Metabolomics in gestational diabetes

Gestational diabetes mellitus (GDM) is a form of diabetes that is first diagnosed during pregnancy in the absence of existing type 1 or type 2 diabetes. Early screening tools for GDM are currently unavailable, but metabolomics is a promising approach for detecting biomarkers of GDM. This review eval...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Clinica chimica acta 2017-12, Vol.475, p.116-127
Hauptverfasser: Mao, Xun, Chen, Xuyang, Chen, Chang, Zhang, Hua, Law, Kai P.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 127
container_issue
container_start_page 116
container_title Clinica chimica acta
container_volume 475
creator Mao, Xun
Chen, Xuyang
Chen, Chang
Zhang, Hua
Law, Kai P.
description Gestational diabetes mellitus (GDM) is a form of diabetes that is first diagnosed during pregnancy in the absence of existing type 1 or type 2 diabetes. Early screening tools for GDM are currently unavailable, but metabolomics is a promising approach for detecting biomarkers of GDM. This review evaluates recent GDM studies employing metabolomic techniques, highlighting the challenges in those studies and envisions the future directions for metabolomic study of GDM. A diverse range of predictive markers and dysregulated metabolic pathways have been associated with the pathogenesis of GDM, but these findings have lacked reproducibility among studies. The case-control study design has been most frequently employed in the studies of GDM, and most of them used specimens acquired in mid-pregnancy. However, this approach might not be adequate to recognise the complexity of the condition. The sample size in some of the studies is limited, and this may result in findings from a participant set that is not representative of the general population. Therefore, we propose that future metabolomic studies pertaining to GDM use a cross-platform approach employing unified diagnostic criteria, a longitudinal cohort, and innovative data processing methods to allow for full-scale identification and comprehensive coverage of the metabolome. In addition, the relationship between the exposure to environmental chemicals such as endocrine disruptors and the development of GDM should be further investigated in future studies. •This review evaluates the impact of metabolomics in the studies of GDM.•Attention is paid to the study design, methodology and predictive biomarkers proposed.•Biomarker candidates proposed have been highly diverse even when the same instrument and study protocol are used.•A proposal of our vision for the future metabolomic studies of GDM is given.
doi_str_mv 10.1016/j.cca.2017.10.019
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1955619714</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0009898117304151</els_id><sourcerecordid>1955619714</sourcerecordid><originalsourceid>FETCH-LOGICAL-c353t-1fcaf439d906d92cee06df11e2f465eb61a06a7835469a790f68dfc21b7ddeeb3</originalsourceid><addsrcrecordid>eNp9kM1LAzEQxYMotlb_AC_Sgwcvu2aS3ewGT1L8gooXPYdsMpGU_ajJVvC_b0qrR0_DG957zPwIuQSaAwVxu8qN0TmjUCWdU5BHZAp1xTNeSHZMppRSmdWyhgk5i3GVZEEFnJIJk1QIBnRKrl9x1M3QDp03ce77-SfGUY9-6HU7t143OGI8JydOtxEvDnNGPh4f3hfP2fLt6WVxv8wML_mYgTPaFVza1G4lM4hpOgBkrhAlNgI0FbqqeVkIqStJnaitMwyaylrEhs_Izb53HYavTTpEdT4abFvd47CJCmRZCpAVFMkKe6sJQ4wBnVoH3-nwo4CqHRy1UgmO2sHZrRKclLk61G-aDu1f4pdGMtztDZie_PYYVDQee4PWBzSjsoP_p34LU9lzsw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1955619714</pqid></control><display><type>article</type><title>Metabolomics in gestational diabetes</title><source>MEDLINE</source><source>Access via ScienceDirect (Elsevier)</source><creator>Mao, Xun ; Chen, Xuyang ; Chen, Chang ; Zhang, Hua ; Law, Kai P.</creator><creatorcontrib>Mao, Xun ; Chen, Xuyang ; Chen, Chang ; Zhang, Hua ; Law, Kai P.</creatorcontrib><description>Gestational diabetes mellitus (GDM) is a form of diabetes that is first diagnosed during pregnancy in the absence of existing type 1 or type 2 diabetes. Early screening tools for GDM are currently unavailable, but metabolomics is a promising approach for detecting biomarkers of GDM. This review evaluates recent GDM studies employing metabolomic techniques, highlighting the challenges in those studies and envisions the future directions for metabolomic study of GDM. A diverse range of predictive markers and dysregulated metabolic pathways have been associated with the pathogenesis of GDM, but these findings have lacked reproducibility among studies. The case-control study design has been most frequently employed in the studies of GDM, and most of them used specimens acquired in mid-pregnancy. However, this approach might not be adequate to recognise the complexity of the condition. The sample size in some of the studies is limited, and this may result in findings from a participant set that is not representative of the general population. Therefore, we propose that future metabolomic studies pertaining to GDM use a cross-platform approach employing unified diagnostic criteria, a longitudinal cohort, and innovative data processing methods to allow for full-scale identification and comprehensive coverage of the metabolome. In addition, the relationship between the exposure to environmental chemicals such as endocrine disruptors and the development of GDM should be further investigated in future studies. •This review evaluates the impact of metabolomics in the studies of GDM.•Attention is paid to the study design, methodology and predictive biomarkers proposed.•Biomarker candidates proposed have been highly diverse even when the same instrument and study protocol are used.•A proposal of our vision for the future metabolomic studies of GDM is given.</description><identifier>ISSN: 0009-8981</identifier><identifier>EISSN: 1873-3492</identifier><identifier>DOI: 10.1016/j.cca.2017.10.019</identifier><identifier>PMID: 29066210</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Adult ; Biomarkers ; Biomarkers - blood ; Biomarkers - urine ; Case-Control Studies ; Chromatography, High Pressure Liquid - instrumentation ; Chromatography, High Pressure Liquid - methods ; Diabetes, Gestational - blood ; Diabetes, Gestational - diagnosis ; Diabetes, Gestational - physiopathology ; Diabetes, Gestational - urine ; Female ; Gestational diabetes mellitus ; Humans ; Magnetic Resonance Spectroscopy - instrumentation ; Magnetic Resonance Spectroscopy - methods ; Metabolic Networks and Pathways - physiology ; Metabolic pathways ; Metabolome ; Metabolomics ; Metabolomics - instrumentation ; Metabolomics - methods ; Pregnancy ; Pregnancy complications ; Reproducibility of Results ; Sample Size ; Tandem Mass Spectrometry - instrumentation ; Tandem Mass Spectrometry - methods</subject><ispartof>Clinica chimica acta, 2017-12, Vol.475, p.116-127</ispartof><rights>2017 Elsevier B.V.</rights><rights>Copyright © 2017 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c353t-1fcaf439d906d92cee06df11e2f465eb61a06a7835469a790f68dfc21b7ddeeb3</citedby><cites>FETCH-LOGICAL-c353t-1fcaf439d906d92cee06df11e2f465eb61a06a7835469a790f68dfc21b7ddeeb3</cites><orcidid>0000-0003-0107-2885</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cca.2017.10.019$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29066210$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mao, Xun</creatorcontrib><creatorcontrib>Chen, Xuyang</creatorcontrib><creatorcontrib>Chen, Chang</creatorcontrib><creatorcontrib>Zhang, Hua</creatorcontrib><creatorcontrib>Law, Kai P.</creatorcontrib><title>Metabolomics in gestational diabetes</title><title>Clinica chimica acta</title><addtitle>Clin Chim Acta</addtitle><description>Gestational diabetes mellitus (GDM) is a form of diabetes that is first diagnosed during pregnancy in the absence of existing type 1 or type 2 diabetes. Early screening tools for GDM are currently unavailable, but metabolomics is a promising approach for detecting biomarkers of GDM. This review evaluates recent GDM studies employing metabolomic techniques, highlighting the challenges in those studies and envisions the future directions for metabolomic study of GDM. A diverse range of predictive markers and dysregulated metabolic pathways have been associated with the pathogenesis of GDM, but these findings have lacked reproducibility among studies. The case-control study design has been most frequently employed in the studies of GDM, and most of them used specimens acquired in mid-pregnancy. However, this approach might not be adequate to recognise the complexity of the condition. The sample size in some of the studies is limited, and this may result in findings from a participant set that is not representative of the general population. Therefore, we propose that future metabolomic studies pertaining to GDM use a cross-platform approach employing unified diagnostic criteria, a longitudinal cohort, and innovative data processing methods to allow for full-scale identification and comprehensive coverage of the metabolome. In addition, the relationship between the exposure to environmental chemicals such as endocrine disruptors and the development of GDM should be further investigated in future studies. •This review evaluates the impact of metabolomics in the studies of GDM.•Attention is paid to the study design, methodology and predictive biomarkers proposed.•Biomarker candidates proposed have been highly diverse even when the same instrument and study protocol are used.•A proposal of our vision for the future metabolomic studies of GDM is given.</description><subject>Adult</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Biomarkers - urine</subject><subject>Case-Control Studies</subject><subject>Chromatography, High Pressure Liquid - instrumentation</subject><subject>Chromatography, High Pressure Liquid - methods</subject><subject>Diabetes, Gestational - blood</subject><subject>Diabetes, Gestational - diagnosis</subject><subject>Diabetes, Gestational - physiopathology</subject><subject>Diabetes, Gestational - urine</subject><subject>Female</subject><subject>Gestational diabetes mellitus</subject><subject>Humans</subject><subject>Magnetic Resonance Spectroscopy - instrumentation</subject><subject>Magnetic Resonance Spectroscopy - methods</subject><subject>Metabolic Networks and Pathways - physiology</subject><subject>Metabolic pathways</subject><subject>Metabolome</subject><subject>Metabolomics</subject><subject>Metabolomics - instrumentation</subject><subject>Metabolomics - methods</subject><subject>Pregnancy</subject><subject>Pregnancy complications</subject><subject>Reproducibility of Results</subject><subject>Sample Size</subject><subject>Tandem Mass Spectrometry - instrumentation</subject><subject>Tandem Mass Spectrometry - methods</subject><issn>0009-8981</issn><issn>1873-3492</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kM1LAzEQxYMotlb_AC_Sgwcvu2aS3ewGT1L8gooXPYdsMpGU_ajJVvC_b0qrR0_DG957zPwIuQSaAwVxu8qN0TmjUCWdU5BHZAp1xTNeSHZMppRSmdWyhgk5i3GVZEEFnJIJk1QIBnRKrl9x1M3QDp03ce77-SfGUY9-6HU7t143OGI8JydOtxEvDnNGPh4f3hfP2fLt6WVxv8wML_mYgTPaFVza1G4lM4hpOgBkrhAlNgI0FbqqeVkIqStJnaitMwyaylrEhs_Izb53HYavTTpEdT4abFvd47CJCmRZCpAVFMkKe6sJQ4wBnVoH3-nwo4CqHRy1UgmO2sHZrRKclLk61G-aDu1f4pdGMtztDZie_PYYVDQee4PWBzSjsoP_p34LU9lzsw</recordid><startdate>201712</startdate><enddate>201712</enddate><creator>Mao, Xun</creator><creator>Chen, Xuyang</creator><creator>Chen, Chang</creator><creator>Zhang, Hua</creator><creator>Law, Kai P.</creator><general>Elsevier 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>7X8</scope><orcidid>https://orcid.org/0000-0003-0107-2885</orcidid></search><sort><creationdate>201712</creationdate><title>Metabolomics in gestational diabetes</title><author>Mao, Xun ; Chen, Xuyang ; Chen, Chang ; Zhang, Hua ; Law, Kai P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-1fcaf439d906d92cee06df11e2f465eb61a06a7835469a790f68dfc21b7ddeeb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Biomarkers - urine</topic><topic>Case-Control Studies</topic><topic>Chromatography, High Pressure Liquid - instrumentation</topic><topic>Chromatography, High Pressure Liquid - methods</topic><topic>Diabetes, Gestational - blood</topic><topic>Diabetes, Gestational - diagnosis</topic><topic>Diabetes, Gestational - physiopathology</topic><topic>Diabetes, Gestational - urine</topic><topic>Female</topic><topic>Gestational diabetes mellitus</topic><topic>Humans</topic><topic>Magnetic Resonance Spectroscopy - instrumentation</topic><topic>Magnetic Resonance Spectroscopy - methods</topic><topic>Metabolic Networks and Pathways - physiology</topic><topic>Metabolic pathways</topic><topic>Metabolome</topic><topic>Metabolomics</topic><topic>Metabolomics - instrumentation</topic><topic>Metabolomics - methods</topic><topic>Pregnancy</topic><topic>Pregnancy complications</topic><topic>Reproducibility of Results</topic><topic>Sample Size</topic><topic>Tandem Mass Spectrometry - instrumentation</topic><topic>Tandem Mass Spectrometry - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mao, Xun</creatorcontrib><creatorcontrib>Chen, Xuyang</creatorcontrib><creatorcontrib>Chen, Chang</creatorcontrib><creatorcontrib>Zhang, Hua</creatorcontrib><creatorcontrib>Law, Kai P.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinica chimica acta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mao, Xun</au><au>Chen, Xuyang</au><au>Chen, Chang</au><au>Zhang, Hua</au><au>Law, Kai P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Metabolomics in gestational diabetes</atitle><jtitle>Clinica chimica acta</jtitle><addtitle>Clin Chim Acta</addtitle><date>2017-12</date><risdate>2017</risdate><volume>475</volume><spage>116</spage><epage>127</epage><pages>116-127</pages><issn>0009-8981</issn><eissn>1873-3492</eissn><abstract>Gestational diabetes mellitus (GDM) is a form of diabetes that is first diagnosed during pregnancy in the absence of existing type 1 or type 2 diabetes. Early screening tools for GDM are currently unavailable, but metabolomics is a promising approach for detecting biomarkers of GDM. This review evaluates recent GDM studies employing metabolomic techniques, highlighting the challenges in those studies and envisions the future directions for metabolomic study of GDM. A diverse range of predictive markers and dysregulated metabolic pathways have been associated with the pathogenesis of GDM, but these findings have lacked reproducibility among studies. The case-control study design has been most frequently employed in the studies of GDM, and most of them used specimens acquired in mid-pregnancy. However, this approach might not be adequate to recognise the complexity of the condition. The sample size in some of the studies is limited, and this may result in findings from a participant set that is not representative of the general population. Therefore, we propose that future metabolomic studies pertaining to GDM use a cross-platform approach employing unified diagnostic criteria, a longitudinal cohort, and innovative data processing methods to allow for full-scale identification and comprehensive coverage of the metabolome. In addition, the relationship between the exposure to environmental chemicals such as endocrine disruptors and the development of GDM should be further investigated in future studies. •This review evaluates the impact of metabolomics in the studies of GDM.•Attention is paid to the study design, methodology and predictive biomarkers proposed.•Biomarker candidates proposed have been highly diverse even when the same instrument and study protocol are used.•A proposal of our vision for the future metabolomic studies of GDM is given.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>29066210</pmid><doi>10.1016/j.cca.2017.10.019</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-0107-2885</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0009-8981
ispartof Clinica chimica acta, 2017-12, Vol.475, p.116-127
issn 0009-8981
1873-3492
language eng
recordid cdi_proquest_miscellaneous_1955619714
source MEDLINE; Access via ScienceDirect (Elsevier)
subjects Adult
Biomarkers
Biomarkers - blood
Biomarkers - urine
Case-Control Studies
Chromatography, High Pressure Liquid - instrumentation
Chromatography, High Pressure Liquid - methods
Diabetes, Gestational - blood
Diabetes, Gestational - diagnosis
Diabetes, Gestational - physiopathology
Diabetes, Gestational - urine
Female
Gestational diabetes mellitus
Humans
Magnetic Resonance Spectroscopy - instrumentation
Magnetic Resonance Spectroscopy - methods
Metabolic Networks and Pathways - physiology
Metabolic pathways
Metabolome
Metabolomics
Metabolomics - instrumentation
Metabolomics - methods
Pregnancy
Pregnancy complications
Reproducibility of Results
Sample Size
Tandem Mass Spectrometry - instrumentation
Tandem Mass Spectrometry - methods
title Metabolomics in gestational diabetes
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T15%3A02%3A01IST&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=Metabolomics%20in%20gestational%20diabetes&rft.jtitle=Clinica%20chimica%20acta&rft.au=Mao,%20Xun&rft.date=2017-12&rft.volume=475&rft.spage=116&rft.epage=127&rft.pages=116-127&rft.issn=0009-8981&rft.eissn=1873-3492&rft_id=info:doi/10.1016/j.cca.2017.10.019&rft_dat=%3Cproquest_cross%3E1955619714%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1955619714&rft_id=info:pmid/29066210&rft_els_id=S0009898117304151&rfr_iscdi=true