ArticleHeliyon2023
Bioinformatics analysis of the diversity of gut microbiota and different microbiota on insulin resistance in diabetes mellitus patients.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Oral-associated bacteria in the gut microbiome of individuals with type 2 diabetes: a secondary analysis of metagenomic data.BMC oral health · 2025Article
- Bioactive Component Screening and Mechanistic Study of the Anti-Diabetic Activity ofCurrent issues in molecular biology · 2025Article
- A Multidisciplinary Approach of Type 1 Diabetes: The Intersection of Technology, Immunotherapy, and Personalized Medicine.Journal of clinical medicine · 2025Review
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Authors and funding
1 author.
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Abstract
It aimed to explore the diversity of gut microbiota (GM) and the effect of different microbiota on insulin resistance in diabetes mellitus (DM) patients through bioinformatics analysis. MATERIAL AND
methodMicroarray data were obtained from GEO database. GM samples from DM patients and healthy controls were collected, and 16S rRNA gene sequencing was carried out adopting high-throughput sequencing technology. The differential expression genes were screened using the Qlucore Omics Explorer 3.0 software. Subsequently, online tools such as STRING and DAVID were utilized for bioinformatics analysis of the differential expression genes. The differences in bacterial diversity between DM patients and healthy controls were evaluated by analyzing the diversity indicators of the microbiota, such as Shannon and Chao1 indexes. Differential abundance and functional prediction analysis were adopted to explore the different microbiota and its possible metabolic pathways between DM patients and controls. And differences in insulin resistance in specific bacterial taxa were analyzed.
resultGM diversity between DM patients and controls had significant differences. GM diversity was lower in DM patients compared with controls, as indicated by a decrease in Shannon and Chao1 indexes. The differential abundance analysis showed that there were multiple different bacterial communities between DM patients and controls, including some bacterial communities at the genus-level. Functional prediction analysis also revealed potential metabolic pathways related to GM and insulin resistance in DM patients. HEXB, ZC3H12A, CCR, CXCR3, GBR10, CDK9, TXN, IGFBP3, PDHA1, and NDUFB3 genes may be potential targets for treatment.
conclusionThere are differences in GM diversity between DM patients and healthy controls, and the different microbiota may be related to the occurrence and development of insulin resistance.
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