Evidence map›Paper›PMID 41471963›Full record

ArticleMicroorganisms2025

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

Yiting Fang, Xi Meng, Rong Cao, Jianhang Li, Hui Cai, Peihua Liao, Xingfen Yang, Guiyuan Ji, Wei Wu

Abstract read
In one paragraph

Article in Microorganisms, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yiting FangNMPA Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Xi MengNMPA Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Rong CaoGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511400, China.
Jianhang LiGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511400, China.
Hui CaiGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511400, China.
Peihua LiaoXinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi 830002, China.
Xingfen YangNMPA Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Guiyuan JiGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511400, China.
Wei WuNMPA Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0001-9172-7616

Funding

National Key Research and Development Program of the Ministry of Science and Technology 2018YFC1314100National Natural Science Foundation of China 81874276Natural Science Foundation of Guangdong Province, China 2018A0303130118
6 · The paper itself

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.

Indexed as

co-abundancecross-sectional studygut microbiotametabolic syndrome

Identifiers

PMID41471963
PMCPMC12736139

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.