The Causal Relationship Between Serum Metabolites and Sjogren’s Syndrome: A Mendelian Randomization Study

Aims/Background Sjogren’s syndrome (SS) is a highly prevalent autoimmune disease with potentially serious consequences if left untreated, but methods for early detection and prevention of SS remain limited. This study aims to investigate the causal relationships between serum metabolites and SS using Mendelian randomization (MR), focusing on identifying key metabolic pathways and biomarkers that contribute to SS pathogenesis. Methods We used a two-sample MR approach to investigate the causal relationships between serum metabolites and SS. The primary method for estimating these causal effects was inverse variance weighting (IVW), with results presented alongside their corresponding 95% confidence intervals (CIs). Sensitivity analyses included the Cochran’s Q statistical analysis and MR-Egger method. Furthermore, an enrichment analysis of metabolic pathways was applied to the identified metabolites. Results Thirty-seven serum metabolites that have causal links with SS, encompassing 7 metabolite ratios and 30 single metabolites (4 unknown and 26 known), were identified. Metabolite ratios, reflecting the balance between specific metabolites, were analyzed to identify metabolic shifts that may contribute to SS pathogenesis. Among the 26 known metabolites, 12 are protective factors and 14 are risk factors. The levels of cis-4-decenoate (cDA) (10:1n6) (odds ratio [OR] = 1.125; 95% CI = 1.026–1.233; p = 0.012) is positively correlated with the incidence of SS, whereas the levels of butyrate/isobutyrate (4:0) (OR = 0.822; 95% CI = 0.701–0.963; p = 0.016) are negatively correlated with the SS incidence. Most of these metabolites are associated with lipid and amino acid metabolism. Among lipids, the strongest risk-increasing factor was 2,3-dihydroxy-2-methylbutyrate (OR = 1.307; 95% CI = 1.054–1.621; p = 0.015), while the strongest risk-decreasing factor was hexadecadienoate (16:2n6) (OR = 0.774; 95% CI = 0.635–0.944; p = 0.011). Among amino acids, the strongest risk-increasing factor was N-acetylproline (OR = 1.178; 95% CI = 1.024–1.355; p = 0.022), and the strongest risk-decreasing factor was N-acetylserine (OR = 0.802; 95% CI = 0.694–0.926; p = 0.003). Furthermore, these metabolites are predominantly enriched in the arginine and proline metabolism pathway. Conclusion This study helped enhance our comprehension of the causal relationship between serum metabolites and SS, showing that some metabolites may influence the risk and development of this disease. These insights offer novel perspectives for the development of SS prediction and diagnosis.