Predicting stroke through genetic risk functions the CHARGE risk score project

Carla A. Ibrahim-Verbaas, Myriam Fornage, Joshua C. Bis, Seung Hoan Choi, Bruce M. Psaty, James B. Meigs, Madhu Rao, Mike Nalls, Joao Daniel T. Fontes, Christopher J. O'Donnell, Sekar Kathiresan, Georg B. Ehret, Caroline S. Fox, Rainer Malik, Martin Dichgans, Helena Schmidt, Jari Lahti, Susan R. Heckbert, Thomas Lumley, Kenneth RiceJerome I. Rotter, Kent D. Taylor, Aaron R. Folsom, Eric Boerwinkle, Wayne D. Rosamond, Eyal Shahar, Rebecca F. Gottesman, Peter J. Koudstaal, Najaf Amin, Renske G. Wieberdink, Abbas Dehghan, Albert Hofman, André G. Uitterlinden, Anita L. DeStefano, Stephanie Debette, Luting Xue, Alexa Beiser, Philip A. Wolf, Charles DeCarli, M. Arfan Ikram, Sudha Seshadri, Thomas H. Mosley, W. T. Longstreth, Cornelia M. Van Duijn, Lenore J. Launer

Research output: Contribution to journalArticle

34 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)403-412
Number of pages10
JournalStroke
Volume45
Issue number2
DOIs
StatePublished - Feb 2014
Externally publishedYes

Fingerprint

Stroke
Area Under Curve
Single Nucleotide Polymorphism
Meta-Analysis
Joints
Case-Control Studies

Keywords

  • Genetic epidemiology
  • Risk factors
  • Stroke

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Clinical Neurology
  • Advanced and Specialized Nursing

Cite this

Ibrahim-Verbaas, C. A., Fornage, M., Bis, J. C., Choi, S. H., Psaty, B. M., Meigs, J. B., ... Launer, L. J. (2014). Predicting stroke through genetic risk functions the CHARGE risk score project. Stroke, 45(2), 403-412. https://doi.org/10.1161/STROKEAHA.113.003044

Predicting stroke through genetic risk functions the CHARGE risk score project. / Ibrahim-Verbaas, Carla A.; Fornage, Myriam; Bis, Joshua C.; Choi, Seung Hoan; Psaty, Bruce M.; Meigs, James B.; Rao, Madhu; Nalls, Mike; Fontes, Joao Daniel T.; O'Donnell, Christopher J.; Kathiresan, Sekar; Ehret, Georg B.; Fox, Caroline S.; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R.; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I.; Taylor, Kent D.; Folsom, Aaron R.; Boerwinkle, Eric; Rosamond, Wayne D.; Shahar, Eyal; Gottesman, Rebecca F.; Koudstaal, Peter J.; Amin, Najaf; Wieberdink, Renske G.; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G.; DeStefano, Anita L.; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A.; DeCarli, Charles; Ikram, M. Arfan; Seshadri, Sudha; Mosley, Thomas H.; Longstreth, W. T.; Van Duijn, Cornelia M.; Launer, Lenore J.

In: Stroke, Vol. 45, No. 2, 02.2014, p. 403-412.

Research output: Contribution to journalArticle

Ibrahim-Verbaas, CA, Fornage, M, Bis, JC, Choi, SH, Psaty, BM, Meigs, JB, Rao, M, Nalls, M, Fontes, JDT, O'Donnell, CJ, Kathiresan, S, Ehret, GB, Fox, CS, Malik, R, Dichgans, M, Schmidt, H, Lahti, J, Heckbert, SR, Lumley, T, Rice, K, Rotter, JI, Taylor, KD, Folsom, AR, Boerwinkle, E, Rosamond, WD, Shahar, E, Gottesman, RF, Koudstaal, PJ, Amin, N, Wieberdink, RG, Dehghan, A, Hofman, A, Uitterlinden, AG, DeStefano, AL, Debette, S, Xue, L, Beiser, A, Wolf, PA, DeCarli, C, Ikram, MA, Seshadri, S, Mosley, TH, Longstreth, WT, Van Duijn, CM & Launer, LJ 2014, 'Predicting stroke through genetic risk functions the CHARGE risk score project', Stroke, vol. 45, no. 2, pp. 403-412. https://doi.org/10.1161/STROKEAHA.113.003044
Ibrahim-Verbaas CA, Fornage M, Bis JC, Choi SH, Psaty BM, Meigs JB et al. Predicting stroke through genetic risk functions the CHARGE risk score project. Stroke. 2014 Feb;45(2):403-412. https://doi.org/10.1161/STROKEAHA.113.003044
Ibrahim-Verbaas, Carla A. ; Fornage, Myriam ; Bis, Joshua C. ; Choi, Seung Hoan ; Psaty, Bruce M. ; Meigs, James B. ; Rao, Madhu ; Nalls, Mike ; Fontes, Joao Daniel T. ; O'Donnell, Christopher J. ; Kathiresan, Sekar ; Ehret, Georg B. ; Fox, Caroline S. ; Malik, Rainer ; Dichgans, Martin ; Schmidt, Helena ; Lahti, Jari ; Heckbert, Susan R. ; Lumley, Thomas ; Rice, Kenneth ; Rotter, Jerome I. ; Taylor, Kent D. ; Folsom, Aaron R. ; Boerwinkle, Eric ; Rosamond, Wayne D. ; Shahar, Eyal ; Gottesman, Rebecca F. ; Koudstaal, Peter J. ; Amin, Najaf ; Wieberdink, Renske G. ; Dehghan, Abbas ; Hofman, Albert ; Uitterlinden, André G. ; DeStefano, Anita L. ; Debette, Stephanie ; Xue, Luting ; Beiser, Alexa ; Wolf, Philip A. ; DeCarli, Charles ; Ikram, M. Arfan ; Seshadri, Sudha ; Mosley, Thomas H. ; Longstreth, W. T. ; Van Duijn, Cornelia M. ; Launer, Lenore J. / Predicting stroke through genetic risk functions the CHARGE risk score project. In: Stroke. 2014 ; Vol. 45, No. 2. pp. 403-412.
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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.",
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author = "Ibrahim-Verbaas, {Carla A.} and Myriam Fornage and Bis, {Joshua C.} and Choi, {Seung Hoan} and Psaty, {Bruce M.} and Meigs, {James B.} and Madhu Rao and Mike Nalls and Fontes, {Joao Daniel T.} and O'Donnell, {Christopher J.} and Sekar Kathiresan and Ehret, {Georg B.} and Fox, {Caroline S.} and Rainer Malik and Martin Dichgans and Helena Schmidt and Jari Lahti and Heckbert, {Susan R.} and Thomas Lumley and Kenneth Rice and Rotter, {Jerome I.} and Taylor, {Kent D.} and Folsom, {Aaron R.} and Eric Boerwinkle and Rosamond, {Wayne D.} and Eyal Shahar and Gottesman, {Rebecca F.} and Koudstaal, {Peter J.} and Najaf Amin and Wieberdink, {Renske G.} and Abbas Dehghan and Albert Hofman and Uitterlinden, {Andr{\'e} G.} and DeStefano, {Anita L.} and Stephanie Debette and Luting Xue and Alexa Beiser and Wolf, {Philip A.} and Charles DeCarli and Ikram, {M. Arfan} and Sudha Seshadri and Mosley, {Thomas H.} and Longstreth, {W. T.} and {Van Duijn}, {Cornelia M.} and Launer, {Lenore J.}",
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T1 - Predicting stroke through genetic risk functions the CHARGE risk score project

AU - Ibrahim-Verbaas, Carla A.

AU - Fornage, Myriam

AU - Bis, Joshua C.

AU - Choi, Seung Hoan

AU - Psaty, Bruce M.

AU - Meigs, James B.

AU - Rao, Madhu

AU - Nalls, Mike

AU - Fontes, Joao Daniel T.

AU - O'Donnell, Christopher J.

AU - Kathiresan, Sekar

AU - Ehret, Georg B.

AU - Fox, Caroline S.

AU - Malik, Rainer

AU - Dichgans, Martin

AU - Schmidt, Helena

AU - Lahti, Jari

AU - Heckbert, Susan R.

AU - Lumley, Thomas

AU - Rice, Kenneth

AU - Rotter, Jerome I.

AU - Taylor, Kent D.

AU - Folsom, Aaron R.

AU - Boerwinkle, Eric

AU - Rosamond, Wayne D.

AU - Shahar, Eyal

AU - Gottesman, Rebecca F.

AU - Koudstaal, Peter J.

AU - Amin, Najaf

AU - Wieberdink, Renske G.

AU - Dehghan, Abbas

AU - Hofman, Albert

AU - Uitterlinden, André G.

AU - DeStefano, Anita L.

AU - Debette, Stephanie

AU - Xue, Luting

AU - Beiser, Alexa

AU - Wolf, Philip A.

AU - DeCarli, Charles

AU - Ikram, M. Arfan

AU - Seshadri, Sudha

AU - Mosley, Thomas H.

AU - Longstreth, W. T.

AU - Van Duijn, Cornelia M.

AU - Launer, Lenore J.

PY - 2014/2

Y1 - 2014/2

N2 - 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.

AB - 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.

KW - Genetic epidemiology

KW - Risk factors

KW - Stroke

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