Type 2 diabetes (T2D) and obesity are metabolic diseases with many causes, including overlapping and distinct genetic features. Polygenic risk scores (PRS) capture multiple genetic risk factors and can estimate whether a person is likely to develop a complex medical condition and how they will cope in the long term. Researchers at Massachusetts General Brigham have built a metabolic PRS to predict obesity and T2D by integrating genetic findings from some of the world’s largest biobanks. It outperformed existing disease prediction models and predicted downstream morbidity and clinical interventions. The survey results are posted below cell metabolism.
Our aim was to not only understand the risk of being diagnosed with obesity and diabetes, but also to better predict health outcomes across the life course by integrating many aspects of metabolic function. In the future, this genomic approach may complement established clinical risk factors and inform patient care and prevention strategies. ”
Min Seo Kim, MD, MSc, co-lead author
The metabolic PRS designed by the researchers includes an obesity-optimized version and a T2D-optimized version. Both scores go beyond widely used variables such as BMI to focus on genes associated with 20 different traits related to metabolic function, such as fat distribution and insulin and glucose control. The team used genome-wide association studies (GWAS) conducted on some of the world’s largest datasets, encompassing a total of more than 8.5 million participants worldwide.
Researchers found that risk scores can identify individuals at high risk for clinical outcomes such as cardiovascular disease and stroke. People with high PRS values and initially good health were approximately twice as likely to later receive GLP-1 agonist medication or bariatric surgery than those with intermediate PRS scores during a median follow-up of 5.5 years.
The use of multi-ancestral GWAS data, with a particular focus on non-European populations, has enabled the construction of obesity and T2D risk scores that exceed previous PRS models in Africans, East Asians, and South Asians.
Researchers hope to continue to further understand the genetic subtypes of T2D and obesity, improve patient classification and stratification in clinical trials, and ultimately facilitate more tailored interventions.
“We want to help clinicians think about metabolic status beyond BMI. We want to focus more broadly on the underlying genetic susceptibility,” said co-senior author Akul Fahed, MD, MPH, from the Massachusetts General Hospital Heart and Vascular Research Center and an interventional cardiologist at the Massachusetts General Brigham Heart and Vascular Institute. “Early identification of people who are most likely to develop a worsening trajectory of metabolic disorders, before they develop these symptoms, can improve prevention and clinical interventions. That’s how we cure disease, and that’s the bold mission we pursue.”
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Reference magazines:
Kim, MS, Others. (2026). Metabolic polygenic risk scores for predicting obesity, type 2 diabetes, and related morbidity. cell metabolism. DOI: 10.1016/j.cmet.2026.02.009. https://www.sciencedirect.com/science/article/abs/pii/S1550413126000525?via%3Dihub
