ArticleFrontiers in cellular and infection microbiology2025
Predicting gut microbiota dynamics in obese individuals from cross-sectional data.
Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dissimilarity measures for generalized Lotka-Volterra systems on networks.Scientific reports · 2026Article
- Gut Microbiota-Mediated Mechanisms of Traditional Chinese Medicine in Obesity Management.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
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5 authors.
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Abstract
Introduction: Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa. Methods: We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals. Results: A total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations. Discussion: These findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as
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