Composition and diversity of gut microbiota in diabetic retinopathy
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Objective: Diabetic retinopathy (DR) is one of the most common complications of type 2 diabetes mellitus. The current study investigates the composition, structure, and function of gut microbiota in DR patients and explores the correlation between gut microbiota and clinical characteristics of DR. Methods: A total of 50 stool samples were collected from 50 participants, including 25 DR patients and 25 healthy controls (HCs). 16S ribosomal RNA gene sequencing was used to analyze the gut microbial composition in these two groups. DNA was extracted from the fecal samples using the MiSeq platform. Results: The microbial structure and composition of DR patients were different from that of HCs. The microbial richness of gut microbiota in DR was higher than that of normal individuals. The alterations of microbiome of DR patients were associated with disrupted Firmicutes, Bacteroidetes, Synergistota, and Desulfobacterota phyla. In addition, increased levels of Bacteroides , Megamonas , Ruminococcus_torques _group, Lachnoclostridium , and Alistipes , and decreased levels of Blautia , Eubacterium_ hallii _group, Collinsella , Dorea , Romboutsia , Anaerostipes , and Fusicatenibacter genera were observed in the DR groups. Additionally, a stochastic forest model was developed to identify a set of biomarkers with seven bacterial genera that can differentiate patients with DR from those HC. The microbial communities exhibited varied functions in these two groups because of the alterations of the above-mentioned bacterial genera. Conclusion: The altered composition and function of gut microbiota in DR patients indicated that gut microbiome could be used as non-invasive biomarkers, improve clinical diagnostic methods, and identify putative therapeutic targets for DR.
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Another study identified seven bacterial genera capable of distinguishing DR from healthy states with robust performance.
“Another study reinforced these findings by identifying seven bacterial genera capable of distinguishing DR from healthy states with robust performance [60].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [60]
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- Authors
- Bai, J., Wan, Z., Zhang, Y., Wang, T., Xue, Y., & Peng, Q.
- Affiliations
- Tongji University (CN)
- Corresponding author
- Peng, Qing
- Venue
- Frontiers in Microbiology
- Publisher
- Frontiers Media SA
- Published
- 2022-08-23
- Volume
- 13
- Pages
- 926926-926926
- ISSN
- 1664-302X (ISSN-L)
- PMC
- PMC9445585
- OpenAlex
- W4292716475
- Cited by
- 70
- References
- 58
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 18 · 2023: 16 · 2022: 1
- FWCI
- 4.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Microbiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (2018–2020, SHDC2020CR5014, 81470025)
- Shanghai Municipal Health Bureau (ZWB-1001-CPJS10)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Health Sciences › Medicine › Gastroenterology › Gastrointestinal motility and disorders
- Keywords
- diabetic retinopathy, gut microbiota, type 2 diabetes, microbial diversity, microbial biomarkers, Firmicutes, Bacteroidetes, Desulfobacterota, Blautia, Ruminococcus torques group, Alistipes, Lachnoclostridium, diagnostic biomarkers
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