Berberine-induced glucagon-like peptide-1 and its mechanism for controlling type 2 diabetes mellitus: a comprehensive pathway review
Bibliographic record
Abstract
Introduction: A growing number of studies have thus far showed the association between type 2 diabetes mellitus (DM) and the intestinal microbiome homoeostasis. As reported, the gut microflora can be significantly different in patients with type 2 DM (T2DM) compared to those in healthy individuals.Methods: The authors collected the relevant articles published until 2022 and these are carefully selected from three scientific databases based on keywords.Discussion: This review highlights research on the anti-diabetic properties of berberine (BBR)-induced glucagon-like peptide-1 (GLP-1), as a glucose-lowering factor and a balance regulator in the microbial flora of the intestines, which plays an important role in adjusting the signalling pathways affecting insulin secretion.Results: Considering the anti-diabetic characteristics of the BBR-induced GLP-1, BBR makes a promising complementary treatment for reducing the clinical symptoms of DM by reducing the hyperglycaemia. Berberin might be a safe and effective drug for T2DM with little or no adverse effects.HighlightsBerberine induces GLP-1 insulin secretion by PLC2 pathway in the intestinalBerberine-induced GLP-1 decreases mitochondrial stress and relocates cytochrome c out of the mitochondria.Berberine induces GLP-1 secretion in the intestine by altering the bacterial profile, thus could possibly lighten diabetes symptomsBerberine-induced SCFA production, SCFA causes GLP-1 secretion from the intestinal L-Cell.Preventing mitochondrial damage, reducing adipose tissue fat, and reducing oxidative stress are thus among the results of BBR-induced GLP-1.The lower costs of BBR, and its limited side effects and higher availability, make it a promising supplementary medicine for DM
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".