CYP2D6 Metabolizer Phenotype Is Associated with Early Antidepressant Discontinuation in the UK Biobank

The metabolizer phenotype inferred from CYP2D6 genotype modulates exposure to paroxetine, venlafaxine and mirtazapine, substrates of this enzyme.

Antidepressant response is highly variable and CYP2D6 metabolizer phenotype has been proposed as one determinant of that variability. Using genetic data and longitudinal primary care prescription records from the UK Biobank, the authors tested associations between CYP2D6 phenotype and prescription-based proxies of treatment outcome — discontinuation, switching, side effects — stratified by antidepressant and adjusted for demographic covariates. Among 26,957 individuals of European ancestry prescribed a CYP2D6-metabolized antidepressant, reduced metabolic capacity was significantly associated with early discontinuation of paroxetine (n = 5718), venlafaxine (n = 2327) and mirtazapine (n = 3340); for paroxetine, poor metabolizers discontinued more often than normal metabolizers and, among discontinuers, were more likely to stop immediately, whereas no association was found for fluoxetine. Associations with switching were limited and no significant association was detected for side effects. The authors conclude that CYP2D6 variation mainly influences discontinuation within the first 30 days of treatment.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

The useful observation is chronological: if the effect plays out within the first thirty days, a genotype returned at week six is of no use, which argues for pre-emptive rather than reactive testing after a first failure. The paradox of this work is that it shows early discontinuation without showing side effects, which mainly reflects the weakness of endpoints reconstructed from prescription records, where perceived intolerance is not coded. It should therefore be read as a refinement of timing for gene-drug pairs already covered by CPIC, not as evidence that genotyping works.

Analysis by Dr Thibaut Benquey