The pathophysiologic events that precede the onset of rheumatoid arthritis (RA) remain incompletely understood. This study was undertaken to identify changes in the serum proteome that precede the onset of RA, with the aim of providing new insights into the pathogenic mechanisms that lead to its development.
In a cohort of first-degree relatives of Indigenous North American RA patients, the SomaScan proteomics platform was used to determine the levels of 1,307 proteins in multiple longitudinal serum samples from 17 individuals who were followed up prospectively to the time of disease onset. Proteomic signatures from this group of individuals (designated the progressor group) were compared to those in a group of individuals who were considered at risk of developing RA, stratified as either positive (n = 63) or negative (n = 47) for anti-citrullinated protein antibodies (ACPAs) (designated the at-risk group). Machine learning was used to identify a protein signature that could accurately classify those individuals at highest risk of future RA development.
A preclinical proteomic signature that differentiated RA progressors from at-risk individuals, irrespective of ACPA status, was identified (area under the curve 0.913, accuracy 91.2%). Importantly, the predictive preclinical proteomic signature was present not only in serum samples obtained close to the onset of RA, but also in serum samples obtained a median of 30.9 months prior to onset. Network analysis implicated the activation of Toll-like receptor 2 and production of tumor necrosis factor and interleukin-1 as key events that precede RA progression.
Alterations in the serum proteome in the preclinical phase of RA can emerge years prior to the onset of disease. Our findings suggest that the serum proteome provides a rich source of proteins serving both to classify at-risk individuals and to identify molecular pathways involved in the development of clinically detectable RA.