Arsenic and gallbladder cancer risk: Mendelian randomization analysis of European prospective data

International Journal of CancerVolume 146, Issue 9 p. 2648-2650 Letter to the EditorFree Access Arsenic and gallbladder cancer risk: Mendelian randomization analysis of European prospective data Carol Barahona Ponce, Carol Barahona Ponce Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago, ChileSearch for more papers by this authorDominique Scherer, Dominique Scherer Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, GermanySearch for more papers by this authorFelix Boekstegers, Felix Boekstegers orcid.org/0000-0002-0587-7624 Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, GermanySearch for more papers by this authorValentina Garate-Calderon, Valentina Garate-Calderon Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago, ChileSearch for more papers by this authorMazda Jenab, Mazda Jenab International Agency for Research on Cancer, World Health Organization, Lyon, FranceSearch for more papers by this authorKrasimira Aleksandrova, Krasimira Aleksandrova Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany Institute of Nutritional Science, University of Potsdam, Potsdam, GermanySearch for more papers by this authorVerena Katzke, Verena Katzke Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, GermanySearch for more papers by this authorElisabete Weiderpass, Elisabete Weiderpass orcid.org/0000-0003-2237-0128 International Agency for Research on Cancer, World Health Organization, Lyon, FranceSearch for more papers by this authorCatalina Bonet, Catalina Bonet Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Barcelona, SpainSearch for more papers by this authorTahereh Moradi, Tahereh Moradi Division of Epidemiology, Department of Environmental Medicine, Karolinska Institutet, Stockholm, SwedenSearch for more papers by this authorKrista Fischer, Krista Fischer Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, EstoniaSearch for more papers by this authorWillem Bossers, Willem Bossers Lifelines, Groningen, NetherlandsSearch for more papers by this authorHermann Brenner, Hermann Brenner Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, GermanySearch for more papers by this authorBen Schöttker, Ben Schöttker orcid.org/0000-0002-1217-4521 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, GermanySearch for more papers by this authorBernd Holleczek, Bernd Holleczek orcid.org/0000-0001-8759-4371 Saarland Cancer Registry, Saarbrücken, GermanySearch for more papers by this authorKristian Hveem, Kristian Hveem The Nord-Trøndelag Health (HUNT) Research Centre, Norwegian University of Science and Technology (NTNU), Trondheim, Norway K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, NorwaySearch for more papers by this authorNiina Eklund, Niina Eklund Genomiikka ja Biomarkkerit, National Institute for Health and Welfare (THL), Helsinki, FinlandSearch for more papers by this authorUwe Völker, Uwe Völker Interfakultäres Institut für Genetik und Funktionelle Genomforschung, Universitätsmedizin Greifswald, GermanySearch for more papers by this authorMelanie Waldenberger, Melanie Waldenberger Research Unit of Molecular Epidemiology and Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorJusto Lorenzo Bermejo, Corresponding Author Justo Lorenzo Bermejo [email protected] orcid.org/0000-0002-6568-5333 Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, GermanyCorrespondence to: Justo Lorenzo Bermejo, E-mail: [email protected]Search for more papers by this author Carol Barahona Ponce, Carol Barahona Ponce Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago, ChileSearch for more papers by this authorDominique Scherer, Dominique Scherer Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, GermanySearch for more papers by this authorFelix Boekstegers, Felix Boekstegers orcid.org/0000-0002-0587-7624 Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, GermanySearch for more papers by this authorValentina Garate-Calderon, Valentina Garate-Calderon Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany Department of Basic and Clinical Oncology, Medical Faculty, University of Chile, Santiago, ChileSearch for more papers by this authorMazda Jenab, Mazda Jenab International Agency for Research on Cancer, World Health Organization, Lyon, FranceSearch for more papers by this authorKrasimira Aleksandrova, Krasimira Aleksandrova Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany Institute of Nutritional Science, University of Potsdam, Potsdam, GermanySearch for more papers by this authorVerena Katzke, Verena Katzke Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, GermanySearch for more papers by this authorElisabete Weiderpass, Elisabete Weiderpass orcid.org/0000-0003-2237-0128 International Agency for Research on Cancer, World Health Organization, Lyon, FranceSearch for more papers by this authorCatalina Bonet, Catalina Bonet Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Barcelona, SpainSearch for more papers by this authorTahereh Moradi, Tahereh Moradi Division of Epidemiology, Department of Environmental Medicine, Karolinska Institutet, Stockholm, SwedenSearch for more papers by this authorKrista Fischer, Krista Fischer Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, EstoniaSearch for more papers by this authorWillem Bossers, Willem Bossers Lifelines, Groningen, NetherlandsSearch for more papers by this authorHermann Brenner, Hermann Brenner Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, GermanySearch for more papers by this authorBen Schöttker, Ben Schöttker orcid.org/0000-0002-1217-4521 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, GermanySearch for more papers by this authorBernd Holleczek, Bernd Holleczek orcid.org/0000-0001-8759-4371 Saarland Cancer Registry, Saarbrücken, GermanySearch for more papers by this authorKristian Hveem, Kristian Hveem The Nord-Trøndelag Health (HUNT) Research Centre, Norwegian University of Science and Technology (NTNU), Trondheim, Norway K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, NorwaySearch for more papers by this authorNiina Eklund, Niina Eklund Genomiikka ja Biomarkkerit, National Institute for Health and Welfare (THL), Helsinki, FinlandSearch for more papers by this authorUwe Völker, Uwe Völker Interfakultäres Institut für Genetik und Funktionelle Genomforschung, Universitätsmedizin Greifswald, GermanySearch for more papers by this authorMelanie Waldenberger, Melanie Waldenberger Research Unit of Molecular Epidemiology and Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorJusto Lorenzo Bermejo, Corresponding Author Justo Lorenzo Bermejo [email protected] orcid.org/0000-0002-6568-5333 Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, GermanyCorrespondence to: Justo Lorenzo Bermejo, E-mail: [email protected]Search for more papers by this author First published: 17 December 2019 https://doi.org/10.1002/ijc.32837Citations: 5AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abbreviations AS3MT arsenite methyltransferase DMA dimethylarsinic acid ESTHER Epidemiologische Studie zu Chancen der Verhütung, Früherkennung und optimierten Therapie chronischer Erkrankungen in der älteren Bevölkerung FTCD formimidoyltransferase cyclodeaminase GBC gallbladder cancer iAs inorganic arsenic MMA monomethylarsonic acid MR Mendelian randomization OR odds ratio SD standard deviation T tertile Dear editor, An inverse association between arsenic in serum and the risk of gallbladder cancer (GBC) was recently reported in a cross-sectional study conducted by Lee et al. in Shanghai, China.1 This result was surprising, because arsenic has been classified as a human carcinogen and arsenic-contaminated water has recently been associated with increased risk of GBC.2, 3 Motivated by this unexpected finding, we applied Mendelian randomization (MR) to assess the causal effect of arsenic on GBC risk. Once arsenic in drinking water and food is absorbed into the bloodstream, inorganic arsenic (iAs) is methylated to monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) to facilitate excretion in urine.4 Lee et al. used inductively coupled plasma mass spectrometry to measure total arsenic in serum but made no distinction among arsenic species. The authors categorized the arsenic levels based on tertiles (Ts) because they noticed a nonlinear relationship between total arsenic and GBC risk. The reported odds ratios (OR) adjusted for age, sex, body mass index, cigarette smoking, alcohol consumption and levels of triglycerides and cholesterol were OR = 0.38 for T2 versus T1 and OR = 0.20 for T3 versus T1 (p trend <0.001). In the discussion of their findings, Lee et al. hypothesize that the inverse association could be attributed to decreased seafood intake by GBC patients; however, information on the amount, frequency and types of seafood consumed by study participants was not available. The authors also postulate that the inverse association could be due to impaired viability and apoptosis of cancer cells after arsenic exposure, but the case–control study design did not permit assessment of whether arsenic exposure preceded GBC development. In addition to the impossibility of (i) distinguishing between arsenic species and (ii) ruling out reverse causality (i.e., GBC causes decreased arsenic levels rather than vice versa), (iii) potential confounding was another limitation of the study by Lee et al. For example, diabetes has been associated with both arsenic exposure and GBC, and these associations could negatively distort the observed relationship between increased arsenic levels in serum and decreased GBC risk.5, 6 The capacity to metabolize arsenic shows considerable interindividual variation, depending partly on the genetic variants inherited by an individual. The relative abundance of arsenic species in urine reflects the individual capacity for arsenic elimination: increased iAs% and MMA%, and decreased DMA% are indicative of poor metabolizing efficiency, which in turn results in a high biologically effective dose of arsenic exposure.4 MR permits assessment of the causal effect of a risk factor (here, the percentages of arsenic species) on a particular phenotype (here, GBC development) using genetic variants as instrumental variables.7 MR makes it possible to rule out the potential effects of reverse causality and confounding. We applied two-sample MR to examine the causal effects of iAs%, MMA% and DMA% on GBC risk. The methodology proposed by Burgess et al., which has been previously applied to other MR studies of arsenic, was used to integrate genotype-arsenic metabolism summary statistics from the literature with genotype-GBC risk summary statistics adjusted for age, gender and the first five genetic principal components based on a collaborative European study set up with the participation of the European Prospective Investigation into Cancer and Nutrition Cohort, the Nord-Trøndelag Health Study, the ESTHER Study, the Swedish Twin Registry, the National FINRISK Study, the Study of Health in Pomerania, the Estonian Genome Project and Lifelines.8 Ethics approval was obtained for all studies and informed consent was provided by all participants. Statistics on the association between the percentages of arsenic species and the instrumental variables rs9527 and rs11191527 near AS3MT, and rs61735836 in exon 3 of FTCD were retrieved from two publications.4, 9 Additive mixed linear regression models were used for association testing in two study populations comprising 2,060 (AS3MT) and 1,660 (FTCD) arsenic-exposed Bangladeshi individuals. The variance in relative abundance of arsenic species explained by the considered genetic variants (for example, ~10% for DMA%) and the available sample size (103 prospective cases and 168 controls) translated into a detectable OR of around 0.39 per standard deviation (SD; type I error rate of 5%).10 In agreement with the surprising results of Lee et al., we found evidence for a protective effect of iAs% on GBC risk (OR = 0.80, p = 0.03, Fig. 1). Adding plausibility to this finding, poor metabolizing capacity, marked by MMA%, also showed a protective effect (OR = 0.85, p = 0.08) and DMA%, a marker of efficient arsenic metabolism, showed a deleterious effect on GBC risk (OR = 1.10, p = 0.06). The variants rs9527 (AS3MT) and rs61735836 (FTCD) showed consistent ORs (Fig. 1). The variant rs11191527 near AS3MT gene showed discrepant results and broader 95% confidence intervals for iAs% and MMA%. Figure 1Open in figure viewerPowerPoint Odds ratios (OR) and corresponding 95% confidence interval (95% CI) for the association between AS3MT variants rs9527 and rs11191527, and variant rs61735836 in FTCD as instrumental variables of the individual capacity to metabolize arsenic and GBC risk. The summary OR was calculated using the methodology proposed by Burgess et al. for linked genetic variants. The present MR study has some limitations. GBC is relatively rare in Europe, and the investigated collective was small compared to traditional MR studies. The measured variation of arsenic species in the study by Pierce et al.—SD = 6.4 for iAs%, SD = 5.1 for MMA% and SD = 8.5 for DMA% (personal communication)—probably results in lower detectable causal ORs for DMA% than for MMA% or iAs%.4 The genetic variants used for MR may not be the best predictors of the individual capacity to metabolize and eliminate arsenic for Europeans: the utilized summary statistics on genotype-arsenic metabolism relied on a study of arsenic exposure in Bangladesh. Differences in allele frequency, linkage disequilibrium patterns and arsenic exposure across populations could translate into alternative, stronger predictors of arsenic elimination efficiency for Europeans. For example, the minor allele frequency of rs9527 near AS3MT is 8% in Bangladeshis compared to 25% in Europeans (ensembl.org). The r2 between AS3MT variants rs9527 and rs11191527 is 0.04 for Bangladeshis and 0.36 for Europeans, and Pierce et al. explicitly state that rs11191527 may not be a strong instrumental variable for DMA% in populations with low arsenic exposure.4 In spite of these limitations, we consider that our MR results may contribute to the meager literature on GBC and hopefully motivate future collaborative research to raise the available sample sizes. Acknowledgements This study was supported by the European Union within the initiative "Biobanking and Biomolecular Research Infrastructure—Large Prospective Cohorts" (Collaborative study "Identification of biomarkers for gallbladder cancer risk prediction—Towards personalized prevention of an orphan disease") under grant agreement no. 313010 (BBMRI-LPC); the German Federal Ministry of Education and Research (BMBF, grant 01DN15021); the Deutsche Forschungsgemeinschaft and the University of Heidelberg within the funding programme Open Access Publishing and the German Research Foundation (DFG, grant LO 2061/1). The funders had no role in the design and conduct of the study; the collection, management, analysis and interpretation of the data; the preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication. Conflict of interest None declared. Disclosure All authors have nothing to disclose. The authors from the International Agency for Research on Cancer/World Health Organization alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization. Open Research Data availability statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. References 1Lee MH, Gao YT, Huang YH, et al. A metallomic approach to assess associations of serum metal levels with gallstones and gallbladder cancer. Hepatology 2020. https://doi.org/10.1002/hep.30861. [Epub ahead of print]. 2Ganesan N, Bambino K, Boffetta P, et al. Exploring the potential carcinogenic role of arsenic in gallbladder cancer. Eur J Cancer Prev 2020. https://doi.org/10.1097/CEJ.0000000000000521. [Epub ahead of print]. 3Straif K, Benbrahim-Tallaa L, Baan R, et al. A review of human carcinogens—part C: metals, arsenic, dusts, and fibres. Lancet Oncol 2009; 10: 453– 4. 4Pierce BL, Tong L, Argos M, et al. Arsenic metabolism efficiency has a causal role in arsenic toxicity: Mendelian randomization and gene-environment interaction. Int J Epidemiol 2013; 42: 1862– 71. 5Scannell Bryan M, Sofer T, Mossavar-Rahmani Y, et al. Mendelian randomization of inorganic arsenic metabolism as a risk factor for hypertension- and diabetes-related traits among adults in the Hispanic community health study/study of Latinos (HCHS/SOL) cohort. Int J Epidemiol 2019; 48: 876– 86. 6Sung TC, Huang JW, Guo HR. Association between arsenic exposure and diabetes: a meta-analysis. Biomed Res Int 2015; 2015:368087. 7Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008; 27: 1133– 63. 8Burgess S, Zuber V, Valdes-Marquez E, et al. Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables. Genet Epidemiol 2017; 41: 714– 25. 9Pierce BL, Tong L, Dean S, et al. A missense variant in FTCD is associated with arsenic metabolism and toxicity phenotypes in Bangladesh. PLoS Genet 2019; 15:e1007984. 10Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 2013; 42: 1497– 501. Citing Literature Volume146, Issue91 May 2020Pages 2648-2650 FiguresReferencesRelatedInformation