Is genetic information helpful in predicting recurrent events in young patients after acute coronary syndrome?

Abstract Coronary Artery Disease (CAD) shares environmental and genetic factors. Young patients with acute coronary syndrome (ACS) carry a poor long-term prognosis. The clinical utility of genetic information in predicting CAD events remains unknown. Aim Evaluate the clinical utility of a multiplicative Genetic Risk Score (mGRS) to predict lifelong residual risk in CAD patients below 55 years with few risk factors. Methods 475 non-diabetic patients with LDL cholesterol levels below 100mg/dL at the first admission who suffered a prior MI at age ≤55 years were followed prospectively for a mean of 5.6±5.3 years. A mGRS was performed with nine variants associated with CAD but not with traditional risk factors (TRF): CDKN2B-AS1 (rs1333049 and rs4977574), TCF21 (rs12190287), PHACTR1 (rs1332844), MIA3 (rs17465637), ADAMTS7 (rs3825807), ZC3HC1 (rs11556924), SMAD3 (rs17228212) and GJA4 (rs618675). The mGRS was subdivided into terciles. Using Cox regression analysis, the predictive and discriminative score ability for events was evaluated. C-statistic discriminated cardiovascular events occurrence. Whether mGRS was included in the TRF model, Net Reclassification Improvement (NRI), or Integrated Discrimination Improvement (IDI) reclassified patients. Kaplan-Meier method estimated survival curves. Results There were 153 patients with at least one cardiovascular event. The bivariate analysis showed the strongest correlation with second and third mGRS tercile, multivessel disease, left ventricular dysfunction, and inflammation. Multivariate Cox regression HR adjusted for events was 1.44 (p=0.103) for the intermediate and 2.00 (p=0.001) for the high-risk tercile. The mGRS inclusion improved C-statistic (∆C-index=0.010; p=0.004), NRI (35.4%; p<0.0001) and IDI (2.7%; p=0.0005). Conclusions Our 9-SNP mGRS better identified and reclassified younger CAD patients with a high probability of cardiovascular events, improving clinical decision-making and reducing costs for therapeutic strategies.