Emulating real-world GLP-1 efficacy in type 2 diabetes through causal learning and virtual patients
Rattachement africain : gb, cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Randomized controlled trials (RCTs) remain the benchmark for assessing treatment effects but are limited to phenotypically narrow populations by design. We introduce a novel generative artificial intelligence (AI) driven emulation method that infers effect size through virtual clinical trials, which can emulate the RCT process and potentially extrapolate into wider populations. We validate the virtual trials by comparing the predicted impact of glucagon-like peptide-1 (GLP-1) agonists on HbA1c in type-2 diabetes (T2DM) with its true efficacy established in the LEAD-5 trial. Our emulation model learns treatment effects from real-world evidence data by a combined generative AI and causal learning approach. Training data comprised pre- and post-treatment outcomes for 5,476 people with T2DM. We considered three treatment arms: GLP-1 (Liraglutide), basal insulin (glargine), and placebo. After training, virtual trials were conducted by sampling 232 virtual patients per arm (according to the LEAD-5 inclusion criteria) and predicting post-treatment outcomes. We used difference-in-differences (DiD) for pairwise comparisons between arms. Our goal was to emulate LEAD-5 by demonstrating a significant DiD in post-treatment HbA1c reduction for GLP-1 compared to basal insulin and placebo. We found significant differences in HbA1c reduction for GLP-1 vs basal insulin (-1.21 mmol/mol (-0.11%); p < 0.001) and GLP-1 vs placebo (-2.58 mmol/mol (-0.24%); p < 0.001) in our virtual populations, consistent with LEAD-5 (Liraglutide vs glargine: -2.62mmol/mol (-0.24%); p = 0.0015, Liraglutide vs placebo: -11.91 mmol/mol (-1.09%); p < 0.0001). The causal AI-powered clinical trials can emulate LEAD-5 in important measurements for T2DM. Our algorithm is specialty agnostic and can explore counterfactual questions, making it suitable for further study in the generalizability of RCT results in real-world populations to support clinical decision-making and policy recommendations.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Emulating real-world GLP-1 efficacy in type 2 diabetes through causal learning and virtual patients
- Date Crossref
- 21/07/2025
- Éditeur
- Public Library of Science (PLoS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Strathclyde Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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NHS Greater Glasgow and Clyde pays non établi dans la noticeÉtablissement de santé
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Queen Elizabeth University Hospital Emergency Department pays non établi dans la noticeÉtablissement de santé
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University of Glasgow Digital Health Validation Lab pays non établi dans la noticeUniversité ou école supérieure
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University of Surrey Surrey Clinical Trials Unit pays non établi dans la noticeUniversité ou école supérieure
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Shandong Women’s University pays non établi dans la noticeUniversité ou école supérieure
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University of Birmingham Institute of Applied Health Research pays non établi dans la noticeUniversité ou école supérieure
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School of Cardiovascular & Metabolic Health pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Electronic Engineering pays non établi dans la noticeUniversité ou école supérieure
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School of Data Science and Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Health and Medical Sciences pays non établi dans la noticeUniversité ou école supérieure
Department of Biomedical Engineering — University of Strathclyde, NHS Greater Glasgow and Clyde et Emergency Department — Queen Elizabeth University Hospital, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.