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Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.
Carla A Ibrahim-Verbaas1, Myriam Fornage, Joshua C Bis
1From the Departments of Epidemiology (C.A.I.-V., P.J.K., N.A., R.G.W., A.D., A.H., A.G.U., C.M.v.D.), Neurology (C.A.I.-V., P.J.K., R.G.W., M.A.I.), Internal Medicine (A.G.U.), and Radiology (M.A.I.), Erasmus University Medical Center, Rotterdam, The Netherlands; Center for Medical Systems Biology, Leiden, The Netherlands (C.A.I.-V., N.A., C.M.v.D.); Institute for Molecular Medicine (M.F.) and Human Genetics Center (M.F., E.B.), University of Texas Health Science Center at Houston; Cardiovascular Health Research Unit (J.C.B., B.M.P.) and Departments of Medicine (J.C.B., B.M.P.), Epidemiology (B.M.P., S.R.H., W.T.L.), Health Services (B.M.P.), Biostatistics (K.R.), and Neurology (W.T.L.), University of Washington, Seattle; Group Health Research Institute, Group Health Cooperative, Seattle, WA (B.M.P.); Department of Biostatistics, Boston University School of Public Health, MA (S.H.C., A.L.D., S.D., L.X., A.B., P.A.W.); Department of Neurology (S.H.C., A.L.D., S.D., L.X., A.B., P.A.W., S.S.) and Cardiology section, Whitaker Cardiovascular Institute (J.D.F.), Boston University School of Medicine, MA; The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (S.H.C., J.D.F, C.J.O., C.S.F., A.L.D., S.D., L.X., A.B., P.A.W., S.S.); Department of Medicine, Harvard Medical School General Medicine Division (J.B.M.), Cardiovascular Research Center and Cardiology Division (S.K.), and Center for Human Genetic Research (S.K.), Massachusetts General Hospital, Boston; Division of Nephrology/Tufts Evidence Practice Center, Tufts University School of Medicine, Tufts Medical Center, Boston, MA (M.R.); Laboratory of Neurogenetics (M.N.) and Laboratory of Epidemiology and Population Sciences (L.J.L.), National Institute on Aging, National Institutes of Health, Bethesda, MD; Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge (S.K.); Center for Complex Disease Genomics, McKusick-Na
A genetic risk score (GRS) using single-nucleotide polymorphisms modestly improves stroke prediction beyond traditional risk factors. This genetic information offers a slight enhancement for forecasting future stroke events.
Area of Science:
- Genetics
- Epidemiology
- Cardiovascular Research
Background:
- Stroke prediction models like the Framingham Stroke Risk Score are crucial for identifying individuals at high risk.
- Integrating genetic information may enhance the accuracy of existing stroke risk prediction tools.
Purpose of the Study:
- To evaluate the predictive performance of a genetic risk score (GRS) for future stroke.
- To determine if a GRS improves stroke prediction beyond established clinical risk factors and scores.
Main Methods:
- A meta-analysis of 4 population-based cohorts (22,720 participants, 2047 strokes) was conducted.
- A GRS was developed using 324 single-nucleotide polymorphisms linked to stroke and its risk factors.
- Cox regression and area under the curve statistics were used to assess GRS predictive properties, with replication in an ischemic stroke case-control study.
Main Results:
- Adding the GRS to age, sex, and Framingham Stroke Risk Score significantly improved stroke prediction discrimination (ΔAUC for all stroke: 0.016; ischemic stroke: 0.021).
- The net reclassification index showed highly significant improvement across all studies (P<10(-4)).
- Despite statistical significance, the overall area under the curve remained low, indicating a small magnitude of improvement.
Conclusions:
- Genetic risk scores based on single-nucleotide polymorphisms offer a statistically significant but modest improvement in predicting future stroke.
- The incremental predictive value of GRS is limited when compared to established epidemiological risk factors for stroke.
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