Association between Gut Microbiota Compositions with MicrovascularComplications in Individuals with Diabetes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Diabetes is one of the chronic and very complex diseases that can lead to microvascular complications. Recent evidence demonstrates that dysbiosis of the microbiota composition might result in low-grade, local, and systemic inflammation, which contributes directly to the development of diabetes mellitus and its microvascular consequences. OBJECTIVE: The aim of this systematic review was to investigate the association between diabetes microvascular complications, including retinopathy, neuropathy, nephropathy, and gut microbiota composition. METHODS: A systematic search was carried out in PubMed, Scopus, and ISI Web of Science from database inception to March 2023. Screening, data extraction, and quality assessment were performed by two independent authors. The Newcastle-Ottawa Quality Assessment Scale was used for quality assessment. RESULTS: About 19 articles were selected from 590 retrieved articles. Among the included studies, nephropathy has been studied more than other complications of diabetes, showing that the composition of the healthy microbiota is changed, and large quantities of uremic solutes that cause kidney injury are produced by gut microbes. Phyla, including Fusobacteria and Proteobacteria, accounted for the majority of the variation in gut microbiota between Type 2 diabetic patients with and without neuropathy. In cases with retinopathy, an increase in pathogenic and proinflammatory bacteria was observed. CONCLUSION: Our results revealed that increases in Bacteroidetes, Proteobacteria and Fusobacteria may be associated with the pathogenesis of diabetic nephropathy, neuropathy, and retinopathy. In view of the detrimental role of intestinal dysbiosis in the development of diabetes-related complications, gut microbiota assessment may be used as a biomarker in the future and interventions that modulate the composition of microbiota in individuals with diabetes can be used to prevent and control these complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".