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Introduction: DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and Methods: Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use. Results: There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001). Conclusions: Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number: NCT04428645. [ABSTRACT FROM AUTHOR]

Insulin therapy has advanced remarkably over the past few decades. Ultra-rapid-acting and ultra-long-acting insulin analogs are now commercially available. Many additional insulin formulations are in development. This review outlines recent advances in insulin therapy and novel therapies in development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article focuses on the continuous glucose monitoring (CGM) has transformed the management of type 1 diabetes (T1D). Topics include automated insulin delivery systems are continuing to evolve to better address exercise-related dysglycemia; mentions use of CGM has been shown to improve time in target range while reducing hemoglobin A1c levels and risk of hypoglycemia; and also mentions exercise significantly increased the time delay between the reference glucose and the CGM readings.

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 (<70 mg/dL) and in a target range (70-180 mg/dL) assessed across the entire study and from the start of the in-clinic exercise until the next meal.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. [ABSTRACT FROM AUTHOR]

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 adaptability. [ABSTRACT FROM AUTHOR]

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 (13)C 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. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses the results of the pivotal trial using the Medtronic 670G insulin delivery system.

OBJECTIVE Glucagon delivery in closed-loop control of type 1 diabetes is effective in minimizing hypoglycemia. However, high insulin concentration lowers the hyperglycemic effect of glucagon, and small doses of glucagon in this setting are ineffective. There are no studies clearly defining the relationship between insulin levels, subcutaneous glucagon, and blood glucose. RESEARCH DESIGN AND METHODS Using a euglycemic clamp technique in 11 subjects with type 1 diabetes, we examined endogenous glucose production (EGP) of glucagon (25, 75, 125, and 175 µg) at three insulin infusion rates (0.016, 0.032, and 0.05 units/kg/h) in a randomized, crossover study. Infused 6,6-dideuterated glucose was measured every 10 min, and EGP was determined using a validated glucoregulatory model. Area under the curve (AUC) for glucose production was the primary outcome, estimated over 60 min. RESULTS At low insulin levels, EGP rose proportionately with glucagon dose, from 5 ± 68 to 112 ± 152 mg/kg (P = 0.038 linear trend), whereas at high levels, there was no increase in glucose output (19 ± 53 to 26 ± 38 mg/kg, P = NS). Peak glucagon serum levels and AUC correlated well with dose (r² = 0.63, P < 0.001), as did insulin levels with insulin infusion rates (r² = 0.59, P < 0.001). CONCLUSIONS EGP increases steeply with glucagon doses between 25 and 175 µg at lower insulin infusion rates. However, high insulin infusion rates prevent these doses of glucagon from significantly increasing glucose output and may reduce glucagon effectiveness in preventing hypoglycemia when used in the artificial pancreas. [ABSTRACT FROM AUTHOR]

Highlights: [•] Because of its ability to prevent and treat hypoglycemia, glucagon shows great promise in closed loop treatment of diabetes. [•] However, glucagon degrades in liquid solution, and the mechanisms of degradation are not well understood. [•] We found that deamidation and isomerization were common, and in many cases, impair the bioactivity of glucagon. [•] Glucagon also undergoes spontaneous oxidation, but oxidized glucagon retains its bioactivity. [•] An understanding of glucagon's inactivating degradation mechanisms might help in the search for stabilizing excipients. [Copyright &y& Elsevier]

Background and Objective: There is a paucity of data regarding tolerability of alkaline drugs administered subcutaneously. The aim of this study was to assess the tolerability of alkaline preparations of human albumin delivered subcutaneously to healthy humans. Methods: We compared the tolerability of neutral versus alkaline (pH 10) formulations of human albumin in ten volunteers. With an intent to minimize the time required to reach physiological pH after injection, the alkaline formulation was buffered with a low concentration of glycine (20 mmol/L). Each formulation was given at two rates: over 5 seconds and over 60 seconds. A six-point scale was used to assess discomfort. Results: For slow injections, there was a significant difference between pH 7.4 and pH 10 injections (0.4±0.2 vs 1.1±0.2, mean±SEM; p=0.025), though the degree of discomfort at pH 10 injections was only 'mild or slight'. For fast injections, the difference between neutral and alkaline formulations was of borderline significance. Inflammation and oedema, as judged by a physician, were very minimal for all injections, irrespective of pH. Conclusion: For subcutaneous drug administration (especially when delivered slowly), there was more discomfort associated with alkaline versus neutral formulations of albumin, though the discomfort was mild. This study suggests that there is little discomfort and inflammation resulting from subcutaneous administration of protein drugs formulated with weak buffers at alkaline pH. [ABSTRACT FROM AUTHOR]

OBJECTIVE--To improve glucose sensor accuracy in subjects with type 1 diabetes by using multiple sensors and to assess whether the benefit of redundancy is affected by intersensor distance. RESEARCH DESIGN AND METHODS--Nineteen adults with type 1 diabetes wore four Dexcom SEVEN PLUS subcutaneous glucose sensors during two 9-h studies. One pair of sensors was worn on each side of the abdomen, with each sensor pair placed at a predetermined distance apart and 20 cm away from the opposite pair. Arterialized venous blood glucose levels were measured every 15 min, and sensor glucose values were recorded every 5 min. Sensors were calibrated once at the beginning of the study. RESULTS--The use of four sensors significantly reduced very large errors compared with one sensor (0.4 vs. 2.6% of errors ≥50% from reference glucose, P , 0.001) and also improved overall accuracy (mean absolute relative difference, 11.6 vs. 14.8%, P , 0.001). Using only two sensors also significantly improved very large errors and accuracy. Intersensor distance did not affect the function of sensor pairs. CONCLUSIONS--Sensor accuracy is significantly improved with the use of multiple sensors compared with the use of a single sensor. The benefit of redundancy is present even when sensors are positioned very closely together (7 mm). These findings are relevant to the design of an artificial pancreas device. [ABSTRACT FROM AUTHOR]

OBJECTIVE- To minimize hypoglycemia in subjects with type 1 diabetes by automated glucagon delivery in a closed-loop insulin delivery system. RESEARCH DESIGN AND METHODS-- Adult subjects with type 1 diabetes underwent one closed-loop study with insulin plus placebo and one study with insulin plus glucagon, given at times of impending hypoglycemia. Seven subjects received glucagon using high-gain parameters, and six subjects received glucagon in a more prolonged manner using low-gain parameters. Blood glucose levels were measured every 10 min and insulin and glucagon infusions were adjusted every 5 min. All subjects received a portion of their usual premeal insulin after meal announcement. RESULTS -- Automated glucagon plus insulin delivery, compared with placebo plus insulin, significantly reduced time spent in the hypoglycemic range (15 ± 6 vs. 40 ± 10 min/day, P = 0.04). Compared with placebo, high-gain glucagon delivery reduced the frequency of hypoglycemic events (1.0 ± 0.6 vs. 2.1 ± 0.6 events/day, P = 0.01) and the need for carbohydrate treatment (1.4 ± 0.8 vs. 4.0 ± 1.4 treatments/day, P = 0.01). Glucagon given with low-gain parameters did not significantly reduce hypoglycemic event frequency (P = NS) but did reduce frequency of carbohydrate treatment (P = 0.05). CONCLUSIONS-- During closed-loop treatment in subjects with type 1 diabetes, high-gain pulses of glucagon decreased the frequency of hypoglycemia. Larger and longer-term studies will be required to assess the effect of ongoing glucagon treatment on overall glycemic control. [ABSTRACT FROM AUTHOR]

The article discusses whether mini-dose glucagon help in preventing exercise related dysglycemia. Topics discussed include use of glucagon in rescue treatment for severe hypoglycemia, need to individualize glucagon dosing to minimize the risk of exercise-related dysglycemia and minimizing the risk of hyperglycemia during the exercise period.

The article discusses the advances in subcutaneous glucose monitoring, referencing the study ‘‘Accuracy of a Fourth-Generation Subcutaneous Continuous Glucose Sensor" published in the issue and states significance of approval of the Dexcom G5 glucose sensor by the U.S. Food & Drug Administration.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, <8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (<57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m², respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise. [ABSTRACT FROM AUTHOR]

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 (AUCEGP) and rate of glucose disappearance (AUCRd) over 45 min from exercise start. A novel application of linear regression of Rd across the three insulin sessions allowed separation of insulin-mediated from non-insulin-mediated glucose uptake before, during, and after exercise. AUCRd 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 AUCEGP 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. AUCEGP 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 AUCRd 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. [ABSTRACT FROM AUTHOR]

Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear. RESEARCH DESIGN AND 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 nonstudy exercise, food intake, and insulin dosing (multiple daily injection [MDI] users) using a custom smart phone application and provided pump (pump users), heart rate, and continuous glucose monitoring data. RESULTS: A total of 497 adults with type 1 diabetes (mean age ± SD 37 ± 14 years; mean HbA1c ± SD 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. The mean (± SD) change in glucose during assigned exercise was −18 ± 39, −14 ± 32, and −9 ± 36 mg/dL for aerobic, interval, and resistance, respectively (P < 0.001), with similar results for closed-loop, standard pump, and MDI users. Time in range 70–180 mg/dL (3.9–10.0 mmol/L) was higher during the 24 h after study exercise when compared with days without exercise (mean ± SD 76 ± 20% vs. 70 ± 23%; P < 0.001). CONCLUSIONS: Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic exercise, followed by interval and resistance exercise, regardless of insulin delivery modality. Even in adults with well-controlled 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. [ABSTRACT FROM AUTHOR]

Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes. [ABSTRACT FROM AUTHOR]

[Display omitted] Position statement guidelines should help people with type 1 diabetes (T1D) improve glucose outcomes during exercise. In a 4-week observational study, continuous glucose, insulin, and nutrient data were collected from 561 adults with T1D. Glucose outcomes were calculated during exercise, post-exercise, and overnight, and were compared for sessions when participants used versus did not use exercise guidelines for open-loop (OL) and automated insulin delivery (AID) therapy. Guidelines requiring behaviour modification were rarely used while guidelines not requiring modification were often used. The guideline recommending reduced meal insulin before exercise was associated with lower time <3.9 mmol/L during exercise (−2.2 %, P=0.02) for OL but not significant for AID (−1.4 %, P=0.27). Compared to exercise with low glucose (<3.9 mmol/L) in prior 24-hours, sessions without recent low glucose had lower time <3.9 mmol/L during exercise (−1.2 %, P<0.001). The AID guideline for no carbohydrates before exercise when CGM is flat, or increasing, was not associated with improved glycaemia. Free-living datasets may be used to evaluate usage and benefit of position statement guidelines. Evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 h had less time below range. [ABSTRACT FROM AUTHOR]

Background: For patients with type 1 diabetes mellitus, a bihormonal artificial endocrine pancreas system utilizing glucagon and insulin has been found to stabilize glycemic control. However, commercially available formulations of glucagon cannot currently be used in such systems because of physical instability characterized by aggregation and chemical degradation. Storing glucagon at pH 10 blocks protein aggregation but results in chemical degradation. Reductions in pH minimize chemical degradation, but even small reductions increase protein aggregation. We hypothesized that common pharmaceutical excipients accompanied by a new excipient would inhibit glucagon aggregation at an alkaline pH. Methods and Results: As measured by tryptophan intrinsic fluorescence shift and optical density at 630 nm, protein aggregation was indeed minimized when glucagon was formulated with curcumin and albumin. This formulation also reduced chemical degradation, measured by liquid chromatography with mass spectrometry. Biological activity was retained after aging for 7 days in an in vitro cell-based bioassay and also in Yorkshire swine. Conclusions: Based on these findings, a formulation of glucagon stabilized with curcumin, polysorbate-80, l-methionine, and albumin at alkaline pH in glycine buffer may be suitable for extended use in a portable pump in the setting of a bihormonal artificial endocrine pancreas. [ABSTRACT FROM AUTHOR]

An introduction is presented din which author discusses articles on topics including focuses on importance to society and health care and those that offer a spectrum of knowledge on a theme, providing insight from basic science to clinical topics.

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 <70 mg/dL higher (9.3% ± 11.0% vs. 7.1% ± 9.1%, P = 0.04) on exercise days compared with sedentary days. Glucose variability and % time <54 mg/dL did not differ significantly between exercise and sedentary days. No significant differences in glucose control by exercise type were observed. Conclusion: Participants had lower 24-h mean glucose levels and a greater time in range on exercise days compared with sedentary days, with mode of exercise affecting glycemia similarly. In summary, this study offers data supporting frequency of exercise as a method of facilitating glucose control but does not suggest an effect for mode of exercise. [ABSTRACT FROM AUTHOR]

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 <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered. [ABSTRACT FROM AUTHOR]

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 <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered. [ABSTRACT FROM AUTHOR]

Background: Despite new glucose sensing technologies, nocturnal hypoglycemia is still a problem for people with type 1 diabetes (T1D) as symptoms and sensor alarms may not be detected while sleeping. Accurately predicting nocturnal hypoglycemia before sleep may help minimize nighttime hypoglycemia. Methods: A support vector regression (SVR) model was trained to predict, before bedtime, the overnight minimum glucose and overnight nocturnal hypoglycemia for people with T1D. The algorithm was trained on continuous glucose measurements and insulin data collected from 124 people (22,804 valid nights of data) with T1D. The minimum glucose threshold for announcing nocturnal hypoglycemia risk was derived by applying a decision theoretic criterion to maximize expected net benefit. Accuracy was evaluated on a validation set from 10 people with T1D during a 4-week trial under free-living sensor-augmented insulin-pump therapy. The primary outcome measures were sensitivity and specificity of prediction, the correlation between predicted and actual minimum nocturnal glucose, and root-mean-square error. The impact of using the algorithm to prevent nocturnal hypoglycemia is shown in-silico. Results: The algorithm predicted 94.1% of nocturnal hypoglycemia events (<3.9 mmol/L, 95% confidence interval [CI], 71.3-99.9) with an area under the receiver operating characteristic curve of 0.86 (95% CI, 0.75-0.98). Correlation between actual and predicted minimum glucose was high (R = 0.71, P < 0.001). In-silico simulations showed that the algorithm could reduce nocturnal hypoglycemia by 77.0% (P = 0.006) without impacting time in target range (3.9-10 mmol/L). Conclusion: An SVR model trained on a big data set and optimized using decision theoretic criterion can accurately predict at bedtime if overnight nocturnal hypoglycemia will occur and may help reduce nocturnal hypoglycemia. [ABSTRACT FROM AUTHOR]

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 (<70 mg/dL) from the start of in-clinic aerobic exercise (45 min at 60% VO2max) to 4 h after.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. [ABSTRACT FROM AUTHOR]

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 < 0.001) and significantly lower GO-based meter values (-10.38 mg/dL, P < 0.001). Pre-HBO2 and post-HBO2 values obtained in NBAir were also significantly different when measured by CGM (+4.13 mg/dL, P = 0.015) or the GO-based meter (-9.04 mg/dL, P < 0.001). Conclusions: In volunteers without DM, HBO2 exposure results in statistically significant differences in glucose measurements obtained with GO-based devices, but not a GD-based device. Standard HBO2 treatment results in statistically significant effects on glucose concentrations. These differences are of unlikely clinical significance. [ABSTRACT FROM AUTHOR]

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<0.001). AUC during intense exercise increased by 0.109 mmol/L (95% CI= 0.079-0.139, p<0.001). Mixed-effects model analysis showed no difference between exercise arms. 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 [ABSTRACT FROM AUTHOR]

Background: Labeling prohibits delivery of insulin at the site of subcutaneous continuous glucose monitoring (CGM). Integration of the sensing and insulin delivery functions into a single device would likely increase the usage of CGM in persons with type 1 diabetes.Methods: To understand the nature of such interference, we measured glucose at the site of bolus insulin delivery in swine using a flexible electrode strip that was laminated to the outer wall of an insulin delivery cannula. In terms of sensing design, we compared H2O2-measuring sensors biased at 600 mV with redox mediator-type sensors biased at 175 mV.Results: In H2O2-measuring sensors, but not in sensors with redox-mediated chemistry, a spurious rise in current was seen after insulin lis-pro boluses. This prolonged artifact was accompanied by electrode poisoning. In redox-mediated sensors, the patterns of sensor signals acquired during delivery of saline and without any liquid delivery were similar to those acquired during insulin delivery.Conclusion: Considering in vitro and in vivo findings together, it became clear that the mechanism of interference is the oxidation, at high bias potentials, of phenolic preservatives present in insulin formulations. This effect can be avoided by the use of redox mediator chemistry using a low bias potential. [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring (CGM) sensors are portable devices, employed in the treatment of diabetes, able to measure glucose concentration in the interstitium almost continuously for several days. However, CGM sensors are not as accurate as standard blood glucose (BG) meters. Studies comparing CGM versus BG demonstrated that CGM is affected by distortion due to diffusion processes and by time-varying systematic under/overestimations due to calibrations and sensor drifts. In addition, measurement noise is also present in CGM data. A reliable model of the different components of CGM inaccuracy with respect to BG (briefly, “sensor error”) is important in several applications, e.g., design of optimal digital filters for denoising of CGM data, real-time glucose prediction, insulin dosing, and artificial pancreas control algorithms. The aim of this paper is to propose an approach to describe CGM sensor error by exploiting n multiple simultaneous CGM recordings. The model of sensor error description includes a model of blood-to-interstitial glucose diffusion process, a linear time-varying model to account for calibration and sensor drift-in-time, and an autoregressive model to describe the additive measurement noise. Model orders and parameters are identified from the n simultaneous CGM sensor recordings and BG references. While the model is applicable to any CGM sensor, here, it is used on a database of 36 datasets of type 1 diabetic adults in which n = 4 Dexcom SEVEN Plus CGM time series and frequent BG references were available simultaneously. Results demonstrates that multiple simultaneous sensor data and proper modeling allow dissecting the sensor error into its different components, distinguishing those related to physiology from those related to technology. [ABSTRACT FROM PUBLISHER]

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 (<70 mg/dL) represented 7.1% and 3.0% of the time in the non-T1D females and males, respectively (P<0.001), vs. 4.4% and 5.0% in the T1D females and males (P=0.67), respectively. 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. [ABSTRACT FROM AUTHOR]

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 min vs. 41.2 ± 78.3 s, P < 0.05). Using a smart-sampling Kalman filtering smoothing algorithm improved sensor accuracy. When using an all-point calibration on all sensors, the smart-sampling Kalman filter reduced the mean absolute relative difference from 10.9% to 9.5% and resulted in 96.7% of the data points falling within the A and B regions of the Clarke error grid. Despite a small artifact, which is likely due to dilution by fluid delivery, it is possible to continuously measure glucose in a cannula that simultaneously delivers insulin. • A redox-mediated glucose sensor mounted on an insulin infusion cannula was fabricated and tested in people with diabetes. • Infusing insulin or saline through a glucose sensing cannula can cause a small dilution artifact. • Polarizing a glucose sensor near the site of insulin infusion at a voltage potential of 175 mV can reduce sensor artifacts. • Strategic sampling and signal processing methods including Kalman filtering can reduce error from dilution artifacts. [ABSTRACT FROM AUTHOR]

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) [ABSTRACT FROM AUTHOR]

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) [ABSTRACT FROM AUTHOR]

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) [ABSTRACT FROM AUTHOR]

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<0.001). Glucose variability was similar between exercise and sedentary days (coefficient of variation: 36 ± 12% vs. 37% ± 12%). 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 [ABSTRACT FROM AUTHOR]

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 (<13%), typical (13-<18%), or high (≥18%) protein and if meals were low (<26%), medium (26-<32%), or high (≥32%) fat. The phone app plus RFPM captured 92±27% of estimated energy needs. Of 1,292 food photos analyzed, 429 contained < 25 g (small), 641 contained 25-75 g (medium), and 222 contained > 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 [ABSTRACT FROM AUTHOR]

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 <0.005), and reduced time-in-hypoglycemia from 3.14% at baseline to 1.37% (56% relative improvement, p < 0.005) after one year. Compared to endocrinologists, the KNN-DSS recommendations to real-world patients were within acceptable agreement 75% of the time, 6% contraindicated, and 19% whereby KNN-DSS gave a recommendation when the physician gave no recommendation. KNN-DSS improves glycemic outcomes in silico and exhibits acceptable agreement with physician recommendations. Future clinical studies are planned. 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) [ABSTRACT FROM AUTHOR]

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 <70 mg/dL for the 3-day study and the 4-hour period from start of exercise until the next meal. From start of exercise until the next meal, DH showed a clinically meaningful reduction of time in hypoglycemia compared with PLGS and SH. For the entire study, DH showed a clinically meaningful lower time <70mg/dL. No serious events or unexpected side effects occurred. 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 [ABSTRACT FROM AUTHOR]