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Multimodal combination of GC × GC-HRTOFMS and SIFT-MS for asthma phenotyping using exhaled breath

Authors
  • Stefanuto, Pierre-Hugues1
  • Zanella, Delphine1
  • Vercammen, Joeri2, 3
  • Henket, Monique4
  • Schleich, Florence4
  • Louis, Renaud4
  • Focant, Jean-François1
  • 1 University of Liège, Allée du 6 Août B6c, Liège, 4000, Belgium , Liège (Belgium)
  • 2 Interscience, Avenue J.E. Lenoir, Louvain-la-Neuve, Belgium , Louvain-la-Neuve (Belgium)
  • 3 Ghent University, Ghent, Belgium , Ghent (Belgium)
  • 4 CHU of Liège, University of Liege, Liège, Belgium , Liège (Belgium)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Sep 30, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41598-020-73408-2
Source
Springer Nature
License
Green

Abstract

Chronic inflammatory lung diseases impact more than 300 million of people worldwide. Because they are not curable, these diseases have a high impact on both the quality of life of patients and the healthcare budget. The stability of patient condition relies mostly on constant treatment adaptation and lung function monitoring. However, due to the variety of inflammation phenotypes, almost one third of the patients receive an ineffective treatment. To improve phenotyping, we evaluated the complementarity of two techniques for exhaled breath analysis: full resolving comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOFMS) and rapid screening selected ion flow tube MS (SIFT-MS). GC × GC-HRTOFMS has a high resolving power and offers a full overview of sample composition, providing deep insights on the ongoing biology. SIFT-MS is usually used for targeted analyses, allowing rapid classification of samples in defined groups. In this study, we used SIFT-MS in a possible untargeted full-scan mode, where it provides pattern-based classification capacity. We analyzed the exhaled breath of 50 asthmatic patients. Both techniques provided good classification accuracy (around 75%), similar to the efficiency of other clinical tools routinely used for asthma phenotyping. Moreover, our study provides useful information regarding the complementarity of the two techniques.

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