Val66Met polymorphism and serum brain‐derived neurotrophic factor in bipolar disorder: an open‐label trial
Bibliographic record
Abstract
OBJECTIVE: Brain-derived neurotrophic factor (BDNF) is consistently associated with acute mood episodes in bipolar disorder, but there is a lack of longitudinal data to support this hypothesis. In this 16-week open-label clinical trial, we tested the predictive role of BDNF Val66Met polymorphism on serum BDNF levels and the relationship of serum BDNF and clinical response in people with bipolar disorder during an acute illness episode. METHOD: Sixty-four people with bipolar disorder who were medication-free at baseline and in an acute mood episode were recruited. They were matched with 64 healthy controls. Clinical evaluation, serum BDNF, and BDNF Val66Met polymorphism were determined at baseline, and change in serum BDNF was assessed in patients at weeks 2, 4, 8 and 16. RESULTS: There were no differences between patients and controls in serum BDNF or in frequencies of the BDNF Val66Met polymorphism genotype at baseline. The multivariable model showed that Met carriers had a significantly different change in BDNF levels compared with Val homozygotes. Not achieving a complete remission was also associated with lower prospectively assessed BDNF levels. CONCLUSION: This study provides the first longitudinal evidence that both the BDNF Val66Met polymorphism and remission status predict change in circulating BDNF levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".