Glutathione S-Transferase M1/T1 Polymorphisms and Schizophrenia Risk: A New Method for Quality Assessment and a Systematic Review
Bibliographic record
Abstract
Background: GST genes were reported to be involved in susceptibility to mental disorder. The results between deletions of GST genes and schizophrenia were inconclusive and confusing. Therefore, we performed this updated meta-analysis to outline the association using a new method for quality assessment. Methods: Sixteen reported studies were selected, and the overall OR and 95% CI were calculated and analyzed by Review Manager 5.4 and STATE 12. The Newcastle-Ottawa Quality Assessment Scale (NOS) for case–control studies was rewritten to evaluate the quality of published studies, as there was no “Exposure” in these studies and other factors should be suggested to assess the quality. Results: There was no significant association between deletions of GST genes and SZ risk ( p > 0.05 in Random model). We also failed to find a significant relation between null genotypes and SZ risk in East Asian population. Based on further analysis of PCR methods, GSTM1 null was weakly associated with SZ risk in 8 studies using multiplex PCR (OR = 1.17, 95% CI = 1.00– 1.37, p = 0.05), but GSTT1 null was a protective factor for SZ risk (OR = 0.73, 95% CI = 0.56– 0.94, p = 0.02). When stratified by rewritten NOS stars and deductions, GSTM1 null was significantly associated with SZ risk in 9 studies with high quality (OR = 1.24, 95% CI = 1.08– 1.43, p = 0.002), and in 10 studies with no deductions (OR = 1.20, 95% CI = 1.05– 1.38, p = 0.007). Conclusion: GSTM1 null genotype may be a genetic risk factor for SZ in studies using multiplex PCR and high-quality studies. However, GSTT1 null might be a protective factor. Besides, we provided a new method for quality assessment and it was useful and should be promoted in further analysis. Keywords: glutathione S-transferase T1 , GSTM1 , polymorphism, schizophrenia, meta-analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.321 | 0.558 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".