Flax oil-mediated activation of PPAR-γ correlates with reduction of hepatic lipid accumulation in obese spontaneously hypertensive/NDmcr-cp rats, a model of the metabolic syndrome
Bibliographic record
Abstract
Flax oil feeding has been proposed to have beneficial effects on the outcome of the metabolic syndrome due to the high n-3 fatty acid content of flax oil; however, the mechanisms of its action remain largely unknown. We investigated the effects of flax oil feeding on hyperlipidaemia, hyperglycaemia, hepatic steatosis and oxidative stress in the spontaneously hypertensive (SHR)/NDmcr-cp rats, a genetic model of the metabolic syndrome. Hepatic gene expression of PPAR-α, PPAR-γ and sterol-regulatory element-binding protein-1c was also assessed in order to investigate the possible underlying mechanisms. Obese and lean SHR/NDmcr-cp rats were fed high-fat diets enriched with either lard or flax oil for a period of 4 weeks. Obese rats exhibited higher body weight, liver weight and mesenteric fat-, epididymal fat- and renal fat-pad weights, and also TAG and cholesterol concentrations in serum and VLDL, LDL and HDL fractions, when compared with the lean rats (P < 0·001), irrespective of the diets. Concentrations of fasting serum insulin and urinary thiobarbituric acid reactive substances were lower in flax oil-fed obese (FO) rats compared with the lard-fed obese (LO) rats (P < 0·01). Flax oil feeding also revealed a significant reduction in hepatic TAG and cholesterol concentrations in obese rats compared with the LO rats (P < 0·05). In addition, FO rats exhibited significantly higher hepatic mRNA expression of PPAR-γ, which negatively correlated (r - 0·98, P < 0·05) with their hepatic lipid levels. These findings suggest that flax oil feeding may activate PPAR-γ-dependent pathways to alter the hepatic lipid metabolism and to increase insulin sensitivity in the obese SHR/NDmcr-cp rats.
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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 | 0.000 |
| 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".