# 16S ribosomal ribonucleic acid sequencing reveals bile microbiome features in gallstone disease and their links to blood lipid subtypes

## TL;DR

This study shows that the bile microbiome in gallstone disease differs between patients with high triglycerides and others, suggesting a link to blood lipid levels and possible metabolic pathways.

## Contribution

The study identifies specific bile microbiome features associated with distinct lipid subtypes in gallstone disease patients.

## Key findings

- Bile microbiome composition differed between hypertriglyceridaemia and non-HTG groups, with Proteobacteria and Firmicutes showing group-specific trends.
- Microbial diversity was reduced in the HTG group, and random forest classification achieved moderate accuracy in predicting lipid subtypes.
- Potential mechanistic links were suggested through bile acid metabolism and farnesoid X receptor–fibroblast growth factor 19 signaling pathways.

## Abstract

Gallstone disease (GSD) represents a major global health burden with complex pathophysiology involving bile microbiome dysbiosis and metabolic dysfunction. Although previous studies have examined bile microbial communities, the relationship between bile microbiome composition and specific lipid phenotypes remains incompletely understood.

We conducted a cross-sectional study of 28 adults undergoing cholecystectomy for radiologically and pathologically confirmed gallstones. Bile samples were collected intraoperatively and subjected to 16S ribosomal ribonucleic acid V3–V4 region sequencing. Patients were stratified by lipid subtypes based on contemporary dyslipidaemia guidelines. Microbial diversity, community structure and differential abundance analyses were performed alongside machine learning classification.

The bile microbiome exhibited distinct compositional patterns between the hypertriglyceridaemia (HTG) and non-HTG (NTG) groups, with key phyla (Proteobacteria, Firmicutes) showing group-specific abundance trends and alpha diversity indices reflecting reduced evenness in HTG. Beta diversity analyses demonstrated mild-to-moderate separation between groups, and the linear discriminant analysis effect size technique identified discriminatory taxa with potential functional relevance. Random forest classification achieved moderate accuracy in predicting lipid subtypes based on microbial features.

This study revealed associations between bile microbiome composition and systemic lipid metabolism in GSD, suggesting potential mechanistic links through bile acid metabolism and farnesoid X receptor–fibroblast growth factor 19 signaling pathways.