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...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2015-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0140993 |
id |
doaj-ff57c2a9894d4988ae77e5504158f700 |
---|---|
record_format |
Article |
spelling |
doaj-ff57c2a9894d4988ae77e5504158f7002021-03-03T19:57:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011011e014099310.1371/journal.pone.0140993Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit.Monica Garcia-SimonJose M MoralesVicente Modesto-AlapontVannina Gonzalez-MarrachelliRosa Vento-RehuesAngela Jorda-MiñanaJose Blanquer-OlivasDaniel MonleonEarly 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.https://doi.org/10.1371/journal.pone.0140993 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Monica Garcia-Simon Jose M Morales Vicente Modesto-Alapont Vannina Gonzalez-Marrachelli Rosa Vento-Rehues Angela Jorda-Miñana Jose Blanquer-Olivas Daniel Monleon |
spellingShingle |
Monica Garcia-Simon Jose M Morales Vicente Modesto-Alapont Vannina Gonzalez-Marrachelli Rosa Vento-Rehues Angela Jorda-Miñana Jose Blanquer-Olivas Daniel Monleon Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit. PLoS ONE |
author_facet |
Monica Garcia-Simon Jose M Morales Vicente Modesto-Alapont Vannina Gonzalez-Marrachelli Rosa Vento-Rehues Angela Jorda-Miñana Jose Blanquer-Olivas Daniel Monleon |
author_sort |
Monica Garcia-Simon |
title |
Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit. |
title_short |
Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit. |
title_full |
Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit. |
title_fullStr |
Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit. |
title_full_unstemmed |
Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit. |
title_sort |
prognosis biomarkers of severe sepsis and septic shock by 1h nmr urine metabolomics in the intensive care unit. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2015-01-01 |
description |
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. |
url |
https://doi.org/10.1371/journal.pone.0140993 |
work_keys_str_mv |
AT monicagarciasimon prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT josemmorales prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT vicentemodestoalapont prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT vanninagonzalezmarrachelli prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT rosaventorehues prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT angelajordaminana prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT joseblanquerolivas prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit AT danielmonleon prognosisbiomarkersofseveresepsisandsepticshockby1hnmrurinemetabolomicsintheintensivecareunit |
_version_ |
1714824887761108992 |