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吸烟者和非吸烟者呼出气体中挥发性有机化合物(VOC)模式的色谱分析。

Chromatographic analysis of VOC patterns in exhaled breath from smokers and nonsmokers.

作者信息

Capone Simonetta, Tufariello Maria, Forleo Angiola, Longo Valentina, Giampetruzzi Lucia, Radogna Antonio Vincenzo, Casino Flavio, Siciliano Pietro

机构信息

National Research Council, Institute for Microelectronics and Microsystem, Lecce, Italy.

National Research Council, Institute of Sciences of Food Production, Lecce, Italy.

出版信息

Biomed Chromatogr. 2018 Apr;32(4). doi: 10.1002/bmc.4132. Epub 2017 Dec 12.

DOI:10.1002/bmc.4132
PMID:29131420
Abstract

Cigarette smoking harms nearly every organ of the body and causes many diseases. The analysis of exhaled breath for exogenous and endogenous volatile organic compounds (VOCs) can provide fundamental information on active smoking and insight into the health damage that smoke is creating. Various exhaled VOCs have been reported as typical of smoking habit and recent tobacco consumption, but to date, no eligible biomarkers have been identified. Aiming to identify such potential biomarkers, in this pilot study we analyzed the chemical patterns of exhaled breath from 26 volunteers divided into groups of nonsmokers and subgroups of smokers sampled at different periods of withdrawal from smoking. Solid-phase microextraction technique and gas chromatography/mass spectrometry methods were applied. Many breath VOCs were identified and quantified in very low concentrations (ppbv range), but only a few (toluene, pyridine, pyrrole, benzene, 2-butanone, 2-pentanone and 1-methyldecyclamine) were found to be statistically significant variables by Mann-Whitney test. In our analysis, we did not consider the predictive power of individual VOCs, as well as the criterion of uniqueness for biomarkers suggests, but we used the patterns of the only statistically significant compounds. Probit prediction model based on statistical relevant VOCs-patterns showed that assessment of smoking status is heavily time dependent. In a two-class classifier model, it is possible to predict with high specificity and sensitivity if a subject is a smoker who respected 1 hour of abstinence from smoking (short-term exposure to tobacco) or a smoker (labelled "blank smoker") after a night out of smoking (long-term exposure to tobacco). On the other side, in our study "blank smokers" are more like non-smokers so that the two classes cannot be well distinguished and the corresponding prediction results showed a good sensitivity but low selectivity.

摘要

吸烟几乎会损害身体的每个器官,并引发多种疾病。对呼出气体中的外源性和内源性挥发性有机化合物(VOCs)进行分析,可以提供有关主动吸烟的基本信息,并深入了解吸烟对健康造成的损害。已有多种呼出的VOCs被报道为吸烟习惯和近期烟草消费的典型特征,但迄今为止,尚未确定合适的生物标志物。为了识别此类潜在的生物标志物,在这项初步研究中,我们分析了26名志愿者呼出气体的化学模式,这些志愿者被分为非吸烟者组和吸烟者亚组,吸烟者亚组是在不同戒烟阶段进行采样的。采用了固相微萃取技术和气相色谱/质谱法。许多呼出的VOCs在极低浓度(ppbv范围)下被识别和定量,但通过曼-惠特尼检验发现,只有少数几种(甲苯、吡啶、吡咯、苯、2-丁酮、2-戊酮和1-甲基癸环胺)是具有统计学意义的变量。在我们的分析中,我们没有考虑单个VOCs的预测能力,也没有按照生物标志物的唯一性标准来考虑,而是使用了仅具有统计学意义的化合物的模式。基于具有统计相关性的VOCs模式的概率预测模型表明,吸烟状态的评估严重依赖于时间。在一个两类分类模型中,如果一个受试者是遵守1小时戒烟(短期接触烟草)的吸烟者,或者是在一晚不吸烟后(长期接触烟草)的吸烟者(标记为“空白吸烟者”),则有可能以高特异性和敏感性进行预测。另一方面,在我们的研究中,“空白吸烟者”更类似于非吸烟者,因此这两类无法很好地区分,相应的预测结果显示出良好的敏感性但选择性较低。

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