Table 1.
Sociodemographic, epidemiological, and clinical characteristics, including laboratory analyses, of the study participants by surveyed group.
Fig 1.
Multivariate analyses from plasma metabolome profile of G1 versus G2, G2 versus G3, and G3 versus G4 patients.
(A) Score scatter plot based on PLS-DA models to explain the diagnosis (G1: green, G2: yellow) (B) rank of to 20 metabolites identified by PLS-DA according to VIP score on x-axis. (C) Score scatter plot based on PLS-DA models to explain the prognosis (G2: yellow, G3: orange), (D) rank of top 20 metabolites identified by the PLS-DA according to VIP score on x-axis. (E) Score scatter plot based on PLS-DA models to explain the prognosis (G3: orange, G4: red), (F) rank of top 20 metabolites identified by PLS-DA according to VIP score on x-axis. The most discriminating metabolites are shown in descending score order. The color boxes indicate whether metabolite concentration was increased (red) or decreased (blue). Figures were produced in MetaboAnalyst software v 4.0 (https://www.metaboanalyst.ca/).
Table 2.
Plasma concentration values (micromoles) of dysregulated metabolites according to disease severity.
Fig 2.
Plasma concentration of types I, II and III interferon response cytokines, inflammatory response cytokines, and neutrophil response cytokines.
(A) IFN-λ1 (B) IFN-α2(C) IFN-λ2/3 (D) IFN-β (E) IL-6(F) IL-10 (G) IL-1β (H) TNF-α (I) IP-10 (J) IFN-γ (K) IL-12p70 (L) IL-8 (M) GM-CSF. All measurements were made using a multiplex flow cytometry assay (LEGENDplex). Results were obtained in a BD FACS Canto II flow cytometer and processed using the LEGENDplex Data Analysis Software v8.0. Graphs were constructed in GraphPad Prism v8.0. The * p value <0.05, ** p value <0.01, *** p value <0.001 and **** p value <0.0001 was calculated using Kruskall Wallis tests with a Dunn´s post-tests.
Fig 3.
Patterns of correlations among all metabolites and cytokines/chemokines measured in the study.
(A) Correlation between metabolites and cytokines/chemokines/ neutrophil to lymphocyte ratio (NLR) in mild patients (G2). (B) correlation between lipids and cytokines/chemokines/NLR in critically ill patients (G2). (C) correlation between metabolites and cytokines/chemokines/NLR in mild patients (G4). (D) correlation between lipids and cytokines/chemokines/NLR in critically ill patients (G4). The correlations between the concentration levels of the metabolites and cytokines and chemokines were done by Spearman’s Correlation Coefficient using the R package “corrr”, correlations plots were done using the “corrplot” package. Each analysis and plot were done in R studio (1.3.959).
Fig 4.
Multivariate and univariate models.
(A) Multivariate ROC curve of model A: G1 vs. G4. (B) ROC curve of model B: G2 vs. G4. (C) ROC curve of the model C: G3 vs. G4. (D) Univariate ROC curve explaining the performance of qSOFA to predict sepsis in G3 and G4. (E) Multivariate ROC curve of model D: non-survivors vs. survivors from G4. (F) Univariate ROC curve explaining the performance of qSOFA to predict mortality in G4. Figures were made in MetaboAnalyst software v 4.0 (https://www.metaboanalyst.ca/).
Table 3.
Comparison of metabolic and immune mediators in sepsis caused by bacterial, fungal and viral infections.
Fig 5.
Differential metabolites involved in sepsis associated to COVID-19 infection.
Black arrows represent the trend (increasing or decreasing) in critically ill patients. The figure was created with BioRender.com under a CC BY license, with permission from BioRender, original copyright 2021.