ATTD 2019 (Advanced Technologies & Treatments for Diabetes)

February 20-23; Berlin, Germany; Day #3 Highlights – Draft

Executive Highlights

  • In CGM, Abbott shared the largest real-world CGM data set ever in a poster on nearly 500,000 FreeStyle Libre users across 26 countries. The combined 4.8 billion (!) glucose reading give an unprecedented global pulse on a sizeable fraction of FreeStyle Libre’s >1.3 million global users. The median Libre user (10 scans/day) has a time-in-range of 56% (70-180 mg/dl), with 34 minutes/day (2%) spent <54 mg/dl and four hours/day (17%) at >240 mg/dl. In a Dexcom Clarity symposium, we got a look at CGM data flowing directly into UVA’s Epic EMR – a big step forward for HCPs. We also saw preliminary results from CGM users in Onduo’s Blue Cross Blue Shield pilot, as well real-world Eversense XL data, showing time-in-range by country ranging from 69% in Austria to 53% in Switzerland.

  • Sanofi’s digital health plans came out of the woodwork in a big way, with updates across the portfolio: Prefilled and durable connected pens are in development, 3,500 people are using its insulin titration products, and ~4,600 people are enrolled in Onduo. We also learned more about Onduo’s 100% remote CGM onboarding approach  and were treated to a look at DreaMed’s decision support pipeline.

  • In automated insulin delivery, we saw full 2-6-year-old MiniMed 670G data over three months (nearly identical to the 7-13-year-old results). Elsewhere, real-world Basal-IQ data (n=2,712) from Tandem showed less time spent in hypoglycemia than that achieved in the PROLOG pivotal trial: at three weeks, Basal-IQ users spent just 1% time <70 mg/dl, besting PROLOG results by 20 minutes per day. This is outstanding.

  • In therapy, Dr. Chantal Mathieu and Prof. Thomas Danne gave a tempered take on the data for 2.5 mg empagliflozin from EASE-3. Substantial excitement followed Lilly/BI’s presentation of phase 3 EASE data at EASD 2018 showing no DKA signal with a 2.5 mg dose – but both speakers emphasized that this single study lasted only six months (DEPICT-1 had no DKA signal at six months, either) and had very few DKA events overall.

    • Additionally, and separately, a Lilly-sponsored series on severe hypoglycemia presented results from the CRASH survey, showing that only 10% of type 1s (n=110) and 5% of type 2s (n=98) injected glucagon to recover from severe hypoglycemia.

  • Our initial exhibit hall highlights include Unomedical (encouraging 7-10 day wear outcomes; dual-hormone set), Roche (our first look at the Accu-Chek Solo patch pump) Medtrum (patch pump PLGS system). We’ll be back tomorrow with our full exhibit report from over 30 booths.

Greetings from Berlin, Germany! We’re back after a travel-filled weekend with installation #3 of our coverage from a very busy ATTD 2019. Read below for our top highlights below plus an exhibit hall aperitif.

Table of Contents 

CGM Highlights

In the largest real-world CGM data set ever shared, an Abbott poster showed CGM metrics from nearly 500,000 FreeStyle Libre users across 26 countries (including the US). The data include a staggering 4.8 billion glucose readings collected from 470,463 readers (September 2014-May 2018). This gives an unprecedented global pulse on time-in-range and outcomes in a sizeable fraction of FreeStyle Libre’s >1.3 million global users. Given that two-thirds of FreeStyle Libre users have type 1, it’s also a read on CGM outcomes to inform benchmarks – e.g., What are realistic goals? How do different technologies and studies compare to global time-in-range? We’ve created the table below to summarizes the key stats, comparing CGM metrics for the median FreeStyle Libre user that scans 10 times per day vs. the lowest-scan group (4 scans/day) vs. the highest-scan group (40 scans/day). Our takeaways follow the table below, and pictures are below those.

N=470,643 readers

Median Libre User
10 Scans/Day

Lowest-Scan Users
4 Scans/Day

Highest-Scan Users
40 Scans/Day

Estimated A1c




(70-180 mg/dl)

13.5 hours/day

11.7 hours/day

16.9 hours/day

Time <54 mg/dl

34 minutes/day

34 minutes/day

24 minutes/day

Time >240

4 hours/day

6 hours/day

2.2 hours/day

  • What are CGM metrics in the median FreeStyle Libre user? A time-in-range of 56% (70-180), 34 minutes per day (2%) spent <54 mg/dl, and a staggering four hours per day (17%) spent >240 mg/dl. Even with a 10 scans per day, this shows a still-dangerous amount of time spent at extremely high and low glucose values – 4.5 hours per day spent <54 mg/dl or >240 mg/dl. And this is only the median – i.e., 50% of the FreeStyle Libre population has outcomes worse than these.

    • Implication: Continuous glucose data alone is not enough – better education on what to do with the data (food, mindset, exercise, sleep) and insulin automation are desperately needed to reduce/eliminate extreme glucose values.

  • Guidance on time-in-range goals? In this enormous global database, the lowest scanning group had a time-in-range of 48% vs. 70% in the highest scan group. Overall, 63% of FreeStyle Libre users had a time-in-range over 50% (12 hours per day). The poster does not allow for further extrapolation – e.g., what percentage of users have a time-in-range above 70% – the proposed goal? It does mention that in users with less than 10 scans/day, only half of them had >50% time-in-range. In those taking more than 10 scans/day, a majority (73%) had >50% time-in-range. (For those wondering about the stats here, the data have been grouped by scan frequency first, and then the mean is found for each scan-frequency group. This means a separate analysis will be needed to answer the “How many have time-in-range >70%?” question.)

    • Implication: A majority of FreeStyle Libre users have a time-in-range >50%, though a sizeable 37% are spending more than half the day outside of 70-180 mg/dl.  

  • Same technology, huge variability – data shows a major 1.5% eA1c difference between the lowest and highest scanning groups (8.2% vs. 6.7%), a 3.4 hour/day difference in time-in-range (56% vs. 70%), and nearly two fewer hours per day spent over 240 mg/dl (17% vs. 9%). Scanning frequency also has huge variability – a 10x daily difference between the lowest and highest groups in this data.

    • Implication: How can we shrink disparities in time-in-range? What else can be learned from Bright Spots – those with >65% time-in-range? Beyond scanning glucose more, what is the high-scanning group doing differently?

  • Even those with an on-target eA1c of 6.7% had a lot of extreme values – two full hours per day over 240 mg/dl and 2% of the day <54 mg/dl.

    • Implication: average glucose is wildly inadequate to characterize dangerous glucose extremes.

  • The difference in time <54 mg/dl (10 minutes/day) was small between the lowest and highest-scanning groups – will FreeStyle Libre 2 with alarms bring the time <54 closer to zero in both groups?

  • Higher glucose levels do not protect against hypoglycemia. Time <54 mg/dl was higher in groups with the highest estimated A1c and lowest in those with the lowest estimated A1c. In other words, running high all the time is not a strategy to avoid lows. 

  • For context, this expanded data set is 9x bigger than the one shared at ATTD 2017, and nearly twice as big as last year. It also shows Abbott’s leadership position on real-world Big Data in CGM – at least in terms of patient base.

    • We hope to see Abbott move to the hypoglycemia and hyperglycemia consensus definitions: “<54” and “>250,” rather than the <54 and >240. Fortunately, the critical 70-180 for time-in-range is being used!


2. Dexcom Clarity in EMRs – Demo of UVA Integration into Epic (PDF upload); Dexcom EMR program to expand this year; more UVA phases

Following a mention at last year’s ATTD, a Dexcom Clarity workshop shared the workflow of Dexcom CGM data flowing directly into the EPIC electronic medical record. Dexcom is using a software company called Redox, which pulls CGM data in a PDF from Clarity and uploads the PDF to the “media” tab of a patient’s EMR. The program has been live within UVA’s Epic system since January, and we got a great look at it today. During a visit, an HCP adds an “order” for the CGM data within Epic, and the report is delivered in less than a minute directly into the EMR – no need to log in separately to Clarity. The process only requires a one-time patient authorization, which can be done on a patient’s phone. (The patient logs in to his/her Dexcom Clarity account and “authorizes” the connection, similar to authorizing other data partner apps like Glooko.) The order is linked to the CPT billing code 95251, allowing the HCP to bill for CGM data interpretation. UVA uses this integration for both telephone and in-person visit encounters. Dexcom plans to expand the program this year, and it also works with Cerner and Athena Health EMRs – nice! Said Director of Data Partnerships Dr. Nate Heintzman, “Scalability and interoperability are baked into this approach.” Ohio State’s Dr. Kittie Wyne, who was in the audience, eagerly asked in Q&A how she could get it immediately in OSU’s version of Epic. Though some work will be required locally – e.g., getting the health system’s IT department to agree – having the technical aspects figured out is a big step.

  • Getting a PDF from Dexcom Clarity automatically into the EMR is an important step, but not the end goal. UVA’s Dr. Ananda Basu shared that there are two more phases for UVA’s CGM data integration into the EMR, which he hopes to complete in the “next 1-2 years.” Phase 2 aims to add time-series CGM data (i.e., not a static PDF), insulin data, heart rate/activity data, and deploy analytics and custom reporting. Phase 3 will expand the automated data collection and storage, including more advanced analytics, sending “mini reports” into the Epic record, alerting healthcare providers, and real-time remote monitoring. Excellent!

  • There are now 13 launched apps that work with Dexcom’s retrospective data API, including new additions Welldoc Bluestar, Social Diabetes, Fitabase, and Validic. The Developer.Dexcom app gallery will be updated soon. The other nine include One Drop, Glucose Buddy, Tidepool, Achievement by Evidation, Ensa, Rimidi, Glooko, Center Health, and 1Bios. Over 1,000 third parties have registered as Dexcom Developers ( and “hundreds of prototype apps” have been created thus far. Dexcom took the lead in September 2017 when it launched this public API for third-party apps to leverage retrospective CGM data; to date, it remains the only program of its kind in CGM.

  • In line with recent Dexcom updates, Dr. Heintzman noted ongoing work on a (i) CGM-based insulin dose calculator (the acquisition of TypeZero should help a lot here); (ii) adding more context around CGM data (diet, activity, insulin – “The more context, the better the insights and outcomes,” said Dr. Heintzman); (iii) population-level data; and (iv) standardized APIs for expanded access and more data partnerships.

3. Year-Long Medtronic iPro 2 Professional CGM Study Results in Mean 1.3% A1c Reduction, Reduced Hyperglycemia and Variability with No Increase in Hypo; Interesting Therapy Adjustment Charts

Following the interim readout of eight-month data a year ago, Medtronic’s Dr. Bob Vigersky presented full 12-month results from the single-arm Portuguese ADJUST study of the iPro 2 professional (blinded) CGM in 102 people with type 2 diabetes. At 12 months, following quarterly CGM applications, each with a follow-up visit (in-person or by phone), A1cs had dropped by a mean of 1.3% (baseline: 9.4%). The greatest A1c reduction (-1.0%) unsurprisingly occurred after the first CGM application, and by study’s end, 81% had seen a drop of ≥0.5% (6% saw their A1cs increase). Mean glucose (185 -> 170 mg/dl), standard deviation (53 -> 49 mg/dl), and percent time >180 mg/dl (48% -> 37%) all improved without a concomitant increase in time <70 mg/dl or the number of hypoglycemia episodes (both low at baseline). Qualitatively, patients reported increased treatment satisfaction, less frequent detection of hyperglycemic symptoms, and no change in perception of hypoglycemia; there were also reports of improved communication between patient and healthcare team and a need for specific professional training on professional CGM protocols. Dr. Vigersky showed an interesting graphic overviewing the types of adjustments providers were making and at what times (below). The pie charts show how therapeutic changes became less common over time (top) as people’s profiles theoretically stabilized, and the types of recommendations providers made over time. Most participants saw tremendous benefit from participating in the study, and we hope to see more validations of the power of intermittent CGM in type 2s to drive positive therapeutic change (and reduce inertia!). 

  • These strong results were obtained in a type 2 population with 53% unemployment (31% were retired, 11% unemployed) and in which ~27% hadn’t progressed in their studies past middle school. We’d be interested to see this population followed over time – following a year of intermittent blinded CGM-guided therapeutic and behavioral changes, what happens to A1c and self-management practices? Do they revert back to baseline? What is the optimal frequency of professional CGM moving forward for most people to maintain their ADJUST gains? Would results be stronger with real-time, intermittent CGM?

4. >3,000 Onduo Participants’ Mean Glucoses Before + After CGM (G5) in BCBS Pilot; Only Given to People Demonstrating ≥7 Fingersticks/Day

Onduo Medical Director Dr. Amit Majithia presented preliminary data from type 2 patients who received CGM as part of Onduo’s Blue Cross Blue Shield pilot conducted in three states. The plot below depicts the distribution of “well over 3,000” participants’ average blood glucose in the 14 days prior to CGM minus the 14 days after CGM. Negative numbers indicate that average blood glucose increased following CGM, while positive numbers reflect a decline. As demonstrated below, the majority of individuals hovered around a neutral effect, with the average suggesting some effect – though a sizeable portion saw an increase in mean blood glucose. This was an odd, obfuscating way to present the data, and it would have been far more instructive to see time-in-range, GMI, and time in extreme glucose ranges in this group. Dr. Majithia justified the surprising increase in that Onduo encouraged people to “go crazy” with their CGM initially, treating it as an “exploratory time.” (We assume this means testing the limits of what they can eat, etc.) Dr. Majithia asserted that for some participants, receiving CGM resulted in an A1c benefit equivalent to that achieved with a pharmacologic therapy – e.g., the trace below from one individual shows a 1.5% A1c reduction in five months (baseline A1c: 8.9%).

  • CGM use was shown to increase app activity. Dr. Majithia showed data reflecting a spike in activity (i.e., meal, medication, and BGM logging, messaging) during the first seven days of CGM. While activity declined a bit during the second week of CGM use, activity reverted to baseline when CGM was discontinued. It’s exciting to see that CGM data may drive further interest in diabetes management.

  • The funnel plot below depicts the drop-off rates for each stage in the CGM acquisition process. About half of participants who logged in had a CGM strategy initiated, consisting of coach education and evaluation of willingness to use. If participants completed the subsequent survey, “most” completed the remainder of the protocol, which includes a “provider visit,” CGM shipment, and CGM worn for at least three weeks. The provider visit could either be a face-to-face telemedicine video chat or an asynchronous review by a physician. One of the pilot states, Arkansas, required a face-to-face interaction prior to shipping the CGM. Interestingly, Dr. Majithia found that less people completed the survey if a face-to-face visit was required; however, these patients were more likely to wear the CGM than if they underwent an asynchronous review. Asynchronous review still ultimately drove ~10% more CGM wear, given the high survey drop-off rate in those who were required to undergo a face-to-face interaction. It’s possible that participants value CGM more after devoting the additional time to the acquisition process. In fact, Dr. Majithia was surprised that overall ~15% of participants who received CGM did not initiate the sensor after three weeks. He believes that because the participants received the device easily and at no cost, they lacked the same urgency than a patient who had to go to a physician’s office might experience. They might also look at the G5 inserter and be deterred, whereas the G6 might be more attractive.

  • Onduo participants were eligible to receive a CGM if they were able to demonstrate 7-14 fingersticks/day. While Dr. Majithia considered this requirement “pretty permissive,” we were shocked to see such a high number. (For context, the median FreeStyle Libre user, as shown above, scans 10 times per day! This is a ludicrous number of fingersticks.) Still, as he pointed out, Onduo was using Dexcom G5, which requires two calibration fingersticks per day. We know it’s early, but if we were designing the intervention, we wouldn’t mandate fingersticks for enrollees to try CGM at least once – everyone can learn something from seeing their glucose values in real-time for a week.    

6. Real-World EU Eversense XL Time-In-Range and Sensor Wear Data by Country; Six-Month Outcomes in N=21 Italian Sample Show ~4 Hour/Day Increase in Time-In-Range

Dr. Concetta Irace (Magna Græcia University of Catanzaro) shared real-world Eversense XL data from the EU registry showing percent time-in-range (70-180 mg/dl) collected from patients across 11 countries as of September 2018 (sample size not provided). Percent time-in-range varied from 69% in Austria (~16.6 hours/day) down to 53% (~12.7 hours/day) in Switzerland. Dr. Irace also presented sensor wear time (percent of time spent wearing the transmitter) data ranging from 92% in Denmark to 89% in the Netherlands; both come out to about ~21-22 hours per day, which is strong but which we’d liketo see even higher.

  • Dr. Irace also presented real-world data from a small sample (n=21) of Eversense XL users in Italy showing significant improvements in time-in-range, A1c, and glycemic variability after the first six months of wear. Time in a narrower range (70-160 mg/dl) increased from 43% to 58% (+3.6 hours) and A1c declined by 0.6% from a low baseline of 7.3%. Glucose standard deviation (SD) also improved by a robust 9 mg/dl, decreasing from 59 mg/dl to 50 mg/dl. Dr. Irace was particularly excited by the glycemic variability results, as they were not included in the pivotal trial.

  • A separate survey of 767 Italian Eversense XL users found high patient satisfaction. 87% of respondents reported feeling “very satisfied” or “satisfied” with the overall experience. An overwhelming 94% of respondents indicated that the procedure was “totally painless.”

7. Helmsley-Funded Jaeb+Cecelia Health Study Investigates Direct-To-Consumer CGM In Type 1 Adults; Participants Choose FreeStyle Libre or Dexcom G6 Plus CDE Support

Dr. Roy Beck shared that a Helmsley-funded study conducted by the Jaeb Center in collaboration with Cecelia Health (formerly Fit4D) and Wisconsin Research and Education Network (WREN – a clinical research network of primary care providers in Wisconsin) will investigate direct-to-consumer CGM uptake primarily in type 1 diabetes. The primary outcome is sustained use of CGM when initiated outside the clinic, with a goal of 6-7 days/week wear time. The study has started as of this month and will also examine outcomes including: (i) occurrence of severe hypoglycemia, DKA, and hospitalizations; (ii) A1c measured at three months; and (iii) CGM metrics at three months. Participants must be adults (≥18 years-old) with type 1 diabetes or type 2 diabetes using basal-bolus injection therapy (pump or MDI) and cannot have used real-time CGM within the past two years. Participants can choose between the Dexcom G6 or Abbott FreeStyle Libre and are provided with information and guidance via a video conference with a CDE. Dr. Beck described an intentionally gradual, staged onboarding process to mitigate patients feeling overwhelmed. Participants are sent their CGMs in the mail and undergo an onboarding session with a CDE. After two weeks, CDEs introduce visualization tools and instruct participants on how to use CGM data for self-management. Two weeks later, CDEs provide troubleshooting tips and tricks. Throughout the whole study, participants will have access to CDEs. Dr. Beck estimates ~10 participants have already enrolled towards a target of 30 participants, and the study population is fairly split in their decision to choose either the G6 or FreeStyle Libre. The study will also collect information on the factors that lead to participants’ selection. As the study expands, Dr. Beck expects to incorporate decision support tools and mental health support/coaching. We are very excited about this study, as it may pave a path towards non-prescription CGM. As Dr. Beck put it: “CGM is getting to the point where, like BGM, it shouldn’t require a prescription.” Dr. Beck anticipates results to be presented at ATTD 2020.

Automated Insulin Delivery Highlights

1. 670G Pivotal in 2-6 Year Olds Full Data: 0.5% A1c Decline (Baseline 8%); +2 Hour/Day TIR Gain; No Change in Hypo; Consistent with 7-13-year data

Following the first topline look on Day #2, Medtronic’s Dr. John Shin presented detailed results from the MiniMed 670G pivotal study in 2-6 year olds over three-months of use (n=46, n=6,697 patient days). Results were nearly identical to the 7-13 year-old cohort: a 0.5% decline in A1c (baseline: 8.0%); an outstanding +2 hours/day spent in 70-180 mg/dl (from 55% to 64%); and no change in time <70 mg/dl (from 3.6% to 3.5%). Time in the tighter 70-140 mg/dl range also increased by two hours per day (34% to 42%) – a meaningful gain in a tough-to-manage age group. After three-months on the 670G, 52% of patients had an A1c <7.5%, up from 32% at run-in. Mean glucose declined from 173 mg/dl (run-in) to 161 mg/dl (670G), with a smaller change in standard deviation (65 mg/dl to 63 mg/dl); as a result, CV increased nominally from 38% to 39%. Median sensor wear was a strong 93%, with 87% of the time spent in Auto Mode – much higher than 81% in 7-13 years and 76% in 14-21 years. There were zero DKA or severe hypoglycemia events. As with the prior 670G pivotals in other ages, the design compared two weeks of run-in (CGM and pump in open loop) vs. three months of hybrid closed loop. The modal day profile shows the expected reduction in variability – especially overnight – though some is obviously due to the imbalance in use time (i.e., 14 days of CGM data from the run-in will always have wider bars than 90 days of data). Still, the 670G’s overnight consistency in this age group probably saved parents a lot of sleep and worry. As of Medtronic’s Day #2 presentation, the company plans to launch the 2-6 year old indication within one year, combined with Bluetooth and a remote monitoring app for caregivers.

  • Dr. Shin also showed some valuable comparisons to other age groups; the 670G has been remarkably consistent across all the key metrics. As shown below, all groups have seen a 0.4-0.6% A1c reduction, a time-in-range gain of 1-2 hours per day (largest in the <14 year groups), and a similar ~3% time spent <70 mg/dl. Of note, the 2-6 and 7-13 year cohorts had the lowest time-in-range at run-in (~56%), and both groups improved by two hours per day while on the 670G (~64%-65%). Auto Mode use was highest in the 2-6 year old (87% )and 22+ year-old groups (88%), and lowest in 14-21 year olds (76%).

  • Participants in this 2-6 year-old group were all previously on a pump for >3 months, and 93% were previously on CGM. Would the outcomes have looked even better in those coming from MDI and/or fingersticks?

2. Real-World Basal-IQ Users (n=2,712) Spend 20 Minutes Less Time <70 mg/dl than PROLOG Pivotal Trial Participants; N=1,000 Basal-IQ Study in the Works

Tandem’s Dr. Steph Habif presented very impressive real-world data (n=2,712) uploaded to t:connect from users with at least three consecutive weeks on Basal-IQ predictive low glucose suspend. Impressively, time-in-hypoglycemia was even lower in real-world users than that reported in the PROLOG pivotal trial across all measures. At three weeks, real-world users spent a remarkably low 1.2% time <70 mg/dl – 20 minutes less than that seen in the pivotal trial with Basal-IQ (2.6%). These improvements in time <70 mg/dl were maintained in real-world users both during the day and night. Real-world users spent 1.2% and 0.7% time <70 mg/dl during daytime and nighttime, respectively, while PROLOG participants spent 2.4% and 2.7% time <70 mg/dl. These results reflect differences of 17 minutes and 29 minutes in time <70 mg/dl during the day and night, respectively. A sub-cohort of 1,437 users with at least six weeks of Basal-IQ data uploaded to t:connect demonstrated that the reductions in hypoglycemia were sustained over time across all measures. Two caveats: (i) We don’t know baseline time <70 mg/dl in the real-world cohort (it was 4.5% on SAP in PROLOG); and (ii) G5 was used in PROLOG, while G6 is used in the real world. To benchmark these data, look at highlight #1 in today’s report, which shows the median FreeStyle Libre user (nearly 500,000 users!) is spending 2% of the day <54 mg/dl; these <70 hypoglycemia metrics for Tandem are over 80% lower.

  • Split out by age, real-world data showed patients over 60 years-old (n=208) spend the lowest time in hypoglycemia, with just 0.92% <70 mg/dl, 0.22% <60 mg/dl, and 0.03% <50 mg/dl. We were a bit surprised to see that those in the 18-60-year-old age group had the highest time <70 mg/dl (1.29%) and <60 mg/dl (0.35%) – we might have thought patients <18 years-old would spend the most time in hypoglycemia. However, no statistical analyses were provided, so it is unclear whether these differences are significant. These age-related trends held true during the day and night for time <70 mg/dl, with those over 60 years-old spending the least amount of time in hypoglycemia, followed by those <18 years-old and then those in the 18-60-year-old age group.

  • Dr. Habif noted that Tandem is currently conducting a longitudinal patient reported outcomes (PROs) study, following 1,000 people on Basal-IQ during their first six months on the system. While data from the study are not yet available, she shared results from a dQ&A-conducted survey (n=417) of patients’ attitudes regarding the Basal-IQ vs. Medtronic’s MiniMed 670G. Participants rated Basal-IQ higher for every measurement presented, including satisfaction, trust, helping to feel more in control of their diabetes, helping to sleep better at night, and ease of use.

3. PROLOG Sub-Analysis Finds CGM Use Alone Does Not Confer Hypoglycemia Reductions Observed in CGM-Naïve Participants

Also at the Tandem symposium, we saw a new sub-analysis of the pivotal PROLOG trial for t:slim X2 with Basal-IQ predictive low glucose suspend showing outcomes split by prior pump and CGM use. Sansum Diabetes Research Institute’s Dr. Jordan Pinsker underscored that for those without previous CGM use, the addition of CGM alone was not responsible for the reductions in time <70 mg/dl achieved with Basal-IQ. As depicted below, non-CGM users (a small n=16) in the sensor-augmented pump (SAP) group spent 6% time <70 mg/dl, while those on Basal-IQ spent 4% time <70 mg/dl – a difference of ~33 minutes. In fact, those in the SAP group actually spent more time <70 mg/dl as compared to baseline (+14 minutes). Interestingly, the same cannot be said for the results broken out by pre-study insulin delivery. Prior MDI users saw 2.6% time <70 mg/dl on SAP and 2.3% time <70 mg/dl on Basal-IQ – an inconsequential difference between the two groups. Overall, these are very, very low rates of hypoglycemia – far lower than typically seen in real-world use.

Diabetes Therapy Highlights

1. Experts Sound Off on Type 1 Adjuncts: Drs. Mathieu and Danne Raise Safety and Efficacy Questions for 2.5 mg Empagliflozin; Dr. Garg Highlights ~15% of Type 1s Using Off-Label Meds

A triumvirate of experts on type 1 diabetes adjunct therapy – comprised of Prof. Thomas Danne, Dr. Satish Garg, and Dr. Chantal Mathieu – delivered a tour-de-force series of presentations on the topic. Of particular note, both Prof. Danne and Dr. Mathieu expressed substantial concern over the quality of data supporting the 2.5 mg empagliflozin dose tested in Lilly/BI’s phase 3 program in type 1. Dr. Mathieu first addressed the EASE program’s efficacy data (see EASD 2018 – 2.5 mg was tested in EASE-3 but not EASE-2), pointing to the gradual loss of efficacy on A1c lowering and less impressive weight loss with the 2.5 mg dose, particularly when compared to the 10 and 25 mg doses that drive more glucosuria. Very notable was her caution over the DKA data for 2.5 mg. While there was no observed increased risk of DKA vs. placebo with the 2.5 mg dose (only 2 DKA events occurred in this arm), Dr. Mathieu offered two strong words of caution: “very few events and only six months.” Indeed, while EASE-2, which tested only 10 mg and 25 mg, was extended out to 52 weeks, EASE-3 was continued only to week 26 – a shorter duration that stands in strong contrast to full 52-week programs from both AZ (DEPICT, for Farxiga) and Sanofi/Lexicon (inTandem, for sotagliflozin). Both speakers also made the highly-compelling point that when six-month data for DEPICT-1 were presented at EASD 2017, there was no DKA imbalance between dapagliflozin and placebo, either – but one did emerge in the 52-week (ADA 2018). In Dr. Mathieu’s assessment, this calls into question whether an imbalance would have emerged had EASE-3 been carried out to a year. Prof. Danne echoed these sentiments and further questioned the efficacy of 2.5 mg empagliflozin on the endpoint that seems to be most important to patients – time in range. As he put it, “What patients are reporting is that their glycemic variability is going down and they feel glucose swings less…But with the low dose we don’t have a very significant improvement in time in range.” In EASE-3, patients saw a mean 6% daily gain in time between 70 and 180 mg/dl, compared to +12% with 10 mg, +8% with 25 mg, and +2% with placebo – translating to a placebo-adjusted gain of just-under 1 hour with 2.5 mg. While any time-in-range gain is a positive, Prof. Danne understandably characterized this effect as underwhelming relative to the consistent ~2-3 hour gains conferred by the higher doses, as well as dapagliflozin and sotagliflozin in DEPICT and inTandem. On balance, both experts admitted that, while 2.5 mg empagliflozin may indeed offer efficacy with a lower or no risk of DKA, the data that exist so far should be taken as no more than an indication that this might be the case.

  • We appreciated Dr. Mathieu’s reminder not to forget about heart and renal outcomes for people with type 1 diabetes and the role adjunct therapy could play in lowering residual CV/renal risk. She emphasized that there’s “still a lot of morbidity and excess mortality” among type 1s, reasoning that the biggest remaining question on this front is whether the CV and renal benefits well-established with SGLT-2 inhibitors in type 2 diabetes will translate to type 1. On the first day of ATTD 2019, Dr. Per-Henrik Groop presented preliminary, mechanism-based evidence in favor of a renal benefit in type 1, while cautioning that bona fide clinical data is still on it way. To be sure, the rise of SGLTs in type 1 has led to increasing and insistent calls for a full CVOT in type 1 diabetes – which, doubtless, we’d love to see.

  • Prof. Danne also presented the recently-published international consensus on DKA risk management with SGLTs in type 1, contextualizing the document as a starting point to be further developed and bolstered by additional investigation. Certainly, there’s a fine line to walk between (i) the need to make effective and actionable recommendations applicable to the current and increasingly common use of SGLTs for type 1 and (ii) the immense lack of data on whether any given risk management and DKA treatment strategy is actually effective, particularly in the real world. In our observation, the consensus committee is doing an excellent job of walking this line, and we hope to see a continued push forward on testing and optimizing DKA risk minimization strategies. This is all the more important in light of a point Prof. Danne highlighted: In the type 1 programs, both placebo and treatment arm DKA rates came in below those reported in general practice; while there is an increase in DKA risk with SGLTs, there is also an enormous opportunity to approach DKA minimization more effectively on a general and global scale. How can the field get smarter about DKA prevention for all type 1s?

  • Dr. Garg reviewed the history of and current utilization of adjunct type 1 treatments, painting a picture of immense unmet need – and high demand – for solutions in addition to insulin. As he reviewed clinical data on pramlintide, metformin, colesevelam, DPP-4 inhibitors, and GLP-1 agonists in type 1 diabetes, a narrative emerged that most strategies tested to date have resulted in less-than-enthusing effects on glycemic control and safety (both hypoglycemia and DKA). At the same time, there is undeniable demand for more options: Most recent T1D Exchange data (n=22,697) indicate that ~15% of people with type 1 in the US are using off-label diabetes medications, including metformin (811 patients, 4%), GLP-1s (300, 1%), SGLT-2s (232, 1%), pramlintide (131, 1%), DPP-4s (9, <1%), and “other” drugs (28, <1%). In particular, we find it notable that metformin remains so highly used as an adjunct despite what Dr. Garg described as “hardly any change in A1c, more GI side effects, and more severe hypoglycemia,” based on a study conducted by Jaeb. To be sure, many type 1s do garner meaningful benefit from off-label adjuncts, but on average, no breakthrough therapy that works for all or even most patients has been found – a trend that was highly evident in Novo Nordisk’s two ADJUNCT studies of Victoza in type 1 (see Dr. Paresh Dandona’s study at ADA 2018). Our sense is that, among adjuncts, inter-patient variability in response to SGLT-2 inhibitors is much lower than with others, leading to more consistent effects across study enrollments (though this is our own interpretation) – Dr. Garg labeled the class the “ideal adjunctive prescription,” particularly due to its insulin-independent mode of action.

  • According to Dr. Garg, AZ’s submission for a type 1 indication for Forxiga in Japan just received a “favorable review.” As a reminder, AZ submitted Forxiga for type 1 to Japan’s Pharmaceutical and Medical Devices Agency (PMDA) in May 2018, well ahead of the late 2018 submission to FDA and a couple of months after EMA began reviewing the EU submission. While this news does not appear to have crossed the wire, a favorable review (or even approval) in Japan comes with little surprise – the country approved Astellas’ Suglat (ipragliflozin) for type 1 diabetes in December 2018, with apparently little concern over DKA. To our understanding, Sanofi/Lexicon have not submitted sotagliflozin in Japan. We’ve previously estimated Japan’s type 1 population at ~17,000 people (~13.5 cases/100,000 persons) – a relatively small market compared to >1 million type 1s in the US. On the other hand, we continue to understand that reimbursement and pricing (and thus, profitability) are favorable in Japan, and approval here could represent a gateway into the rest of Asia.

Selected Questions and Answers

Q: Is there any correlation of DKA risk with weight?

Prof. Danne: I’m happy with the 27 kg/m2 recommendation – basically, I think it tells us this drug not for everyone. I think the statistics for that are weak but it’s a sign that this isn’t out there for everyone. So, as an evidence-based person I think the evidence is rather weak but it is a starting point. There is some protection and those people have an opportunity perhaps for more benefit because weight loss is important; however, it isn’t a sure thing that above 27 kg/m2 is safe against DKA – that’s certainly not the case.

Q: Are there guidelines for restarting the SGLT-2 inhibitor if DKA is secondary to other causes, like pneumonia?

Prof. Danne: If the patient understands the need for doing ketone monitoring the next time he feels sick and for stopping the SGLT-2 inhibitor so it doesn’t get that far – you can start again on the lowest effective dose and make sure he has access to ketone monitoring and you as a medical expert.

Q: Should SGLTs be discontinued on hospital admission regardless of whether a surgical procedure is planned?

Prof. Danne: Any patient getting surgery should stop: The risk of DKA is far too high in my mind, even if it’s just a tooth extraction. I think a hospital state is high stress. If the patient does good ketone monitoring, they could continue, but I’d always be on the safe side. Why put the patient at risk? It’s easy to top and restart. We all know how little knowledge there is about diabetes in so many hospitals – Adam Brown just wrote a crazy article about what happened when his appendix ruptured.

Q: Would you recommend SGLT inhibitors for people with an A1c below 7.5%?

Prof. Danne: I would – the main point is reducing glycemic variability and improving time in range. Even a low A1c is sometimes caused by a lot of hypoglycemia, and glucose variability is a factor promoting hypoglycemia. If you have a patient with near-normal A1c and high glucose variability, I wouldn’t hesitate to start it.

2. Lilly Symposium Covers Unmet Needs in Hypoglycemia Rescue Therapy; CRASH Results on Response to Severe Hypo in the Real World

Dr. Pratik Choudhary discussed results from the cross-sectional CRASH study (also presented on poster 145) during a Lilly-sponsored symposium on severe hypoglycemia. People with type 1 (n=110) and type 2 diabetes (n=98) were surveyed on their experiences of severe hypo. A majority of these events happened at home (88% for type 1s and 77% for type 2s), and a majority of patients only ate or drank sugars to treat the low (76% of type 1s and 79% of type 2s). In contrast, only 10% of type 1s and 5% of type 2s injected glucagon to recover from severe hypoglycemia, and shockingly, only 43% of type 1s and 33% of type 2s mentioned the episode to a healthcare professional within one week. The reason for this became clear during Ms. Cajsa Lindberg’s talk, which offered the patient perspective on severe hypoglycemia. Ms. Lindberg, who was diagnosed with T1D 17 years ago, described how challenging it is to discuss severe lows with an HCP; she told the story of a recent conversation she had with a new diabetes nurse, who upon hearing about Cajsa’s hypos, said only “okay, I’ll keep tabs on that because it may affect your driver’s license.” Fear of losing a driver’s license is not at all trivial for people with diabetes, and Dr. Choudhary cited data showing that when a rule to revoke licenses from those at-risk for hypoglycemia is implemented, reports of severe hypo went down by half almost immediately in a Danish study. He concluded his talk with a call-to-action, charging HCPs to advise patients/caregivers about severe hypoglycemia and glucagon without exacerbating stress and fear. This is a fine like to walk, and it’s no doubt easier said done, but it’s an imperative.

  • In CRASH, nearly a third of type 1s and nearly half of type 2s who experienced severe hypo did not possess a glucagon prescription. After their event, only 3% of type 1s and 1% of type 2s obtained a glucagon prescription. A major factor contributing to under-utilization of glucagon is the complexity of current options, which require a cumbersome mixing process prior to injection, and we’ve also heard from US patients that glucagon often represents an additional, pricey diabetes supply that they don’t anticipate needing to use – or feel others won’t be able to use, given the complexity. We imagine CRASH is one way for Lilly to set the stage for its next-gen nasal glucagon, submitted to FDA in 2Q18 with a decision expected in 2Q or 3Q19. An audience member inquired about the status of nasal glucagon during Q&A, and although the faculty couldn’t discuss the drug openly (since it’s not yet approved), they agreed with her that it will be a more patient-friendly option in hypoglycemia rescue therapy (compared to glucagon reconstitution kits). As Prof. Battelino put it, “there are seven steps to prepare glucagon before injection; I can’t even list a couple.” The next-gen glucagon competitive landscape also includes autoinjectors from Xeris (filed with FDA in 3Q18, decision anticipated by 3Q19) and Zealand (on track for 4Q19 FDA submission). We’re eager to see these advanced products on the market, as glucagon is one of the areas of diabetes therapy most in need of improvement, from our view.

  • Global prevalence of severe hypoglycemia is only climbing as diabetes prevalence rises. In this same session, Professor Tadej Battelino showed that the number of severe hypoglycemic events requiring emergency medical services nearly tripled between 1997-1998 and 2011-2012 (which reflects the tremendous economic toll of severe hypo). He displayed WHO data on all-cause mortality related to severe hypo, which peaked in 2010 but has remained high since then (the odds ratio for death is ~1.5 comparing individuals who have experienced severe hypoglycemia vs. those who have not); Prof. Battelino attributed this to the growing type 2 diabetes epidemic. Dr. Choudhary commented that the number of type 2s on insulin now exceeds the total number of people on the planet with type 1 diabetes, reinforcing that severe hypoglycemia is a pressing problem in T1D and T2D alike, one that must be explicitly addressed by the medical community.

  • Ms. Lindberg told a moving story about her journey with chronic disease. She was diagnosed with type 1 diabetes at age 13 and was later diagnosed with brain cancer, which multiplied her severe hypos 10-fold. She described the intense fear she felt one night after a severe nocturnal low, sharing that she was “scared to go back to sleep and never wake up again.” Ms. Lindberg emphasized the need to disentangle hypoglycemia from feelings of failure. Prevention is key, and advanced technology can lessen the burden of hypoglycemia, but low blood glucose will happen anyway, she explained, echoing Dr. Choudhary’s charge that HCPs increase focus on glucagon training. “If I could pick one thing to be without,” Ms. Lindberg surmised, “it would be the hypos. They have a big impact on me physically, emotionally, and socially. They’re probably the scariest thing about type 1 diabetes.” Ms. Lindberg is President of the Swedish Diabetes Association and serves as a global advocate in non-communicable diseases.

3. MannKind Poster Demonstrates Comparable Effects of BMI, A1c, Diabetes Duration, and Afrezza Use on Pulmonary Function

A poster from MannKind found that BMI, A1c, and diabetes duration had comparable effects on pulmonary function to inhalable insulin Afrezza. Over a two-year study period, Afrezza use was associated with the greatest decline in FEV1 score (a test of lung capacity, healthy values range from ~2-5 L depending on age and height) of any variable presented for that time period: 0.056 L in type 1 (p=0.019) and 0.041 L in type 2 (p=0.011), both vs. usual care. For comparison, each additional five years of type 1 diabetes at baseline was associated with 0.065 L decline (p=0.0028), and, in type 2, each additional A1c or BMI point was associated with a 0.047 L (p=0.0014) or 0.025 L (p<0.001) decline, respectively – see below. Therefore, although Afrezza was associated with the greatest transient effect on FEV1 – which has also been shown to be reversible after Afrezza discontinuation – the reduction in lung capacity is comparable to the effects produced by greater BMI, A1c, and diabetes duration. Moreover, although all of these effects were statistically significant, the poster’s results section stated that none were clinically significant. Doubtless, this poster was meant to put to bed any remaining concerns about the pulmonary effects of inhaling Technosphere insulin, and the lack of a clinically significant FEV1 decline could palliate HCP concerns.

As has been reported, the most common pulmonary reaction with Afrezza is cough, which occurs in about 1 in 4 individuals.  The product label reports a discontinuation rate of 2.8% based on the Afrezza clinical program and cough is reported to diminish over time for most individuals.  For additional perspective, our team recently spoke to Afrezza expert Mr. Mark Harmel – the CDE responsible for onboarding participants into the STAT trial – who stated that inhaling more slowly or sipping water prior to inhalation usually mitigates cough. While few would question the impressive glucose-lowering efficacy of Afrezza as an alternative to injectable mealtime insulin, lung-related risks have affected Afrezza specifically and inhaled insulins broadly since the market entry (and exit) of Pfizer’s Exubera. However, FDA recently removed Afrezza’s REMS program a year earlier than scheduled.  The REMS for Afrezza required the company to explain the risk of acute bronchospasm for patients with chronic lung disease taking the inhaled insulin to healthcare providers.

Digital Health + Decision Support Highlights

1. Sanofi Out of the Digital Woodwork: Launching Durable, Prefilled Connected Pens and “All-in-One” Connected Patch Pump in “Very Next Few Years”; 8,000+ Using Digital Solutions, Including ~3,500 on Titration

In an early morning symposium, Sanofi Head of Integrated Care Mr. Gilles Litman provided a long-awaited updated on the company’s digital health strategy. Of note: (i) The company is internally developing connected insulin pens in both the prefilled and durable form factors; (ii) work on the prefilled, connected type 2 patch pump with Verily and Sensile continues; and (iii) the company’s insulin titration products (Diabeo, My Dose Coach) are being used by ~3,500 people in six countries, and a recent study in India showed that My Dose Coach resulted in a 2.7% A1c reduction in individuals initiating basal insulin therapy. Overall, there are 8,000+ people using Sanofi’s digital products globally – early days to be sure! With these new details, Sanofi’s strategy is becoming more clear; relative to Lilly and Novo Nordisk, the Paris-based insulin manufacturer had leaned toward fewer details. Read on for more and pictures.

  • Sanofi is developing both prefilled and durable pens, both with connectivity built into the body of the pen (i.e., not a cap or other attachment); in a separate conversation, Mr. Litman told us they will launch “in the very next few years.” In his talk, he added: “We want people to engage in a different way by bringing connectivity to our devices to capture data in a seamless way. We’re developing disposable and reusable pens. They will enable better dialogue, data and decision-support, and solutions for self-titration. And this is basal and basal-bolus patients.” Sanofi seems well behind both Novo Nordisk (launching first connected pens in EU in early 2019 following a Swedish pilot in 2018) and Lilly (filing first dose capture technology with the FDA this year). Sanofi has also invested in and is working with Common Sensing’s Gocap (currently 510(k) exempt) in a pilot with Innovation Health (Aetna) and One Drop, and the company didn’t explicitly rule out further collaborations with the startup in our conversation. Sanofi has made the decision to install connectivity directly into its devices, but the cap form factor has the advantage of working with disposable pens; will it pivot to use Common Sensing? Since Sanofi currently manufactures 300 million pens per year, connectivity is no small investment – further underscoring the consensus view that better monitoring and decision support is the way forward in insulin injection therapy.

  • Confirming our suspicion of a deliberate roll-out, Sanofi’s insulin titration app portfolio (Diabeo/My Dose Coach/Voluntis Insulia) are used by just 3,500 people in six countries (including India, France, Germany, Canada, and Mexico). There are currently 10 pilot programs and four commercial launches. A 12-week study in India (n=137 type 2s initiating 100 U insulin glargine) reported toward the end of last year a 2.7% A1c reduction from a high baseline of 9.9% with no incidents of symptomatic low blood glucose or hypoglycemia hospitalizations. Average insulin dose increased by seven units per day, and 38% of participants achieved <7% A1c. “Very promising outcomes,” Mr. Litman commented. There was no control group initiating basal insulin without My Dose Coach so the marginal impact of the app is an unknown, but a 2.7% A1c reduction is obviously quite meaningful. A Sanofi Integrated Care rep estimated that commencing basal insulin generally results in a ~1.5% A1c drop without My Dose Coach – loosely implying an effect size of -1.2% – though she added that “that’s not in people with baseline A1cs >10%, like they are here.” In France, basal-bolus titration/telemedicine through Diabeo are reimbursed by the government, and we are also glad to see the company’s focus on implementation of decision support in low-resource countries with health systems of varying means and composition. Arguably, these self-titration apps can have the biggest public health impact in these settings. It is taking longer than expected for titration to take off in the US, where there are five FDA-cleared apps, including Sanofi’s My Dose Coach and Voluntis’ Insulia (a Sanofi commercial partner) and the just-cleared Hygieia d-Nav. For his part, Mr. Litman said Sanofi is learning from experiences, “How do people use it? How is it reimbursed? How do physicians use it? Testing and learning with stakeholders. What we do with titration is different in different countries. Cultural, psychological aspects are different. This is valuable for us.”

    • “EGIDE” in the slide below is an independent group of “former health ministers, payers, clinicians, academics” assembled by Sanofi with a goal of accelerating connected care acceptance, reimbursement, and development.

  • Sanofi boasts at least five collaborations in the diabetes vertical, each laid out on the slide below. Sanofi and Evidation continue to leverage the latter’s Real Life Study Platform to better understand the impact of behaviors on health and economic outcomes at scale and what is the best intervention for a given individual. The company already has a framework for what works best for which person, which it is currently testing with Evidation. Science37’s virtual clinical study platform is presumably being used to validate various aspects of Sanofi’s digital portfolio, though Mr. Litman didn’t specify.

2. DreaMed Pipeline: “Advisor Dose” Patient-Facing Bolus Calculator, Pump<->Transition Support (w/ JDRF), Insulin Titration for SMBG/MDI (w/ Harvard), Glucose Forecasting (?); PWD Survey on Decision Support

DreaMed’s Dr. Revital Nimri covered the company’s decision support future (including the patient-facing Advisor Dose) and presented survey data from Schneider Children’s Hospital on patients’ needs and desires when it comes to decision support.

  • Dr. Nimri displayed a few screengrabs of an in-development real-time, patient-facing decision support system with actionable alarms and a “smart bolus” called “Advisor Dose.” CSO Prof. Moshe Philip has hinted at a patient-facing version of the FDA-cleared Advisor Pro pump settings decision support (retrospective) system a number of times in the past, but we’re not sure that’s what this is (at least not in its current form). Rather, this app appears to integrate CGM data, track last insulin dose – possibly based on injection dose capture with Biocorp’s Mallya cap, though the “Add Bolus” button implies manual entry. There also appears to be manual qualitative food and meal entry. Dr. Nimri didn’t say whether this app was limited to MDI or if it worked with pumps as well, nor how far along it is in development. Another concern is regulatory, since the bolus calculator seems to be based off CGM data – as FDA’s Dr. Lias said last week, CGM’s lower point accuracy may require the consideration of trend and historical data in addition to the current glucose number. We could see Advisor Dose becoming the framework for future patient-facing DreaMed decision support innovations.

  • For the first time since it was announced in May 2017, we heard a public update on the research collaboration between DreaMed, Schneider Children’s, and Harvard to develop a novel dosing support for type 1s on MDI and fingersticks. There’s still no commercial timeline attached to this project, but as seen in the slide below, researchers are tackling some of the thornier problems, such as offering MDI support with minimal fingerstick data. In this particular slide, an in silico simulation of a patient who is only performing three fingersticks a day, according to Dr. Nimri. We’re not entirely clear about what the slide is showing beyond that; we’d guess it’s either (i) a representation of how the researchers are going about imputing “missing” blood glucose data or (ii) a simulation of a patient who is only taking three fingersticks per day but receiving dosing support advice.

  • As shown in the slide below, JDRF has funded DreaMed’s work on supporting patients as they navigate the insulin challenges of transitioning between MDI and pump (apparently in collaboration with Dr. Cobelli et al. at the University of Padova). We’ve also heard this potential use case from companies developing closed loop and MDI dosing support, such as Lilly and Bigfoot.

  • DreaMed is also working on glucose prediction one hour into the future (likely with type 1s, given that focus in the group’s other work). In the trace below, the 21-day profiles appear to track very well (with a 13% error rate), though there is room for improvement. The true positive rate for hypoglycemia prediction is just 56%, meaning 34% of hypoglycemic events are not predicted by the algorithm. Plus, there is one false positive hypoglycemia alarm every 1.5 days. Hyperglycemia prediction is more robust since the data set is bigger (i.e., it is more common than hypoglycemia). On the high end, the true positive rate is 91%, meaning fewer than 1 in 10 hyperglycemic events are missed. However, the false positive rate is higher than it is for hypoglycemia, at one per day. We’re not sure if this module would be used simply to inform insulin dose recommendations or actually be presented to patients to aid their decision-making, but we suspect 1.66 false alerts per day – 0.66 false hypoglycemia and 1 false hyperglycemia – is too high. Medtronic and One Drop have both already launched patient-facing blood glucose prediction software; the real-world accuracy of these efforts remains a big question.

  • Dr. Nimri also presented some interesting data obtained from surveying 47 type 1 pumpers (+68% “constant” CGM users) ages 6-30 at Schneider Children’s. Some of the most notable findings:

    • 54% of patients never change their insulin pump settings between visits. Of those who do, basal rate is the most commonly-adjusted parameter, a fact which surprised Dr. Nimri.

    • 29% never/almost never amend insulin boluses according to the sensor glucose trend arrows; 29% do it all the time. For those who do not adjust their insulin dose based on arrows, the most frequently-cited reasons are lack of confidence (37.5%) and the high difficulty of doing so (25%). This is a small, highly-selected sample, but there was a significant correlation between adjusting insulin dose according to glucose trends and the level of A1c. This is a reminder of the desperate need for trend-adjusted, CGM-based bolus calculators – preventing a high or low blood sugar with a smarter bolus is better than ignoring the arrows and dealing with a subsequent prolonged high or low.

    • See the following table for attitudes on various decision support products/modalities. These are very encouraging numbers for adoption, though this is a young, tech-friendly population. We’d be super interested to see this kind of work expanded in larger, more representative surveys.


Willing to try


Would you be willing to use an algorithm that adjusts your pump setting after downloading your devices at home?


Would you be willing to use an algorithm that suggests insulin-dosing in real-time?


Would you be willing to get text messages each time you need insulin adjustment?


Would you trust dosing recommendations given by automatic algorithm?


Do you think that automated algorithms for insulin dosing will release some of the burden of managing diabetes?


Questions and Answers

Q: Whose responsibility is it when an adverse event like severe hypoglycemia happens with this software? Is it the patient or is it the healthcare provider or is it manufacturer who is going to take responsibility for the damage?

Dr. Nimri: It’s a complicated question. Good question. I don’t know all the answers for this question. I believe that if we give patients our Advisor, it will be the same, like closed loop. This needs to be examined in the future. This was the first question I had for closed loop. Now we have closed loop out there – so who’s responsible if severe hypoglycemia? It’s the same situation.

Prof. Eric Renard: The difficult part is to validate your algorithms. You have so many pieces of advice – if you have ten doctors, who is right?

Dr. Nimri: The outcome!

Prof. Renard: But you can only make one decision, so you can’t live the outcome.

3. Screenshots of Onduo App Tracking, Remote CGM Onboarding (“CGM Hyperloop”); RCT Coming Down the Pike; Management “Agnostic” on Integrating Other Data Sources

Onduo Head of Clinical Affairs Dr. Ronald Dixon presented screenshots of the tracking and CGM capabilities of the “virtual diabetes clinic” (connected BGM/CGM + remote coaching + app), which is currently in the midst of a 2019 national launch with payers, employers, and health systems. Log-able events in the app include meals (photos), snacks, physical activity, and even mood, representing a strong start to comprehensive education on blood glucose affecters (see below). The app looks pretty much the same as when it launched one year ago in three states.

  • We also got a breakdown of the onboarding process for CGM within Onduo – reportedly the first such 100% remote system of its kind (see photos below). The slide refers to the process as a “CGM hyperloop.” After establishing a willingness to use CGM with a coach, participants have a telemedicine visit with a physician (asynchronous or synchronous), followed by shipment, insertion, pairing, and onboarding of CGM (either assisted by coach or following videos in the Onduo app). The participant is then set to use CGM in Onduo, gaining insights from coaches and the app (see screenshot below with time-in-range [80-200 mg/dl], steps, and a “dinner challenge”) without ever physically entering the clinic. The app sets a goal for the user to spend 75% of time in the wide 80-200 mg/dl range, and tracks peak glucose post-meal to help users identify spike-prone foods. As a reminder, CGM-derived glucose values in the app are three hours delayed.

  • Prompted by a salient question from Dr. Satish Garg on the proportion of Onduo benefit attributable to CGM, Dr. Dixon revealed that the company plans to conduct a study in the next year to tease out the variables most responsible for Onduo outcomes and behavior change. The company is currently conducting a single-arm study (size, methods, outcomes not shared), that will inform an RCT on this subject. We’re eager for more details (will different arms be “knock-outs” of the various parts of the Onduo platform?) and expect, as was implied by Dr. Garg, that access to real-time CGM will be one of the principal factors motivating behavior change.

    • Dr. Dixon presented a compelling case study in which CGM drove significant time-in-range improvements for one Onduo member who was already “well-controlled” (A1c=6.4% at baseline). In the first seven days of CGM, the participant’s time-in-range hovered around 50% with an average blood glucose of ~180 mg/dl. Three months later, time-in-range was often greater than 90% and average blood glucose had dropped to 144 mg/dl. Importantly, adjustments were made only to the patients’ diet, based on a heightened understanding of how eating patterns affected their glucose levels through Onduo; no medication changes or increases in steps were needed.

  • After refinement of current processes, Onduo may allow data integration from other sources. As Dr. Dixon put it, “We are agnostic on what data sources [Onduo] could take, as long as they are going to help people with diabetes.” The app does pull activity data from Apple Health/Google Fit.

4. Barriers, Pitfalls, and Must-Haves of Diabetes Digital Health from Prof. Barnard

University of Bournemouth’s Prof. Katharine Barnard enumerated the pitfalls of digital health apps that have led to poor uptake to-date: (i) Health-tech is not as whiz-bang or attractive as consumer tech, which does not have regulatory hurdles slowing it down; (ii) poor previous experience make people unwilling experimenters (we hear this most often re: CGM); (iii) poor functionality – if it’s not intuitive and easy-to-use, people won’t use it; (iv) lack of infrastructure supporting the app, causing results to fall short of potential (i.e., integration into EHRs, availability to HCPs); and (v) lack of access – not just to smartphones but also inadequate health literacy, numeracy, or physical barriers such as colorblindness. On smartphone access, Prof. Barnard mentioned that 77% of US adults in 2018 had access to a smartphone regardless of socioeconomic status or ethnicity, and 36% of the world’s population (2.5 billion people) will have access to a smartphone in 2019, creating a fantastic opportunity to provide direct, personalized healthcare at a low cost to many people. However, digital health tools must hit some baseline characteristics: (i) Respect of privacy and security, including user safety; (ii) a clinically-meaningful benefit (for both user and HCP) relevant to clinical guidelines; (iii) an economic benefit for payers; and (iv) for users, timely information (e.g., medical, dietary, physical, practical, peer support, health services access, etc.) that is relevant, easy to understand, encouraging, and supportive. (See Adam’s ADA 2018 presentation for a different framework for diabetes apps.) In short, people with diabetes want an integrated care experience that reduces management burden and facilitates easier control; superfluous, complex apps with little value will simply not be used.

  • In Q&A, Prof. Barnard struck us with a particularly salient quote on assumptions of technological capability (or lack thereof) across populations: “I think onboarding is crucial for all of these tools. My own word of advice when it comes to technology is to never think that someone can’t or won’t use it. Offer the opportunity, because you never know what their ability or willingness is. Never exclude somebody based on your own filter of who will benefit, because invariably you will be wrong.”

Exhibit Hall

Convatec (Unomedical)

Unomedical showed a number of brand-new infusion set concepts in its booth, including sets for dual-hormone delivery, a patch pump base plate design leveraging the all-in-one Mio Advance inserter, and a combined set-CGM sensor design. Pictures are included below. A poster also showed encouraging data from the ongoing Stanford study (n=24) testing up to 10-day wear with the coated Lantern set that includes slits along the sides. Interim outcomes (n=10) look encouraging, as 100% of sets have lasted seven days or more (mean: 8.9 days) and half of sets have gone for 10 days. There is still unexplained hyperglycemia (see table below), and clearly quite a bit of variability between different people. A follow-on, larger crossover study (masked, randomized) will compare the coated Lantern catheter against commercially available infusion sets. Based on these interim outcomes, we’d guess the company will go for seven-day wear and not ten. As noted on Day #2, Unomedical is also working with Medtronic on a separate seven-day wear set that uses a different technology; a launch is ambitiously expected in one year, according to Medtronic’s presentation. We also asked about a Mio Advance launch in the US – the excellent inserter has been available OUS since last ATTD and FDA cleared for nearly a year – and the team told us it is still building capacity to support a US launch. This is prudent, as Mio Advance is such a tremendous upgrade and we know demand will be sky high. Medtronic does have an exclusive deal on Mio Advance for an unspecified amount of time, a downside for Tandem.

  • Coated Lantern study at Stanfordinterim data from n=10 patients, with two patients excluded in red:

  • Unomedical also debuted some never-before-seen infusion set concepts: (i) dual hormone delivery (Beta Bionics?); (ii) a dual CGM sensor/insulin catheter set with a side-by-side design and Mio-Advance-like insertion (2+ years away, per Medtronic’s pipeline); (iii) a patch pump base plate that uses the Mio Advance insertion device (looks ideal for Roche’s Solo); (iv) an all-in-one inserter like Mio Advance, but with the tubing bundled into the lid; and (v) an all-in-one inserter like Mio Advance, but with an angled insertion.


The Roche booth displayed its highly-anticipated Accu-Chek Solo patch pump, which has launched to ~200 people in Austria, Switzerland, Poland, and the UK. Roche expects the pilot launch will stay fairly limited and “definitely” will not exceed 10,000 patients in 2019. A booth representative shared that a pre-submission of Solo to the FDA is “running.” The rep added that Solo has received “excellent feedback” from patients so far. We were impressed by the very sleek on-body form factor, but the demo confirmed our view that the insertion process is more burdensome and complicated than Omnipod (see multiple components below). Solo consists of: (i) a plastic baseplate with adhesive; (ii) a 200-unit disposable reservoir – the representative emphasized that patients do not have to fill up the entire 200U so as to mitigate insulin waste; (iii) the reusable pump itself, consisting of the electronics, which last 120 days; and (v) a touchscreen, Bluetooth-enabled handheld controller with an integrated BGM. Solo is recommended for 3-4 days of use, although the representative underscored that this is not due to adhesive wear or battery life, but instead related to insulin absorption and utilization. The inserter is reusable (it looks like a computer mouse) and lasts for two years, and the pump holder and cannula (available in 6 mL and 9 mL) come together as a sterile package. Users are guided through the insertion process on the handheld controller. The handheld also includes a camera to scan a unique code located on the pump base for pump activation and pairing. The booth representative expects that the “last phase” of Solo will include pump control via a smartphone app, which will also integrate CGM data. We wonder if Roche may wait for this final version to launch in the US – the current controller doesn’t come close to Insulet’s excellent user experience on Omnipod Dash. The booth representative also anticipates “several cloud-based platforms,” including Glooko and Roche’s “in-house solution,” to be compatible with Solo.

  • In addition to the section on Solo, the Roche booth also had portions dedicated to mySugr and Senseonics’ 180-day Eversense XL CGM. Notably, “Roche” and “mySugr” were printed at equal size beneath “Accu-Chek” on the booth banner, reflecting Roche’s commitment to its digital ecosystem. At the Eversense XL section of the booth, representatives performed sensor insertion/removal demonstrations. We learned that Eversense XL just received reimbursement from three major insurance companies in Germany – a big win, which the booth representative believes will help to increase adoption. As expected, Senseonics recently renewed its distribution agreement for Eversense XL with Roche, adding 17 new countries.


Shanghai-based Medtrum boldly advertised three integrated patch pump and CGM products, including a predictive low-glucose-suspend system reportedly available in six European countries and with ~200 users so far (Germany, Denmark, Sweden, UK, Spain, Italy). The current A6 system includes a semi-disposable tubeless patch pump, a handheld controller, a seven-day CGM (two calibrations/day), and predictive low glucose suspend. The A7 system adds a secondary display mobile app and improves the CGM to 14-day wear and one calibration/day, with an expected launch this year in Europe and “2020” in the US (seems highly ambitious). The P7 system, currently in R&D, adds direct smartphone control (no handheld) and a no-calibration CGM. Like Roche’s Solo, Medtrum’s tubeless patch pump is semi-disposable – in this case, the reusable part (colored) reportedly lasts four years, while the disposable part is replaced every three days. The company also offers its Bluetooth-enabled S7 CGM as a standalone product (countries unclear). This was Medtrum’s largest booth following smaller and less-confidence-inspiring EASD exhibit hall appearances (e.g., 2018, 2015). We’re not convinced of the CGM’s accuracy nor the ability to reliably scale the patch pump’s manufacturing. Still, it is notable that Medtrum owns its own tubeless patch pump and CGM – a combined offering that no other company has at this point.



-- by Adam Brown, Ann Carracher, Brian Levine, Payal Marathe, Peter Rentzepis, Maeve Serino, and Kelly Close