Genetic predictors of response to treatment with citalopram in depression secondary to traumatic brain injury
Bibliographic record
Abstract
OBJECTIVES: To determine which serotonergic system-related single nucleotide polymorphisms (SNPs) predicted variation in treatment response to citalopram in depression following a traumatic brain injury (TBI). METHODS: Ninety (50 M/40 F, aged 39.9, SD = 18.0 years) post-TBI patients with a major depressive episode (MDE) were recruited into a 6-week open-label study of citalopram (20 mg/day). Six functional SNPs in genes related to the serotonergic system were examined: serotonin transporter (5HTTLPR including rs25531), 5HT1A C-(1019)G and 5HT2A T-(102)C, methylene tetrahydrofolate reductase (MTHFR) C-(677)T, brain-derived neurotrophic factor (BDNF) val66met and tryptophan hydroxylase-2 (TPH2) G-(703)T. Regression analyses were performed using the six SNPs as independent variables: Model 1 with response (percentage Hamilton Depression (HAMD) change from baseline to endpoint) as the dependent variable and Model 2 with adverse event index as the dependent variable (Bonferroni corrected p-value < 0.025). RESULTS: MTHFR and BDNF SNPs predicted greater treatment response (R(2)= 0.098, F = 4.65, p = 0.013). The 5HTTLPR predicted greater occurrence of adverse events (R(2)= 0.069, F = 5.72, p = 0.020). CONCLUSION: Results suggest that polymorphisms in genes related to the serotonergic system may help predict short-term response to citalopram and tolerability to the medication in patients with MDE following a TBI.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".