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Metabolic Responses to Exercise and Nutritional Strategies in Type 1 Diabetes Using Automated Insulin Delivery
Desirée Victoria-Montesinos1, Inmaculada Llopis-Alonso1, Ana María García-Muñoz1
1Faculty of Pharmacy and Nutrition, UCAM Universidad Católica de Murcia, 30107 Murcia, Spain.
Metabolites
|July 27, 2026
Summary
Managing type 1 diabetes (T1D) with automated insulin delivery (AID) systems during exercise and nutrition requires dynamic metabolic adjustments. Current strategies focus on user input and metabolic biomarkers, with emerging technologies promising further improvements.
Area of Science:
- Metabolic Physiology
- Endocrinology
- Biotechnology
Background:
- Automated insulin delivery (AID) systems enhance type 1 diabetes (T1D) management but face challenges with exercise and nutrition due to rapid metabolic shifts.
- Understanding the interplay between AID, physical activity, and nutrition is crucial for optimizing glucose control in individuals with T1D.
Purpose of the Study:
- To review and synthesize current evidence on the interaction between AID systems, physical activity, and nutritional strategies from a metabolic perspective.
- To identify key factors influencing glucose metabolism during exercise in T1D patients using AID.
Main Methods:
- A narrative literature review integrating data from clinical trials, observational studies, technical studies, consensus statements, and reviews.
- Focus on T1D across pediatric, adolescent, adult, and pregnancy stages, examining AID, exercise physiology, nutrition, and metabolic biomarkers.
Main Results:
- AID systems generally improve glycemic control (time in range) and reduce hypoglycemia.
- Exercise responses are influenced by user input, exercise type, insulin on board, meal timing, and metabolic status.
- Key strategies include planned exercise announcements, prandial bolus adjustments, and individualized carbohydrate intake. Metabolic biomarkers offer insights into variability.
Conclusions:
- Nutrition and exercise management in AID users represent a complex metabolic interface requiring dynamic adjustments.
- Emerging technologies like adaptive algorithms, dual-hormone systems, and metabolomics-informed personalization show potential for improved safety and reduced burden, but require further validation.
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