Resveratrol prevents insulin resistance caused by short-term elevation of free fatty acids in vivo
Bibliographic record
Abstract
Elevated levels of plasma free fatty acids (FFA), which are commonly found in obesity, induce insulin resistance. FFA activate protein kinases including the proinflammatory IκBα kinase β (IKKβ), leading to serine phosphorylation of insulin receptor substrate 1 (IRS-1) and impaired insulin signaling. To test whether resveratrol, a polyphenol found in red wine, prevents FFA-induced insulin resistance, we used a hyperinsulinemic-euglycemic clamp with a tracer to assess hepatic and peripheral insulin sensitivity in overnight-fasted Wistar rats infused for 7 h with saline, Intralipid plus 20 U·mL(-1) heparin (IH; triglyceride emulsion that elevates FFA levels in vivo; 5.5 μL·min(-1)) with or without resveratrol (3 mg·kg(-1)·h(-1)), or resveratrol alone. Infusion of IH significantly decreased glucose infusion rate (GIR; P < 0.05) and peripheral glucose utilization (P < 0.05) and increased endogenous glucose production (EGP; P < 0.05) during the clamp compared with saline infusion. Resveratrol co-infusion, however, completely prevented the effects induced by IH infusion: it prevented the decreases in GIR (P < 0.05 vs. IH), peripheral glucose utilization (P < 0.05 vs. IH), and insulin-induced suppression of EGP (P < 0.05 vs. IH). Resveratrol alone had no effect. Furthermore, IH infusion increased serine (307) phosphorylation of IRS-1 in soleus muscle (∼30-fold, P < 0.001), decreased total IRS-1 levels, and decreased IκBα content, consistent with activation of IKKβ. Importantly, all of these effects were abolished by resveratrol (P < 0.05 vs. IH). These results suggest that resveratrol prevents FFA-induced hepatic and peripheral insulin resistance and, therefore, may help mitigate the health consequences of obesity.
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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 | 0.001 | 0.001 |
| 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".