Predictors of treatment response in rheumatoid arthritis: a narrative review

Rheumatoid arthritis (RA) is a common autoimmune disease having diverse responses to disease-modifying antirheumatic drugs. Although treat-to-target strategies have improved disease management, therapeutic selection remains largely empirical and a substantial proportion of patients fail to achieve adequate clinical response to their therapies. Therefore, reliable biomarkers capable of predicting treatment response before therapy initiation are urgently needed to guide personalised treatment decisions. Current evidence suggests that multiple categories of biomarkers may contribute to the prediction of treatment responses across different therapeutic agents in RA, including clinical characteristics, serological markers, cytokine profiles, genetic and transcriptomic signatures, immune-cell phenotypes, synovial pathotypes and microbiome features. This review summarises current biomarker strategies for predicting therapeutic response in RA and discusses their potential utility in improving precision medicine. However, most individual biomarkers show limited predictive power, partly due to the complex and dynamic nature of RA pathogenesis. Combining biomarkers from multiple biological dimensions, together with the continued exploration of novel predictive markers, may improve the robustness and accuracy of treatment response prediction in RA.