Exploring biomarkers for treatment response in psoriatic arthritis: a focus on multi-omics technologies

Psoriatic arthritis (PsA) is a complex inflammatory disease characterised by a combination of cutaneous and musculoskeletal symptoms, which complicates both diagnosis and management. Despite advances in targeted therapy, the treatment of PsA remains challenging, with up to 40% of patients experiencing inadequate response. Predictive biomarkers may serve as valuable tools to help clinicians select the most appropriate therapy at the optimal time. However, despite substantial research activity, the current evidence remains inconclusive. Moreover, biomarkers that predict treatment response may differ from those that dynamically change in response to therapy. In this narrative review we therefore examine both biomarkers with potential predictive value and those that demonstrate treatment-related changes over time. Multi-omics approaches have enhanced understanding of the molecular determinants of therapeutic response. Most genetic analyses have investigated TNF inhibitor response, whereas certain transcriptomic studies highlight genes related to apoptosis and immune-mediated pathways as key contributors to therapeutic response across different treatments and throughout the treatment course. Several non-coding RNAs and protein panels have also shown promise as indicators of treatment outcome, but metabolomic studies remain scarce. Reported molecular signatures nonetheless vary widely, likely reflecting disease heterogeneity as well as differences in treatment modalities, study design, and outcome measures, and are summarised herein. Future studies integrating multi-omics and computational frameworks are essential to validate candidate biomarkers and facilitate personalised treatment strategies in PsA.