Genetic risk score and epicardial adipose tissue: the new tools with impact on cardiovascular risk assessment

Abstract

Abstract Background Cardiovascular disease (CVD) remains the leading cause of death worldwide. One of its main contributors is coronary artery disease (CAD), a complex multifactorial disease influenced by hereditary and environmental factors. A better cardiovascular risk assessment is a real challenge in our daily clinical practice. Evidence points to high Epicardial Adipose Tissue (EAT) volume as an essential player in the pathophysiology of CAD. On the other hand, genetic predisposition to CAD remains crucial to improve cardiovascular risk assessment and treatment. It is unknown whether the association between these two risk markers improved the ability to predict CV events. Objective Evaluate whether a high EAT volume added to a Genetic Risk Score (GRS) improves the predictive ability to discriminate CV events in an asymptomatic population without known CVD. Methods A prospective cohort was performed with 1024 participants (mean age 51.6±8.2 years, 75.6% male) selected from controls of the GENEMACOR Study. The GRS was created from 33 genetic variants associated with CAD by GWAS, choosing those with a hazard ratio (HR) higher than 1. EAT volume was measured with a quantitative semi-automated procedure using a postprocessing workstation-TeraRecon Aquarius Workstation (version 4.4.7, TeraRecon, Inc., San Mateo, CA, USA). We evaluated the discriminative ability of the GRS model without (model 1) and with EAT volume (model 2) using the Harrel C statistics. Categorical free Net Reclassification Improvement (cfNRI) and Integrated Discrimination Index (IDI) reclassified patients. Results Cox regression analysis showed that GRS and EAT remained in the equation with an HR of 1.140 (p=0.002) and HR of 1.221 (p=0.002), respectively. C-statistic demonstrated that the predictive value for MACE was 0.588 (95%CI 0.445-0.731) for GRS and increased to 0.689 (95%CI 0.577-0.801) when EAT volume was added to GRS, showing a better discrimination capacity for MACE. The difference between the two C indexes was significant (p=0.003). CfNRI reclassified 58.9% of the population (p=0.001), and IDI improved the discrimination when EAT was included in the GRS model (IDI=0.012; p=0.015). Conclusion Our results displayed that the GRS associated with a high EAT volume increased the discriminative ability to predict MACE occurrence. Improving the identification of high-risk patients at a subclinical stage could avoid atherosclerosis progression and events occurrence through more rigorous and earlier preventive and even therapeutic measures.