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Prediction of acute coronary syndromes by urinary proteome analysis.

Identifieur interne : 000F29 ( PubMed/Curation ); précédent : 000F28; suivant : 000F30

Prediction of acute coronary syndromes by urinary proteome analysis.

Auteurs : Nay M. Htun [Australie] ; Dianna J. Magliano [Australie] ; Zhen-Yu Zhang [Belgique] ; Jasmine Lyons [Australie] ; Thibault Petit [Belgique] ; Esther Nkuipou-Kenfack [Allemagne] ; Adela Ramirez-Torres [Allemagne] ; Constantin Von Zur Muhlen [Allemagne] ; David Maahs [États-Unis] ; Joost P. Schanstra [France] ; Claudia Pontillo [Allemagne] ; Martin Pejchinovski [Allemagne] ; Janet K. Snell-Bergeon [États-Unis] ; Christian Delles [Royaume-Uni] ; Harald Mischak [Allemagne] ; Jan A. Staessen [Belgique] ; Jonathan E. Shaw [Australie] ; Thomas Koeck [Allemagne] ; Karlheinz Peter [Australie]

Source :

PloS one [ 1932-6203 ] ; 2017.

RBID : pubmed:28273075

Descripteurs français

KwdFr :
- Adulte d'âge moyen, Aire sous la courbe, Analyse de survie, Courbe ROC, Facteurs de l'âge, Facteurs de risque, Femelle, Humains, Machine à vecteur de support, Marqueurs biologiques (urine), Mâle, Peptides (urine), Pronostic, Protéome (analyse), Protéomique, Spectrométrie de masse, Sujet âgé, Syndrome coronarien aigu (diagnostic), Syndrome coronarien aigu (mortalité), Syndrome coronarien aigu (métabolisme), Syndrome coronarien aigu (urine), Électrophorèse capillaire, Études cas-témoins.
MESH :
- analyse : Protéome.
- diagnostic : Syndrome coronarien aigu.
- mortalité : Syndrome coronarien aigu.
- métabolisme : Syndrome coronarien aigu.
- urine : Adulte d'âge moyen, Aire sous la courbe, Analyse de survie, Courbe ROC, Facteurs de l'âge, Facteurs de risque, Femelle, Humains, Machine à vecteur de support, Marqueurs biologiques, Mâle, Peptides, Pronostic, Protéomique, Spectrométrie de masse, Sujet âgé, Syndrome coronarien aigu, Électrophorèse capillaire, Études cas-témoins.

English descriptors

KwdEn :
- Acute Coronary Syndrome (diagnosis), Acute Coronary Syndrome (metabolism), Acute Coronary Syndrome (mortality), Acute Coronary Syndrome (urine), Age Factors, Aged, Area Under Curve, Biomarkers (urine), Case-Control Studies, Electrophoresis, Capillary, Female, Humans, Male, Mass Spectrometry, Middle Aged, Peptides (urine), Prognosis, Proteome (analysis), Proteomics, ROC Curve, Risk Factors, Support Vector Machine, Survival Analysis.
MESH :
- chemical , analysis : Proteome.
- chemical , urine : Biomarkers, Peptides.
- diagnosis : Acute Coronary Syndrome.
- metabolism : Acute Coronary Syndrome.
- mortality : Acute Coronary Syndrome.
- urine : Acute Coronary Syndrome.
- Age Factors, Aged, Area Under Curve, Case-Control Studies, Electrophoresis, Capillary, Female, Humans, Male, Mass Spectrometry, Middle Aged, Prognosis, Proteomics, ROC Curve, Risk Factors, Support Vector Machine, Survival Analysis.

Abstract

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

DOI: 10.1371/journal.pone.0172036
PubMed: 28273075

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Le document en format XML

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<author><name sortKey="Shaw, Jonathan E" sort="Shaw, Jonathan E" uniqKey="Shaw J" first="Jonathan E" last="Shaw">Jonathan E. Shaw</name>
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<author><name sortKey="Koeck, Thomas" sort="Koeck, Thomas" uniqKey="Koeck T" first="Thomas" last="Koeck">Thomas Koeck</name>
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<author><name sortKey="Peter, Karlheinz" sort="Peter, Karlheinz" uniqKey="Peter K" first="Karlheinz" last="Peter">Karlheinz Peter</name>
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<author><name sortKey="Htun, Nay M" sort="Htun, Nay M" uniqKey="Htun N" first="Nay M" last="Htun">Nay M. Htun</name>
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<country xml:lang="fr">Australie</country>
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</affiliation>
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<author><name sortKey="Magliano, Dianna J" sort="Magliano, Dianna J" uniqKey="Magliano D" first="Dianna J" last="Magliano">Dianna J. Magliano</name>
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<country xml:lang="fr">Australie</country>
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<author><name sortKey="Zhang, Zhen Yu" sort="Zhang, Zhen Yu" uniqKey="Zhang Z" first="Zhen-Yu" last="Zhang">Zhen-Yu Zhang</name>
<affiliation wicri:level="1"><nlm:affiliation>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.</nlm:affiliation>
<country xml:lang="fr">Belgique</country>
<wicri:regionArea>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven</wicri:regionArea>
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<author><name sortKey="Lyons, Jasmine" sort="Lyons, Jasmine" uniqKey="Lyons J" first="Jasmine" last="Lyons">Jasmine Lyons</name>
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<country xml:lang="fr">Australie</country>
<wicri:regionArea>Clinical Diabetes and Epidemiology, Baker IDI Heart and Diabetes Institute, Melbourne</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Petit, Thibault" sort="Petit, Thibault" uniqKey="Petit T" first="Thibault" last="Petit">Thibault Petit</name>
<affiliation wicri:level="1"><nlm:affiliation>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.</nlm:affiliation>
<country xml:lang="fr">Belgique</country>
<wicri:regionArea>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Nkuipou Kenfack, Esther" sort="Nkuipou Kenfack, Esther" uniqKey="Nkuipou Kenfack E" first="Esther" last="Nkuipou-Kenfack">Esther Nkuipou-Kenfack</name>
<affiliation wicri:level="1"><nlm:affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Mosaiques Diagnostics GmbH, Hanover</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Ramirez Torres, Adela" sort="Ramirez Torres, Adela" uniqKey="Ramirez Torres A" first="Adela" last="Ramirez-Torres">Adela Ramirez-Torres</name>
<affiliation wicri:level="1"><nlm:affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Mosaiques Diagnostics GmbH, Hanover</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Von Zur Muhlen, Constantin" sort="Von Zur Muhlen, Constantin" uniqKey="Von Zur Muhlen C" first="Constantin" last="Von Zur Muhlen">Constantin Von Zur Muhlen</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Cardiology, University Heart Centre Freiburg, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Department of Cardiology, University Heart Centre Freiburg</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Maahs, David" sort="Maahs, David" uniqKey="Maahs D" first="David" last="Maahs">David Maahs</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Paediatrics, Stanford School of Medicine, Stanford, California, United States of America.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Paediatrics, Stanford School of Medicine, Stanford, California</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Schanstra, Joost P" sort="Schanstra, Joost P" uniqKey="Schanstra J" first="Joost P" last="Schanstra">Joost P. Schanstra</name>
<affiliation wicri:level="1"><nlm:affiliation>Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Pontillo, Claudia" sort="Pontillo, Claudia" uniqKey="Pontillo C" first="Claudia" last="Pontillo">Claudia Pontillo</name>
<affiliation wicri:level="1"><nlm:affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Mosaiques Diagnostics GmbH, Hanover</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Pejchinovski, Martin" sort="Pejchinovski, Martin" uniqKey="Pejchinovski M" first="Martin" last="Pejchinovski">Martin Pejchinovski</name>
<affiliation wicri:level="1"><nlm:affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Mosaiques Diagnostics GmbH, Hanover</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Snell Bergeon, Janet K" sort="Snell Bergeon, Janet K" uniqKey="Snell Bergeon J" first="Janet K" last="Snell-Bergeon">Janet K. Snell-Bergeon</name>
<affiliation wicri:level="1"><nlm:affiliation>Barbara Davis Centre for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, United States of America.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Barbara Davis Centre for Diabetes, University of Colorado School of Medicine, Aurora, Colorado</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Delles, Christian" sort="Delles, Christian" uniqKey="Delles C" first="Christian" last="Delles">Christian Delles</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Mischak, Harald" sort="Mischak, Harald" uniqKey="Mischak H" first="Harald" last="Mischak">Harald Mischak</name>
<affiliation wicri:level="1"><nlm:affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Mosaiques Diagnostics GmbH, Hanover</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Staessen, Jan A" sort="Staessen, Jan A" uniqKey="Staessen J" first="Jan A" last="Staessen">Jan A. Staessen</name>
<affiliation wicri:level="1"><nlm:affiliation>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.</nlm:affiliation>
<country xml:lang="fr">Belgique</country>
<wicri:regionArea>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Shaw, Jonathan E" sort="Shaw, Jonathan E" uniqKey="Shaw J" first="Jonathan E" last="Shaw">Jonathan E. Shaw</name>
<affiliation wicri:level="1"><nlm:affiliation>Clinical Diabetes and Epidemiology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Clinical Diabetes and Epidemiology, Baker IDI Heart and Diabetes Institute, Melbourne</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Koeck, Thomas" sort="Koeck, Thomas" uniqKey="Koeck T" first="Thomas" last="Koeck">Thomas Koeck</name>
<affiliation wicri:level="1"><nlm:affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</nlm:affiliation>
<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>Mosaiques Diagnostics GmbH, Hanover</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Peter, Karlheinz" sort="Peter, Karlheinz" uniqKey="Peter K" first="Karlheinz" last="Peter">Karlheinz Peter</name>
<affiliation wicri:level="1"><nlm:affiliation>Atherothrombosis and Vascular Biology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Atherothrombosis and Vascular Biology, Baker IDI Heart and Diabetes Institute, Melbourne</wicri:regionArea>
</affiliation>
</author>
</analytic>
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<idno type="eISSN">1932-6203</idno>
<imprint><date when="2017" type="published">2017</date>
</imprint>
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</sourceDesc>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Acute Coronary Syndrome (diagnosis)</term>
<term>Acute Coronary Syndrome (metabolism)</term>
<term>Acute Coronary Syndrome (mortality)</term>
<term>Acute Coronary Syndrome (urine)</term>
<term>Age Factors</term>
<term>Aged</term>
<term>Area Under Curve</term>
<term>Biomarkers (urine)</term>
<term>Case-Control Studies</term>
<term>Electrophoresis, Capillary</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Mass Spectrometry</term>
<term>Middle Aged</term>
<term>Peptides (urine)</term>
<term>Prognosis</term>
<term>Proteome (analysis)</term>
<term>Proteomics</term>
<term>ROC Curve</term>
<term>Risk Factors</term>
<term>Support Vector Machine</term>
<term>Survival Analysis</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adulte d'âge moyen</term>
<term>Aire sous la courbe</term>
<term>Analyse de survie</term>
<term>Courbe ROC</term>
<term>Facteurs de l'âge</term>
<term>Facteurs de risque</term>
<term>Femelle</term>
<term>Humains</term>
<term>Machine à vecteur de support</term>
<term>Marqueurs biologiques (urine)</term>
<term>Mâle</term>
<term>Peptides (urine)</term>
<term>Pronostic</term>
<term>Protéome (analyse)</term>
<term>Protéomique</term>
<term>Spectrométrie de masse</term>
<term>Sujet âgé</term>
<term>Syndrome coronarien aigu (diagnostic)</term>
<term>Syndrome coronarien aigu (mortalité)</term>
<term>Syndrome coronarien aigu (métabolisme)</term>
<term>Syndrome coronarien aigu (urine)</term>
<term>Électrophorèse capillaire</term>
<term>Études cas-témoins</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Proteome</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="urine" xml:lang="en"><term>Biomarkers</term>
<term>Peptides</term>
</keywords>
<keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Protéome</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Acute Coronary Syndrome</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Syndrome coronarien aigu</term>
</keywords>
<keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Acute Coronary Syndrome</term>
</keywords>
<keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Acute Coronary Syndrome</term>
</keywords>
<keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Syndrome coronarien aigu</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Syndrome coronarien aigu</term>
</keywords>
<keywords scheme="MESH" qualifier="urine" xml:lang="en"><term>Acute Coronary Syndrome</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Age Factors</term>
<term>Aged</term>
<term>Area Under Curve</term>
<term>Case-Control Studies</term>
<term>Electrophoresis, Capillary</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Mass Spectrometry</term>
<term>Middle Aged</term>
<term>Prognosis</term>
<term>Proteomics</term>
<term>ROC Curve</term>
<term>Risk Factors</term>
<term>Support Vector Machine</term>
<term>Survival Analysis</term>
</keywords>
<keywords scheme="MESH" qualifier="urine" xml:lang="fr"><term>Adulte d'âge moyen</term>
<term>Aire sous la courbe</term>
<term>Analyse de survie</term>
<term>Courbe ROC</term>
<term>Facteurs de l'âge</term>
<term>Facteurs de risque</term>
<term>Femelle</term>
<term>Humains</term>
<term>Machine à vecteur de support</term>
<term>Marqueurs biologiques</term>
<term>Mâle</term>
<term>Peptides</term>
<term>Pronostic</term>
<term>Protéomique</term>
<term>Spectrométrie de masse</term>
<term>Sujet âgé</term>
<term>Syndrome coronarien aigu</term>
<term>Électrophorèse capillaire</term>
<term>Études cas-témoins</term>
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<front><div type="abstract" xml:lang="en">Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28273075</PMID>
<DateCreated><Year>2017</Year>
<Month>03</Month>
<Day>08</Day>
</DateCreated>
<DateCompleted><Year>2017</Year>
<Month>08</Month>
<Day>30</Day>
</DateCompleted>
<DateRevised><Year>2017</Year>
<Month>08</Month>
<Day>30</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>12</Volume>
<Issue>3</Issue>
<PubDate><Year>2017</Year>
</PubDate>
</JournalIssue>
<Title>PloS one</Title>
<ISOAbbreviation>PLoS ONE</ISOAbbreviation>
</Journal>
<ArticleTitle>Prediction of acute coronary syndromes by urinary proteome analysis.</ArticleTitle>
<Pagination><MedlinePgn>e0172036</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0172036</ELocationID>
<Abstract><AbstractText>Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Htun</LastName>
<ForeName>Nay M</ForeName>
<Initials>NM</Initials>
<AffiliationInfo><Affiliation>Atherothrombosis and Vascular Biology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Medicine, Monash University, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Magliano</LastName>
<ForeName>Dianna J</ForeName>
<Initials>DJ</Initials>
<AffiliationInfo><Affiliation>Clinical Diabetes and Epidemiology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhang</LastName>
<ForeName>Zhen-Yu</ForeName>
<Initials>ZY</Initials>
<AffiliationInfo><Affiliation>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lyons</LastName>
<ForeName>Jasmine</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Clinical Diabetes and Epidemiology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Petit</LastName>
<ForeName>Thibault</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Nkuipou-Kenfack</LastName>
<ForeName>Esther</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ramirez-Torres</LastName>
<ForeName>Adela</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>von Zur Muhlen</LastName>
<ForeName>Constantin</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Department of Cardiology, University Heart Centre Freiburg, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Maahs</LastName>
<ForeName>David</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department of Paediatrics, Stanford School of Medicine, Stanford, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Barbara Davis Centre for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Schanstra</LastName>
<ForeName>Joost P</ForeName>
<Initials>JP</Initials>
<AffiliationInfo><Affiliation>Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Université Toulouse III Paul-Sabatier, Toulouse, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pontillo</LastName>
<ForeName>Claudia</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pejchinovski</LastName>
<ForeName>Martin</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Snell-Bergeon</LastName>
<ForeName>Janet K</ForeName>
<Initials>JK</Initials>
<AffiliationInfo><Affiliation>Barbara Davis Centre for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Delles</LastName>
<ForeName>Christian</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mischak</LastName>
<ForeName>Harald</ForeName>
<Initials>H</Initials>
<AffiliationInfo><Affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Staessen</LastName>
<ForeName>Jan A</ForeName>
<Initials>JA</Initials>
<AffiliationInfo><Affiliation>Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>R&D VitaK Group, Maastricht University, Maastricht, Netherlands.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Shaw</LastName>
<ForeName>Jonathan E</ForeName>
<Initials>JE</Initials>
<AffiliationInfo><Affiliation>Clinical Diabetes and Epidemiology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Koeck</LastName>
<ForeName>Thomas</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Mosaiques Diagnostics GmbH, Hanover, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Peter</LastName>
<ForeName>Karlheinz</ForeName>
<Initials>K</Initials>
<AffiliationInfo><Affiliation>Atherothrombosis and Vascular Biology, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Medicine, Monash University, Melbourne, Australia.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2017</Year>
<Month>03</Month>
<Day>08</Day>
</ArticleDate>
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<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>PLoS One</MedlineTA>
<NlmUniqueID>101285081</NlmUniqueID>
<ISSNLinking>1932-6203</ISSNLinking>
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<MeshHeadingList><MeshHeading><DescriptorName UI="D054058" MajorTopicYN="N">Acute Coronary Syndrome</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
<QualifierName UI="Q000401" MajorTopicYN="N">mortality</QualifierName>
<QualifierName UI="Q000652" MajorTopicYN="N">urine</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000367" MajorTopicYN="N">Age Factors</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019540" MajorTopicYN="N">Area Under Curve</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D015415" MajorTopicYN="N">Biomarkers</DescriptorName>
<QualifierName UI="Q000652" MajorTopicYN="N">urine</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016022" MajorTopicYN="N">Case-Control Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019075" MajorTopicYN="N">Electrophoresis, Capillary</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D013058" MajorTopicYN="N">Mass Spectrometry</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D010455" MajorTopicYN="N">Peptides</DescriptorName>
<QualifierName UI="Q000652" MajorTopicYN="Y">urine</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D020543" MajorTopicYN="N">Proteome</DescriptorName>
<QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D040901" MajorTopicYN="Y">Proteomics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012372" MajorTopicYN="N">ROC Curve</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D060388" MajorTopicYN="N">Support Vector Machine</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016019" MajorTopicYN="N">Survival Analysis</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year>
<Month>11</Month>
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   |texte=   Prediction of acute coronary syndromes by urinary proteome analysis.
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Prediction of acute coronary syndromes by urinary proteome analysis.

Prediction of acute coronary syndromes by urinary proteome analysis.

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