Brain-derived neurotrophic factor Val66Met and CYP2B6 polymorphisms as predictors for ketamine effectiveness in patients with treatment-resistant depression
Bibliographic record
Abstract
BACKGROUND: Converging lines of evidence indicate that ketamine is a rapid antidepressant for individuals with treatment-resistant depression. Hitherto, no reliable a priori predictors of ketamine response have been reported. Pharmacogenetic biomarkers have yielded mixed results regarding potential candidate genes associated with ketamine's biochemistry as reliable predictors of response. AIMS: No studies have examined the effects of Val66Met and CYP2B6 genotypes on patients receiving repeated infusions of intravenous ketamine. METHODS: In all, 85 participants with major depressive disorder who had previously received four infusions of intravenous ketamine were recruited to the foregoing study. Buccal swabs were collected and genotype variants across the Val66Met and CYP2B6 genes were analyzed. A repeated measures mixed linear model was used to assess change in depressive symptoms, suicidality, and anxiety, correcting for sex and age. Multiple regression was run to determine whether these genetic markers were associated with treatment efficacy for depressive severity, suicidal ideation, anxiolytic response, and degree of dissociation to intravenous ketamine. RESULTS: Participants experienced significant overall reductions in depression, suicide, and anxiety. Overall, 25% met the response criteria and 15% met the remission criteria. However, Val66Met and CYP2B6 did not significantly predict changes in symptoms of depression, suicide, anxiety, or average dissociation. CONCLUSIONS: This study contributes to the growing literature that ketamine efficacy is unlikely to be predicted by single genes, and a pleiotropic approach may likely be necessary for developing reliable predictors of clinical benefits.
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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 | 0.001 | 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".