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. 2023 Jul 5;27(1):263.
doi: 10.1186/s13054-023-04552-0.

A targeted metabolomics approach for sepsis-induced ARDS and its subphenotypes

Youjin Chang  1 , Hyun Ju Yoo  2 , Su Jung Kim  2 , Kwangha Lee  3 , Chae-Man Lim  4 , Sang-Bum Hong  4 , Younsuck Koh  4 , Jin Won Huh  5
Affiliations

A targeted metabolomics approach for sepsis-induced ARDS and its subphenotypes

Youjin Chang et al. Crit Care. .

Abstract

Background: Acute respiratory distress syndrome (ARDS) is etiologically and clinically a heterogeneous disease. Its diagnostic characteristics and subtype classification, and the application of these features to treatment, have been of considerable interest. Metabolomics is becoming important for identifying ARDS biology and distinguishing its subtypes. This study aimed to identify metabolites that could distinguish sepsis-induced ARDS patients from non-ARDS controls, using a targeted metabolomics approach, and to identify whether sepsis-induced direct and sepsis-induced indirect ARDS are metabolically distinct groups, and if so, confirm their metabolites and associated pathways.

Methods: This study retrospectively analyzed 54 samples of ARDS patients from a sepsis registry that was prospectively collected from March 2011 to February 2018, along with 30 non-ARDS controls. The cohort was divided into direct and indirect ARDS. Metabolite concentrations of five analyte classes (energy metabolism, free fatty acids, amino acids, phospholipids, sphingolipids) were measured using liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry by targeted metabolomics.

Results: In total, 186 metabolites were detected. Among them, 102 metabolites could differentiate sepsis-induced ARDS patients from the non-ARDS controls, while 14 metabolites could discriminate sepsis-induced ARDS subphenotypes. Using partial least-squares discriminant analysis, we showed that sepsis-induced ARDS patients were metabolically distinct from the non-ARDS controls. The main distinguishing metabolites were lysophosphatidylethanolamine (lysoPE) plasmalogen, PE plasmalogens, and phosphatidylcholines (PCs). Sepsis-induced direct and indirect ARDS were also metabolically distinct subgroups, with differences in lysoPCs. Glycerophospholipid and sphingolipid metabolism were the most significant metabolic pathways involved in sepsis-induced ARDS biology and in sepsis-induced direct/indirect ARDS, respectively.

Conclusion: Our study demonstrated a marked difference in metabolic patterns between sepsis-induced ARDS patients and non-ARDS controls, and between sepsis-induced direct and indirect ARDS subpheonotypes. The identified metabolites and pathways can provide clues relevant to the diagnosis and treatment of individuals with ARDS.

Keywords: Adult; Biomarkers; Metabolomics; Pathways; Respiratory distress syndrome; Sepsis.

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Conflict of interest statement

All authors report no conflict of interest.

Figures

Fig. 1
Fig. 1
Statistical analysis of the data obtained for acute respiratory distress syndrome (ARDS) patients and non-ARDS controls. A Partial least squares discriminant analysis (PLS-DA) showing the separation of sepsis-induced ARDS patients (green) from non-ARDS controls (red). B Permutation test statistics using separation distance based on sum of squares between and sum of squares within (B/W) ratio. This test indicates PLS-DA between ARDS patients and non-ARDS controls was statistically significant (p < 0.01). C Variable importance in projection (VIP) score. The metabolites responsible for discrimination between ARDS patients and non-ARDS controls are shown. Metabolites with high VIP scores are more important in class separation. D Concentrations of significant metabolites for the discrimination of sepsis-induced ARDS and non-ARDS controls
Fig. 1
Fig. 1
Statistical analysis of the data obtained for acute respiratory distress syndrome (ARDS) patients and non-ARDS controls. A Partial least squares discriminant analysis (PLS-DA) showing the separation of sepsis-induced ARDS patients (green) from non-ARDS controls (red). B Permutation test statistics using separation distance based on sum of squares between and sum of squares within (B/W) ratio. This test indicates PLS-DA between ARDS patients and non-ARDS controls was statistically significant (p < 0.01). C Variable importance in projection (VIP) score. The metabolites responsible for discrimination between ARDS patients and non-ARDS controls are shown. Metabolites with high VIP scores are more important in class separation. D Concentrations of significant metabolites for the discrimination of sepsis-induced ARDS and non-ARDS controls
Fig. 2
Fig. 2
Pathways affected in sepsis-induced ARDS biology. *Color gradient and circle size indicate the significance of the pathway ranked by p-value (yellow: higher p-value, red: lower p-value) and pathway impact score (larger circle indicates higher impact score)
Fig. 3
Fig. 3
Statistical analysis of the data obtained for 54 patients with 27 direct ARDS and 27 indirect ARDS. A PLS-DA 3D score plot for the discrimination of patients with sepsis-induced direct ARDS (P-ARDS) and indirect ARDS (E-ARDS) (left) and permutation test (right) indicating that the PLS-DA between ARDS patients and non-ARDS controls was statistically significant (p = 0.01). B Important metabolites discriminating the two groups. Variable importance in projection (VIP) score: the metabolites are responsible for discrimination between direct ARDS and indirect ARDS. Metabolites with high VIP scores are more important in class separation. C Concentrations of significant metabolites for the discrimination of sepsis-induced direct ARDS and indirect ARDS. D Hierarchical heatmap for top-15 discriminating metabolites between sepsis-induced direct ARDS and indirect ARDS (red bar: direct ARDS, green bar: indirect ARDS)
Fig. 3
Fig. 3
Statistical analysis of the data obtained for 54 patients with 27 direct ARDS and 27 indirect ARDS. A PLS-DA 3D score plot for the discrimination of patients with sepsis-induced direct ARDS (P-ARDS) and indirect ARDS (E-ARDS) (left) and permutation test (right) indicating that the PLS-DA between ARDS patients and non-ARDS controls was statistically significant (p = 0.01). B Important metabolites discriminating the two groups. Variable importance in projection (VIP) score: the metabolites are responsible for discrimination between direct ARDS and indirect ARDS. Metabolites with high VIP scores are more important in class separation. C Concentrations of significant metabolites for the discrimination of sepsis-induced direct ARDS and indirect ARDS. D Hierarchical heatmap for top-15 discriminating metabolites between sepsis-induced direct ARDS and indirect ARDS (red bar: direct ARDS, green bar: indirect ARDS)
Fig. 4
Fig. 4
Metabolic pathway analysis significantly discriminating between direct acute respiratory distress syndrome (ARDS) and indirect ARDS. *Color gradient and circle size indicate the significance of the pathway ranked by p-value (yellow: higher p-value, red: lower p-value) and pathway impact score (larger circle indicates higher impact score)
Fig. 5
Fig. 5
Sphingolipid metabolic pathway involving acute respiratory distress syndrome (ARDS) subphenotypes. *Sepsis-induced direct ARDS (P-ARDS) vs. sepsis-induced indirect ARDS (E-ARDS)
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