Differential effects of 3 classes of antidiabetic drugs on olanzapine-induced glucose dysregulation and insulin resistance in female rats
Bibliographic record
Abstract
BACKGROUND: The second-generation antipsychotic drug olanzapine is an effective pharmacological treatment for psychosis. However, use of the drug is commonly associated with a range of metabolic side effects, including glucose intolerance and insulin resistance. These symptoms have been accurately modelled in rodents. METHODS: We compared the effects of 3 distinct classes of antidiabetic drugs, metformin (100 and 500 mg/kg, oral), rosiglitazone (6 and 30 mg/kg, oral) and glyburide (2 and 10 mg/kg, oral), on olanzapineinduced metabolic dysregulation. After acutely treating female rats with lower (7.5 mg/kg) or higher (15 mg/kg) doses of olanzapine, we assessed glucose intolerance using the glucose tolerance test and measured insulin resistance using the homeostatic model assessment of insulin resistance equation. RESULTS: Both doses of olanzapine caused pronounced glucose dysregulation and insulin resistance, which were significantly reduced by treatment with metformin and rosiglitazone; however, glucose tolerance did not fully return to control levels. In contrast, glyburide failed to reverse the glucose intolerance caused by olanzapine despite increasing insulin levels. LIMITATIONS: We evaluated a single antipsychotic drug, and it is unknown whether other antipsychotic drugs are similarly affected by antidiabetic treatments. CONCLUSION: The present study indicates that oral hypoglycemic drugs that influence hepatic glucose metabolism, such as metformin and rosiglitazone, are more effective in regulating olanzapine-induced glucose dysregulation than drugs primarily affecting insulin release, such as glyburide. The current model may be used to better understand the biological basis of glucose dysregulation caused by olanzapine and how it can be reversed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".