Predicting the Role of the Human Gut Microbiome in Constipation Using Machine-Learning Methods: A Meta-Analysis

Area of Science:

• Microbiology
• Gastroenterology
• Bioinformatics

Background:

• Constipation significantly impacts patient health and quality of life.
• Emerging research links gut microbiome alterations to constipation, but often lacks multi-cohort validation.
• Understanding these links is crucial for developing novel diagnostic and therapeutic strategies.

Purpose of the Study:

• To construct a robust classification model for constipation using fecal bacterial data.
• To identify specific gut microbial species that can serve as biomarkers for constipation.
• To validate findings across multiple independent research cohorts.

Main Methods:

• Analysis of 3056 fecal amplicon sequence datasets from five distinct cohorts.
• Comprehensive microbiome analysis including alpha- and beta-diversity, and phylogenetic profiling.
• Application of machine learning, specifically Gradient Boosted Regression Trees with chi2 feature selection, for model development.

Main Results:

• Patients with constipation exhibited significantly higher alpha diversity in their gut bacterial communities.
• Distinct differences in gut microbiota composition were observed between constipated and non-constipated individuals (beta diversity).
• The developed machine learning model achieved a 70.7% validation accuracy in classifying constipation.

Conclusions:

• A reliable discriminant model for constipation has been established based on fecal microbiome data.
• Fifteen key bacterial genera, including Serratia, Dorea, and Aeromonas, were identified as potential biomarkers for constipation.
• These findings highlight the gut microbiome's role in constipation and offer potential for targeted interventions.