Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
Systematic Reviews
Published in: Systematic Reviews 1/2017

Open Access 01-12-2017 | Protocol

Influence of genetic variants on toxicity to anti-tubercular agents: a systematic review and meta-analysis (protocol)

Authors: Marty Richardson, Jamie Kirkham, Kerry Dwan, Derek Sloan, Geraint Davies, Andrea Jorgensen

Published in: Systematic Reviews | Issue 1/2017

Login to get access

Abstract

Background

Tuberculosis patients receiving anti-tuberculosis treatment may experience serious adverse drug reactions, such as hepatotoxicity. Genetic risk factors, such as polymorphisms of the NAT2, CYP2E1 and GSTM1 genes, may increase the risk of experiencing such toxicity events. Many pharmacogenetic studies have investigated the association between genetic variants and anti-tuberculosis drug-related toxicity events, and several meta-analyses have synthesised data from these studies, although conclusions from these meta-analyses are conflicting. Many meta-analyses also have serious methodological limitations, such as applying restrictive inclusion criteria, or not assessing the quality of included studies. Most also only consider hepatotoxicity outcomes and specific genetic variants. The purpose of this systematic review and meta-analysis is to give a comprehensive evaluation of the evidence base for associations between any genetic variant and anti-tuberculosis drug-related toxicity.

Methods

We will search for studies in MEDLINE, EMBASE, BIOSIS and Web of Science. We will also hand search reference lists from relevant studies and contact experts in the field. We will include cohort studies, case–control studies and randomised controlled trials that recruited patients with tuberculosis who were either already established on anti-tuberculosis treatment or were commencing treatment and who were genotyped to investigate the effect of genetic variants on any anti-tuberculosis drug-related toxicity outcome. One author will screen abstracts to identify potentially relevant studies and will then obtain the full text for each potentially relevant study in order to assess eligibility. At each of these stages, a second author will independently screen/assess 10% of studies. Two authors will independently extract data and assess the quality of studies using a pre-piloted data extraction form. If appropriate, we will pool estimates of effect for each genotype on each outcome using meta-analyses stratified by ethnicity.

Discussion

Our review and meta-analysis will update and add to the existing research in this field. By not restricting the scope of the review to a specific drug, genetic variant, or toxicity outcome, we hope to synthesise data for associations between genetic variants and anti-tuberculosis drug-related toxicity outcomes that have previously not been summarised in systematic reviews, and consequently, add to the knowledge base of the pharmacogenetics of anti-tuberculosis drugs.

Systematic review registration

PROSPERO CRD42017068448
Appendix
Available only for authorised users
Literature
1.
2.
Yee D, Valiquette C, Pelletier M, Parisien I, Rocher I, Menzies D. Incidence of serious side effects from first-line antituberculosis drugs among patients treated for active tuberculosis. Am J Respir Crit Care Med. 2003;167(11):1472–7.CrossRefPubMed
3.
Lundkvist J, Jönsson B. Pharmacoeconomics of adverse drug reactions. Fundam Clin Pharmacol. 2004;18(3):275–80.CrossRefPubMed
4.
Tostmann A, Boeree MJ, Aarnoutse RE, De Lange WCM, Van Der Ven AJAM, Dekhuijzen R. Antituberculosis drug-induced hepatotoxicity: concise up-to-date review. J Gastroenterol Hepatol. 2008;23(2):192–202.CrossRefPubMed
5.
Ohno M, Yamaguchi I, Yamamoto I, Fukuda T, Yokota S, Maekura R, et al. Slow N-acetyltransferase 2 genotype affects the incidence of isoniazid and rifampicin-induced hepatotoxicity. Int J Tuberc Lung Dis. 2000;4(3):256–61.PubMed
6.
Huang Y-S, Chern H-D, Su W-J, Wu J-C, Lai S-L, Yang S-Y, et al. Polymorphism of the N-acetyltransferase 2 gene as a susceptibility risk factor for antituberculosis drug-induced hepatitis. Hepatology. 2002;35(4):883–9.CrossRefPubMed
7.
Shimizu Y, Dobashi K, Mita Y, Endou K, Moriya S, Osano K, et al. DNA microarray genotyping of N-acetyltransferase 2 polymorphism using carbodiimide as the linker for assessment of isoniazid hepatotoxicity. Tuberculosis. 2006;86(5):374–81.CrossRefPubMed
8.
Higuchi N, Tahara N, Yanagihara K, Fukushima K, Suyama N, Inoue Y, et al. NAT2 6A, a haplotype of the N-acetyltransferase 2 gene, is an important biomarker for risk of anti-tuberculosis drug-induced hepatotoxicity in Japanese patients with tuberculosis. World J Gastroenterol. 2007;13(45):6003–8.CrossRefPubMedPubMedCentral
9.
Çetintaş VB, Erer OF, Kosova B, Özdemir I, Topçuoğlu N, Aktoğu S, et al. Determining the relation between N-acetyltransferase-2 acetylator phenotype and antituberculosis drug induced hepatitis by molecular biologic tests. Tuberk Toraks. 2008;56(1):81–6.
10.
Khalili H, Fouladdel S, Sistanizad M, Hajiabdolbaghi M, Azizi E. Association of n-acetyltransferase-2 genotypes and anti-tuberculosis induced liver injury: first case-controlled study from Iran. Curr Drug Saf. 2011;6(1):17–22.CrossRefPubMed
11.
An H, Wu X, Wang Z, Liang Y, Zhang J. The associations of polymorphism of N-acetyltransferase 2 gene is associated with antituberculosis drug-induced hepatotoxicity in tuberculosis patients. Chin J Prev Med. 2011;45(1):36–40.
12.
Possuelo L, Castelan J, De Brito T, Ribeiro A, Cafrune P, Picon P, et al. Association of slow N-acetyltransferase 2 profile and anti-TB drug-induced hepatotoxicity in patients from Southern Brazil. Eur J Clin Pharmacol. 2008;64(7):673–81.CrossRefPubMed
13.
Bose PD, Sarma MP, Medhi S, Das BC, Husain SA, Kar P. Role of polymorphic N‐acetyl transferase2 and cytochrome P4502E1 gene in antituberculosis treatment‐induced hepatitis. J Gastroenterol Hepatol. 2011;26(2):312–8.CrossRefPubMed
14.
Yamada S, Tang M, Richardson K, Halaschek-Wiener J, Chan M, Cook VJ, et al. Genetic variations of NAT2 and CYP2E1 and isoniazid hepatotoxicity in a diverse population. Pharmacogenomics. 2009;10(9):1433–45.CrossRefPubMed
15.
Kim S-H, Kim S-H, Bahn J-W, Kim Y-K, Chang Y-S, Shin E-S, et al. Genetic polymorphisms of drug-metabolizing enzymes and anti-TB drug-induced hepatitis. Pharmacogenomics. 2009;10(11):1767–79.CrossRefPubMed
16.
Cho HJ, Koh WJ, Ryu YJ, Ki CS, Nam MH, Kim JW, et al. Genetic polymorphisms of NAT2 and CYP2E1 associated with antituberculosis drug-induced hepatotoxicity in Korean patients with pulmonary tuberculosis. Tuberculosis (Edinb). 2007;87(6):551–6.CrossRef
17.
Lee S, Chung L, Huang H, Chuang T, Liou Y, Wu L. NAT2 and CYP2E1 polymorphisms and susceptibility to first-line anti-tuberculosis drug-induced hepatitis. Int J Tuberc Lung Dis. 2010;14(5):622–6.PubMed
18.
Sotsuka T, Sasaki Y, Hirai S, Yamagishi F, Ueno K. Association of isoniazid-metabolizing enzyme genotypes and isoniazid-induced hepatotoxicity in tuberculosis patients. In Vivo. 2011;25(5):803–12.PubMed
19.
Roy B, Ghosh SK, Sutradhar D, Sikdar N, Mazumder S, Barman S. Predisposition of antituberculosis drug induced hepatotoxicity by cytochrome P450 2E1 genotype and haplotype in pediatric patients. J Gastroenterol Hepatol. 2006;21(4):784–6.CrossRefPubMed
20.
Huang Y-S, Chern H-D, Su W-J, Wu J-C, Chang S-C, Chiang C-H, et al. Cytochrome P450 2E1 genotype and the susceptibility to antituberculosis drug-induced hepatitis. Hepatology. 2003;37(4):924–30.CrossRefPubMed
21.
Vuilleumier N, Rossier MF, Chiappe A, Degoumois F, Dayer P, Mermillod B, et al. CYP2E1 genotype and isoniazid-induced hepatotoxicity in patients treated for latent tuberculosis. Eur J Clin Pharmacol. 2006;62(6):423–9.CrossRefPubMed
22.
Wang T, Yu HT, Wang W, Pan YY, He LX, Wang ZY. Genetic polymorphisms of cytochrome P450 and glutathione S-transferase associated with antituberculosis drug-induced hepatotoxicity in Chinese tuberculosis patients. J Int Med Res. 2010;38(3):977–86.CrossRefPubMed
23.
Huang YS, Su WJ, Huang YH, Chen CY, Chang FY, Lin HC, et al. Genetic polymorphisms of manganese superoxide dismutase, NAD(P)H : quinone oxidoreductase, glutathione S-transferase M1 and T1, and the susceptibility to drug-induced liver injury. J Hepatol. 2007;47(1):128–34.CrossRefPubMed
24.
Roy B, Chowdhury A, Kundu S, Santra A, Dey B, Chakraborty M, et al. Increased risk of antituberculosis drug‐induced hepatotoxicity in individuals with glutathione S‐transferase M1 ‘null’mutation. J Gastroenterol Hepatol. 2001;16(9):1033–7.CrossRefPubMed
25.
Sun F, Chen Y, Xiang Y, Zhan S. Drug-metabolising enzyme polymorphisms and predisposition to anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis. 2008;12(9):994–1002.PubMed
26.
Lauterburg B, Smith C, Todd E, Mitchell J. Pharmacokinetics of the toxic hydrazino metabolites formed from isoniazid in humans. J Pharmacol Exp Ther. 1985;235(3):566–70.PubMed
27.
Cai Y, Yi J, Zhou C, Shen X. Pharmacogenetic study of drug-metabolising enzyme polymorphisms on the risk of anti-tuberculosis drug-induced liver injury: a meta-analysis. PLoS One. 2012;7(10):e47769.CrossRefPubMedPubMedCentral
28.
Wang P, Xie S, Hao Q, Zhang C, Jiang B. NAT2 polymorphisms and susceptibility to anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis. 2012;16(5):589–95.PubMed
29.
Li LM, Chen L, Deng GH, Tan WT, Dan YJ, Wang RQ, et al. SLCO1B1 *15 haplotype is associated with rifampin-induced liver injury. Mol Med Rep. 2012;6(1):75–82.PubMed
30.
Girling D. The hepatic toxicity of antituberculosis regimens containing isoniazid, rifampicin and pyrazinamide. Tubercle. 1977;59(1):13–32.CrossRef
31.
Cai L, Cai MH, Wang MY, Xu YF, Chen WZ, Qin SY, et al. Meta-analysis-based preliminary exploration of the connection between ATDILI and schizophrenia by GSTM1/T1 gene polymorphisms. PLoS One. 2015;10(6):e0128643.CrossRefPubMedPubMedCentral
32.
Deng R, Yang T, Wang Y, Tang N. CYP2E1 RsaI/PstI polymorphism and risk of anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis. 2012;16(12):1574–81.CrossRefPubMed
33.
Du H, Chen X, Fang Y, Yan O, Xu H, Li L, et al. Slow N-acetyltransferase 2 genotype contributes to anti-tuberculosis drug-induced hepatotoxicity: a meta-analysis. Mol Biol Rep. 2013;40(5):3591–6.CrossRefPubMed
34.
Li C, Long J, Hu X, Zhou Y. GSTM1 and GSTT1 genetic polymorphisms and risk of anti-tuberculosis drug-induced hepatotoxicity: an updated meta-analysis. Eur J Clin Microbiol Infect Dis. 2013;32(7):859–68.CrossRefPubMed
35.
Sheng Y-J, Wu G, He H-Y, Chen W, Zou Y-S, Li Q, et al. The association between CYP2E1 polymorphisms and hepatotoxicity due to anti-tuberculosis drugs: a meta-analysis. Infect Genet Evol. 2014;24:34–40.CrossRefPubMed
36.
Shi J, Xie M, Wang J, Xu Y, Liu X. Susceptibility of N-acetyltransferase 2 slow acetylators to antituberculosis drug-induced liver injury: a meta-analysis. Pharmacogenomics. 2015;16(18):2083–97.CrossRefPubMed
37.
Tang N, Deng R, Wang Y, Lin M, Li H, Qiu Y, et al. GSTM1 and GSTT1 null polymorphisms and susceptibility to anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis. 2013;17(1):17–25.CrossRefPubMed
38.
Wang FJ, Wang Y, Niu T, Lu WX, Sandford A, He JQ. Update meta‐analysis of the CYP2E1 RsaI/PstI and DraI polymorphisms and risk of antituberculosis drug‐induced hepatotoxicity: evidence from 26 studies. J Clin Phar Ther. 2016;41(3):334–40.CrossRef
39.
Little J, Bradley L, Bray MS, Clyne M, Dorman J, Ellsworth DL, et al. Reporting, appraising, and integrating data on genotype prevalence and gene-disease associations. Am J Epidemiol. 2002;156(4):300–10.CrossRefPubMed
40.
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.CrossRefPubMedPubMedCentral
41.
42.
Higgins J, Green S, editors. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from http://​handbook.​cochrane.​org. Accessed 1 Nov 2016.
43.
44.
Jorgensen AL, FitzGerald RJ, Oyee J, Pirmohamed M, Williamson PR. Influence of CYP2C9 and VKORC1 on patient response to warfarin: a systematic review and meta-analysis. PLoS One. 2012;7(8):e44064.CrossRefPubMedPubMedCentral
45.
Salanti G, Higgins J. Meta‐analysis of genetic association studies under different inheritance models using data reported as merged genotypes. Stat Med. 2008;27(5):764–77.CrossRefPubMed
46.
Minelli C, Thompson JR, Abrams KR, Thakkinstian A, Attia J. The choice of a genetic model in the meta-analysis of molecular association studies. Int J Epidemiol. 2005;34(6):1319–28.CrossRefPubMed
47.
Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.CrossRefPubMed
48.
Kirkham JJ, Dwan KM, Altman DG, Gamble C, Dodd S, Smyth R, et al. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. BMJ. 2010;340:c365.CrossRefPubMed
49.
Dwan K, Gamble C, Kolamunnage-Dona R, Mohammed S, Powell C, Williamson PR. Assessing the potential for outcome reporting bias in a review: a tutorial. Trials. 2010;11(1):52.CrossRefPubMedPubMedCentral
50.
Williamson P, Gamble C. Identification and impact of outcome selection bias in meta‐analysis. Stat Med. 2005;24(10):1547–61.CrossRefPubMed
51.
52.
Copas J, Dwan K, Kirkham J, Williamson P. A model-based correction for outcome reporting bias in meta-analysis. Biostatistics. 2014;15(2):370–83.CrossRefPubMed
53.
Ioannidis JP, Boffetta P, Little J, O'Brien TR, Uitterlinden AG, Vineis P, et al. Assessment of cumulative evidence on genetic associations: interim guidelines. Int J Epidemiol. 2008;37(1):120–32.CrossRefPubMed
Metadata
Title
Influence of genetic variants on toxicity to anti-tubercular agents: a systematic review and meta-analysis (protocol)
Authors
Marty Richardson
Jamie Kirkham
Kerry Dwan
Derek Sloan
Geraint Davies
Andrea Jorgensen
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Systematic Reviews / Issue 1/2017
Electronic ISSN: 2046-4053
DOI
https://doi.org/10.1186/s13643-017-0533-4

Other articles of this Issue 1/2017

Systematic Reviews 1/2017 Go to the issue

Protocol

The sleep and circadian modulation of neural reward pathways: a protocol for a pair of systematic reviews

Research

A systematic review of the processes used to link clinical trial registrations to their published results

Research

Are child-centric aspects in newborn and child health systematic review and meta-analysis protocols and reports adequately reported?—two systematic reviews

Protocol

Evaluating overweight and obesity prevalence in survivors of childhood brain tumors: a systematic review protocol

Research

Producing Cochrane systematic reviews—a qualitative study of current approaches and opportunities for innovation and improvement

Protocol

The association between child and adolescent emotional disorder and poor attendance at school: a systematic review protocol