Supplementary Material for: Genetical predicted causal relationship between gut microbiota and different types of kidney diseases

Introduction: Although recent research suggests that alterations in gut microbiota play a critical role in the pathophysiology of kidney diseases, the causal relationship between specific intestinal flora and the risk of kidney diseases remains unclear. Here, we investigated the causal relationship between gut microbiota and different kidney diseases through Mendelian randomization analysis.
Methods: Gut microbiota and three types of kidney diseases, including diabetic nephropathy, IgA nephropathy, and membranous nephropathy, were identified from large-scale genome-wide association studies summary data. Inverse variance weighted method was employed to estimate causal relationships. Cochran’s Q test was utilized to uncover any heterogeneity. The Mendelian randomization Egger intercept test was employed to detect horizontal pleiotropy, and the leave-one-out method was used for testing the stability. In addition, the reverse, multivariable, and two step Mendelian randomization analysis was conducted to assess the causation possibilities. Furthermore, the associations between three types of kidney diseases and immune infiltration were determined.
Results: We identified 1,531 single nucleotide polymorphisms. There were 6 positive and 9 negative causal effects between gut microbiota and three types of kidney diseases. Specifically, Dialister was a protective factor for diabetic nephropathy while LachnospiraceaeUCG008 was a risk factor. Clostridiuminnocuum was a protective factor for IgA nephropathy, while ChristensenellaceaeR.7, Clostridiumsensustricto1, LachnospiraceaeUCG004, LachnospiraceaeUCG010, Oscillospira, RuminococcaceaeUCG010, and Terrisporobacter were risk factors for IgA nephropathy. Butyricicoccus, Catenibacterium, Flavonifractor, and Lachnospira were associated with an increased risk of membranous nephropathy, while RuminococcaceaeUCG011 was associated with a decreased risk of membranous nephropathy. Sensitivity analysis indicated the results were robust. No significant pleiotropy or heterogeneity was identified. Notably, the reverse Mendelian randomization analysis did not reveal any causal relationship. After adjusting for environmental confounders, including CO, PM 2.5, PM10, and exposure to tobacco smoke at home, these causal relationships still exist. Additionally, immune infiltration analysis indicated unique immune cell distribution in each type of kidney disease, which are largely consistent with later two step approach.
Conclusion: This study uncovered the causal relationship between gut microbiota and three types of kidney diseases. This discovery provides fresh perspectives on how microbes contribute to kidney diseases, paving the way for more in-depth clinical studies.