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Objective <p>Primary 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.</p> Methods <p>Untargeted 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.</p> Results <p>Compared 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.</p> Conclusion <p>This 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.</p>

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Discovery of biomarkers for primary Sj ögren’s syndrome based on multi-omics data, construction of diagnostic models, and clinical correlation analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Le Qiang,
  • Nan Wang,
  • Yuhan Jia,
  • Jiahui Xue,
  • Yue Jin,
  • Lei Sun,
  • Xiaohan Ni,
  • Yanlin Wang,
  • Min Feng,
  • Chong Gao,
  • Jing Luo

摘要

Objective

Primary 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.

Methods

Untargeted 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.

Results

Compared 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.

Conclusion

This 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.