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

Ena Melvan; Andrew P. Allen; Tea Vuckovic; Irena Soljic; Antonio Starcevic

doi:10.3389/fcimb.2025.1485791

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

Ena Melvan^*, Andrew P. Allen, Tea Vuckovic, Irena Soljic, Antonio Starcevic

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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 Optibiomics.

Original language	English
Article number	1485791
Pages (from-to)	1-11
Number of pages	11
Journal	Frontiers in Cellular and Infection Microbiology
Volume	15
Early online date	10 Jun 2025
DOIs	https://doi.org/10.3389/fcimb.2025.1485791
Publication status	Published - 2025

Bibliographical note

Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

dietary interventions
GLV method
gut microbiota
microbial interactions
microbiome dynamics
obesity
personalized nutrition

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10.3389/fcimb.2025.1485791

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title = "Predicting gut microbiota dynamics in obese individuals from cross-sectional data",

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 Optibiomics.",

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author = "Ena Melvan and Allen, \{Andrew P.\} and Tea Vuckovic and Irena Soljic and Antonio Starcevic",

note = "Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. ",

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doi = "10.3389/fcimb.2025.1485791",

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AU - Melvan, Ena

AU - Allen, Andrew P.

AU - Vuckovic, Tea

AU - Soljic, Irena

AU - Starcevic, Antonio

N1 - Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2025

Y1 - 2025

N2 - 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 Optibiomics.

AB - 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 Optibiomics.

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KW - GLV method

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KW - microbial interactions

KW - microbiome dynamics

KW - obesity

KW - personalized nutrition

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JF - Frontiers in Cellular and Infection Microbiology

M1 - 1485791

ER -

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

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