Routine exercise ameliorates the metabolic side-effects of treatment with the atypical antipsychotic drug olanzapine in rats
Bibliographic record
Abstract
Second generation antipsychotic (SGA) drugs are effective treatments for psychosis. Common side-effects of SGAs include metabolic dysregulation and risk of cardiometabolic disorders. Metabolic side-effects, including glucose intolerance, can be accurately modelled in rodents. The benefits of interventions used for treating metabolic side-effects of SGAs are mostly unknown. In a 9 wk longitudinal study, female rats were given daily olanzapine (10 mg/kg s.c.) or vehicle. Animals were either sedentary or allowed 1 or 3 h daily access to a running wheel, with total wheel revolutions electronically quantified to reflect exercise intensity. Glucose tolerance tests were performed once weekly to measure glycemic control. Drug levels were measured at week 4. At week 9, abdominal fat and skeletal muscle levels of Glucose Transporter 4 (GLUT4) were measured. Exercise intensity progressively increased over time in all groups given access to running wheels; however, rats treated with olanzapine consistently exercised less than those given the vehicle. Olanzapine caused acute and persistent glucose intolerance throughout the study, which was markedly, though incompletely, ameliorated by exercise. Exercise did not affect glycemic regulation in vehicle-treated rats. Olanzapine-treated rats showed greater central adiposity. Levels of GLUT4 in skeletal muscle were higher in both groups of exercising than in sedentary rats, and GLUT4 values were negatively correlated with glucose intolerance. Routine exercise reduced olanzapine-induced glucose intolerance and increased skeletal muscle levels of GLUT 4, the insulin-responsive transporter that mediates glucose uptake into cells. The current animal model is suitable for evaluating physiological pathways involved with glucose intolerance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".