The diabetogenic effects of atypical antipsychotic medication: an animal model
Bibliographic record
Abstract
In the schizophrenia population, the increased prevalence of atypical antipsychotic use has been paralleled by an increase in reported incidence of obesity, type 2 diabetes, diabetic ketoacidosis and other metabolic abnormalities. These side effects contribute additional health risks to a population that is already burdened by issues of increased morbidity and mortality. We have developed an animal model to evaluate a number of variables relating to antipsychotic treatment and disruptions in metabolism. Investigating measures of insulin sensitivity, and secretion we assessed the chronic versus acute effects of atypical antipsychotic treatment, the differential effect of various antipsychotics, and a neural component by which the atypical antipsychotics induce glucose dysregulation. Chronic administration of olanzapine resulted in a decrease in insulin sensitivity. This insulin resistance was also observed with acute administration of olanzapine. Clozapine and risperidone induced insulin resistance, while ziprasidone and haloperidol did not change insulin sensitivity. We report novel data that show olanzapine and clozapine can have an immediate and significant impact on pancreatic beta cell function. We also initiated experiments to help uncover the pathways mediating antipsychotic-induced glucose dysregulation. Our results show that ICV injections of olanzapine affect both insulin sensitivity and secretion. Our acute data challenge prior notions that weight gain is the primary side effect of treatment with atypical antipsychotic medication and that the host of metabolic problems observed are merely a result of this increased adiposity. We highlight the significant risk to homeostatic glucoregulatory mechanisms in the acute phase of treatment with atypicals reiterating the need for clinicians to monitor metabolic status from the earliest stages of pharmacotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".