Association of BDNF, HTR2A, TPH1, SLC6A4, and COMT polymorphisms with tDCS and escitalopram efficacy: ancillary analysis of a double-blind, placebo-controlled trial
Bibliographic record
Abstract
OBJECTIVE: We investigated whether single nucleotide polymorphisms (SNPs) associated with neuroplasticity and activity of monoamine neurotransmitters, such as the brain-derived neurotrophic factor (BDNF, rs6265), the serotonin transporter (SLC6A4, rs25531), the tryptophan hydroxylase 1 (TPH1, rs1800532), the 5-hydroxytryptamine receptor 2A (HTR2A, rs6311, rs6313, rs7997012), and the catechol-O-methyltransferase (COMT, rs4680) genes, are associated with efficacy of transcranial direct current stimulation (tDCS) in major depression. METHODS: Data from the Escitalopram vs. Electrical Current Therapy for Treating Depression Clinical Study (ELECT-TDCS) were used. Participants were antidepressant-free at baseline and presented with an acute, moderate-to-severe unipolar depressive episode. They were randomized to receive escitalopram/tDCS-sham (n=75), tDCS/placebo-pill (n=75), or placebo-pill/sham-tDCS (n=45). General linear models assessed the interaction between treatment group and allele-wise carriers. Additional analyses were performed for each group and each genotype separately. RESULTS: Pairwise group comparisons (tDCS vs. placebo, tDCS vs. escitalopram, and escitalopram vs. placebo) did not identify alleles associated with depression improvement. In addition, exploratory analyses also did not identify any SNP unequivocally associated with improvement of depression in any treatment group. CONCLUSION: Larger, combined datasets are necessary to identify candidate genes for tDCS response.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".