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Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit.

Mickiewicz B, Tam P, Jenne CN, Leger C, Wong J, Winston BW, Doig C, Kubes P, Vogel HJ, Alberta Sepsis Netwo - Crit Care (2015)

Bottom Line: Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients.The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality.

View Article: PubMed Central - PubMed

Affiliation: Bio-NMR-Centre, Department of Biological Sciences, University of Calgary, 2500 University Drive Northwest, Calgary, AB, T2N 1N4, Canada. bmmickie@ucalgary.ca.

ABSTRACT

Introduction: Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU.

Methods: Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.

Results: By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock.

Conclusions: Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

No MeSH data available.


Related in: MedlinePlus

Septic shock patients versus ICU controls. Statistical analysis for septic shock patients (red) and ICU controls (green) based on the combined metabolomics and cytokine/chemokine dataset. (A) Three-dimensional PCA score scatter plot; (B) OPLS-DA score scatter plot; (C) ‘Predicted vs. Observed’ plot. The groups are well clustered along the axes of the three principal components in the three-dimensional PCA plot. Three septic shock samples are placed outside the sphere that indicates the 95% confidence interval of the Hotelling’s T-squared distribution. ICU, intensive care unit; OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis.
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Fig1: Septic shock patients versus ICU controls. Statistical analysis for septic shock patients (red) and ICU controls (green) based on the combined metabolomics and cytokine/chemokine dataset. (A) Three-dimensional PCA score scatter plot; (B) OPLS-DA score scatter plot; (C) ‘Predicted vs. Observed’ plot. The groups are well clustered along the axes of the three principal components in the three-dimensional PCA plot. Three septic shock samples are placed outside the sphere that indicates the 95% confidence interval of the Hotelling’s T-squared distribution. ICU, intensive care unit; OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis.

Mentions: Figure 1A presents the score scatter plot for the combined dataset. Similar plots for the individual NMR and cytokine/chemokine datasets are shown in Additional file 1. Three principal components (PCs) were calculated to build the PCA models for metabolomics, cytokine/chemokine (Additional file 1) and the combined dataset (Figure 1A).The percentage of variation explained by each component is as follows: for the metabolomics data: PC1 = 13.6%, PC2 = 12.1% and PC3 = 11.5%; for the cytokine/chemokine data: PC1 = 34%, PC2 = 12.4% and PC3 = 6.8%; and for the combined dataset: PC1 = 18.1%, PC2 = 11.1% and PC3 = 9.5%. Some of the septic shock samples (one sample for the metabolomics and cytokine/chemokine model and three samples for the combined dataset) appear far outside of the area of 95% confidence interval of the Hotelling’s T-squared distribution. It is well known that such outliers may disturb the model and incorrectly influence the results [25], thus in the next steps of statistical analysis the data for these samples were excluded. The outlying sample detected in the NMR dataset was exactly the same as in the cytokine/chemokine model. This same outlier was also observed in the combined dataset and from the clinical data this outlying sample was collected from the oldest patient in the whole cohort (88 years old) who was assessed with the admission SOFA score = 0 and who did not survive during the ICU stay.Figure 1


Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit.

Mickiewicz B, Tam P, Jenne CN, Leger C, Wong J, Winston BW, Doig C, Kubes P, Vogel HJ, Alberta Sepsis Netwo - Crit Care (2015)

Septic shock patients versus ICU controls. Statistical analysis for septic shock patients (red) and ICU controls (green) based on the combined metabolomics and cytokine/chemokine dataset. (A) Three-dimensional PCA score scatter plot; (B) OPLS-DA score scatter plot; (C) ‘Predicted vs. Observed’ plot. The groups are well clustered along the axes of the three principal components in the three-dimensional PCA plot. Three septic shock samples are placed outside the sphere that indicates the 95% confidence interval of the Hotelling’s T-squared distribution. ICU, intensive care unit; OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4340832&req=5

Fig1: Septic shock patients versus ICU controls. Statistical analysis for septic shock patients (red) and ICU controls (green) based on the combined metabolomics and cytokine/chemokine dataset. (A) Three-dimensional PCA score scatter plot; (B) OPLS-DA score scatter plot; (C) ‘Predicted vs. Observed’ plot. The groups are well clustered along the axes of the three principal components in the three-dimensional PCA plot. Three septic shock samples are placed outside the sphere that indicates the 95% confidence interval of the Hotelling’s T-squared distribution. ICU, intensive care unit; OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis.
Mentions: Figure 1A presents the score scatter plot for the combined dataset. Similar plots for the individual NMR and cytokine/chemokine datasets are shown in Additional file 1. Three principal components (PCs) were calculated to build the PCA models for metabolomics, cytokine/chemokine (Additional file 1) and the combined dataset (Figure 1A).The percentage of variation explained by each component is as follows: for the metabolomics data: PC1 = 13.6%, PC2 = 12.1% and PC3 = 11.5%; for the cytokine/chemokine data: PC1 = 34%, PC2 = 12.4% and PC3 = 6.8%; and for the combined dataset: PC1 = 18.1%, PC2 = 11.1% and PC3 = 9.5%. Some of the septic shock samples (one sample for the metabolomics and cytokine/chemokine model and three samples for the combined dataset) appear far outside of the area of 95% confidence interval of the Hotelling’s T-squared distribution. It is well known that such outliers may disturb the model and incorrectly influence the results [25], thus in the next steps of statistical analysis the data for these samples were excluded. The outlying sample detected in the NMR dataset was exactly the same as in the cytokine/chemokine model. This same outlier was also observed in the combined dataset and from the clinical data this outlying sample was collected from the oldest patient in the whole cohort (88 years old) who was assessed with the admission SOFA score = 0 and who did not survive during the ICU stay.Figure 1

Bottom Line: Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients.The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality.

View Article: PubMed Central - PubMed

Affiliation: Bio-NMR-Centre, Department of Biological Sciences, University of Calgary, 2500 University Drive Northwest, Calgary, AB, T2N 1N4, Canada. bmmickie@ucalgary.ca.

ABSTRACT

Introduction: Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU.

Methods: Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.

Results: By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock.

Conclusions: Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

No MeSH data available.


Related in: MedlinePlus