Rosemary extract activates AMPK, inhibits mTOR and attenuates the high glucose and high insulin-induced muscle cell insulin resistance
Bibliographic record
Abstract
Impaired action of insulin in skeletal muscle, termed insulin resistance, leads to increased blood glucose levels resulting in compensatory increase in insulin levels. The elevated blood glucose and insulin levels exacerbate insulin resistance and contribute to the pathogenesis of type 2 diabetes mellitus. In previous studies we found attenuation of free fatty acid-induced muscle cell insulin resistance by rosemary extract (RE). In the present study we investigated the effects of RE on high glucose (HG) and high insulin (HI)-induced muscle cell insulin resistance. Exposure of L6 myotubes to 25 mmol/L glucose and 100 nmol/L insulin for 24 h, to mimic hyperglycemia and hyperinsulinemia, abolished the acute insulin-stimulated glucose uptake, increased the serine phosphorylation of IRS-1 and the phosphorylation/activation of mTOR and p70S6K. Treatment with RE significantly improved the insulin-stimulated glucose uptake and increased the acute insulin-stimulated tyrosine phosphorylation while reducing the HG+HI-induced serine phosphorylation of IRS-1 and phosphorylation of mTOR and p70S6K. Additionally, treatment with RE significantly increased the phosphorylation of AMPK, its downstream effector ACC and the plasma membrane GLUT4 levels. Our data indicate a potential of RE to counteract muscle cell insulin resistance and more studies are required to investigate its effectiveness in vivo. Novelty: RE phosphorylated muscle cell AMPK and ACC under both normal and HG+HI conditions. The HG+HI-induced serine phosphorylation of IRS-1 and activation of mTOR and p70S6K were attenuated by RE. RE restored the insulin-stimulated glucose uptake by enhancing GLUT4 glucose transporter translocation to plasma membrane.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".