A rodent model of rapid-onset diabetes (ROD) induced by glucocorticoids and high-fat feeding
Bibliographic record
Abstract
Glucocorticoids (GCs) are potent pharmacological agents used to treat a number of immune conditions. GCs are also naturally occurring steroid hormones (e.g. cortisol, corticosterone) produced in response to stressful conditions that are thought to increase the preference for calorie dense 'comfort' foods. If chronically elevated, GCs can contribute to the development of type 2 diabetes mellitus (T2DM), although the mechanisms for the diabetogenic effects are not entirely clear. The present study proposes a new rodent model to investigate the combined metabolic effects of elevated GCs and high-fat feeding on ectopic fat deposition and diabetes development. Male Sprague-Dawley rats (aged 7-8 weeks) received exogenous corticosterone or wax (placebo) pellets, implanted subcutaneously, and were fed either a standard chow diet (SD) or a 60% high-fat diet (HFD) for 16 days. Animals given corticosterone and a HFD (cort-HFD) had lower body weight and smaller relative glycolytic muscle mass, but increased relative epididymal mass, compared with controls (placebo-SD). Cort-HFD rats exhibited severe hepatic steatosis and increased muscle lipid deposition compared with placebo-SD animals. Moreover, cort-HFD animals were found to exhibit severe fasting hyperglycemia (60% increase), hyperinsulinemia (80% increase), insulin resistance (60% increase) and impaired β-cell response to oral glucose load (20% decrease) compared with placebo-SD animals. Thus, a metabolic syndrome or T2DM phenotype can be rapidly induced in young Sprague-Dawley rats by using exogenous GCs if a HFD is consumed. This finding might be valuable in examining the physiological and molecular mechanisms of GC-induced metabolic disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".