COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD. We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patient...
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| Veröffentlicht in: | PloS one 2016-08, Vol.11 (8), p.e0161129-e0161129 |
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| creator | Leung, Janice M Chen, Virginia Hollander, Zsuzsanna Dai, Darlene Tebbutt, Scott J Aaron, Shawn D Vandemheen, Kathy L Rennard, Stephen I FitzGerald, J Mark Woodruff, Prescott G Lazarus, Stephen C Connett, John E Coxson, Harvey O Miller, Bruce Borchers, Christoph McManus, Bruce M Ng, Raymond T Sin, Don D |
| description | Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD.
We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72). Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate 1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109) using leave-pair-out cross-validation methods.
Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p |
| doi_str_mv | 10.1371/journal.pone.0161129 |
| format | Article |
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We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72). Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate <0.01 and fold change >1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109) using leave-pair-out cross-validation methods.
Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p<0.001). The receiver operating characteristic cross-validation area under the curve (CV-AUC) statistic was 0.73 in Cohort A, while in the replication cohorts the CV-AUC was 0.77 for Cohort B and 0.79 for Cohort C.
A panel of five biomarkers shows promise in distinguishing AECOPD from convalescence and may provide the basis for a clinical blood test to diagnose AECOPD. Further validation in larger cohorts is necessary for future clinical translation.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0161129</identifier><identifier>PMID: 27525416</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acute Disease ; Aged ; Analysis ; Apolipoproteins ; Biology and Life Sciences ; Biomarkers ; Biomarkers - blood ; Chronic obstructive lung disease ; Chronic obstructive pulmonary disease ; Clinical trials ; Convalescence ; Critical care ; Dyspnea ; Female ; Heart attacks ; Hospitals ; Humans ; Identification methods ; Lipoproteins ; Lung diseases ; Male ; Mass Spectrometry ; Mass spectroscopy ; Medicine ; Medicine and Health Sciences ; Middle Aged ; Monitoring ; Morbidity ; Mortality ; Obstructive lung disease ; Patients ; Protein folding ; Proteins ; Proteomics ; Pulmonary Disease, Chronic Obstructive - blood ; R&D ; Regression analysis ; Regression models ; Research & development ; Risk factors ; Rodents ; Scientific imaging ; Sleep ; Spectroscopy ; Statistical analysis ; Statistical methods</subject><ispartof>PloS one, 2016-08, Vol.11 (8), p.e0161129-e0161129</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Leung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Leung et al 2016 Leung et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c618t-62c93d9fbd0d93ac4b197a3b2516cb6eec843a136813b60b6c026d4db107a0763</citedby><cites>FETCH-LOGICAL-c618t-62c93d9fbd0d93ac4b197a3b2516cb6eec843a136813b60b6c026d4db107a0763</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4985129/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4985129/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27525416$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leung, Janice M</creatorcontrib><creatorcontrib>Chen, Virginia</creatorcontrib><creatorcontrib>Hollander, Zsuzsanna</creatorcontrib><creatorcontrib>Dai, Darlene</creatorcontrib><creatorcontrib>Tebbutt, Scott J</creatorcontrib><creatorcontrib>Aaron, Shawn D</creatorcontrib><creatorcontrib>Vandemheen, Kathy L</creatorcontrib><creatorcontrib>Rennard, Stephen I</creatorcontrib><creatorcontrib>FitzGerald, J Mark</creatorcontrib><creatorcontrib>Woodruff, Prescott G</creatorcontrib><creatorcontrib>Lazarus, Stephen C</creatorcontrib><creatorcontrib>Connett, John E</creatorcontrib><creatorcontrib>Coxson, Harvey O</creatorcontrib><creatorcontrib>Miller, Bruce</creatorcontrib><creatorcontrib>Borchers, Christoph</creatorcontrib><creatorcontrib>McManus, Bruce M</creatorcontrib><creatorcontrib>Ng, Raymond T</creatorcontrib><creatorcontrib>Sin, Don D</creatorcontrib><title>COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD.
We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72). Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate <0.01 and fold change >1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109) using leave-pair-out cross-validation methods.
Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p<0.001). The receiver operating characteristic cross-validation area under the curve (CV-AUC) statistic was 0.73 in Cohort A, while in the replication cohorts the CV-AUC was 0.77 for Cohort B and 0.79 for Cohort C.
A panel of five biomarkers shows promise in distinguishing AECOPD from convalescence and may provide the basis for a clinical blood test to diagnose AECOPD. Further validation in larger cohorts is necessary for future clinical translation.</description><subject>Acute Disease</subject><subject>Aged</subject><subject>Analysis</subject><subject>Apolipoproteins</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Chronic obstructive lung disease</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Clinical trials</subject><subject>Convalescence</subject><subject>Critical care</subject><subject>Dyspnea</subject><subject>Female</subject><subject>Heart attacks</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Lipoproteins</subject><subject>Lung diseases</subject><subject>Male</subject><subject>Mass Spectrometry</subject><subject>Mass spectroscopy</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Monitoring</subject><subject>Morbidity</subject><subject>Mortality</subject><subject>Obstructive lung disease</subject><subject>Patients</subject><subject>Protein folding</subject><subject>Proteins</subject><subject>Proteomics</subject><subject>Pulmonary Disease, Chronic Obstructive - blood</subject><subject>R&D</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Research & development</subject><subject>Risk factors</subject><subject>Rodents</subject><subject>Scientific imaging</subject><subject>Sleep</subject><subject>Spectroscopy</subject><subject>Statistical analysis</subject><subject>Statistical 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Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry</title><author>Leung, Janice M ; Chen, Virginia ; Hollander, Zsuzsanna ; Dai, Darlene ; Tebbutt, Scott J ; Aaron, Shawn D ; Vandemheen, Kathy L ; Rennard, Stephen I ; FitzGerald, J Mark ; Woodruff, Prescott G ; Lazarus, Stephen C ; Connett, John E ; Coxson, Harvey O ; Miller, Bruce ; Borchers, Christoph ; McManus, Bruce M ; Ng, Raymond T ; Sin, Don D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c618t-62c93d9fbd0d93ac4b197a3b2516cb6eec843a136813b60b6c026d4db107a0763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Acute Disease</topic><topic>Aged</topic><topic>Analysis</topic><topic>Apolipoproteins</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Chronic obstructive lung disease</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Clinical trials</topic><topic>Convalescence</topic><topic>Critical care</topic><topic>Dyspnea</topic><topic>Female</topic><topic>Heart attacks</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Lipoproteins</topic><topic>Lung diseases</topic><topic>Male</topic><topic>Mass Spectrometry</topic><topic>Mass spectroscopy</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Monitoring</topic><topic>Morbidity</topic><topic>Mortality</topic><topic>Obstructive lung disease</topic><topic>Patients</topic><topic>Protein folding</topic><topic>Proteins</topic><topic>Proteomics</topic><topic>Pulmonary Disease, Chronic Obstructive - blood</topic><topic>R&D</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Research & development</topic><topic>Risk factors</topic><topic>Rodents</topic><topic>Scientific 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Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leung, Janice M</au><au>Chen, Virginia</au><au>Hollander, Zsuzsanna</au><au>Dai, Darlene</au><au>Tebbutt, Scott J</au><au>Aaron, Shawn D</au><au>Vandemheen, Kathy L</au><au>Rennard, Stephen I</au><au>FitzGerald, J Mark</au><au>Woodruff, Prescott G</au><au>Lazarus, Stephen C</au><au>Connett, John E</au><au>Coxson, Harvey O</au><au>Miller, Bruce</au><au>Borchers, Christoph</au><au>McManus, Bruce M</au><au>Ng, Raymond T</au><au>Sin, Don D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-08-15</date><risdate>2016</risdate><volume>11</volume><issue>8</issue><spage>e0161129</spage><epage>e0161129</epage><pages>e0161129-e0161129</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD.
We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72). Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate <0.01 and fold change >1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109) using leave-pair-out cross-validation methods.
Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p<0.001). The receiver operating characteristic cross-validation area under the curve (CV-AUC) statistic was 0.73 in Cohort A, while in the replication cohorts the CV-AUC was 0.77 for Cohort B and 0.79 for Cohort C.
A panel of five biomarkers shows promise in distinguishing AECOPD from convalescence and may provide the basis for a clinical blood test to diagnose AECOPD. Further validation in larger cohorts is necessary for future clinical translation.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27525416</pmid><doi>10.1371/journal.pone.0161129</doi><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_1812543120 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Acute Disease Aged Analysis Apolipoproteins Biology and Life Sciences Biomarkers Biomarkers - blood Chronic obstructive lung disease Chronic obstructive pulmonary disease Clinical trials Convalescence Critical care Dyspnea Female Heart attacks Hospitals Humans Identification methods Lipoproteins Lung diseases Male Mass Spectrometry Mass spectroscopy Medicine Medicine and Health Sciences Middle Aged Monitoring Morbidity Mortality Obstructive lung disease Patients Protein folding Proteins Proteomics Pulmonary Disease, Chronic Obstructive - blood R&D Regression analysis Regression models Research & development Risk factors Rodents Scientific imaging Sleep Spectroscopy Statistical analysis Statistical methods |
| title | COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-20T20%3A00%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=COPD%20Exacerbation%20Biomarkers%20Validated%20Using%20Multiple%20Reaction%20Monitoring%20Mass%20Spectrometry&rft.jtitle=PloS%20one&rft.au=Leung,%20Janice%20M&rft.date=2016-08-15&rft.volume=11&rft.issue=8&rft.spage=e0161129&rft.epage=e0161129&rft.pages=e0161129-e0161129&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0161129&rft_dat=%3Cgale_plos_%3EA460819094%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1812543120&rft_id=info:pmid/27525416&rft_galeid=A460819094&rft_doaj_id=oai_doaj_org_article_01437b8b5ec945a5b6af48adf023c0af&rfr_iscdi=true |