Personalized insulin dosing using reinforcement learning for high-fat meals and aerobic exercises in type 1 diabetes: a proof-of-concept trial

Adnan Jafar, Alessandra Kobayati, Michael A. Tsoukas and Ahmad Haidar ()
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Adnan Jafar: McGill University
Alessandra Kobayati: The Research Institute of McGill University Health Centre
Michael A. Tsoukas: The Research Institute of McGill University Health Centre
Ahmad Haidar: McGill University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract In type 1 diabetes, high-fat meals require more insulin to prevent hyperglycemia while meals followed by aerobic exercises require less insulin to prevent hypoglycemia, but the adjustments needed vary between individuals. We propose a decision support system with reinforcement learning to personalize insulin doses for high-fat meals and postprandial aerobic exercises. We test this system in a single-arm 16-week study in 15 adults on multiple daily injections therapy (NCT05041621). The primary objective of this study is to assess the feasibility of the novel learning algorithm. This study looks at glucose outcomes and patient reported outcomes. The postprandial incremental area under the glucose curve is improved from the baseline to the evaluation period for high-fat meals (378 ± 222 vs 38 ± 223 mmol/L/min, p = 0.03) and meals followed by exercises (−395 ± 192 vs 132 ± 181 mmol/L/min, p = 0.007). The postprandial time spent below 3.9 mmol/L is reduced after high-fat meals (5.3 ± 1.6 vs 1.8 ± 1.5%, p = 0.003) and meals followed by exercises (5.3 ± 1.2 vs 1.4 ± 1.1%, p = 0.003). Our study shows the feasibility of automatically personalizing insulin doses for high-fat meals and postprandial exercises. Randomized controlled trials are warranted.

Date: 2024
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DOI: 10.1038/s41467-024-50764-5

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