Exercise training maintains normal HPA feedback and prevents hypercortisolemia in the ZDF rat
Bibliographic record
Abstract
Zucker Diabetic Fatty (ZDF) rats develop type 2 diabetes (T2D), resulting in elevations in circulating corticosterone (CORT) concomitantly with the onset of hyperglycaemia. We hypothesize that prolonged training results in increased negative feedback sensitivity of the hypothalamic‐pituitary‐adrenal axis, thus maintaining normal CORT values. At 5 wks of age, rats were assigned to either: basal (B), exercise (E), or sedentary (S) groups. E was given free access to running wheels for 10 wks. Plasma CORT and intraperitoneal glucose tolerance test values were collected prior to sacrifice at age 6 wks in B, and 16 wks in S and E. Food consumption and weight gain were similar among S and E, despite E running 4‐7 km/day. S demonstrated a T2D phenotype by 12 wks of age (fasting glycemia >11 mM), while E had fasting glucose levels comparable to B (both groups <6 mM). S also expressed higher plasma CORT values compared to E (129.9±43.7 vs. 33.3±7.3 ng/ml, p<0.05) or B (25.6±5.9, p<0.05). S had lower hippocampal glucocorticoid receptor (GR) expression than E (0.64±0.11 vs. 1.11±0.06, p<0.05) and B (1.00±0.09, p<0.05). GR expression in the pituitary gland was not different between groups. This data suggests that exercise training may prevent the hypercorticosteronemia and hyperglycemia associated with sedentary behaviour, likely through maintained protein expression of hippocampal GR. This study was funded by CIHR and NSERC.
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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".