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|Title:||Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case-control study in the HUNT and Lifelines cohorts||Authors:||Gängler, Stephanie
Van Bolhuis, Jurjen N.
Sørgjerd, Elin Pettersen
Van Vliet-Ostaptchouk, Jana V.
Makris, Konstantinos C.
|Major Field of Science:||Medical and Health Sciences||Field Category:||Clinical Medicine||Keywords:||Type 2 diabetes;Metabolomics;Disinfection byproducts;Trihalomethanes;HUNT;Lifelines;LASSO;Brominated disinfection byproducts||Issue Date:||8-Apr-2019||Source:||Metabolomics, 2019, vol. 15, no. 4||Volume:||15||Issue:||4||Journal:||Metabolomics||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.||ISSN:||1573-3882||DOI:||10.1007/s11306-019-1519-0||Rights:||© Springer Nature||Type:||Article||Affiliation :||German Research Center for Environmental Health
German Center for Diabetes Research
Technical University of Munich
The Lifelines Cohort
Norwegian University of Science and Technology
University of Groningen
University Medical Center Groningen
University of Singapore
Cyprus University of Technology
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