Differential effects of low-fat and high-fat diets on fed-state hepatic triacylglycerol secretion, hepatic fatty acid profiles, and DGAT-1 protein expression in obese-prone Sprague–Dawley rats
Bibliographic record
Abstract
The purpose of this study was to compare the effects of short-term low-fat (LF) and high-fat (HF) diets on fed-state hepatic triacylglycerol (TAG) secretion, the content of proteins involved in TAG assembly and secretion, fatty acid oxidation (FAO), and the fatty acid profile of stored TAG. Using selectively bred obese-prone Sprague-Dawley rats, we directly measured fed-state hepatic TAG secretion, using Tyloxapol (a lipoprotein lipase inhibitor) and a standardized oral mixed meal (45% carbohydrate, 40% fat, 15% protein) bolus in animals fed a HF or LF diet for 2 weeks, after which the rats were maintained on their respective diet for 1 week (washout) prior to the liver being excised to measure protein content, FAO, and TAG fatty acid profiles. Hepatic DGAT-1 protein expression was ∼27% lower in HF- than in LF-fed animals (p < 0.05); the protein expression of all other molecules was similar in the 2 diets. The fed-state hepatic TAG secretion rate was ∼39% lower (p < 0.05) in HF- (4.62 ± 0.18 mmol·h(-1)) than in LF- (7.60 ± 0.57 mmol·h(-1)) fed animals. Hepatic TAG content was ∼2-fold higher (p < 0.05) in HF- (1.07 ± 0.15 nmol·g(-1) tissue) than in LF- (0.50 ± 0.16 nmol·g(-1) tissue) fed animals. In addition, the fatty acid profile of liver TAG in HF-fed animals closely resembled the diet, whereas in LF-fed animals, the fatty acid profile consisted of mostly de novo synthesized fatty acids. FAO was not altered by diet. LF and HF diets differentially alter fed-state hepatic TAG secretion, hepatic fatty acid profiles, and DGAT-1 protein expression.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".