Anxiety symptom remission is associated with genetic variation of PTPRZ1 among patients with major depressive disorder treated with escitalopram
Bibliographic record
Abstract
OBJECTIVES: Genome-wide analyses of antidepressant response have suggested that genes initially associated with risk for schizophrenia may also serve as promising candidates for selective serotonin reuptake inhibitor (SSRI) efficacy. Protein tyrosine phosphatase, receptor-type, zeta-1 (PTPRZ1) has previously been shown to be associated with schizophrenia, but it has not been investigated as a predictor of antidepressant efficacy. The main objective of the study was to assess whether SSRI-mediated depressive and anxiety symptom remission in Chinese patients with major depressive disorder (MDD) are associated with specific PTPRZ1 variants. METHODS: Two independent cohorts were investigated, the first sample (N = 344) received an SSRI (i.e. fluoxetine, sertraline, citalopram, escitalopram, fluvoxamine, or paroxetine) for 8 weeks. The second sample (N = 160) only received escitalopram for 8 weeks. Hamilton Depression and Hamilton Anxiety Rating Scale scores at 8-weeks post-baseline in both cohorts were used to determine remission status. Five PTPRZ1 variants (rs12154537, rs6466810, rs6466808, rs6955395, and rs1918031) were genotyped in both cohorts. RESULTS: Anxiety symptom remission was robustly associated with PTPRZ1 rs12154537 (P = 0.004) and the G-G-G-G haplotype (rs12154537-rs6466810-rs6466808-rs6955395; P = 0.005) in cohort 2 but not cohort 1 (mixed SSRI use). Associations with depressive symptom remission did not survive correction for multiple testing. CONCLUSIONS: These findings suggest that PTPRZ1 variants may serve as a marker of escitalopram-mediated anxiety symptom remission in MDD.
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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".