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Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.


ABSTRACT: BACKGROUND AND PURPOSE:Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. METHODS:The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ?55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. RESULTS:In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: ?joint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: ?joint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). CONCLUSIONS:The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

SUBMITTER: Ibrahim-Verbaas CA 

PROVIDER: S-EPMC3955258 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.

Ibrahim-Verbaas Carla A CA   Fornage Myriam M   Bis Joshua C JC   Choi Seung Hoan SH   Psaty Bruce M BM   Meigs James B JB   Rao Madhu M   Nalls Mike M   Fontes Joao D JD   O'Donnell Christopher J CJ   Kathiresan Sekar S   Ehret Georg B GB   Fox Caroline S CS   Malik Rainer R   Dichgans Martin M   Schmidt Helena H   Lahti Jari J   Heckbert Susan R SR   Lumley Thomas T   Rice Kenneth K   Rotter Jerome I JI   Taylor Kent D KD   Folsom Aaron R AR   Boerwinkle Eric E   Rosamond Wayne D WD   Shahar Eyal E   Gottesman Rebecca F RF   Koudstaal Peter J PJ   Amin Najaf N   Wieberdink Renske G RG   Dehghan Abbas A   Hofman Albert A   Uitterlinden André G AG   Destefano Anita L AL   Debette Stephanie S   Xue Luting L   Beiser Alexa A   Wolf Philip A PA   Decarli Charles C   Ikram M Arfan MA   Seshadri Sudha S   Mosley Thomas H TH   Longstreth W T WT   van Duijn Cornelia M CM   Launer Lenore J LJ  

Stroke 20140116 2


<h4>Background and purpose</h4>Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.<h4>Methods</h4>The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphi  ...[more]

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