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Objective: Maintenance of glycemic control during and following exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, resistance), and the effect of activity type on glycemic control following exercise remains unclear. Research Design-Methods: The Type 1 Diabetes Exercise Initiative (T1Dexi) was a real-world study of at-home exercise. Adult participants were randomly assigned to complete six structured aerobic, interval, or resistance exercise sessions over 4-weeks. Participants self-reported study and non-study exercise, food intake, and insulin dosing (multiple-daily injection [MDI] users) using a custom smart phone application, and provided pump data (pump users), heart rate, and continuous glucose monitoring (CGM) data. Results: 497 adults with type 1 diabetes, mean±SD age 37±14 years, HbA1c 6.6±0.8% (49±8.7 mmol/mol) assigned to structured aerobic (N=162), interval (N=165), or resistance (N=170) exercise were analyzed. Mean change in glucose during assigned exercise was -18±39, -14±32, and -9±36 mg/dL for aerobic, interval, and resistance, respectively (P Conclusion: Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic followed by interval and resistance exercise, regardless of insulin delivery modality. Even in well controlled adults with type 1 diabetes, days with structured exercise sessions contributed to clinically meaningful improvement in glucose time-in-range but may have slightly increased time below range.

Glycemic responses differ by exercise classification (i.e. aerobic, interval, resistance) in individuals with T1D, but the impact of insulin delivery modality (multiple daily injections [MDI], pump, closed loop) on these differences is unclear. Data from the Type 1 Diabetes Exercise Initiative study allowed us to compare glycemic responses by insulin delivery method. Adults (N=497) with T1D ([mean ± SD] age= 37 ± 14 years; HbA1C= 6.6 ± 0.8%) were assigned to complete six at-home sessions of aerobic, interval, or resistance exercise over 4 weeks, using study-developed instructional videos approximately 30 minutes in duration. Mean [95% CI] change in glucose during the assigned exercise was -[95% CI -22, -16], -15 [-18, -12], and -9 [-12, -7] mg/dL for aerobic, interval, and resistance exercise, respectively (P In summary, regardless of insulin delivery modality, adults with T1D experienced the largest glucose drop for aerobic exercise followed by interval exercise and resistance exercise. Disclosure M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Consultant; Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. P.Calhoun: None. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. R.Beck: Consultant; Diasome, Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Research Support; Ascensia Diabetes Care, Beta Bionics, Inc., Bigfoot Biomedical, Inc., Dexcom, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk, Roche Diabetes Care, Tandem Diabetes Care, Inc. M.R.Rickels: Advisory Panel; Sernova, Corp., Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S, Consultant; L-Nutra Inc. S.R.Patton: None. C.K.Martin: Advisory Panel; Wondr Health, Board Member; EHE Health, Other Relationship; ABGIL, ABGIL, Academy of Nutrition and Dietetics. Z.Li: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. M.B.Gillingham: None. R.L.Gal: None. F.J.Doyle: Other Relationship; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care, Stock/Shareholder; Mode AGC. M.A.Clements: Consultant; Glooko, Inc., Research Support; Abbott Diabetes, Dexcom, Inc.

DailyDose is a smart-phone decision support system developed at Oregon Health & Science University that uses Dexcom G6 CGM and Medtonic’s InPen. The app calculates insulin doses using CGM value and trend, insulin-on-board, carbohydrate amount, and exercise information. Insulin dosing and carbohydrate intake recommendations before and after exercise are adjusted according to the 2017 consensus statement by Riddell et al. known as the PEAK Guidelines. Twenty-four adults with T1D, using multiple daily injections of insulin at baseline, completed a two-week run-in period then used the DailyDose intervention for 8 weeks. Participants completed 3 at-home exercise sessions per week, one aerobic exercise video and two other sessions of their choice. We examined the impact of the use of PEAK guidelines on glucose outcomes during exercise comparing 176 exercise sessions done during run-in and 471 sessions done during the intervention period. Glucose outcomes were assessed from start of exercise to 4 hours after the start or until either a meal was consumed, insulin dosed or new exercise session initiated. Mixed effects analysis was used to determine significance of the intervention on glucose outcomes. The nadir of the glucose was lower for the run-in compared with the intervention (120.6 vs. 130.9 mg/dL, P=.012) . Change in glucose from the start of exercise to the nadir was 58.0 for the run-in versus 46.9 for the intervention period (P=.016) . Time in hyperglycemia (>180mg/dL) during the exercise periods trended toward being lower during the intervention (43.8% run-in, 35.0% intervention, P=.059) , as did time in range of 70-180 mg/dL, (54.2% run-in, 62.2% intervention, P=.081) . There was no difference in time in hypoglycemia. Data suggest that use of PEAK within a decision support app can help prevent more severe glucose drops during and after exercise with a trend toward improving time in range. Disclosure L.M.Wilson: n/a. S.M.Oganessian: None. D.Branigan: None. V.Gabo: None. J.H.Eom: None. J.El youssef: None. K.Winters-stone: None. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. F.H.Guillot: Stock/Shareholder; Pacific Diabetes Technologies. N.S.Tyler: None. T.Kushner: Consultant; Tandem Diabetes Care, Inc. A.Z.Espinoza: None. C.M.Mosquera-lopez: None. J.Pinsonault: None. R.Dodier: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (Grant 2018PG-T1D001) , OHSU Medical Research Foundation, Supplies provided by Dexcom

Glucose dynamics during and after resistance exercise in people with T1D is not well understood. We recruited 25 people with T1D to perform 3 separate sessions of in-clinic resistance exercise under low (basal) , medium (1.5x basal) , or high (3.0x basal) insulin infusion levels. Blood glucose was clamped by intravenous dextrose infusion 3 hours prior to, and 4 hours after a 45-minute session of strength training. Glucose tracer was infused to match the rate of endogenous glucose production (EGP) . Glucose and tracer data were used to fit a two-compartment model and quantify the rate of glucose disposal (Rd) and EGP. For 58 sessions across 25 participants, blood glucose levels remained constant during and after resistance exercise. The mean change in area under the curve (AUC) for Rd was not higher during resistance exercise compared to baseline (+1.2 mM, p=0.69) , but increased during previously published aerobic exercise (+12.5 mM, p Disclosure G.Young: None. M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Consultant; Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. J.El youssef: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. N.S.Tyler: None. T.P.Nguyen: None. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. L.M.Wilson: n/a. D.Branigan: None. V.Gabo: None. F.H.Guillot: Stock/Shareholder; Pacific Diabetes Technologies. Funding National Institutes of Health (5R01DK110175, 1F30DK128914)

Different types of exercise (i.e., aerobic vs. interval vs. resistance) may have different immediate glycemic effects in adults with T1D, but the impact of exercise type on percent time in range 70-180 mg/dL (TIR) on physically active and inactive days is unknown. The Type 1 Diabetes Exercise Initiative (T1DEXI) study examined CGM data on active vs. sedentary days to assess the effect of exercise type on TIR. Adults (N=497) with T1D ([mean ± SD] age= 37 ± 14 years; HbA1C= 6.6 ± 0.8%) , completed six aerobic, interval, or resistance study-designed exercise videos at home over a 4-week period. TIR was higher and mean glucose concentration lower on days when the study-designed exercise video was completed, as compared with sedentary days (Table) . Time below range was low in general in this cohort, although slightly higher on exercise days. Results were remarkably similar for each exercise type, with marginally less increase in hypoglycemia with resistance exercise. In summary, adults with type 1 diabetes have clinically significant improvements in TIR on active days as compared to inactive days, regardless of the exercise type. Disclosure Z.Li: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. C.K.Martin: Advisory Panel; Wondr Health, Board Member; EHE Health, Other Relationship; ABGIL, ABGIL, Academy of Nutrition and Dietetics. S.R.Patton: None. P.Calhoun: None. M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Consultant; Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. M.R.Rickels: Advisory Panel; Sernova, Corp., Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S, Consultant; L-Nutra Inc. R.Beck: Consultant; Diasome, Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Research Support; Ascensia Diabetes Care, Beta Bionics, Inc., Bigfoot Biomedical, Inc., Dexcom, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk, Roche Diabetes Care, Tandem Diabetes Care, Inc. J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. M.A.Clements: Consultant; Glooko, Inc., Research Support; Abbott Diabetes, Dexcom, Inc. F.J.Doyle: Other Relationship; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care, Stock/Shareholder; Mode AGC. R.L.Gal: None. M.B.Gillingham: None.

DailyDose is a decision support system developed at Oregon Health & Science University designed for people with T1D on MDI to improve glycemic control. It connects with Dexcom G6 and Medtronic's InPen. DailyDose runs on a smartphone and calculates insulin doses using CGM value and trend, IOB, carbohydrate amount, and exercise information. The system analyzes CGM and insulin data and automatically provides weekly recommendations on insulin settings, such as basal insulin dose and carbohydrate ratios, based on a k-nearest neighbors algorithm. Twenty-four adults with T1D used DailyDose for 8 weeks. The primary outcome was change in % time in range (TIR, 70-180 mg/dL) on CGM comparing the two week run-in period before starting DailyDose vs. final two weeks of DailyDose use. A mixed effects model was used to determine the impact of the % of accepted recommendations on change in % TIR. Users who accepted and followed recommendations showed a mean week-to-week improvement in TIR of 2.0% (Figure) . The mixed effects model shows week-by-week TIR increased by 7.8% when recommendations were accepted compared with not accepted (CI, 3-12%, P=.001) . Overall, there were no significant differences between TIR or time in hypoglycemia comparing the run-in period and the final two weeks of use. Further work is needed to encourage people using decision support systems to follow recommendations. Disclosure J.R.Castle: Advisory Panel; Insulet Corporation, Novo Nordisk, Zealand Pharma A/S, Stock/Shareholder; Pacific Diabetes Technologies. V.Gabo: None. J.H.Eom: None. J.El youssef: None. K.Ramsey: None. T.Kushner: Consultant; Tandem Diabetes Care, Inc. K.Winters-stone: None. J.A.Cafazzo: None. P.G.Jacobs: Other Relationship; Pacific Diabetes Technologies, Research Support; Dexcom, Inc. A.Z.Espinoza: None. N.S.Tyler: None. L.M.Wilson: n/a. C.M.Mosquera-lopez: None. J.Pinsonault: None. R.Dodier: None. S.M.Oganessian: None. D.Branigan: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (Grant 2018PG-T1D001) .

Aerobic exercise in type 1 diabetes (T1D) causes rapid increase in glucose utilization due to muscle work during exercise, followed by increased insulin sensitivity after exercise. Better understanding of these changes is necessary for models of exercise in T1D. Twenty-six individuals with T1D underwent three sessions at three insulin rates (100%, 150%, 300% of basal). After 3-h run-in, participants performed 45 min aerobic exercise (moderate or intense). We determined area under the curve for endogenous glucose production (AUC(EGP)) and rate of glucose disappearance (AUC(Rd)) over 45 min from exercise start. A novel application of linear regression of R(d) across the three insulin sessions allowed separation of insulin-mediated from non-insulin-mediated glucose uptake before, during, and after exercise. AUC(Rd) increased 12.45 mmol/L (CI = 10.33–14.58, P < 0.001) and 13.13 mmol/L (CI = 11.01–15.26, P < 0.001) whereas AUC(EGP) increased 1.66 mmol/L (CI = 1.01–2.31, P < 0.001) and 3.46 mmol/L (CI = 2.81–4.11, P < 0.001) above baseline during moderate and intense exercise, respectively. AUC(EGP) increased during intense exercise by 2.14 mmol/L (CI = 0.91–3.37, P < 0.001) compared with moderate exercise. There was significant effect of insulin infusion rate on AUC(Rd) equal to 0.06 mmol/L per % above basal rate (CI = 0.05–0.07, P < 0.001). Insulin-mediated glucose uptake rose during exercise and persisted hours afterward, whereas non-insulin-mediated effect was limited to the exercise period. To our knowledge, this method of isolating dynamic insulin- and non-insulin-mediated uptake has not been previously employed during exercise. These results will be useful in informing glucoregulatory models of T1D. The study has been registered at www.clinicaltrials.gov as NCT03090451. NEW & NOTEWORTHY Separating insulin and non-insulin glucose uptake dynamically during exercise in type 1 diabetes has not been done before. We use a multistep process, including a previously described linear regression method, over three insulin infusion sessions, to perform this separation and can graph these components before, during, and after exercise for the first time.

OBJECTIVE To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia ( RESULTS DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0–4.2], SH 8.3% [0.0–12.5], P = 0.025). There was an increased time in hyperglycemia (>180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70–180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study. CONCLUSIONS The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.

Background: People with diabetes-related ulcers may benefit from hyperbaric oxygen (HBO2) therapy and from continuous glucose monitors (CGM). Although blood glucose (BG) meters based on glucose oxidase (GO) report erroneously low values at high pO2, BG meters based on glucose dehydrogenase (GD) do not. We therefore examined the performance of a GO-based CGM system in comparison to GO-based and GD-based BG systems in normobaric air (NBAir), hyperbaric air (HBAir), and HBO2 environments. Materials and Methods: Twenty-six volunteers without diabetes mellitus (DM) wore Dexcom G6 CGM systems and provided periodic blood samples before, during, and after a standard HBO2 treatment consisting of three 30-min intervals of HBO2 separated by two 5-min intervals of HBAir. Accuracy of the CGM and GO-based BG meter were assessed by comparisons with the GD-based values. Results: The mean absolute relative difference for the CGM system was 15.96% and for the GO-based meter was 8.52%. Compared to NBAir, HBO2 exposure resulted in significantly higher CGM values (+3.76 mg/dL, P

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Background: Using continuous glucose monitoring (CGM), we investigated potential sex-related differences in glycemia during exercise in persons living with type 1 diabetes (T1D) or without diabetes. Methods: Participants used CGM (Dexcom G6), exercise/meal diaries, or an activity monitor with a meal capture smart phone app, to help identify home based exercise and meal events over a 10 day to 4 wk period. Results: The analyses included 40 T1D (age: 36±15 yrs; BMI: 26.3±3.1 kg/m2; HbA1c: 7.5±1.5%; 35% female; mean±SD) and 120 persons without diabetes (age: 30±20 years; BMI: 24.4±3.2 kg/m2; HbA1c: 5.1±0.3; 64% female) and 283 and 619 exercise events, respectively. The frequency distribution of all CGM values were comparable between males and females overall, but values had a wider distribution among T1D as compared to non-T1D (Fig). Hyperglycemia (> 180 mg/dL) represented ~0.1% of the exercise time in participants without T1D vs. 31% and 24% of the time in the female and male participants without diabetes, respectively. Hypoglycemic values ( Conclusions: Based on CGM, mild hypoglycemic exposure during exercise is surprisingly common in individuals with and without diabetes and may be particularly prevalent in females without diabetes. Disclosure M. Riddell: Advisory Panel; Self; Zealand Pharma A/S. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Novo Nordisk Inc., Sanofi. Stock/Shareholder; Self; Zucara Therapeutics Inc. Z. Li: None. N. DSouza: None. C. Yeung: None. D. Kesibi: None. S. R. Patton: None. R. Beck: Consultant; Self; Bigfoot Biomedical, Inc., Diasome Pharmaceuticals, Inc., Insulet Corporation, Lilly Diabetes, vTv Therapeutics. Research Support; Self; Beta Bionics, Inc., Dexcom, Inc., Medtronic, Novo Nordisk, Tandem Diabetes Care. P. G. Jacobs: Board Member; Self; Pacific Diabetes Technologies. Research Support; Self; Dexcom, Inc. M. A. Clements: Consultant; Self; Eli Lilly and Company. Employee; Self; Glooko, Inc. Research Support; Self; Abbott Diabetes, Dexcom, Inc. R. L. Gal: None. F. J. Doyle: Advisory Panel; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. C. K. Martin: Advisory Panel; Self; EHE Health. Board Member; Self; NaturallySlim. Research Support; Self; American Society for Nutrition, Leona M. and Harry B. Helmsley Charitable Trust, Lilly, National Institutes of Health, Patient-Centered Outcomes Research Institute, U. S. Department of Agriculture, WW. Other Relationship; Self; ABGIL, Academy of Nutrition and Dietetics. P. Calhoun: None. J. Sherr: Advisory Panel; Self; Cecelia Health, Insulet Corporation, Medtronic. Consultant; Self; Insulet Corporation, Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Medtronic. Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic. Speaker’s Bureau; Self; Lilly Diabetes. J. R. Castle: Advisory Panel; Self; Novo Nordisk, Zealand Pharma A/S. Consultant; Self; ADOCIA. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Spouse/Partner; Insulet Corporation. Stock/Shareholder; Self; Pacific Diabetes Technologies. M. R. Rickels: Advisory Panel; Self; Semma Therapeutics, Sernova, Corp. Research Support; Self; Xeris Pharmaceuticals, Inc.

Background: People with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals’ different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions. Methods: A new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient’s insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control. Results: Incorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%. Conclusion: Adapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.

Background: Decision support smartphone applications integrated with continuous glucose monitors may improve glycemic control in type 1 diabetes (T1D). We conducted a survey to understand trends and needs of potential users to inform the design of decision support technology. Methods: A 70-question survey was distributed October 2017 through May 2018 to adults aged 18-80 with T1D from a specialty clinic and T1D Exchange online health community ( myglu.org ). The survey responses were used to evaluate potential features of a diabetes decision support tool by Likert scale and open responses. Results: There were 1542 responses (mean age 46.1 years [SD 15.2], mean duration of diabetes 26.5 years [SD 15.8]). The majority (84.2%) have never used an app to manage diabetes; however, a large majority (77.8%) expressed interest in using a decision support app. The ability to predict and avoid hypoglycemia was the most important feature identified by a majority of the respondents, with 91% of respondents indicating the highest level of interest in these features. The task that respondents find most difficult was management of glucose during exercise (only 47% of participants were confident in glucose management during exercise). The respondents also highly desired features that help manage glucose during exercise (85% of respondents were interested). The responses identified integration and interoperability with peripheral devices/apps and customization of alerts as important. Responses from participants were generally consistent across stratified categories. Conclusions: These results provide valuable insight into patient needs in decision support applications for management of T1D.

Objective We introduce an adaptive learning algorithm to better adjust postprandial basal and pre-meal bolus insulin for reducing postprandial hypoglycemia in a hybrid artificial pancreas (AP). An AP uses a control algorithm and sensed glucose to automate the delivery of insulin to people with type 1 diabetes (T1D). A hybrid AP requires the person to dose insulin in advance of a meal. Insulin sensitivity is dynamic in people with T1D, making it challenging for an AP to maintain euglycemia. Adaptive approaches to meal dosing can help prevent postprandial hypoglycemia. Methods An adaptive learning postprandial hypoglycemia-prevention algorithm (ALPHA) is introduced. One implementation of ALPHA adjusts the rate of postprandial insulin (ALPHA-BR) proportionally in response to prior postprandial episodes. This is achieved by an adaptive aggressiveness factor applied to postprandial basal insulin. The second implementation adaptively updates the pre-meal bolus insulin by changing the insulin-to-carbohydrate ratio (ALPHA-ICR), also proportionally in response to prior postprandial hypoglycemia. Both implementations were evaluated within an AP on an in-silico T1D virtual population of 99 subjects with circadian insulin sensitivity variations and 30% errors on meal estimations. Twenty real-world 4-day meal scenarios were given and glycemic outcomes were compared with an AP with no adaptation. Results Out of the 99 in-silico subjects, 23 of them experienced postprandial hypoglycemia leading to greater than 1% overall time in hypoglycemia. Of these 23 subjects, we evaluated the benefit of using ALPHA-BR and ALPHA-ICR to prevent postprandial hypoglycemia. ALPHA-BR yielded substantially fewer percent time in hypoglycemia compared to AP (0.54% vs 1.92%, p Conclusion Incorporating adaptive meal dosing into an AP can help reduce postprandial hypoglycemia, and the reduction is primarily due to changes in postprandial insulin delivery rather than pre-meal bolus. Significance Adapting postprandial insulin can lead to substantial reduction in postprandial hypoglycemia and the adaptive algorithm presented can be used both to tune an algorithm prior to a study and to adapt to individuals during real-time usage.

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15–68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with

Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15–68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70–180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Objective: To assess the efficacy and feasibility of a dual-hormone closed loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed loop system and a predictive low glucose suspend system. Research Design and Methods: In a 76-hour, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: (1) dual-hormone (DH) closed loop control, (2) insulin-only single-hormone (SH) closed loop control and (3) predictive low glucose suspend (PLGS). The primary endpoint was percent time in hypoglycemia (2max) to 4 hours after. Results: DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [0.0-4.2], SH 8.3% [0.0-12.5], p=0.025). There was an increased time in hyperglycemia (>180mg/dL) during and after exercise for DH vs SH (20.8% DH vs. 6.3% SH, p=0.038). Mean glucose during the entire study duration was: DH 159.2, SH 151.6, PLGS 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70-180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, p=0.044). For the entire study duration, DH had 28.2% time in hyperglycemia versus 25.1% for SH (p=0.044) and 34.7% for PLGS (p=0.140). Four participants experienced nausea related to glucagon leading 3 to withdraw from the study. Conclusions: The glucagon formulation demonstrated feasibility in a closed loop system. The dual-hormone system reduced hypoglycemia during and after exercise with some increase in hyperglycemia.

The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6&ndash, 9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: &minus, 6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: &minus, 8.3 to &minus, 2.9%, HIIT MARD: 12.1 to 16.8% and MRD: &minus, 14.3 to &minus, 9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

Treatments for type 1 diabetes have advanced significantly over recent years. There are now multiple hybrid closed-loop systems commercially available and additional systems are in development. Challenges remain, however. This review outlines the recent advances in closed-loop systems and outlines the remaining challenges, including post-prandial hyperglycemia and exercise-related dysglycemia.

People with type 1 diabetes (T1D) have difficulty controlling glucose during exercise. Different types, intensities, and durations of exercise impact glucose differently. There is currently no algorithm that can accurately predict glucose changes during aerobic, resistance, interval, and free-living exercise. A support vector regression (SVR) model was designed to predict the maximum change in glucose during exercise. The model was trained and validated on 30-minute exercise sessions collected twice weekly over 4 weeks from 33 people with T1D (age 33±13 years, BMI 26.3±2.9 kg/m2, 18±12 years since diagnosis). Participants completed study assigned aerobic (n = 40), resistance (n= 33), or interval (n=20) exercise sessions in addition to their typical exercise regimen (n = 254). Participants wore a Dexcom (G5 or G6) or Medtronic CGM and used their own insulin pen (n = 10) or pump to administer insulin. The exercise information and food consumed was reported using a custom app. Heart rate, accelerometry, and sleep metrics were acquired using a Garmin vivosmart watch. Training was done on 90% of the observations while 10% of data were used for testing using 10-fold cross validation. The mean absolute error (MAE) of the model-predicted change in glucose during exercise was 23.59 mg/dL. The SVR algorithm may be useful within a decision support tool to help people with T1D better manage their glucose levels during exercise. Disclosure G. Young: None. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Funding Exercise in Diabetes Initiative (T1-DEXI Main Study)

While automated insulin delivery (AID) systems are now commercially available, over 40 percent of people with type 1 diabetes manage their insulin with multiple daily injection therapy (MDI). AID systems can improve time-in-range in adults, however it is not clear if optimal MDI therapy can achieve the same glycemic targets. We have previously shown that a K-nearest-neighbors decision support system (KNN-DSS) for MDI can deliver safe recommendations and achieves high agreement with endocrinologists. Here, we report glycemic outcomes as we compare KNN-DSS with two artificial pancreas systems: fading-memory-proportional-derivative (FMPD), and model-predictive control (MPC). We utilized a platform accepted by U.S. Food and Drug Administration for the evaluation of artificial pancreas technologies, the UVA-Padova simulator. In silico, 100 adults with varying insulin sensitivities, weight, and insulin dosing behaviors underwent a 3-arm study comparing KNN-DSS, FMPD, and MPC algorithms. Each UVA-Padova subject received a unique study meal pattern that was repeated across study arms. The two primary outcome measures were % time-in-range and % time-in-hypoglycemia. Secondary outcome measures included LGBI and HGBI risk index. The KNN-DSS was able to achieve 79.6 ± 15.4 %time-in-range after 12 weeks compared with 83.6 ± 8.3% for the MPC and 84.2 ± 9.24% for the FMPD. The KNN-DSS achieved comparable time below 70 mg/dL (0.79%, [0 1.6]) with the MPC-AP (0.62%, [0 1.8]) and FMPD (0%, [0 0.72]). The KNN-DSS also achieved comparable LGBI (2.55) with the MPC-AP (2.68). This simulation assumes perfect adherence to insulin dosing regimens, and while % time-in-range may be higher than expected, the results indicate that decision support in MDI may yield comparable glycemic outcomes with automated insulin delivery systems if people are compliant. Results of this study will be used to guide the design of an upcoming human clinical trial. Disclosure N.S. Tyler: None. W. Hilts: None. C.M. Mosquera-Lopez: None. L.M. Wilson: None. R. Dodier: None. J. El Youssef: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-T1D001)

Exercise increases nocturnal hypoglycemia risk which may impact sleep in T1D. Adults with T1D (n=25; 19 CSII; age 33±14yrs; HbA1c 7.6±1.3%; T1D duration 15yrs [IQR: 10, 22]) were divided into 3 exercise groups: aerobic, interval, or resistance, performed twice/week using instructional videos for 4 weeks. Participants used CGM, and wore an activity/sleep monitor (Garmin Vivosmart 3). Sleep duration was lower on nights following interval exercise compared to nights following sedentary days (p=0.04, Figure 1a). Participants experienced hypoglycemia earlier in the night following interval or aerobic exercise, compared to sedentary or resistance exercise (Figure 1b). Exercise types differentially impact sleep loss and nocturnal hypoglycemia. Disclosure P.G. Jacobs: Consultant; Self; SFC Fluidics. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Pacific Diabetes Technologies. Other Relationship; Self; AgaMatrix. Z. Li: None. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; ADOCIA, Dexcom, Inc., Zealand Pharma A/S. Other Relationship; Self; Pacific Diabetes Technologies, Roche Diabetes Care. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc. E. Dassau: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. F.J. Doyle: Research Support; Self; DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Stock/Shareholder; Self; Mode AGC. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE Health, NaturallySlim. Consultant; Self; Florida Hospital, Gila Therapeutics, OpenFit, Zafgen, Inc. Research Support; Self; Egg Board, Helmsley Trust, IDEA Public Schools, National Institutes of Health, NIHR, Patient-Centered Outcomes Research Institute, USDA, Weight Watchers International, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics, Louisiana State Univ./Pennington Biomedical Research Center. M.R. Rickels: Consultant; Self; Semma Therapeutics, Inc. Research Support; Self; Xeris Pharmaceuticals, Inc. S.R. Patton: None. M. Riddell: Advisory Panel; Self; Zucara Therapeutics Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. Funding The Leona M. and Harry B. Helmsley Charitable Trust (T1-DEXI Main Study)

Automated insulin delivery systems for people with type 1 diabetes rely on an accurate subcutaneous glucose sensor and an infusion cannula that delivers insulin in response to measured glucose. Integrating the sensor with the infusion cannula would provide substantial benefit by reducing the number of devices inserted into subcutaneous tissue. We describe the sensor chemistry and a calibration algorithm to minimize impact of insulin delivery artifacts in a new glucose sensing cannula. Seven people with type 1 diabetes undergoing automated insulin delivery used two sensing cannulae whereby one delivered a rapidly-acting insulin analog and the other delivered a control phosphate buffered saline (PBS) solution with no insulin. While there was a small artifact in both conditions that increased for larger volumes, there was no difference between the artifacts in the sensing cannula delivering insulin compared with the sensing cannula delivering PBS as determined by integrating the area-under-the-curve of the sensor values following delivery of larger amounts of fluid (P=0.7). The time for the sensor to recover from the artifact was found to be longer for larger fluid amounts compared with smaller fluid amounts (10.3 ± 8.5 minutes vs. 41.2 ± 78.3 seconds, P

Glycemic control is the mainstay of preventing diabetes complications at the expense of increased risk of hypoglycemia. Severe hypoglycemia negatively impacts the quality of life of patients with type 1 diabetes and can lead to morbidity and mortality. Currently available glucagon emergency kits are effective at treating hypoglycemia when correctly used, however use is complicated especially by untrained persons. Better formulations and devices for glucagon treatment of hypoglycemia are needed, specifically stable liquid glucagon. Out of the scope of this review, other potential uses of stable liquid glucagon include congenital hyperinsulinism, post–bariatric surgery hypoglycemia, and insulinoma induced hypoglycemia. In the 35 years since Food and Drug Administration (FDA) approval of the first liquid stable human recombinant insulin, we continue to wait for the glucagon counterpart. For mild hypoglycemia, a commercially available liquid stable glucagon would enable more widespread implementation of mini-dose glucagon use as well as glucagon in dual hormone closed-loop systems. This review focuses on the current and upcoming pharmaceutical uses of glucagon in the treatment of type 1 diabetes with an outlook on stable liquid glucagon preparations that will hopefully be available for use in patients in the near future.

Most people with type 1 diabetes use multiple daily injections (MDI) therapy, yet there are limited decision support tools for this population. We designed and evaluated a K-nearest-neighbors decision support system (KNN-DSS) that utilizes continuous glucose data and Bluetooth-enabled insulin dose capture devices to detect problematic glycemic patterns and recommend insulin dosage adjustments to maximize time-in-range (70-180 mg/dL) and minimize hypoglycemia (< 70 mg/dL). The KNN-DSS employs distance and class weighting, a carb ratio aggressiveness algorithm, and an expert-opinion quality-control algorithm to filter erroneous recommendations. The KNN-DSS was evaluated both in silico and in comparison with physician recommendations. In silico virtual patients (n=30) of varying weight, total daily insulin requirement, insulin sensitivity, and compliance with care were evaluated in a 1-year study whereby they received weekly recommendations. The primary outcome measures were the % time-in-range and % time-in-hypoglycemia. The KNN-DSS was further evaluated for accuracy compared with endocrinologist recommendations collected during a 4-week, at-home clinical trial of CGM-augmented MDI therapy (N = 12, 7 female, 30.8 ± 5 years, 79.6 ± 18.9 kg), and compared using Dice similarity coefficient. In silico, the KNN-DSS improved time-in-range from 66.8% at baseline to 81.0% (21% relative improvement, p Disclosure N.S. Tyler: None. C.M. Mosquera-Lopez: None. L.M. Wilson: None. N. Resalat: None. R. Dodier: None. D. Branigan: None. V. Gabo: None. J. El Youssef: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-T1D001)

Introduction: Glycemic control during exercise is challenging for people with type 1 diabetes. Better quantification of insulin dynamics at different exercise intensities and across insulin loading conditions is necessary for accurate insulin dosing during exercise. Methods: Twenty seven participants with type 1 diabetes were evaluated during 3 separate 10-hour fasting aerobic exercise studies with low, medium, and high levels of insulin infusion (100%, 150%, and 300% of basal rate) across two exercise arms: moderate (40-45% of VO2 max), and intense (60-65% of VO2 max) exercise. Glucose levels were clamped during a 3-hour run-in period, followed by 45 minutes of exercise on a treadmill. 6,6-2H2-dideuterated glucose was infused to match endogenous glucose production during the entire trial. Glucose tracer data were fitted to a two-compartment ODE model, solved via Bayesian inference. We intend to quantify insulin and non-insulin mediated disposal of glucose by observing the rate of disposal (Rd) across different rates of insulin infusion in each exercise arm. Results: Area under the curve (AUC) of Rd during 45 minutes of moderate exercise increased by 0.084 mmol/L for every percent increase in baseline insulin infusion rate (95% CI=0.064-0.104, p Conclusion: Results indicate Rd is substantial during exercise but that the difference in glucose uptake during aerobic exercise at 40-45% VO2 max may be comparable with 60-65% VO2 max. Disclosure T.P. Nguyen: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. L.M. Wilson: None. D. Branigan: None. V. Gabo: None. B. Senf: None. F.H. Guillot: None. J. El Youssef: None. Funding National Institutes of Health

People with type 1 diabetes (T1D) estimate the carbohydrate (CHO) content of meals to enable accurate insulin dosing, yet protein and fat content of meals also influences post-prandial glycemia. As part of an observational study examining the impact of exercise and nutrient consumption on glycemia, we examined accuracy in estimating macronutrient content of free-living meals via a novel phone app. Participant estimates of nutrient content were compared to expert nutrition analyses performed via the Remote Food Photography Method (RFPM). We report results from 30 of 33 randomized with analyzable food photos. Participants were 15-65 years (32±14 years); 27% identified as female. Participants were asked to take photos before and after meals/snacks on up to 16 days over a 28-day period, enter CHO estimates and estimate if meals were low ( 75 g (large) amounts of CHO. Participants estimated CHO in small or medium meals within 10 g of the expert analyses. They were less accurate estimating CHO for larger meals (-56 ± 46 g). Likewise, most correctly categorized low and typical protein (63%, 50%) as well as low and typical fat (62%, 67%) meals. Few correctly categorized meals high in protein (17%) or fat (16%). Participants’ estimation accuracy for larger meals did not differ by T1D duration. The phone app successfully collected individuals’ food intake and estimated macronutrient intake, but showed that participants consistently underestimated nutrient intake for large meals. Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop systems; development of tools to improve macronutrient estimation skills should be considered. Disclosure M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE. Consultant; Self; ACAP Health, Florida Hospital, WW, Zafgen, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics. S.R. Patton: None. Z. Li: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. M. Riddell: Advisory Panel; Self; Xeris Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Insulet Corporation, Medtronic MiniMed, Inc. Stock/Shareholder; Self; Zucara Therapeutics Inc. M.R. Rickels: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. M.A. Clements: Advisory Panel; Self; Glooko, Inc. Consultant; Self; Eli Lilly and Company. Speaker's Bureau; Self; Medtronic. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Dual-hormone systems show promise to reduce hypoglycemia, but require a stable liquid glucagon formulation. XeriSol™ glucagon is a shelf-stable glucagon product for this use. Nineteen subjects with T1D are completing a single-center three-day outpatient study comparing control modes of the Oregon Artificial Pancreas system: 1) dual hormone (DH) closed-loop with Novolog™ insulin and XeriSol™ glucagon, 2) insulin-only single hormone (SH) closed-loop, 3) insulin-only predictive low glucose suspend system (PLGS). In clinic aerobic exercise (45 minutes at 60% VO2max) and home exercise was completed with each arm. SH and DH used automated exercise detection to adaptively dose insulin/glucagon for predicted hypoglycemia and tailored mealtime dosing based on past meal responses with an adaptive algorithm. An interim analysis after 7 subjects was completed for safety. The primary outcome measures are % time 70-180 mg/dL and % time Disclosure L.M. Wilson: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. N. Resalat: None. R. Reddy: None. J. El Youssef: None. D. Branigan: None. J.A. Leitschuh: Other Relationship; Self; AgaMatrix. B. Senf: None. V. Gabo: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. Funding JDRF

Structured exercise can help patients with type 1 diabetes (T1D) achieve physical activity targets, however the effects of exercise type on percent time in range (TIR) and glucose variability are unclear. The Type 1 Diabetes Exercise Initiative Pilot Study provided the opportunity to examine glycemic control on structured exercise days compared to sedentary days. Adults with T1D ([mean ± SD] age= 32 ± 14 years; HbA1C= 7.8 ± 1.6%), using a continuous glucose monitor, were assigned to complete twice weekly aerobic, interval or resistance exercise sessions, using study-developed instructional videos for a period of 4 weeks. TIR was higher (Figure 1) and mean glucose concentration was lower overall on exercise days compared with sedentary days (160 ± 52 vs. 176 ± 49 mg/dL, P In summary, participants had lower 24-hour mean glucose levels and a greater percent time in range on active days compared with inactive days. Disclosure M. Riddell: Advisory Panel; Self; Xeris Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Insulet Corporation, Medtronic MiniMed, Inc. Stock/Shareholder; Self; Zucara Therapeutics Inc. Z. Li: None. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care. R.L. Gal: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. M.B. Gillingham: None. M.A. Clements: Advisory Panel; Self; Glooko, Inc. Consultant; Self; Eli Lilly and Company. Speaker's Bureau; Self; Medtronic. S.R. Patton: None. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. C.K. Martin: Advisory Panel; Self; EHE. Consultant; Self; ACAP Health, Florida Hospital, WW, Zafgen, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. M.R. Rickels: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Background: Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise. Methods: We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D. Results: Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity. Conclusions: Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.

Background: There is currently no stable liquid form of glucagon commercially available. The aim of this study is to assess the speed of absorption and onset of action of G-Pump™ glucagon at 3 doses as compared to GlucaGen®, all delivered subcutaneously via an OmniPod®. Methods: Nineteen adult subjects with type 1 diabetes participated in this Phase 2, randomized, double-blind, cross-over, pharmacokinetic/pharmacodynamic study. Subjects were given 0.3, 1.2, and 2.0 µg/kg each of G-Pump glucagon and GlucaGen via an OmniPod. Results: G-Pump glucagon effectively increased blood glucose levels in a dose-dependent fashion with a glucose Cmax of 183, 200, and 210 mg/dL at doses of 0.3, 1.2, and 2.0 µg/kg, respectively ( P = ns vs GlucaGen). Mean increases in blood glucose from baseline were 29.2, 52.9, and 77.7 mg/dL for G-Pump doses of 0.3, 1.2, and 2.0 µg/kg, respectively. There were no statistically significant differences between treatments in the glucose T50%-early or glucagon T50%-early with one exception. The glucagon T50%-early was greater following G-Pump treatment at the 2.0 μg/kg dose (13.9 ± 4.7 min) compared with GlucaGen treatment at the 2.0 μg/kg dose (11.0 ± 3.1 min, P = .018). There was more pain and erythema at the infusion site with G-Pump as compared to GlucaGen. No serious adverse events were reported, and no unexpected safety issues were observed. Conclusions: G-Pump glucagon is a novel, stable glucagon formulation with similar PK/PD properties as GlucaGen, but was associated with more pain and infusion site reactions as the dose increased, as compared to GlucaGen.

Objectives Physical exercise is recommended for individuals with type 1 diabetes, yet the effects of exercise on glycemic control are not well established. We evaluated the impact of different modes of exercise on glycemic control in people with type 1 diabetes. Methods In a 3-week randomized crossover trial, 10 adults with type 1 diabetes (4 men and 6 women, aged 33±6 years; duration of diabetes, 18±10 years; glycated hemoglobin level, 7.4%±1%) were assigned to 3 weeks of intervention: aerobic exercise (treadmill at 60% of maximum volume of oxygen utilization), resistance training (8 to 12 repetitions of 5 upper and lower body exercises at 60% to 80% of 1 repetition maximum) or no exercise (control). During each exercise week, participants completed 2 monitored 45 min exercise sessions. For each week of the study, we analyzed participants’ insulin pump data, sensor glucose data and meal intake using a custom smart-phone application. The primary outcome was the percentage of time in range (glucose >3.9 mmol/L and ≤10 mmol/L) for the 24 h after each bout of exercise or rest during the control week. The study was registered on ClinicalTrials.gov (NCT:02687893). Results Aerobic exercise caused a mean glucose reduction during exercise of 3.94±2.67 mmol/L, whereas the reduction during resistance training was 1.33±1.78 mmol/L (p=0.007). The mean percentage time in range for the 24 h after resistance training was significantly greater than that during the control period (70% vs. 56%, p=0.013) but not after aerobic exercise (60%). Conclusions The results indicate that when various confounders are considered, resistance training could improve glycemic control in this population.

Regular exercise is recommended to individuals with type 1 diabetes (T1D) as it is associated with improved longevity and reduced diabetes-related complications, yet the effects of exercise on glucose control have not been proven. We evaluated the impact of different modes of exercise on glycemic control in people with T1D during a 24-hour period following exercise. In a 3-week crossover trial, 10 adults with T1D who self-managed their glucose levels with their own insulin pump (4 M, 6 F; age 33 ± 6 years, HbA1c 7.4 ± 1%) were randomized to perform aerobic, resistance or no exercise (control). During each exercise week, participants completed two monitored in-clinic 45-minute sessions. Participant’s insulin pump data were downloaded, glucose sensor data was recorded using a continuous glucose monitor and meal intake was recorded using a custom phone app including photographs of the meals which were analysed post-hoc by a nutritionist. The primary outcome was percentage of time in euglycemia (70mg/dL Results from this study indicate that resistance exercise could improve glycemic control in adults with T1D. Disclosure R. Reddy: None. A. Wittenberg: None. D. Branigan: None. K. Winters-Stone: None. J.R. Castle: Consultant; Self; Zealand Pharma A/S. Advisory Panel; Self; Novo Nordisk Inc.. J. El Youssef: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies.

The complexity of T1D results in inadequate glucose control for patients which can lead to acute and chronic complications. We are developing a decision support smartphone application (app) for patients using multiple daily injection (MDI) therapy and continuous glucose monitoring (CGM). This app will use glucose trends, insulin dosing, and physical activity to provide recommendations for titration of insulin dosing to improve glycemic control. We performed an online survey of patients age 18-80 with T1D from a diabetes specialty clinic, targeting MDI users, to determine current needs and preferences of candidates for decision support tools. Twenty-seven respondents are included in the analysis: mean age of 39.4 years, 54% female, 17.5 years mean duration of T1D, 89% use MDI. A total of 41% of respondents used CGM 50-100% of the time with all checking their CGM >6 times per day. Over 90% use a smartphone, 88% have the phone with them 75-100% of the time. Only 19% used an app to calculate insulin doses, while 81% were interested in using an app to manage diabetes. Participants wanted the following features in a decision support app: hypoglycemia avoidance (89%), insulin dose calculation (78%), behavioral change suggestions (74%), predicted glucose trends (70%), and insulin-on-board (70%). Many expressed low confidence in their ability to manage glucose during exercise (51%). Most respondents were interested in viewing predicted glucose trends prior to exercise (85%). Preferences about alerts to improve glucose control were varied with 44% saying they would be comfortable with 1-3 alerts per day. Preferences about alerts for predicted hypoglycemia were also varied; 11% would want to be alerted 5 minutes prior, 19% 15 minutes, 44% 30 minutes, and 26% >1 hour. These survey results confirm the need for and interest in a smartphone-based decision support app that will assist with management of T1D. The results will enable intelligent app design to better meet the needs of end users. Disclosure L.M. Wilson: None. V. Gabo: None. N.S. Tyler: None. R. Reddy: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Consultant; Self; Zealand Pharma A/S. Advisory Panel; Self; Novo Nordisk Inc..

Several groups are developing fully integrated dual-hormone artificial pancreas systems, which deliver insulin or glucagon in response to changes in glucose to maintain euglycemia. These systems hold the potential to transform management of type 1 diabetes, but in order to be realized these systems require a stable-in-solution glucagon analog suitable for chronic use. Dasiglucagon is a novel glucagon analog stable in liquid formulation. The present studies evaluated safety and tolerability of chronically administered dasiglucagon in rats and dogs. Rats and dogs were dosed s.c. daily for 26 and 39 weeks, respectively, in 4 groups of 20 rats with 0 (vehicle), 0.5, 2, or 8 mg/kg/day; 4 groups of 4 beagle dogs with 0, 0.02, 0.1, or 0.3 mg/kg/day. Glucose and insulin levels increased in a dose-dependent manner in all treated animals. In dogs, heart rate increased at the 0.1 and 0.3 mg/kg/day dose level. Liver and kidney weights increased at all dose levels for in both species, and microscopic pathology demonstrated increase in hepatocyte glycogen vacuolation and increase in chronic progressive nephropathy (a normal finding in aging rats). Heart weights increased with no histopathological correlate. All findings showed full/partial recovery after 4-week treatment free periods. Some animals developed anti-drug-antibodies but with no apparent effect on pharmacodynamics. The NOAEL (no observed adverse effect level) dose of 2 mg/kg/day in rats and 0.1 mg/kg/day in dogs represents exposure multiples (AUC) of 22 and 2 compared to anticipated human doses of up to 1 mg/day. Chronic administration of dasiglucagon multiple times above relevant human doses was well tolerated. All findings were consistent with the known pharmacological effects of glucagon. The risk of glycogen accumulation is not considered relevant for humans treated with dasiglucagon to maintain euglycemia. These findings support long-term human testing of dasiglucagon in dual-hormonal artificial pancreas systems. Disclosure J.R. Castle: Consultant; Self; Zealand Pharma A/S. Advisory Panel; Self; Novo Nordisk Inc. M. Elander: Employee; Self; Zealand Pharma A/S. S.A. O'Halloran: None.

Exercise-related hypoglycemia in T1D is frequent and can be life-threatening. The ability to predict glucose changes at the start of exercise could significantly improve exercise outcomes for people with T1D. We developed multivariate adaptive regression splines (MARS) to predict changes to blood glucose dynamics and to assess whether including past glucose changes during exercise improves prediction accuracy. Data was collected from 20 adults during a 4-arm, 4-day, outpatient artificial pancreas (AP) study (14 F, weight: 76.3 ±14.6 kg, age: 35.2 ± 4.6 years, height: 172.0 ± 10.6 cm). Days 1 and 4 of the study consisted of identical controlled meals and aerobic exercise events at an academic medical center. Insulin delivery data, heart rate, continuous glucose measurement (CGM) data, and carbohydrate intake were recorded. Subjects exercised at 60% of their maximal VO2 for 45 minutes. Using the 160 aerobic exercise sessions, 2 linear predictive models were developed to estimate glucose drop during exercise: (1) a model excluding past exercise history (naïve), and (2) a model including past exercise (historical). A greedy approach was used to eliminate non-predictive features and build sparse models consisting of CGM at the start of exercise, the rate of change of CGM within 5 minutes of exercise, the metabolic expenditure 5 minutes into exercise, and participant height. Leave-one-out cross validation was done to fit the model and estimate root-mean squared error (RMSE). R2 was calculated between empirical and predicted glucose drops. The naïve model was able to predict glucose drop to within 18.7 mg/dL. Including historical exercise data improved accuracy and increased R2 only nominally (naïve R2=0.78, RMSE=18.7, historical R2= 0.81, RMSE=17.54). Results demonstrate prediction accuracy of linear models in estimating exercise-related glucose changes with moderate improvement by including prior exercise data. Disclosure N.S. Tyler: None. R. Reddy: None. J. El Youssef: None. J.R. Castle: Consultant; Self; Zealand Pharma A/S. Advisory Panel; Self; Novo Nordisk Inc. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies.

OBJECTIVE Healthy pancreatic β-cells secrete the hormones insulin and amylin in a fixed ratio. Both hormones are lacking in type 1 diabetes, and postprandial glucose control using insulin therapy alone is difficult. This study tested the pharmacodynamic effects of the amylin analog pramlintide and insulin delivered in a fixed ratio over a 24-h period. RESEARCH DESIGN AND METHODS Patients with type 1 diabetes were stabilized on insulin pump therapy with insulin lispro before a randomized, single-masked, two-way crossover, 24-h inpatient study in which regular human insulin was administered with pramlintide or placebo using separate infusion pumps in a fixed ratio (9 μg/unit). Meal content and timing and patient-specific insulin doses were the same with each treatment. The primary outcome measure was change in mean glucose by continuous glucose monitoring (CGM). Profiles of laboratory-measured glucose, insulin, glucagon, and triglycerides were also compared. RESULTS Mean 24-h glucose measured by CGM was lower with pramlintide versus placebo (8.5 vs. 9.7 mmol/L, respectively; P = 0.012) due to a marked reduction of postprandial increments. Glycemic variability was reduced, and postprandial glucagon and triglycerides were also lower with pramlintide versus placebo. Gastrointestinal side effects were more frequent during use of pramlintide; no major hypoglycemic events occurred with pramlintide or placebo. CONCLUSIONS Coadministration of fixed-ratio pramlintide and regular human insulin for 24 h improved postprandial hyperglycemia and glycemic variability in patients with type 1 diabetes. Longer studies including dose titration under daily conditions are needed to determine whether this regimen could provide long-term improvement of glycemic control.

BACKGROUND Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. OBJECTIVE The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. METHODS We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. RESULTS Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P CONCLUSIONS Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

OBJECTIVE Automated insulin delivery is the new standard for type 1 diabetes, but exercise-related hypoglycemia remains a challenge. Our aim was to determine whether a dual-hormone closed-loop system using wearable sensors to detect exercise and adjust dosing to reduce exercise-related hypoglycemia would outperform other forms of closed-loop and open-loop therapy. RESEARCH DESIGN AND METHODS Participants underwent four arms in randomized order: dual-hormone, single-hormone, predictive low glucose suspend, and continuation of current care over 4 outpatient days. Each arm included three moderate-intensity aerobic exercise sessions. The two primary outcomes were percentage of time in hypoglycemia ( RESULTS The analysis included 20 adults with type 1 diabetes who completed all arms. The mean time (SD) in hypoglycemia was the lowest with dual-hormone during the exercise period: 3.4% (4.5) vs. 8.3% (12.6) single-hormone (P = 0.009) vs. 7.6% (8.0) predictive low glucose suspend (P < 0.001) vs. 4.3% (6.8) current care where pre-exercise insulin adjustments were allowed (P = 0.49). Time in hypoglycemia was also the lowest with dual-hormone during the entire 4-day study: 1.3% (1.0) vs. 2.8% (1.7) single-hormone (P < 0.001) vs. 2.0% (1.5) predictive low glucose suspend (P = 0.04) vs. 3.1% (3.2) current care (P = 0.007). Time in range during the entire study was the highest with single-hormone: 74.3% (8.0) vs. 72.0% (10.8) dual-hormone (P = 0.44). CONCLUSIONS The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.

OBJECTIVE To evaluate subjects with type 1 diabetes for hepatic glycogen depletion after repeated doses of glucagon, simulating delivery in a bihormonal closed-loop system. RESEARCH DESIGN AND METHODS Eleven adult subjects with type 1 diabetes participated. Subjects underwent estimation of hepatic glycogen using 13C MRS. MRS was performed at the following four time points: fasting and after a meal at baseline, and fasting and after a meal after eight doses of subcutaneously administered glucagon at a dose of 2 µg/kg, for a total mean dose of 1,126 µg over 16 h. The primary and secondary end points were, respectively, estimated hepatic glycogen by MRS and incremental area under the glucose curve for a 90-min interval after glucagon administration. RESULTS In the eight subjects with complete data sets, estimated glycogen stores were similar at baseline and after repeated glucagon doses. In the fasting state, glycogen averaged 21 ± 3 g/L before glucagon administration and 25 ± 4 g/L after glucagon administration (mean ± SEM) (P = NS). In the fed state, glycogen averaged 40 ± 2 g/L before glucagon administration and 34 ± 4 g/L after glucagon administration (P = NS). With the use of an insulin action model, the rise in glucose after the last dose of glucagon was comparable to the rise after the first dose, as measured by the 90-min incremental area under the glucose curve. CONCLUSIONS In adult subjects with well-controlled type 1 diabetes (mean A1C 7.2%), glycogen stores and the hyperglycemic response to glucagon administration are maintained even after receiving multiple doses of glucagon. This finding supports the safety of repeated glucagon delivery in the setting of a bihormonal closed-loop system.

Background: This study evaluated the accuracy and performance of a fourth-generation subcutaneous glucose sensor (Guardian™ Sensor 3) in the abdomen and arm. Methods: Eighty-eight subjects (14–75 years of age, mean ± standard deviation [SD] of 42.0 ± 19.1 years) with type 1 or type 2 diabetes participated in the study. Subjects wore two sensors in the abdomen that were paired with either a MiniMed™ 640G insulin pump, or an iPhone® or iPod® touch® running a glucose monitoring mobile application (Guardian Connect system) and a third sensor in the arm, which was connected to a glucose sensor recorder (GSR). Subjects were also asked to undergo in-clinic visits of 12–14 h on study days 1, 3, and 7 for frequent blood glucose sample testing using a Yellow Springs Instrument (YSI) reference. Results: The overall mean absolute relative difference (MARD ± SD) between abdomen sensor glucose (SG) and YSI reference values was 9.6% ± 9.0% and 9.4% ± 9.8% for the MiniMed 640G insulin pump and Guardian Connect system, respectively; and 8.7% ± 8.0% between arm SG and YSI reference values. The percentage of SG values within 20% agreement of the YSI reference value (for YSI >80 mg/dL) was 90.7% with the MiniMed 640G insulin pump, 91.8% with the Guardian Connect system, and 93.1% for GSR-connected arm sensors. Mean functional sensor life, when calibrating 3–4 times/day, was 145.9 ± 39.3 h for sensors paired with the MiniMed 640G insulin pump, 146.1 ± 41.6 h for sensors paired with the Guardian Connect system, and 147.6 ± 40.4 h for sensors connected to the GSR. Responses to survey questions regarding sensor comfort and ease of use were favorable. Conclusions: The Guardian Sensor 3 glucose sensor, whether located in abdomen or the arm, provided accurate glucose readings when compared with the YSI reference and demonstrated functional life commensurate with the intended 7-day use. ClinicalTrials.gov: NCT02246582

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input–output characteristics, form factor, and adaptab...