Gut Microbiota and Metabolites: Biomarkers and Therapeutic Targets for Diabetes Mellitus and Its Complications
Bibliographic record
Abstract
Diabetes mellitus (DM) is a complex metabolic disease characterized by significantly elevated blood glucose levels as a result of dysfunctional or impaired pancreatic β-cells, leading to insulin deficiency. This condition can result in severe complications, including cardiovascular diseases, kidney failure, vision impairment, and nerve damage. Currently available anti-diabetic drugs do not fully prevent the progression of these complications. Moreover, they often have significant side effects. The gut microbiota plays a crucial role in influencing diet, energy metabolism, and blood glucose levels. Research shows a strong link between microbiota dysbiosis and DM, as well as the severity of its complications. Commensal bacteria can help manage blood glucose levels, reduce inflammation, regulate metabolism, and enhance the gut barrier. Conversely, opportunistic pathogens can worsen insulin resistance, promote metabolic disorders, disrupt gut integrity, and affect appetite and weight. This article describes the characteristics of gut microbiota in various types of DM and explores the role of the "gut microbiota-metabolite-signaling pathway" axis in DM and its complications. In addition, it highlights the therapeutic potential of traditional Chinese medicine and dietary interventions through modulation of the gut microbiota and metabolites. The aim is to provide comprehensive evidence supporting the integration of TCM dietary therapy, targeted dietary strategies, and specific probiotics as alternative and complementary therapies for DM and its 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".