The aim of this pilot study was to investigate the effect of exercise on sleep and nocturnal hypoglycaemia in adults with type 1 diabetes ( T1D ). In a 3‐week crossover trial, 10 adults with T1D were randomized to perform aerobic, resistance or no exercise. During each exercise week, participants completed 2 separate 45‐minutes exercise sessions at an academic medical center. Participants returned home and wore a continuous glucose monitor and a wrist‐based activity monitor to estimate sleep duration. Participants on average lost 70 (±49) minutes of sleep ( P = .0015) on nights following aerobic exercise and 27 (±78) minutes ( P = .3) following resistance exercise relative to control nights. The odds ratio with confidence intervals of nocturnal hypoglycaemia occurring on nights following aerobic and resistance exercise was 5.4 (1.3, 27.2) and 7.0 (1.7, 37.3), respectively. Aerobic exercise can cause sleep loss in T1D possibly from increased hypoglycaemia.
OBJECTIVE Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI −0.05%, 0.13%; P = 0.41). HbA1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference −0.34% [−3.7 mmol/mol]; 95% CI −0.57% [−6.2 mmol/mol], −0.11% [1.2 mmol/mol]; P = 0.0035). CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.
A BSTRACT Type 2 diabetes (T2D) is a large and growing epidemic. Importantly, new technologies and pharmaceutical options are improving the management of T2D. Continuous glucose monitoring (CGM) systems have advanced glucose-sensing technology, which has made it easier for users to monitor their glucose levels. Glucagon-like peptide 1-based therapies and dual agonists have similarly revolutionized the treatment of T2D. In this article, we present four cases of individuals with T2D who, in collaboration with their healthcare provider, used the data from their CGM systems to inform therapy changes, including the initiation and titration of glucagon-like peptide 1-based therapies. Combined use of CGM systems and glucagon-like peptide 1-based therapies could improve people’s diabetes as well as their overall health.
Aims To test whether adjusting insulin and glucagon in response to exercise within a dual‐hormone artificial pancreas ( AP ) reduces exercise‐related hypoglycaemia. Materials and methods In random order, 21 adults with type 1 diabetes ( T1D ) underwent three 22‐hour experimental sessions: AP with exercise dosing adjustment ( APX ); AP with no exercise dosing adjustment ( APN ); and sensor‐augmented pump ( SAP ) therapy. After an overnight stay and 2 hours after breakfast, participants exercised for 45 minutes at 60% of their maximum heart rate, with no snack given before exercise. During APX , insulin was decreased and glucagon was increased at exercise onset, while during SAP therapy, subjects could adjust dosing before exercise. The two primary outcomes were percentage of time spent in hypoglycaemia (<3.9 mmol/ L ) and percentage of time spent in euglycaemia (3.9‐10 mmol/ L ) from the start of exercise to the end of the study. Results The mean (95% confidence interval) times spent in hypoglycaemia (<3.9 mmol/ L ) after the start of exercise were 0.3% (−0.1, 0.7) for APX , 3.1% (0.8, 5.3) for APN , and 0.8% (0.1, 1.4) for SAP therapy. There was an absolute difference of 2.8% less time spent in hypoglycaemia for APX versus APN (p = .001) and 0.5% less time spent in hypoglycaemia for APX versus SAP therapy (p = .16). Mean time spent in euglycaemia was similar across the different sessions. Conclusions Adjusting insulin and glucagon delivery at exercise onset within a dual‐hormone AP significantly reduces hypoglycaemia compared with no adjustment and performs similarly to SAP therapy when insulin is adjusted before exercise.
Amperometric glucose sensors have advanced the care of patients with diabetes and are being studied to control insulin delivery in the research setting. However, at times, currently available sensors demonstrate suboptimal accuracy, which can result from calibration error, sensor drift, or lag. Inaccuracy can be particularly problematic in a closed-loop glycemic control system. In such a system, the use of two sensors allows selection of the more accurate sensor as the input to the controller. In our studies in subjects with type 1 diabetes, the accuracy of the better of two sensors significantly exceeded the accuracy of a single, randomly selected sensor. If an array with three or more sensors were available, it would likely allow even better accuracy with the use of voting.
<p> </p> <p><strong>Objective:</strong> 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.</p> <p><strong>Research Design-Methods: </strong>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.</p> <p><strong>Results: </strong>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<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-hours following study exercise when compared to days without exercise (76±20% vs. 70±23%; P<0.001).</p> <p><strong>Conclusion: </strong>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.</p>
Background: Despite a vigorous research effort, to date, the development of systems that achieve glucagon stability in aqueous formulations (without reconstitution) has failed to produce any clinical candidates. We have developed a novel, nonaqueous glucagon formulation based on a biocompatible pharmaceutical solvent, dimethyl sulfoxide, which demonstrates excellent physical and chemical stability at relatively high concentrations and at high temperatures. Methods: This article reports the development of a novel, biocompatible, nonaqueous native human glucagon formulation for potential use in subcutaneous infusion pump systems. Results: Data are presented that demonstrate physical and chemical stability under presumed storage conditions (>2 years at room temperature) as well as “in use” stability and compatibility in an Insulet’s OmniPod ® infusion pump. Also presented are results of a skin irritation study in a rabbit model and pharmacokinetics/pharmacodynamics data following pump administration of glucagon in a diabetic swine model. Conclusions: This nonaqueous glucagon formulation is suitable for further clinical development in pump systems.
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 <=glucose<=180 mg/dL) for the 24 hours after each bout of exercise as compared with the same period during the control week. Time in range following resistance exercise was significantly greater than during the control period (70% vs. 56%, respectively, p<0.05) while time in range following aerobic exercise (60%) was not statistically different. 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.
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.View largeDownload slideView largeDownload slide DisclosureM. 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.
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