Individualized network analysis reveals link between the gut microbiome, diet intervention and Gestational Diabetes Mellitus.
Yimeng LiuGuy AmitXiaolei ZhaoNa WuDaqing LiAmir BashanPublished in: PLoS computational biology (2023)
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.
Keyphrases
- microbial community
- physical activity
- weight loss
- end stage renal disease
- network analysis
- newly diagnosed
- ejection fraction
- pregnant women
- chronic kidney disease
- randomized controlled trial
- antibiotic resistance genes
- healthcare
- endothelial cells
- prognostic factors
- cardiovascular disease
- adipose tissue
- mental health
- electronic health record
- risk assessment
- machine learning
- insulin resistance
- pregnancy outcomes
- replacement therapy
- health promotion