A simple tool developed by researchers at Queen Mary University of London and the Berlin Health Institute Charité could help identify which people living with obesity or overweight are most likely to develop serious obesity-related diseases such as type 2 diabetes and heart disease.
This study natural medicineshow that 20 commonly collected health indicators, such as blood test results and demographic information, can be used to predict future risk for 18 obesity-related diseases. This tool complements the use of BMI and provides a more accurate and personalized way to identify individuals at increased risk of developing diseases such as heart disease and cancer, potentially leading to better monitoring, earlier intervention, and improved health outcomes.
Obesity is a major global health challenge, with 60-70% of adults in Western countries living with overweight or obesity. If left untreated, obesity can lead to a variety of illnesses, from type 2 diabetes and heart disease to other chronic diseases. However, the health outcomes of people living with overweight or obesity vary widely, with some people able to stay healthy for years and others suffering from health problems. Identifying those most at risk early in the development of symptoms could help health care professionals better select appropriate interventions and prioritize treatment to those who need it most.
To address this clinical challenge, researchers at the Queen Mary and Charité Berlin Institute for Health Research developed and validated an obesity risk model that can early and accurately identify individuals at highest risk of obesity-related complications.
Researchers analyzed health data from 200,000 overweight or obese participants whose data is held in the UK Biobank, a large-scale study that combines detailed health assessments with long-term medical records. They used interpretable machine learning to evaluate more than 2,000 health metrics, including blood test data, body measurements, lifestyle information, and molecular data.
From this assessment, the research team identified 20 health indicators that most effectively predicted future risk for 18 obesity-related diseases or complications, which they called the OBSCORE model. The model is easy to use in clinical practice and was also validated by researchers in the independent Genes & Health and European Investigation into Cancer (EPIC) – Norfolk studies. Following further validation and evaluation of cost-effectiveness in appropriate clinical trials, OBSCORE will help doctors identify people with overweight or obesity who are likely to benefit most from early intervention, closer monitoring or enhanced treatment, thereby helping the NHS and potentially saving lives.
Professor Claudia Langenberg, lead author of the study and director of the University Institute for Precision Healthcare at Queen Mary University of London and head of the Computational Medicine Group at the Berlin Institute for Health Research, said: “With obesity affecting a growing proportion of the world’s population, it is important to “Prevention of serious health complications is a major challenge for health systems. Our study shows how large-scale, deeply phenotypical health data can be used to develop data-driven frameworks to identify individuals at high risk of developing the disease.” It may help reduce complications and support a more risk-based approach to managing obesity. ”
The researchers also found significant differences in the risk profiles of 18 obesity-related complications examined between individuals within the same BMI category. Importantly, those identified as being at highest risk are not necessarily those with the highest BMI. A significant proportion of individuals predicted to be at highest risk were those living with overweight rather than obesity, and a combination of metabolic and clinical factors increased the likelihood of developing complications.
Two people of similar weight can have very different risks of developing diseases such as diabetes or heart disease. By systematically analyzing a wide range of health factors in a data-driven manner, we have identified a small number of factors that may help detect the most at-risk individuals early, providing a clearer picture of future risk for obesity-related diseases. ”
Dr. Kamil Demircan, DFG Walter Benjamin Fellow at the University Institute for Precision Healthcare, Queen Mary University of London and the Berlin Health Institute
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Queen Mary University of London
Reference magazines:
Demircan, K. Others. (2026). Data-based prioritization of weight loss interventions for high-risk individuals. natural medicine. DOI: 10.1038/s41591-026-04353-2. https://www.nature.com/articles/s41591-026-04353-2
