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2025 ; 8
(1
): 623
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
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English Wikipedia
Location and amount of joint involvement differentiates rheumatoid arthritis into
different clinical subsets
#MMPMID41131344
Maarseveen TD
; Maurits MP
; Coletto LA
; Perniola S
; Böhringer S
; Steinz N
; Bergstra SA
; Bruno D
; Gigante MR
; Pacucci VA
; Petricca L
; Boxma-de Klerk B
; Glas HK
; Di Mario C
; Campobasso D
; Tolusso B
; Veris-van Dieren J
; van der Helm-van Mil AHM
; Gremese E
; D'Agostino MA
; Reinders MJT
; Gessi M
; Huizinga TWJ
; Alivernini S
; van den Akker EB
; Knevel R
NPJ Digit Med
2025[Oct]; 8
(1
): 623
PMID41131344
show ga
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.