Characterization of a metabolomic profile associated with responsiveness to therapy in the acute phase of septic shock

Abstract

The early metabolic signatures associated with the progression of septic shock and with responsiveness to therapy can be useful for developing target therapy. The Sequential Organ Failure Assessment (SOFA) score is used for stratifying risk and predicting mortality. This study aimed to verify whether different responses to therapy, assessed as changes in SOFA score at admission (T1, acute phase) and 48 h later (T2, post-resuscitation), are associated with different metabolite patterns. We examined the plasma metabolome of 21 septic shock patients (pts) enrolled in the Shockomics clinical trial (NCT02141607). Patients for which SOFAT2 was >8 and Δ = SOFAT1 - SOFAT2 < 5, were classified as not responsive to therapy (NR, 7 pts), the remaining 14 as responsive (R). We combined untargeted and targeted mass spectrometry-based metabolomics strategies to cover the plasma metabolites repertoire as far as possible. Metabolite concentration changes from T1 to T2 (Δ = T2 - T1) were used to build classification models. Our results support the emerging evidence that lipidome alterations play an important role in individual patients' responses to infection. Furthermore, alanine indicates a possible alteration in the glucose-alanine cycle in the liver, providing a different picture of liver functionality from bilirubin. Understanding these metabolic disturbances is important for developing any effective tailored therapy for these patients.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1

Untargeted metabolomics. Metabolites whose peak intensity is significantly different between responsive (R) and non-responsive (NR) patients at T2 (Wilcoxon rank-sum test p 

Figure 2

Untargeted metabolomics. Metabolites whose change in peak intensity from T1 to T2 in the two groups is statistically significant. Box-plots in the top right corner show differences in metabolite peak intensity between T1 and T2 expressed as delta (Δ = T2 − T1). We did the Wilcoxon rank-sum test for the delta of the two groups and Wilcoxon signed rank between T1 and T2 in each group separately. Significant differences are marked with *(p-value 

Figure 3

Targeted metabolomics. Metabolites whose concentration (μM) is significantly different between responsive (R) and non-responsive (NR) patients at T2 (Wilcoxon rank-sum test p-value 

Figure 4

Targeted metabolomics. Metabolites whose concentration (μM) from T1 to T2 in the two groups is statistically different. The figure shows only 4 metabolites as an example of those differing overtime (see Table 5). Box-plots in the top right corner show the differences in metabolite concentrations between T1 and T2, expressed as delta (Δ = T2 − T1). We did the Wilcoxon rank-sum test for the delta of the two groups and Wilcoxon signed rank between T1 and T2 in each group separately. Significant differences are marked with *(p-value 

Figure 5

Coefficients values of the logistic regression models for targeted metabolomics (panel A) and for integration of targeted and untargeted metabolomics (panel B).

Figure 6

Three-dimensional PLS-DA score plots on 20 features for targeted metabolomics model (panel A) and for targeted and untargeted metabolomics model (panel B).

Figure 7

Plasma sPLA2-IIA levels (μg/L) in responsive (R) and non-responsive (NR) patients at T1 and T2 (panel A) and comparison of time trend variation, expressed as delta (Δ = T2 − T1), between the two groups (panel B). Distributions are shown as box-plots where the central line is the median concentration, the edges of the box are the 25th and 75th percentiles and the outliers are defined as 1.5 times the interquartile range and highlighted by +. Significant differences between groups are marked with *(Wilcoxon rank-sum test p-value