Association between gut microbiota and seven gastrointestinal diseases: A Mendelian randomized study

BackgroundObservational research has shed light on the ability of gut microbes to influence the onset and progression of gastrointestinal diseases. The causal relationships between specific gut microbiomes and various gastrointestinal conditions, however, remain unknown.MethodsWe investigated the relationship between gut microbiota and seven specific gastrointestinal disorders using a robust two-sample Mendelian randomization (MR) approach. The inverse variance-weighted (IVW) method was used as the primary analysis tool in our study. Furthermore, we conducted multiple sensitivity analyses to strengthen the robustness of our findings and ensure the reliability of the IVW method.ResultsOur research has discovered significant links between the composition of gut microbiota and a variety of gastrointestinal ailments. We found compelling links between 13 gut microbiota and fatty liver, four gut microbiota and cirrhosis, eight gut microbiota and hepatocellular carcinoma, four gut microbiota and cholelithiasis, 12 gut microbiota and acute pancreatitis, eight gut microbiota and chronic pancreatitis, and 11 gut microbiota and pancreatic cancer. These findings shed light on the intricate relationship between gut microbes and the emergence of these specific gastrointestinal conditions.ConclusionsThe findings of this extensive study not only validate the potential role of specific gut microbiota in gastrointestinal diseases, but also fill a critical gap in previous research. The discovery of these specific gut microbiota is a significant step forward because they may serve as novel and promising biomarkers for both the prevention and treatment of gastrointestinal conditions. Through the estimation of the instrumental variables that we selected, we used the Mendelian randomization's randomization method to show the causal relationship between 196 intestinal microorganisms and seven gastrointestinal diseases and verified our results through multiple sensitivity analyses.image