Objective: Previous observational studies suggest a potential link between gut microbiota, metabolites, and diabetic nephropathy. However, the exact causal relationship among these factors remains unclear.
Method: We conducted a two-sample bidirectional Mendelian randomization study using summary statistics from the IEU OpenGWAS Project database to investigate gut microbiota, metabolites, and diabetic nephropathy. A range of methods, including inverse variance weighting, MR-Egger, weighted median, and simple median, were applied to examine causal associations. Sensitivity analyses were performed to assess the robustness of the results. Additionally, reverse Mendelian randomization analysis was conducted, treating significant gut microbiota as the outcome, to evaluate effects and perform sensitivity testing. This comprehensive approach provided an in-depth assessment of the interactions among gut microbiota, metabolites, and diabetic nephropathy.
Result: The Inverse Variance Weighted estimates revealed that the abundance of Lachnospiraceae, Parasutterella, and Eubacterium exhibited negative causal effects on diabetic nephropathy, while Coprococcus, Sutterella, Faecalibacterium prausnitzii, and Bacteroides vulgatus showed protective causal effects against the condition. However, reverse Mendelian randomization analysis did not identify any significant associations between diabetic nephropathy and the identified gut microbiota. Furthermore, the estimates indicated that Cholesterol, Pyridoxate, Hexanoylcarnitine, X-12007, Octanoylcarnitine, 10-nonadecenoate (19:1n9), X-12734, and the average number of double bonds in a fatty acid chain had negative causal effects on diabetic nephropathy. In contrast, Methionine, Glycodeoxycholate, X-06351, 1-stearoylglycerol (1-monostearin), 5-dodecenoate (12:1n7), X-13859, 2-hydroxyglutarate, Glycoproteins, Phospholipids in IDL, and the concentration of small HDL particles demonstrated protective causal effects. Notably, sensitivity analyses did not detect any heterogeneity or horizontal pleiotropy, ensuring the robustness of the findings.
Conclusion: Modulating gut microbiota diversity and composition offers a promising strategy for improving the incidence and prognosis of diabetic nephropathy. This highlights the need for future clinical trials focusing on microbiome-based interventions, potentially utilizing microbiome-dependent metabolites. Such approaches could transform the treatment and management of diabetic nephropathy and its associated risk factors, paving the way for more effective therapeutic strategies to combat this debilitating condition.
Keywords: bidirectional; diabetic nephropathy; gut microbiota; mendelian randomization analysis; metabolites.
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