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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.
|Keywords:||Type 2 diabetes;Metabolomics;Disinfection byproducts;Trihalomethanes;HUNT;Lifelines;LASSO;Brominated disinfection byproducts||Category:||Clinical Medicine||Field:||Medical and Health Sciences||Issue Date:||8-Apr-2019||Source:||Metabolomics, 2019, Volume 15, Issue 4, Article number 60||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/13617||ISSN:||1573-3890||DOI:||10.1007/s11306-019-1519-0||Rights:||© 2019, Springer Science+Business Media, LLC, part of Springer Nature.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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