title: Assessment of the value of a genetic risk score in improving the estimation of coronary risk.
abstract: BACKGROUND
  The American Heart Association has established criteria for the evaluation of novel markers of cardiovascular risk. In accordance with these criteria, we assessed the association between a multi-locus genetic risk score (GRS) and incident coronary heart disease (CHD), and evaluated whether this GRS improves the predictive capacity of the Framingham risk function.
  
  
  RESULTS
  Using eight genetic variants associated with CHD but not with classical cardiovascular risk factors (CVRFs), we generated a multi-locus GRS, and found it to be linearly associated with CHD in two population based cohorts: The REGICOR Study (n=2351) and The Framingham Heart Study (n=3537) (meta-analyzed HR [95%CI]: ~1.13 [1.01-1.27], per unit). Inclusion of the GRS in the Framingham risk function improved its discriminative capacity in the Framingham sample (c-statistic: 72.81 vs.72.37, p=0.042) but not in the REGICOR sample. According to both the net reclassification improvement (NRI) index and the integrated discrimination index (IDI), the GRS improved re-classification among individuals with intermediate coronary risk (meta-analysis NRI [95%CI]: 17.44 [8.04; 26.83]), but not overall.
  
  
  CONCLUSIONS
  A multi-locus GRS based on genetic variants unrelated to CVRFs was associated with a linear increase in risk of CHD events in two distinct populations. This GRS improves risk reclassification particularly in the population at intermediate coronary risk. These results indicate the potential value of the inclusion of genetic information in classical functions for risk assessment in the intermediate risk population group.
authors.affiliation: Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain.
authors.forename: Carla
authors.initials: C
authors.lastname: Lluis-Ganella
authors.forename: Isaac
authors.initials: I
authors.lastname: Subirana
authors.forename: Gavin
authors.initials: G
authors.lastname: Lucas
authors.forename: Marta
authors.initials: M
authors.lastname: Tomás
authors.forename: Daniel
authors.initials: D
authors.lastname: Muñoz
authors.forename: Mariano
authors.initials: M
authors.lastname: Sentí
authors.forename: Eduardo
authors.initials: E
authors.lastname: Salas
authors.forename: Joan
authors.initials: J
authors.lastname: Sala
authors.forename: Rafel
authors.initials: R
authors.lastname: Ramos
authors.forename: Jose M
authors.initials: JM
authors.lastname: Ordovas
authors.forename: Jaume
authors.initials: J
authors.lastname: Marrugat
authors.forename: Roberto
authors.initials: R
authors.lastname: Elosua
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood; D001794:Blood Pressure; D003327:Coronary Disease / Q000097:blood / Q000453:epidemiology / Q000235:genetics* / Q000503:physiopathology; D016002:Discriminant Analysis; D005260:Female; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D015994:Incidence; D053208:Kaplan-Meier Estimate; D016014:Linear Models; D008297:Male; D008404:Massachusetts / Q000453:epidemiology; D008875:Middle Aged; D058885:Multilocus Sequence Typing; D010641:Phenotype; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013030:Spain / Q000453:epidemiology; D013997:Time Factors
chemical_list: D015415:Biomarkers