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. 2020 Sep 1;10(9):359.
doi: 10.3390/metabo10090359.

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome

Giovana Colozza Mecatti  1 , Salvador Sánchez-Vinces  1 , Anna Maria A P Fernandes  1 , Marcia C F Messias  1 , Gabrielle K D de Santis  1 , Andreia M Porcari  1 , Fernando A L Marson  2 , Patrícia de Oliveira Carvalho  1
Affiliations

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome

Giovana Colozza Mecatti et al. Metabolites. .

Abstract

Systemic inflammatory response syndrome (SIRS) and sepsis are two conditions which are difficult to differentiate clinically and which are strongly impacted for prompt intervention. This study identified potential lipid signatures that are able to differentiate SIRS from sepsis and to predict prognosis. Forty-two patients, including 21 patients with sepsis and 21 patients with SIRS, were involved in the study. Liquid chromatography coupled to mass spectrometry and multivariate statistical methods were used to determine lipids present in patient plasma. The obtained lipid signatures revealed 355 features for the negative ion mode and 297 for the positive ion mode, which were relevant for differential diagnosis of sepsis and SIRS. These lipids were also tested as prognosis predictors. Lastly, L-octanoylcarnitine was found to be the most promising lipid signature for both the diagnosis and prognosis of critically ill patients, with accuracies of 75% for both purposes. In short, we presented the determination of lipid signatures as a potential tool for differential diagnosis of sepsis and SIRS and prognosis of these patients.

Keywords: SIRS; lipidomics; multivariate analysis; sepsis.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Principal component analysis (PCA) score plot between the first 2 principal components (PC) for negative ionization mode (A) and positive ionization mode (B). Areas of 95% confidence are highlighted in red and green. Variance explanation (%) for each PC is indicated.
Figure 2
Figure 2
Volcano plot of features for negative ionization mode (A) and positive ionization mode (C). Heat map of clustered differential features and samples for negative mode (B) and positive mode (D). In the volcano plot, highlighted features with adjusted p-value of 0.05 and log (fold change) of 1. Heatmap depicts top 50 features with lowest adjusted p-values.
Figure 3
Figure 3
Summary of pathway analysis.
Figure 4
Figure 4
Prognostic classification using identified lipids from diagnosis classification model. Lipids ranked by importance for the random forest based model.
Figure 5
Figure 5
Classification performance of L-octanoylcarnitine for diagnosis (A) and prognosis (B). In (A), the receiver operating characteristic (ROC) curve shows AUC = 0.89 for training/test and ROC AUC = 0.625 for validation (holdout). (B) For prognostic classification, it shows the receiver operating characteristic (ROC) curve AUC = 0.713 for training/test and ROC AUC = 0.688 for validation (holdout).
Figure 6
Figure 6
(A) Heatmap of standardized intensities of significant lipids obtained by two-way ANOVA. The upper bars indicate the group to which the samples belong (columns). Lipid clusters on the left side. (B,D) Boxplot of the 2 most relevant lipids. Boxplot of subgroups (diagnosis + prognosis) with statistical significance values obtained by ANOVA and Tukey′s test. Boxplots for diagnosis and prognosis classification for (C) L-octanoylcarnintie and (E) FAHFA 36:4.
See this image and copyright information in PMC

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