Effectiveness of pharmacogenomic testing across age groups for depressive disorder treatment and its age-dependent effects on cognitive function: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Pharmacogenomic testing may optimize antidepressant treatment in depression. Its effects on cognition and across age groups remain unclear. METHODS: A total of 843 patients with depressive disorder were stratified by age (adolescent, young adult, middle-aged, elderly) and assigned to pharmacogenomic-guided or treatment-as-usual groups. Guided treatment was based on pharmacogenomic results. Primary outcomes were changes in Montreal Cognitive Assessment (MoCA), remission (HDRS<8), and response (≥50 % HDRS reduction) at weeks 4, 8, and 12. RESULTS: MoCA improvement inversely correlated with age (week 8: r = -0.078, P = 0.036; week 12: r = -0.076, P = 0.041). Pharmacogenomic guidance was associated with greater cognitive gains in younger participants, with sustained improvement in adolescents (all P ≤ 0.036), improvement in young adults at weeks 8 and 12 (both P ≤ 0.013), and transient benefit in middle-aged patients at week8 (P = 0.048). No cognitive benefit was observed in the elderly. Multi-way ANOVA for depressive symptoms (HDRS) revealed a Group × Time interaction (P < 0.001), and for cognitive function (MoCA) showed both a Group × Time interaction (P = 0.011) and an Age × Time interaction (P = 0.024), consistent with age-dependent recovery trajectories. Pharmacogenomic guidance was associated with increased remission and response rates from week 8 through week 12 across all ages. LIMITATIONS: single blinded; single-center design. CONCLUSIONS: Pharmacogenomic-guided treatment consistently improves depressive symptom remission but shows age-dependent cognitive effects: sustained in youth, transient in middle age, absent in elderly. Age is a key modifier of cognitive benefit, supporting prioritized use in younger patients, while elderly individuals may require integrated interventions beyond pharmacogenomics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".