OA28 Exploring the potential of polygenic risk scores for predicting coronary artery disease in patients with rheumatoid arthritis. (23rd April 2022)

• Record Type: Journal Article
• Authors: Soomro, Mehreen; Stadler, Michael; Viatte, Sebastien; MacGregor, Alexander; Verstappen, Suzanne; Barton, Anne; Bowes, John
• Is Part Of: Rheumatology. Volume 61(2022)Supplement 1
• Publication Date: 2022-04-23
• Subjects: Rheumatism -- Periodicals; Rheumatology -- Periodicals; 616.723005
• DOI: 10.1093/rheumatology/keac132.028
• Languages: English
• ISSNs: 1462-0324

Abstract

Abstract: Background/Aims: Patients with rheumatoid arthritis (RA) have a higher prevalence of coronary artery disease (CAD) than the general population which contributes to early mortality. Current screening tools for CAD, which are developed in the general population, are less effective for estimating CAD risk in patients with RA. This reduced performance is mainly due to the differing contribution from traditional risk factors and the contribution from disease-specific factors. Our understanding of the genetic basis of CAD has improved over recent years and shows promise for improving risk prediction in the form of polygenic risk scores (PRS). We hypothesise that PRS can help us improve CAD risk prediction in patients with RA by providing more accurate models of risk. Methods: Patients were recruited from the Norfolk Arthritis Register (NOAR), a detailed community-based longitudinal observational study focused on the cause and outcome of inflammatory polyarthritis, between 1990 and 2017. 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. We developed a prediction model based on traditional risk factors and explored the inclusion of a PRS (49K SNPs) 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 using the Random Forest algorithm to replace missing values. Measures of calibration and discrimination were determined in the validation cohort of 423 individuals. Results: A total of 2123 patients were included in the analysis with 136 incident cases of self-reported CAD. The model using only traditional risk factors achieved an AUC of 0.72 (95% CI 0.71, 0.73), with a calibration slope of 1.03, and explained approximately 50% (95% CI 47, 52%) of the variance of the outcome. We found that being male reduces the risk by a factor of 0.82 (95% CI 0.68, 1.00). 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 PRS increased the performance with an AUC of 0.76 (95% CI 0.75, 0.77), explained variance of 53% (95% CI 49, 56%) but with a slightly worse calibration slope of 0.91. Conclusion: An integrated risk score, that combines traditional risk factors with a PRS, 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 PRS, we hope to further improve CAD risk prediction in RA patients, through this integrated approach. Disclosure: M. Soomro: None. M. Stadler: None. S. Viatte: None. A. MacGregor: None. S. Verstappen: None. A. Barton: None. J. Bowes: None.