Metabolic Risk Score Outperforms Disease Prediction Models | 8.5M Individuals

A new genetic risk score, developed by researchers at Mass General Brigham, demonstrates improved accuracy in predicting the development of type 2 diabetes (T2D) and obesity, as well as related health complications, compared to existing prediction models. The metabolic polygenic risk score (MetPRS) integrates genetic data from over 8.5 million individuals, focusing on 20 traits linked to metabolic function.

The findings, published in Cell Metabolism on March 16, 2026, suggest the MetPRS can move beyond traditional risk factors like body mass index to identify individuals predisposed to metabolic dysfunction before the onset of clinical symptoms. “Our intention was to not only capture the risk of being diagnosed with obesity or diabetes, but as well to better predict health consequences across the life course by integrating many aspects of metabolic function,” said Min Seo Kim, MD, MSc, a co-first author of the study.

The research team created two versions of the score: one optimized for obesity and another for T2D. Both scores analyze genes associated with factors such as fat distribution, insulin regulation, and glucose control. Genome-wide association studies (GWAS) performed on datasets encompassing over 8.5 million participants worldwide were utilized in the development of the MetPRS, according to a report from ScienceDirect.

Akl Fahed, MD, MPH, of the Cardiovascular Research Center at Massachusetts General Hospital, emphasized the potential for proactive intervention. “Early identification of people who are likely to have a worse trajectory of poor metabolic health, before they even develop these conditions, can help us improve prevention and clinical interventions,” Fahed stated. “That is how we can cure disease, and that is the bold mission that we are after.”

The new score outperformed existing disease-prediction models and demonstrated the ability to forecast downstream health issues and the necessitate for clinical interventions. The development builds on the understanding that both T2D and obesity have complex, overlapping genetic components, and that a polygenic risk score can effectively capture multiple genetic risk factors to estimate an individual’s susceptibility to these conditions.

Researchers hope the genomic approach will complement existing clinical risk factors, ultimately informing patient care and preventative strategies. Further research is planned to assess the clinical utility of the MetPRS in diverse populations and to refine its predictive capabilities.