Machine learning-based characterization of the gut microbiome associated with the progression of primary biliary cholangitis to cirrhosis

Background

Primary biliary cholangitis (PBC) is associated closely with the gut microbiota. This study aimed to explore the characteristics of the gut microbiota after the progress of PBC to cirrhosis.

Method

This study focuses on utilizing the 16S rRNA gene sequencing method to screen for differences in gut microbiota in PBC patients who progress to cirrhosis. Then, we divided the data into training and verification sets and used seven different machine learning (ML) models to validate them respectively, calculating and comparing the accuracy, F1 score, precision, and recall, and screening the dominant intestinal flora affecting PBC cirrhosis.

Result

PBC cirrhosis patients showed decreased diversity and richness of gut microbiota. Additionally, there are alterations in the composition of gut microbiota in PBC cirrhosis patients. The abundance of Faecalibacterium and Gemmiger bacteria significantly decreases, while the abundance of Veillonella and Streptococcus significantly increases. Furthermore, machine learning methods identify Streptococcus and Gemmiger as the predominant gut microbiota in PBC patients with cirrhosis, serving as non-invasive biomarkers (AUC = 0.902).

Conclusion

Our study revealed that PBC cirrhosis patients gut microbiota composition and function have significantly changed. Streptococcus and Gemmiger may become a non-invasive biomarker for predicting the progression of PBC progress to cirrhosis.