The role of NAT2 genetic variants in anti-tuberculosis drug-induced liver injury (AT-DILI): a meta-analysis
Bibliographic record
Abstract
Background Anti-tuberculosis drug-induced liver injury (AT-DILI) is one of the significant adverse effects of first-line tuberculosis therapy, frequently resulting in treatment discontinuation. Genetic polymorphisms in N-acetyltransferase 2 (NAT2), a key enzyme in the metabolism of isoniazid (an anti-TB drug), are suggested to influence AT-DILI susceptibility.Methods A meta-analysis of published studies was conducted to evaluate the association between NAT2 polymorphisms and the risk of AT-DILI. Literature searches were conducted in PubMed, Web of Science, Wiley, ScienceDirect, and Medline up to December 2024. A total of 48 studies comprising 11,035 patients were included. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Subgroup analyses were conducted based on region, study design, genotyping method, hepatotoxicity definitions, and NAT2 genetic variants. Heterogeneity, publication bias, quality assessment, and sensitivity analysis were assessed using the I2 statistic, Egger’s test, the Newcastle–Ottawa Scale (NOS), and the leave-one-out method, respectively.Results Slow acetylator genotypes were significantly associated with an increased risk of AT-DILI (pooled OR = 3.02; 95% CI = 2.50–3.64; p < 0.001). Moderate heterogeneity was observed (I2 = 58.74%). No significant publication bias was observed (p = 0.199).Conclusion NAT2 acetylator status was significantly associated with the likelihood of experiencing hepatotoxicity related to anti-tuberculosis drugs.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.072 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".