Background and aims: Studies have demonstrated that the risk of atherosclerotic cardiovascular disease (ASCVD) can be assessed by polygenic risk score (PRS) using common genetic variants. Because metabolic syndrome is a well-known, robust risk factor of ASCVD, we established PRS of metabolic disease and analyzed whether this PRS could predict incident ASCVD.
Methods: We constructed PRSs for eight quantifiable metabolic phenotypes-systolic/diastolic blood pressure, body mass index (BMI), four blood lipid components, and fasting blood glucose-by genome-wide association studies of two prospective Korean cohorts (n = 37,285). We conducted a grid search of combinations of metabolic PRSs to identify the most optimal weighted score for incident ASCVD (PRSMetS-ASCVD). The utility of PRSMetS-ASCVD was validated in an independent prospective cohort (n = 4333).
Results: The individuals in the highest PRS quintile demonstrated a 1.4-2.0-fold increased risk of incident hypertension, obesity, hyperlipidemia, and diabetes. Using the PRSMetS-ASCVD, we identified 6.7% of the population as a high risk group demonstrating a 3.3-fold (95% confidence interval 1.7-6.1, p < 0.001) higher risk for incident ASCVD. The model combining the PRSMetS-ASCVD demonstrated a better performance for predicting ASCVD than that consisting of only conventional risk factors, such as age, sex, BMI, smoking, hypertension, diabetes and hyperlipidemia. The population with high PRSMetS-ASCVD minimally overlapped with that of high Framingham risk score, thus suggesting the additive independent benefits beyond the Framingham risk score, especially in younger individuals.
Conclusions: The polygenic risk of metabolic disease independently predicts those at an increased risk of ASCVD, identifying those at a genetically high risk of incident ASCVD.
Keywords: Atherosclerotic cardiovascular disease; Metabolic syndrome; Polygenic risk score.
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