Pharmacogenetic Study of Drug-Metabolising Enzyme Polymorphisms on the Risk of Anti-Tuberculosis Drug-Induced Liver Injury: A Meta-Analysis
Shen X (2012) Pharmacogenetic Study of Drug-Metabolising Enzyme Polymorphisms on the Risk of Anti-Tuberculosis Drug-Induced
Liver Injury: A Meta-Analysis. PLoS ONE 7(10): e47769. doi:10.1371/journal.pone.0047769
Pharmacogenetic Study of Drug-Metabolising Enzyme Polymorphisms on the Risk of Anti-Tuberculosis Drug- Induced Liver Injury: A Meta-Analysis
Yu Cai 0
JiaYong Yi 0
ChaoHui Zhou 0
XiZhong Shen 0
Rui Medeiros, IPO, Inst Port Oncology, Portugal
0 1 Department of Gastroenterology, Zhongshan Hospital , Fudan Unversity, Shanghai , People's Republic of China, 2 Departments of Orthopedics, Zhongshan Hospital , Fudan Unversity, Shanghai , People's Republic of China
Background: Three first-line antituberculosis drugs, isoniazid, rifampicin and pyrazinamide, may induce liver injury, especially isoniazid. This antituberculosis drug-induced liver injury (ATLI) ranges from a mild to severe form, and the associated mortality cases are not rare. In the past decade, many investigations have focused the association between drugmetabolising enzyme (DME) gene polymorphisms and risk for ATLI; however, these studies have yielded contradictory results. Methods: PubMed, EMBASE, ISI web of science and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. A meta-analysis was performed to examine the association between polymorphisms from 4 DME genes (NAT2, CYP2E1, GSTM1 and GSTT1) and susceptibility to ATLI. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Heterogeneity among articles and their publication bias were also tested. Results: 38 studies involving 2,225 patients and 4,906 controls were included. Overall, significantly increased ATLI risk was associated with slow NAT2 genotype and GSTM1 null genotype when all studies were pooled into the meta-analysis. Significantly increased risk was also found for CYP2E1*1A in East Asians when stratified by ethnicity. However, no significant results were observed for GSTT1. Conclusions: Our results demonstrated that slow NAT2 genotype, CYP2E1*1A and GSTM1 null have a modest effect on genetic susceptibility to ATLI.
. These authors contributed equally to this work.
Tuberculosis (TB) is an important infectious disease plaguing
the developed and developing countries worldwide, with more
than nine million new cases of active tuberculosis reported
annually. Isoniazid (INH), rifampicin (RIF) and pyrazinamide
(PZA) are all widely used as first-line multidrug therapy for TB.
Anti-tuberculosis drug induced liver injury (ATLI), a common
serious adverse drug reaction, is one of the most challenging
clinical problems, cause of hospitalization and life-threatening
events. ATLI can be fatal if therapy is not interrupted on time, and
the subsequent adherence problem may cause treatment failure
and, relapse or drug resistance [1,2].
Of the various antituberculosis regimens, isoniazid is the main
drug to induce hepatotoxicity . Metabolic intermediates of
isoniazid are incriminated to be the cause of hepatotoxicity. In the
liver, isoniazid is first metabolized into acetylisoniazid via
Nacetyltransferase (NAT) , followed by hydrolysis to
acetylhydrazine. Acetylhydrazine is proposed to be oxidized into
hepatotoxic intermediates by cytochrome P450 2E1 (CYP2E1)
 and some hepatotoxic intermediates can be detoxified by
glutathione S-transferase (GST) enzyme . Drug-metabolizing
enzymes (DME) have critical effects on both the synthesis and
detoxification of reactive metabolites . Therefore, studies on
genetic predisposition for anti-TB drug-induced liver injury have
focused on a few metabolizing enzymes including
N-acetyltransferase 2 (NAT2), CYP2E1, GSTM1 and GSTT1.
Over the past few years, considerable efforts have been devoted
to exploring the relationships between the DME polymorphisms
and ATLI among various populations. However, existing studies
have yielded inconsistent results. These disparate findings may be
due partly to insufficient power, false-positive results, and
publication biases. Therefore, we performed a meta-analysis of
the published studies to clarify this inconsistency and obtain
summary risk estimates for the association of specific
polymorphism in DME and risk of ATLI.
Materials and Methods
Literature Search Strategy
Eligible literature published before the end of May 2012 were
identified through a search of PubMed, EMBASE, ISI Web of
Science and CNKI (Chinese National Knowledge Infrastructure)
without language restriction. Search term combinations were as
follows: drug-metabolising enzymes, antitubercular agents,
druginduced hepatotoxicity, drug-induced liver injury, genetic
polymorphism, arylamine N-acetyltransferase,
glutathione-S-transferase, cytochrome 2E1, acetylator phenotype, genetic susceptibility.
All reference lists from the main reports and relevant reviews
were hand searched for additional eligible studies.
Eligible Studies and Data Extraction
Eligible studies had to meet all of the following criteria: (i) the
studies were published in peer-reviewed journals and were
independent studies using original data; (ii) the studies provided
genotype distribution information of polymorphism in both cases
and controls or odds ratio (OR) with its 95% confidence interval
and P value; (iii) the studies investigated the DME polymorphism
using either case-control or cohort design; (iv) the studies described
the genotyping method, equipment, and protocols used or
provided reference to them and (v) the studies used TB patients
without ATLI as controls.
For each study, the following data were extracted independently
by two authors: first authors surname, year of publication,
diagnosis criterion, age, sex, ethnicity, genotyping method, total
number of cases and controls and genotype frequency in cases and
controls. The results were compared, and disagreements were
discussed and resolved with consensus.
The proposed risk genotypes are NAT2 slow acetylator (without
wild-type NAT2*4 allele), CYP2E1*1A/*1A (homozygous wild
type c1/c1) and homozygous null GST genotype (GSTM1 null/
null or GSTT1 null/null). Our primary analysis for measuring the
overall effects of every DME was to compare the genotype
distribution for ATLI patients against controls by the contrast of
the above risk genotypes vs. other combined genotypes.
Odds ratio (OR) with 95% confidence intervals (CIs) was used
to assess the strength of association between gene polymorphism
and ATLI risk. Cochrans chi-square-based Q statistic test was
performed in order to assess possible heterogeneity between the
individual studies and thus to ensure that each group of studies was
suitable for meta-analysis. ORs were pooled according to the
method of DerSimonian and Laird that takes into account the
variation between studies, and 95% CI were constructed using
Woolfs method [8,9]. The Z test was used to determine the
significance of the pooled OR. Sensitivity analyses were performed
to assess the stability of the results, namely, a single study in the
meta-analysis was deleted each time to reflect the influence of the
individual data set to the overall OR. Publication bias was assessed
using Eggers test  and Beggs funnel plots . All P values
are two-sided, and P,0.05 were considered statistically significant.
Statistical analyses were done with Stata (version 10.0).
Characteristics of Studies
The combined search yielded 412 references. Study selection
process was shown in Figure S1. A total of 38 studies were finally
included with 2,225 patients and 4,906 controls . For the
CYP2E1, 13 studies were available, including a total of 674 cases
and 1,990 controls. For the NAT2, 24 studies involved a total of
1,116 cases and 2,655 controls. For the GST M1, 11 studies
involved a total of 896 cases and 1,604 controls. For the GST T1,
10 studies involved a total of 792 cases and 1,493 controls. Of the
cases, 80% were East Asian, 8% were Indian, 7% were Caucasian,
and 5% were of other ethnic populations. The detailed
characteristics of the studies included in this meta-analysis are shown in
Association of CYP2E1 Gene with ATLI
Meta-analysis revealed no statistically significant association
between ATLI and CYP2E1 c1/c1 genotype [OR = 1.28; 95%
CI: 0.971.69; P(Z) = 0.08; P(Q) = 0.11]. When studies were
stratified for ethnicity, significant risks were found among East
Asians [OR = 1.35; 95% CI: 1.011.81; P(Z) = 0.04; P(Q) = 0.08].
However, no significant associations were detected among Indian,
Caucasian and other ethnic populations (Figure 1). In the
subgroup analyses by sample size, the summary OR for big
studies of the c1/c1 genotype was 1.36 [95% CI: 0.922.00;
P(Z) = 0.12; P(Q) = 0.05] and for small studies was 1.17 [95% CI:
0.751.81; P(Z) = 0.49; P(Q) = 0.28].
Association of NAT2 Gene with ATLI
Overall, there was evidence of an association between the
increased risk of ATLI and the variant when all eligible studies
were pooled into the meta-analysis. Using random effect model,
the summary OR of the NAT2 slow acetylator genotype for ATLI
was 3.18 [95% CI: 2.494.07; P(Z) ,1025; P(Q) = 0.03]. When
stratifying for ethnicity, an OR of 3.32 [95% CI: 2.434.53; P(Z)
,1025; P(Q) = 0.06], 2.96 [95% CI: 1.834.76; P(Z) ,1024;
P(Q) = 0.49], 6.64 [95% CI: 3.0114.66; P(Z) ,1024; P(Q) = 0.36]
and 5.24 [95% CI: 2.1812.60; P(Z) ,1024; P(Q) = 0.93] resulted
for the slow acetylator genotype, among East Asian, Indian,
Middle Eastern and other ethnic population, respectively
(Figure 2). Unfortunately, we failed to detect any association to
ATLI risk for Caucasians. Subsidiary analyses of control source
yielded an overall OR for big studies of 2.48 [95% CI: 1.873.30;
P(Z) ,1025; P(Q) = 0.26] and for small studies of 3.92 [95% CI:
2.755.58; P(Z) ,1025; P(Q) = 0.06].
Association of GSTM1 and GSTT1 Gene with ATLI
For ATLI risk and the null genotype of GSTM1, our
metaanalysis gave an overall OR of 1.43 [95% CI: 1.081.88;
P(Z) = 0.01; P(Q) = 0.02] with statistically significant
betweenstudy heterogeneity. This analysis is based on pooling of data from
a number of different ethnic populations. When stratifying for
ethnicity, an OR of 1.55 [95% CI: 1.122.13; P(Z) = 0.008;
P(Q) = 0.02] resulted for null genotype among East Asians; while
no significant association was found among Caucasian and Indian
populations (Figure 3). By considering sample size subgroups, the
OR was 1.61 [95% CI: 1.182.19; P(Z) = 0.003; P(Q) = 0.005] in
big studies, compared to 1.26 [95% CI: 0.542.95; P(Z) = 0.59;
P(Q) = 0.08] in small studies.
The meta-analysis resulted in a statistically non-significant
association between GSTT1 deficiency and ATLI. The overall
OR was 1.07 [95% CI: 0.821.39; P(Z) = 0.61; P(Q) = 0.16].
When stratifying for ethnicity, an OR of 0.96 [95% CI: 0.781.18;
P(Z) = 0.69; P(Q) = 0.40] and 2.92 [95% CI: 0.7910.89;
P(Z) = 0.11; P(Q) = 0.45] resulted for null genotype, among East
Asian and Indian populations, respectively (Figure 4). No
significant association was found in stratified analyses according
to sample size. The OR was 0.97 [95% CI: 0.791.19; P(Z) = 0.76;
P(Q) = 0.44] in big studies, 1.88 [95% CI: 0.685.20; P(Z) = 0.23;
P(Q) = 0.15] in small studies.
Mean age of
No. of case/control case/control
The effect of each genotype of GSTs was independently
assessed. The data on both null genotype of GSTs among cases
and controls were available in five studies, which included 460
cases and 1006 controls. The interaction between GSTM1 null
and GSTT1 null, for which an OR of 1.12 [95% CI: 0.861.48;
P(Z) = 0.40; P(Q) = 0.69] for ATLI appeared in compared with
individuals with the positive genotypes.
Sensitivity Analyses and Publication Bias
Sensitivity analyses were performed to assess the influence of
each individual study on the pooled OR by sequential removal of
individual studies. The results suggested that no individual study
significantly affected the pooled OR, thus suggesting that the
results of this meta-analysis are stable.
A funnel plot of these 24 included studies concerning NAT2 and
ATLI suggested a possibility of the preferential publication of
positive findings in smaller studies (t = 3.72, P = 0.001; Figure S2).
The shape of the funnel plots was symmetrical (Figures S3, S4 and
S5) for CYP2E1, GSTM1 and GSTT1. The statistical results still
did not show publication bias in these studies for CYP2E1
(t = 21.27, P = 0.23), GSTM1 (t = 0.32, P = 0.76) and GSTT1
polymorphism (t = 1.65, P = 0.14).
This is the most comprehensive meta-analysis concerning the
relationship between polymorphisms of three DME genes and
ATLI risk. Its strength was based on the accumulation of
published data giving greater information to detect significant
differences. In total, the meta-analysis involved 38 studies for
ATLI that provided 2,225 patients and 4,906 controls. Our
results demonstrated that the slow NAT2 genotype and GSTT1
null polymorphism is a risk factor for developing ATLI.
Although no significant association between CYP2E1 *1A
polymorphism and ATLI susceptibility was detected in the
overall comparison, we found that the CYP2E1 *1A
polymorphism was associated with increased risk of ATLI among East
Asian populations when stratified by ethnicity. Ethnic differences
may contribute to these different results, since the CYP2E1 c2
allele distribution of the polymorphism varies between East Asian
and other ethnic populations . However, we failed to detect
any positive relationship between ATLI and GSTT1 null
As the pathogenic mechanism of ATLI is poorly understood,
most studies were based on INH metabolic pathway. For
predicting ATLI, the NAT2 genotype is seemingly more
important than other genotypes, because this genotype possesses
high susceptibility to ATLI with OR of 3.18 compared with
CYP2E1 (OR = 1.28) and GSTM1 (OR = 1.43). Slow acetylators
not only acetylate isoniazid more slowly but also
monoacetylhydrazine, the immediate precursor of the toxic intermediates, to the
harmless diacetylhydrazine . This protective acetylation is
further suppressed by isoniazid. Therefore, slow acetylators may
critically increase the accumulation of toxic metabolites indirectly.
The action of NAT in the disposition of isoniazid is followed by
CYP2E1. Earlier reports demonstrated CYP2E1 activity was less
inhibited by isoniazid in subjects with CYP2E1 *1A/*1A genotype
than in those with other genotypes . Therefore, under the
administration of isoniazid, subjects with CYP2E1 *1A/*1A
genotype have higher CYP2E1 activity than those with other
genotypes, and, hence, may produce more hepatotoxins and
finally increase the risk of liver injury.
GST, as an important phase II detoxification enzyme, was
correlated to the susceptibility of alcoholic liver disease and many
cancers . Subjects with homozygous null mutant genotype of
GSTM1 or GSTT1 have been found to lose enzymatic activity
. It is speculated that people with null GSTM1 or GSTT1
genotypes could not detoxify the toxic reactive metabolites
efficiently, and thus have higher risk of drug-induced liver injury
and many cancers. However, failing to identify the association
between GSTT1 polymorphism and ATLI may be due partly to
the small sample size and low frequency of patients with ATLI;
additional studies with large sample size are therefore needed to
confirm our finds.
In interpreting the results, some limitations of this meta-analysis
should be addressed. Firstly, lack of clarification regarding the
exact criteria used for the diagnosis of ATLI, and the
HardyWeinberg equilibrium status among controls in several studies may
over inflate our results. Secondly, variation in the anti-TB drugs
administered in the studies we analyzed limited the possibility of
examining the association of DME with any specific drug. In these
studies, multiple anti-TB drugs were utilized in some research,
while some other adopts the single-drug therapy. Thirdly, our
meta-analysis is based on unadjusted estimates, whereas a more
precise analysis could be performed if individual data were
available, which would allow for an adjustment estimate (by age,
sex, alcohol consumption, cigarette smoking and other lifestyle).
Additionally, it should be noted that the studies included patients
from several different ethnic groups, with an overrepresentation of
East Asian patients (e.g., Chinese, Korean, and Japanese
populations) and an underrepresentation of individuals of
Caucasian descent. Since allele frequencies may vary considerably
between ethnic groups, careful consideration of the potential effect
of population genetics on genotypic and phenotypic distribution is
warranted, but the limited samples currently available have
hampered this effort.
As a result of the heterogeneity of medication used, the duration
of illness in different samples, and the different racial groups, it is
possible that we have underestimated the effect size of the
genedrug response association. Furthermore, none of the studies
formally accounted for medication noncompliance, which is
prevalent in patients with TB. Put simply, when a patient does
not take the prescribed anti-TB drug, the measured effect size of
gene-drug response association is assessed as zero, whereas the true
effect of genotype on the phenotype is perhaps larger.
Nevertheless, despite the potential underestimation of effect size produced
by these uncontrolled factors, we were still able to detect a
significant association between the NAT2, GSTM1 and CYP2E1
polymorphism and anti-TB drug response. It is suggested that
patients with high-risk genotypes should have regular liver
biochemical tests in the first few months following administration
of anti-TB drugs. Tailoring the anti-TB regimen to patients
according to individual genetic profiles is expected in the coming
In summary, our meta-analysis indicates that CYP2E1, NAT2
and GSTM1 genetic variation is significantly associated with
antituberculosis drug-induced liver injury. Polymorphisms in these
DME, such as NAT2*4, may be particularly important in
predicting clinical response to anti-TB drug treatment.
Furthermore, interaction between candidate genes and other risk factors,
such as diet, alcohol consumption, smoking, existing liver disease
and other comorbid diseases, should be explored to realize the
modification effect of these extrinsic factors to the expression of
The flow chart of the included studies.
Conceived and designed the experiments: CHZ. Performed the
experiments: YC JYY XZS. Analyzed the data: YC JYY. Contributed reagents/
materials/analysis tools: YC JYY XZS. Wrote the paper: CHZ XZS.
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