Serum Proteomic Signatures Before the Diagnosis of Rheumatoid Arthritis: Evolving Biologic Pathways and Specific Periods of Disease Development

Abstract

Objective

This longitudinal case-control study evaluated serum proteomics before a clinical diagnosis of rheumatoid arthritis (RA) (ie, pre-RA) to evaluate biologic pathways of disease development and inform prediction of timing of onset of future disease.

Methods

Patients (n = 213) meeting the 1987 American College of Rheumatology classification criteria for RA and matched controls without RA (n = 215) were identified in the Department of Defense Serum Repository. Serum samples from patients before and after RA diagnosis and controls were tested for RA-related autoantibodies (anti–cyclic citrullinated peptide-3 [anti-CCP3] and rheumatoid factor [RF] isotypes IgM and IgA) and 197 proteins using a commercial platform (Olink). We applied linear mixed effect models to identify biomarkers distinguishing patients from controls before 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 patients and controls ≤5 years before RA diagnosis. Longitudinal analyses identified 104 proteins that were differentially expressed between patients and controls; 60 proteins were differentially expressed ≤5 years before diagnosis, 42 proteins were differentially expressed within and before five years of diagnosis, and 2 proteins were differentially expressed >5 years before diagnosis. Kyoto Encyclopedia of Genes and Genomes analyses identified that these proteins were associated with 32 pathways, including 21 pathways that were enriched ≤5 years before diagnosis. Within the anti–citrullinated protein antibody–positive samples from before RA diagnosis and controls, a set of features classified if that sample was from a period <3 years before RA diagnosis, with an area under the receiver operating characteristic (ROC) curve of 0.78 (95% confidence interval 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 before 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 biologic pathways to target with preventive interventions.