Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese...

Area of Science:

• Microbiome research
• Computational biology
• Human health

Background:

• The gut microbiome influences obesity, but consistent biomarkers and targeted modulation remain challenging across diverse populations.
• Identifying reliable gut microbial signatures for obesity is crucial for developing effective interventions.

Purpose of the Study:

• To develop a machine learning framework for identifying shared gut microbiome biomarkers across different geographic locations.
• To enable personalized modulation of the gut microbiome for individuals with obesity.

Main Methods:

• Applied a machine learning framework to 870 human stool metagenomes from five countries.
• Utilized heterogeneous ensemble feature selection for biomarker identification.
• Employed deep reinforcement learning for personalized gut microbiome modulation via counterfactual inference.

Main Results:

• Identified 42 species as regional shared biomarkers for obesity with high accuracy (AUC = 0.85).
• Discovered potential broad-spectrum targets for modulation, including Akkermansia muciniphila and Faecalibacterium prausnitzii.
• Demonstrated the framework's capability for personalized intervention strategies.

Conclusions:

• The proposed machine learning framework effectively identifies comprehensive and accurate gut microbiome biomarkers for obesity.
• The framework facilitates personalized modulation analysis, offering a valuable tool for obesity research and other diseases.
• This approach advances the understanding and treatment of obesity through data-driven microbiome insights.