Clinical impact of functional CYP2C19 and CYP2D6 gene variants on treatment outcomes in patients with depression: a Danish cohort study
Bibliographic record
Abstract
Introduction Pharmacogenetic (PGx) targets to optimize drug therapy, but its implementation is rare. Objectives We evaluate the clinical utility of PGx testing in psychiatry by investigating the one-year risks of clinical outcomes in patients with depression taking sertraline, (es)citalopram or fluoxetine by their Cytochrome P450 (CYP) 2C19/2D6 phenotypes. Methods We investigated 17,297 individuals born between 1981-2005 with a depression diagnosis between 1996-2012 from the iPsych2012 case-cohort. Based on array-based single-nucleotide-polymorphism genotype data, individuals were phenotyped as CYP2C19/CYP2D6 normal (NM, reference group), ultra-rapid- (UM), rapid- (RM), intermediate- (IM), or poor-metabolizer (PM). Outcomes were treatment switching or discontinuation, psychiatric in-, out-, and emergency room contacts (ER), and suicide attempt/self-harm. Incidence rate ratios (IRR) by age groups were estimated using Poisson regression analysis with 95% confidence intervals, adjusted for potential confounders. Results Risks of switching (IRR=1.89[1.22-2.93]), ERs (1.69 [1.01-2.81]) and suicide attempt/self-harm (2.73 [1.49-5.01]) were higher in CYP2C19 PMs <19 years taking (es)citalopram. Fluoxetine users <19 years had a decreased risk of discontinuation in CYP2D6 PMs (0.5 [0.27-0.95]) and decreased risk of out-patient contacts in CYP2D6 PMs and IMs (IRRIM=0.83 [0.68-1.00] and IRRPM=0.59 [ 0.37-0.96]). We observed an increased risk for ERs in CYP2D6 PMs aged 19-25 years taking fluoxetine (4.53 [1.54-13.35]). In CYP2C19 UMs >25 years taking (es)citalopram the risk of suicide attempt/self-harm was more than three-fold increased (3.64 [ 1.01-13.19]). We found no significant results in users of sertraline. Conclusions PGx variability was associated with treatment outcomes in depression in patients with CYP2C19 PM or UM status taking (es)citalopram, or CYP2D6 PM or IM status taking fluoxetine. Disclosure No significant relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".