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Record W2174762029 · doi:10.1192/bjp.bp.105.010587

Brain-derived neurotrophic factor (BDNF) gene and rapid-cycling bipolar disorder

2006· article· en· W2174762029 on OpenAlexafffund
Daniel J. Müller, Vincenzo De Luca, Tricia Sicard, Nicole King, John S. Strauss, James L. Kennedy

Bibliographic record

VenueThe British Journal of Psychiatry · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsBipolar disorderSingle-nucleotide polymorphismHaplotypeBrain-derived neurotrophic factorGeneticsNeurotrophic factorsPsychologyGeneAlleleBiologyGenotypePsychiatryLithium (medication)

Abstract

fetched live from OpenAlex

BACKGROUND: We have previously reported the Val66Met and GT(n) repeat polymorphisms of the brain-derived neurotrophic factor (BDNF) gene to be associated with bipolar disorder. However, these findings have not been replicated consistently. AIMS: To dissect the association of the BDNF gene with bipolar disorder by examining additional markers at the DNA level and by testing the illness categories of bipolar disorder I and II and rapid cycling. METHOD: We performed a family-based association study and haplotype analyses with 312 nuclear families using four single nucleotide polymorphisms (SNPs) and the Val66Met and GT(n) repeat polymorphisms. RESULTS: The SNPs hCV11592756 and rs2049045, the Val66Met and GT(n) were significantly associated with bipolar disorder using transmission disequilibrium analyses (P=0.02, 0.009, 0.001 and 0.008 respectively). The effect atthese markers was mainly driven by the rapid-cycling patients. CONCLUSIONS: Within bipolar disorder, variation in the BDNF gene appears to predict risk for developing rapid cycling according to DSM-IV. Incorporating this clinical sub-phenotyping into other studies of the BDNF gene may help to resolve some of the inconsistencies reported thus far concerning BDNF and bipolar disorder.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.238
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations111
Published2006
Admission routes2
Has abstractyes

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