Associations between polygenic risk of coronary artery disease and type 2 diabetes, lifestyle, and cardiovascular mortality: A prospective UK Biobank study - Yun, Jae-Seung; Jung, Sang-Hyuk; Shivakumar, Manu; Xiao, Brenda; Khera, Amit V.; Park, Woong-Yang; Won, Hong-Hee; Kim, Dokyoon (Frontiers in Cardiovascular Medicine, 2022)
Background Previous studies primarily targeted the ability of polygenic risk scores (PRSs) to predict a specific disease, and only a few studies have investigated the association between genetic risk scores and cardiovascular (CV) mortality. We assessed PRSs for coronary artery disease (CAD) and type 2 diabetes (T2DM) as predictive factors for CV mortality, independent of traditional risk factors, and further investigated the additive effect between lifestyle behavior and PRS on CV mortality. Methods We used genetic and phenotypic data from UK Biobank participants aged 40–69 years at baseline, collected with standardized procedures. Genome-wide PRSs were constructed using >6 million genetic variants. Cox proportional hazard models were used to analyze the relationship between PRS and CV mortality with stratification by age, sex, disease status, and lifestyle behavior. Results Of 377,909 UK Biobank participants having European ancestry, 3,210 (0.8%) died due to CV disease during a median follow-up of 8.9 years. CV mortality risk was significantly associated with CAD PRS (low vs. very high genetic risk groups, CAD PRS hazard ratio [HR] 2.61 [2.02–3.36]) and T2DM PRS (HR 2.08 [1.58–2.73]), respectively. These relationships remained significant even after adjustment for a comprehensive range of demographic and clinical factors. In the very high genetic risk group, adherence to an unfavorable lifestyle was further associated with a substantially increased risk of CV mortality (favorable versus unfavorable lifestyle with very high genetic risk for CAD PRS, HR 8.31 [5.12–13.49]; T2DM PRS, HR 5.84 [3.39–10.04]). Across all genetic risk groups, 32.1% of CV mortality was attributable to lifestyle behavior (population attributable fraction [PAF] 32.1% [95% CI 28.8–35.3%]) and 14.1% was attributable to smoking (PAF 14.1% [95% CI 12.4–15.7%]). There was no evidence of significant interaction between PRSs and age, sex, or lifestyle behavior in predicting the risk of CV mortality. Conclusions PRSs for CAD or T2DM and lifestyle behaviors are independent predictive factors for future CV mortality in the white, middle-aged population. PRS-based risk assessment could be useful to identify individuals who need intensive behavioral or therapeutic interventions to reduce the risk of CV mortality.