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Peer-reviewedCC BY-NC-NDCirculation. Genomic and precision medicine

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

  • Quan Sun1,2,3
  • Amarnath Marthi4
  • Jia Wen5
  • Bridget M. Lin1,4
  • Jiawen Du1
  • Ana C. Ricardo6
  • Alan S. Go7
  • Panduranga S. Rao8
  • Mahboob Rahman9
  • Jiang He10
  • Yun Li1,5
  • Nora Franceschini4
1 more authors
  • the CRIC Study Investigators
10 affiliations
  1. 1Dept of Biostatistics, Univ of North Carolina at Chapel Hill, Chapel Hill, NC
  2. 2Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Univ of Pennsylvania, Philadelphia, PA
  3. 3Dept of Pathology and Laboratory Medicine, Univ of Pennsylvania, Philadelphia, PA
  4. 4Dept of Epidemiology, Univ of North Carolina at Chapel Hill, Chapel Hill, NC
  5. 5Dept of Genetics, Univ of North Carolina at Chapel Hill, Chapel Hill, NC
  6. 6College of Medicine, Division of Nephrology, Univ of Illinois at Chicago, Chicago, IL
  7. 7Division of Research, Kaiser Permanente Northern California, Oakland, CA; Depts of Epidemiology, Biostatistics and Medicine, Univ of California San Francisco, San Francisco, CA
  8. 8Dept of Medicine, Division of Nephrology, Univ of Michigan, Ann Arbor, Ann Arbor, MI
  9. 9Dept of Medicine, Case Western Reserve Univ, Cleveland, OH
  10. 10Dept of Internal Medicine, UT Southwestern Medical Center, Dallas, TX
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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: ). 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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Cite

BibTeXRIS
BibTeX
@article{sun2026polygenic,
  title = {{Polygenic Risk Scores of Coronary Heart Disease in Chronic Kidney Disease Population: The CRIC Study}},
  author = {Quan Sun and Amarnath Marthi and Jia Wen and Bridget M. Lin and Jiawen Du and Ana C. Ricardo and Alan S. Go and Panduranga S. Rao and Mahboob Rahman and Jiang He and Yun Li and Nora Franceschini and the CRIC Study Investigators},
  journal = {Circulation. Genomic and precision medicine},
  year = {2026},
  month = sep,
  doi = {10.1161/circgen.126.005684},
  url = {https://doi.org/10.1161/circgen.126.005684},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Polygenic Risk Scores of Coronary Heart Disease in Chronic Kidney Disease Population: The CRIC Study
AU  - Quan Sun
AU  - Amarnath Marthi
AU  - Jia Wen
AU  - Bridget M. Lin
AU  - Jiawen Du
AU  - Ana C. Ricardo
AU  - Alan S. Go
AU  - Panduranga S. Rao
AU  - Mahboob Rahman
AU  - Jiang He
AU  - Yun Li
AU  - Nora Franceschini
AU  - the CRIC Study Investigators
PY  - 2026
DA  - 2026/09/30
JO  - Circulation. Genomic and precision medicine
DO  - 10.1161/circgen.126.005684
UR  - https://doi.org/10.1161/circgen.126.005684
AB  - 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.
SN  - 2574-8300
ER  -