Analysis of volatile organic compounds in the breath of patients with stable or acute exacerbation of chronic obstructive pulmonary disease
Analysis of volatile organic compounds in the breath of patients with stable or acute exacerbation of chronic obstructive pulmonary disease
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DOI：
10.1088/1752-7163/aaa4c5
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发表时间：
2018-07-01
影响因子：
3.8
通讯作者：
Bellmann-Weiler, Rosa
中科院分区：
文献类型：
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作者：
Pizzini, Alex;Filipiak, Wojciech;Bellmann-Weiler, Rosa
慢性阻塞性肺疾病（COPD）是世界范围内的一个主要死亡原因。慢性阻塞性肺病急性加重（AECOPD）是由传染性和非传染性因素引起的，可导致死亡率增加。AECOPD的诊断程序主要基于临床特征。本初步研究的目的是确定呼吸中的挥发性有机化合物（VOCs）是否可以用于区分急性加重COPD。本对照研究包括三组患者：AECOPD患者（n = 14，年龄平均+/- SD: 71.4 +/- 7.46）、稳定期COPD患者（n = 16，年龄平均+/- SD: 66.9 +/- 9.05）和健康志愿者（n = 24，年龄平均+/- SD: 28 +/- 6.08）。呼气样本是通过优化我们开发的采样策略收集的。然后使用热解吸-气相色谱-飞行时间质谱仪（TD-GC-ToF-MS）对这些样品进行分析。在呼吸样本中共鉴定出105种挥发性有机化合物。随后，通过总体发生率、肺泡梯度（AG）阳性频率（即呼出和吸入VOCs浓度的差异）、“吸烟相关”VOCs的排除以及三组间AGs的显著差异来选择相关物质。这些步骤大大减少了相关分析物的数量，并产生了12种具有判别值的关键挥发性有机化合物。使用所有12种物质的受试者工作特征（ROC）曲线描述的患者分类表现为曲线下面积（AUC）为0.97。进一步减少到4个VOCs （AGs仅在AECOPD和COPD之间不同），AUC为0.92。这些结果表明，呼气分析与TD-GC-ToF-MS有望准确和容易地进行AECOPD和COPD的鉴别诊断。在这方面，AECOPD呼出气体中酮类含量最高，其中一些也与潜在的细菌病原体有关。使用一组可以区分AECOPD的VOCs， ROC曲线分析中计算出的auc与血清AECOPD生物标志物（如c反应蛋白）相比，结果要好得多。识别出的挥发性有机化合物应该在转化研究中进一步研究，以解决它们开发用于呼吸气体分析的高度特异性纳米传感器的潜力，这将为临床医生提供一种在护理点对AECOPD进行非侵入性诊断的工具。
Chronic obstructive pulmonary disease (COPD) is a major cause of death worldwide. Acute exacerbations COPD (AECOPD), caused by infectious and non-infectious agents, contribute to an increase in mortality. The diagnostic procedure of AECOPD is mainly based on clinical features. The aim of this pilot study was to identify whether volatile organic compounds (VOCs) in breath could be used to discriminate for acute exacerbated COPD. Three patient groups were included in this controlled study: AECOPD patients (n = 14, age mean +/- SD: 71.4 +/- 7.46), stable COPD patients (n = 16, age mean +/- SD: 66.9 +/- 9.05) and healthy volunteers (n = 24, age mean +/- SD: 28 +/- 6.08). Breath samples were collected by optimizing a sampling strategy developed by us. These samples were then analyzed using a thermal desorption-gas chromatography-time of flight-mass spectrometer (TD-GC-ToF-MS). A total of 105 VOCs were identified in the breath samples. Relevant substances were subsequently selected by overall occurrence rate, the frequency of positive alveolar gradient (AG) (i.e. the difference in exhaled and inhaled VOCs concentration), exclusion of 'smoking related' VOCs and significant differences in AGs between the three groups. These steps dramatically reduced the number of relevant analytes and resulted in 12 key VOCs having discriminative values. The performance of patients' classification described by the Receiver Operating Characteristic (ROC) curve using all 12 substances delineates an area under the curve (AUC) of 0.97. A further reduction to four VOCs (AGs only different between AECOPD and COPD) delineates an AUC of 0.92. These results indicate that breath analysis with TD-GC-ToF-MS holds promise for an accurate and easy to perform differential diagnosis between AECOPD and COPD. In this regard, ketones were observed at the highest levels in exhaled breath of AECOPD, some of which are also related to potential bacterial pathogens. Using a set of VOCs that can discriminate for AECOPD, the calculated AUCs in ROC curve analysis show far superior results in comparison to serum AECOPD biomarkers, such as C-reactive protein. The identified VOCs should be further investigated in translational studies addressing their potential for developing highly specific nanosensors for breath gas analysis which would give clinicians a tool for non-invasive diagnosis of AECOPD at the point of care.