Organic and Biological Analytical Chemistry Group, MOLSYS Research Unit, University of Liège, Allée du 6 Août B6c, 4000, Liège, Belgium.
Interscience, Avenue J.E. Lenoir, Louvain-la-Neuve, Belgium.
Sci Rep. 2020 Sep 30;10(1):16159. doi: 10.1038/s41598-020-73408-2.
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.
慢性炎症性肺病影响着全球超过 3 亿人。由于这些疾病无法治愈,它们对患者的生活质量和医疗保健预算都有很大的影响。患者病情的稳定主要依赖于持续的治疗适应和肺功能监测。然而,由于炎症表型的多样性,几乎三分之一的患者接受的治疗效果不佳。为了改善表型分析,我们评估了两种呼气分析技术的互补性:全解析综合二维气相色谱与高分辨率飞行时间质谱联用(GC×GC-HRTOFMS)和快速筛选选择离子流管质谱(SIFT-MS)。GC×GC-HRTOFMS 具有高分辨率能力,提供了对样品组成的全面概述,深入了解正在进行的生物学过程。SIFT-MS 通常用于靶向分析,允许对定义的样本组进行快速分类。在这项研究中,我们在可能的非靶向全扫描模式下使用 SIFT-MS,它提供基于模式的分类能力。我们分析了 50 名哮喘患者的呼气。两种技术都提供了良好的分类准确性(约 75%),与其他常规用于哮喘表型分析的临床工具的效率相似。此外,我们的研究提供了关于两种技术互补性的有用信息。