Association of Environmental and Genetic Factors and Gene–Environment Interactions With Risk of Developing Rheumatoid Arthritis

Authors


Department of Medicine, Division of Rheumatology, Allergy, and Immunology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115. E-mail: ekarlson@partners.org

Abstract

Objective

We developed rheumatoid arthritis (RA) risk models based on validated environmental factors (E), genetic risk scores (GRS), and gene–environment interactions (GEI) to identify factors that can improve accuracy and reclassification.

Methods

Models including E, GRS, and GEI were developed among 317 white seropositive RA cases and 551 controls from the Nurses' Health Studies (NHS) and validated in 987 white anti–citrullinated protein antibody–positive cases and 958 controls from the Swedish Epidemiologic Investigation of Rheumatoid Arthritis (EIRA), stratified by sex. Primary analyses included age, smoking, alcohol, parity, weighted GRS using 31 non-HLA alleles and 8 HLA–DRB1 alleles, and the HLA × smoking interaction. Expanded models included reproductive, geographic, and occupational factors and additional GEI terms. Hierarchical models were compared for discriminative accuracy using the area under the receiver operating characteristic curve (AUC) and reclassification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement.

Results

The mean age at RA diagnosis was 56 years in the NHS and 51 years in the EIRA. Primary models produced AUCs of 0.716 in the NHS, 0.716 in women in the EIRA, and 0.756 in men in the EIRA. Expanded models produced improvements in discrimination with AUCs of 0.738 in the NHS, 0.724 in women in the EIRA, and 0.769 in men in the EIRA. Models including genetic factors (G) or G + GEI improved reclassification over E models; the full E + G + GEI model provided the optimal predictive ability by IDI analyses.

Conclusion

We have developed comprehensive RA risk models incorporating E, G, and GEI that have improved the discriminative accuracy for RA. Further work developing and assessing highly specific prediction models in prospective cohorts is still needed to inform primary RA prevention trials.

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