Alterations in Gut Microbial Co-Abundance Networks in Metabolic Syndrome: A Population-Based Cross-Sectional Study

Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular diseases and type 2 diabetes. Gut microbiota dysbiosis has been implicated in the pathogenesis of MetS, but the mechanisms remain poorly understood. This study investigates gut microbiota interaction networks in MetS and explores their potential role in host metabolic regulation. In this population-based cross-sectional study, 221 MetS patients and 382 healthy controls were analyzed. Co-abundance network analysis was used to examine microbial interactions across the study. Significant differences in microbial co-abundance patterns were observed between MetS and healthy participants. In MetS, the gut microbiota displayed fewer and generally weaker co-abundance correlations compared with healthy controls. These changes appear to be more strongly associated with the synergistic effects of microbial interactions than solely with the abundance of individual taxa investigated here. Specific microbiota combinations were found to influence key metabolic functions, contributing to MetS development. The findings suggest that microbial interactions, rather than the abundance of individual bacteria, are associated with MetS. This study provides new insights into the role of disrupted gut microbiota networks in MetS pathogenesis.

Authors

Institutions

Publication Details

Journal
Microorganisms
Published
2025-12-04
DOI
https://doi.org/10.3390/microorganisms13122759
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Alterations in Gut Microbial Co-Abundance Networks in Metabolic Syndrome: A Population-Based Cross-Sectional Study

Yu Fang, X. M. Meng, Xingfen Yang, Peihua Liao et al.
Microorganisms
Gut microbiota and health
article

Alterations in Gut Microbial Co-Abundance Networks in Metabolic Syndrome: A Population-Based Cross-Sectional Study

Yu Fang, X. M. Meng, Xingfen Yang, Peihua Liao, Jiawen Li, Hui Cai, Rong Cao, Guiyuan Ji, Wei Wu
article en

Abstract

Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular diseases and type 2 diabetes. Gut microbiota dysbiosis has been implicated in the pathogenesis of MetS, but the mechanisms remain poorly understood. This study investigates gut microbiota interaction networks in MetS and explores their potential role in host metabolic regulation. In this population-based cross-sectional study, 221 MetS patients and 382 healthy controls were analyzed. Co-abundance network analysis was used to examine microbial interactions across the study. Significant differences in microbial co-abundance patterns were observed between MetS and healthy participants. In MetS, the gut microbiota displayed fewer and generally weaker co-abundance correlations compared with healthy controls. These changes appear to be more strongly associated with the synergistic effects of microbial interactions than solely with the abundance of individual taxa investigated here. Specific microbiota combinations were found to influence key metabolic functions, contributing to MetS development. The findings suggest that microbial interactions, rather than the abundance of individual bacteria, are associated with MetS. This study provides new insights into the role of disrupted gut microbiota networks in MetS pathogenesis.

MicroorganismsVol. 13(12)
Xinjiang Uygur Autonomous Region Disease Prevention and Control Center (CN), Guangdong Provincial Center for Disease Control and Prevention (CN), Southern Medical University (CN)
Openalex Percentile: Top 65%
Gut microbiota and health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.