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Demircan, K.* ; Carrasco-Zanini, J.* ; Williamson, A.* ; Beuchel, C.* ; Jackson, L.* ; Römisch-Margl, W. ; Hansen, A.L.* ; Finer, S.* ; van Heel, D.A.* ; Kastenmüller, G. ; Coghlan, M.* ; Moeller, I.* ; Wareham, N.J.* ; Pietzner, M.* ; Langenberg, C.*

Data-driven prioritization of high-risk individuals for weight loss interventions.

Nat. Med., DOI: 10.1038/s41591-026-04353-2 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Obesity
ISSN (print) / ISBN 1078-8956
e-ISSN 1546-170X
Zeitschrift Nature medicine
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen 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