Literature DB >> 27780846

Clinical Utility of Multimarker Genetic Risk Scores for Prediction of Incident Coronary Heart Disease: A Cohort Study Among Over 51 000 Individuals of European Ancestry.

Carlos Iribarren1, Meng Lu2, Eric Jorgenson2, Manuel Martínez2, Carla Lluis-Ganella2, Isaac Subirana2, Eduardo Salas2, Roberto Elosua2.   

Abstract

BACKGROUND: We evaluated whether including multilocus genetic risk scores (GRSs) into the Framingham Risk Equation improves the predictive capacity, discrimination, and reclassification of asymptomatic individuals with respect to coronary heart disease (CHD) risk. METHODS AND
RESULTS: We performed a cohort study among 51 954 European-ancestry members of a Northern California integrated healthcare system (67% female; mean age 59) free of CHD at baseline (2007-2008). Four GRSs were constructed using between 8 and 51 previously identified genetic variants. After a mean (±SD) follow-up of 5.9 (±1.5) years, 1864 incident CHD events were documented. All GRSs were linearly associated with CHD in a model adjusted by individual risk factors: hazard ratio (95% confidence interval) per SD unit: 1.21 (1.15-1.26) for GRS_8, 1.20 (1.15-1.26) for GRS_12, 1.23 (1.17-1.28) for GRS_36, and 1.23 (1.17-1.28) for GRS_51. Inclusion of the GRSs improved the C statistic (ΔC statistic =0.008 for GRS_8 and GRS_36; 0.007 for GRS_12; and 0.009 for GRS_51; all P<0.001). The net reclassification improvement was 5% for GRS_8, GRS_12, and GRS_36 and 4% for GRS_51 in the entire cohort and was (after correcting for bias) 9% for GRS_8 and GRS_12 and 7% for GRS_36 and GRS_51 when analyzing those classified as intermediate Framingham risk (10%-20%). The number required to treat to prevent 1 CHD after selectively treating with statins up-reclassified subjects on the basis of genetic information was 36 for GRS_8 and GRS_12, 41 for GRS_36, and 43 for GRS_51.
CONCLUSIONS: Our results demonstrate significant and clinically relevant incremental discriminative/predictive capability of 4 multilocus GRSs for incident CHD among subjects of European ancestry.
© 2016 American Heart Association, Inc.

Entities:  

Keywords:  clinical effectiveness; cohort studies; coronary disease; genetic predisposition to disease; risk factors

Mesh:

Substances:

Year:  2016        PMID: 27780846     DOI: 10.1161/CIRCGENETICS.116.001522

Source DB:  PubMed          Journal:  Circ Cardiovasc Genet        ISSN: 1942-3268


  12 in total

1.  Effect of Disclosing Genetic Risk for Coronary Heart Disease on Information Seeking and Sharing: The MI-GENES Study (Myocardial Infarction Genes).

Authors:  Sherry-Ann N Brown; Hayan Jouni; Tariq S Marroush; Iftikhar J Kullo
Journal:  Circ Cardiovasc Genet       Date:  2017-08

Review 2.  Leveraging information from genetic risk scores of coronary atherosclerosis.

Authors:  Themistocles L Assimes; Elias L Salfati; Liana C Del Gobbo
Journal:  Curr Opin Lipidol       Date:  2017-04       Impact factor: 4.776

Review 3.  Hypertension genomics and cardiovascular prevention.

Authors:  Fu Liang Ng; Helen R Warren; Mark J Caulfield
Journal:  Ann Transl Med       Date:  2018-08

4.  Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction.

Authors:  Vincent Plagnol; Peter Donnelly; Fernando Riveros-Mckay; Michael E Weale; Rachel Moore; Saskia Selzam; Eva Krapohl; R Michael Sivley; William A Tarran; Peter Sørensen; Alexander S Lachapelle; Jonathan A Griffiths; Ayden Saffari; John Deanfield; Chris C A Spencer; Julia Hippisley-Cox; David J Hunter; Jack W O'Sullivan; Euan A Ashley
Journal:  Circ Genom Precis Med       Date:  2021-03-02

5.  The Genetic Sphygmomanometer: an argument for routine genome-wide genotyping in the population and a new view on its use to inform clinical practice.

Authors:  Nicholas John Timpson; Frank Dudbridge
Journal:  Wellcome Open Res       Date:  2018-10-31

6.  Genetic risk score associations for myocardial infarction are comparable in persons with and without rheumatoid arthritis: the population-based HUNT study.

Authors:  S Rostami; M Hoff; H Dalen; K Hveem; V Videm
Journal:  Sci Rep       Date:  2020-11-24       Impact factor: 4.379

Review 7.  The Interface of Therapeutics and Genomics in Cardiovascular Medicine.

Authors:  E F Magavern; J C Kaski; R M Turner; A Janmohamed; P Borry; M Pirmohamed
Journal:  Cardiovasc Drugs Ther       Date:  2021-02-02       Impact factor: 3.727

8.  Predictive Accuracy of a Polygenic Risk Score Compared With a Clinical Risk Score for Incident Coronary Heart Disease.

Authors:  Jonathan D Mosley; Deepak K Gupta; Jingyi Tan; Jie Yao; Quinn S Wells; Christian M Shaffer; Suman Kundu; Cassianne Robinson-Cohen; Bruce M Psaty; Stephen S Rich; Wendy S Post; Xiuqing Guo; Jerome I Rotter; Dan M Roden; Robert E Gerszten; Thomas J Wang
Journal:  JAMA       Date:  2020-02-18       Impact factor: 157.335

9.  A clinical-genetic approach to assessing cardiovascular risk in patients with CKD.

Authors:  Emilio Rodrigo; Sara Pich; Isaac Subirana; Gema Fernandez-Fresnedo; Paloma Barreda; Carles Ferrer-Costa; Ángel Luis M de Francisco; Eduardo Salas; Roberto Elosua; Manuel Arias
Journal:  Clin Kidney J       Date:  2017-06-22

10.  Weighted Multi-marker Genetic Risk Scores for Incident Coronary Heart Disease among Individuals of African, Latino and East-Asian Ancestry.

Authors:  Carlos Iribarren; Meng Lu; Eric Jorgenson; Manuel Martínez; Carla Lluis-Ganella; Isaac Subirana; Eduardo Salas; Roberto Elosua
Journal:  Sci Rep       Date:  2018-05-01       Impact factor: 4.379

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