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Data-driven prioritization of high-risk individuals for weight loss interventions.
Nat. Med. 32, 2117–2127 (2026)
New obesity medications have demonstrated efficacy in trials, but their real-world deployment is partly limited by the absence of approaches that identify individuals for treatment based on risks for obesity-related complications. Here we present a risk prediction model to guide prioritization of high-risk individuals. In a population-based sample of similar to 200,000 individuals with a body mass index (BMI) exceeding 27 kg m(-2), our machine learning framework identified the 20 most informative features, from among thousands tested, that predict future onset of 18 complications of obesity, providing information beyond BMI. An integrated model (OBSCORE) successfully stratified individuals into risk groups based on incidence over 10 years: for example, 5.7%, 1.8%, 0.9%, 0.4% and 0.1% for cardiovascular mortality. We demonstrate generalizability of the model in independent populations of European and non-European ancestry and, in SURMOUNT-1 trial participants, show that weight loss was similar across baseline OBSCORE risk groups and that predicted risks decreased following treatment with tirzepatide. In summary, OBSCORE provides a framework for prioritizing high-risk individuals with overweight or obesity based on their risk of obesity-related complications, complementing BMI-based frameworks.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Obesity
ISSN (print) / ISBN
1078-8956
e-ISSN
1546-170X
Journal
Nature medicine
Quellenangaben
Volume: 32,
Pages: 2117–2127
Publisher
Nature Publishing Group
Publishing Place
New York, NY
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Supported by a BHF (British Heart Foundation) - DZHK (German Centre for Cardiovascular Research) (Grant Number: 81X2100281), a UKRI/NIHR Strategic Priorities Award in Multimorbidity Research for the Multimorbidity Mechanism and Therapeutics Research Colla
Genes Health is/has recently been core-funded by Wellcome (WT102627, WT210561), the Medical Research Council (UK) (M009017, MR/X009777/1, MR/X009920/1), Higher Education Funding Council for England Catalyst, Barts Charity (845/1796), Health Data Research
Friede Springer - Cardiovascular Prevention Center
Genes Health is/has recently been core-funded by Wellcome (WT102627, WT210561), the Medical Research Council (UK) (M009017, MR/X009777/1, MR/X009920/1), Higher Education Funding Council for England Catalyst, Barts Charity (845/1796), Health Data Research
Friede Springer - Cardiovascular Prevention Center