Genetic predictors of antidepressant side effects: A grouped candidate gene approach in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study
Bibliographic record
Abstract
BACKGROUND: The unwanted side effects associated with antidepressants are key determinants of treatment adherence in depression; propensity to experience these adverse drug reactions (ADRs) may be influenced by genetic variation. However, previous work attempting to ascertain the genetic variants involved has had limited success, in part due to the range of ADRs reported with antidepressants. METHOD: ADRs reported with antidepressant treatment were categorised using their likely pharmacological basis; adrenergic, cholinergic, serotonergic and histaminergic. To identify genetic predictors of susceptibility to each group of ADRs, a candidate gene analysis was performed with data from 431 depressed patients (from a total sample size of 811 patients) enrolled in the Genome-Based Therapeutic Drugs for Depression (GENDEP) project, who were randomly allocated to receive treatment with escitalopram or nortriptyline. Data from 474 patients treated with citalopram or reboxetine in the GenPod project (total sample of 601 patients) were used for replication of significant findings. RESULTS: We found no significant predictors of presumed adrenergic, cholinergic and histaminergic ADRs. Putative serotonergic ADRs were significantly associated with variation in the gene encoding the serotonin 2C receptor (HTR2C, rs6644093, odds ratio (OR)=1.72, 95% confidence interval (CI)=1.31-2.25, p=7.43×10(-5)) in GENDEP. However, this finding was not replicated in GenPod. CONCLUSIONS: The association between serotonergic side effects and variation in the HTR2C gene in the GENDEP sample supports the hypothesis that serotonin receptor-mediated mechanisms underlie these adverse reactions, however this finding was not replicated in GenPod.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".