| アブストラクト | INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data. RESEARCH DESIGN AND METHODS: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals >/=18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden. RESULTS: Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset. CONCLUSIONS: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction. |
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| 投稿者 | Mizani, Mehrdad A; Dashtban, Ashkan; Pasea, Laura; Zeng, Qingjia; Khunti, Kamlesh; Valabhji, Jonathan; Mamza, Jil Billy; Gao, He; Morris, Tamsin; Banerjee, Amitava |
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| 組織名 | University College London, London, UK.;British Heart Foundation Data Science Centre, Health Data Research UK, London,;UK.;Peking Union Medical College Hospital, Beijing, China.;Diabetes Research Department, University of Leicester, Leicester, UK.;NHS England and NHS Improvement London, London, UK.;Imperial College Healthcare NHS Trust, London, UK.;AstraZeneca Cambridge Biomedical Campus, Cambridge, UK.;AstraZeneca, Cambridge, UK.;University College London, London, UK ami.banerjee@ucl.ac.uk.;Barts Health NHS Trust, London, UK. |
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