Key result
Adding a coronary heart disease polygenic risk score to PREVENT equations improved 10-year CHD event prediction in chronic kidney disease, increasing the area under the curve from 70.8% to 72.4%.
Why the study?
Studies had not assessed whether a polygenic risk score of coronary heart disease developed from the general population can improve cardiovascular risk prediction in individuals with chronic kidney disease when added to PREVENT equations.
Does adding a polygenic risk score for CHD to the PREVENT equations improve 10-year CHD event prediction in individuals with chronic kidney disease?
Cohort
Yes
Does adding a polygenic risk score for CHD to the PREVENT equations improve 10-year CHD event prediction in individuals with chronic kidney disease?
Absolute Event Rate: 72.4% vs 70.8%
Adding a polygenic risk score for CHD to the PREVENT risk prediction models improves the prediction of 10-year CHD events in individuals with chronic kidney disease.
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: ). 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.
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Sun et al. (2026) conducted a cohort in chronic kidney disease. Polygenic risk score (PRS) of coronary heart disease + PREVENT equations vs. PREVENT equations alone was evaluated on 10-year composite CHD event prediction (area under the curve). Adding a coronary heart disease polygenic risk score to PREVENT equations improved 10-year CHD event prediction in chronic kidney disease, increasing the area under the curve from 70.8% to 72.4%.
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