Location and amount of joint involvement differentiates rheumatoid arthritis into different clinical subsets
Tjardo D. Maarseveen (Leiden University Medical Center)
Marc P. Maurits (Leiden University Medical Center)
Lavinia Agra Coletto (Fondazione Policlinico Universitario Agostino Gemelli IRCCS)
Simone Perniola (Fondazione Policlinico Universitario Agostino Gemelli IRCCS)
Stefan Böhringer (Leiden University Medical Center)
Nils Steinz (Leiden University Medical Center)
M.J.T. Reinders (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
Erik Ben van den Akker (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
R. Knevel (Newcastle University, Leiden University Medical Center)
More authors (External organisation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.
help_outline