NAT2 variants and toxicity related to anti-tuberculosis agents: a systematic review and meta-analysis

M Richardson, J Kirkham, K Dwan, D J Sloan, G Davies, A L Jorgensen

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Tuberculosis (TB) patients receiving anti-tuberculosis treatment may experience serious adverse drug reactions (ADRs) such as hepatotoxicity. Variants of the N-acetyltransferase 2 (NAT2) gene may increase the risk of experiencing such toxicity events.

OBJECTIVE: To provide a comprehensive evaluation of the evidence base for associations between NAT2 variants and anti-tuberculosis drug-related toxicity.

METHOD: This was a systematic review and meta-analysis. We searched for studies in Medline, PubMed, EMBASE, BIOSIS and Web of Science. We included data from 41 articles (39 distinct cohorts of patients). We pooled effect estimates for each genotype on each outcome using meta-analyses stratified by country.

RESULTS: We assessed the quality of the included studies, which was variable, with many areas of concern. Slow/intermediate NAT2 acetylators were statistically significantly more likely to experience hepatotoxicity than rapid acetylators (OR 1.59, 95%CI 1.26-2.01). Heterogeneity was not detected in the overall pooled analysis (I² = 0%). NAT2 acetylator status was significantly associated with the likelihood of experiencing anti-tuberculosis drug-related hepatotoxicity.

CONCLUSION: We encountered several challenges in performing robust syntheses of data from pharmacogenetic studies, and we outline recommendations for the future reporting of pharmacogenetic studies to enable high-quality systematic reviews and meta-analyses to be performed.

Original languageEnglish
Pages (from-to)293-305
Number of pages13
JournalInternational Journal of Tuberculosis and Lung Disease
Volume23
Issue number3
DOIs
Publication statusPublished - 1 Mar 2019

Fingerprint

Dive into the research topics of 'NAT2 variants and toxicity related to anti-tuberculosis agents: a systematic review and meta-analysis'. Together they form a unique fingerprint.

Cite this