Polygenic Risk Scores of Coronary Heart Disease in Chronic Kidney Disease Population: The CRIC Study.

Abstract

Background: Cardiovascular disease is a major cause of death in individuals with chronic kidney disease; thus, improving its prediction is critical for cardiovascular disease risk reduction. Studies have not assessed whether a polygenic risk score (PRS) of coronary heart disease (CHD) developed from genetic studies in the general population can improve cardiovascular disease risk prediction in individuals with chronic kidney disease, especially when PRS is added to the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations.

Methods: Focusing on a 10-year composite CHD event, we applied PRSs of CHD obtained from the polygenic score catalog to the multi-center CRIC (Chronic Renal Insufficiency Cohort) participants, selected the best-performing PRS and evaluated the added value of the best PRS while accounting for PREVENT linear predictors. Three Cox proportional hazard models were fitted with PRS only, PREVENT predictors only, and PRS+PREVENT risk factors. We evaluated the prediction accuracy, discrimination, and calibration of the combined model compared with the PREVENT model.

Results: A PRS of CHD was significantly associated with a 21% hazard of CHD in the PRS+PREVENT risk model (95% CI, [1.06-1.39], 2-sided Wald test P=5.5×10^-3). The PRS+PREVENT model demonstrated a higher area under the curve (72.4% [95% CI, 0.66-0.79] versus 70.8% [95% CI, 0.64-0.77]), better discrimination ability, and improved calibration compared with the PREVENT model predictors. Individuals were more correctly classified in prediction quartiles using the PRS+PREVENT model for the 10-year CHD event proportions.

Conclusions: Our results demonstrate the added value of a CHD PRS to the PREVENT risk prediction models in individuals with chronic kidney disease. The combined PRS+PREVENT model has the potential to improve clinical decisions on disease prevention and treatment strategies.