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

doi:10.5588/ijtld.18.0324

. 2019 Mar;23(3):293–316. doi: 10.5588/ijtld.18.0324

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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 ^*

PMCID: PMC6421944 PMID: 30871660

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.

Keywords: tuberculosis, pharmacogenetics, adverse events, evidence synthesis

TUBERCULOSIS (TB) is one of the most important challenges in global health. There were an estimated 1.3 million TB deaths in 2016 among human immunodeficiency virus (HIV) negative people and 374 000 deaths among HIV-positive people.¹ The World Health Organization (WHO) recommends a combination of four first-line drugs for individuals with drug-susceptible TB: isoniazid (INH), rifampicin (RMP), ethambutol (EMB) and pyrazinamide (PZA).¹

TB patients receiving a combination of these drugs may experience adverse drug reactions (ADRs), the most serious of which is anti-tuberculosis drug-induced hepatotoxicity (ATDH). Reported incidence rates of ATDH among patients treated with standard multidrug treatment vary from 2% to 28%, depending on the regimen given, definition of ATDH and patient characteristics such as age, race and sex.² ATDH can be fatal, with reported mortality rates of 6–12% if drugs are not promptly stopped.³ ATDH and other anti-tuberculosis drug-related adverse effects also contribute to non-adherence, eventually leading to treatment failure, relapse and the emergence of drug resistance.²

The proposed genetic risk factors for ATDH include polymorphisms of the N-acetyltransferase 2 (NAT2) gene, which codes for the drug-metabolising enzyme, NAT2.⁴^,⁵ NAT2 polymorphisms may affect the activity of the NAT2 enzyme, altering the chemical modification of anti-tuberculosis drugs and their metabolites in the liver, leading to hepatic adverse reactions.⁶ Toxic metabolites may also cause other toxicity events, such as peripheral neuropathy and maculopapular eruption, although the majority of evidence on the pharmacogenetics of anti-tuberculosis drugs focuses on hepatotoxicity.

INH is the anti-tuberculosis drug for which the genetic contribution to ATDH has been most widely studied and is best understood. Specifically, it is thought that NAT2 acetylator status may be associated with INH-related hepatotoxicity because NAT2 is one of the main enzymes involved in INH metabolism in the liver. There are three phenotypes of acetylator status. Individuals who are slow NAT2 acetylators have higher plasma drug concentrations. This may be beneficial for treatment efficacy, but may also cause an accumulation of toxic metabolites as part of the metabolic activation of acetylhydrazine to harmless diacetylhydrazine. INH suppresses the acetylation of acetylhydrazine to produce more toxic metabolites, which contributes to the increased risk of hepatitis.⁷ Fast acetylators have lower plasma drug concentrations, and so treatment may be less effective, but also less toxic. Intermediate acetylators fall between these two extremes.

RMP and PZA have also been reported to be hepatotoxic;⁸ however, the mechanisms for RMP-and PZA-induced hepatotoxicity are not known.⁹ The OATP1B1^*15 haplotype has been reported to be a predictor of RMP-induced liver injury;¹⁰ no research into the genetic predictors of PZA-induced hepatotoxicity has been reported.¹¹ No hepatotoxicity has been described for EMB.⁸

The objective of this systematic review and meta-analysis was to evaluate evidence on the effect of NAT2 on anti-tuberculosis drug-related toxicity in TB patients receiving anti-tuberculosis treatment. Meta-analyses investigating the effect of NAT2 on toxicity outcomes have been published,⁶^,^12–15 but the conclusions from these have been conflicting. Our review and meta-analysis updates and adds to the evidence base on associations between NAT2 and anti-tuberculosis drug-related toxicity.

METHODS

This review was conducted in line with the methods outlined in our protocol (PROSPERO registration number: CRD42017068448).¹⁶ A search strategy and study selection process enabled identification of studies that investigated the association between any genetic variant and anti-tuberculosis drug-related toxicity. However, in this article, we focus only on the subset of studies that considered NAT2 variants. Studies investigating associations between other genetic variants and anti-tuberculosis drug-related toxicity will be reported separately.

Selection criteria

Types of studies

We included cohort studies, case-control studies and randomised controlled trials (RCTs). We did not include studies on case series because this type of study design would be inappropriate to investigate the effect of genetic variants on anti-tuberculosis drug-related toxicity. We did not require a minimum number of enrolled patients for a study to be included in our review.

Types of participants

We included studies that recruited TB patients who were either already established on anti-tuberculosis treatment or commencing treatment (at least one of INH, RMP, PZA or EMB), and who were genotyped to investigate the effect of genetic variants on anti-tuberculosis drug-related toxicity. We only included studies where >50% of included patients were TB patients receiving anti-tuberculosis treatment.

Types of outcomes

We included studies that measured any drug-related toxicity outcomes.

Search strategy

An information specialist (EK) designed the search strategy (Appendix Tables A.1^* and A.2), and searched for relevant studies in Medline, PubMed, EMBASE, BIOSIS and Web of Science (date of search: 3 March 2016). We searched reference lists from relevant studies manually, and contacted experts to identify eligible studies. We included studies published in English only. We did not restrict by year of publication or publication status.

Study selection

The search results were imported to Covidence.¹⁷ We removed duplicates, and one author (MR) scanned the study abstracts to remove irrelevant studies. A second author (AJ, JK or KD) independently screened a sample of 10% of studies.

We obtained the full text for each potentially relevant study. One reviewer (MR) assessed eligibility based on the selection criteria. A second author (AJ, JK or KD) independently assessed a sample of 10% of studies for eligibility. Disagreements between the two reviewers at the abstract and full-text screening stages were resolved through discussion, and by consulting a third author if necessary.

Outcomes

The primary outcome of this review was hepatotoxicity by any definition used by the original investigators. The secondary outcomes were all other toxicity outcomes.

Data collection

We designed and piloted a data extraction form. We collected data on study design, participant characteristics, and treatment regimen and outcomes. One author (MR) extracted data in accordance with the methods outlined in the Cochrane Handbook¹⁸ and The HuGENet HuGE Review Handbook.¹⁹ A second author (AJ, JK or KD) independently extracted all outcome data. Disagreements between the two reviewers were resolved through discussion, and by consulting a third author if necessary. We contacted study authors if outcome data necessary for inclusion in a meta-analysis were not published in the paper.

We contacted individuals who were listed as authors of multiple included articles to enquire whether there was overlap between articles in terms of the patient cohorts. We examined locations, dates of recruitment and other study characteristics to identify articles that reported outcomes for the same patient cohort. If an author confirmed that multiple articles reported outcomes for the same patient cohort, or if we suspected this based on reported study characteristics, we assigned a group identifier (GI) to these articles, and ensured that no data for the same patient cohort were included more than once in any meta-analysis.

Quality assessment

One author applied criteria for the quality assessment of pharmacogenetic studies²⁰ to each study. A second author (AJ) independently assessed the quality of a sample of 10% of studies. Disagreements between the two reviewers were resolved through discussion. We obtained the number of studies meeting each criterion and summarised this information in the text.

Data synthesis

We performed meta-analyses for associations between NAT2 and any anti-tuberculosis drug-related toxicity outcome that were investigated by at least two studies. The effects of both NAT2 acetylator status (as predicted using genotyping methods) and individual NAT2 single-nucleotide polymorphisms (SNPs) were investigated.

Primary analysis

The primary analysis compared risk of hepatotoxicity for slow/intermediate acetylators in comparison with rapid acetylators. Data were pooled from studies that reported data for each acetylator group separately together with data from studies that combined slow and intermediate acetylator groups.

Two sensitivity analyses were conducted. The first was pairwise comparisons of slow vs. rapid acetylator status, and intermediate vs. rapid acetylator status. Here, it was only possible to include data from studies that reported on each acetylator group separately. The second was comparison of slow vs. rapid/intermediate acetylator status. Here, data were pooled from studies that combined data for intermediate and rapid acetylator groups, and from studies that reported data for each acetylator group separately.

Secondary analysis

The secondary analysis compared the risk of hepatotoxicity between genotype groups for NAT2 SNPs. For each SNP, two pairwise comparisons were undertaken: heterozygous genotype vs. homozygous wild-type (wt), and homozygous mutant-type vs. homozygous wt. For SNPs investigated by one study only, odds ratios (ORs) comparing genotype groups were calculated and summarised in a table, together with the pooled estimates from the meta-analyses. There were insufficient data to perform meta-analyses for an association between NAT2 (acetylator status and individual SNPs) and other toxicity outcomes; ORs and 95% confidence intervals (CIs) for each pairwise comparison were calculated and reported in a table.

Meta-analyses were performed using Stata v 14 (metan package) (StataCorp, College Station, TX, USA);²¹ ORs with 95%CIs were the chosen measure of effect. We used the random-effects model because we anticipated heterogeneity between studies due to differences in study design, methodological quality, ethnicity of participants and outcome definitions. The random-effects model used the method of DerSimonian and Laird,²² with the estimate of heterogeneity being taken from the Mantel-Haenszel model.²³ If zero events were observed in one of the genotype groups, a continuity correction of 0.5 was used. Data were excluded from the analysis if there were no patients in one of the genotype groups in a comparison.

The HuGENet HuGE Review Handbook recommends that meta-analyses of genetic association studies be stratified by ethnicity, and that meta-analyses should only be performed if effect estimates for different ethnic groups appear sufficiently similar.¹⁹ However, information on participants' ethnicity was sparsely reported in the studies included in our review. We therefore performed analyses stratified by the countries in which studies were conducted as a proxy for ethnicity.

Investigation of heterogeneity

We assessed heterogeneity by visually examining forest plots, and by referring to the I² statistic. If substantial heterogeneity had been observed (>50%),¹⁸ we planned to undertake subgroup analyses according to study design, outcome definitions, treatment regimens and date of study publication.

Selective reporting

We assessed the possibility of selective reporting as part of the quality assessment. Potential sources of selective reporting considered were genetic variants, outcomes and modes of inheritance.²⁰

Publication bias

We produced a funnel plot for the primary analysis to assess the risk of publication bias.

RESULTS

Included and excluded studies

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart showing the selection of studies during the literature search is provided in Figure 1 (for more information, visit www.prisma-statement.org).²⁴ The initial search identified 77 articles investigating the association between any genetic variant and anti-tuberculosis drug-related toxicity, from which 52 distinct cohorts of patients were identified (Figure 1).

Flow chart of study according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).²⁴ NAT2 = N-acetyltransferase 2.

Forty-six articles reported data for the association between NAT2 variants and anti-tuberculosis drug-related toxicity; from these articles, 40 distinct patient cohorts were identified. In this review, we include data from 40 articles (39 distinct patient cohorts).^25–64 We did not include data from the remaining six articles.^65–70 Of those six articles, five reported data for patient cohorts for whom data were also reported in other articles (or we suspected that this was the case); for the sixth article,⁷⁰ the numbers of patients in each genotype group were not reported, and we were unable to obtain this information from the authors. The characteristics of studies included in this review are provided in Appendix Table A.3.

Quality assessment

Choosing which genes and SNPs to genotype

Twenty-seven articles reported the reasons for choosing all genes and SNPs investigated. For the 13 articles²⁸^,³¹^,³³^,³⁷^,⁴⁸^,⁵¹^,⁵³^,⁵⁷^,⁵⁹^,⁶⁰^,^62–64 that did not report this information, no articles limited their reporting to only statistically significant associations. Therefore, there was no evidence to suggest that selective reporting of genes and SNPs had occurred.

Sample size

The median sample size was 170 (interquartile range 108.5–285). Only two articles²⁶^,⁶³ provided details of the a priori power to detect pre-specified effect sizes.

Study design

Eleven articles described case-control studies, 27 articles described prospective cohorts, one article described a retrospective cohort and one article described an RCT. For one case-control study,³³ the case and control groups were not clearly defined. No articles describing case-control studies reported that the two groups were genotyped in mixed batches.

Reliability of genotypes

Only three articles²⁶^,³²^,⁴⁶ mentioned genotype quality control procedures, and only 12 articles²⁶^,³³^,³⁵^,³⁷^,³⁸^,⁴¹^,⁴⁵^,^49–51^,⁵³^,⁵⁵ compared the genotype frequencies of all investigated SNPs to those previously published for the same population. Of the articles describing case-control studies and retrospective cohorts, only two⁴⁵^,⁴⁶ mentioned that genotyping personnel were blinded to outcome status.

Missing genotype data

For most articles (29/40), on comparison of the number of participants included in the analyses with the study sample size, it was apparent there were no missing genotype data. For the remaining 11 articles,³²^,³³^,^42–44^,⁵³^,⁵⁶^,⁵⁸^,⁶⁰^,⁶³^,⁶⁴ only five articles³²^,⁵⁶^,⁵⁸^,⁶³^,⁶⁴ summarised the extent of missing data for all the genes and SNPs analysed. None of these articles described checking whether missing data were randomly distributed.

Population stratification

One article mentioned undertaking tests for population stratification;⁵³ no population stratification was identified. One article used a study design that ensured that the included patients were from a non-diverse ethnic group.⁴⁸ All other studies were at potential risk from confounding due to population stratification.

Hardy-Weinberg equilibrium

Twenty-three articles³⁰^,³²^,^34–39^,^41–43^,^46–49^,⁵³^,⁵⁷^,⁵⁸^,^60–64 reported testing for Hardy-Weinberg equilibrium (HWE) for all investigated SNPs, and a further three²⁵^,⁵¹^,⁵⁶ tested for HWE for a subset of SNPs. The remaining 14 articles reported no testing for HWE.

Mode of inheritance

Nineteen articles made a specific assumption regarding the underlying mode of inheritance.²⁵^,²⁹^,³¹^,³⁴^,³⁵^,⁴⁰^,⁴³^,⁴⁴^,⁴⁸^,⁵⁰^,⁵³^,^55–57^,^59–61^,⁶³^,⁶⁴ Of these, only two provided justification;²⁹^,⁶⁰ for the remaining 17 articles, there was a risk of selective reporting under different modes of inheritance. Two articles⁴²^,⁵⁸ applied models assuming different modes of inheritance to the genotype data, although only one of these articles⁴² adjusted these analyses for multiplicity of testing.

Choice and definition of outcomes

There was large variation in the definition of hepatotoxicity (Appendix Table A.4). Of the 37 articles reporting hepatotoxicity data, one did not provide a definition,⁶² one provided a vague definition,³⁰ and the remaining 35 articles provided 31 different definitions. Definitions of other toxicity outcomes were generally not sufficiently detailed (Appendix Table A.5).

Nine articles did not provide justification for the choice of outcomes, but outcomes were in line with the main study aim as conveyed in the Introduction section of the article.²⁷^,³²^,³⁸^,⁴⁹^,⁵⁰^,⁵²^,⁵⁶^,⁵⁷^,⁶³ The remaining articles all provided justification for the choice of outcomes. There was therefore no evidence to suggest that selective reporting of outcomes had occurred.

Treatment adherence

Six articles³¹^,³²^,⁴³^,⁴⁵^,⁵⁰^,⁵⁷ mentioned assessing treatment adherence. One article⁴⁸ reported that treatment was administered under DOTS; it was therefore not necessary to measure adherence. Of the six articles that reported assessing adherence, one did not report adjusting the analyses for adherence.⁵⁰ It was not necessary to adjust for adherence in the analyses of two articles because patients were reported to have good treatment adherence.³¹^,³²

Association between NAT2 variants and anti-tuberculosis drug-related toxicity

NAT2 acetylator status and hepatotoxicity

A forest plot displaying the results of the primary analysis is given in Figure 2. Slow/intermediate acetylators were significantly more likely to experience hepatotoxicity than rapid acetylators (OR 1.59, 95%CI 1.26–2.01). No heterogeneity was detected in this analysis (I² = 0%).

Slow/intermediate vs. rapid acetylator status for the outcome of hepatotoxicity. ^* Conducted in LTBI populations. ^†Caucasian: 38 (43%), Hispanic: 8 (9%), African: 22 (25%), South American: 15 (17%), Asian: 5 (6%), Middle Eastern: 1 (1%). ^‡Asian: 72 (42%), Caucasian: 49 (29%), South Asian: 22 (13%), Hispanic: 7 (4%), Middle Eastern: 8 (5%), First nations: 5 (3%), Other/mixed/unknown: 7 (4%). OR = odds ratio; CI = confidence interval; GI = group identifier; LTBI = latent tuberculous infection.

Results of the sensitivity analyses are provided in Appendix Figures A.1–A.3. Results from the pairwise comparisons suggested that slow acetylators were significantly more likely to experience hepatotoxicity than rapid acetylators (OR 3.68, 95%CI 2.23–6.09, I² = 60.0%), but there were no significant differences between intermediate and rapid acetylators (OR 1.12, 95%CI 0.87–1.45, I² = 0%). The sensitivity analysis that compared slow acetylators with rapid/intermediate acetylators suggested that slow acetylator status significantly increased the risk of hepatotoxicity (OR 3.12, 95%CI 2.45–3.97, I² = 59.0%).

Moderate heterogeneity was observed in the sensitivity analyses of slow vs. rapid acetylator status, and slow vs. rapid/intermediate acetylator status. Such moderate heterogeneity may have been due to the variable distribution of genotypes in different geographic areas.

The funnel plot for the primary analysis (Appendix Figure A.4) provided no evidence of publication bias.

NAT2 SNPs and hepatotoxicity

The included studies reported data for 12 NAT2 SNPs. A summary of all data for the association between NAT2 SNPs and hepatotoxicity is provided in Table 1. There were sufficient data to perform meta-analyses for six SNPs. Forest plots showing the results of these meta-analyses are provided in Figure 2. The four main findings from these meta-analyses are shown below.

1 For 590G-A and 857G-A, both heterozygous genotype and homozygous mutant-type significantly increased hepatotoxicity risk compared with homozygous wt (590G-A: GA vs. GG, OR 1.30, 95%CI 1.06–1.59, I² =0%; AA vs. GG, OR 2.05, 95%CI 1.24–3.40, I² =47.7%; 857G-A: GA vs. GG, OR 1.30, 95%CI 1.03–1.64, I² = 0.9%; AA vs. GG, OR 1.99, 95%CI 1.02–3.91, I² = 11.3%).
2 For 282C-T, homozygous mutant-type significantly increased hepatotoxicity risk compared with homozygous wt (OR 3.95, 95%CI 2.21–7.05, I² = 5.5%); however, no significant difference was observed for heterozygous genotype compared with homozygous wt (OR 1.27, 95%CI 0.80–2.02, I² = 0%).
3 For 481C-T, heterozygous genotype significantly increased hepatotoxicity risk compared with homozygous wt (OR 1.48, 95%CI 1.12–1.97, I² = 0%); however, no significant difference was observed for homozygous mutant-type compared with homozygous wt (OR 1.91, 95%CI 0.93–3.92, I² = 34.1%). The lack of statistical significance for the latter comparison may have been caused by the relatively small number of homozygous mutant-type patients (n = 162) among the patients contributing data to this analysis (n = 3604)
4 For 341T-C and 803A-G, no significant differences were observed for either pairwise comparison (341T-C: TC vs. TT, OR 1.15, 95%CI 0.72–1.82, I²=0%; CC vs. TT, OR 1.54, 95%CI 0.58–4.04, I² = 0%; 803A-G: AG vs. AA, OR 1.14, 95%CI 0.67–1.96, I² =0%; GG vs. AA, OR 1.90, 95%CI 0.66–5.52, I² = 0%).

Table 1.

Summary of all reported data for the association between NAT2 SNPs and hepatotoxicity

NAT2 SNP	Comparison	Country (number of studies)	Ethnicity	OR (95%CI)	Cases n	Controls n	I²
190C-T	Het (CT) vs. hom wt (CC)	China (1 study)	NR	0.21 (0.01–4.38)	101	107	NA
190C-T	Hom MT (TT) vs. hom wt (CC)	China (1 study)	NR	Data excluded^*
191G-A (rs1801279)	Het (GA) vs. hom wt (GG)	Taiwan (1 study)	NR	Data excluded^*
		Turkey (1 study)	NR	Data excluded^*
		All (0 studies)		NA	NA	NA	NA
	Hom MT (AA) vs. hom wt (GG)	Taiwan (1 study)	NR	Data excluded^*
		Turkey (1 study)	NR	Data excluded^*
		All (0 studies)		NA	NA	NA	NA
282C-T (rs1041983)	Het (CT) vs. hom wt (CC)	China (1 study)	NR	1.28 (0.67–2.44)	65	98	NA
		Taiwan (1 study)	NR	0.50 (0.06–4.06)	70	284	NA
		Indonesia (1 study)	100% Indonesian	1.25 (0.51–3.05)	27	148	NA
		Brazil (1 study)	NR	1.67 (0.56–5.00)	14	216	NA
		All (4 studies)		1.27 (0.80, 2.02)	176	746	0.0
	Hom MT (TT) vs. hom wt (CC)	China (1 study)	NR	7.00 (2.89–16.98)	60	51	NA
		Taiwan (1 study)	NR	1.33 (0.05–32.91)	69	277	NA
		Indonesia (1 study)	100% Indonesian	3.41 (1.38–8.40)	31	94	NA
		Brazil (1 study)	NR	2.07 (0.59–7.25)	12	185	NA
		All (4 studies)		3.95 (2.21, 7.05)	172	607	5.5
341T-C (rs1801280)	Het (TC) vs. hom wt (TT)	China (1 study)	NR	1.63 (0.45–5.94)	101	107	NA
		Taiwan (2 studies)	NR	1.26 (0.58–2.75)	114	376	0.0
		Indonesia (1 study)	100% Indonesian	1.13 (0.54–2.35)	49	188	NA
		Brazil (1 study)	NR	0.66 (0.18–2.42)	10	187	NA
		All (5 studies)		1.15 (0.72–1.82)	274	858	0.0
	Hom MT (CC) vs. hom wt (TT)	China (1 study)	NR	Data excluded^*
		Taiwan (2 studies)	NR	1.18 (0.08–16.93)	105	355	41.4
		Indonesia (1 study)	100% Indonesian	1.32 (0.13–13.01)	38	149	NA
		Brazil (1 study)	NR	1.75 (0.50–6.13)	12	122	NA
		All (4 studies)		1.54 (0.58, 4.04)	155	626	0.0
481C-T (rs1799929)	Het (CT) vs. hom wt (CC)	China (3 studies)	1 study, 100% Chinese; 2 studies, NR	1.66 (1.11–2.48)	259	2027	0.0
		Taiwan (1 study)	NR	4.12 (0.25–66.63)	70	285	NA
		Indonesia (1 study)	100% Indonesian	1.01 (0.47–2.14)	49	188	NA
		India (1 study)	NR	1.82 (0.89–3.71)	39	154	NA
		Tunisia (1 study)	NR	1.33 (0.29–6.06)	8	42	NA
		Turkey (1 study)	NR	2.17 (0.88–5.36)	28	63	NA
		Brazil (1 study)	NR	0.44 (0.14–1.37)	14	216	NA
		All (9 studies)		1.48 (1.12, 1.97)	467	2975	0.0
	Hom MT (TT) vs. hom wt (CC)	China (3 studies)^†	1 study, 100% Chinese; 2 studies, NR	0.81 (0.19–3.41)	41	1155	NA
		Taiwan (1 study)	NR	Data excluded^*
		Indonesia (1 study)	100% Indonesian	1.28 (0.13–12.66)	39	149	NA
		India (1 study)	NR	5.38 (1.99–14.49)	27	97	NA
		Tunisia (1 study)	NR	3.60 (0.83–15.57)	10	34	NA
		Turkey (1 study)	NR	0.93 (0.17–5.08)	14	46	NA
		Brazil (1 study)	NR	1.19 (0.34–4.09)	13	132	NA
		All (6 studies)		1.91 (0.93, 3.92)	144	1613	34.1
499G-A	Het (GA) vs. hom wt (GG)	China (1 study)	NR	0.21 (0.01–4.38)	101	107	NA
499G-A	Hom MT (AA) vs. hom wt (GG)	China (1 study)	NR	Data excluded^*
590G-A (rs1799930)	Het (GA) vs. hom wt (GG)	China (3 studies)	1 study, 100% Chinese; 2 studies, NR	1.19 (0.86–1.66)	236	1921	15.6
		Taiwan (2 studies)	NR	1.16 (0.74–1.82)	104	356	0.0
		South Korea (1 study)	NR	1.99 (1.06–3.74)	57	145	NA
		Indonesia (1 study)	100% Indonesian	1.17 (0.58–2.36)	38	173	NA
		India (1 study)	NR	1.38 (0.70–2.72)	45	137	NA
		Tunisia (1 study)	NR	0.77 (0.22–2.77)	12	50	NA
		Turkey (1 study)	NR	2.63 (1.00–6.87)	24	67	NA
		Brazil (1 study)	NR	2.36 (0.27–20.76)	18	247	NA
		All (11 studies)		1.30 (1.06, 1.59)	534	3096	0.0
	Hom MT (AA) vs. hom wt (GG)	China (3 studies)	1 study, 100% Chinese; 2 studies, NR	1.63 (0.66–4.00)	165	1356	58.1
		Taiwan (2 studies)	NR	1.52 (0.68–3.40)	74	250	0.0
		South Korea (1 study)	NR	5.26 (1.61–17.26)	39	107	NA
		Indonesia (1 study)	100% Indonesian	3.29 (1.34–8.08)	29	102	NA
		India (1 study)	NR	0.64 (0.22–1.88)	25	100	NA
		Tunisia (1 study)	NR	3.71 (0.44–31.26)	9	28	NA
		Turkey (1 study)	NR	9.11 (1.91–43.46)	15	44	NA
		Brazil (1 study)	NR	1.25 (0.07–23.62)	17	246	NA
		All (11 studies)		2.05 (1.24–3.40)	373	2233	47.7
803A-G (rs1208)	Het (AG) vs. hom wt (AA)	China (1 study)	NR	1.63 (0.45–5.94)	101	107	NA
		Taiwan (1 study)	NR	1.36 (0.14–13.30)	70	285	NA
		Indonesia (1 study)	100% Indonesian	1.15 (0.55–2.41)	49	187	NA
		Brazil (1 study)	NR	0.82 (0.27–2.52)	13	219	NA
		All (4 studies)		1.14 (0.67–1.96)	233	798	0.0
	Hom MT (GG) vs. hom wt (AA)	China (1 study)	NR	Data excluded^†
		Taiwan (1 study)	NR	Data excluded^†
		Indonesia (1 study)	100% Indonesian	0.99 (0.11–9.09)	38	150	NA
		Brazil (1 study)	NR	2.32 (0.69–7.78)	12	140	NA
		All (2 studies)		1.90 (0.66–5.52)	50	290	0.0%
857G-A (rs1799931)	Het (GA) vs. hom wt (GG)	China (3 studies)	1 study, 100% Chinese; 2 studies, NR	1.28 (0.74–2.22)	254	2069	61.5
		Taiwan (2 studies)	NR	1.13 (0.70–1.82)	103	368	0.0
		South Korea (1 study)	NR	1.11 (0.56–2.20)	65	150	NA
		Indonesia (1 study)	100% Indonesian	1.41 (0.72–2.75)	49	190	NA
		Tunisia (1 study)	NR	0.70 (0.03–15.34)	14	52	NA
		Turkey (1 study)	NR	3.39 (0.84–13.67)	29	69	NA
		Brazil (1 study)	NR	2.19 (0.73–6.55)	17	250	NA
		All (10 studies)		1.30 (1.03–1.64)	531	3148	0.9
	Hom MT (AA) vs. hom wt (GG)	China (3 studies)	1 study, 100% Chinese; 2 studies, NR	0.98 (0.38–2.51)	184	1677	0.0
		Taiwan (2 studies)	NR	5.05 (0.47–54.88)	82	268	74.2
		South Korea (1 study)	NR	1.18 (0.10–13.36)	50	118	NA
		Indonesia (1 study)	100% Indonesian	4.31 (0.26–70.80)	33	139	NA
		Tunisia (1 study)	NR	Data excluded^*
		Turkey (1 study)	NR	2.71 (0.16–45.03)	25	66	NA
		Brazil (1 study)	NR	8.75 (0.74–103.44)	13	212	NA
		All (9 studies)		1.99 (1.02–3.91)	387	2480	11.3
rs1495741	Het (AG) vs. hom wt (AA)	Taiwan (1 study)	NR	0.19 (0.07–0.52)	19	249	NA
rs1495741	Hom MT (GG) vs. hom wt (AA)	Taiwan (1 study)	NR	0.07 (0.01–0.56)	14	152	NA
rs4646244	Het (TA) vs. hom wt (TT)	South Korea (1 study)	NR	2.03 (1.09–3.78)	57	152	NA
rs4646244	Hom MT (AA) vs. hom wt (TT)	South Korea (1 study)	NR	4.06 (1.36–12.13)	37	110	NA
Rs4646267	Het (AG) vs. hom wt (AA)	South Korea (1 study)	NR	0.50 (0.25–0.98)	52	127	NA
Rs4646267	Hom MT (GG) vs. hom wt (AA)	South Korea (1 study)	NR	0.63 (0.27–1.45)	35	66	NA

Outcome	Variant	Study	Country	Ethnicity	Comparison	OR (95%CI)	Cases n	Controls n
Peripheral neuropathy	Acetylator status	Azuma, 2013	Japan	NR	Intermediate vs. rapid	1.36 (0.32–5.75)	8	104
	Acetylator status	Azuma, 2013	Japan	NR	Slow vs. rapid	4.29 (0.66–27.8)	6	67
	191G-A (rs1801279)	Dhoro, 2013	Zimbabwe	NR	Het (GA) vs. hom wt (GG)	0.69 (0.33–1.41)	102	56
	191G-A (rs1801279)	Dhoro, 2013	Zimbabwe	NR	Hom MT (AA) vs. hom wt (GG)	2.48 (0.12–53.02)	79	38
	341T-C (rs1801280)	Dhoro, 2013	Zimbabwe	NR	Het (TC) vs. hom wt (TT)	1.01 (0.50–2.07)	84	48
	341T-C (rs1801280)	Dhoro, 2013	Zimbabwe	NR	Hom MT (CC) vs. hom wt (TT)	1.34 (0.32–5.62)	54	30
Adverse DIH outcome	Acetylator status	Bose, 2011	India	NR	Slow vs. rapid/intermediate	3.31 (1.03–10.62)	16	202
ADRs	Acetylator status	Costa, 2012	Brazil	84% Black/mixed race, 16% other	Slow vs. rapid/intermediate	3.20 (1.31–7.80)	40	47
Skin rash	Acetylator status	Higuchi, 2007	Japan	NR	Intermediate vs. rapid	0.83 (0.32–2.19)	22	68
Skin rash	Acetylator status	Higuchi, 2007	Japan	NR	Slow vs. rapid	1.21 (0.27–5.46)	15	41
Eosinophilia	Acetylator status	Higuchi, 2007	Japan	NR	Intermediate vs. rapid	1.44 (0.60–3.45)	31	59
Eosinophilia	Acetylator status	Higuchi, 2007	Japan	NR	Slow vs. rapid	0.98 (0.22–4.35)	17	39
ATD-induced MPE	R197Q (590G-A, rs1799930)	Kim, 2011 (GI: KIM)	South Korea	NR	Hom MT (AA) or het (GA) vs. hom wt (GG)	0.96 (0.50–1.84)	58	150
	G286E (857G-A, rs1799931)	Kim, 2011 (GI: KIM)	South Korea	NR	Hom MT (AA) or het (GA) vs. hom wt (GG)	1.65 (0.86–3.18)	59	152
	-9796 T-A (rs4646244)	Kim, 2011 (GI: KIM)	South Korea	NR	Hom MT (AA) or het (TA) vs. hom wt (TT)	1.08 (0.59–2.00)	62	159
	-9601A-G (rs4646267)	Kim, 2011 (GI: KIM)	South Korea	NR	Hom MT (GG) or het (AG) vs. hom wt (AA)	0.65 (0.33–1.27)	61	159
Gastrointestinal ADRs	Acetylator status	Possuelo, 2008(GI: POSSUELO)	Brazil	57% White	Slow vs. rapid/intermediate	1.18 (0.51–2.70)	33	207

Databases	Date searched	Number retrieved
MEDLINE (Ovid) and MEDLINE In-Process (Ovid)	3 March 2016	3029
EMBASE (Ovid)	3 March 2016	4778
PubMed	3 March 2016	379
Web of science	3 March 2016	421
Biosis	3 March 2016	328

A) Database: Web of science and Biosis

Approximately 1 634 627		#10 OR #7 OR #6 OR #5 OR #4
Approximately 9 565		TITLE: ((((Genetic or gene) near/2 associat near/2 (studies or study or analys*))))
Approximately 93 935		#2 OR #1
Approximately 382 014		TITLE: ((((TB or Tuberculosis* or Antitubercul*))))
Approximately 219 324		TITLE: ((((gene* or genetic) near/5 (mutat or variant*))))
Approximately 1 388 831		TITLE: ((((SNP or Genotyp* or Phenotyp* or Allele* or Pharmacogenet* or Pharmacogenom* or Polymorph*))))
Approximately 38 430		TITLE: (((single* near/2 nucleotid* near/2 polymorph*)))
Approximately 45 745		TITLE: ((((genetic* or gene) near/3 (suscept or predisposit* or anticipat*))))
Approximately 47 961		TITLE: (((aminosalicylic acid or diarylquinoline* or ethambutol* or ethionamide* or isoniazid* or prothionamide* or pyrazinamide* or thioacetazone* or capreomycin* or cycloserine* or enviomycin* or rifabutin* or rifampin* or viomycin*)))
Approximately 49 386		TITLE: ((((Antitubercul* or tuberculos* or TB) Near/4 (agent* or drug* or antibiotic* or medicine* or medication* or treatment*))))

B) Database: Medline

# ▴	Searches		Results

1	antitubercular agents/ or aminosalicylic acid/ or diarylquinolines/ or ethambutol/ or ethionamide/ or isoniazid/ or prothionamide/ or pyrazinamide/ or thioacetazone/ or antibiotics, antitubercular/ or capreomycin/ or cycloserine/ or enviomycin/ or rifabutin/ or rifampin/ or viomycin/		73 943
2	((Antitubercul* or tuberculos* or TB) adj4 (agent* or drug* or antibiotic* or medicine* or medication* or treatment*)).tw.		29 293
3	(aminosalicylic acid or diarylquinoline* or ethambutol* or ethionamide* or isoniazid* or prothionamide* or pyrazinamide* or thioacetazone* or capreomycin* or cycloserine* or enviomycin* or rifabutin* or rifampin* or viomycin*).tw.		26 053
4	1 or 2 or 3		93 357
5	Polymorphism, Genetic/		103 705
6	genetic predisposition to disease/ or anticipation, genetic/		101 390
7	Pharmacogenetics/		9 595
8	Genetic Association Studies/		14 210
9	((Genetic or gene) adj2 associat adj2 (studies or study or analys*)).tw.		4 883
10	((genetic* or gene) adj3 (suscept or predisposit* or anticipat*)).tw.		40 247
11	Polymorphism, Single Nucleotide/		77 811
12	(single* adj2 nucleotid* adj2 polymorph*).tw.		46 260
13	(SNP or Genotyp* or Phenotyp* or Allele* or Pharmacogenet* or Pharmacogenom* or Polymorph*).tw.		774 469
14	((gene* or genetic) adj5 (mutat or variant*)).tw.		182 197
15	Genotype/ or Phenotype/ or Alleles/		381 555
16	or/5–15		1 035 512
17	exp Tuberculosis/		175 110
18	(TB or Tuberculosis*).tw.		153 175
19	Antitubercul*.tw.		11 635
20	or/17–19		213 138
21	4 and 16 and 20		2 846
22	animal/ not human/		4 159 388
23	21 not 22		2 730

C) Database: Embase

# ▴	Searches		Results

1	antitubercular agents/ or aminosalicylic acid/ or diarylquinolines/ or ethambutol/ or ethionamide/ or isoniazid/ or prothionamide/ or pyrazinamide/ or thioacetazone/ or antibiotics, antitubercular/ or capreomycin/ or cycloserine/ or enviomycin/ or rifabutin/ or rifampin/ or viomycin/		151 901
2	((Antitubercul* or tuberculos* or TB) adj4 (agent* or drug* or antibiotic* or medicine* or medication* or treatment*)).tw.		40 664
3	(aminosalicylic acid or diarylquinoline* or ethambutol* or ethionamide* or isoniazid* or prothionamide* or pyrazinamide* or thioacetazone* or capreomycin* or cycloserine* or enviomycin* or rifabutin* or rifampin* or viomycin*).tw.		34 743
4	1 or 2 or 3		172 588
5	Polymorphism, Genetic/		102 257
6	genetic predisposition to disease/ or anticipation, genetic/		97 585
7	Pharmacogenetics/		17 431
8	Genetic Association Studies/		876
9	((Genetic or gene) adj2 associat adj2 (studies or study or analys*)).tw.		7 890
10	((genetic* or gene) adj3 (suscept or predisposit* or anticipat*)).tw.		61 544
11	Polymorphism, Single Nucleotide/		98 303
12	(single* adj2 nucleotid* adj2 polymorph*).tw.		75 841
13	(SNP or Genotyp* or Phenotyp* or Allele* or Pharmacogenet* or Pharmacogenom* or Polymorph*).tw.		1 171 894
14	((gene* or genetic) adj5 (mutat or variant*)).tw.		294 715
15	Genotype/ or Phenotype/ or Alleles/		777 386
16	or/5–15		1 548 879
17	exp Tuberculosis/		197 008
18	(TB or Tuberculosis*).tw.		187 590
19	Antitubercul*.tw.		16 330
20	or/17–19		253 048
21	4 and 16 and 20		5 380
22	animal/ not human/		1 357 016
23	21 not 22		5 360
24	limit 23 to em=188300-201608		4 778

D) Database: PubMed

#1	Search (((Antitubercul* or tuberculos* or TB))) AND ((agent* or drug* or antibiotic* or medicine* or medication* or treatment*))		124 242
#2	Search ((aminosalicylic acid or diarylquinoline* or ethambutol* or ethionamide* or isoniazid* or prothionamide* or pyrazinamide* or thioacetazone* or capreomycin* or cycloserine* or enviomycin* or rifabutin* or rifampin* or viomycin*))		49 591
#3	Search (#1 or #2)		151 329
#4	Search ((((Genetic or gene) near/2 near/2 ))) AND associat) AND ((studies or study or analys*))		3 922
#5	Search (((genetic* or gene))) AND ((suscept or predisposit* or anticipat*))		235 548
#6	Search ((single) AND nucleotid) AND polymorph*		98 071
#7	Search ((SNP or Genotyp* or Phenotyp* or Allele* or Pharmacogenet* or Pharmacogenom* or Polymorph*))		997 538
#8	Search (((gene* or genetic))) AND ((mutat or variant*))		743 819
#9	Search (#4 or #5 or #6 or #7 or #8)		1 553 428
#10	Search (((((TB or Tuberculosis* or Antitubercul*)))))		251 923
#11	Search (#3 and #9 and #10)		7 671
#12	Search (“2015/08/01”[Date - Entrez] : “3000”[Date - Entrez])		658 085
#13	Search (#11 and #12)		379

Author, year	Outcome and definition
An, 2012	ATDH was defined as an increase of >2× ULN range in ALT or conjugated bilirubin levels or a concurrent increase in AST levels, according to the criteria of DILI developed at an international consensus meeting¹
Azuma, 2013	INH-DILI was assessed according to the diagnostic criteria of the Manual for Serious Side Effects of Drug-induced Liver Injury from the Ministry of Health, Labor and Welfare of Japan.²,³ In brief, hepatocellular injury was defined as a >2-fold increase in the ULN concentration of ALT alone or a serum ALT ratio/ALP ratio > 5, where the ALT ratio = ALT value/ULN of ALT, and ALP ratio = ALP value/ULN of ALP. Cholestatic injury was defined as an increase above 2-fold of the ULN range of ALP or a serum ALT ratio/ALP ratio < 2. Mixed injury was defined as a serum ALT ratio/ALP ratio of between 2 and 5. Causality assessments showed a relationship to the INH administration if the total score was more than grade 3, i.e. ‘possible’
Bose, 2011; Yimer 2011	ATDH (Bose 2011)/DILI (Yimer 2011) in patients was defined according to the international consensus criteria.¹ Liver biochemical parameters >2 times the ULN value was considered as hepatotoxicity
Çetintaş 3 2008	Drug-induced hepatitis criteria were defined as follows: 1) an increase in AST and ALT levels of >3-fold above normal or >5-fold above starting level or, 2) a greater than normal increase in ALT and AST levels together with hepatitis symptoms or, 3) a high bilirubin level
Chamorro, 2013	Hepatotoxicity was defined as when serum transaminase concentrations were at least 3× ULN (normal values: AST 0–32 IU/l and ALT 0–31 IU/l) with report of jaundice (bilirubin normal values: 0–1 mg/dl) and/or hepatitis symptoms (nausea, vomiting, abdominal pain), or >5× ULN with or without symptoms
Chang, 2012	ATDH was ‘defined according to the classification of the CIOMS’.¹ No further information was provided
Cho, 2007; Jung, 2015; Lee, 2010	ATDH was designated as an increase in serum ALT level > 2× ULN after anti-tuberculosis treatment, according to the criteria for DILI developed by the international consensus meeting¹
Feng, 2014; Teixeira, 2011	Anti-tuberculosis drug-induced hepatitis (Teixeira 2011)/anti-tuberculosis drug-induced hepatic injury (Feng 2014): an increase in serum transaminase values to >3× ULN values (40 IU/l ALT in Feng) and symptoms compatible with hepatitis
Fredj, 2016	The causality of drug-induced hepatotoxicity was determined according to the report of an international consensus meeting.¹ These criteria include 1) an increase of liver transaminases levels of >2 times above the normal value (<40 IU/l) for AST and ALT, 2) an improvement of this pattern after the drug withdrawal, and 3) the absence of alternative causes of this disorder
Gupta, 2013 (GI: GUPTA)	Increase in ALT > 2× ULN or a combined increase in AST and bilirubin levels, provided one of them is >2× ULN, was defined as ATDH according to the international consensus meeting¹
Higuchi, 2007	DIH was defined according to the criteria of the international consensus meeting,¹ i.e., development of a ⩾2-fold increase in serum ALT level above the ULN range: N (642 IU/l), or a combined increase of > 2 N in serum AST (N ⩽ 33 IU/l) and total bilirubin (N ⩽ 1.5 mg/dl)
Ho, 2013	The criteria for the diagnosis of hepatotoxicity was an elevation in liver function tests, AST and/or ALT of >5 × ULN; or AST and/or ALT of >3× ULN in the presence of symptoms such as nausea, vomiting, poor appetite, abdominal pain or jaundice; or AST and/or ALT of >3× ULN in the presence of total bilirubin of >2× ULN
Huang, 2003 (GI: HUANG)	Anti-tuberculosis drug-induced hepatitis was diagnosed as 1) an increase in serum ALT level > 2× ULN during treatment, according to the criteria established by the international consensus meeting;¹ 2) negative serum HBV surface antigen, IgM antibody to HAV, and antibody to HCV when ALT or AST is elevated; 3) without any other major hepatic or systemic diseases that may induce elevation of liver biochemical tests, such as alcoholic liver disease, autoimmune hepatitis, congestive heart failure, hypoxia, and bacteremia; and 4) a causality assessment score > 5 (when classified as ‘probable’ or ‘highly probable’ drug-induced hepatitis), as derived from the international consensus meeting¹
Khalili, 2011	Hepatotoxicity was defined as 1) increased levels of liver transaminases > 3 times above the normal value (<40 U/l for AST and ALT) with any other clinical signs and symptoms; or 2) elevation of transaminases > 5× ULN, if patients had no symptoms. For evaluation of causality, The Roussel Uclaf Causality Assessment Method scoring system was used²
Kim, 2009 (GI: KIM)	Anti-tuberculosis drug-induced hepatitis was defined as an elevation in the serum levels of ALT > 2× ULN (⩽40 U/ml) during treatment and normalisation of these values after cessation of medication according to the criteria from the international consensus meeting¹
Leiro-Fernandez, 2011	ATDH was defined as an increase in serum transaminase (either AST or ALT) to values > 3× ULN (i.e., >120 IU/l) at any time during the treatment period
Lv, 2012	ATDH was designated as an increase of >2× ULN value in ALT or a combined increase in AST and total bilirubin provided one of them is >2× ULN. In this study, the ULN of ALT, AST and total bilirubin were respectively 40 U/l, 40 U/l and 19 μmol/l Causality assessment result was highly probable, probable or possible based on the CIOMS scale¹
Mahmoud, 2012	DIH was diagnosed as 1) an increase in serum ALT level greater than twice the ULN during the treatment, according to the criteria established by the international consensus meeting;¹ 2) negative serum HBV surface antigen, IgM antibody to HAV, and antibody to HCV when ALT or AST was elevated; 3) without any other major hepatic or systemic diseases that may induce elevation of liver biochemical tests, such as alcoholic liver disease, autoimmune hepatitis, congestive heart failure, hypoxia, and bacteremia; when the French imputability score⁴ was classified as ‘probable’ or ‘likely’ or ‘certain’
Ng, 2014	All cases of DILI met at least one of the following biochemical criteria for enrolment into this study: 1) ALT > 5× ULN, 2) ALP > 2× ULN, or 3) ALT > 3× ULN and bilirubin > 2× ULN
Ohno, 2000	Hepatotoxicity was estimated as follows: AST and/or ALT > 1.5× ULN and 2× before administration
Possuelo, 2008 (GI: POSSUELO)	Criteria for the diagnosis of hepatotoxicity was an elevation in liver function tests, AST and/or ALT of >3× ULN (reference: respectively 40 and 65 U/l) and/or in total bilirubin up to >2.0 mg/dl in the presence of such gastrointestinal symptoms as anorexia, nausea, vomiting and/or jaundice, with a normalisation of serum ALT level after discontinuation of the anti-tuberculosis drugs
Rana, 2014 (GI: RANA)	ATDH was defined according to international consensus criteria.¹ Patients with a rise in serum AST or ALT levels ⩾ 5× ULN, irrespective of symptoms and serum bilirubin levels, or patients with rise in serum AST or ALT levels ⩾ 2× ULN with hyperbilirubinaemia and an absence of serological evidence of infection with hepatitis viruses (A, B, C and E) were considered as having ATDH
Santos, 2013 (GI: SANTOS)	Hepatotoxicity was defined as an increase in serum ALT level in excess of three times the ULN after INH treatment
Shimizu, 2006	Hepatotoxicity was defined as an ALT and/or AST level more than twice the institutional ULN according to the modified criteria of the international consensus meeting for drug-induced liver disorders.¹ The ULN for AST was 33 IU/l and that for ALT was 42 IU/l
Singla, 2014	International consensus criteria¹ define ATDH as development of >2× ULN value of ALT and AST. The ULN values used in this study were 35 U/l ALT and 40 U/l AST
Sotsuka, 2011	The severity of hepatotoxicity (hepatotoxicity A–D) was judged by the increase in either AST or ALT levels from the ULN range (AST, 33 U/l; ALT, 42 U/l): hepatotoxicity A, above the upper limit and less than 2-fold increase; hepatotoxicity B, 2- to 3-fold increase; hepatotoxicity C, 3- to 4-fold increase; hepatotoxicity D, greater than 4-fold increase. Results for grades B–D of hepatotoxicity were used in this review as clinical opinion was that the hepatotoxicity A patients would not have met the criteria for hepatotoxicity in many of the other studies included in this review
Vuilleumier, 2006	Criteria for the diagnosis of INH-H comprised elevation in AST and/or ALT levels 4-fold above the upper reference limit (168 UI/l) with or without symptoms. CDS were used to assess the likelihood of drug involvement when INH-H was suspected.⁵ Based on the CDS, causality assessment of INH-H was then categorised as definite (score > 17), probable (14–17), possible (10–13), unlikely (6–9) or excluded (<6). INH-H with possible to probable scores were considered for statistical analysis; unlikely scores were still considered when no other factor was identifiable
Wang, 2011 (GI: NTUH)	HATT was defined as increased serum AST and/or ALT > 1.5 times the baseline level. Results are presented for drug-induced HATT and virus-induced HATT separately. In this review, we used the results for drug-induced HATT. The diagnosis of INH- or RMP-induced HATT required a positive re-challenge test (at least doubling of serum AST or ALT level and recurrence of clinical symptoms of hepatitis after re-challenge), whereas PZA-induced HATT was diagnosed by exclusion
Wang, 2015 (GI: NTUH)	Hepatitis during anti-tuberculosis treatment was defined as increased serum AST and/or ALT > 3× ULN in symptomatic patients, or >5× ULN in asymptomatic patients. The diagnosis of INH- or RMP-induced hepatitis required a positive re-challenge test (at least doubling of serum AST or ALT levels and recurrence of clinical symptoms of hepatitis after re-challenge), whereas PZA-induced hepatitis was diagnosed either by a positive re-challenge test or by exclusion. Results are presented for overall drug-induced HATT and INH-induced HATT separately. In this review, we used the results for overall drug-induced HATT as our review focuses on hepatotoxicity induced by any anti-tuberculosis drug
Xiang, 2014	ATLI was defined as an ALT, AST or bilirubin value > 2× ULN. The ULN used in the study was 40 Ul for ALT, 40 U/l for AST, and 19 mmol/l for total bilirubin
Yamada, 2009	ATDH was defined as an increase in serum AST level > 2× ULN during 9 months of treatment with INH according to the criteria of the international consensus meeting in Paris;¹ normalisation of serum AST level after discontinuation of INH; and a causality assessment score² of >8, corresponding to the category of highly probable hepatotoxicity
Yuliwulandari, 2016	ATLI: definition not reported
Zaverucha-do-Valle, 2014	Hepatotoxicity was defined as 2× ULN (ALT 42 IU/l) or at least a 2-fold increase in ALT initial levels in patients with a baseline ALT of >84 IU/l during the treatment period

PERMALINK

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

Abstract

BACKGROUND:

OBJECTIVE:

METHOD:

RESULTS:

CONCLUSION:

Abstract

CADRE :

OBJECTIF :

MÉTHODE:

RÉSULTATS:

CONCLUSION:

Abstract

MARCO DE REFERENCIA:

OBJETIVO:

MÉTODO:

RESULTADOS:

CONCLUSIÓN:

METHODS

Selection criteria

Types of studies

Types of participants

Types of outcomes

Search strategy

Study selection

Outcomes

Data collection

Quality assessment

Data synthesis

Primary analysis

Secondary analysis

Investigation of heterogeneity

Selective reporting

Publication bias

RESULTS

Included and excluded studies

Figure 1.

Quality assessment

Choosing which genes and SNPs to genotype

Sample size

Study design

Reliability of genotypes

Missing genotype data

Population stratification

Hardy-Weinberg equilibrium

Mode of inheritance

Choice and definition of outcomes

Treatment adherence

Association between NAT2 variants and anti-tuberculosis drug-related toxicity

NAT2 acetylator status and hepatotoxicity

Figure 2.

NAT2 SNPs and hepatotoxicity

Table 1.

Table 1.

NAT2 variants and other toxicity outcomes

Table 2.

DISCUSSION

Meta-analyses

Quality assessment

Limitations

Recommendations for authors of pharmacogenetic studies

CONCLUSION

Acknowledgments

APPENDIX

Table A.1.

Table A.2.

Table A.2.

Table A.3.

Table A.3.

Table A.3.

Table A.3.

Table A.3.