Metabolomic derangements are associated with mortality in critically ill adult patients

To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. We performed metabolomic profiling of plasma from 90 ICU subje...

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Published inPloS one Vol. 9; no. 1; p. e87538
Main Authors Rogers, Angela J, McGeachie, Michael, Baron, Rebecca M, Gazourian, Lee, Haspel, Jeffrey A, Nakahira, Kiichi, Fredenburgh, Laura E, Hunninghake, Gary M, Raby, Benjamin A, Matthay, Michael A, Otero, Ronny M, Fowler, Vance G, Rivers, Emanuel P, Woods, Christopher W, Kingsmore, Stephen, Langley, Ray J, Choi, Augustine M K
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 30.01.2014
Public Library of Science (PLoS)
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Abstract To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
AbstractList Objective To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Rationale Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. Methods We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. Results We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). Conclusion Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
Objective To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Rationale Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. Methods We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. Results We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). Conclusion Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
OBJECTIVETo identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. RATIONALEComprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. METHODSWe performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. RESULTSWe tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). CONCLUSIONBoth individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
Audience Academic
Author Gazourian, Lee
Kingsmore, Stephen
Matthay, Michael A
Raby, Benjamin A
Hunninghake, Gary M
Langley, Ray J
Baron, Rebecca M
Nakahira, Kiichi
Otero, Ronny M
McGeachie, Michael
Woods, Christopher W
Rogers, Angela J
Haspel, Jeffrey A
Fowler, Vance G
Choi, Augustine M K
Rivers, Emanuel P
Fredenburgh, Laura E
AuthorAffiliation 9 Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
1 Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
7 National Center for Genome Resources, Santa Fe, New Mexico, United States of America
4 Department of Medicine and Anesthesia, University of California San Francisco, San Francisco, California, United States of America
2 Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
6 Duke Institute for Genome Sciences and Policy, and Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America
8 Lovelace Respiratory Research Institute, Santa Fe, New Mexico, United States of America
Beth Israel Deaconess Medical Center, United States of America
3 Division of Pulmonary and Critical Care Medicine, Stanford University, Stanford, California, United States of America
5 D
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/24498130$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2014 Public Library of Science
2014 Rogers 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.
2014 Rogers et al 2014 Rogers et al
Copyright_xml – notice: COPYRIGHT 2014 Public Library of Science
– notice: 2014 Rogers 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.
– notice: 2014 Rogers et al 2014 Rogers et al
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Competing Interests: The CAPSOD trial was supported in part by Pfizer and Roche diagnostics. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: AJR MM RMB LG KN LF BAR RMO VGF EPR CWW SK RL AMC. Performed the experiments: LG RMB RL AMC. Analyzed the data: AJR MM GMH BAR AMC. Contributed reagents/materials/analysis tools: RL SK AMC. Wrote the paper: AJR MM MAM RMB LG JAH KN GMH SK AMC.
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Snippet To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Comprehensive metabolomic profiling of plasma at ICU admission...
Objective To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Rationale Comprehensive metabolomic profiling of...
To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Comprehensive metabolomic profiling of plasma at ICU admission...
OBJECTIVETo identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. RATIONALEComprehensive metabolomic profiling of plasma...
To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults.Comprehensive metabolomic profiling of plasma at ICU admission...
Objective To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Rationale Comprehensive metabolomic profiling of...
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SubjectTerms Adults
Aged
Amino acids
Analysis
Bayes Theorem
Bayesian analysis
Biology
Biomarkers
Biomarkers - metabolism
Carbohydrates
Community-Acquired Infections - metabolism
Community-Acquired Infections - mortality
Critical care
Critical Illness - mortality
Emergency medical care
Enrollments
Female
Gene expression
Genomes
Genotype & phenotype
Hormones
Hospital Mortality
Hospital patients
Hospitals
Humans
Identification methods
Infection
Intensive care
Intensive Care Units
Lactates
Lactic acid
Lipid metabolism
Lipids
Male
Mathematics
Medicine
Metabolism
Metabolites
Metabolomics
Metabolomics - methods
Middle Aged
Mortality
Nucleotides
Patient outcomes
Patients
Prognosis
Replication
Sepsis
Sepsis - metabolism
Sepsis - mortality
Sucrose
Sugar
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Title Metabolomic derangements are associated with mortality in critically ill adult patients
URI https://www.ncbi.nlm.nih.gov/pubmed/24498130
https://www.proquest.com/docview/1977446043
https://search.proquest.com/docview/1499124464
https://pubmed.ncbi.nlm.nih.gov/PMC3907548
https://doaj.org/article/af0c2a2810304bc993f1c1f3b566b073
http://dx.doi.org/10.1371/journal.pone.0087538
Volume 9
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