POS0395 EXPLORING THE POTENTIAL OF GENOMIC RISK PREDICTION FOR CORONARY ARTERY DISEASE IN PATIENTS WITH RHEUMATOID ARTHRITIS

Abstract

BackgroundPatients with rheumatoid arthritis (RA) have a higher prevalence of coronary artery disease (CAD) than the general population which contributes to early mortality. However, CAD screeing tools developed in the general population are less effective for estimating CAD risk in RA patients. This is mainly due to the differing contribution from traditional risk factors and the contribution from disease-specific factors. Understanding of the genetic basis of CAD has improved over recent years and shows promise for improving risk prediction in the form of genetic risk scores (GRs), in particular with the development of the metaGRS approach, which combines multiple polygenic risk scores.ObjectivesThis study hypothesise that the metaGRS approach can help us improve CAD risk prediction in patients with RA.MethodsPatients were recruited from the Norfolk Arthritis Register (NOAR), a longitudinal observational study focused on the cause and outcome of inflammatory polyarthritis. Analysis was restricted to patients who satisfied the 2010 ACR criteria cumulatively over five years and had detailed clinical history at baseline and follow-up for ten years. We developed a prediction model based on traditional risk factors[1], and explored the inclusion of a metaGRS. We used a meta-analytic approach to calculate a new metaGRS for CAD, using the effect-sizes from three large-scale, genome-wide, and targeted GRs derived from 1,745,179 [2], 6,630,150 [3], and 40,079 SNPs [4]. We tested the metaGRS in combination with available data on traditional risk factors in a subset of patients with available genetic data. Cox proportional hazards models were used to derive risk equations for evaluation of 10-year risk of CAD. We applied multiple imputations with chained equations to replace missing values. Calibration and discrimination were determined in a separate cohort of 423 individuals.ResultsA total of 2123 patients were included in the analysis with 136 incident cases of self-reported CAD (defined as a composite outcome of myocardial infarction, angina, heart attack, arrhythmia, angioplasty, and coronary artery bypass grafting).The model using only traditional risk factors achieved an AUC of 0.81 (95% CI 0.80, 0.82), with a calibration slope of 1.10, and explained approximately 71% (95% CI 69, 72%) of the variance of the outcome. The hazard ratio for age was found to be 1.00 (95% CI 0.99, 1.01) indicating risk remains the same across all age groups. Inclusion of a CAD metaGRS improves the AUC to 0.82 (95% CI 0.80, 0.83), explains more of the variance at 81% (95% CI 79, 82%) but worsens calibration slope to 0.93. A likelihood ratio test indicates that the integrated model is a better fit (p = 0.04).ConclusionAn integrated risk score, that combines traditional risk factors with a metaGRS, improves CAD prediction in patients with RA. Further research is required to better understand the role of heritable components contributing to CAD risk in RA patients. By refining the underlying GRS, we hope to further improve risk prediction, through this integrated approach.