Voluntary exercise improves metabolic profile in high-fat fed glucocorticoid-treated rats
Bibliographic record
Abstract
Diabetes is rapidly induced in young male Sprague-Dawley rats following treatment with exogenous corticosterone (CORT) and a high-fat diet (HFD). Regular exercise alleviates insulin insensitivity and improves pancreatic β-cell function in insulin-resistant/diabetic rodents, but its effect in an animal model of elevated glucocorticoids is unknown. We examined the effect of voluntary exercise (EX) on diabetes development in CORT-HFD-treated male Sprague-Dawley rats (∼6 wk old). Animals were acclimatized to running wheels for 2 wk, then given a HFD, either wax (placebo) or CORT pellets, and split into 4 groups: placebo-sedentary (SED) or -EX and CORT-SED or -EX. After 2 wk of running combined with treatment, CORT-EX animals had reduced visceral adiposity, and increased skeletal muscle type IIb/x fiber area, oxidative capacity, capillary-to-fiber ratio and insulin sensitivity compared with CORT-SED animals (all P < 0.05). Although CORT-EX animals still had fasting hyperglycemia, these values were significantly improved compared with CORT-SED animals (14.3 ± 1.6 vs. 18.8 ± 0.9 mM). In addition, acute in vivo insulin response to an oral glucose challenge was enhanced ∼2-fold in CORT-EX vs. CORT-SED (P < 0.05) which was further demonstrated ex vivo in isolated islets. We conclude that voluntary wheel running in rats improves, but does not fully normalize, the metabolic profile and skeletal muscle composition of animals administered CORT and HFD.
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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 | 0.000 | 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".