Assessing improved risk prediction of rheumatoid arthritis by environmental, genetic, and metabolomic factors

Objective: We sought to improve seropositive rheumatoid arthritis (RA) risk prediction using a novel weighted genetic risk score (wGRS) and preclinical plasma metabolites associated with RA risk. Predictive performance was compared to previously validated models including RA-associated environmental factors. Methods: This nested case-control study matched incident seropositive RA cases (meeting ACR 1987 or EULAR/ACR 2010 criteria) in the Nurses' Health Studies (NHS) to two controls on age, blood collection fea-tures, and post-menopausal hormone use at pre-RA blood draw. Environmental variables were measured at the questionnaire cycle preceding blood draw. Four models were generated and internally validated using a bootstrapped optimism estimate: (a) base with environmental factors (E), (b) environmental, genetic and gene-environment interaction factors (E + G + GEI), c) environmental and metabolic factors (E + M), and d) all factors (E + G + GEI + M). A fifth model including all factors and interaction terms was fit using ridge regres-sion and cross-validation. Models were compared using area under the receiver operating characteristic curve (AUC). Results: 150 pre-RA cases and 455 matched controls were included. The E model yielded an optimism-cor-rected AUC of 0.622. The E + M model did not show improvement over the E model (corrected AUC 0.620). Including genetic factors increased prediction, producing corrected AUCs of 0.677 in the E + G + GEI model and 0.674 in the E + G + GEI + M model. Similarly, the performance of the cross-validated ridge regression model yielded an AUC of 0.657. Conclusion: Addition of wGRS and gene-environment interaction improved seropositive RA risk prediction models. Preclinical metabolite levels did not significantly contribute to prediction. (c) 2021 Elsevier Inc. All rights reserved.