CYP2D6 genotype and venlafaxine‐XR concentrations in depressed elderly
Bibliographic record
Abstract
INTRODUCTION: The elderly are at increased risk for medication-related adverse events. Recent reports indicate that venlafaxine may put the elderly at increased risk of cardio- and cerebrovascular adverse events. We investigated the relationship between the CYP2D6 polymorphism and steady-state plasma concentration of venlafaxine (VEN) and its primary metabolite o-desmethylvenlafaxine (ODV) in elderly participants receiving venlafaxine-XR for major depression in order to explore the contribution of pharmacogenetics to medication tolerability. METHODS: Forty-six elderly participants received venlafaxine-XR for the treatment of major depression. CYP2D6 genotype and steady-state plasma levels of VEN and ODV were determined. RESULTS: Sixty-five percent of participants were homozygous of the wild type (WT) allele, whereas 35% carried one or more variant alleles associated with intermediate and poor 2D6 metabolizer status. VEN concentration per unit dose was significantly higher and ODV concentration per unit dose was significantly lower in participants who carried one or more variant alleles compared to participants who were homozygous for the WT allele. The VEN and ODV concentrations per unit dose were also correlated with creatinine clearance. CYP2D6 genotype was not associated with medication associated side-effects. CONCLUSIONS: Plasma dose-corrected concentrations of VEN and ODV correlated with genetically determined CYP2D6 enzymatic activity in depressed elders treated with venlafaxine-XR. This relationship was not masked by the effects of age-related illness or polypharmacy. Future clinical application of pharmacogenetics to examine 2D6-dependent medications may help reduce the incidence of medication adverse events particularly in those elders at higher risk for medication adverse events due to impaired renal or cardiac function.
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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 | 0.000 |
| 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) | 0.001 | 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".