Genetic dissection of atypical antipsychotic-induced weight gain: novel preliminary data on the pharmacogenetic puzzle.
Bibliographic record
Abstract
Atypical antipsychotics such as clozapine represent a significant improvement over typical antipsychotics in the treatment of schizophrenia, particularly regarding extrapyramidal symptoms. Despite their benefits, use is limited by the occurrence of adverse reactions such as sedation and weight gain. This article provides a comprehensive review and discussion of obesity-related pathways and integrates these with the known mechanisms of atypical antipsychotic action to identify candidate molecules that may be disrupted during antipsychotic treatment. Novel preliminary data are presented to genetically dissect these obesity pathways and elucidate the genetic contribution of these candidate molecules to clozapine-induced weight gain. There is considerable variability among individuals with respect to the ability of clozapine to induce weight gain. Genetic predisposition to clozapine-induced weight gain has been suggested. Therefore, genetic variation in these candidate molecules may predict patient susceptibility to clozapine-induced weight gain. This hypothesis was tested for 10 genetic polymorphisms across 9 candidate genes, including the serotonin 2C, 2A, and 1A receptor genes (HTR2C/2A/1A); the histamine H1 and H2 receptor genes (H1R/H2R); the cytochrome P450 1A2 gene (CYPIA2); the beta3 and alpha,alpha-adrenergic receptor genes (ADRB3/ADRAIA); and tumor necrosis factor alpha (TNF-alpha). Prospective weight gain data were obtained for 80 patients with schizophrenia who completed a structured clozapine trial. Trends were observed for ADRB3, ADRA1A, TNF-alpha, and HTR2C; however, replication in larger, independent samples is required. Although in its infancy, psychiatric pharmacogenetics will in the future aid clinical practice in the prediction of response and side effects, such as antipsychotic-induced weight gain, and minimize the current "trial and error" approach to prescribing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".