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A simple tool, developed by Researchers from Queen Mary University of London and the Berlin Institute of Health at Charité, could help identify which people living with obesity or overweight are most likely to develop serious obesity-related conditions such as type 2 diabetes and heart disease.
The study, published in Nature Medicine, shows that future risk of 18 obesity-related diseases can be predicted using just 20 commonly collected health measures, offering a more personalised way to assess risk. The tool could complement the use of BMI to more accurately identify high-risk individuals, leading to earlier monitoring, interventions and improved health outcomes.
Obesity is a major global health challenge, with 60-70 per cent of adults in the western countries living with overweight or obesity. If untreated, obesity can lead to several conditions ranging from type 2 diabetes and heart disease to other chronic illnesses. However, people living with overweight or obesity can have vastly different health trajectories. While some remain healthy for many years, others go on to develop serious health-related conditions. Identifying those individuals at highest risk early is increasingly important and could help healthcare professionals choose the appropriate intervention and prioritise treatments to those that need them the most, particularly as new and promising treatment options for treating obesity – such as GLP-1 medicines – are becoming increasingly available.
Analysing over 2,000 indicators of health examined in 200,000 people
To address this clinical challenge, researchers from Queen Mary and the Berlin Institute of Health at Charité developed and validated an obesity risk model that can accurately identify individuals at highest risk of obesity-related complications early.
The researchers analysed health data from 200,000 participants with overweight or obesity in the UK Biobank, a large population study that links detailed health assessments with long-term medical records. Using interpretable machine-learning, they evaluated more than 2,000 general, lifestyle, clinical, blood tests, body measurements, molecular, and other indicators of health. To develop the model (OBSCORE), they analysed the data to identify 20 health indicators or routine blood tests that most effectively predicts future risk of developing 18 obesity-related diseases or complications, ensuring that the test would not only be accurate, but also simple to use in clinical settings. The researchers then validated the model in independent studies.
Moving beyond BMI
For decades, doctors have heavily relied on body weight and body mass index (BMI) when assessing obesity and its risk for future health complications. While BMI is a simple and widely used, it does not fully capture the large differences and complex ways in how people’s bodies deal with and respond to excess body weight. The findings show that the tool could complement BMI-based approaches to build a more accurate indicator to identify those at high risk of obesity- related disease.
“Two people with similar body weight can have very different risks of developing diseases such as diabetes or heart conditions,” says first author Dr Kamil Demircan, DFG Walter Benjamin Fellow at PHURI at Queen Mary University of London and at the Berlin Institute of Health. “By systematically analysing a wide range of health factors in a data-driven manner, we identified a small set of factors that together may help detect individuals at highest risk earlier, providing a clearer picture of their future risk for obesity-related conditions.”
The researchers found substantial differences in risk profiles for the 18 obesity-related complications tested among individuals within the same BMI category. Importantly, those people identified as being at the highest risk were not always those with the highest BMI. A considerable proportion of individuals predicted to be at highest risk were people living with overweight rather than obesity, whose combination of metabolic and clinical factors increased their likelihood of developing complications.
Toward more personalised obesity care
Following further validation and evaluation of cost-effectiveness in appropriate clinical trials, tools like OBSCORE could eventually help doctors identify which patients may benefit most from early intervention, closer monitoring, or intensified treatment.
“With obesity affecting a growing proportion of the global population, preventing its long-term health complications has become a major challenge for healthcare systems,” says the study’s lead author, Professor Claudia Langenberg, director of PHURI at Queen Mary University of London and head of the Computational Medicine group at BIH. “Our work shows how deeply phenotyped large-scale health data can be used to develop data-driven frameworks that identify individuals at higher risk of developing complications and may help support more risk-based approaches to manage obesity.”
Demircan, K. et al. Data-driven prioritisation of high-risk individuals for weight loss interventions. Nature Medicine. DOI: 10.1038/s41591-026-04353-2
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