- 2025-12-03
- Clinical rheumatology 45(2)
Study Design
- Type
- Meta-Analysis
- Methods
- We used Mendelian randomization (MR) methods, including inverse-variance weighted (IVW), MR-Egger, and weighted median (WM) models, along with metabolic pathway analysis, meta-analysis, colocalization analysis, and genetic correlation studies
Introduction
METHODS: We used Mendelian randomization (MR) methods, including inverse-variance weighted (IVW), MR-Egger, and weighted median (WM) models, along with metabolic pathway analysis, meta-analysis, colocalization analysis, and genetic correlation studies to explore the relationship between plasma metabolites and SS.Results
Our analysis uncovered 53 metabolites with potential causal links to SS, among which nine demonstrated statistically significant associations. In the validation stage, two metabolites were found to play a role in SS pathogenesis: gluconate, which exhibited a protective effect, and 1,3,7-trimethylurate, which was associated with increased risk. The reliability of these results was further reinforced by sensitivity analyses and validation procedures. Additionally, metabolic pathway analysis identified four key pathways associated with SS risk: cysteine and methionine metabolism, glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, and oxaloacetate and dicarboxylate metabolism. Although no genetic correlations were identified, colocalization analysis suggested that the top single nucleotide polymorphism (SNP) in the LINC01572 gene may contribute to increased SS risk.Conclusion
These findings provide novel insights into the metabolic etiology of SS, highlighting both protective and harmful metabolites.Key points
• Identified 53 metabolites linked to Sjögren's syndrome (SS), with 9 showing significant associations. • Validated gluconate (protective) and 1,3,7-trimethylurate (risk-increasing) in SS pathogenesis. • Found four key metabolic pathways associated with SS risk. • First two-sample MR study to assess plasma metabolites and SS risk using the largest GWAS dataset.