Unraveling the Rheumatoid Factor autoantibody repertoire profile preceding disease onset

About 1 in 10 individuals will develop autoimmune disease. Early detection and correct diagnosis of these diseases is important for effective treatment, both avoiding overtreatment and unnecessary 2nd and 3rd line referrals as well as enabling swift early targeted interventions. 
In pilot studies we have shown that ‘rheumatoid factors’ (RF), anti-IgG autoantibodies prevalent in rheumatoid arthritis as well as other autoimmune diseases and healthy individuals, comprise a range of different subsets. We demonstrated the feasibility to dissect individual binding profiles and observed differences between RFs in disease settings and in healthy individuals.
In this project, we aim to investigate the development of these reactivity patterns over time prior to disease onset, and link these to the development of specific autoimmune diseases, rheumatoid arthritis (RA) in particular. To this end, we will use our modified human IgGs that only capture specific subsets of RF reactivities. We will analyze a large sample of the general population (using the Lifelines cohort) for rheumatoid factors, dissect binding profiles at time of inclusion, and analyze how these relate to development of disease during follow-up (median 10 years). We expect to identify specific reactivities or patterns that are predictive of future disease onset, and to uncover how these differ from patterns found across healthy individuals. These results can be used to develop novel RF tests for sensitive early prediction of multiple autoimmune diseases, and improving diagnosis at an early stage, thereby ultimately replacing the current, widely used RF tests that lack specificity.