Metabolic Polygenic Risk Scores for Prediction of Obesity, Type 2 Diabetes, and Related Morbidities

Obesity and type 2 diabetes (T2D) are metabolic diseases with shared pathophysiology. Traditional polygenic risk scores (PRS) have focused on these conditions individually, yet the single disease approach falls short in capturing the full dimension of metabolic dysfunction. We derived biologically enriched metabolic PRS (MetPRS), a composite score that uses multi-ancestry genome-wide association studies of 22 metabolic traits from over 10 million people. MetPRS, optimized to predict obesity (O-MetPRS) and T2D (D-MetPRS), was validated in the UK Biobank (UKB, n=15,000), and tested in UKB hold-out set (n=49,377), then externally tested in 3 cohorts - All of Us (n=245,394), Mass General Brigham (MGB) Biobank (n=53,306), and a King Faisal Specialist Hospital and Research Center cohort (n=6,416). O-MetPRS and D-MetPRS outperformed existing PRSs in predicting obesity and T2D across 6 ancestries (European, African, East Asian, South Asian, Latino/admixed American, and Middle Eastern). O-MetPRS and D-MetPRS also predicted morbidities and downstream complications of obesity and T2D, as well as the use of GLP-1 receptor agonists in contemporary practice. Among 37,329 MGB participants free of T2D and obesity at baseline, those in the top decile of O-MetPRS had a 103% relatively higher chance, and those in the top decile of D-MetPRS had an 80% relatively higher chance of receiving a GLP-1 receptor agonist prescription compared to individuals at the population median of MetPRS. The biologically enriched MetPRS is poised to have an impact across all layers of clinical utility, from predicting morbidities to informing management decisions.