Farnesol Decreases Serum Triglycerides in Rats: Identification of Mechanisms Including Up‐Regulation of PPARα and Down‐Regulation of Fatty Acid Synthase in Hepatocytes
Bibliographic record
Abstract
Obesity is associated with impaired fatty acid (FA) oxidation and increased de novo hepatic lipogenesis that may contribute to the development of hypertriglyceridemia, an important risk factor for the development of cardiovascular disease. Strategies to improve hepatocyte FA metabolism, including dietary interventions, are therefore important for the prevention of obesity-associated co-morbidities. Farnesol is consumed in the diet as a component of plant products. In the present study, we administered farnesol orally to rats for seven days and found significantly reduced serum triglyceride concentrations compared with controls. Potential mechanisms underlying the hypotriglyceridemic effect of farnesol were investigated using clone-9 cultured rat hepatocytes. Farnesol significantly upregulated expression of peroxisome proliferator-activated receptor alpha (PPARalpha) and the PPARalpha-regulated genes fatty acyl-CoA oxidase and carnitine palmitoyl transferase 1a, suggesting that increased hepatic FA oxidation may contribute to serum triglyceride lowering in rats. Farnesol did not change SREBP-1c mRNA levels, but significantly down-regulated fatty acid synthase (FAS) mRNA and protein levels and activity, indicating that attenuated lipogenesis may also contribute to hypotriglyceridemic effects of farnesol in vivo. Rescue experiments revealed that down-regulation of FAS by farnesol was not related to activation of PPARalpha, but rather was caused by a 9-cis retinoic acid mediated mechanism that involved down-regulation of retinoid X receptor beta. Diets rich in plant products are associated with a lower risk of cardiovascular disease. Our findings suggest that farnesol may contribute to this protective effect by lowering serum TG levels.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".