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. 2016 Sep 1;6(3):26.
doi: 10.3390/metabo6030026.

Specific Metabolome Profile of Exhaled Breath Condensate in Patients with Shock and Respiratory Failure: A Pilot Study

Affiliations

Specific Metabolome Profile of Exhaled Breath Condensate in Patients with Shock and Respiratory Failure: A Pilot Study

Brice Fermier et al. Metabolites. .

Abstract

Background: Shock includes different pathophysiological mechanisms not fully understood and remains a challenge to manage. Exhaled breath condensate (EBC) may contain relevant biomarkers that could help us make an early diagnosis or better understand the metabolic perturbations resulting from this pathological situation.

Objective: we aimed to establish the metabolomics signature of EBC from patients in shock with acute respiratory failure in a pilot study.

Material and methods: We explored the metabolic signature of EBC in 12 patients with shock compared to 14 controls using LC-HRMS. We used a non-targeted approach, and we performed a multivariate analysis based on Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) to differentiate between the two groups of patients.

Results: We optimized the procedure of EBC collection and LC-HRMS detected more than 1000 ions in this fluid. The optimization of multivariate models led to an excellent model of differentiation for both groups (Q2 > 0.4) after inclusion of only 6 ions.

Discussion and conclusion: We validated the procedure of EBC collection and we showed that the metabolome profile of EBC may be relevant in characterizing patients with shock. We performed well in distinguishing these patients from controls, and the identification of relevant compounds may be promising for ICC patients.

Keywords: biomarkers; exhaled breath condensate; mass spectrometry; metabolomics; shock.

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Figures

Figure A1
Figure A1
DModXPS data based metabolome obtained from quality controls. All quality controls are under the Dcrit (0.05) line.
Figure 1
Figure 1
Score scatter plot of principal component analysis (PCA) from quality controls from (A) positive mode and (B) negative mode.
Figure 2
Figure 2
Score scatter plot of OPLS-DA from the metabolome of exhaled breath condensates showing the discrimination of patients (blue) and controls (green) using (A) positive mode and (B) negative mode.
Figure 3
Figure 3
Contribution plot of the most relevant VIP included in the OPLS-DA models built from the metabolome of exhaled breath condensate based on (A) positive mode and (B) negative mode. Metabolites with positive score contributions are higher in patients than in controls.

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