Integrated serum proteomics and autoantibody analyses reveal a biomarker signature predictive of flare during biologic tapering in rheumatoid arthritis.

چکیده اصلی

BACKGROUND: Reliable biomarkers predicting which patients with rheumatoid arthritis (RA) can safely undergo biologic tapering are currently lacking. This study aimed to identify circulating protein biomarkers predictive of flare during biologic disease-modifying anti-rheumatic drug tapering in RA. METHODS: Baseline serum samples from patients with RA in sustained remission enrolled in the Biologic Optimisation (OPTIBIO) clinical trial were analysed using a two-stage design comprising an exploratory mass spectrometry in a discovery sample subset (n=44, 22 in the optimisation arm and 22 in the control arm), followed by ELISA validation of selected protein biomarkers and anti-cytokine autoantibody profiling in the full cohort (n=194). The primary outcome was a disease flare during follow-up. RESULTS: During follow-up, disease flare occurred in 46.4% of patients in the optimisation arm and 30.9% in the control arm. Within the discovery subset of the optimisation arm, 87 proteins were differentially abundant between patients who flared and those who remained in remission. V-set immunoglobulin-domain-containing 4 (VSIG4) was validated as a biomarker associated with increased flare risk while anti-interferon-γ (anti-IFNγ) autoantibodies were also independently associated with flare. A combined model integrating VSIG4, anti-IFNγ and Disease Activity Score 28-joint count using C reactive protein improved discrimination compared with the clinical model alone (area under the curve (AUC) 0.760 vs 0.666) and showed higher sensitivity and greater potential clinical utility in decision curve analysis. In a secondary exploratory subgroup of patients with prolonged remission, predictive performance further improved (AUC 0.839). CONCLUSION: These findings identify a promising protein biomarker signature associated with flare during biologic tapering in RA, supporting biomarker-guided risk stratification in treatment optimisation strategies. External validation is required before clinical implementation.

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