Serologic Biomarkers of Progression Toward Diagnosis of Rheumatoid Arthritis in Active Component Military Personnel

Objective

To identify a panel of serum biomarkers that could specifically identify imminent cases of rheumatoid arthritis (RA) before diagnosis.

Methods

Serum samples were collected at 4 time points from active component US military personnel, including 157 anti–citrullinated protein antibody (ACPA)–seropositive and 50 ACPA‐seronegative RA subjects, 100 reactive arthritis (ReA) subjects, and 76 healthy controls. The cohorts were split into 2 phases, with samples tested on independent proteomic platforms for each phase. Classification models of RA diagnosis based on samples obtained within 6 months prior to diagnosis were developed both in univariate analyses and by multivariate random forest modeling of training sample sets and testing sample sets from each phase.

Results

Increases in serum analytes, including C‐reactive protein levels, serum amyloid A, and soluble programmed cell death 1 (PD‐1), were observed in seropositive RA subjects at the time point closest to diagnosis, up to several years before diagnosis. Only a small fraction of RA subjects had levels above the 95th percentile of healthy control levels until the time period within 6 months of diagnosis. For classification of RA diagnosis using samples obtained within 6 months prior to diagnosis, soluble PD‐1 provided superior specificity compared to ReA cases (>89%), with a sensitivity of 48% for RA classification. An 8‐analyte model provided superior sensitivity (69%), with comparable specificity relative to ReA (>82%).

Conclusion

Our findings demonstrate that imminent RA diagnosis could be classified with high specificity, relative to healthy controls and ReA cases, using a panel of cytokines measured in serum samples collected within 6 months before actual diagnosis.