HTR2C haplotypes and antipsychotics‐induced weight gain: X‐linked multimarker analysis
Bibliographic record
Abstract
UNLABELLED: The 5HT2C receptor (HTR2C) has been hypothesized to represent an important modulator in feeding behavior. Evidence was based on the observation that knock-out mice for the HTR2C receptor gene develop obesity and that many antipsychotics (AP) with potent HTR2C antagonism may induce weight gain in susceptible individuals. Pharmacogenetic studies focusing either on the Cys23Ser polymorphism or on the -759C/T promoter polymorphism of the X-linked HTR2C receptor gene revealed significant findings for the -759C/T polymorphism, however, no study has performed haplotype analyses for both polymorphisms. METHODS: We analyzed three functional polymorphisms (Cys23Ser, -759C/T, and (GT)12-18/(CT) 4-5) of the HTR2C in 139 schizophrenic patients mainly treated with clozapine. Weight gain was assessed over a time course of 6-14 weeks (mean 8.2 weeks). RESULTS: Single marker and haplotype analysis revealed no significant associations with AP-induced weight gain. The haplotype Long-C-Ser was protective against weight gain, but the number of subjects available for that analysis was small. CONCLUSIONS: Our pilot study did not detect any significant haplotype conferring risk for antipsychotic-induced weight gain although the statistical model took into account the X-linked heterogeneity and did correct for confounding factors (i.e., ethnicity, medications, clinical response, time of assessment).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".