{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T10:08:11Z","timestamp":1790158091136,"version":"4.1.0"},"reference-count":98,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Psoriasis (PsO) is a chronic immune-mediated inflammatory disease that progresses to psoriatic arthritis (PsA) in up to 30% of patients. The transition from localized cutaneous inflammation to systemic articular involvement represents a critical window for early intervention; however, accurately predicting which patients will develop PsA remains a significant clinical challenge. Growing evidence highlights immunometabolic dysregulation as a central driver of this systemic shift. The progression from PsO to PsA is fueled by a complex interplay between immune dysregulation\u2014centered on the IL-23\/Th17 axis, tissue-resident memory T cell recirculation, and regulatory T cell dysfunction\u2014and systemic metabolic disturbances, including obesity, insulin resistance, dyslipidemia, and\u00a0adipokine imbalance. This review comprehensively examines the immunometabolic mechanisms bridging skin and joint pathology and evaluates emerging candidate biomarkers for predicting disease progression. Promising indicators include immunological markers, such as declining serum CXCL10 levels and circulating skin-homing CD8+CCR10+ T cells, as well as metabolic signatures, including an elevated leptin-to-adiponectin ratio and distinct lipidomic and metabolomic profiles. Given the multifactorial nature of psoriatic disease, individual biomarkers often lack sufficient predictive power. Therefore, we highlight the paradigm shift toward multi-omics integration. Leveraging machine learning to synthesize genomic, transcriptomic, proteomic, metabolomic, and epigenomic data offers a powerful strategy for developing robust predictive models. The identification and validation of integrated immunometabolic biomarker profiles will facilitate early risk stratification, enable preemptive disease-modifying and lifestyle interventions, and advance precision medicine in the management of psoriatic disease.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1944289","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T09:26:01Z","timestamp":1790155561000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The immunometabolic tipping point: forecasting the systemic shift from psoriasis to psoriatic arthritis"],"prefix":"10.3389","volume":"17","author":[{"given":"Xi","family":"Chen","sequence":"first","affiliation":[{"name":"First Affiliated Hospital of Huzhou University","place":["Huzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Chen","sequence":"additional","affiliation":[{"name":"Huzhou Central 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