Type 2 diabetes (T2D) and obesity are complex metabolic conditions shaped by a combination of genetic and environmental factors, with some biological pathways overlapping while others remain distinct. Increasingly, researchers are turning to polygenic risk scores (PRS) to understand these conditions better. A PRS aggregates the effects of many genetic variants across the genome, offering an estimate of an individual’s likelihood of developing a disease as well as potential long-term health outcomes. Drawing on genetic data from several of the world’s largest biobanks, investigators at Mass General Brigham developed advanced metabolic PRSs for both obesity and T2D. These new models not only surpassed existing approaches in predicting disease risk but also demonstrated a strong ability to anticipate future complications and medical interventions. The findings have been reported in Cell Metabolism.
The researchers emphasised that their goal extended beyond predicting whether someone might be diagnosed with obesity or diabetes. Instead, they sought to create a more comprehensive tool that captures broader aspects of metabolic health across an individual’s life course. By integrating diverse genetic signals linked to metabolic function, the team aimed to improve predictions of downstream health consequences. This approach, they suggest, could eventually complement traditional clinical risk factors, supporting more informed decision-making in both prevention and patient care.
To achieve this, the team designed two related but distinct PRSs—one optimised for obesity and the other for T2D. Unlike conventional models that rely heavily on measures such as body mass index (BMI), these scores incorporate genetic variants associated with approximately 20 metabolic traits. These include factors such as fat distribution, insulin sensitivity, and glucose regulation. The models were built using genome-wide association studies (GWAS) drawn from exceptionally large datasets, collectively including more than 8.5 million individuals worldwide. This extensive data foundation allowed for a more nuanced and biologically grounded assessment of metabolic risk.
Importantly, the study demonstrated that individuals identified as high risk by these genetic scores were more likely to experience serious clinical outcomes, including cardiovascular disease and stroke. Among participants who were initially healthy, those with elevated PRS values were roughly twice as likely to require interventions such as GLP-1 receptor agonist therapies or bariatric surgery over a median follow-up period of 5.5 years. These findings highlight the potential of genetic risk profiling to identify individuals who may benefit from earlier or more targeted intervention strategies.
A notable strength of the study lies in its inclusion of multi-ancestry genetic data. By placing particular emphasis on non-European populations, the researchers were able to develop PRSs that performed more effectively across diverse groups, including individuals of African, East Asian, and South Asian ancestry. This represents an important step forward, as many earlier genetic models have been limited by a lack of diversity, reducing their applicability in global populations.
Looking ahead, the research team plans to further refine these tools by exploring genetic subtypes within T2D and obesity. A deeper understanding of these subgroups could improve how patients are classified and selected for clinical trials, ultimately enabling more personalised treatment approaches. The broader vision is to shift clinical thinking beyond simple measures like BMI towards a more sophisticated understanding of genetic susceptibility. By identifying individuals at risk of poor metabolic health trajectories before disease onset, clinicians may be better equipped to intervene early, improving prevention efforts and long-term outcomes.
More information: Min Seo Kim et al, Metabolic polygenic risk scores for prediction of obesity, type 2 diabetes, and related morbidities, Cell Metabolism. DOI: 10.1016/j.cmet.2026.02.009
Journal information: Cell Metabolism Provided by Mass General Brigham
