Vitamin A status affects weight gain and hepatic glucose metabolism in rats fed a high-fat diet
Bibliographic record
Abstract
Whether vitamin A (VA) has a role in the development of metabolic abnormalities associated with intake of a high-fat diet (HFD) is unclear. Sprague-Dawley rats after weaning were fed an isocaloric VA sufficient HFD (VAS-HFD) or a VA deficient HFD (VAD-HFD) for 8 weeks. Body mass, food intake, liver and adipose tissue mass, and the hepatic expression levels of key proteins for metabolism were determined. VAD-HFD rats had lower body, liver, and epididymal fat mass than VAS-HFD rats. VAD-HFD rats had lower hepatic protein expression levels of cytochrome P450 26A1, glucokinase, and phosphoenolpyruvate carboxykinase than VAS-HFD rats. VAD-HFD rats had higher protein levels of glycogen synthase kinase (GSK)-3α and lower levels of GSK-3β, but not glycogen synthase, than VAS-HFD rats. VAD-HFD rats had higher hepatic levels of insulin receptor substrate-1 (IRS-1), insulin receptor β-subunit, mitogen-activated protein kinase proteins, and peroxisome proliferator-activated receptor-gamma coactivator 1α mRNA, and lower level of IRS-2 protein than VAS-HFD rats. These results indicate that in a HFD setting, VA deficiency attenuated HFD-induced obesity, and VA status altered the expression levels of proteins required for glucose metabolism and insulin signaling. We conclude that VA status contributes to the regulation of hepatic glucose and lipid metabolism in a HFD setting, and may regulate hepatic carbohydrate metabolism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".