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Genomic prediction of coronary heart disease
AIMS: Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD ri...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Society of Cardiology
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5146693/ https://www.ncbi.nlm.nih.gov/pubmed/27655226 http://dx.doi.org/10.1093/eurheartj/ehw450 |
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author | Abraham, Gad Havulinna, Aki S. Bhalala, Oneil G. Byars, Sean G. De Livera, Alysha M. Yetukuri, Laxman Tikkanen, Emmi Perola, Markus Schunkert, Heribert Sijbrands, Eric J. Palotie, Aarno Samani, Nilesh J. Salomaa, Veikko Ripatti, Samuli Inouye, Michael |
author_facet | Abraham, Gad Havulinna, Aki S. Bhalala, Oneil G. Byars, Sean G. De Livera, Alysha M. Yetukuri, Laxman Tikkanen, Emmi Perola, Markus Schunkert, Heribert Sijbrands, Eric J. Palotie, Aarno Samani, Nilesh J. Salomaa, Veikko Ripatti, Samuli Inouye, Michael |
author_sort | Abraham, Gad |
collection | PubMed |
description | AIMS: Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores. METHODS AND RESULTS: We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61–1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18–1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5–1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6–5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12–18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking. CONCLUSIONS: A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores. |
format | Online Article Text |
id | pubmed-5146693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | European Society of Cardiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-51466932016-12-12 Genomic prediction of coronary heart disease Abraham, Gad Havulinna, Aki S. Bhalala, Oneil G. Byars, Sean G. De Livera, Alysha M. Yetukuri, Laxman Tikkanen, Emmi Perola, Markus Schunkert, Heribert Sijbrands, Eric J. Palotie, Aarno Samani, Nilesh J. Salomaa, Veikko Ripatti, Samuli Inouye, Michael Eur Heart J Article AIMS: Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores. METHODS AND RESULTS: We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61–1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18–1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5–1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6–5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12–18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking. CONCLUSIONS: A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores. European Society of Cardiology 2016-09-21 2016-11-14 /pmc/articles/PMC5146693/ /pubmed/27655226 http://dx.doi.org/10.1093/eurheartj/ehw450 Text en © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Article Abraham, Gad Havulinna, Aki S. Bhalala, Oneil G. Byars, Sean G. De Livera, Alysha M. Yetukuri, Laxman Tikkanen, Emmi Perola, Markus Schunkert, Heribert Sijbrands, Eric J. Palotie, Aarno Samani, Nilesh J. Salomaa, Veikko Ripatti, Samuli Inouye, Michael Genomic prediction of coronary heart disease |
title | Genomic prediction of coronary heart disease |
title_full | Genomic prediction of coronary heart disease |
title_fullStr | Genomic prediction of coronary heart disease |
title_full_unstemmed | Genomic prediction of coronary heart disease |
title_short | Genomic prediction of coronary heart disease |
title_sort | genomic prediction of coronary heart disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5146693/ https://www.ncbi.nlm.nih.gov/pubmed/27655226 http://dx.doi.org/10.1093/eurheartj/ehw450 |
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