Association between <i>5-HTR1A</i> gene C-1019G polymorphism and antidepressant response in patients with major depressive disorder: A meta-analysis
Bibliographic record
Abstract
BACKGROUND Major depressive disorder (MDD) is a substantial global health concern, and its treatment is complicated by the variability in individual response to antidepressants. AIM To consolidate research and clarify the impact of genetic variation on MDD treatment outcomes. METHODS Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search across PubMed, EMBASE, Web of Science, and the Cochrane Library was conducted without date restrictions, utilizing key terms related to MDD, serotonin 1A receptor polymorphism (5-HTR1A), C-1019G polymorphism, and antidepressant response. Studies meeting inclusion criteria were thoroughly screened, and quality assessed using the Newcastle-Ottawa Scale. Statistical analyses, including χ 2 and I² values, were used to evaluate heterogeneity and fixed-effect or random-effect models were applied accordingly. RESULTS The initial search yielded 1216 articles, with 11 studies meeting criteria for inclusion. Analysis of various genetic models showed no significant association between the 5-HTR1A C-1019G polymorphism and antidepressant efficacy. The heterogeneity was low to moderate, and no publication bias was detected through funnel plot symmetry and Egger's and Begg's tests. CONCLUSION This meta-analysis does not support a significant association between the 5-HTR1A C-1019G polymorphism and the efficacy of antidepressant treatment in MDD. The findings call for further research with larger cohorts to substantiate these results and enhance the understanding of antidepressant pharmacogenetics.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.059 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".