Towards rheumatoid arthritis prevention: integrating mechanistic and omics insights

Seropositive rheumatoid arthritis (RA) is an autoimmune disease whose course typically begins before joint involvement, during a period termed pre-RA, which is initially characterized by the presence of autoantibodies, including anti-citrullinated protein antibodies and rheumatoid factors. The ability to identify autoantibody-positive individuals who are at an increased risk of developing clinical RA has catalysed efforts to define the immune mechanisms underlying the initial break in self-tolerance and the subsequent transition to clinically apparent disease. Although not mutually exclusive, these investigations have broadly followed two complementary approaches: hypothesis-testing studies and agnostic multi-omics analysis. Hypothesis-testing studies have identified site-specific and isotype-specific patterns of mucosal anti-citrullinated protein antibodies and rheumatoid factor production, together with inflammation and/or dysbiosis associated with systemic autoimmunity, supporting a 'mucosal origins hypothesis'. Cross-sectional and longitudinal multi-omics studies have demonstrated epitope spreading, pro-inflammatory biomarkers and dysregulated peripheral blood immune-cell populations with autoimmune characteristics. Epidemiological studies have further implicated certain environmental exposures in RA development. Collectively, these observations suggest that integrating molecular, cellular and environmental data through emerging data science and artificial intelligence approaches could help to identify pathogenic mechanisms underlying RA development and inform future prevention strategies.