Serum proteomic signatures prior to the diagnosis of rheumatoid arthritis demonstrate evolving biological pathways and identify specific periods of disease development

Objective This longitudinal case‐control study evaluated serum proteomics prior to a clinical diagnosis of rheumatoid arthritis (i.e. pre‐RA) to evaluate biologic pathways of disease development and inform prediction of timing of onset of future disease. Methods Patients (cases, n=213) meeting the 1987 American College of Rheumatology (ACR) classification criteria for RA, and matched controls without RA (n=215) were identified in the Department of Defense Serum Repository. Serum samples from cases pre‐ and post‐RA diagnosis and controls were tested for RA‐related autoantibodies (anti‐cyclic citrullinated peptide [anti‐CCP3], rheumatoid factor [RF] isotypes immunoglobulin [Ig] M and A, and 197 proteins using a commercial platform (Olink). We applied linear mixed effect models to identify biomarkers distinguishing cases from controls prior to RA diagnosis and analyzed longitudinal patterns of enriched pathways; in addition, models were developed to classify the time of a sample in relationship to the time of RA diagnosis. Results Levels of anti‐CCP3, RFIgA, and RFIgM demonstrated the greatest differences between cases and controls ≤5 years prior to RA diagnosis. Longitudinal analyses identified 104 proteins that were differentially expressed between cases and controls; 60 were differentially expressed ≤5 years prior to diagnosis, 42 within and prior to 5 years of diagnosis, and 2 >5 years prior. KEGG analyses identified that these proteins were associated with 32 pathways, including 21 pathways that were enriched ≤5 years prior to diagnosis. Within the ACPA positive samples from prior to RA diagnosis and controls, a set of features classified if that sample was from a period <3 years prior to RA diagnosis with an area under the curve (AUC) of 0.78 [0.67, 0.89] in a training set, and 0.80 [0.68, 0.92] in a validation set. Conclusion Autoantibodies and protein signatures evolve in distinct stages prior to a diagnosis of RA. Furthermore, protein biomarkers may identify biologic pathways relevant to specific stages. These can be further explored to potentially improve prediction of disease onset and identify stage‐specific biological pathways to target with preventive interventions.