Association of glutathione S-transferases (GSTT1, GSTM1 and GSTP1) genes polymorphisms with nonalcoholic fatty liver disease susceptibility: A PRISMA-compliant systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Glutathione S-transferases (GSTs) genes single-nucleotide polymorphisms (SNPs) have been connected with the susceptibility of nonalcoholic fatty liver disease (NAFLD), but with inconsistent results across the current evidences. The present work was schemed to explore the association between GSTs genes polymorphisms and the NAFLD vulnerability via meta-analysis. METHODS: PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure and Wanfang were retrieved for eligible literatures previous to March 10, 2021. The odds ratio (OR) of the dichotomic variables and the standardized mean difference of quantitative variables with corresponding 95% confidence intervals (95%CIs) were computed to evaluate the strength of the associations. The quality of included studies were assessed via using Newcastle-Ottawa Scale (NOS). RESULTS: In total, 7 case-control studies encompassing 804 NAFLD patients and 1362 disease-free controls in this meta-analysis. Ultimately, this analysis included 6, 5 and 5 studies for GSTM1, GSTT1 and GSTP1 polymorphisms, respectively. The pooled data revealed that the GSTs genes SNPs had conspicuous associations with NAFLD susceptibility: for GSTM1, null versus present, OR = 1.46, 95%CI 1.20 to 1.79, P = .0002; for GSTT1, null versus present, OR = 1.34, 95%CI 1.06 to 1.68, P = .01; for GSTP1, Ile/Val or Val/Val versus Ile/Ile, OR = 1.60, 95%CI 1.23 to 2.09, P = .0005. CONCLUSION: This work revealed that the GSTM1 null, GSTT1 null and GSTP1-Val genotypes might be related to increased NAFLD susceptibility.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".