Clinical trial NCT05736263: Exercise-induced Glycemic Variations and Hybrid Closed-loop Systems
Official title: Setting a New Algorithm For the Management of Exercise-induced Glycemic Variations in Patients With Type 1 Diabetes in Intensive Therapy With Hybrid Closed-loop Systems
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Type 1 Diabetes
Exercise
Keywords: Exercise-induced glycemic variations
hybrid closed-loop systems
Type 1 Diabetes
Lead sponsor: University of Roma La Sapienza
Sponsor class: OTHER
Collaborators: name: University of Rome Foro Italico
name: University of Padova
First posted: 2023-02-21
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Primary purpose: OTHER

Brief summary: Type 1 diabetes is characterized by high risk of hypoglycemia and associated fear of hypoglycemia. Hypoglycemia risk is higher during and after physical activity, especially aerobic activity of long duration. Fear of hypoglycemia can result in avoidance of exercise or overcompensatory eating, both related to worse metabolic control and increased cardiometabolic risk. Hybrid closed-loop (HCL)systems have significantly improved risk of hypoglycemia. They also offer the possibility to set a temporary target for physical activity, further reducing the risk of hypoglycemia during physical activity. Although temporary target seems to work rather well with moderate-intensity aerobic exercise, little data is available for other types of exercise, like resistance exercise, high-intensity interval exercise, combined modalities of exercise, in which the temporary target seems to perform less well. The present study aims to test the performance of current HCL systems under different exercise conditions and evaluate the relationship between different exercise variables (recorded during exercise), physical activity variables (measured by accelerometry) and glycemic variations in HCL system users.

Detailed description: People with type 1 diabetes mellitus (T1DM) are continuously at risk of hypoglycemia, which is one of the main barriers to achieving optimal glycemic control.

Physical activity (PA) in T1DM is characterized by an imbalance between hepatic glucose production and glucose disposal into the muscle, increased insulin sensitivity and impaired counterregulatory hormonal response. Thus, PA could increase the risk of hypoglycemia in T1DM. Hypoglycemia can occur during exercise, as well as during recovery, and fear of hypoglycemia often results in either avoidance of exercise or overcompensatory treatment behaviors, which in turn result in worsened metabolic control and increased cardiometabolic risk.

Complexity of glucose homeostasis and an insufficient level of technology prevents tight blood glucose (BG) control regulation. Closed-loop artificial pancreas studies have shown reduction in the risk of hypoglycemia and increase in time in range (70-180 mg/dl) in T1DM patients. Although these systems work fairly well on overnight hypoglycemia, preventing low BG during and immediately after exercise remains a problem, due to the combination of a dramatic increase in insulin sensitivity with the delayed onset of subcutaneous insulin. Informing insulin dosing of PA could decrease this risk. Glycemic response to exercise varies based on type of exercise (aerobic or resistance), but also based on intensity and duration of exercise. Most existing hybrid closed-loop (HCL) systems use the Dexcom G6 glucose sensor, which has demonstrated good accuracy during aerobic, resistance and high intensity interval training (HIIT) exercise.

Current HCL systems offer the possibility to announce exercise to the system and this information is accounted for in the insulin dosing calculation. As a result, the system sets a higher glycemic target, increases the insulin sensitivity factor and/or avoids correction boluses during exercise. These systems perform well in preventing hypoglycemia, but compensatory hyperglycemia, due to higher carbohydrate intake and lower insulin delivery, often follows. One of the major issues with existing HCL systems is that exercise is considered a binomial entity, either present or absent, and factors like intensity, duration or type of exercise are not taken into consideration. However, these exercise variables can contribute to elicit completely different glycemic responses to exercise. Even more, these systems do not account for the delayed effect of exercise, i.e. the so-called activity-on-board (AOB), a quantitative representation of the previously performed PA that is still affecting the BG levels, and which is responsible for the post-exercise hypoglycemia, sometimes several hours after an exercise session. Current recommendations on how to manage BG levels in exercise have not yet been fully tested in the context of a closed-loop system.

The present study aims to develop a new and improved algorithm that uses information on exercise/physical activity variables to predict glycemic variations and modulate insulin therapy accordingly in order to avoid hypo- and hyper-glycemia and maintain glycemic levels in the desired range. This major aim is to be achieved in a multi-step manner. The first step will consist in data collection. Data relative to exercise/physical activity will be collected in two different settings. First, through the different exercise sessions, exercise-related data derived from heart rate monitor, strength training machines will be gathered. Second, data relative to spontaneous physical activity/sedentary behavior, outside the experimental setting will be gathered through movement trackers. Data relative to glycemic variations, derived from CGMs and data relative to insulin and carbohydrate intake from insulin pumps and food diaries. Through the exercise experimental sessions, investigators will be able to evaluate the effectiveness of hybrid closed-loop systems on maintaining glucose levels in range (70-180 mg/dl) and in preventing both hypo- and hyperglycemia, during and after exercise of different durations, intensities and types. In a second step, the relationships between exercise variables derived from both experimental sessions and PA monitoring outside of the experimental sessions, and glycemic, insulin and carbohydrate variations with physical activity will be investigated.

The third, and last step, will include further data analysis, testing, modelling and in-silico simulations in order to estimate the performances of a new decision support system for PA-informed insulin dosing against standard insulin dosing.

Study design Participants Fifty men and women with T1DM will participate in the study. Preliminary testing Upon signing the informed consent, participants' baseline data will be collected. Then, preliminary testing will take place in the Exercise Physiology Lab at the University of Rome Foro Italico. Peak cardiopulmonary oxygen consumption (VO2peak) will be assessed using a graded cycle ergometer (Lode, Groningen, Netherlands) exercise test to volitional exhaustion. The maximum power output (Wmax) achieved during the test will be used to standardize exercise prescription.

The 1-RM test will be performed for the evaluation of the dynamic maximal muscle strength. The test will be performed before randomization, for lower body push movements and upper body push and pull movements on the leg press, chest press and low row Biostrength® line machines, respectively (Technogym S.p.A., Cesena, Italy). The 1-RM evaluation will be useful for the determination of the individuals' load during the exercise program. During the test, the velocity of the movement will be registered for the analysis of the muscle peak power. Before being assigned to any of the exercise trials, participants will be asked to wear a GENEActiv activity monitor at the wrist for seven consecutive days in free-living conditions. GENEActiv devices are wrist-worn instruments used for evaluation of free-living activities. These devices, which will be provided by the Exercise Physiology Lab, provide raw data for physical activity, sedentary behavior and sleep analysis, capturing movement, light and temperature data. Participants will be provided with a standardized diet for the whole duration of the study and will be asked to keep a food diary on the seven days they wear the accelerometer, 24 hours before the exercise trials and on the days of exercise trials.

Exercise trials Patients will undergo four different exercise trials, moderate intensity aerobic exercise (MIE), high-intensity interval exercise (HIIE), resistance exercise (RE) and combined aerobic and resistance exercise (COMB). The same exercise trials will be performed in two different conditions: Exercise mode set on "on" or exercise mode set on "off" on the insulin pump. Patients will also wear an accelerometer and a heart rate monitor on the trial days. The four exercise sessions will have the same duration of 50 min subdivided into 5 min of warmup, 40 min of work and 5 min of cool down. The 40 min of work will differ between trials as follows.

Moderate intensity exercise will consist of continuous exercise on a cycle ergometer at 40% Wmax.

High-intensity interval exercise will consist in three 10-min bouts of interval exercise at 1:1 work:rest ratio (5 x 1-min intervals at 90% Wmax with one minute recovery at 25% Wmax between intervals) interspersed by 3 min of recovery at 25% Wmax.

Resistance exercise will comprise a circuit of 8 groups of exercises repeated in 3 sets of 12 repetitions each at 70% of 1-RM. All exercises will be performed on the same machines used for strength evaluations during preliminary testing (Biostrength® line machines, Technogym S.p.A., Cesena, Italy), which will allow precise individualization of resistance exercise, recording of sessions and exact reproducibility on the next exercise trial for each patient.

Combined exercise will consist in 20 minutes of moderate-intensity exercise at 40% Wmax on a cycle ergometer followed by 20 minutes of resistance exercise consisting in a circuit training of 4 groups of exercises repeated in 3 sets of 12 repetitions each at 70% of 1-RM.

Experimental design Participants will be assigned the eight exercise trials (MIE-ON, HIIT-ON, RE-ON, COMB-ON, MIE-OFF, HIIT-OFF, RE-OFF, COMB-OFF) in random order. Randomization will be done by a computer-generated sequence. Each exercise session will be separated by at least 7 days.

Participants will be asked to abstain from strenuous exercise 48 hours before the exercise trial. The day before the exercise trials, participants will be asked to follow a standard diet and keep a food dairy.

On the morning of the trial, patients will present at the lab at 9.00 am, 2 hours after a standard breakfast, which was proceeded by an insulin bolus calculated by the participants bolus calculator. When allocated to one of the "ON" exercise trials, the patients will be asked to activate the exercise mode (temporary higher target) from 2 hours before the expected exercise trial until the end of the exercise trial. When allocated to one of the "OFF" exercise trials, the patients will start the exercise trial without announcing exercise to the pump. Heart rate, rate of perceived exertion (RPE), power output, angular velocity, number of repetitions and exercise load will all be recorded during the exercise sessions. CGM data, carbohydrate intake and insulin delivery data will be recorded for the following 24 hours.

Eligibility criteria: Inclusion Criteria:

* Age ≥ 18 years and ≤ 65 years old
* T1DM duration ≥ 1 year;
* Automated insulin pump therapy (Hybrid closed-loop) ≥ 12 weeks; HbA1c \< 10 %
* Physically able to complete the study protocol

Exclusion Criteria:

* severe diabetic nephropathy, retinopathy and neuropathy;
* acute cardiovascular events in the last 6 months;
* presence of diabetic foot ulcers;
* severe hypoglycemia, diabetic ketoacidosis in the past month;
* severe visual impairment; systemic steroid therapy;
* pregnancy;
* any major life-threatening disease.

Primary outcomes:
measure: CGM-derived percentage time in range (TIR) during and after exercise trials
timeFrame: 3 hours
measure: Comparison of CGM-derived percentage time in range (TIR) when temporary target is enabled or disabled during exercise
timeFrame: 3 hours
Secondary outcomes:
measure: CGM-derived percentage time in glycemic range range (TIR 70-180 mg/dl), during and after exercise trials
timeFrame: Immediate (3 hours) and delayed (24 hours) glucose response to exercise
measure: CGM-derived percentage time in tight glycemic range range 80-140 mg/dl, during and after exercise trials
timeFrame: Glucose response 24 hours after exercise
measure: CGM-derived percentage time above range (TAR > 180 mg/dl) during and after exercise trials
timeFrame: Immediate (3 hours) and delayed (24 hours) glucose response to exercise
measure: CGM-derived percentage time above range (TAR >250 mg/dl) during and after exercise trials
timeFrame: Immediate (3 hours) and delayed (24 hours) glucose response to exercise
measure: Calculation of Hypo/hyperglycemia risk from CGM data
timeFrame: Immediate (3 hours) and delayed (24 hours) glucose response to exercise
measure: CGM-derived percentage time below range (TBR <70 mg/dl) during and after exercise trials
timeFrame: Immediate (3 hours) and delayed (24 hours) glucose response to exercise
measure: CGM-derived percentage time below range (TBR < 54 mg/dl) during and after exercise trials
timeFrame: Immediate (3 hours) and delayed (24 hours) glucose response to exercise
Other outcomes:
measure: Associations between physical activity and glycemic variations
timeFrame: 7 days
measure: Associations between sleep quantity and glycemic variations
timeFrame: 7 days
measure: Associations between sleep quality and glycemic variations
timeFrame: 7 days
Locations:
city: Roma
country: Italy
facility: Azienda Ospedaliera Sant'Andrea
state: RM
Study officials:
affiliation: University of Roma La Sapienza
name: Giuseppe Pugliese
role: PRINCIPAL_INVESTIGATOR
References:
citation: Cryer PE. Hypoglycaemia: the limiting factor in the glycaemic management of Type I and Type II diabetes. Diabetologia. 2002 Jul;45(7):937-48. doi: 10.1007/s00125-002-0822-9. Epub 2002 Apr 26.
citation: McMahon SK, Ferreira LD, Ratnam N, Davey RJ, Youngs LM, Davis EA, Fournier PA, Jones TW. Glucose requirements to maintain euglycemia after moderate-intensity afternoon exercise in adolescents with type 1 diabetes are increased in a biphasic manner. J Clin Endocrinol Metab. 2007 Mar;92(3):963-8. doi: 10.1210/jc.2006-2263. Epub 2006 Nov 21.
citation: Goodyear LJ, Kahn BB. Exercise, glucose transport, and insulin sensitivity. Annu Rev Med. 1998;49:235-61. doi: 10.1146/annurev.med.49.1.235.
citation: Dohm GL. Invited review: Regulation of skeletal muscle GLUT-4 expression by exercise. J Appl Physiol (1985). 2002 Aug;93(2):782-7. doi: 10.1152/japplphysiol.01266.2001.
citation: Younk LM, Mikeladze M, Tate D, Davis SN. Exercise-related hypoglycemia in diabetes mellitus. Expert Rev Endocrinol Metab. 2011 Jan 1;6(1):93-108. doi: 10.1586/eem.10.78.
citation: van Bon AC, Verbitskiy E, von Basum G, Hoekstra JB, DeVries JH. Exercise in closed-loop control: a major hurdle. J Diabetes Sci Technol. 2011 Nov 1;5(6):1337-41. doi: 10.1177/193229681100500604.
citation: Toni S, Reali MF, Barni F, Lenzi L, Festini F. Managing insulin therapy during exercise in type 1 diabetes mellitus. Acta Biomed. 2006;77 Suppl 1:34-40.
citation: Riddell MC, Milliken J. Preventing exercise-induced hypoglycemia in type 1 diabetes using real-time continuous glucose monitoring and a new carbohydrate intake algorithm: an observational field study. Diabetes Technol Ther. 2011 Aug;13(8):819-25. doi: 10.1089/dia.2011.0052. Epub 2011 May 20.
citation: Breton M, Farret A, Bruttomesso D, Anderson S, Magni L, Patek S, Dalla Man C, Place J, Demartini S, Del Favero S, Toffanin C, Hughes-Karvetski C, Dassau E, Zisser H, Doyle FJ 3rd, De Nicolao G, Avogaro A, Cobelli C, Renard E, Kovatchev B; International Artificial Pancreas Study Group. Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes. 2012 Sep;61(9):2230-7. doi: 10.2337/db11-1445. Epub 2012 Jun 11.
citation: Sherr JL, Cengiz E, Palerm CC, Clark B, Kurtz N, Roy A, Carria L, Cantwell M, Tamborlane WV, Weinzimer SA. Reduced hypoglycemia and increased time in target using closed-loop insulin delivery during nights with or without antecedent afternoon exercise in type 1 diabetes. Diabetes Care. 2013 Oct;36(10):2909-14. doi: 10.2337/dc13-0010. Epub 2013 Jun 11.
citation: Jackson M, Castle JR. Where Do We Stand with Closed-Loop Systems and Their Challenges? Diabetes Technol Ther. 2020 Jul;22(7):485-491. doi: 10.1089/dia.2019.0469. Epub 2020 May 22.
citation: Breton MD, Brown SA, Karvetski CH, Kollar L, Topchyan KA, Anderson SM, Kovatchev BP. Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes. Diabetes Technol Ther. 2014 Aug;16(8):506-11. doi: 10.1089/dia.2013.0333. Epub 2014 Apr 4.
citation: Breton MD. Physical activity-the major unaccounted impediment to closed loop control. J Diabetes Sci Technol. 2008 Jan;2(1):169-74. doi: 10.1177/193229680800200127.
citation: Yardley JE, Kenny GP, Perkins BA, Riddell MC, Balaa N, Malcolm J, Boulay P, Khandwala F, Sigal RJ. Resistance versus aerobic exercise: acute effects on glycemia in type 1 diabetes. Diabetes Care. 2013 Mar;36(3):537-42. doi: 10.2337/dc12-0963. Epub 2012 Nov 19.
citation: Reddy R, Wittenberg A, Castle JR, El Youssef J, Winters-Stone K, Gillingham M, Jacobs PG. Effect of Aerobic and Resistance Exercise on Glycemic Control in Adults With Type 1 Diabetes. Can J Diabetes. 2019 Aug;43(6):406-414.e1. doi: 10.1016/j.jcjd.2018.08.193. Epub 2018 Aug 30.
citation: Guillot FH, Jacobs PG, Wilson LM, Youssef JE, Gabo VB, Branigan DL, Tyler NS, Ramsey K, Riddell MC, Castle JR. Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes. Biosensors (Basel). 2020 Sep 29;10(10):138. doi: 10.3390/bios10100138.
citation: Franc S, Benhamou PY, Borot S, Chaillous L, Delemer B, Doron M, Guerci B, Hanaire H, Huneker E, Jeandidier N, Amadou C, Renard E, Reznik Y, Schaepelynck P, Simon C, Thivolet C, Thomas C, Hannaert P, Charpentier G. No more hypoglycaemia on days with physical activity and unrestricted diet when using a closed-loop system for 12 weeks: A post hoc secondary analysis of the multicentre, randomized controlled Diabeloop WP7 trial. Diabetes Obes Metab. 2021 Sep;23(9):2170-2176. doi: 10.1111/dom.14442. Epub 2021 Jun 3.
citation: Paldus B, Morrison D, Zaharieva DP, Lee MH, Jones H, Obeyesekere V, Lu J, Vogrin S, La Gerche A, McAuley SA, MacIsaac RJ, Jenkins AJ, Ward GM, Colman P, Smart CEM, Seckold R, King BR, Riddell MC, O'Neal DN. A Randomized Crossover Trial Comparing Glucose Control During Moderate-Intensity, High-Intensity, and Resistance Exercise With Hybrid Closed-Loop Insulin Delivery While Profiling Potential Additional Signals in Adults With Type 1 Diabetes. Diabetes Care. 2022 Jan 1;45(1):194-203. doi: 10.2337/dc21-1593.
citation: Riddell MC, Gallen IW, Smart CE, Taplin CE, Adolfsson P, Lumb AN, Kowalski A, Rabasa-Lhoret R, McCrimmon RJ, Hume C, Annan F, Fournier PA, Graham C, Bode B, Galassetti P, Jones TW, Millan IS, Heise T, Peters AL, Petz A, Laffel LM. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes Endocrinol. 2017 May;5(5):377-390. doi: 10.1016/S2213-8587(17)30014-1. Epub 2017 Jan 24.
citation: Ozaslan B, Patek SD, Fabris C, Breton MD. Automatically accounting for physical activity in insulin dosing for type 1 diabetes. Comput Methods Programs Biomed. 2020 Dec;197:105757. doi: 10.1016/j.cmpb.2020.105757. Epub 2020 Sep 21.