The Antidiabetic Potential of Quercetin: Underlying Mechanisms
Bibliographic record
Abstract
The dramatic increase in modern lifestyle diseases such as cancer, cardiovascular diseases and diabetes has renewed researchers' interest to explore nature as a source of novel therapeutic agents. Flavonoids are a large group of polyphenols that are widely present in the human diet. They have shown promising therapeutic activities against a wide variety of ailments. One of the most widely distributed and most extensively studied flavonoid is the flavonol quercetin. Its powerful antioxidant and anti-inflammatory activities are well documented and are thought to play a role in treating and protecting against diseases including diabetes, cancer, neurodegenerative and cardiovascular diseases. The purpose of this review is to shed light on quercetin therapeutic potential as an antidiabetic agent. Quercetin was reported to interact with many molecular targets in small intestine, pancreas, skeletal muscle, adipose tissue and liver to control whole-body glucose homeostasis. Mechanisms of action of quercetin are pleiotropic and involve the inhibition of intestinal glucose absorption, insulin secretory and insulin-sensitizing activities as well as improved glucose utilization in peripheral tissues. Initial studies suggested poor bioavailability of quercetin. However, recent reports have shown that quercetin was detected in the plasma after food or supplements consumption and has a long half-life in human body. Despite the wealth of in vitro and in vivo results supporting the antidiabetic potential of quercetin, its efficacy in diabetic human subjects is yet to be explored.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".