Krishna G Aragam1, Amanda Dobbyn2, Renae Judy3, Mark Chaffin4, Kumardeep Chaudhary2, George Hindy4, Andrew Cagan5, Phoebe Finneran6, Lu-Chen Weng7, Ruth J F Loos8, Girish Nadkarni2, Judy H Cho9, Rachel L Kember10, Aris Baras11, Jeffrey Reid11, John Overton11, Anthony Philippakis4, Patrick T Ellinor7, Scott T Weiss12, Daniel J Rader10, Steven A Lubitz7, Jordan W Smoller13, Elizabeth W Karlson14, Amit V Khera15, Sekar Kathiresan15, Ron Do16, Scott M Damrauer17, Pradeep Natarajan18. 1. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 2. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York. 3. Department of Surgery, Perlman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 4. Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts. 5. Research Computing, Partners HealthCare, Charlestown, Massachusetts. 6. Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 7. Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 8. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York. 9. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. 10. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 11. Regeneron Genetics Center, Tarrytown, New York. 12. Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 13. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute, Boston, Massachusetts. 14. Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts. 15. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts. 16. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: RON.DO@MSSM.EDU. 17. Department of Surgery, Perlman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania. 18. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: PNATARAJAN@PARTNERS.ORG.
Abstract
BACKGROUND: Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear. OBJECTIVES: This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention. METHODS: A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy. RESULTS: Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p < 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p < 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor. CONCLUSIONS: Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.
BACKGROUND: Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear. OBJECTIVES: This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention. METHODS: A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy. RESULTS: Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p < 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p < 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor. CONCLUSIONS: Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.
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