Circulating inflammatory cytokines and the risk of myasthenia gravis: a bidirectional Mendelian randomization study
BMC Neurology, 2025
Su B., He Z., Shi L., Li M., Huang X.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Immunological & Inflammatory Diseases Neurology | Pathophysiology | Plasma |  Olink Target 96 |
Abstract
Background
Myasthenia gravis (MG) is an autoimmune disorder of the neuromuscular junction. Increasing evidence has suggested inflammation is involved in the pathogenesis of MG, but whether it is the cause or a downstream effect remains unclear. In this study, a two-sample Mendelian randomization (TSMR) analysis was performed to explore the causal relationship between 91 circulating inflammatory cytokines and MG.
Method
In this study, the data of 91 circulating inflammatory cytokines from 4824 Europeans and the largest GWAS database of MG (1873 patients and 36370 controls) were used to screen instrumental variables (IVs). Inverse variance weighting (IVW), Bayesian weighted MR (BWMR), MR-Egger regression, weighted median (WM), simple mode and weighted mode were used to evaluate the association between MG and inflammatory cytokines. The MR-Egger intercept test and Cochran’s Q test were used to test the pleiotropy and heterogeneity of IVs.
Result
Our results showed that adenosine deaminase (ADA) and CD40 Ligand (CD40L) are positively associated with the risk of MG (OR = 1.16, 95%CI: 1.00-1.33, P = 0.041; OR = 1.20, 95%CI: 1.02–1.40, P = 0.025), while interleukin-1-alpha (IL-1α), glial-cell-line-derived neurotrophic factor (GDNF), Osteoprotegerin (OPG) and tumor necrosis factor-beta (TNF-β) are negatively associated with the risk of MG (OR = 0.80, 95% CI: 0.64 ~ 0.99, P = 0.042; OR = 0.74, 95%CI:0.58 ~ 0.0.96, P = 0.022; OR = 0.76, 95% CI: 0.61 ~ 0.94, P = 0.013; OR = 0.76, 95% CI: 0.61 ~ 0.94, P = 0.012; OR = 0.80, 95% CI: 0.68 ~ 0.93, P = 0.006). In addition, genetically predicted MG affected the expression of seven cytokines. Sensitivity analysis showed no horizontal pleiotropy and significant heterogeneity of all results.
Conclusions
Our results provided promising clues for the treatment of MG. We evaluated the association between inflammatory cytokines and the disease by genetic informatics approach, which may help to better understand the underlying mechanisms of MG.