Exercise Training Protects Against Overexposure to Glucocorticoids in Skeletal Muscle and Liver Tissue in the Fructose‐Fed Hamster
Bibliographic record
Abstract
Elevated exposure to glucocorticoids (GC) in insulin target tissues has been linked to insulin resistance. Tissue exposure to GC is determined by both the receptor (GR) and the pre‐receptor enzyme, 11beta‐hydroxysteroid dehydrogenase type 1(11βHSD1), which converts inactive GC to the active form. We tested the hypothesis that beneficial effects of exercise are mediated through changes in the expression of 11βHSD1 and GR in skeletal muscle and liver. Male Syrian hamsters were divided into 3 groups; fructose‐fed exercise (FE), fructose‐fed sedentary (FS), and chow‐fed sedentary (CS). FE had access to running wheels for 6 wks and all were sacrificed following an intraperitoneal glucose tolerance test (IPGTT). Combining the AUC for glucose and insulin yields an insulin sensitivity index, which indicated that FS was significantly more insulin resistant than either CS or FE (p<0.05). Gastrocnemius 11βHSD1 protein level was lower in FE (0.66 0.12 relative optical density [ROD]) compared to CS (1.00 0.05 ROD) or FS (0.92±0.06 ROD, p<0.05), while GR was similar between groups. Liver GR was lower in FE (0.66±0.09 ROD) compared to both CS (1.00±0.08 ROD) and FS (0.95±0.09 ROD, p<0.05), while 11βHSD1 was similar between groups. Thus, exercise decreases the determinants of tissue GC exposure in muscle and liver and is associated with retention in insulin sensitivity despite a high‐fructose diet.
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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) | 0.000 | 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 | 0.000 |
| 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".