Abstract
Introduction
Environmental chemicals acting as metabolic disruptors have been implicated with diabetogenesis, but evidence is weak among short-lived chemicals, such as disinfection byproducts (trihalomethanes, THM composed of chloroform, TCM and brominated trihalomethanes, BrTHM).
Objectives
We assessed whether THM were associated with type 2 diabetes (T2D) and we explored alterations in metabolic profiles due to THM exposures or T2D status.
Methods
A prospective 1:1 matched case–control study (n = 430) and a cross-sectional 1:1 matched case–control study (n = 362) nested within the HUNT cohort (Norway) and the Lifelines cohort (Netherlands), respectively, were set up. Urinary biomarkers of THM exposure and mass spectrometry-based serum metabolomics were measured. Associations between THM, clinical markers, metabolites and disease status were evaluated using logistic regressions with Least Absolute Shrinkage and Selection Operator procedure.
Results
Low median THM exposures (ng/g, IQR) were measured in both cohorts (cases and controls of HUNT and Lifelines, respectively, 193 (76, 470), 208 (77, 502) and 292 (162, 595), 342 (180, 602). Neither BrTHM (OR = 0.87; 95% CI: 0.67, 1.11 | OR = 1.09; 95% CI: 0.73, 1.61), nor TCM (OR = 1.03; 95% CI: 0.88, 1.2 | OR = 1.03; 95% CI: 0.79, 1.35) were associated with incident or prevalent T2D, respectively. Metabolomics showed 48 metabolites associated with incident T2D after adjusting for sex, age and BMI, whereas a total of 244 metabolites were associated with prevalent T2D. A total of 34 metabolites were associated with the progression of T2D. In data driven logistic regression, novel biomarkers, such as cinnamoylglycine or 1-methylurate, being protective of T2D were identified. The incident T2D risk prediction model (HUNT) predicted well incident Lifelines cases (AUC = 0.845; 95% CI: 0.72, 0.97).
Conclusion
Such exposome-based approaches in cohort-nested studies are warranted to better understand the environmental origins of diabetogenesis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- T2D:
-
Type 2 diabetes
- ALT:
-
Alanine-aminotransferase
- BDCM:
-
Bromodicholormethane
- BrTHM:
-
Brominated trihalomethanes
- DBCM:
-
Dibromochloromethane
- FINDRISC:
-
Finnish diabetes risk score
- HDL:
-
High density lipoprotein
- LASSO:
-
Least absolute shrinkage and selection operator
- LOD:
-
Limit of detection
- LOQ:
-
Limit of quantification
- TBM:
-
Bromoform
- TCM:
-
Chloroform
- THM:
-
Trihalomethanes
References
Andra, S. S., Charisiadis, P., & Makris, K. C. (2014). Obesity-mediated association between exposure to brominated trihalomethanes and type II diabetes mellitus: An exploratory analysis. The Science of the Total Environment, 485–486, 340–347. https://doi.org/10.1016/j.scitotenv.2014.03.075.
Andrianou, X. D., Charisiadis, P., Andra, S. S., & Makris, K. C. (2014). Spatial and seasonal variability of urinary trihalomethanes concentrations in urban settings. Environmental Research, 135, 289–295. https://doi.org/10.1016/j.envres.2014.09.015.
Aylward, L. L., Hays, S. M., & Zidek, A. (2017). Variation in urinary spot sample, 24 h samples, and longer-term average urinary concentrations of short-lived environmental chemicals: implications for exposure assessment and reverse dosimetry. Journal of Exposure Science & Environmental Epidemiology, 27(6), 582–590. https://doi.org/10.1038/jes.2016.54.
Barker, H. A. (1981). Amino acid degradation by anaerobic bacteria. Annual Review of Biochemistry, 50(1), 23–40. https://doi.org/10.1146/annurev.bi.50.070181.000323.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological). https://doi.org/10.2307/2346101.
Burch, J. B., Everson, T. M., Seth, R. K., Wirth, M. D., & Chatterjee, S. (2015). Trihalomethane exposure and biomonitoring for the liver injury indicator, alanine aminotransferase, in the United States population (NHANES 1999–2006). Science of the Total Environment, 521, 226–234. https://doi.org/10.1016/j.scitotenv.2015.03.050.
Charisiadis, P., Andra, S. S., Makris, K. C., Christodoulou, M., Christophi, C. A., Kargaki, S., et al. (2014). Household cleaning activities as noningestion exposure determinants of urinary trihalomethanes. Environmental Science and Technology, 48(1), 770–780. https://doi.org/10.1021/es404220z.
Charisiadis, P., & Makris, K. C. (2018). Cohort-friendly protocol for a sensitive and fast method for trihalomethanes in urine using gas chromatography—Triple quadrupole mass spectrometry. Journal of Chromatography B, 1072, 336–340. https://doi.org/10.1016/J.JCHROMB.2017.11.045.
Cobb, J., Eckhart, A., Perichon, R., Wulff, J., Mitchell, M., Adam, K.-P., et al. (2015). A novel test for IGT utilizing metabolite markers of glucose tolerance. Journal of Diabetes Science and Technology, 9(1), 69–76. https://doi.org/10.1177/1932296814553622.
Evans, A., Bridgewater, B., Liu, Q., Mitchell, M., Robinson, R., Dai, H., et al. (2014). High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Metabolomics, 04(02), 132. https://doi.org/10.4172/2153-0769.1000132.
Fabris, R., Chow, C. W. K., Drikas, M., & Eikebrokk, B. (2008). Comparison of NOM character in selected Australian and Norwegian drinking waters. Water Research, 42(15), 4188–4196. https://doi.org/10.1016/J.WATRES.2008.06.023.
Fleiner, H. F., Bjøro, T., Midthjell, K., Grill, V., & Åsvold, B. O. (2016). Prevalence of Thyroid dysfunction in autoimmune and type 2 diabetes: The population-based HUNT Study in Norway. The Journal of Clinical Endocrinology & Metabolism, 101(2), 669–677. https://doi.org/10.1210/jc.2015-3235.
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01.
Friedrich, N., Budde, K., Suhre, K., Völker, U., John, U., Felix, S. B., et al. (2015). Sex differences in urine metabolites related with risk of diabetes using NMR spectroscopy: Results of the study of health in pomerania, Metabolomics, 11, 1405–1415. N. Friedrich, Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Str. NK, Greifswald 17475, Germany: Springer New York LLC (E-mail: journals@springer-sbm.com). https://doi.org/10.1007/s11306-015-0795-6.
Friedrich, N., Skaaby, T., Pietzner, M., Budde, K., Thuesen, B. H., Nauck, M., et al. (2017). Identification of urine metabolites associated with 5-year changes in biomarkers of glucose homoeostasis. Diabetes & Metabolism. https://doi.org/10.1016/j.diabet.2017.05.007.
Gall, W. E., Beebe, K., Lawton, K. A., Adam, K.-P., Mitchell, M. W., Nakhle, P. J., et al. (2010). alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE, 5(5), e10883. https://doi.org/10.1371/journal.pone.0010883.
Gängler, S., Charisiadis, P., Seth, R., Chatterjee, S., & Makris, K. C. (2018). Time of the day dictates the variability of biomarkers of exposure to disinfection byproducts. Environment International, 112, 33–40. https://doi.org/10.1016/j.envint.2017.12.013.
Gängler, S., Makris, K. C., Bouhamra, W., & Dockery, D. W. (2017). Coupling external with internal exposure metrics of trihalomethanes in young females from Kuwait and Cyprus. Journal of Exposure Science & Environmental Epidemiology, 00(July), 1–7. https://doi.org/10.1038/jes.2017.27.
Garg, R. C. (2016). Fenugreek. In R. C. Gupta (Ed.), Nutraceuticals (pp. 599–617). Amsterdam: Elsevier. https://doi.org/10.1016/b978-0-12-802147-7.00044-9.
GBD 2016 Causes of Death Collaborators*. (2017). Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 390, 1151–1210. https://doi.org/10.1016/S0140-6736(17)32152-9.
Grosso, G., Godos, J., Galvano, F., & Giovannucci, E. L. (2017). Coffee, caffeine, and health outcomes: An umbrella review. Annual Review of Nutrition, 37(1), 131–156. https://doi.org/10.1146/annurev-nutr-071816-064941.
Guasch-Ferré, M., Hruby, A., Toledo, E. E., Clish, C. B., Martínez-González, M. A., Salas-Salvadó, J., et al. (2016). Metabolomics in prediabetes and diabetes: A systematic review and meta-analysis. Diabetes Care, 39(5), 833–846. https://doi.org/10.2337/dc15-2251.
Haddad, S., Tardif, G.-C., & Tardif, R. (2006). Development of physiologically based toxicokinetic models for improving the human indoor exposure assessment to water contaminants: Trichloroethylene and trihalomethanes. Journal of Toxicology and Environmental Health: Part A, 69(23), 2095–2136. https://doi.org/10.1080/15287390600631789.
Harrell, F. E. (2016). Harrell Miscellaneous (Hmisc); Package “Hmisc.”
Helsel, D. R. (2006). Fabricating data: How substituting values for nondetects can ruin results, and what can be done about it. Chemosphere, 65(11), 2434–2439. https://doi.org/10.1016/j.chemosphere.2006.04.051.
Ioannou, S., Andrianou, X. D., Charisiadis, P., & Makris, K. C. (2017). Biomarkers of end of shift exposure to disinfection byproducts in nurses. Journal of Environmental Sciences, 58, 217–223. https://doi.org/10.1016/J.JES.2017.06.031.
Kaprio, J., Tuomilehto, J., Koskenvuo, M., Romanov, K., Reunanen, A., Eriksson, J., et al. (1992). Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia, 35(11), 1060–1067.
Kim, W. J., & Park, C.-Y. (2013). 1,5-Anhydroglucitol in diabetes mellitus. Endocrine, 43(1), 33–40. https://doi.org/10.1007/s12020-012-9760-6.
Klijs, B., Scholtens, S., Mandemakers, J. J., Snieder, H., Stolk, R. P., & Smidt, N. (2015). Representativeness of the LifeLines Cohort Study. PLoS ONE, 10(9), e0137203. https://doi.org/10.1371/journal.pone.0137203.
Kraus, D., Yang, Q., Kong, D., Banks, A. S., Zhang, L., Rodgers, J. T., et al. (2014). Nicotinamide N-methyltransferase knockdown protects against diet-induced obesity. Nature, 508(7495), 258–262. https://doi.org/10.1038/nature13198.
Krokstad, S., Langhammer, A., Hveem, K., Holmen, T. L., Midthjell, K., Stene, T. R., et al. (2013). Cohort profile: The HUNT study, Norway. International Journal of Epidemiology, 42(4), 968–977. https://doi.org/10.1093/ije/dys095.
Le Cao, K.-A., Rohart, F., Gonzalez, I., Dejean, S., Key Contributors, Gautier, B., et al. (2017). mixOmics: Omics Data Integration Project.
Lee, L. (2017). NADA: Nondetects and Data Analysis for Environmental Data.
Lee, S. H., Kim, S. O., Lee, H. D., & Chung, B. C. (1998). Estrogens and polyamines in breast cancer: Their profiles and values in disease staging. Cancer Letters, 133(1), 47–56.
Lee, S., Zhang, C., Kilicarslan, M., Piening, B. D., Bjornson, E., Hallström, B. M., et al. (2016a). Integrated network analysis reveals an association between plasma mannose levels and insulin resistance. Cell Metabolism, 24(1), 172–184. https://doi.org/10.1016/j.cmet.2016.05.026.
Lee, S., Zhang, C., Serlie, M. J., Boren, J., Mardinoglu, A., Kilicarslan, M., et al. (2016b). Integrated network analysis reveals an association between plasma mannose levels and insulin resistance cell metabolism resource. Cell Metabolism, 24, 172–184. https://doi.org/10.1016/j.cmet.2016.05.026.
Li, J. H., Wang, Z. H., Zhu, X. J., Deng, Z. H., Cai, C. X., Qiu, L. Q., et al. (2015). Health effects from swimming training in chlorinated pools and the corresponding metabolic stress pathways. PLoS ONE, 10(3), e0119241. https://doi.org/10.1371/journal.pone.0119241.
Lindström, J., & Tuomilehto, J. (2003). The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725–731.
Liu, J., Semiz, S., van der Lee, S. J., van der Spek, A., Verhoeven, A., van Klinken, J. B., et al. (2017). Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study. Metabolomics, 13(9), 104. https://doi.org/10.1007/s11306-017-1239-2.
Lustgarten, M. S., Price, L. L., Chalé, A., & Fielding, R. A. (2014). Metabolites related to gut bacterial metabolism, peroxisome proliferator-activated receptor-alpha activation, and insulin sensitivity are associated with physical function in functionally-limited older adults. Aging Cell, 13(5), 918–925. https://doi.org/10.1111/acel.12251.
Makris, K. C., Andrianou, X. D., Charisiadis, P., Burch, J. B., Seth, R. K., Ioannou, A., et al. (2016). Association between exposures to brominated trihalomethanes, hepatic injury and type II diabetes mellitus. Environment International, 92, 486–493. https://doi.org/10.1016/j.envint.2016.04.012.
Martin, E., Gonzalez-Horta, C., Rager, J., Bailey, K. A., Sanchez-Ramirez, B., Ballinas-Casarrubias, L., et al. (2015). Metabolomic characteristics of arsenic-associated diabetes in a prospective cohort in Chihuahua, Mexico. Toxicological Sciences, 144(2), 338–346. https://doi.org/10.1093/toxsci/kfu318.
Menni, C., Fauman, E., Erte, I., Perry, J. R. B. B., Kastenmuller, G., Shin, S.-Y. Y., et al. (2013). Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes, 62(12), 4270–4276. https://doi.org/10.2337/db13-0570.
Michalik, L., Auwerx, J., Berger, J. P., Chatterjee, V. K., Glass, C. K., Gonzalez, F. J., et al. (2006). International union of pharmacology. LXI. Peroxisome proliferator-activated receptors. Pharmacological Reviews, 58(4), 726–741.
Midthjell, K., Lee, C. M. Y., Langhammer, A., Krokstad, S., Holmen, T. L., Hveem, K., et al. (2013). Trends in overweight and obesity over 22 years in a large adult population: The HUNT Study, Norway. Clinical Obesity, 3(1–2), 12–20. https://doi.org/10.1111/cob.12009.
Miyake, T., Kumagi, T., Hirooka, M., Koizumi, M., Furukawa, S., Ueda, T., et al. (2012). Metabolic markers and ALT cutoff level for diagnosing nonalcoholic fatty liver disease: A community-based cross-sectional study. Journal of Gastroenterology, 47(6), 696–703. https://doi.org/10.1007/s00535-012-0534-y.
Mook-Kanamori, D. O., Selim, M. M. E.-D., Takiddin, A. H., Al-Homsi, H., Al-Mahmoud, K. A. S., Al-Obaidli, A., et al. (2014). 1,5-Anhydroglucitol in saliva is a noninvasive marker of short-term glycemic control. Journal of Clinical Endocrinology and Metabolism, 99(3), E479–E483. https://doi.org/10.1210/jc.2013-3596.
Murray, K. E., Shaw, K. J., Adams, R. F., & Conway, P. L. (1993). Presence of N-acyl and acetoxy derivatives of putrescine and cadaverine in the human gut. Gut, 34(4), 489–493.
Nano, J., Muka, T., Ligthart, S., Hofman, A., Darwish Murad, S., LA Janssen, H., et al. (2017). Gamma-glutamyltransferase levels, prediabetes and type 2 diabetes: A Mendelian randomization study. International Journal of Epidemiology, 46(5), 1400–1409. https://doi.org/10.1093/ije/dyx006.
Neafsey, P., Ginsberg, G., Hattis, D., Johns, D. O., Guyton, K. Z., & Sonawane, B. (2009). Genetic polymorphism in CYP2E1: Population distribution of CYP2E1 activity. Journal of Toxicology and Environmental Health, Part B, Critical reviews, 12(5–6), 362–388. https://doi.org/10.1080/10937400903158359.
Padberg, I., Peter, E., Gonzalez-Maldonado, S., Witt, H., Mueller, M., Weis, T., et al. (2014). A new metabolomic signature in type-2 diabetes mellitus and its pathophysiology. PLoS ONE, 9(1), e85082. https://doi.org/10.1371/journal.pone.0085082.
Patel, C. J., Bhattacharya, J., Butte, A. J., Zeggini, E., & Freathy, R. (2010). An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS ONE, 5(5), e10746. https://doi.org/10.1371/journal.pone.0010746.
Peddinti, G., Cobb, J., Yengo, L., Froguel, P., Kravic, J., Balkau, B., et al. (2017). Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia. https://doi.org/10.1007/s00125-017-4325-0.
Perichon, R., Bell, L. N., Wulff, J., Nguyen, U. T., & Watkins, S. M. (2017). Patent: 20170370954, “Biomarkers for Fatty Liver Disease and Methods Using the Same.” USA.
Playdon, M. C., Sampson, J. N., Cross, A. J., Sinha, R., Guertin, K. A., Moy, K. A., et al. (2016). Comparing metabolite profiles of habitual diet in serum and urine. The American Journal of Clinical Nutrition, 104(3), 776–789. https://doi.org/10.3945/ajcn.116.135301.
R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Rappaport, S. M. (2016). Genetic factors are not the major causes of chronic diseases. PLoS ONE, 11(4), e0154387. https://doi.org/10.1371/journal.pone.0154387.
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., et al. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1), 77. https://doi.org/10.1186/1471-2105-12-77.
Salas, L. A., Bustamante, M., Gonzalez, J. R., Gracia-Lavedan, E., Moreno, V., Kogevinas, M., et al. (2015). DNA methylation levels and long-term trihalomethane exposure in drinking water: An epigenome-wide association study. Epigenetics, 10(7), 650–661. https://doi.org/10.1080/15592294.2015.1057672.
Scholtens, S., Smidt, N., Swertz, M. A., Bakker, S. J., Dotinga, A., Vonk, J. M., et al. (2015). Cohort Profile: LifeLines, a three-generation cohort study and biobank. International Journal of Epidemiology, 44(4), 1172–1180. https://doi.org/10.1093/ije/dyu229.
Slagter, S. N., van Vliet-Ostaptchouk, J. V., van Beek, A. P., Keers, J. C., Lutgers, H. L., van der Klauw, M. M., et al. (2015). Health-related quality of life in relation to obesity grade, type 2 diabetes, metabolic syndrome and inflammation. PLOS ONE, 10(10), e0140599. https://doi.org/10.1371/journal.pone.0140599.
Slagter, S. N., van Waateringe, R. P., van Beek, A. P., van der Klauw, M. M., Wolffenbuttel, B. H. R., & van Vliet-Ostaptchouk, J. V. (2017). Sex, BMI and age differences in metabolic syndrome: The Dutch Lifelines Cohort Study. Endocrine Connections, 6(4), 278–288. https://doi.org/10.1530/EC-17-0011.
Smeets, P. W. M. H., Medema, G. J., & Van Dijk, J. C. (2009). The Dutch secret: How to provide safe drinking water without chlorine in the Netherlands. Drinking Water Engineering and Science, 2, 1–14.
Stekhoven, D. J., & Bühlmann, P. (2012). Missforest-non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118. https://doi.org/10.1093/bioinformatics/btr597.
Suhre, K., Meisinger, C., Döring, A., Altmaier, E., Belcredi, P., Gieger, C., et al. (2010). Metabolic footprint of diabetes: A multiplatform metabolomics study in an epidemiological setting. PLoS ONE, 5(11), e13953. https://doi.org/10.1371/journal.pone.0013953.
Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67.
van Veldhoven, K., Keski-Rahkonen, P., Barupal, D. K., Villanueva, C. M., Font-Ribera, L., Scalbert, A., et al. (2018). Effects of exposure to water disinfection by-products in a swimming pool: A metabolome-wide association study. Environment International, 111, 60–70. https://doi.org/10.1016/j.envint.2017.11.017.
Wang, Z., Hall, S. D., Maya, J. F., Li, L., Asghar, A., & Gorski, J. C. (2003). Diabetes mellitus increases the in vivo activity of cytochrome P450 2E1 in humans. British Journal of Clinical Pharmacology, 55(1), 77–85.
Wei, T., & Simko, V. (2016). R package “corrplot”: Visualization of a Correlation Matrix (Version 0.77). Available from https://github.com/taiyun/corrplot.
Wickham, Hadley. (2009). ggplot2: Elegant graphics for data analysis. New York: Springer. ISBN 978-0-387-98140-6.
Wikoff, W. R., Anfora, A. T., Liu, J., Schultz, P. G., Lesley, S. A., Peters, E. C., et al. (2009). Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proceedings of the National Academy of Sciences of the United States of America, 106(10), 3698–3703. https://doi.org/10.1073/pnas.0812874106.
Wild, C. P. (2005). Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology, Biomarkers & Prevention, 14(8), 1847–1850. https://doi.org/10.1158/1055-9965.EPI-05-0456.
Wild, C. P., Scalbert, A., & Herceg, Z. (2013). Measuring the exposome: A powerful basis for evaluating environmental exposures and cancer risk. Environmental and Molecular Mutagenesis, 54(7), 480–499. https://doi.org/10.1002/em.21777.
Yoshida, K., & Bohn, J. (2015). tableone: Create “Table 1” to Describe Baseline Characteristics.
Yousri, N. A., Mook-Kanamori, D. O., Selim, M. M. E.-D., Takiddin, A. H., Al-Homsi, H., Al-Mahmoud, K. A. S., et al. (2015). A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control. Diabetologia, 58(8), 1855–1867. https://doi.org/10.1007/s00125-015-3636-2.
Yu, D., Moore, S. C., Matthews, C. E., Xiang, Y.-B., Zhang, X., Gao, Y.-T., et al. (2016). Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults. Metabolomics, 12(1), 1–11. https://doi.org/10.1007/s11306-015-0890-8.
Zhen, Y., Krausz, K. W., Chen, C., Idle, J. R., & Gonzalez, F. J. (2007). Metabolomic and genetic analysis of biomarkers for peroxisome proliferator-activated receptor alpha expression and activation. Molecular Endocrinology, 21(9), 2136–2151. https://doi.org/10.1210/me.2007-0150.
Acknowledgements
This research was funded by a Biobanking and Biomolecular Resources Research Infrastructure -Large prospective cohort (BBMRI-LPC) grant given to Dr. K.C. Makris. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No 313010 (BBMRI-LPC). We would like to thank the Lifelines Biobank initiative, which has been made possible by funds from FESS (Fonds Economische Structuurversterking), SNN (Samenwerkingsverband Noord Nederland) and REP (Ruimtelijk Economisch Programma). The Lifelines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (Grant 175.010.2007.006), the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), The Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. We would also like to thank the The Nord-Trøndelag Health Study (The HUNT Study), which is a collaboration between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. J.V. van Vliet-Ostaptchouk was supported by a Diabetes Funds Junior Fellowship from the Dutch Diabetes Research Foundation (Project No. 2013.81.1673).
Author information
Authors and Affiliations
Contributions
KCM conceived and designed the study. KCM, EPS, JvVO and JB acquired the cohort data. SG and MW, AA and JA acquired biomarker and metabolomics data. SG and KCM undertook the statistical analyses. All authors were involved in data interpretation. SG wrote the initial draft of the manuscript. These drafts were revised for important scientific content by all authors. All authors gave final approval of the version to be published. KCM is the guarantor of this work.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare they have no conflict of interest.
Human and animal rights
The respective bioethics committees of the University Medical Center Groningen (UMCG), the Netherlands, and the Regional Committee for Ethics in Medical Research in Norway approved this study.
Informed consent
Informed consent was obtained for all participants.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Gängler, S., Waldenberger, M., Artati, A. et al. Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case–control study in the HUNT and Lifelines cohorts. Metabolomics 15, 60 (2019). https://doi.org/10.1007/s11306-019-1519-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11306-019-1519-0