Early metabolic markers identify potential targets for the prevention of type 2 diabetes.
Identifieur interne : 000B13 ( PubMed/Corpus ); précédent : 000B12; suivant : 000B14Early metabolic markers identify potential targets for the prevention of type 2 diabetes.
Auteurs : Gopal Peddinti ; Jeff Cobb ; Loic Yengo ; Philippe Froguel ; Jasmina Kravi ; Beverley Balkau ; Tiinamaija Tuomi ; Tero Aittokallio ; Leif GroopSource :
- Diabetologia [ 1432-0428 ] ; 2017.
Abstract
The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.
DOI: 10.1007/s00125-017-4325-0
PubMed: 28597074
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pubmed:28597074Le document en format XML
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<author><name sortKey="Yengo, Loic" sort="Yengo, Loic" uniqKey="Yengo L" first="Loic" last="Yengo">Loic Yengo</name>
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<author><name sortKey="Froguel, Philippe" sort="Froguel, Philippe" uniqKey="Froguel P" first="Philippe" last="Froguel">Philippe Froguel</name>
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<affiliation><nlm:affiliation>Lund University Diabetes Center, Lund, Sweden.</nlm:affiliation>
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<author><name sortKey="Balkau, Beverley" sort="Balkau, Beverley" uniqKey="Balkau B" first="Beverley" last="Balkau">Beverley Balkau</name>
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<author><name sortKey="Tuomi, Tiinamaija" sort="Tuomi, Tiinamaija" uniqKey="Tuomi T" first="Tiinamaija" last="Tuomi">Tiinamaija Tuomi</name>
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<author><name sortKey="Aittokallio, Tero" sort="Aittokallio, Tero" uniqKey="Aittokallio T" first="Tero" last="Aittokallio">Tero Aittokallio</name>
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<author><name sortKey="Groop, Leif" sort="Groop, Leif" uniqKey="Groop L" first="Leif" last="Groop">Leif Groop</name>
<affiliation><nlm:affiliation>Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.</nlm:affiliation>
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<series><title level="j">Diabetologia</title>
<idno type="eISSN">1432-0428</idno>
<imprint><date when="2017" type="published">2017</date>
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<front><div type="abstract" xml:lang="en">The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.</div>
</front>
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<pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">28597074</PMID>
<DateCreated><Year>2017</Year>
<Month>06</Month>
<Day>09</Day>
</DateCreated>
<DateRevised><Year>2017</Year>
<Month>08</Month>
<Day>27</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1432-0428</ISSN>
<JournalIssue CitedMedium="Internet"><PubDate><Year>2017</Year>
<Month>Jun</Month>
<Day>08</Day>
</PubDate>
</JournalIssue>
<Title>Diabetologia</Title>
<ISOAbbreviation>Diabetologia</ISOAbbreviation>
</Journal>
<ArticleTitle>Early metabolic markers identify potential targets for the prevention of type 2 diabetes.</ArticleTitle>
<ELocationID EIdType="doi" ValidYN="Y">10.1007/s00125-017-4325-0</ELocationID>
<Abstract><AbstractText Label="AIMS/HYPOTHESIS" NlmCategory="OBJECTIVE">The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors.</AbstractText>
<AbstractText Label="CONCLUSIONS/INTERPRETATION" NlmCategory="CONCLUSIONS">This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Peddinti</LastName>
<ForeName>Gopal</ForeName>
<Initials>G</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0002-8767-968X</Identifier>
<AffiliationInfo><Affiliation>Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland. gopal.peddinti@vtt.fi.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>, Tietotie 2, P. O. Box 1000, FIN-02044 VTT, Espoo, Finland. gopal.peddinti@vtt.fi.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Cobb</LastName>
<ForeName>Jeff</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Metabolon Inc., Durham, NC, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Yengo</LastName>
<ForeName>Loic</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>CNRS UMR8199, Pasteur Institute of Lille, Lille, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Lille University, Lille, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Froguel</LastName>
<ForeName>Philippe</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>CNRS UMR8199, Pasteur Institute of Lille, Lille, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Lille University, Lille, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Kravić</LastName>
<ForeName>Jasmina</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Lund University Diabetes Center, Lund, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Balkau</LastName>
<ForeName>Beverley</ForeName>
<Initials>B</Initials>
<AffiliationInfo><Affiliation>CESP, Faculty of Medicine - University Paris-South; Faculty of Medicine - University Versailles-St Quentin; Inserm U1018, University Paris-Saclay, Villejuif, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Tuomi</LastName>
<ForeName>Tiinamaija</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Endocrinology, Abdominal Centre, Helsinki University Central Hospital, Helsinki, Finland.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Folkhalsan Research Center and Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Aittokallio</LastName>
<ForeName>Tero</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Mathematics and Statistics, University of Turku, Turku, Finland.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Groop</LastName>
<ForeName>Leif</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Lund University Diabetes Center, Lund, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
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<Keyword MajorTopicYN="N">Early prediction</Keyword>
<Keyword MajorTopicYN="N">Kallikrein–kinin system</Keyword>
<Keyword MajorTopicYN="N">Machine learning</Keyword>
<Keyword MajorTopicYN="N">Metabolomics</Keyword>
<Keyword MajorTopicYN="N">Multivariate models</Keyword>
<Keyword MajorTopicYN="N">Prevention</Keyword>
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