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Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case–control study in the HUNT and Lifelines cohorts

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 asse...

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Published in:Metabolomics 2019-04, Vol.15 (4), p.60-16, Article 60
Main Authors: Gängler, Stephanie, Waldenberger, Melanie, Artati, Anna, Adamski, Jerzy, van Bolhuis, Jurjen N., Sørgjerd, Elin Pettersen, van Vliet-Ostaptchouk, Jana, Makris, Konstantinos C.
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container_title Metabolomics
container_volume 15
creator Gängler, Stephanie
Waldenberger, Melanie
Artati, Anna
Adamski, Jerzy
van Bolhuis, Jurjen N.
Sørgjerd, Elin Pettersen
van Vliet-Ostaptchouk, Jana
Makris, Konstantinos C.
description 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.
doi_str_mv 10.1007/s11306-019-1519-0
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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.</description><identifier>ISSN: 1573-3882</identifier><identifier>EISSN: 1573-3890</identifier><identifier>DOI: 10.1007/s11306-019-1519-0</identifier><identifier>PMID: 30963292</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Adult ; Biochemistry ; Biomarkers ; Biomarkers - blood ; Biomarkers - urine ; Biomedical and Life Sciences ; Biomedicine ; Case-Control Studies ; Cell Biology ; Chloroform ; Chloroform - adverse effects ; Cohort Studies ; Developmental Biology ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - etiology ; Diabetes Mellitus, Type 2 - metabolism ; Disinfectants - adverse effects ; Disinfection ; Female ; Halogenation ; Humans ; Life Sciences ; Male ; Mass spectroscopy ; Metabolism ; Metabolites ; Metabolomics ; Metabolomics - methods ; Middle Aged ; Molecular Medicine ; Original Article ; Prediction models ; Prospective Studies ; Risk Factors ; Trihalomethanes ; Trihalomethanes - adverse effects ; Trihalomethanes - metabolism</subject><ispartof>Metabolomics, 2019-04, Vol.15 (4), p.60-16, Article 60</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Metabolomics is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-d68418a1827228e8cb3a6e8b7dbb46a017dc440dfeb6b06c50f7889e5f59b3733</citedby><cites>FETCH-LOGICAL-c415t-d68418a1827228e8cb3a6e8b7dbb46a017dc440dfeb6b06c50f7889e5f59b3733</cites><orcidid>0000-0001-5251-8619</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30963292$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gängler, Stephanie</creatorcontrib><creatorcontrib>Waldenberger, Melanie</creatorcontrib><creatorcontrib>Artati, Anna</creatorcontrib><creatorcontrib>Adamski, Jerzy</creatorcontrib><creatorcontrib>van Bolhuis, Jurjen N.</creatorcontrib><creatorcontrib>Sørgjerd, Elin Pettersen</creatorcontrib><creatorcontrib>van Vliet-Ostaptchouk, Jana</creatorcontrib><creatorcontrib>Makris, Konstantinos C.</creatorcontrib><title>Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case–control study in the HUNT and Lifelines cohorts</title><title>Metabolomics</title><addtitle>Metabolomics</addtitle><addtitle>Metabolomics</addtitle><description>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.</description><subject>Adult</subject><subject>Biochemistry</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Biomarkers - urine</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Case-Control Studies</subject><subject>Cell Biology</subject><subject>Chloroform</subject><subject>Chloroform - adverse effects</subject><subject>Cohort Studies</subject><subject>Developmental Biology</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - etiology</subject><subject>Diabetes Mellitus, Type 2 - metabolism</subject><subject>Disinfectants - adverse effects</subject><subject>Disinfection</subject><subject>Female</subject><subject>Halogenation</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Male</subject><subject>Mass spectroscopy</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Metabolomics - methods</subject><subject>Middle Aged</subject><subject>Molecular Medicine</subject><subject>Original Article</subject><subject>Prediction models</subject><subject>Prospective Studies</subject><subject>Risk Factors</subject><subject>Trihalomethanes</subject><subject>Trihalomethanes - adverse effects</subject><subject>Trihalomethanes - metabolism</subject><issn>1573-3882</issn><issn>1573-3890</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kc1qFTEYhgdRbK1egBsJuHEzmp_Jz7iTUlvhoJt2HfLzjU2dkxyTDHh2vQfv0CtpxlMrCG6SQJ73yRferntJ8FuCsXxXCGFY9JiMPeFtwY-6Y8Il65ka8eOHs6JH3bNSbjAehlHip90Rw6NgdKTH3e3Zj10qSwZUE_KhhDiBqyFFZPe7nPziakEmepRD-YbShOp-B4g21FioUN4jgyKUCh45U-DX7U-XYs1pRqUufo9CRPUa0MXV58vfmk2YYA4tgVy6TrmW592TycwFXtzvJ93Vx7PL04t-8-X80-mHTe8GwmvvhRqIMkRRSakC5SwzApSV3tpBGEykd8OA_QRWWCwcx5NUagQ-8dEyydhJ9-bgbb_6vrSJ9TYUB_NsIqSlaEqxaGYqVvT1P-hNWnJs060UH4TkHDeKHCiXUykZJr3LYWvyXhOs13r0oR7d6tFrPXrNvLo3L3YL_iHxp48G0ANQ2lX8Cvnv0_-33gGfWJuz</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Gängler, Stephanie</creator><creator>Waldenberger, Melanie</creator><creator>Artati, Anna</creator><creator>Adamski, Jerzy</creator><creator>van Bolhuis, Jurjen N.</creator><creator>Sørgjerd, Elin Pettersen</creator><creator>van Vliet-Ostaptchouk, Jana</creator><creator>Makris, Konstantinos C.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-5251-8619</orcidid></search><sort><creationdate>20190401</creationdate><title>Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case–control study in the HUNT and Lifelines cohorts</title><author>Gängler, Stephanie ; 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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.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>30963292</pmid><doi>10.1007/s11306-019-1519-0</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-5251-8619</orcidid><oa>free_for_read</oa></addata></record>
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subjects Adult
Biochemistry
Biomarkers
Biomarkers - blood
Biomarkers - urine
Biomedical and Life Sciences
Biomedicine
Case-Control Studies
Cell Biology
Chloroform
Chloroform - adverse effects
Cohort Studies
Developmental Biology
Diabetes
Diabetes mellitus
Diabetes mellitus (non-insulin dependent)
Diabetes Mellitus, Type 2 - etiology
Diabetes Mellitus, Type 2 - metabolism
Disinfectants - adverse effects
Disinfection
Female
Halogenation
Humans
Life Sciences
Male
Mass spectroscopy
Metabolism
Metabolites
Metabolomics
Metabolomics - methods
Middle Aged
Molecular Medicine
Original Article
Prediction models
Prospective Studies
Risk Factors
Trihalomethanes
Trihalomethanes - adverse effects
Trihalomethanes - metabolism
title Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case–control study in the HUNT and Lifelines cohorts
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T04%3A42%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Exposure%20to%20disinfection%20byproducts%20and%20risk%20of%20type%202%20diabetes:%20a%20nested%20case%E2%80%93control%20study%20in%20the%20HUNT%20and%20Lifelines%20cohorts&rft.jtitle=Metabolomics&rft.au=G%C3%A4ngler,%20Stephanie&rft.date=2019-04-01&rft.volume=15&rft.issue=4&rft.spage=60&rft.epage=16&rft.pages=60-16&rft.artnum=60&rft.issn=1573-3882&rft.eissn=1573-3890&rft_id=info:doi/10.1007/s11306-019-1519-0&rft_dat=%3Cproquest_cross%3E2205467550%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c415t-d68418a1827228e8cb3a6e8b7dbb46a017dc440dfeb6b06c50f7889e5f59b3733%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2205467550&rft_id=info:pmid/30963292&rfr_iscdi=true