Impact of histamine receptors H1 and H3 polymorphisms on antipsychotic-induced weight gain
Bibliographic record
Abstract
Objectives: A positive correlation between antipsychotic-induced weight gain (AIWG) and the antagonist effect of antipsychotic drugs at the histamine H1 receptor (HRH1) as well as the agonist effect at the histamine H3 receptor (HRH3) in the brain has been consistently demonstrated. We investigated the potential impact of single-nucleotide polymorphisms (SNPs) in HRH1 and HRH3 genes on AIWG.Methods: We analysed 40 tagSNPs in HRH1 (n = 34) and HRH3 (n = 6) in schizophrenia/schizoaffective disorder patients (n = 193) primarily treated with clozapine or olanzapine for up to 14 weeks. Linear regression was used to evaluate the association between SNPs and AIWG, with baseline weight and treatment duration as covariates.Results: In HRH1, a nominal association of rs7639145 with AIWG was observed in patients of European ancestry treated with either clozapine or olanzapine (P = 0.043; β = 1.658; n = 77). We observed nominal association for two HRH1 SNPs rs346074 (P = 0.002; β = –5.024) and rs13064530 (P = 0.004; β = –5.158) in patients of African ancestry treated with either clozapine or olanzapine (n = 37). However, the above associations are not significant after correcting for multiple testing. In HRH3, we did not observe association in either ancestry.Conclusions: The current study suggests that SNPs in HRH1 and HRH3 may not have a major role in AIWG.
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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 | 0.001 |
| 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 | 0.000 | 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".