Resveratrol protects against nonalcoholic fatty liver disease by improving lipid metabolism and redox homeostasis via the PPARα pathway
Bibliographic record
Abstract
Resveratrol (RSV), a well-known bioactive compound, has been reported to exert a broad range of health benefits. Accumulating evidence suggests that RSV is beneficial for many metabolic diseases, including nonalcoholic fatty liver disease (NAFLD). This study investigated the preventive and therapeutic effects of RSV on high-fat diet (HFD)-induced NAFLD in rats and palmitate acid (PA)-induced hepatocyte steatosis in HepG2 cells. Hepatocytes were incubated with inhibitors of peroxisome proliferator-activated receptor α (PPARα) or short interfering RNAs (siRNAs) targeting PPARα, AMP-activated protein kinase (AMPK), and protein kinase A (PKA) to determine the underlying mechanisms. We found that RSV noticeably ameliorated HFD-induced hepatic steatosis in rats and inhibited PA-induced lipid accumulation in HepG2 cells. Moreover, RSV improved lipid metabolism, enhanced antioxidant capacity, and restored mitochondrial respiratory chain activities. Incubation with inhibitors of PPARα or PPARα siRNA abolished the protective effects of RSV on lipid metabolism and redox homeostasis. Furthermore, RSV activated the PKA/AMPK/PPARα signaling pathway. Our results provided direct evidence for a novel, PPARα-mediated mechanism responsible for the beneficial effects of RSV on hepatic steatosis. These findings may have important theoretical and application prospects for the prevention and treatment of NAFLD. Novelty RSV improved lipid metabolism and redox homeostasis and oxidative stress in NAFLD via the PKA/AMPK/PPARα signaling pathway. RSV may have a greater beneficial effect in the early prevention of hepatic steatosis.
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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 | 0.001 |
| 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".