BDNF Val66Met polymorphism and resilience in major depressive disorder: the impact of cognitive psychotherapy
Bibliographic record
Abstract
OBJECTIVE: Clinical and biological correlates of resilience in major depressive disorder are scarce. We aimed to investigate the effect of the Val66Met polymorphism in the BDNF gene on resilience scores in major depressive disorder patients and evaluate the polymorphism's moderation effect on resilience scores in response to cognitive therapy. METHOD: A total of 106 major depressive disorder patients were enrolled in this clinical randomized study. The Resilience Scale and the Hamilton Rating Scale for Depression were applied at baseline, post-treatment, and at six months of follow-up. Blood samples were obtained at baseline for molecular analysis. RESULTS: The baseline resilience scores were higher in patients with the Met allele (114.6±17.6) than in those with the Val/Val genotype (104.04±21.05; p = 0.037). Cognitive therapy treatment increased resilience scores (p ≤ 0.001) and decreased depressive symptoms (p ≤ 0.001). In the mixed-effect model, the Val/Val genotype represented a decrease in resilience scores (t218 = -1.98; p = 0.048), and the Val66Met polymorphism interacted with sex to predict an increase in total resilience scores during cognitive treatment (t218 = 2.69; p = 0.008). CONCLUSION: Our results indicate that cognitive therapy intervention could improve resilience in follow-up, considering that gender and genetic susceptibility are predicted by the Val66Met polymorphism.
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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) | 0.002 | 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".