The promise of biomarkers in Psoriatic Arthritis: Moving towards precision medicine.

Psoriatic arthritis is a heterogeneous disease where delayed diagnosis and variable treatment responses remain significant challenges. This review addresses how interconnected metabolic, immunological, and genetic pathways provide opportunities for precision medicine. We examine a distinct metabolic endotype involving altered apolipoproteins and gut metabolites, alongside immunological signatures such as pathogenic tissue-resident memory T cells. Genetic and multi-omic analyses, supported by machine learning, highlight susceptibility loci and signaling pathways that refine disease heterogeneity. Clinically, cytokine profiles and extracellular matrix markers show potential for treatment stratification and monitoring. Furthermore, specific chemokines and inflammatory indices are identified as predictors of the transition from psoriasis to arthritis, enabling early intervention. Integrating these diverse domains through machine learning may facilitate earlier diagnosis and personalized therapy. By defining what is known regarding these biomarkers, this chapter outlines a path toward improved long-term outcomes for patients with psoriatic arthritis.