Mendelian randomization analyses of known and suspected risk factors and biomarkers for myasthenia gravis overall and by subtypes.

BACKGROUND: Myasthenia gravis (MG) is an autoimmune disease that affects neuromuscular junction. The literature suggests the involvement of circulating cytokines (CK), gut microbiota (GM), and serum metabolites (SM) with MG. However, this research is limited to observational trials, and comprehensive causal relationship studies have not been conducted. Based on published datasets, this investigation employed Mendelian Randomization (MR) to analyze the known and suspected risk factors and biomarkers causal association of MG and its subtypes. METHODS: This research used two-sample MR and linkage disequilibrium score (LDSC) regression of multiple datasets to aggregate datasets acquired from the genome-wide association studies (GWAS) to assess the association of MG with 41-CK, 221-GM, and 486-SM. For sensitivity analysis and to validate the robustness of the acquired data, six methods were utilized, including MR-Egger regression, inverse variance weighting (IVW), weighted median, and MR-PRESSO. RESULTS: The MR method identified 20 factors significantly associated with MG, including 2 CKs, 6 GMs, and 9 SMs. Further analysis of the factors related to the two MG subtypes, early-onset MG (EOMG) and late-onset MG (LOMG), showed that EOMG had a high overlap with MG in the intestinal flora, while LOMG had a greater similarity in CKs and SMs. Furthermore, LDSC regression analysis indicated that Peptococcaceae, oxidized biliverdin, and Kynurenine had significant genetic correlations with general MG, whereas EOMG was highly correlated with Intestinibacter, while LOMG had significant genetic associations with Kynurenine and Glucose. CONCLUSION: This research furnishes evidence for the potential causal associations of various risk factors with MG and indicates a heterogeneous relationship between CKs, GMs, and SMs with MG subtypes.

Identifying serum metabolite biomarkers for autoimmune diseases: a two-sample mendelian randomization and meta-analysis.

Background: Extensive evidence suggests a link between alterations in serum metabolite composition and various autoimmune diseases (ADs). Nevertheless, the causal relationship underlying these correlations and their potential utility as dependable biomarkers for early AD detection remain uncertain. Objective: The objective of this study was to employ a two-sample Mendelian randomization (MR) approach to ascertain the causal relationship between serum metabolites and ADs. Additionally, a meta-analysis incorporating data from diverse samples was conducted to enhance the validation of this causal effect. Materials and methods: A two-sample MR analysis was performed to investigate the association between 486 human serum metabolites and six prevalent autoimmune diseases: systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), dermatomyositis (DM), type 1 diabetes (T1D), and celiac disease (CeD). The inverse variance weighted (IVW) model was employed as the primary analytical technique for the two-sample MR analysis, aiming to identify blood metabolites linked with autoimmune diseases. Independent outcome samples were utilized for further validation of significant blood metabolites. Additional sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and retention rate analysis, were conducted. The results from these analyses were subsequently meta-integrated. Finally, metabolic pathway analysis was performed using the KEGG and Small Molecule Pathway Databases (SMPD). Results: Following the discovery and replication phases, eight metabolites were identified as causally associated with various autoimmune diseases, encompassing five lipid metabolism types: 1-oleoylglycerophosphoethanolamine, 1-arachidonoylglycerophosphoethanolamine, 1-myristoylglycerophosphocholine, arachidonate (20:4 n6), and glycerol. The meta-analysis indicated that three out of these eight metabolites exhibited a protective effect, while the remaining five were designated as pathogenic factors. The robustness of these associations was further confirmed through sensitivity analysis. Moreover, an investigation into metabolic pathways revealed a significant correlation between galactose metabolism and autoimmune diseases. Conclusion: This study revealed a causal relationship between lipid metabolites and ADs, providing novel insights into the mechanism of AD development mediated by serum metabolites and possible biomarkers for early diagnosis.