Analysis of the causal relationship between immune cells and rheumatoid arthritis from the perspective of genetic variation: a bidirectional two-sample Mendelian randomization study

Abstract

Abstract Background Immune factors are crucial in the pathogenesis of rheumatoid arthritis (RA), and immune cells play a key role in the development of RA. However, there is still disagreement regarding the specific roles of each type of immune cell in the pathological process of RA. Methods This study used bidirectional two-sample Mendelian randomization (MR) analysis to determine the causal relationship between immune cell characteristics and RA. Utilizing publicly available genetic data, we initially treated immune cell characteristics as exposures to investigate their causal effects on the risk of RA. Subsequently, we performed reverse two-sample MR using the positively selected cells from the initial analysis as outcomes, aiming to identify the core immune cells involved. Finally, a comprehensive sensitivity analysis was conducted to validate the robustness, heterogeneity, and horizontal pleiotropy of the results. Results Using data from 731 immune cells as exposures and cell SNPs as instruments, we independently conducted two-sample MR analysis for each patient with RA. The main analytical method used was the IVW method, with a significance level set at P < 0.05 for inclusion. In total, we identified 42 immune cell phenotypes that were causally associated with the onset of RA. For the reverse MR analysis, we used RA as the exposure factor and focused on 42 immune cell phenotypes as outcomes. Our analysis revealed causal relationships between the onset of RA and 7 immune cell phenotypes. Among these, 6 showed positive causal relationships, while 1 exhibited a negative causal relationship. Conclusions Our study emphasized the causal relationship between immune cells and RA through bidirectional two-sample MR analysis, identifying the immune cells causally associated with RA.

Keywords

• Rheumatoid arthritis
• Immune cells
• Causal inference
• Genetic variation
• MR analysis
• Sensitivity