Zinc supplementation reduces diet-induced obesity and improves insulin sensitivity in rats
Bibliographic record
Abstract
Rates of obesity have been growing at alarming rates, compromising the health of the world population. Thus, the search for interventions that address the metabolic repercussions of obesity are necessary. Here we evaluated the metabolic and antioxidant effects of zinc and branched-chain amino acids (BCAA) supplementation on obese rats. Male Wistar rats were fed either a high-fat/high-fructose diet (HFD) or a standard diet (SD) for 19 weeks. From the fifteenth week until the end of the experiment, HFD- and SD-fed rats received zinc (6 mg/kg) or BCAA (750 mg/kg) supplementation. Body weight, abdominal fat, lipid profile, blood glucose, insulin, leptin, and hepatic transaminases were evaluated. In the liver, superoxide dismutase and catalase activities and lipid peroxidation were also analyzed. HFD-fed animals showed increased weight gain, abdominal fat pad, plasma insulin, leptin, and triglycerides levels in comparison with SD-fed rats. Zinc supplementation reduced all these parameters, suggesting a beneficial role for the treatment of obesity. BCAA, on the other hand, did not show any beneficial effect. Liver antioxidant enzymes and hepatic transaminases plasma levels did not change among groups. Lipid peroxidation was higher in HFD-fed rats and was not reverted by zinc or BCAA supplementation. In conclusion, zinc supplementation may be a useful strategy for the treatment of the metabolic dysfunction associated with obesity.
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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) | 0.001 | 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".