Berberine as a therapy for type 2 diabetes and its complications: From mechanism of action to clinical studies
Bibliographic record
Abstract
The incidence of type 2 diabetes is increasing rapidly worldwide, and the development of novel anti-diabetic drugs is emerging. However, most anti-diabetic drugs cannot be used in patients with hepatic dysfunction, renal disease, and heart disease, which makes pharmacological therapy of type 2 diabetes complicated. Despite continued introduction of novel agents, the search for an ideal drug that is useful as both a hypoglycemic agent and to reduce diabetes-related complications remains elusive. Berberine is an isoquinoline alkaloid extract that has shown promise as a hypoglycemic agent in the management of diabetes in animal and human studies. Mechanistic studies have revealed beneficial effects of berberine on diabetes-related complications. Although there have been few clinical reports of the anti-diabetic effects of berberine, little documentation of adverse effects in humans positions it as a potential candidate drug to treat type 2 diabetes. In the present review, the anti-diabetic mechanism of berberine, its effect on diabetes-related complications, and its recent use in human clinical studies is highlighted. In addition, we summarize the different treatments for type 2 diabetes in adults and children.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".