ArticlePLoS computational biology2023
Individualized network analysis reveals a link between the gut microbiome, diet intervention and Gestational Diabetes Mellitus.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Dynamic involvement of the core gut microbiome XNP_Guild1 in the evolution of gestational diabetes mellitus.Gut microbes · 2026Article
- The interplay between gut microbiota and gestational diabetes mellitus: mechanisms, characteristics, and intervention strategies.Frontiers in microbiology · 2026Review
- Gut microbiota composition in early pregnancy as a diagnostic tool for gestational diabetes mellitus.Microbiology spectrum · 2025Article
- Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.Frontiers in microbiology · 2025Review
- Gestational diabetes-related gut microbiome dysbiosis is not influenced by different Asian ethnicities and dietary interventions: a pilot study.Scientific reports · 2024Article
- Suppression of certain intestinal microbiota metabolites may lead to gestational diabetes in mice fed a high-fat diet.Frontiers in microbiology · 2024Article
- Network impact of a single-time-point microbial sample.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
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Abstract
Gestational Diabetes Mellitus (GDM), a serious complication during pregnancy which is defined by abnormal glucose regulation, is commonly treated by diabetic diet and lifestyle changes. While recent findings place the microbiome as a natural mediator between diet interventions and diverse disease states, its role in GDM is still unknown. Here, based on observation data from healthy pregnant control group and GDM patients, we developed a new network approach using patterns of co-abundance of microorganism to construct microbial networks that represent human-specific information about gut microbiota in different groups. By calculating network similarity in different groups, we analyze the gut microbiome from 27 GDM subjects collected before and after two weeks of diet therapy compared with 30 control subjects to identify the health condition of microbial community balance in GDM subjects. Although the microbial communities remain similar after the diet phase, we find that the structure of their inter-species co-abundance network is significantly altered, which is reflected in that the ecological balance of GDM patients was not "healthier" after the diet intervention. In addition, we devised a method for individualized network analysis of the microbiome, thereby a pattern is found that GDM individuals whose microbial networks are with large deviations from the GDM group are usually accompanied by their abnormal glucose regulation. This approach may help the development of individualized diagnosis strategies and microbiome-based therapies in the future.
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