Review on the Role of Polyphenols in Preventing and Treating Type 2 Diabetes: Evidence from In Vitro and In Vivo Studies
Bibliographic record
Abstract
Type 2 diabetes (T2D) is one of the leading causes of death globally. There was a 70% increase in diabetes-related deaths between 2000 and 2020, particularly among males. This non-communicable disease is characterized by increased insulin resistance, leading to elevated blood sugar levels and, if untreated, resulting in complications such as nerve damage, kidney disease, blindness, and poor wound healing. T2D management includes dietary intervention, physical exercise, and the administration of blood sugar-lowering medication. However, these medications often have side effects related to intestinal discomfort. Therefore, natural alternatives to standard diabetes medications are being sought to improve the quality of life for individuals with this condition. Polyphenols, which are naturally occurring plant metabolites, have emerged as strong candidates for T2D control. Various phenolic acids (e.g., chlorogenic acid), flavonoids (e.g., quercetin), proanthocyanidins (e.g., procyanidin B2), gallotannins (e.g., monogalloyl hexoside), and ellagitannins (e.g., ellagic acid hexoside) can enhance insulin sensitivity in tissues, reduce chronic inflammation, scavenge free radicals, improve insulin secretion, inhibit enzymes involved in carbohydrate digestion, regulate glucose transport across cell membranes, and modulate gut microbiota. This contribution compiles up-to-date evidence from in vitro and in vivo studies on the role of polyphenols in the prevention and management of T2D, emphasizing the mechanisms of action underlying these effects.
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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 | 0.003 | 0.003 |
| 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) | 0.007 | 0.002 |
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".