AUTHOR=Melvan Ena , Allen Andrew P. , Vuckovic Tea , Soljic Irena , Starcevic Antonio 

TITLE=Predicting gut microbiota dynamics in obese individuals from cross-sectional data

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1485791

DOI=10.3389/fcimb.2025.1485791

ISSN=2235-2988

ABSTRACT=IntroductionObesity 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.MethodsWe 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.ResultsA 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.DiscussionThese 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 Optibiomics.