ArticleArthritis research & therapy2026
Discovery of biomarkers for primary Sj ögren's syndrome based on multi-omics data, construction of diagnostic models, and clinical correlation analysis.
Article in Arthritis research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivePrimary Sjogren's syndrome(pSS) exhibits significant clinical heterogeneity, which poses challenges for accurately assessing disease activity, predicting organ involvement, and diagnosing seronegative patients(SNSS). This study aimed to delineate the metabolic landscape of pSS by integrating metabolomic, lipidomic, and multi-dimensional clinical data to identify novel biomarkers for these purposes and to uncover the intrinsic links between metabolic dysregulation and immune dysfunction.
methodsUntargeted metabolomic and lipidomic analyses were performed on plasma samples from a discovery cohort and an independent validation cohort. A machine learning-based metabolic model was developed using selected features, and its diagnostic performance was evaluated by receiver operating characteristic curve (ROC) analysis.
resultsCompared with healthy controls(HC), pSS patients exhibited significant alterations in 65 metabolites from the metabolomic analysis and 63 lipids from the lipidomic analysis, indicating systemic metabolic pathway disruptions.Network analysis revealed extensive correlations of metabolomic/lipidomic profiles with clinical parameters and immune cell subsets. A panel of four biomarkers was identified and validated, demonstrating high efficacy in distinguishing SNSS. Furthermore, the levels of redox-related metabolites were significantly associated with age, sex, and anti-SSA antibody status. Seven biomarkers showed significant correlations with the EULAR Sjögren's Syndrome Disease Activity Index(ESSDAI). Specific metabolic signatures were also identified for different organ involvement phenotypes, achieving AUC values of 0.763 and 0.871 for predicting pulmonary and hematological involvement, respectively.
conclusionThis study systematically defines specific metabolic features of pSS, establishes a validated diagnostic model for SNSS, and confirms the close association between metabolic disturbances and clinical heterogeneity. The findings provide novel metabolic biomarkers and insights for the precise diagnosis and management of pSS.
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