Antipsychotic-Induced Metabolic Syndrome: A Review
Bibliographic record
Abstract
Schizophrenia, a serious psychiatric disorder, is among the top 10 global causes of disability and affects nearly 1% of the world population. Antipsychotics constitute the best treatment for patients with schizophrenia, however, this treatment class carries a high risk of metabolic syndrome, including lipid abnormalities. Indeed, the risk of metabolic syndrome would be increased in the population with schizophrenia compared to the general population. The objective is to summarize the prevalence, the mechanisms, and the potential treatments of antipsychotic-induced metabolic syndrome. This is a narrative review of the literature. We searched the electronic database Medline, accessed through PubMed, to find studies that investigated the prevalence and treatments of metabolic syndrome in the adult population using antipsychotics. The prevalence of metabolic syndrome in patients treated with antipsychotics ranges from 37% to 63%. Antipsychotic iatrogenic effects include weight gain/increased waist circumference, dyslipidemia, insulin resistance/type 2 diabetes, and hypertension. Clozapine and olanzapine are reported to precipitate the onset of metabolic syndrome features. In patients with metabolic syndrome, an antipsychotic with less metabolic side effects such as lurasidone, lumateperone, ziprasidone, and aripiprazole should be prioritized. Unlike medications, aerobic exercise and dietetic counseling were found to be efficient as the nonpharmacologic treatment of antipsychotic-induced metabolic syndrome. Few pharmacological treatments were proven effective against weight gain in this patient population. The risk of metabolic syndrome induced by antipsychotics should be early recognized and closely monitored. Primary and secondary prevention of metabolic syndrome or onset of its feature might help reduce the risk of death for patients using antipsychotics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".