Effect of CYP2D6 genetic variation on patient-reported symptom improvement and side effects among children and adolescents treated with amphetamines
Bibliographic record
Abstract
OBJECTIVES: Amphetamine-based medications are recommended as a first-line pharmacotherapy for the treatment of attention-deficit/hyperactivity disorder in children and adolescents. However, the efficacy and tolerability of these medications vary across individuals, which could be related to interindividual differences in amphetamine metabolism. This study examined if genotype-predicted phenotypes of the cytochrome P450 isozyme CYP2D6 were associated with self-reported side effects and symptom improvement in youth treated with amphetamines. METHODS: Two hundred fourteen participants aged 6-24 who had a history of past or current amphetamine treatment were enrolled from Western Canada. Amphetamine dose and duration information was collected from the participants along with questions regarding adherence, concomitant medications, symptom improvement and side effects. DNA was extracted from saliva samples and genotyped for CYP2D6 . Binomial logistic regression models were used to determine the effect of CYP2D6 metabolizer phenotype with and without correction for phenoconversion on self-reported symptom improvement and side effects. RESULTS: Genotype-predicted CYP2D6 poor metabolizers had significantly higher odds of reporting symptom improvement when compared to intermediate metabolizers (OR = 3.67, 95% CI = 1.15-11.7, P = 0.029) after correction for phenoconversion and adjusting for sex, age, dose, duration, and adherence. There was no association between CYP2D6 metabolizer phenotype and self-reported side effects. CONCLUSION: Our findings indicate that phenoconverted and genotype-predicted CYP2D6 poor metabolizer phenotype is significantly associated with higher odds of symptom improvement in children and adolescents treated with amphetamine. If replicated, these results could inform the development of future dosing guidelines for amphetamine treatment in children and adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".