Stephanie Gängler1, Melanie Waldenberger2,3, Anna Artati4, Jerzy Adamski4,5,6,7, Jurjen N van Bolhuis8, Elin Pettersen Sørgjerd9, Jana van Vliet-Ostaptchouk10,11, Konstantinos C Makris12. 1. Water and Health Laboratory, Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Irenes 95, 3041, Limassol, Cyprus. 2. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Bavaria, Germany. 3. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Bavaria, Germany. 4. Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany. 5. German Center for Diabetes Research (DZD e.V.), 85764, Neuherberg, Germany. 6. Chair of Experimental Genetics, Technical University of Munich, 85350, Freising, Germany. 7. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117596, Singapore, Singapore. 8. Lifelines Research Office, The Lifelines Cohort, Bloemsingel 1, 9713 BZ, Groningen, The Netherlands. 9. HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Forskningsvegen 2, 7600, Levanger, Norway. 10. Department of Endocrinology, University Medical Center Groningen, University of Groningen, 9700, Groningen, The Netherlands. 11. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 12. Water and Health Laboratory, Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Irenes 95, 3041, Limassol, Cyprus. konstantinos.makris@cut.ac.cy.
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.
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.
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