Multi-parametric model for the improved diagnosis and stratification of rheumatoid arthritis patients (2020)

Introduction Rheumatoid arthritis (RA) is one of the most prevalent autoimmune diseases. Despite several serological markers are known for this disease and two of them, rheumatoid factor (RF) and anticitrullinated protein antibodies (ACPA), are included in the RA classification criteria, there are still many patients that are negative for these two biomarkers. For this reason, there is a need to identify novel biomarkers that can help close this serological gap in RA, as well as to improve patient stratification, prediction of prognosis and response to treatment, and disease monitoring. Objectives The main objective of this thesis was to develop a multi-parametric model that helps to improve RA diagnosis, patient stratification and prediction of prognosis. The specific objectives included, firstly, the assessment of the current practice, the evaluation of several immunoassays for the detection of the RA classification criteria markers, and the analysis of combinatory approaches. Secondly, among the RA novel biomarkers, the anti-protein-arginine deiminase (PAD) antibodies were selected to study their clinical and immunological relevance in RA. We then aimed to characterize the PAD enzymes as antigenic targets, to develop immunoassays for the detection of these antibodies, and to determine their clinical significance and utility in RA in several characterized cohorts. Methods Several methodologies were utilized to achieve the thesis objectives. These included literature screening; feasibility studies, early development and optimization of a range of immunoassays for the detection of autoantibodies in human samples; biochemical characterization of the antigens and the autoantibodies; measurement of biomarkers in different clinical cohorts using the immunoassays; data analysis; and drafting of combinatory approaches for the integration of the different data points. Results Combinatory approaches based on RF and ACPA showed an improved diagnostic performance over the individual markers and can help correctly classify a higher number of patients. During the study of the PAD enzymes and anti-PAD antibodies, several linear epitopes were identified on the five PAD family members, with data suggesting the presence of epitopes unique to PAD3 or to PAD4, in addition of the cross-reactive epitope. The presence of anti-PAD4 antibodies of the IgA isotype in RA was confirmed and an association with erosive disease was identified, a link also confirmed for anti-PAD3 IgG. We identified anti-PAD4 IgM and antibodies to PAD1 and 6 for the first time, and confirmed that anti-PAD4 IgG can help close the serological gap in RA and are associated with a more severe disease phenotype. We confirmed that antibodies to PAD2 are also present in the sera of RA patients, however the reported association with milder disease could not be reproduced. Conclusions A combinatory approach based on ACPA and RF IgM represents a promising tool to improve the diagnosis of RA. Anti-PAD3 and 4 antibodies are useful biomarkers for patient stratification. A multiparametric model for the improvement of RA diagnosis and patient stratification based on these autoantibodies and other features has been proposed.