Abstract
BACKGROUND: Cardiometabolic diseases (CMD) are a leading cause of morbidity and mortality. While both family history (FH) and polygenic risk scores (PRS) are predictive of CMD risk, few studies have systematically evaluated their independent and joint effects. This study aimed to quantify the individual contributions of FH and PRS, as well as their combined impact on CMD risk. METHODS: We conducted a cross-sectional analysis of 105,633 adults from the All of Us Research Program with available genotypic and FH data. CMDs including type 2 diabetes (T2D), obesity, hypertension (HTN), and coronary artery disease (CAD) were ascertained from electronic health records. FH was derived from self-reported survey responses, and family history scores (FHS) were constructed by weighting the number and degree of affected relatives. PRSs were computed using validated multi-ancestry PRS weights from the PGS catalog. Logistic regression was used to assess associations of FH, FHS and PRS independently and jointly with CMD. We also tested for FHS × PRS interactions and conducted mediation analysis to quantify the proportion of the FHS effect mediated by PRS. RESULTS: Positive FH was significantly associated with increased risk of all CMDs, with the strongest effect observed for obesity (OR: 2.09, 95% CI: 2.01-2.16). FHS showed the strongest association with T2D (OR: 1.40, 95% CI: 1.38-1.42). Higher PRS values were also associated with elevated disease risk, most strongly for T2D (OR: 2.25, 95% CI: 2.18-2.33). A statistically significant interaction between FHS and PRS was observed for obesity (p = < 0.001). A composite variable combining FH and PRS revealed a stepwise increase in disease odds across risk categories. Mediation analysis indicated that PRS accounted for between 13-17% of the total effect of FHS across all traits. CONCLUSIONS: Both FH and PRS are associated with CMD risk and provide complementary but distinct insights into disease risk. PRS adds predictive value beyond FH and partially mediates its effect. Integration of both measures may enhance risk stratification and guide precision prevention strategies.