Gut microbiome relationship with arrhythmias and conduction blocks: A two-sample Mendelian randomization study

Past research based on observations has suggested that the gut microbiome (GM) could play a role in developing arrhythmias and conduction blocks. Nonetheless, the nature of this association remains uncertain due to the potential for reverse causation and confounding factors in observational research. The aim of this investigation is to elucidate the causal relationship between GM and the development of arrhythmias as well as conduction blocks. This study collected summary statistics regarding GM, arrhythmias, and conduction blocks. Two-sample Mendelian randomization (MR) analysis was carried out employing various methods, with inverse variance weighted being the primary approach, followed by weighted median, simple mode, MR-Egger, and MR-PRESSO. Moreover, the MR findings were corroborated through multiple sensitivity analyses. Among them, for atrial fibrillation and flutter (AF), phylum_Actinobacteria and genus_RuminococcaceaeUCG004 demonstrated a negative correlation, while order_Pasteurellales, family_Pasteurellaceae, and genus_Turicibacter were associated with an increased risk. In the case of paroxysmal tachycardia (PT), genus_Holdemania and genus_Roseburia were found to reduce risk. For atrioventricular block (AVB), order_Bifidobacteriales, family_Bifidobacteriaceae, and genus_Alistipes exhibited a negative correlation, whereas genus_CandidatusSoleaferrea showed a positive correlation. Concerning the left bundle-branch block (LBBB), family_Peptococcaceae appeared to decrease the risk, while genus_Flavonifractor was linked to an increased risk. Lastly, no causative GM was identified in the right bundle-branch block (RBBB) context. We have uncovered potential causal links between some GM, arrhythmias, and conduction blocks. This insight may aid in designing microbiome-based interventions for these conditions and their risk factors in future trials. Additionally, it could facilitate the discovery of novel biomarkers for targeted prevention strategies.