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Predicting gut microbiota dynamics in obese individuals from cross-sectional data
Ena Melvan1,2, Andrew P Allen1, Tea Vuckovic2
1School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.
Frontiers in Cellular and Infection Microbiology
|June 25, 2025
Summary
Obesity is linked to gut microbiome changes. Our study reveals that microbial interaction networks, not just abundance, are key in obesity, enabling personalized dietary interventions.
Area of Science:
- Microbiome research
- Systems biology
- Obesity research
Background:
- Obesity affects 39% of adults globally.
- Gut microbiota is implicated in obesity, but research often overlooks microbial interactions.
Purpose of the Study:
- To investigate the role of dynamic microbial interactions in obesity.
- To develop a method for inferring microbiota dynamics from static data.
Main Methods:
- Applied the BEEM-Static model (a Lotka-Volterra model) to 16S rRNA gut microbiome data.
- Analyzed cross-sectional data from 2,435 lean and obese individuals across six public datasets.
Main Results:
- Identified 57 significant microbial interactions in obese individuals (79% negative) vs. 37 in lean (92% negative).
- Observed stronger inhibition of Firmicutes by Bacteroidetes in obese individuals.
- Found higher carrying capacities for Firmicutes and Proteobacteria in obese populations.
Conclusions:
- Microbial interaction networks are crucial in obesity-related dysbiosis, beyond taxonomic abundance.
- The developed approach, termed Optibiomics, allows microbiota dynamics inference from single time points.
- This opens avenues for tailored dietary interventions for obesity management.
