Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit

Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by (1)H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patie...

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Bibliographic Details
Published in:PloS one 2015-11, Vol.10 (11), p.e0140993-e0140993
Main Authors: Garcia-Simon, Monica, Morales, Jose M, Modesto-Alapont, Vicente, Gonzalez-Marrachelli, Vannina, Vento-Rehues, Rosa, Jorda-Miñana, Angela, Blanquer-Olivas, Jose, Monleon, Daniel
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Arginine
Bioindicators
Biomarkers
Biomarkers - metabolism
Biomarkers - urine
Discriminant Analysis
Ethanol
Failure analysis
Female
Glutamine
Human behavior
Humans
Intensive Care Units
Least-Squares Analysis
Magnetic resonance spectroscopy
Male
Mathematical models
Medical prognosis
Metabolic response
Metabolites
Metabolomics
Metabolomics - methods
Methionine
Middle Aged
Mortality
Multivariate analysis
NMR
NMR spectroscopy
Nuclear magnetic resonance
Patients
Phenylalanine
Prediction models
Prognosis
Proton Magnetic Resonance Spectroscopy - methods
Sepsis
Sepsis - diagnosis
Sepsis - metabolism
Sepsis - urine
Septic shock
Shock, Septic - diagnosis
Shock, Septic - metabolism
Shock, Septic - urine
Spectroscopy
Spectrum analysis
Urinalysis - methods
Urine
Young Adult
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Summary:Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by (1)H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a (1)H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0140993