Genetic Polymorphisms of Glutathione S-Transferase P1 (GSTP1) and the Incidence of Anti-Tuberculosis Drug-Induced Hepatotoxicity
Bibliographic record
Abstract
BACKGROUND: Anti-tuberculosis drug-induced hepatotoxicity (ATDH) is one of the most common adverse effects associated with tuberculosis (TB) therapy. Animal studies have demonstrated important roles of glutathione S-transferases in the prevention of chemical-induced hepatotoxicity. The aim of this study was to investigate the relationship between single nucleotide polymorphisms (SNPs) of glutathione S-transferase P1 (GSTP1) and ATDH in TB patients. METHODS: We used two independent samples for this genetic association study. In the initial prospective study, 322 newly diagnosed TB patients were followed up for three months after initiating anti-TB therapy. In an independent retrospective study, 115 ATDH patients and 116 patients without ATDH were selected to verify the results of the prospective study. Tag-SNPs of GSTP1 were genotyped either with the MassARRAY platform or the improved multiple ligase detection reaction (iMLDR) method. The associations between SNPs and ATDH were analyzed by logistic regression analysis adjusting for confounding factors. RESULTS: Of the 322 patients recruited in the prospective cohort, 35 were excluded during the 3 months of follow-up, and 30 were diagnosed with ATDH and were considered as the ATDH group. The remaining 257 subjects without ATDH were considered as the non-ATDH group. After correction for potential confounding factors, significant differences were found for rs1695 (A>G) under an allelic model (OR = 3.876, 95%CI: 1.258011.905; P = 0.018). In the retrospective study, rs1695 allele A also had a higher risk of ATDH (OR = 2.10, 95%CI: 1.17-3.76; P = 0.012). We only found rs4147581AA genotype under a dominant model was related to ATDH in the prospective study (OR = 2.578, 95%CI: 1.076-6.173; P = 0.034). CONCLUSIONS: This is the first study to suggest that GSTP1 genotyping can be an important tool for identifying patients who are susceptible to ATDH. This result should be verified in independent large sample studies and also in other ethnic populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".