Data-driven prioritization of high-risk individuals for weight loss interventions
Summary
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 ~200,000 individuals with a body mass index (BMI) exceeding 27 kg m-2, our machine learning framework identified the 20 most informative feat
Content
# Data-driven prioritization of high-risk individuals for weight loss interventions
*Published: 2026 Apr 30*
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 ~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.
DOI: 10.1038/s41591-026-04353-2