The molecular basis of the antidiabetic action of quercetin in cultured skeletal muscle cells and hepatocytes
Bibliographic record
Abstract
BACKGROUND: Quercetin is universally distributed in the plant kingdom and is the most abundant flavonoid in the human diet. In a previous study, we have reported that quercetin stimulated glucose uptake in cultured C2C12 skeletal muscle through an insulin-independent mechanism involving adenosine monophosphate-activated protein kinase (AMPK). AMPK is a key regulator of the whole body-energy homeostasis. In skeletal muscle, activation of AMPK increases glucose uptake through the stimulation of the glucose transporter GLUT4 translocation to the plasma membrane. In liver, AMPK decreases glucose production mainly through the downregulation of the key gluconeogenesis enzymes such as phosphoenolpyruvate carboxylase (PEPCK) and Glucose -6-phosphate (G6Pase). OBJECTIVE: To study the effect of quercetin on glucose homeostasis in muscle and liver. MATERIALS AND METHODS: L6 skeletal muscle cells, murine H4IIE and human HepG2 hepatocytes were treated with quercetin (50 μM) for 18 h. RESULTS: An 18 h treatment with quercetin (50 μM) stimulated AMPK and increased GLUT4 translocation and protein content in cultured rat L6 skeletal muscle cells. On the other hand, we report that quercetin induced hepatic AMPK activation and inhibited G6pase in H4IIE hepatocytes. Finally, we have observed that quercetin exhibited a mild tendency to increase the activity of glycogen synthase (GS), the rate-limiting enzyme of glycogen synthesis, in HepG2 hepatocytes. CONCLUSIONS: Overall, these data demonstrate that quercetin positively influences glucose metabolism in the liver and skeletal muscle, and therefore appear to be a promising therapeutic candidate for the treatment of in type 2 diabetes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".