High glucocorticoids, in combination with high‐fat feeding, induces insulin resistance and hyperglycaemia: Mechanisms related to beta cell dysfunction?
Bibliographic record
Abstract
Glucocorticoids (GCs) are known to increase insulin resistance and β cell mass. High-fat feeding induces insulin resistance but causes β cell death. Rarely has GCs and high-fat feeding together been investigated on β cell function. We hypothesize that the addition of high-fat feeding to GC therapy will result in β cell exhaustion and hyperglycaemia. We examined the short-term effect of these stressors on β cell dynamics in male Sprague-Dawley rats (age ~6 wks). Rats were given corticosterone pellets (CORT) (400mg/rat) or wax pellets (controls) with or without high-fat feeding (HF) (n=5–6 per group). After 5 days of treatment, fed blood glucose levels in CORT-HF rats only were found to be elevated (>11 mM), while fasted insulin concentrations were 5-fold higher vs. controls (P<0.05). Insulin positive staining indicated β cell mass in CORT and CORT-HF treated animals to be 1.5 fold higher compared to controls (p<0.05). Mean islet areas and β cell number per islet were found to be increased by 1.5 and 1.9 fold, respectively, in CORT/CORT-HF vs. controls (both p<0.05). Interestingly, β cell size was smaller in these animals (by 23 %) compared to controls. There was no evidence of β cell neogenesis as measured by ductal growth. We conclude that CORT and high-fat feeding act synergistically to exhaust normal β cell function, thereby resulting in accelerated T2DM development. This project was funded by CDA 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".