Genetic Determinants of Clozapine-Induced Metabolic Side Effects
Bibliographic record
Abstract
Objective: Atypical antipychotics are linked to a higher incidence of metabolic side effects, including weight gain, dyslipidemia, and diabetes. In this study, we examined the prevalence and potential genetic predictors of metabolic side effects in 60 adult patients on clozapine. Method: Genetic variants of relevance to clozapine metabolism, clearance, and response were assessed through targeted genotyping of cytochrome P450 enzymes CYP1A2 and CYP2C19, the efflux transporter ABCB1, the serotonin receptor (HTR2C), leptin (LEP), and leptin receptor (LEPR). Clozapine levels and other potential confounders, including concurrent medications, were also included in the analysis. Results: More than half of the patients were obese (51%), had metabolic syndrome (52.5%), and 30.5% were overweight. There was a high prevalence of antipsychotic polypharmacy (61.9%). With multivariable linear regression analysis, LEP –2548G>A, LEPR c.668A>G, and HTR2C c.551-3008 C>G were identified as genetic predictors of body mass index (BMI) after considering effects of clozapine dose, blood level, and concurrent medications (adjusted R 2 = 0.305). Metabolic syndrome was found to be significantly associated with clozapine level and CYP2C19*2 and LEPR c.668 G alleles. Clozapine levels in patients with metabolic syndrome were significantly higher compared to those without metabolic syndrome (1886 ± 895 vs. 1283 ± 985 ng/mL, P < 0.01) and were associated with the CYP2C19*2 genotype. No association was found between the genetic variants studied and lipid or glucose levels. Conclusion: This study confirms a high prevalence of metabolic side effects with clozapine and suggests higher clozapine level and pharmacogenetic markers in CYP2C19, LEP, LEPR, and HTR2C receptors as important predictors of BMI and metabolic syndrome.
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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 | 0.000 | 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) | 0.002 | 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".