Role of 5-HT2C receptor gene variants in antipsychotic-induced weight gain
Bibliographic record
Abstract
Tessa JM Wallace, Clement C Zai, Eva J Brandl, Daniel J MüllerNeurogenetics Section, Center for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, CanadaAbstract: Antipsychotic-induced weight gain is a serious side effect of antipsychotic medication that can lead to increased morbidity, mortality, and non-compliance in patients. Numerous single nucleotide polymorphisms have been studied for association with antipsychotic-induced weight gain in an attempt to find genetic predictors of this side effect. An ability to predict this side effect could lead to personalized treatment plans for predisposed individuals, which could significantly decrease the prevalence and severity of weight gain. Variations in the serotonin receptor 2c gene (HTR2C) have emerged as promising candidates for prediction of antipsychotic-induced weight gain. Specifically, the well-studied -759C/T promoter polymorphism has been associated with weight gain in diverse populations, although some studies have reported no association. This discrepancy is likely due to heterogeneity in study design with respect to ethnicity, treatment duration, and other variables. Notably, the association between HTR2C and antipsychotic-induced weight gain appears strongest in short-term studies on patients with limited or no previous antipsychotic treatment. Other, less extensively studied promoter polymorphisms (-697C/G, -997G/A, and -1165A/G) have also emerged as potential predictors of antipsychotic-induced weight gain. Conversely, the well-studied intronic polymorphism Cys23Ser does not appear to be associated. With further research on both HTR2C and other genetic and environmental predictors of antipsychotic-induced weight gain, a predictive test could one day be created to screen patients and provide preventative or alternative treatment for those who are predisposed to this serious side effect.Keywords: HTR2C, pharmacogenomics, promoter polymorphism
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".