Antipsychotic polypharmacy increases metabolic dysregulation in female rats.
Bibliographic record
Abstract
Antipsychotic polypharmacy refers to the clinical practice of treating a patient with two or more antipsychotic drugs concurrently. There is abundant evidence in the clinical literature that treatment with antipsychotic polypharmacy is associated with an increased prevalence of drug side effects compared with monotherapy. This includes drug-induced metabolic side effects, such as glucose intolerance and insulin resistance. As these metabolic side effects have been accurately modeled in preclinical rodent paradigms using drug monotherapy, the goal of the present study was to determine the metabolic effects of antipsychotic polypharmacy using an established rodent model. In the first experiment, adult female rats were treated with clozapine (5 mg/kg), risperidone (1 mg/kg), vehicle, or clozapine + risperidone. In the second experiment, rats were treated with clozapine (5 mg/kg), haloperidol (0.1 mg/kg), vehicle, or clozapine + haloperidol. Animals were then subjected to a glucose tolerance test. Compared with vehicle-treated control animals, risperidone and haloperidol had no effect on any of the metabolic indices when administered on their own. Addition of risperidone to clozapine significantly increased fasting glucose, fasting insulin, and insulin resistance compared with the clozapine-only group. The addition of haloperidol to clozapine significantly increased fasting insulin levels, insulin resistance, and glucose intolerance compared with clozapine-only rats. These results are consistent with clinical studies and therefore indicate that animal models can successfully be used to study the metabolic side effects of antipsychotic drugs. Future studies related to understanding the physiological mechanisms involved remain a priority.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".