Exploring the Relationship Between Immune Cells and Chronic Kidney Disease by Mendelian Randomization, Colocalization Analysis, and SMR

利用孟德尔随机化、共定位分析和SMR方法探索免疫细胞与慢性肾脏病之间的关系

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Abstract

Background: Chronic kidney disease (CKD) impacts millions of individuals annually. Current research suggests that immune factors played a significant role in CKD. However, the potential causal relationship between them remains unclear. Methods: We conducted a comprehensive Mendelian randomization (MR) analysis to assess the potential causal association between 731 immune cells and CKD. Sensitivity analysis was performed to test for heterogeneity and horizontal pleiotropy, including the Cochran Q test, leave-one-out test, MR-Egger intercept analysis, and MR-PRESSO test. The bidirectional MR was utilized to investigate the bidirectional relationship between the immune cells and CKD. Multivariable MR was also conducted to mitigate confounding among immune cells. The colocalization analysis was performed to find the key genes of immune cells. We used the Summary data-based MR (SMR) analysis to generate effect estimates between the cis-eQTL and immune cells. The heterogeneity in dependent instruments (HEIDIs) test was used to test the heterogeneity between dependent instrumental variables. Results: We identified 14 potential pathogenic factors and six potential protective factors through the univariable MR. Moreover, we did not find reverse causation by using the bidirectional MR. We finally identified one risk factor and two protective factors after multivariate MR adjustment for effects between immune cells. CD28 on CD28(+) CD45RA(+) CD8(+) T cell could increase the risk of CKD (Pval: 0.033, OR: 1.112, 95% CI: 1.009-1.227). CD11c on myeloid dendritic cell (DC) could decrease the risk of CKD (Pval: 0.02, OR: 0.854, 95% CI: 0.748-0.975). CD45RA on naive CD4(+) T cell could decrease the risk of CKD (Pval: 0.026, OR: 0.918, 95% CI: 0.852-0.990). Importantly, we observed no evidence of heterogeneity and pleiotropy, signifying the robustness of our results. BACH2 (PPH4.abf = 0.999, P_SMR: <0.001, P_HIEDI: 0.132) and HLA-G (PPH4.abf = 0.990, P_SMR: <0.001, P_HIEDI: 0.141) shared the same variant with CD28 on CD28(+) CD45RA(+) CD8(+) T cell. PAQR9 (PPH4.abf = 0.992, P_SMR: <0.001, P_HIEDI: 0.215) shared the same variant with CD11c on myeloid DC. Conclusion: MR identified a potential correlation between CKD and immune cells. Colocalization and SMR found the key genes of immune cells. Our findings offer insights into the prevention and management of CKD. However, further investigation is required to elucidate the precise mechanisms underlying this relationship.

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