Exogenous Glucocorticoids and a High-Fat Diet Cause Severe Hyperglycemia and Hyperinsulinemia and Limit Islet Glucose Responsiveness in Young Male Sprague-Dawley Rats
Bibliographic record
Abstract
Corticosterone (CORT) and other glucocorticoids cause peripheral insulin resistance and compensatory increases in β-cell mass. A prolonged high-fat diet (HFD) induces insulin resistance and impairs β-cell insulin secretion. This study examined islet adaptive capacity in rats treated with CORT and a HFD. Male Sprague-Dawley rats (age ∼6 weeks) were given exogenous CORT (400 mg/rat) or wax (placebo) implants and placed on a HFD (60% calories from fat) or standard diet (SD) for 2 weeks (N = 10 per group). CORT-HFD rats developed fasting hyperglycemia (>11 mM) and hyperinsulinemia (∼5-fold higher than controls) and were 15-fold more insulin resistant than placebo-SD rats by the end of ∼2 weeks (Homeostatic Model Assessment for Insulin Resistance [HOMA-IR] levels, 15.08 ± 1.64 vs 1.0 ± 0.12, P < .05). Pancreatic β-cell function, as measured by HOMA-β, was lower in the CORT-HFD group as compared to the CORT-SD group (1.64 ± 0.22 vs 3.72 ± 0.64, P < .001) as well as acute insulin response (0.25 ± 0.22 vs 1.68 ± 0.41, P < .05). Moreover, β- and α-cell mass were 2.6- and 1.6-fold higher, respectively, in CORT-HFD animals compared to controls (both P < .05). CORT treatment increased p-protein kinase C-α content in SD but not HFD-fed rats, suggesting that a HFD may lower insulin secretory capacity via impaired glucose sensing. Isolated islets from CORT-HFD animals secreted more insulin in both low and high glucose conditions; however, total insulin content was relatively depleted after glucose challenge. Thus, CORT and HFD, synergistically not independently, act to promote severe insulin resistance, which overwhelms islet adaptive capacity, thereby resulting in overt hyperglycemia.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".