# Topological and Functional Diversity of Gut Microbiota Metabolism Across the Human Lifespan

## TL;DR

This study explores how gut microbiota metabolism changes with age by analyzing metabolic networks across infants, adults, and the elderly.

## Contribution

The study introduces a network-based framework to analyze age-related differences in gut microbiota metabolism using metabolic Directed Acyclic Graphs.

## Key findings

- Gut microbiota metabolic networks in adults show lower redundancy and structural complexity compared to infants and the elderly.
- Elderly samples were split into two clusters, one grouping with adults and the other with infants, indicating heterogeneous network configurations.
- Functional reactions were identified that distinguish adult metabolic networks from those of other age groups.

## Abstract

Background: The human gut microbiota plays a central role in host physiology by influencing digestion, immune function, and metabolism. Characterizing age-associated differences in the organization of microbial metabolism may provide insights into functional variation in the gut microbiome across the human lifespan. Methods: Gut microbiota metabolic organization was analyzed in a cohort of 30 individuals spanning three age groups (infants, adults, and elderly individuals) and comprising 156 stool samples. Community metabolic networks were reconstructed using the metabolic Directed Acyclic Graph (m-DAG) framework derived from KEGG Ortholog annotations. Network topology was characterized to assess whether the resulting networks conform to previously described global structural patterns and to examine age-associated variability. Pairwise m-DAG dissimilarities were computed, and hierarchical clustering was applied to evaluate similarities among samples. Results: All samples revealed a conserved global network organization, alongside marked variability in specific structural features. Hierarchical clustering did not strictly reflect chronological age. A homogeneous cluster composed exclusively of adult samples was identified, whereas elderly samples were distributed across two clusters, one grouping with adults and the other with infants. Exploratory discriminative analyses identified functional reactions contributing to the separation between the adult cluster and the remaining samples, indicating age-associated differences in metabolic network organization. Conclusions: Gut microbiota metabolic networks in adults tend to exhibit lower redundancy and structural complexity, whereas those in infant and elderly samples display more heterogeneous network configurations. This network-based analysis provides a functional perspective on age-associated variation in gut microbiota metabolism and offers a framework for future integrative studies.