Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial.

BACKGROUND: Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data. METHODS: Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403. FINDINGS: Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred. INTERPRETATION: AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia. FUNDING: JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes.

Exercise can cause dangerous fluctuations in blood glucose in people living with type 1 diabetes (T1D). Aerobic exercise, for example, can cause acute hypoglycemia secondary to increased insulin-mediated and noninsulin-mediated glucose utilization. Less is known about how resistance exercise (RE) impacts glucose dynamics. Twenty-five people with T1D underwent three sessions of either moderate or high-intensity RE at three insulin infusion rates during a glucose tracer clamp. We calculated time-varying rates of endogenous glucose production (EGP) and glucose disposal (Rd) across all sessions and used linear regression and extrapolation to estimate insulin- and noninsulin-mediated components of glucose utilization. Blood glucose did not change on average during exercise. The area under the curve (AUC) for EGP increased by 1.04 mM during RE (95% CI: 0.65-1.43, P < 0.001) and decreased proportionally to insulin infusion rate (0.003 mM per percent above basal rate, 95% CI: 0.001-0.006, P = 0.003). The AUC for Rd rose by 1.26 mM during RE (95% CI: 0.41-2.10, P = 0.004) and increased proportionally with insulin infusion rate (0.04 mM per percent above basal rate, CI: 0.03-0.04, P < 0.001). No differences were observed between the moderate and high resistance groups. Noninsulin-mediated glucose utilization rose significantly during exercise before returning to baseline roughly 30-min postexercise. Insulin-mediated glucose utilization remained unchanged during exercise sessions. Circulating catecholamines and lactate rose during exercise despite relatively small changes observed in Rd. Results provide an explanation of why RE may pose a lower overall risk for hypoglycemia.NEW & NOTEWORTHY Aerobic exercise is known to cause decreases in blood glucose secondary to increased glucose utilization in people living with type 1 diabetes (T1D). However, less is known about how resistance-type exercise impacts glucose dynamics. Twenty-five participants with T1D performed in-clinic weight-bearing exercises under a glucose clamp. Mathematical modeling of infused glucose tracer allowed for quantification of the rate of hepatic glucose production as well as rates of insulin-mediated and noninsulin-mediated glucose uptake experienced during resistance exercise.

Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.

We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by 10.8% (P = 0.04) and trends toward increasing time in range (70-180 mg/dL) by 9.1% compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC.

Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections.

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.

Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes.

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.

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes.

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 0.6-9.9). 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: -6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: -8.3 to -2.9%, HIIT MARD: 12.1 to 16.8% and MRD: -14.3 to -9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

Dual-Hormone Closed-Loop System Using a Liquid Stable Glucagon Formulation Versus Insulin-Only Closed-Loop System Compared With a Predictive Low Glucose Suspend System: An Open-Label, Outpatient, Single-Center, Crossover, Randomized Controlled Trial.

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.

An artificial intelligence decision support system for the management of type 1 diabetes.

Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl-1) and hyperglycaemia (>180 mg dl-1), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform3 to generate over 50,000 glucose observations to train a k-nearest neighbours4 decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41-45%)5. In silico3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.

Patient Input for Design of a Decision Support Smartphone Application for Type 1 Diabetes.

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.

Randomized Outpatient Trial of Single- and Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using Wearable Sensors.

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.

Genetic Candidate Variants in Two Multigenerational Families with Childhood Apraxia of Speech.

Childhood apraxia of speech (CAS) is a severe and socially debilitating form of speech sound disorder with suspected genetic involvement, but the genetic etiology is not yet well understood. Very few known or putative causal genes have been identified to date, e.g., FOXP2 and BCL11A. Building a knowledge base of the genetic etiology of CAS will make it possible to identify infants at genetic risk and motivate the development of effective very early intervention programs. We investigated the genetic etiology of CAS in two large multigenerational families with familial CAS. Complementary genomic methods included Markov chain Monte Carlo linkage analysis, copy-number analysis, identity-by-descent sharing, and exome sequencing with variant filtering. No overlaps in regions with positive evidence of linkage between the two families were found. In one family, linkage analysis detected two chromosomal regions of interest, 5p15.1-p14.1, and 17p13.1-q11.1, inherited separately from the two founders. Single-point linkage analysis of selected variants identified CDH18 as a primary gene of interest and additionally, MYO10, NIPBL, GLP2R, NCOR1, FLCN, SMCR8, NEK8, and ANKRD12, possibly with additive effects. Linkage analysis in the second family detected five regions with LOD scores approaching the highest values possible in the family. A gene of interest was C4orf21 (ZGRF1) on 4q25-q28.2. Evidence for previously described causal copy-number variations and validated or suspected genes was not found. Results are consistent with a heterogeneous CAS etiology, as is expected in many neurogenic disorders. Future studies will investigate genome variants in these and other families with CAS.