Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.
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State: Public
Version: author
Serval ID
serval:BIB_87DD4E4DB47A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.
Journal
Stroke
ISSN
1524-4628 (Electronic)
ISSN-L
0039-2499
Publication state
Published
Issued date
2014
Peer-reviewed
Oui
Volume
45
Number
2
Pages
403-412
Language
english
Notes
Publication types: Journal Article ; Meta-Analysis ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
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: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint 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.
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: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint 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.
Keywords
Age Factors, Aged, Aged, 80 and over, Area Under Curve, Case-Control Studies, Cohort Studies, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide/genetics, ROC Curve, Regression Analysis, Risk Factors, Sex Factors, Stroke/epidemiology, Stroke/genetics
Pubmed
Web of science
Open Access
Yes
Create date
11/04/2014 17:45
Last modification date
20/08/2019 14:47