Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning.

Area of Science:

• Microbiology
• Bioinformatics
• Gerontology

Background:

• The human gut microbiome is a complex ecosystem influenced by host factors like age.
• Previous studies noted correlations between gut microbes and host age, but lacked predictive accuracy.
• No reliable method existed to determine host age solely from gut microbial composition.

Purpose of the Study:

• To develop a predictive model for host age using gut microflora taxonomic profiles.
• To identify specific microbes associated with host aging and potential aging biomarkers.
• To establish a quantitative model of gut microbiome aging.

Main Methods:

• Utilized a cross-study dataset of gut microflora taxonomic profiles.
• Applied deep learning, specifically a deep neural network architecture.
• Validated the model on external data to assess prediction accuracy.

Main Results:

• Developed a deep neural network model for age prediction.
• Achieved a mean absolute error of 5.91 years on external test data.
• Advanced a procedure to infer microbial roles in aging and identify biomarkers.

Conclusions:

• The developed 'intestinal clock' provides a novel, quantitative method for estimating host age from gut microbiome data.
• Identified potential microbial aging biomarkers, advancing our understanding of gut aging.
• Offers a foundation for integrating host aging dynamics with gut community succession.