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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".