeable fraction of individuals, given all the inter-patient variability in glycation. Moreover, we like how time-in-range, hyperglycemia, hypoglycemia, and AGP are actionable on a titration basis – “Oh, you seem to be running high in the morning, let’s change X in your medication.” We expect the next-generation of therapies (e.g., closed-loop, SGLT-2s in type 1) will likely drive greater improvements in time-in-range than on A1c, and we hope regulators and payers appreciate the value accordingly.
    • A JDRF-partnered, 12-week study of Lexicon’s SGLT-1/2 inhibitor sotagliflozin in young adults with a high baseline A1c exemplified the importance of outcomes beyond A1c. The primary endpoint of A1c reduction was not met, but sotagliflozin did appear to produce improvements vs. placebo on time-in-range, postprandial glucose, and body weight. We were impressed by the CGM data from the study – sotagliflozin therapy drove a strong increase in time spent in range (70-180 mg/dl), which increased from 33% at baseline to 44% on sotagliflozin. That translates into an extra 2+ hours per day (!) spent in range, a very meaningful win for people with type 1. Under traditional criteria (A1c alone), this useful therapy might not move ahead for this populations. But with the tools available to now measure time-in-range, companies will be far better equipped to characterize more nuanced benefits of their therapies. We look forward to a world where glucose sensors are standard in trials of new therapies.
  • In a HUGE victory for data standardization, Dexcom added the one-page AGP report to its Clarity data management software. It’s outstanding to see Dexcom incorporate the report following the 2012 IDC/Helmsley Charitable Trust expert panel on glucose data reporting, the subsequent publication of AGP in JDST/DT&T in 2013, the 2016 AACE Glucose Monitoring Consensus statement, Abbott’s incorporation of AGP into FreeStyle Libre’s software; additional AGP commitments at ADA 2016 from Roche, Glooko/Diasend, and Abbott (expanded); and about a month after Drs. George Grunberger and Vivian Fonseca co-authored an Endocrine Practice Letter to the Editor recommending standard AGP-like reports. Clinicians have told us for a long time that the AGP is a great way to display data and have one report to look at, regardless of the device. We love that the momentum continues to build and that more companies see the value of adding this report on top of their own innovative ways to display data – the field needs both standardization and innovation! Dr. Grunberger presented the idealized view of standardization in Dexcom’s press release, going back to a familiar analogy: “AGP can become the EKG report of diabetology - where there is one standard glucose report that all clinicians can interpret.” Yes! Will Medtronic also add AGP to CareLink, rounding out the major players?

The Value of Connected Devices – Enabling Real-World Data Collection and More Insightful Products

  • This ADA brought some notable examples of passive data collection from connected devices driving valuable real-world insights and evidence. We saw brand new, encouraging data from Medtronic/IBM Watson’s Sugar.IQ app – relative to baseline metrics (one month prior), a small group of 81 Sugar.IQ users experienced a solid 37-minute/day improvement in time-in-range, an 11% reduction in sustained hypoglycemia (>120 minutes), and an 8% drop in sustained hyperglycemia (>120 minutes). Notably, within three days of the app delivering a pattern “insight,” 65% of users experienced fewer lows and 55% experienced fewer highs. (It’s unclear when Sugar.IQ will launch fully, but we assume the biggest gating factor is approval of the standalone Guardian Connect mobile CGM, which remains under FDA review and is currently in human factors testing.) Meanwhile, Abbott continued to share fascinating real-world data from a >55,000-user-strong cohort on FreeStyle Libre – increased scanning was linked to higher time in range and decreased time in hyperglycemia. We also loved the FreeStyle Libre country-by-country breakdown, data that is now possible to collect at scale and passively with connected devices! In insulin dose capture, a Common Sensing poster shared very eye-opening results that combined its Bluetooth-enabled pen cap with Dexcom CGM data – boy is this going to make the “invisible” data behind injections visible, driving meaningful dose titration and decision support. Roche and Livongo also presented encouraging data on their connected BGM platforms – A1c changes (-0.9% for Roche, -1.2% for Livongo), higher treatment satisfaction (Roche), cost savings (Livongo: $136 per member per month), and more. Connectivity is a must-have in devices in our view, and the next step will be building excellent systems around products that use the data in meaningful ways – driving more insight for patients/HCPs, collecting real-world evidence for payers, and better understanding real-world product use to inform design improvements.

Expanding CGM Adoption – Value in Type 2 Diabetes, HCP Prescribing, and Psychosocial Impact

  • Drs. Bill Polonsky (BDI) and Jeremy Pettus (UCSD) debated use of CGM in type 2 – MDI, basal-only, and non-insulin – concluding it “will eventually become the standard of care for type 2 diabetes, especially as the technology becomes easier to use and less costly.” Obviously, those latter two caveats are critical and will drive the field’s expansion. The final slides shared six major points of agreement concerning the future of CGM in this population: (i) with proper support, CGM could become a powerful motivational tool in type 2 diabetes; (ii) innovative training materials are needed for type 2s; (iii) new methods for providing CGM feedback are needed; (iv) episodic use of CGM may be best for many type 2s; (v) much more evidence on CGM in type 2 is needed; and (vi) we need to determine which patient types will benefit (e.g., the disengaged, hypoglycemia prone, chronically poor glycemic control, those trying to select the best medication, etc.). We loved this list and completely agree with the key improvements that need to happen, particularly on ease of use (factory calibration will be critical), proper feedback mechanism (what to do with the data), and trials to support that this technology actually drives meaningful improvement. Of course, the path to “eventually” making CGM “standard of care” in type 2 diabetes will likely be a long one – after all, we’re only at an estimated ~15%-20% CGM penetration in US type 1s!
    • We believe intermittent-use products like Abbott’s FreeStyle Libre Pro and Medtronic’s iPro have encouraging potential in type 2 diabetes at a population level – even though they may not be as valuable for behavior change as real-time CGM, they will be FAR better than the current standard of care (barely any/no glucose data at all to make therapeutic adjustments). Indeed, several FreeStyle Libre Pro posters at ADA showed the value of this technology across the world, including in India. There was high enthusiasm about the Pro device in Abbott’s exhibit hall booth and corporate symposium. We’ll be fascinated to see what kind of business Abbott can build with this very intuitive, HCP/patient-friendly product.
    • A big question for type 2 diabetes and CGM will center on patient segmentation – who will benefit from professional CGM 1-3 times a year vs. 24/7 real time? What will the healthcare system pay for? What will patients be willing to pay for? Will comparative efficacy trials need to be done, and if so, who will run them? What novel business models are possible with factory calibrated CGM? For example, what about a mail order professional CGM service, where a sensor could be placed by the patient and mailed back for analysis, similar to 23andMe or Ubiome? What can professional CGM learn from other therapeutic areas, such as cardiology? How will Medtronic’s iPro3 stack up to Libre Pro? How quickly will Dexcom/Verily’s first-gen sensor drive into type 2 diabetes (launch expected by the end of 2018)?
  • Additional analyses from Dexcom’s DIaMonD trial of CGM in MDI affirmed the value of real-time sensors in type 2 diabetes. Dexcom’s Dr. David Price shared combined type 1 + type 2 data from the DIaMonD study testing CGM (n=179) vs. SMBG (n=128) in MDIs, the first pooled analysis following type 1 results at ADA 2016 (later published in JAMA) and type 2 data at ATTD 2017. The combined outcomes were very consistent with the by-group data: from a pooled baseline A1c of 8.6%, CGM drove a 0.9% reduction in A1c at 24 weeks vs. 0.4% in the SMBG group (adjusted mean difference: 0.5%; p<0.001). Time-in-range metrics also strongly favored CGM – at 12/24 weeks (pooled), CGM users were spending 72 more minutes per day in range (70-180 mg/dl) vs. 9 fewer minutes in the SMBG group (p<0.001). Taken with the results of Abbott’s IMPACT and REPLACE, DIaMonD definitely shows the value of CGM in the type 1 AND type 2 populations on insulin, though we see incremental benefit to be gained once decision support is added. According to Dr. Price, type 2 results are currently pending publication, and we wonder if they could drive more reimbursement of CGM in the insulin-using type 2 population. In a huge twist of irony, Medicare (US) and Germany’s Federal Joint Committee have really taken the lead on this – both organizations have approved reimbursement of CGM in intensive insulin-using type 2s. (Of course, actually getting it reimbursed is proving to be another thing entirely.) Will private payers follow CMS/Germany’s decisions, particular once DiaMonD type 2 data is published?
    • A separate satisfaction analysis from the DIaMonD study reached a very important conclusion: contrary to common clinical belief, patients with type 2 on MDI are likely to find CGM at least as engaging and valuable as patients with type 1 diabetes. In fact, mean perceived benefits were significantly higher (p <0.05) among participants with type 2 vs. type 1. Here’s hoping these results spawn more studies that counter the conventional views!
  • Provider anxieties surrounding the time, cost, and technological savvy associated with supervising CGM use continue to be an issue, based on T1D Exchange data from Stanford’s Dr. Molly Tanenbaum. Survey results suggested type 1 diabetes clinicians (n=209) can be divided into three distinct profiles related to CGM: 20% are “Ready” to prescribe, 41% are “Cautious,” and 39% fall in the “Not Yet” category. Clinicians who fall in the “Not Yet” bin have the hardest time keeping up with new technology, see the lowest proportion of patients with type 1 diabetes, and generally have inadequate clinic time in clinic to review CGM data. No surprise there. For “Not Yet” clinicians, Dr. Tanenbaum suggested that addressing healthcare system barriers may be needed to enable increased acceptance. Remedying hesitations for the other two groups is simpler: “Ready” clinicians should be encouraged to think about what’s working well, and what might change if uptake increases; the “Cautious” group should assess whether patient and provider barriers align and feel comfortable with education and coaching techniques. Indeed, the current model of prescribing and paying for CGM still demands additional effort from clinicians (prior authorizations chief among them), and hesitant HCPs must see a clearer cost-benefit win to make the prescribing leap. Clinical inertia also plays a big role in medicine and will continue to affect diabetes technology – CGM is still rarely/not covered in medical school, and older clinicians may have baggage from previous generations of the technology. Though CGM has been around for more than a decade, it really hasn’t come into its prime until the past five years. (And with connectivity and accessible data, it’s really only the past two years.)
  • In a talk revolving around her often-raised motto, “right device, right time,” psychologist Dr. Katharine Barnard noted the importance of keeping psychosocial outcomes in mind. CGM has enormous potential, but it may not be the right tool for an individual troubled by accuracy issues, intruding alarms, on-body burden, and a “perfectionist” mentality. We see ALL of these as solvable barriers, since they revolve around sensor design, better wearables, and novel user experiences tailored to different individuals. For instance, could CGM have a “less engaged teenager” mode vs. an “über type A” mode? Now that products use smartphones for display, we hope to see different UI/UX evolve for different patient segments.

Companies are Getting the Message: User Experience, Patient Feedback, and Integration with Consumer Electronics Really Matters

  • More than ever, device companies are hearing the message: user experience, patient feedback, and consumer electronics integration are critical. Dexcom secured Android G5 approval just before ADA and launched on the Google Play store shortly thereafter – the app already has between 500-1,000 downloads and received an update just after ADA on June 18. Also on the eve of ADA, Apple’s Worldwide Developer conference talked about Dexcom’s G5 on stage, which will communicate directly from transmitter-to-watch in the next version of WatchOS – talk about a victory for the patient experience and a major sign of Apple’s commitment to this partnership! Dexcom was a pioneer on mobile medical apps and it shows in the product’s continued uptake, particularly in MDI. The Clarity mobile app also got a terrific facelift, giving users expanded retrospective data reporting right on the phone. Insulet was a standout in this area too, headlined by a first look at the upcoming Omnipod Dash platform. The locked down Android phone has substantially upgraded the PDM user experience, while the next-gen Horizon system (putting automated insulin delivery on Dash) has undergone six usability studies already (31 unique participants to date, including some 670G users). Said Program Director of Advanced Technologies Jason O’Connor, “It’s so critical to not rush a product to market, just because it has functionality. It has to have the right user experience to deliver a product that is going to integrate into people’s lives. We may not be the first to market, but it does mean we’ll deliver an exceptional product.” Insulet has even solicited feedback from OpenAPS community members, which would have been unheard of a few years ago. Tandem’s compelling media day also reminded us that the Device Updater – allowing t:slim X2 pump users to upgrade their software from home, including to add G5 CGM integration and automated insulin delivery – will be a key differentiator going forward. We expect the most successful diabetes devices/apps will be driven by continuous software updating and learning from users, just as the most successful consumer technology is. That said, software is a whole different ballgame than hardware, and continuously building/maintaining apps for a growing number of devices can be burdensome – which companies will nail this?

New Devices in Exhibit Hall, but Clear Changes in Device Industry – Startups had Booths, While Roche Did Not Appear

  • We were glad to see several notable new tech products in an ADA Scientific Sessions’ exhibit hall for the first time: Medtronic’s MiniMed 670G hybrid closed loop and Guardian Connect CGM for MDIs, Insulet’s new OmniPod Dash PDM, J&J’s OneTouch Via bolus-only patch insulin delivery device, Dexcom’s Android G5 and upcoming touchscreen receiver, and many others.
    • Medtronic’s newly launched MiniMed 670G was probably the most talked-about device at ADA 2017, drawing sizeable crowds to the booth and dedicated product theater. The on-device experience was what we expected – a definite pump screen and display improvement over older Medtronic pumps, but still definite room to improve on simplicity, a consumer-grade experience, and connectivity/remote software updates. The company is managing expectations well, though we’ll have to see what early reviews are like once it really rolls out.
    • Insulet’s new Omnipod Dash PDM won our award for most improved device, bringing marked advantages in user experience on the locked-down Android phone (FDA submission in 2H17). Insulet’s emphasis on leveraging user feedback really shined in its product theater and in-booth demonstrations. We think this device will be quite well received from the loyal Omnipod user base, and it clearly gives Insulet a logical path to add Horizon automation and concentrated insulin.
    • J&J’s OneTouch Via bolus-only insulin delivery patch device drew crowds to see the device in the flesh for the first time in an exhibit hall. Reps disclosed that the product’s updated manufacturing process (submitted in November) has received FDA 510(k) clearance, but did not give any specifics on the launch timing front. It was great to finally get to hold the device, and it was clear that booth-goers were intrigued – we overheard one who was amazed by the patch’s small form factor. 
    • Dexcom’s just-approved Android G5 and new touchscreen G5 receiver were on display for the first time following recent FDA approvals. We would describe the new receiver as “built like a tank” – it’s bigger than the current option, and the touchscreen is more resistive than one found on a consumer-grade phone. This thing looks like it could be thrown at the wall and not break, which brings two deliberate advantages: (i) fixing the reliability issues that have challenged Dexcom’s current receiver; and (ii) addressing the Medicare reimbursement requirement that the durable G5 receiver last for three years. (This latter point we only heard in hallway chatter.) We assume once this new receiver is out, more non-Medicare users will switch to displaying data on a smartphone – it definitely loses some cool factor relative to the current iPod Nano-like receiver.
    • Abbott’s FreeStyle Libre Pro and Tandem’s t:slim X2 were not new to US exhibit halls, but did draw notable crowds interested in the next-gen devices with meaningful improvements – the former to professional CGM and the latter to Bluetooth connectivity/remote software updating.
  • One of our first observations upon entering the massive exhibit hall was: “Where’s Roche?” In fact, the major BGM player did not have a booth at this year’s conference to show off its new Accu-Chek Guide BGM or soft-launched Insight CGM system in Europe. This was not highly surprising from one perspective – it’s a tough time for Roche’s US BGM business and it has discontinued new US pump sales – but the juxtaposition with other players was noted. Glooko, Companion Medical, One Drop, and many other smaller device companies were indeed present in the massive ADA hall. This was perhaps a sign that: (i) Roche is being more choosey with its marketing resources (do exhibit hall booths have good ROI relative to other investments?); (ii) reflective of Roche’s smaller geographic sales base in the US; (iii) fewer new Roche products to showcase at this ADA; and/or (iv) something else. While bigger device companies like Medtronic, Abbott, BD, etc. were of course present in a big way in the hall, it felt like a greater number of small, innovative players were present this year and the average age is definitely younger! It will be fascinating to see how large AND small players drive innovation in the years ahead, and whether the current trends of tech partnerships will continue/increase at the next few ADAs.

Detailed Discussion and Commentary

Symposium: Reaching an International Consensus on Standardizing Continuous Glucose Monitoring (CGM) Outcomes―Aligning Clinicians, Researchers, Patients, and Regulators

Bruce A. Buckingham, MD (Stanford University, Palo Alto, CA), Kelly L. Close (The diaTribe Foundation, San Francisco, CA), Richard M. Bergenstal, MD (Park Nicollet Methodist Hospital, Minneapolis, MN), Thomas Danne, MD (Hannover Medical School, Hannover, Germany), Aaron J. Kowalski, PhD (JDRF, New York, NY), Simon R. Heller, MD (University of Sheffield, UK)

Our very own Ms. Kelly Close and Dr. Bruce Buckingham chaired a star-studded (and packed!) session on CGM outcomes standardization with Drs. Rich Bergenstal, Thomas Danne, George Grunberger, Anne Peters, Simon Heller, and Aaron Kowalski. We’ve never felt that a consensus on CGM outcomes has been so within reach! Dr. Rich Bergenstal led off with a number of statistics: There have been nine CGM outcomes consensus statements (six published, three pending) since 2013, comprised of 100 total CGM experts – he concluded that “we probably don’t need more consensus meetings, it’s time to align on a consensus.” And after representatives from a number of those committees presented their group’s work (Dr. Danne on the ATTD International Consensus Statement, Dr. Grunberger on the AACE/ACE Consensus Conference on CGM, Dr. Peters on the EASD and ADA Technology Working Group, Prof. Heller on the International Hypoglycaemia Study Group, and Dr. Kowalski on the T1D Outcomes Program), Dr. Buckingham concluded that “we’re pretty close to consensus, if we’re not there.” His final slide stole a line from the smash musical Hamilton, claiming “You were all in the room where it happened.” The excitement in the room was palpable, even though it was the last session of day one. We as an organization are over the moon. There has been so much discussion and so many stakeholders involved – it is amazing that we’ve come to a near-consensus on the time-in-range and variability thresholds to use for clinical trial reporting and beyond (see below).

  • The table below fleshes out the near-agreed upon core CGM metrics, based on the slide shown: (i) Level two hyperglycemia is >250 mg/dl; (ii) Level one hyperglycemia is >180 mg/dl; (iii) Time in target range is 70-180 mg/dl; (iv) level one (alert) hypoglycemia is <70 mg/dl; (v) level two hypoglycemia is <54 mg/dl; (vi) glycemic variability should be measured in coefficient of variation and possibly standard deviation; (vii) Mean glucose and eA1c should be presented; (viii) AGP is the CGM visualization medium of choice; (ix) An episode of hypoglycemia or hyperglycemia is comprised of 15 minutes; (x) Sleep/wake time blocks are from 12pm-6pm and 6pm-12pm; (xi) data should be collected for a minimum of two weeks with 70-80% CGM wear to ensure sufficiency. Not all working groups settled on exactly these numbers or even addressed every question, but those who did tackle a problem settled on numbers and metrics that look very similar.

Standardizing 14 Core CGM Metrics

Time in Range

70-180 mg/dl


Level 1

<70 mg/dl

Level 2

<54 mg/dl


Clinical diagnosis: Event requiring assistance


Level 1

>180 mg/dl

Level 2

>250 mg/dl


Clinical diagnosis: ketones, acidosis (hyperglycemia)

Glycemic Variability

Coefficient of Variation

Standard Deviation (?)

Mean glucose


Estimated A1c


CGM Visualization


Episode of Hyperglycemia/Hypoglycemia

15 minutes

Sleep-Wake Time Blocks

12am-6am, 6am-12am

Data Sufficiency Recommended

Two weeks of collection

70-80% of CGM readings (minimum)

  • The next step, of course, is to bring regulators and payers on board with the recommendations of 100 experts; the diaTribe Foundation, along with partners in the diabetes community, took a big step by organizing an FDA Workshop on Outcomes Beyond A1c last August, and will hold another Consensus Conference entitled “Glycemic Outcomes Beyond A1c: Standardization & Implementation” in Bethesda, MD in the presence of CDER Director Dr. Janet Woodcock on July 21. For other imminent events on CGM metrics consensus, see this slide (credit to Prof. Heller). This means so much to the whole diabetes community – standardization of data visualization and agreement on which outcomes matter, how to measure them, and what threshold should be used (in =
  • Dr. Aaron Kowalski pointed out that the device side of FDA (CDRH) has largely adopted Outcomes Beyond A1c, but the drug side (CDER) hasn’t to the same degree. This is not a surprise to anyone who follows diabetes regulatory science; Drs. Courtney Lias and Stayce Beck have done an incredible job engaging patients and compromising to enable them to get beneficial technology quickly and with endpoints of relevance like time-in-range and hypoglycemia. This comes partly from the presence of a very active and vocal type 1 patient community in technology, which hasn’t been the case on the drug side. We are strong proponents for outcomes beyond A1c to be considered on the drug side as well, and we have every confidence this division will understand the value. We see no reason why CGM shouldn’t be included in every trial possible!
  • Dr. Peters’ EASD/ADA Technology Working Group discussed five main themes in a manuscript, which is pending approval: (i) More systematic and structured pre-marketing evaluation of the performance of CGM systems; (ii) Greater investment in trials to provide evidence of CGM value and reliability for all patient groups; (iii) Standardization of CGM-measured glucose data reporting from clinical trials (see above); (iv) Improved consistency and accessibility of safety reports to regulatory authorities after market approval; and (v) Improved consistency and accessibility of safety reports to regulatory authorities after market approval. We’ll be keeping an eye out for this paper for sure!

Questions and Answers

Q: Everyone agrees that 70-180 mg/dl is the target range – should I tell my patients that as well?

A: Dr. Grunberger: The key is that every patient is an individual. For an individual, these are your targets, but it’s different for someone who’s pregnant or say nine years old. That range is quote-on-quote “normal” that you’ll see in healthy individuals. Advice for individual patients can differ.

Dr. Bergenstal: With hybrid closed loop, control is getting tighter and tighter overnight, and some people are considering going for 70-140 mg/dl overnight. This is a starting point.

Ms. Close: A lot of patients will have exactly this question. 140 mg/dl is also being measured, so we can individualize what patients see. Many people also have this concern, so thanks for bringing it up.

Q: Can you give specific guidance for individual products that have inherently different properties?

Dr. Danne: You’re really putting your finger in the wound. Obviously we try to be independent of manufacturing, industry, etc. And this is very much changing. A device calibrated twice per day today might be factory calibrated tomorrow, so it’s difficult to make recommendations on specific technology, but we try to give a grand scheme so that we can use CGM safely.

Current Issues

Should Continuous Glucose Monitoring Be Prescribed for People with Type 2 Diabetes? A Pro/Con Discussion

Jeremy Pettus, MD (UCSD, San Diego, CA) and William H. Polonsky, PhD (UCSD, San Diego, CA)

In what was a both instructive and entertaining session of ADA 2017, Drs. Bill Polonsky (BDI) and Jeremy Pettus (UCSD) debated use of CGM in type 2 – MDI, basal-only, and non-insulin – concluding it “will eventually become the standard of care for type 2 diabetes, especially as the technology becomes easier to use and less costly.” The final slides shared six major points of agreement following a vociferous debate: (i) with proper support, CGM could become a powerful motivational tool; (ii) innovative training materials are needed; (iii) new methods for providing CGM feedback are needed; (iv) episodic use of CGM may be best for many; (v) much more evidence on CGM in type 2 is needed; and (vi) we need to determine which patient types will benefit (e.g., the disengaged, hypoglycemia prone, chronically poor glycemic control, selecting the best medication, etc.). Still, don’t let these concluding remarks fool you – this session included hearty and hilarious debate on both sides, with Dr. Pettus taking the “pro” CGM side for type 2s on MDI and basal-only, and then switching to the “con” side for type 2s not on insulin. (For the switchover, the two actually exchanged sides of the stage, and in a dramatic gesture, humorously swapped sportcoats.) We include their key arguments for and against CGM in the different type 2 groups below. Both remain “very convinced that CGM is awesome” and holds a lot of promise in type 2, and their arguments provided a nice lens as to how it might help and where it definitely needs to improve.

Patients with type 2 diabetes on intensive insulin therapy:

  • Dr. Pettus was staunchly in favor of CGM use in type 2 patients on intensive insulin therapy, while Dr. Polonsky argued that it may not always be a home run. Dr. Pettus structured his case around four tenets, largely informed by data from the DIaMonD type 2 cohort: (i) CGM is not more costly than other therapies. SGLT-2 inhibitor empagliflozin costs $400/month, GLP-1 agonist liraglutide costs $720/month, and CGM costs just $445/month – “people often overestimate the cost of CGM,” said Dr. Pettus, “but it is coming down.” In addition, there are no side effects to CGM use, unlike with adjunctive therapies, and patients usually want to be able to stop taking drugs, not the other way around. Plus, Dexcom’s G5 is now covered by Medicare for type 1s and type 2s (though the details of administering coverage are still pending), and many hope other payers will follow suit. (ii) Type 2s will wear CGM. In the DIaMonD type 2 cohort, 93% of patients were still using CGM ≥6 days per week in month six – presumably, they continued to wear it because they saw a benefit (though a clinical trial effect should not be totally discounted, especially because the study screening required consistent CGM use in the run-in). (iii) Some argue that type 2s aren’t tech savvy enough for CGM, but DIaMonD showed equivalent A1c drops regardless of education (bachelor’s degree or not), age, and numeracy. (iv) Dispelling rumors that CGM requires intensive education and time from providers, participants in the CGM group in DIaMonD had four visits in six months, and were only handed a trifold handout for education materials. “I don’t think the people are getting a benefit because they came into the office, but because they see data in real time and live a healthier lifestyle.”
  • Dr. Polonsky also channeled DIaMonD to suggest that the benefit of CGM in hypoglycemia in type 2s on intensive insulin therapy is nil, and wondered if CGM is doable in all cases of MDI-using type 2s. In DIaMonD, he said, there was no impact of CGM on severe hypoglycemia because there were zero episodes over six months in either group – to be fair, patients with recurrent severe hypoglycemia were excluded from the study, but Dr. Polonsky’s point is that the point of glycemic benefit should be proven by studying the appropriate population. We agree that more extensive hypoglycemia would be good to have, though the A1c drop and higher time in range seen in DIaMonD are encouraging. In terms of feasibility, Dr. Polonsky cast doubt on Dr. Pettus’ claims that CGM is doable in the real world: He argued that (i) in the real world, contact with a provider is much less frequent than seen in DIaMonD, and patients may require more support; and (ii) numeracy may be a greater concern than one might think (A&W’s third-pounder sold less than McDonald’s quarter-pounder “because four is bigger than three.”) In addition, he suggested that CGM use may be useful for some, but perhaps not all patients, but providing close support and education may be of great benefit.

Patients with type 2 diabetes on basal-only

  • The second question of the debate – whether CGM should be prescribed for people with type 2 diabetes on basal insulin – swung unsurprisingly toward the “pro” side. Dr. Pettus spent his allotted time debunking what he felt were the biggest myths about CGM in this population: (i) that basal insulin is perfectly well titrated with SMBG; (ii) that patients won’t do anything with the results; and (iii) that hypoglycemia is uncommon. Indeed, he was quite masterful in weaving his way through a host of literature, poking holes in each myth one by one: (i) highlighting the prevalence of overnight hypoglycemia in this population and the inability of SMBG to capture this window; (ii) citing the actionable decision-making that has been documented in type 2s on basal insulin and the lifestyle changes that can persist for years (Yoo et al. 2008; Vigersky et al. 2012); and (iii) pointing out the massively underreported prevalence of hypoglycemia and severe hypoglycemia in this population. Altogether, Dr. Pettus kept his conclusion very simple, “I honestly think CGM should be standard of care in this population.” On the other hand, Dr. Polonsky took a more measured approach in suggesting that we do not yet have the evidence to indisputably call CGM the tool of choice in this population. Instead, he pointed out that studies of CGM in type 2 patients on basal insulin have been confounded by the vast (bordering on unrealistic) support provided to patients in clinical trials. Until we have data specifically looking at the effect of CGM independent of the extra support patients in clinical trials receive, Dr. Polonsky suggested that he’d put his final judgment on hold – we’re not sure how this would be parsed out, since any clinical trial will include extra support.

Patients with type 2 diabetes not on insulin:

  • Dr. Polonsky, switching to the pro side, argued that some of the poor outcomes in type 2 diabetes might relate to “perceived treatment efficacy” – patients feeling that a therapy is actually working helps build momentum and a sense of progress. In diabetes, he said, we want to “help people to see that their actions are having a positive, tangible difference. Then, they get enthused. This is our opportunity with CGM! Humans respond to short-term, powerful, positive reinforcers.” Dr. Polonsky memorably noted that “feedback is the most underused motivational tool we have in diabetes,” and CGM provides an unparalleled level of feedback in real time. He reviewed the oft-cited Vigersky et. al paper (2012) testing CGM in non-insulin users, noting that episodic CGM use may be the way to go for those not on insulin (e.g., quarterly or at critical times like diagnosis, during diabetes education, when medication changes are needed, etc.).
  • Dr. Pettus, though a strong CGM supporter, took the con side quite persuasively, highlighting the grim realities of current medication adherence: nearly 1/3 of diabetes prescriptions are never filled, and of those that are filled, adherence rates at one year are <50% (Fischer et al., J Gen Intern Med 2010). He added that since 2005, 40 different treatment options have been approved for type 2 diabetes, with “approximately no change in A1c.” “Is CGM the 41st new therapy to move the needle,” he asked, particularly when it is more complicated to use than a once-daily pill? Dr. Pettus added that CGM’s training complexity might be a tall order for PCPs, who take care of ~94% of patients with type 2 diabetes on orals and don’t have time to fill out prior authorizations and train patients. He also underscored the realities of clinical inertia – after reaching an A1c of 8%, average time to add a medication was over one year (Brown et al., Diabetes Care 2004). “That’s one year to do one click (in the EMR) to prescribe a drug. A complex and time consuming therapy like CGM (harder to prescribe) will not help overcome patient and physician inertia.” Reimbursement also doesn’t exist for CGM in this group, meaning in a best-case scenario a prescription would be denied.

Oral Presentations: Where is Glucose Monitoring Taking Us?

Sugar.IQ Insights: An Innovative Personalized Machine-Learning Model For Diabetes Management

Huzefa Neemuchwala, PhD, MBA (Head of Innovation, Medtronic Diabetes, Northridge, CA)

Medtronic’s very smart Head of Innovation Dr. Huzefa Neemuchwala shared the first data from the “limited learning launch” phase of the Sugar.IQ app with Watson – now tag-lined, “Intelligent Diabetes Assistant App.” Results came from de-identified CareLink data in 81 users of the Sugar.IQ app using MiniMed 530G/Enlite + MiniMed Connect to send CGM data to the app. Relative to baseline metrics (one month prior), this small group of Sugar.IQ users has experienced a solid 37-minute/day improvement in time-in-range (p=0.04; baseline not shared), an 11% reduction in sustained hypoglycemia (>120 minutes; p<0.001), and an 8% drop in sustained hyperglycemia (>120 minutes; p<0.001). Within three days of the app delivering a pattern “insight,” 65% of users have experienced fewer lows and 55% experienced fewer highs. In total, Sugar.IQ has now been used by 97 people for an average of two weeks each, and engagement has been encouraging in this limited launch: an average of 1.5 unique app sessions per day, 78% of users logging food, and 4.8 logged food items per day – persistence over time, particularly with food and opening the app up, will be THE key question ahead. The very cool Glycemic Assist feature, allowing users to “follow” a particular food item over time (we love this!), has been popular: 1,886 views in these 97 users so far. Users have “followed” their glycemic response to Dunkin Donuts, Panera Bread, corn flakes, ice cream, buttermilk biscuits, etc. – pretty squarely in the junk food (“Diabetes Landmines”) category, but hopefully the app will gradually nudge people away from eating them! We include examples below of the insights Sugar.IQ delivers – so far, this group of users has received 1,119 different contextual, personalized insights ranging from glycemic control and behavior to hyper- and hypoglycemia to rapid rate-of-change to boluses. Users have “liked” a notable 89% of the insights, indicating they are finding useful patterns. Dr. Neemuchwala also shared two Sugar.IQ case studies from patients with longstanding diabetes – the app identified trends (over-correcting highs, eating a high-carb lunch) and nudged them to change their behavior (the latter is a phrase Dr. Neemuchwala emphasized). More details and screenshots below!

  • It’s unclear when Sugar.IQ will launch fully, but we assume the biggest gating factor is approval of the standalone Guardian Connect mobile CGM (under FDA review and currently in human factors testing). Guardian Connect will stream CGM data to Sugar.IQ directly via Bluetooth, and should help Medtronic differentiate its standalone mobile CGM offering from rising competition (Abbott, Dexcom). Per Medtronic’s JPM presentation (the last update), a full launch of Sugar.IQ and Guardian Connect were expected in May-October, though today’s Medtronic Diabetes Analyst Day update said that human factors work is ongoing. Medtronic also told us it is working with IBM to finalize algorithm for the Sugar.IQ commercial launch.
    • As a reminder, this app has been fairly delayed. The plan as of last year was to launch Sugar.IQ by the end of 2016, timing that was updated at JPM. Sugar.IQ was demoed and “beta launched” in September at Health 2.0 – presumably “the beta launch” was a previous group, and this data is from the wider release that was alluded to at ATTD.
  • Sugar.IQ insight examples: In a word, wow!  “Planning your day? I see you tend to go low on Saturday between 12 PM and 3 PM.” “I notice that you tend to go low after meals with >20g of protein.” “Great! I noticed that you had only 1 nighttime low(s) in the last month. Whatever you’re doing, seems to be working very well.” “I see that between 6AM and 9AM, your glucose often goes high (300+ mg/dl) after taking an insulin injection.” “After your glucose is high for more than 120 minutes, you then tend to go low.”
  • Looking ahead, Medtronic has also expanded the research on the hypoglycemia prediction feature, which now has >90% accuracy at predicting hypoglycemia within a 2-4 hour window (80%+sensitivity, 67% positive alert rate). This feature is now using 100+ behavioral models based on unsupervised clustering techniques. Last we heard, this will be included in a future version of the app, but not the one at launch.
  • Dr. Neemuchwala provided two case studies of Sugar.IQ noticing a specific glycemic/insulin/food pattern, giving the user an objective insight, and a resulting human behavior change. “Simple judgment-free nudges can lead to sustained behavior improvement.” Both were in people with long-standing diabetes (one with type 2 for 20 years on insulin, another with . One case concerned over-correcting highs, while another concerned eating a high-carb vs. slightly lower-carb lunch. See the slides below!

  • Sugar.IQ uses machine learning to find patterns in diabetes data, and ultimately, hopes to combine many data sources: CGM and insulin, biometrics, meals/logbook, CRM, medical and claims, mood, sleep, location. The focus is on putting all this data in one place, then driving insights and predictions.

Effect of Continuous Glucose Monitoring on Glycemic Control in Adults Using Multiple Daily Insulin Injections

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

Dexcom’s Dr. David Price shared combined type 1 + type 2 data from the DIaMonD study testing CGM (n=179) vs. SMBG (n=128) in MDIs, the first pooled analysis following type 1 results at ADA 2016 (later published in JAMA) and type 2 data at ATTD 2017. The combined outcomes were very consistent with the by-group data: from a pooled baseline A1c of 8.6%, CGM drove a 0.9% reduction in A1c at 24 weeks vs. 0.4% in the SMBG group (adjusted mean difference: 0.5%; p<0.001). Consistent with the by-group results, age, numeracy, and education had no impact on the A1c benefit of CGM – a very important finding for expanding CGM to broader populations, especially seniors. Time-in-range metrics strongly favored CGM – at 12/24 weeks (pooled), CGM users were spending 72 more minutes per day in range (70-180 mg/dl) vs. 9 fewer minutes in the SMBG group (p<0.001). This improvement came almost entirely from less time spent over 180 mg/dl: -50 minutes per day in the CGM group vs. -13 minutes per day in the SMBG group (p=0.003). Time in hypoglycemia (<70 mg/dl) also favored the CGM group, but just barely missed statistical significance: -8 minutes per day vs. +3 minutes per day (p=0.05) from baseline. Notably, 93% of study participants were using CGM >6 days per week at six months, a sign of (i) strong patient acceptance of the technology; and (ii) excellent screening by investigators to ensure those in the study would actually wear CGM. Dr. Price said the type 2 results are pending publication, and we wonder if they could drive more reimbursement of CGM in the insulin-using type 2 population. Taken with the results of Abbott’s IMPACT and REPLACE, DIaMonD definitely shows the value of CGM in the type 1 AND type 2 populations, though there is definitely benefit to be gained once decision support is paired with glucose data.

  • As expected, the impact of CGM rose with baseline A1c – those starting DIaMonD at a baseline of >7.5% saw a 0.9% improvement in A1c, while those starting at >9.0% saw a 1.4% improvement. Again, this is a good sign that those not in good control stand to have greater A1c reduction benefits from CGM.
  • Interestingly, only 16% of the CGM group achieved an A1c <7.0% at 24 weeks, lower than we would have guessed but still much higher than 9% in usual care (p=0.05). Patients were obviously starting at quite a high baseline A1c in this study, so perhaps a greater percentage was unrealistic with CGM alone. We expect further behavioral feedback and insulin dosing decision support will help close the gap further.
  • Patients in DIaMonD were not a young, tech savvy, “early adopter” population: those in the CGM group had a mean A1c of 8.6%, a mean age of 52 years, a median diabetes duration of 17 years, a mean SMBG frequency of just 3.6/day, a mean BMI of 31 kg/m2, 5% had >1 severe hypoglycemia episode in the last year, and 52% had less than bachelor’s degree.

A Randomised Controlled Trial of Self-Monitoring of Blood Glucose in Noninsulin-Treated Type 2 Diabetes: The SMBG Study

David Owens, MD (Swansea, United Kingdom)

Swansea’s Dr. David Owens presented rather positive data from the SMBG study, an RCT of SMBG in non-insulin treated type 2 diabetes. Random assignment to a group that performed structured SMBG resulted in an early and sustained A1c drop vs. assignment to a group that did not test. Following screening and basic diabetes education, ~450 patients were assigned to either (i) No SMBG; (ii) SMBG alone; or (iii) SMBG + telecare. Both SMBG groups received structured SMBG education in an additional visit. Every three months, A1c, cholesterol, weight, patient-reported outcomes, and (when applicable) 7-point SMBG profiles were collected – A1c at 12 months was the primary outcome. At one year, the no SMBG group experienced a 0.3% decline in A1c (baseline 8.6%), while the SMBG and SMBG+telecare groups had whopping declines of 1.1% (baseline 8.5%) and 1.3% (baseline 8.6%), respectively. We suspect (but have no way to know) that time in ranges would have been better for the SMBG and SMBG+telecare groups. Impressively, these curves began to diverge as early as three months into the study, when the no SMBG group had only declined 0.1% and the SMBG groups had both dropped ~0.6%-0.7%. At the end of the 12-month period, there were nearly three times as many patients at goal (A1c=7.0%) in both of the SMBG groups vs. the no SMBG group (p<0.001). Notably, there was no difference in A1c reduction between the SMBG alone and SMBG+telecare groups, suggesting that remote provider contact in this study was non-inferior to clinic visits. These findings bode positively for the debate over SMBG in non-insulin users, and fit well into the context of existing literature – the STeP study, the oft-cited Diabetes Care study from the T1D Exchange (Miller et al. 2013), etc. Even though the patients in this study didn’t take insulin, checking BG clearly caused A1c to drop meaningfully, presumably from behavioral modifications resulting from the structured testing approach. We wonder what would happen if this huge trial was run with CGM – SMBG without CGM, blinded CGM, real-time CGM? 

Diabetes Devices and Profiles of the Clinicians Who Prescribe Them

Molly Tanenbaum, MD (Stanford University, Palo Alto, CA)

Stanford’s Dr. Molly Tanenbaum presented T1D Exchange/dQ&A data showing that surveyed type 1 diabetes clinicians (n=209) can be divided into three distinct profiles with respect to supporting patients with CGM: 20% are “Ready,” 41% are “Cautious,” and 39% fall in the “Not Yet” category. The profile determinations were based on self-reported attitudes toward technology, time in clinic to review data, ability to keep up with technology, perceived patient barriers to using CGM, as well as provider and practice characteristics. Clinicians who fall in the “Not Yet” bin have the hardest time keeping up with new technology, the lowest proportion of patients with type 1 diabetes, and generally have inadequate clinic time to review CGM data. “Ready” clinicians – which make up a greater proportion of surveyed clinicians than CGM penetration metrics might predict – have the easiest time keeping up with new technology, the highest proportion of patients with type 1 diabetes, and have more time to review CGM data in clinic. Finally, the “Cautious” group generally had positive attitudes toward CGM, but falls in the middle with regards to ability to keep up with new technology, number of type 1 patients, etc. Interestingly, this group was the most likely to endorse barriers to patient uptake – cost, data overload, on-body burden – they consider CGM to be net beneficial, but they are hesitant to change current therapy. Segmenting providers in this way is necessary, Dr. Tanenbaum said, if we wish to support and make them feel comfortable maintaining or shifting to positive attitudes toward CGM. Dr. Tanenbaum suggested that “Ready” clinicians should be encouraged to think about what’s working well, and what will change if uptake increases; the “Cautious” group should assess whether patient and provider barriers align and feel comfortable with education and coaching techniques; and for “Not Yet” clinicians, Dr. Tanenbaum suggested systemic change – if possible, healthcare system barriers should be addressed to enable increased acceptance. She reminded the audience that “physicians aren’t the reason for low uptake of CGM, but they can be part of the solution” – the fact is that the current model of prescribing and paying for CGM still demands additional effort from clinicians (see below image). We absolutely love this work – it’s easy to fall into the trap of assuming that the market will respond as soon as a product becomes available or the tech improves, but changing clinical practice takes a long time, and providers must see a clear cost-benefit win to making the prescribing leap.

  • Dr. Tanenbaum bookended her presentation with a theoretical case study to illustrate how crucial it is to get patients and providers on the same page. At first, “Megan” likes the accuracy of CGM A, but her doctor wants her to wear CGM B. Because the two aren’t aligned, Megan doesn’t wear a CGM. At the end of the presentation, the doctor has agreed to learn how to access and review data for Megan’s preferred CGM, and Megan is wearing her CGM regularly, is less concerned about hypoglycemia, and has seen a reduction in A1c. This is obviously a simplified case, but it shows how provider attitude and initiative can have a direct impact on the patient’s wellbeing.
  • In November, Dr. Tanenbaum and colleagues published an important paper in Diabetes Care, an online survey of T1D Exchange adults (n=1,503) investigating barriers to pump/CGM uptake and profiling device users versus nonusers. The Stanford team is taking a multi-pronged attack to address adoption of diabetes devices, focusing on reducing barriers for patients and providers alike.

Oral Presentations: Potential Implications of the Affordable Care Act on Diabetes Care

Connected Glucose Meter Plus Coaching Improves Diabetes Clinical Outcomes and Decreases Costs

Jennifer Bollyky, MD (VP Clinical Research & Analytics, Livongo, Mountain View, CA)

Livongo VP of Clinical Research and Analytics Dr. Jennifer Bollyky presented retrospective data showing that Livongo users achieved improved glycemic control and cost savings compared to non-Livongo users. The study compared medical claims and clinical lab outcomes for Livongo users (n=646) with non-Livongo users (n=3,014) 12 months before and 12 months after the launch of the Livongo program. At the end of the period of study, there was a 1.2 percentage point decrease in A1c (p=0.12; we did not catch the baseline), a significant 37% reduction (64 point total decrease) in total cholesterol (p=0.04), and an 8.3% reduction (10 point decrease) in triglycerides (p=0.80) seen in Livongo users compared with non-users. Dr. Bollyky also presented cost data showing that Livongo users experienced significantly slowed increase in the cost of medical claims relative to non-users (5% vs. 13% growth, respectively), resulting in a savings of $136 per Livongo member per month. We’d note that these savings dwarf the per-month DTC price of the Livongo service ($49.99 promotional price until August 10, $65 per month thereafter). We assume Livongo still charges employers and health systems roughly ~$70 per person per month, meaning the ROI is quite good based on these retrospective results. Of course, a prospective study would tease out the real benefits, but given the company’s growth and continued funding, we assume the cost data is quite promising in real-world implementation too.

  • Dr. Bollyky opened with the rationale of Livongo, emphasizing that today’s acute care management approach to diabetes is ineffective. With this, she introduced Livongo’s focus on: a cellular-enabled, two-way messaging BGM device that measures blood glucose; free unlimited blood glucose test strips; and access to CDEs for real-time support and goal-setting.

Questions and Answers

Q: You’re working with specific employers now. What’s the cost of service and plans for expansion to other populations?

A: We just launched a DTC campaign, which is $50 a month (promotional price). It includes the cost of test strips so you can test as much as you want.

Q: What does the retention of people look like?

A: We have a turnover rate of about 1%, meaning that someone may not be eligible due to employer change. This usually happens because the person is no longer eligible for the benefits for some reason. We also have something called “last users,” who are not checking their blood glucose. But, employers introduce different incentives to check. We try to make it as fun as possible and give external reasons to check.


The Disruption Continues: Negative Impact of Medicare Competitive Bidding Program

G Puckrein, IB Hirsch, C Parkin, L Xu, DG Marrero

This late-breaking poster shared new data documenting continued striking disruptions of Medicare beneficiary access to prescribed SMBG supplies following CMS’s expansion of its competitive bidding program (CBP) in 2013. The results follow up on the study’s original results (published in Diabetes Care) that showed an increase in mortality associated with the CBP in nine test markets in 2011. Scarily, it appears that the negative impact of the program has only worsened since 2013, when – as a reminder – CMS expanded the program nationally to both mail order and retail channels with lower reimbursement. This iteration of the study investigated changes in the acquisition of SMBG supplies by beneficiaries in those nine test markets (n=43,939) and all non-test markets (n=485,688) in the six months following the national CBP rollout and identified two major trends: (i) a significant increase in the percentage of beneficiaries who migrated from full SMBG to partial/no SMBG access in both test and non-test markets; and (ii) a significant increase in the percentage of insulin-treated beneficiaries with no record for SMBG (from 54.1% in January 2013 to 62.5% by December 2013, p<0.0001). Indeed, the authors estimate that as of January 2014, 37.5% (n=90,923) of insulin-treated beneficiaries were calculating their insulin dosage with partial/no SMBG. These results differ greatly from CMS’ April 2012 report on adverse outcomes associated with competitive bidding, which suggested that there was no disruption of access to supplies and no negative healthcare consequences associated with the program. However, the continued criticism and evidence to the contrary raises red flags for what is already a heavily scrutinized program. Our biggest question now that the evidence seems overwhelming … When will CMS actually listen? What will it take to reverse this policy?

Performance of Two Tissue Glucose Monitoring Systems Intended for Nonadjunctive Use (917-P)

A poster from Dr. Guido Freckmann and colleagues examined the accuracy of the Dexcom G5 CGM and real-time FreeStyle Libre in a head-to-head comparison of the two EU sensors approved for non-adjunctive use. The small study assigned 20 patients to wear two of each sensor in parallel for 14 days (G5 sensors were replaced after seven days). Patients returned to the center three times (48 hours per visit), during which up to 115 comparison measurements with a BGM were performed. Results indicated that combined MAD/MARD was 9.6 for the G5 and 11.0 for Libre, while individual sensor MAD/MARD values ranged from 4.8 to 21.6 for the G5 and from 6.9 to 37.2 for Libre. The distribution of individual sensor results is shown below – showing good clustering around the mean, but definitely some outliers. Dexcom had more sensors with MAD/MARD under 10, while Abbott’s concentrated around 8-12. While the study is small and the accuracy comparator was BGM, it’s highly notable to see head-to-head results, especially because they are in line with what the companies themselves have reported. Significant outlier values (MAD/MARD values in >20) occurred with both sensors, though not too often. We hope more studies like this are done to illustrate real world and comparative performance of different systems. Ultimately, we think both systems are safe for insulin dosing on aggregate, though obviously some sensors still see outlier values. The key point, however, is to remember the paucity of data most non-CGM (SMBG) users are using every day to titrate insulin – just a few sporadic measurements.

Satisfaction with Continuous Glucose Monitoring: How Do the Experiences of Insulin-Using Adults with Type 1 Diabetes vs. Type 2 Diabetes Differ? (924-P)

W Polonsky, D Hessler, K Ruedy, and R Beck

A satisfaction analysis from the DIaMonD study reached a very important conclusion: contrary to common clinical belief, patients with type 2 on MDI are likely to find CGM at least as engaging and valuable as patients with type 1 diabetes. The study gathered results from the 44-item CGM Satisfaction Scale that was administered at the completion of DIaMonD (24 weeks) in 102 adults with type 1 and 76 adults with type 2. Notably, CGM satisfaction was high in both groups, with no significant differences between the type 2 (4.31) and type 1 groups (4.26) – we emphasize continued design improvement over time as contributing to this result. Deeper analysis of the satisfaction subscales demonstrated that mean perceived benefits were significantly higher (p <0.05) among participants with type 2 vs. type 1 (4.39 vs. 4.24), though no significant differences in mean perceived hassles were observed (1.76 vs. 1.71). In the individual questionnaire, type 2 patients reported greater satisfaction on those items focused on acquiring new knowledge or skills (e.g., “CGM taught me new things about diabetes that I didn’t know before”). Ultimately, we love this focus on the type 2 patient experience of wearing CGM, especially because it runs so counter to common beliefs: “Type 2s just won’t wear CGM ... type 2s are unengaged, type 2s don’t need real-time CGM, etc.” Three cheers for providing everyone with diabetes the opportunity to wear real-time sensors and learn things about their own physiology, food choices, and behavior! Here’s hoping these results spawn more studies that counter the conventional views.

Use of the Accu-Chek Connect System is Associated with Increased Treatment Satisfaction and Improved Glycemic Control in Individuals with Insulin-Treated Diabetes (105-LB)

P Mora, A Buskirk, M Lyden, C Parkin, L Borsa, and B Petersen

In a six-month, prospective, multi-center study (n=84), Mora et al. found use of the Accu-Chek Connect system significantly reduced A1c levels, boosted patient satisfaction, and reduced diabetes-related distress in insulin users. The Accu-Chek Connect system includes a Bluetooth-enabled BGM (Aviva Connect), smartphone app (including a bolus advisor), and online web portal, allowing for automatic transmission of patient data to clinician and patient platforms. At three months, use of the integrated system resulted in a mean A1c drop of 1.1% (baseline: 8.8%; p<0.001), which was sustained out to six months (-0.9% vs. baseline; p<0.0001). Satisfaction and distress were measured using the DTSQ and DDS (diabetes distress scale) respectively: The DTSQ showed high baseline treatment satisfaction (~30 out of a possible 36), and satisfaction significantly improved nevertheless (see below). Importantly, mean diabetes distress scores dropped significantly by 0.3 points (p<0.0001), with a notable reduction in regimen-related distress from “moderate distress” to “not distressed.” Daily SMBG frequency increased non-significantly from 2.4 times/day at baseline to 2.6 times/day at six months. These results strongly point towards the psychosocial and clinical benefits of connected meters, a major win.( There was no control group, however, so some of the benefit may be due to a study effect.) We be fascinated to know how much of the benefits could be attributed to the connected meter and use of the data vs. the bolus calculator function in the Accu-Chek Connect app. We’d also be interested to see future studies in patients with lower baseline patient satisfaction who may be less likely to engage with the system regularly. Overall, it’s terrific to see a prospective, longer-term study showing the benefit of a connected meter, which is obviously not a given!

Sharing the Outcomes and User Experience from India in the First 750 Type 2 Diabetes Patients with the New Libre Pro 14-Day Glucose Sensor (108-LB)

J Kesavadev, L Ramachandran, A Shankar, A David, G Krishnan, S Srinivas, A Ajai, G Sanal, and S Jothydev

A poster from Jothydev’s Diabetes & Research Centers detailed a retrospective analysis of type 2 patients (n=425; ~60% on insulin) who had used FreeStyle Libre Pro in India. Use of Libre Pro resulted in a 0.37% drop in A1c at six months (p<0.0001), while a matched control group dropped 0.11% (p=0.07). Fasting blood glucose fell a notable ~14 mg/dl in the Libre Pro group (p<0.0001), while it only fell 3 mg/dl in the control group (p=0.17). It was unclear from the poster if patients wore only one 14-day sensor, or if they wore multiple sensors split over time. The big question for scaling this to a broad population is not whether sensor data adds value over SMBG data, but how to deploy it for maximum benefit. When should patients get Libre Pro vs. real-time Libre, who should get it, and how well can clinicians/patients use the sensor data to change therapies or behavior? The relative 0.26% A1c drop was not very clinically significant, though perhaps it will improve as experience with Pro mounts and decision support improves. We would’ve loved to see a third arm comprised of a matched population that used the unblinded consumer version – the benefits from in-the-moment feedback would presumably have a larger impact on A1c and other glucose-centric measures. The poster also delves into patient-reported outcomes from 750 users: ~100% of the patients had positive experiences in many of the categories measured, including “discreet and convenient use,” “complete glycemic profile over several days and easier integration of report,” “painless sensor insertion,” and “more product clinician-patient interaction facilitating better disease management.” 90% would be willing to repeat the procedure, and 80% found the procedure cost-effective (this is huge – a majority of patients in India pay for medical goods and services out of pocket). We wonder if the providers who guided these patients through the process would also recount the experience positively, as so many here in the US have at conferences.  

  • In a separate retrospective FreeStyle Libre Pro analysis from India presented at ADA, patients in the control group saw an average A1c reduction of 0.7% over the time period (baseline 9.3%), while the FreeStyle Libre Pro group declined 1.0% (from 9.3%). In both of these studies, we might have expected larger A1c declines with Pro relative to matched controls, though the trial effect could play a role and perhaps more decision support is needed. 
  •  Interestingly, the most common cause of sensor damage was “accidental wiping off while taking bath and oil massage.” This is a downside to the otherwise awesome back-of-the-arm wear location. We’ll be interested to see if Abbott changes the adhesive to make it stickier, or gets other wear locations approved. (Of course, patients will wear it wherever, but clinicians are more likely to follow the back-of-the-arm labeling.)

The Economic Impact of Adopting Professional Continuous Glucose Monitoring with the FreeStyle Libre Pro System (109-LB)

S Yu

Based on interviews at eight US endocrinology offices, Abbott’s Dr. Shensheng Yu estimated that the first-year equipment cost per office of existing professional CGM technologies (Medtronic iPro2; Dexcom G4), excluding the sensor, is ~$1,170, while this cost is just ~$75 with the FreeStyle Libre Pro system. The Libre Pro’s inexpensive setup - $65 for the reusable reader, $10 for cable and adapter, and $60 per 14-day sensor – allows for ~18 individuals to experience professional CGM for the same cost that it takes to simply set up clinics on one of the other systems. Wow! The hands-off design of the Libre Pro system also cuts down on time, and therefore cost: Dr. Yu estimates that Libre Pro workflow (outside of analysis/interpretation) takes 10 minutes – five minutes for consultation and application, and five minutes for download. Other reusable, more hands-on systems (requiring calibration, training, disinfection, etc.) require closer to 45 minutes – 20 minutes for CGM training, 10 minutes for cleaning and disinfecting, and 15 minutes for calibration data entry. Assuming a nurse’s salary of $40/hour, the workflow cost per procedure is under $7 for the Libre Pro system, but $30 for existing technology. Per procedure, Dr. Yu also calculated the clinical economic benefit of using Libre Pro: a ~0.4% reduction in A1c yields an estimated ~$100 in savings, while a 43% reduction in severe hypoglycemia event rate yields nearly $25 in savings per procedure. Taken together, and subtracting the $60 cost per procedure (assuming high volume), this comes out to ~$65 saved per patient year on Libre. The lower equipment and staff costs from Libre Pro, along with the potential clinical benefit, make it a compelling and quite scalable at a population level. Two real-world retrospective studies of FreeStyle Libre Pro use in India each demonstrated a ~0.3% reduction in A1c – hypoglycemia was not reported, but we would love to see an economic analysis from these real-world cohorts. Did Dr. Yu’s projections translate into real world use?

FreeStyle Libre Use for Self-Management of Diabetes in Children and Adolescents (110-LB)

F Campbell, O Kordonouri, N Murphy, and C Stewart

Results from the SELFY study in UK, Irish, and German children with type 1 diabetes aged 4-17 years-old indicate that using Abbott’s FreeStyle Libre results in several glycemic and extra-glycemic benefits, including increased time in range, decreased glucose variability, reduced A1c (-0.4%),  improved patient and parent satisfaction, and nearly eliminated fingerstick. After a 14-day run-in with SMBG and a blinded sensor, participants (n=76) were unmasked and used the FreeStyle Libre (real-time) system for self-management of diabetes for the next eight weeks. At the end of the eight-week period, relative to baseline, mean time in range (70-180 mg/dl; the primary outcome!) improved by one hour per day (p=0.0056), time >180 mg/dl diminished by 1.2 hours per day (p=0.0038), and A1c fell by 0.4% from a baseline of 7.9%. Time spent <70 mg/dl did not change significantly vs. baseline, even though a decrease in hypoglycemia was the primary achievement in the IMPACT study of FreeStyle Libre in type 1 adults – then again, in IMPACT, adults were spending around three hours per day <70 mg/dl at baseline, while these kids and adolescents were just over 60 minutes per day at baseline. Both teens and parents expressed increased satisfaction using the DTSQ – likely a byproduct of improved glycemia and all-but-eliminated fingersticks. Indeed, SMBG readings dropped from a median of 8.0 times per day at baseline to just one time per day at week eight! FreeStyle Libre scan frequency was consistent at 12.9 times per day over the course of the eight weeks. (Nearly as high as the 15.1 scans per day seen in adults in IMPACT – again, quite impressive considering this study was in a pretty young population.) There was no formal control group in this study, but pediatric users clearly derived glycemic benefit from using FreeStyle Libre, gathered far more glucose data than at baseline, AND stopped taking fingersticks. In line with the age inclusion threshold for this study, Abbott announced in May national reimbursement in France for all people with diabetes on intensive insulin therapy ages four and up – we’re not sure where the age cutoffs are for other countries where Libre is covered.

Clinical Comparison of iWel and Another Continuous Glucose Monitoring (CGM) Product (121-LB)

D Zeng, JA Pagan, Y Li, X Chen, and A Yu

This head-to-head, company-sponsored study compared Glutalor’s off-the-radar iWel CGM (see picture below) to a Medtronic’s Enlite CGM (MiniMed 530G) device. This trial was conducted in China and assessed iWel performance in 24 people with diabetes ages 16-68 years-old. Both devices were used simultaneously, and glucose values from SMBG were paired with CGM values to determine accuracy. The iWel CGM reported an MARD of 11.1% vs. SMBG, while Medtronic’s Enlite came in at 15.9%. iWel had 87% of values fall in zone A of the Clarke Error Grid; 72% of points from Medtronic’s CGM fell in this zone by comparison. iWel has an appealing set of features – no inserter necessary due to a small/short needle-based sensor (we’re not quite sure how this works), real-time readings available directly on smartphones, and only one calibration needed per day. The wearable is pretty massive, as seen in the picture below. According to the abstract and website, the iWel device has been used by “more than 36,000 patients/sensors” in several countries around the world and will be launched in Europe in 2017 (currently filed). It’s hard to read too much into this data right now, since this was a small, company-sponsored study (310 paired points analyzed for iWel and 241 for Medtronic’s Enlite CGM) and devices can always be cherry-picked from the manufacturing line in such evaluations. (With CGM, manufacturing scale and reliability is a huge part of the challenge!) We wonder if this sensor will actually launch in Europe and how accurate it will prove to be in real-world use. The company’s website does not inspire confidence.

  • A few other trial design notes: iWel was placed on the arm in this trial, while the Medtronic CGM was put on the abdomen. Is that a fair comparison? The study also used  reference SMBG and not YSI. Still, the 15.9% MARD for Enlite is about right, based on the labeling.

Device-Supported vs. Routine Titration of Insulin Glargine 300 U/mL (Gla-300) in T2DM: Efficacy and Safety (131-LB)

S Edelman, S Bain, C Hasslacher, G Charpentier, G Vespasiani, F Flacke, H Goyeau, M Woloschak, and M Davies

Sanofi presented encouraging results from the AUTOMATIX study comparing its myStar DoseCoach BGM (integrated insulin glargine dose titration algorithm and BGM) to investigator-recommended Toujeo titration regimens. In a randomized, multicenter treat-to-target trial, patients with type 2 diabetes (n=151) were randomized 1:1 to device-supported or routine titrations. After 16 weeks, a higher proportion of patients achieved the mean fasting glucose target of 90-130 mg/dl without severe hypoglycemia with the DoseCoach BGM: 46% vs. 37% (not significant). We’d emphasize that in these sorts of studies – comparing best-case scenario HCP titration vs. patient-driven automatic systems – showing non-inferiority is a huge achievement and positive sign. Between study arms, comparable numbers of patients experienced hypoglycemia and adverse events. In addition, 34% of patients using DoseCoach reached mean fast glucose of 90-130 mg/dl without confirmed blood glucose reading ≤70 mg/dl, while this percentage was just 15% in the investigator-recommended titration group (superiority not determined). Similarly, fasting glucose dropped five additional mg/dl and A1c fell an additional 0.15% in the DoseCoach group, with slightly fewer events of hypoglycemia (both during the day and at night). In line with many other studies at this ADA, we see this one as another vote of confidence in automatic basal insulin titration – it works as well as best-case scenario HCP-driven titration (and often better!), but is much more efficient for the healthcare system and for patients. We’d love to see how myStar DoseCoach compares to other insulin titration products that are starting to emerge on the market (see our insulin dose titration competitive landscape) – is an integrated BGM with titration preferred to an app? How will Sanofi choose to commercialize the DoseCoach BGM vs. DoseCoach app in the US – will both be offered?

Glucose Monitoring in Noninsulin-Treated Type 2 Diabetes: A Pragmatic, Randomized Clinical Trial (891-P)

L Young, CM Mitchell, T Gregory, J Buse, M Vu, M Weaver, J Rees, K Grimm, and K Donahue

In this poster, Dr. John Buse’s UNC group shows that SMBG in type 2 patients doesn’t improve glycemia, nor health-related quality of life (HRQOL). 450 patients (baseline A1c: ~7.5%) were randomized to one of three groups: no SMBG, once-daily SMBG, or once-daily SMBG with “enhanced patient feedback including automatic tailored messages delivered via the meter.” After 52 weeks of these interventions, the group with no SMBG had experienced a 0.04% increase in A1c (baseline 7.52%), the once-daily SMBG group had experienced a -0.05% A1c drop, and the one-daily SMBG + messaging group had experienced a 0.10% drop in A1c. None of these changes were statistically, nor clinically, significant, relative to the others (p=0.74). HRQOL, as measured by the “physical” and “mental” arms of the SF-36 survey, was also unchanged over the span of a year. Secondary outcomes – the Problem Areas in Diabetes and Diabetes Empowerment Scale surveys – were not changed significantly in any of the groups, while the Diabetes Symptoms Checklist, a measure of symptom distress, indicated a decrease in symptomatic burden for the SMBG group (no messaging) vs. the other groups that approached significance (p=0.06). The authors conclude that “routine SMBG should not be recommended for patients with type 2 diabetes not treated with insulin.” We understand this study’s conclusion – one fingerstick per day did not benefit a fairly well-controlled group of non-insulin users  (baseline A1c: ~7.5%) – though we wonder if the real problem was that it is too little data to drive any meaningful change. More thoughts on this below.

  • These findings contrast those seen in the SMBG Study, which was presented on Day 2 of ADA. In that trial, random assignment to a group that performed structured SMBG resulted in an early and sustained ~1% A1c drop vs. assignment to a group that did not test. Notably, the baseline A1cs were slightly higher in the SMBG Study (~8.5%), but we’re not sure what the “structured SMBG” looked like in terms of how many times per day and when the enrolled patients were encouraged to test. These conflicting studies add to this controversial area, and we’ll be fascinated to see what is possible once low-cost real-time CGM is applied to this group. 
  • On a related note to this controversial topic, Drs. Bill Polonsky and Larry Fisher wrote an interesting Diabetes Care article a few years ago: “Right answer, but wrong question: self-monitoring of blood glucose can be clinically valuable for noninsulin users.” From many patients’ perspectives, the biggest value of SMBG in diabetes (aside from titrating therapy) is to see the impact of different choices on glucose. This is only possible, however, with paired/more frequent and structured checking – e.g., before and after meals, before and after exercise, etc. In other words, in a paradigm of glucose data -> patient learning -> behavior change, a once daily glucose value might not be expected to achieve miracles, simply because it is not enough data to drive a feedback loop! Perhaps low-cost CGM will drive more improvement with glucose monitoring in this population, since it gives more data and immediate real-time feedback. “Oh, eating X does Y to my glucose? Oh, walking actually lowers my glucose?” Of course, this theory is nice, but only studies can confirm if this makes a difference in real-world practice.

User Acceptability of a Long-Term, Fully-Implanted CGM System—Interim Clinical Results (929-P)

J Lucisano, T Bailey, K Bertsch, P Gupta, L Kurbanyan, S Martha, L Morrow, and J Wilensky

Glysens presented interim user acceptability results from the six-month Figs-GC and one-year FIGS-2 trials (combined n=26) of the company’s fully implantable CGM (no on-body device). A Composite Device Tolerance Questionnaire shows that Acceptance Index (+2 = unaware of the sensor; 0 = indifferent; -2 = strong negative) starts around zero, and increases to settle around +1, indicating that the patients are between unaware of and indifferent to the fully implanted sensor. Acceptance appears notably higher with the second-gen sensor than the first, and patients at FIGS-2 Site 2 (AMCR Institute) have higher rates of acceptance thanks to an unspecified “evolved technique” than do those at Site 1 (Prosciento, Inc.). In a Device Tolerance Questionnaire administered to all FIGS-2 participants, responses suggest that the patients largely found the implant procedure to be simple, that the implant is in a good location, and that the site is mostly comfortable (not itchy, not painful, not uncomfortable). The least favorable responses, though still mostly positive, pertained to having awareness of the device and favoring the implant site, fashion choices, and thoughts of the implant – still, well over 60% of the patients at least “somewhat agree” that they sometimes forget they have an implant. The FIGS-2 trial is in progress and we look forward to seeing accuracy/reliability data for the implantable sensor, along with the form factor of viewing data (receiver vs. phone vs. watch). A possible indicator for the acceptance of Glysens’ sensor will be uptake of Senseonics’ Eversense CGM (smaller implant than Glysens but still requires an on-body transmitter) – this device has rolled out in a very limited fashion in Europe and was most recently slated for an FDA approval in 4Q17. GlySens hasn’t disclosed a timeline for pivotal trials, FDA submission, or commercialization in the US – of course, the challenge with such an ambitious wear time (6-12 months) is that the studies take a long time to do.

Impact of Continuous Glucose Monitoring (CGM) on Hypoglycemia in T1D Adults on Multiple Daily Insulin Injections (MDI) (932-P)

A Olafsdottir, I Hirsch, J Bolinder, T Heise, W Polonsky, S Dahlqvist, A Frid, E Ahlen, J Hellman, H Wedel, and M Lind

In an analysis from the recently JAMA-published Swedish GOLD study (n=161), CGM in type 1 patients on MDI not only significantly reduced A1c by a statistically significant 0.4% vs. SMBG, but also significantly reduced time <70 mg/dl and <54 mg/dl. Adults with type 1 diabetes (baseline A1c =8.6%) were randomized to Dexcom G4 or SMBG for 26 weeks, followed by a 17-week washout, and then 26 weeks in the other treatment arm. The JAMA publication contained high-level data showing A1c and hypoglycemia reduction on CGM, but this poster further breaks down the improvements in hypoglycemia: overall time <70 mg/dl was cut from 4.8% on SMBG to 2.8% on CGM, while time <54 mg/dl was reduced from 1.9% on SMBG to 0.8% on CGM. CGM reduced daytime <70 mg/dl by 40% (4.5% to 2.7%) and <54 mg/dl by 54% (from 1.7% to 0.8%). CGM also drove strong reductions in nighttime hypoglycemia: time <70 mg/dl was reduced by 49% (5.5% to 2.8%), and time <54 mg/dl was cut by a striking 65% (2.5% to 0.9%). These marked improvements can be seen clearly in the graphs below. The poster also shared that the coefficient of variation (CV) was reduced slightly from 40% on SMBG to 37% on CGM. As in Dexcom’s DIaMonD trial, CGM significantly reduced time spent in hypoglycemia for those on MDI – to our knowledge, granular day/night DIaMonD data hasn’t been published, but we’d expect to see the same patterns. We hope that the growing data supporting the superiority of CGM over SMBG, even for MDI, begets positive decisions from payers, regulators, and even prescribers, so that more people can have access to this life-changing technology. 

Lifestyle Coaching Plus Connected Glucose Meter with CDE Support Improves Blood Glucose and Weight Loss for People with Type 2 Diabetes (957-P)

J Bollyky, D Bravata, J Yang, and J Schneider

In this poster, Livongo teamed up with Restore Health to investigate the incremental effect of supplementing its service with intensive lifestyle coaching in overweight patients with type 2 diabetes and elevated A1C levels. Study participants who been on the Livongo program for at least 80 days but had still not achieved their target glucose were randomized to receive one of four possible interventions over the course of 12 weeks: Livongo only, meaning a cellular BGM and CDE coaching (n=75); Livongo + a connected weight scale (n=115); Livongo, the connected scale, and “lightweight” coaching (n=73); and Livongo, the connected scale, and “intensive” coaching (n=67). Coaching consisted of an on-boarding call and daily text messages and activities aimed at improving factors associated with insulin resistance. Lightweight and intensive coaching differed in the length of the on-boarding call and the degree of lesson, meal rating, text, and activity personalization. The investigators found that the Livongo program, alone, significantly boosted blood glucose control (mean estimated A1c decreased from 8.5% to 7.5%; p=0.01). Adding coaching into the equation, mean weight loss and mean blood glucose improved more among those in the intensive and lightweight groups as compared to the scale only groups (-6.4, -4.1, -1.1 pounds, respectively; and  -19.4, -11.3, -2.9 mg/dl respectively). While the intensive coaching resulted in greater improvements as compared to lightweight coaching, the differences were not found to be significant. Interestingly, even when participants did not reach their target A1c, many still achieved weight loss and improved blood glucose control. Intensive coaching also boasted greater engagement, as those in the intensive group had on average 36 more coaching conversations than those in the lightweight group (44 vs. just 8). Although the clinical data suggests Livongo + intensive coaching as an effective option for those experiencing a plateau in reaching their diabetes management goals, it should be noted that the 12-week program costs were a whopping 5.5 times higher for those receiving intensive versus lightweight coaching. This begs for a deeper analysis from a health economics perspective – how much does the intensive coaching save in healthcare costs vs. lightweight coaching? We’d also be interested in sub-analyses sorting out whether some patients are more likely to be responders in one group or another. Plus, we weren’t clear on how intensive coaching alone compares to Livongo alone. To mitigate overall costs, future work should also focus on investigating which aspects of the intensive coaching are integral to the major improvements indicated in this study.

Cost Calculation and Adherence to Guidelines for a Flash Continuous Glucose Monitoring System for Adults with Type 1 Diabetes Mellitus Using Intensive Insulin: a UK NHS Perspective (1325-P)

R Hellmund

Using statistics from the IMPACT study of Abbott’s FreeStyle Libre in type 1 patients and real-world data presented at ATTD, Mr. Richard Hellmund made a compelling cost-effectiveness argument for the use of FreeStyle Libre over SMBG. A FreeStyle Libre sensor costs £48.29 (~$60 USD), and Mr. Hellmund’s analysis assumes that patients use 26 per year (one every two weeks), equating to £1,255.54/patient-year. In the IMPACT trial, patients also performed 0.5 SMBGs per day, which Mr. Hellmund’s calculated adds an additional £60.23/patient-year for supplies, resulting in a FreeStyle Libre-wearing total of £1,315/patient-year. He then compared this cost to three different SMBG scenarios: (i) 2015 UK NICE guidelines calling for 10 SMBGs per day; (ii) 5.6 SMBGs per day seen in the run-in period of the IMPACT trial; and (iii) 16 SMBGs per day – the number of times people check their blood glucose with FreeStyle Libre in the real-world. Based on reasonable assumptions for cost per lancet (£0.04) and strip (£0.29), Mr. Hellmund estimated the following cost differentials by scenario: (i) UK NICE guidelines: FreeStyle Libre costs £111.27 more per patient-year than SMBG; (ii) RCT testing frequency: FreeStyle Libre costs £641.25 more per patient-year than SMBG; and (iii) Real-world testing frequency: FreeStyle Libre costs £611.43 less than SMBG per patient-year (see table below). The first two scenarios are certainly more realistic than the third, and SMBG will likely be cheaper than flash glucose monitoring for the foreseeable future, but Mr. Hellmund pointed out that flash monitoring has the potential to reduce costly complications. For example, in IMPACT, Libre was associated with a 48.5% reduction in low glucose events (<45 mg/dl) – one prevented episode of severe hypoglycemia, with a single hospital admission estimated to cost approximately £1134 ($1433) at 2016 prices, would completely flip the script and rationalize the value of Libre. Additionally, implementation of flash monitoring systems may decrease occurrence of cardiovascular complications through decreased hypoglycemia and hyperglycemia, while also decreasing overall utilization of healthcare resources (as demonstrated in IMPACT). Patients test nearly three times as much with Libre vs. SMBG, potentially aiding in reduced complications long term, which are a much bigger economic drain on the system than the sensor itself. At least 13 countries, including France, Belgium, and Germany, have come to this realization and offer full or partial reimbursement for the device.


Assumed: # of fingersticks per day

Annual Cost of SMBG per patient (£)

Annual Cost of FreeStyle Libre + 0.5 SMBGs/day per patient (£)

Annual cost of FreeStyle Libre relative to SMBG per patient (£)

NICE Guideline Testing Frequency





RCT Testing Frequency (based on IMPACT trial)





Real-world testing frequency (based on real-world data from 55,000+ patients using Libre)





Joint ADA/AACC Symposium: The Role and Utility of Glycated Proteins – Beyond A1c

Technical Challenges

David Sacks, MD (NIH, Bethesda, MD)

Dr. David Sacks discussed the ways in which alternative measures of glycemic control can complement and account for insufficiencies in A1c. His thoughts spanned from the reasons that A1c deviates from reality (e.g., anemia) to the strengths and limitations of fructosamine, glycated albumin (GA), and 1,5 anhydroglucitol (1,5 AG). In his view, GA has a number of advantages considering that there is no impact from renal disease, pregnancy, or cirrhosis and that there is no impact from serum albumin levels. It also better correlates to postprandial hyperglycemia than A1c, and with efforts to standardize this metric underway, he shared confidence that the use of GA will begin gaining some traction “in the next few years.” Ultimately, he stressed that a multi-modal approach using a combination of biomarkers may ultimately better predict long-term complications. Instead of using a new biomarker to assess glycemia, we’d prefer to see wider use of CGM sensors in all forms (real time and professional) to actually measure glucose – this is the metric that matters, and according to Dr. Rich Bergenstal, two weeks of CGM data is quite predictive of 90 days of data.

Symposium: Technology and Automation for Inpatient Insulin Management

Advances in Glucose Monitoring in the ICU

Stanley Nasraway, MD (Tufts Medical Center, Boston, MA)

Dr. Stanley Nasraway characterized the future of the inpatient glucose management field as “chilling,” in part due to ambiguous FDA metric targets and the NICE sugar trial results, which put a major damper on the field and greatly reduced investment. According to Dr. Nasraway, there are just three companies pursuing inpatient glucose management devices – OptiScan, Glysure, and Admetsys – and the OptiScanner is the only continuous glucose monitoring system to have completed a pivotal study in the US. The OptiScanner 5000 is currently under FDA review and has been shown to be incredibly accurate in critically ill patients: A multi-center, accuracy trial (n=200) demonstrated a MARD of 7.6% with over 95% of the data collected located in Zone A of the Clarke Error Grid. Furthermore, population coefficient of variation (PCV) was reported to be an impressive 9.8% (PCV commonly falls in the 20%-30% range for similar systems). The device connects to a venous line, extracts just 0.1 ml of blood, centrifuges the sample, and performs midinfrared spectroscopy to directly measure plasma blood glucose. Spectroscopy requires zero calibration, and the system provides automated readings, generally set to record every 15 minutes. We especially like the automatic and accurate readings, which translate to huge reductions in burdens for nurses, as well as improved information on trends and time in range, particularly at night. We do expect the subcutaneous CGM companies to move into this area, though it will obviously take them time as they continue to focus on the outpatient markets. Cost-effective and accurate inpatient glucose monitoring is critical, as patients are often unable to self-administer their intensive insulin therapy and may have extraneous physiological factors altering their glycemia. We hope this field sees a resurgence as the technology improves and studies show the clear ROI of saving nursing time and (hopefully) improving patient outcomes.

  • In an already incredibly limited market, the statuses of the remaining two companies, GlySure and Admetsys, are currently unknown. The GlySure system operates via a fiber optic sensor, which sits in the blood stream, requiring an initial three calibrations followed by once-daily calibrations for the remainder of use. A somewhat promising MARD of 9.9% was reported; however, Dr. Nasraway claims GlySure is now targeting the outpatient market, so progress for the inpatient population is unknown. Admetsys is a closed-loop system that mixes dextrose and insulin through a central venous catheter with another line for a glucose oxidase sensor. The system aims to prevent hypoglycemia and keep glucose levels within a defined range. Whether this will be implemented in the ICU is unclear and the company has no plans to test in the inpatient setting until at least 2018.
  • Just days before ADA began, Admetsys received an investment (of undisclosed size) from T1D Exchange. The funding was part of T1D Exchange’s “multi-million dollar initiative” to support the development of automated insulin delivery technologies. As a reminder, Bigfoot Biomedical won the first investment from this initiative back in February.

Corporate Symposium: Clinical Benefits of Flash Glucose Monitoring (Sponsored by Abbott)

Flash Glucose Self Monitoring in Children and Adolescents with Type 1 Diabetes: the Selfy Study

Olga Kordonouri, MD (Children’s Hospital, Hanover, Germany)

Dr. Olga Kordonouri (Children’s Hospital, Hannover, Germany) presented data from the single-arm SELFY Study of FreeStyle Libre in pediatrics (ages 4-17), showing strong 1+ hr improvements in time-in-range per day vs. baseline. At the end of the study period (two weeks blinded compared to eight weeks unblinded), participants (n=76; 58% pump users) saw time in range improve by a whopping 1 hour/day (p=0.0056), A1c drop by 0.4% (p<0.0001; baseline 7.9%), and time >180 mg/dl fall by 1.2 hours/day (p=0.0038). To our surprise, time spent <70 mg/dl did not change significantly vs. baseline, even though decrease in hypoglycemia was the primary achievement in the IMPACT study of FreeStyle Libre in type 1 adults – perhaps this pediatric group had parents much more mindful of hypoglycemia. Pediatric patients scanned 12.7 times per day, on average, nearly as frequently as the 15.1 times per day seen in their adult counterparts in IMPACT, whereas SMBG frequency fell from a median of 8.0 to 1.0 per day during the eight week portion – talk about confidence in the system! Median sensor duration was 13.4 days (a good sign for adhesive in an active population), there were only three reported mild device-related events, and both parents and teens indicated increased treatment satisfaction. There was no formal control group in this study, but it is evident that pediatric patients have improved glycemic outcomes on FreeStyle Libre and will use the sensor, and the increased discretion and reduced need to perform fingersticks are equally compelling, particularly for this age group. This data will also be presented Sunday in the poster hall (110-LB).

Flash Glucose Monitoring: Patterns and Impact on Glycemia in Real Life Settings

Ramzi Ajjan, MD (University of Leeds, UK)

Dr. Ramzi Ajjan supplemented real-world FreeStyle Libre data presented at ATTD with new cuts showing increased time in range and decreased time in hyperglycemia from increased scanning, and even a country-by-country breakdown! The ATTD data showed that when individuals from the >55,000-user-strong cohort scanned at higher frequencies, they presented with lower A1cs and spent less time in hypoglycemia. The additional results today also suggested that scanning with a higher frequency increased time in range and reduced time in hyperglycemia – we didn’t get clear pictures to parse out the magnitude of the effect, but we’ll be back in short order as soon as we do. On a more encouraging note, we were able to snag the very cool photo below – it depicts the regional differences in baseline hyperglycemia (hours per day above 180 mg/dl) and hypoglycemia (hours per day below 70 mg/dl), as well as the effects of sustained use (clockwise from top left: Germany, Spain, France, UK, Italy, and other). It is fascinating to see how the healthcare (and diabetes care) systems, diets, and other customs contribute to slightly different population-wide glycemic patterns. That said, more FreeStyle Libre scans, no matter which country, consistently resulted in lower hyperglycemia and hypoglycemia – very confidence inspiring. With two major outcomes trials (IMPACT, REPLACE), 300,000+ patients using the device (many non-adjunctively) overseas, and real world in this >55,000-user group, it will be interesting to see how the FDA approaches this product review (especially for non-adjunctive use).

Clinical Benefits of Professional Flash Glucose Monitoring: the Indian Experience

V. Mohan, MD, PhD (Diabetes Specialty Center, Chennai, India)

Acclaimed diabetologist Dr. V. Mohan presented retrospective, real-world data demonstrating significant A1c reductions from the use of FreeStyle Libre Pro across seven diabetes clinics in India. Dr. Mohan et al. mined the EMRs of these diabetes clinics for data from patients from March 2015 to October 2016 – one group had used FreeStyle Libre Pro (n=2,536), while the other consisted of matched controls (n=2,536). Overall, patients in the control group saw an average A1c reduction of 0.7% over the time period (baseline 9.3%), while the FreeStyle Libre Pro group declined 1.0% (from 9.3%). The type 1 cohort who used Pro saw a 0.7% reduction (baseline 9.6%) vs. a 0.2% reduction in the matched controls (baseline 9.6%), while the type 2 Libre Pro cohort experienced a 0.9% decline (baseline 9.2%) vs. a 0.7% reduction in the matched controls. There were no significant differences between male and females in the study, and impressively, Libre Pro use yielded A1c reductions across the board, irrespective of age. We did find it odd that the control group improved so much, since this wasn’t a clinical trial. The incremental gain with Libre Pro was a 0.5% A1c advantage in type 1 and a 0.2% advantage in type 2, both from very high baselines. We might have expected larger declines, but this wasn’t a formal study. A1c reductions were seen at all seven sites, though there was a very wide range of observed declines from site to site – the smallest A1c drop was 0.4%, while the largest was 2.1%! Why are some clinics doing so much better than others, despite starting from similar baselines? Is it an inherent property of the demographic a clinic serves, or is it a modifiable procedural component of care, or a mixture of both? Dr. Mohan and co. did perform multiple regressions to determine the factors that made patients more likely to be Libre Pro non-responders (aka to not see a marked A1c drop after 14 days of wear): They found that longer duration of diabetes, time to follow-up A1c test (the beneficial A1c effect disappears), and insulin user were all associated with a lack of A1c reduction.

  • Dr. Mohan offered some grim commentary on diabetes care in India: He reminded us that patients pay for everything out of pocket – for this reason, there are a miniscule number of patients on pumps, and “you’re lucky if you can get them on MDI.” He later added in Q&A that a sensor in India costs 2,000 rupees (~$30), but physicians tack on ~1,000 rupees for application, data download, data review, etc., putting the total closer to ~$45. A Libre Pro sensor in the US costs $60, but is covered by most insurance plans for up to four bouts per year. FreeStyle Libre is definitely a technology that can scale to meet a market like India, so we hope with volume the cost of Pro can come down even further – or perhaps other relationships could be forged with government to cover some of the costs.

Corporate Symposium: Continuous Glucose Monitoring as Standard of Care: Clinical Outcomes, Improved Access and Therapeutic Use (Sponsored by Dexcom)

Continuous Glucose Monitoring in Patients Treated With MDI

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

Dr. Richard Bergenstal provided a thorough overview of the utility of CGM in patients on MDI (both type 1 and type 2), focusing on the proven benefit of CGM independent of pumps. He pulled from a host of recent literature – including the JAMA-published DiaMonD and GOLD studies – to make a number of important points: (i) CGM indisputably improves glucose control when used consistently; (ii) CGM improves time-in-range without increasing hypoglycemia; (iii) CGM is beneficial regardless of education, age, numeracy, and a number of other demographic factors; and (iv) CGM reduces diabetes distress. He emphasized, too, that access to CGM readings has been shown to improve glucose control WITHOUT changing daily insulin dosage, suggesting that there is a significant effect on lifestyle (that, in his view, is often underappreciated). Dr. Bergenstal closed by applauding the FDA’s non-adjunctive label approval for Dexcom’s G5 (“a HUGE step forward”) and sharing big hopes for the benefit of CGM in the Medicare population (once the details of administering coverage are figured out).

Clinical Review Of Patients

Steve Edelman, MD (University of California San Diego, CA)

In a discussion of the clinical benefits of CGM, the outspoken Dr. Steve Edelman left no doubt about his views on the value of the technology, stating “CGM is the single most important advance for type 1 diabetes since the discovery of insulin,” and going on to note even more emphatically that, “not using CGM in type 1s is bordering on malpractice.” He highlighted the value of trend arrows (“if you don’t look at those, you’re missing one of the most important parts of your CGM”), the challenge of adjusting insulin around glucose variability (“every day is different for a person with type 1 … despite following the same rules”), the importance of education around alerts and alarms, and the even brighter future for CGM (more accurate, less interference, etc.). Of course – as he has done before – he also did not pass on the opportunity to share with providers his “strong” view against  blinded CGM: “If you can engage a patient, then that’s the key. Engagement is the KEY with CGM. You want patients to see in real time, ‘Gee, look what happens when I forget my medication!’ Do we blind home glucose monitoring? No, of course not!” (We’d note that for those not willing to wear real-time CGM or unable to afford it, blinded CGM is better than very limited/no glucose data at all.)

iDCL Update

Boris Kovatchev, PhD (UVA, Charlottesville, VA)

Dr. Boris Kovatchev’s broad overview of his team’s contribution to automated insulin delivery research was highlighted by new preliminary data from the now “complete” training phase (n=20) of the NIH-Funded International Diabetes Closed-Loop (iDCL) Trial that continues to show promising time-in-range and hypoglycemia numbers in a small group of patients. Dr. Kovatchev shared data from 11 individuals in which the training protocol achieved strong glucose control consistent with past results shared at ATTD: overall mean glucose of 145 mg/dl (150 mg/dl in the “first half of the night”; 125 mg/dl in the “second half of the night”), just 1% of the time <70 mg/dl, and 77% of the time in 70-180 mg/dl (75% in the “first half of the night”; 91% in the “second half of the night”). We have not seen this cut of the overnight iDCL data before, though the improvement as patients get further into sleep is consistent with this algorithm and awesome for the user experience and waking up around 120 mg/dl to start every day. Notably, Dr. Kovatchev also highlighted the very notable win that patients saw no readings <60 mg/dl or >300 mg/dl, evidence that the system is mitigating the most dangerous of highs and lows, particularly with automatic correction boluses to stem highs. We hope this data will also be borne out by the main phase of the trial ( posting here), which Dr. Kovatchev confirmed will begin “soon” (no specific timing update, though Tandem said the same yesterday). As a reminder, the main study will serve as Tandem/TypeZero’s hybrid closed loop pivotal (still not yet open for recruitment, according to and will randomize 240 patients in a 2:1 ratio of closed-loop control vs. sensor-augmented pump therapy over six months.

  • Dr. Kovatchev included a critique of the Bionic Pancreas, noting that some of the results with insulin-only systems have actually seen less hypoglycemia than those with the dual hormonal system – “Interesting to note considering that glucagon is supposed to reduce this.” Of course, we’d emphasize that it’s impossible to compare across trials given the entirely different study designs (sedentary vs. exercise/meals), study populations (pediatrics vs. adults), meal bolus strategies (announcement or not), study lengths, and study settings (super challenging diabetes camp settings vs. less challenging daily ambulatory life settings vs. less challenging inpatient studies). In our view, this debate will only be settled when head-to-head data AND user preference information is available. The benefits vs. costs of different systems will obviously vary greatly between users and even within users over time.

Product Theater: It's Time to Rethink Professional Continuous Glucose Monitoring – The New FreeStyle Libre Pro System (Sponsored by Abbott)

It’s Time to Rethink Professional CGM: The New FreeStyle Libre Pro System

Daniel Einhorn, MD (UC San Diego, CA)

Speaking to an overflow audience (“overflow” is actually an understatement, as every seat was filled and about 70 folks were left either sitting on the floor or standing), Dr. Daniel Einhorn espoused the tremendous benefits he has seen in his own practice with Abbott’s FreeStyle Libre Pro: “This technology is so good and has had such enormous impact on my practice and my patients, I couldn’t resist [speaking here today].” Dr. Einhorn stressed a number of the classic arguments we hear in favor of Libre Pro (highly accurate after Day 1, no calibrations, no effort on the part of patients, no disinfecting, easy start and download with a single HCP-owned reader) and his enthusiasm was evident throughout his lecture:  “Anybody worthy of getting an A1c is worthy of getting this kind of monitoring because it provides that granularity that is needed for therapy decisions”; “Patients with SMBG may be way off and may not know it because they don’t understand the nuances of BG testing … So if I care enough to have you come in and talk with me, then I’m going to use Libre and look at that data.” He also addressed attendees’ angst about the real-time version of Libre, noting that that is a “different animal” but stressing that retrospective data – in and of itself – can be extremely valuable for patients and providers alike. Notably, a poll of the audience showed that only about 10-15% of the room was currently using Libre Pro in their practices (though we’re not sure what percentage of the audience were clinicians based where Pro is available). We see tremendous runway for this technology to revolutionize the glycemic picture for those unwilling/unable to wear real-time CGM.

Special Event: U.S. Diabetes Exchange & Experience event (dX2) (Sponsored by Abbott)

Visualizing the Future of Diabetes Management

Jo Boaler, PhD (Stanford University, Palo Alto, CA), George Grunberger, MD (AACE, Bloomfield Hills, MI), Joel Goldsmith (Abbott, San Francisco, CA)

Abbott’s Mr. Joel Goldsmith boiled down the digital transformation of diabetes care to three main trends: The shifts from strips to sensors, from proprietary handheld devices to connected consumer electronics as the preferred user interface, and from desktop application analytics to cloud-based services. CGM is becoming the standard of care, making the capture of dense glucose data almost effortless and much more cost effective – this dense data makes it easier to visualize trends and patterns. [He took the opportunity to remind attendees that FreeStyle Libre consumer is currently available in 35 countries and used by 300,000+ patients, though is still under review by FDA.] Similarly, smartphones are becoming intertwined with standards of medical care – they are pervasive, and are more and more an integral part of traditionally highly-regulated medical devices. Not only do they offer a familiar user interface and a constant source of connectivity, but they reduce the burden associated with carrying additional devices on one’s person. Finally, moving to the cloud is enabling instantaneous, widespread sharing and new forms of advanced data analytics, “helping to deliver on the promise of precision medicine.” Taken together, these three shifts are lowering the barrier to both insight generation and access, and are beginning to deliver outcomes. Mr. Goldsmith concluded by explaining why diabetes may be the “perfect candidate” for machine learning and AI: It is data-intensive, largely self-managed (increasingly through connected consumer electronics), and a growing global epidemic. We are seeing signs of life in this turf – automated retinopathy screening, Medtronic/IBM Watson’s Sugar.IQ (more on this in an oral session tomorrow), and One Drop just laid the foundation for future AI intervention this morning with its Amazon Alexa integration.


-- by Adam Brown, Varun Iyengar, Brian Levine, Maeve Serino, and Kelly Close