دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Personalized insulin dosing using reinforcement learning for high-fat meals and aerobic exercises in type 1 diabetes: a proof-of-concept trial.
المؤلفون: Jafar A; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.; The Research Institute of McGill University Health Centre, Montreal, QC, Canada., Kobayati A; The Research Institute of McGill University Health Centre, Montreal, QC, Canada., Tsoukas MA; The Research Institute of McGill University Health Centre, Montreal, QC, Canada., Haidar A; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada. ahmad.haidar@mcgill.ca.; The Research Institute of McGill University Health Centre, Montreal, QC, Canada. ahmad.haidar@mcgill.ca.
المصدر: Nature communications [Nat Commun] 2024 Aug 03; Vol. 15 (1), pp. 6585. Date of Electronic Publication: 2024 Aug 03.
نوع المنشور: Journal Article; Clinical Trial
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مواضيع طبية MeSH: Diabetes Mellitus, Type 1*/therapy , Diabetes Mellitus, Type 1*/drug therapy , Diabetes Mellitus, Type 1*/blood , Insulin*/administration & dosage , Exercise*/physiology , Postprandial Period* , Blood Glucose*/metabolism , Meals*, Humans ; Male ; Female ; Adult ; Proof of Concept Study ; Middle Aged ; Hypoglycemic Agents/administration & dosage ; Diet, High-Fat/adverse effects ; Reinforcement, Psychology ; Precision Medicine/methods ; Hypoglycemia/prevention & control ; Algorithms ; Young Adult
مستخلص: 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.
(© 2024. The Author(s).)
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معلومات مُعتمدة: Dr Ahmad Haidar Canada Research Chairs (Chaires de recherche du Canada); Dr Ahmad Haidar Canada Research Chairs (Chaires de recherche du Canada); Dr Ahmad Haidar Canada Research Chairs (Chaires de recherche du Canada); Dr Ahmad Haidar Canada Research Chairs (Chaires de recherche du Canada)
المشرفين على المادة: 0 (Insulin)
0 (Blood Glucose)
0 (Hypoglycemic Agents)
تواريخ الأحداث: Date Created: 20240803 Date Completed: 20240803 Latest Revision: 20240806
رمز التحديث: 20240806
مُعرف محوري في PubMed: PMC11297938
DOI: 10.1038/s41467-024-50764-5
PMID: 39097566
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-024-50764-5