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

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.