ORIGINAL RESEARCH article
Front. Cell. Infect. Microbiol.
Sec. Intestinal Microbiome
Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1485791
This article is part of the Research TopicUnderstanding Diet-Gut Microbiome Interactions for Optimal Gut Health using Artificial IntelligenceView all articles
Predicting Gut Microbiota Dynamics in Obese Individuals from Cross-Sectional Data
Provisionally accepted
- 1Macquarie University, Sydney, Australia
- 2Metabelly, Split, Croatia
- 3Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia
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Obesity affects ~39% of adults (1.9 billion people) worldwide. Our understanding of the gut microbiome's function in health and disease has primarily focused on correlations between disease status and microbiota composition, with limited exploration of microbial interactions. We employed BEEM-Static, a dynamic mathematical model, to analyze microbial interactions from six datasets, totaling 2,435 gut microbiome profiles. Our meta-analysis identified 57 significant interactions in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes exhibited a stronger inhibitory effect on Firmicutes in obese individuals (interaction strength = –0.41) than in lean individuals (–0.26), indicating a need for personalized dietary interventions. The proposed model predicts microbiota dynamics from a single time point with high accuracy, enabling the development of tailored dietary strategies, termed Optibiomics.
Keywords: Gut Microbiota, Obesity, Microbial Interactions, personalized nutrition, GLV method, Dietary interventions, Microbiome Dynamics Font: Not Italic Font: (Default) Times New Roman, 12 pt
Received: 24 Aug 2024; Accepted: 20 May 2025.
Copyright: © 2025 Melvan, Allen, Vuckovic, Soljic and Starcevic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ena Melvan, Macquarie University, Sydney, Australia
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