Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca−). Exhaled breath samples from 49 Ca+ patients, 36 Ca− patients and 52 healthy controls (HC) were analyzed by an SPME–GC–MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca− patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca− patients, which was not achieved by the targeted approach.     

纳米材料在用于癌症诊断的呼出气挥发性有机物检测中的应用

细胞新陈代谢的变化会导致挥发性有机化合物（VOCs）类型及含量发生变化,因此可通过分析某些标志性VOCs简立起多种疾病早期诊断的模型. 人体呼出物中特征VOCs的检测作为一种非侵入性、无损的检测手段,近些年在疾病检测领域已成为世界范围内的研究热点. 其中,纳米材料可用于增强传感器性能,并使传感器便携式小型化,推进检测传感器进入临床. 在这篇综述中,作者将种类繁多的传感器中用到的纳米材料归纳总结为金属、金属氧化物、碳基、复合物和MOFs基纳米材料等几类,并讨论了不同类纳米材料在VOCs检测中的优劣势. 本文所建立起的分析方法及讨论有助于进一步了解检测技术的优越性与局限性. 最后,作者对利用VOCs的检测实现癌症早期筛选的研究及发展提出了个人观点.     

人体呼出气分析的技术进展及其在非侵入式医学诊断方面的临床应用前景

人体呼出气被视为血液顶空气体,通过肺泡交换排出体外,可在一定程度上反映人体的内源代谢情况。近些年,随着医学诊断无损化的不断发展,呼出气分析由于其无创性,无痛性,便捷性,具有临床疾病早期诊断与大规模筛查的潜力,受到越来越多的关注。研究表明,呼出气中包含的挥发性有机化合物和人体的疾病代谢状态密切相关,如丙酮与糖尿病,醛类物质与乳腺癌,烷烃类与氧化应激水平等。现代分析方法(如气相色谱、质谱、光谱、传感器等)对呼出气中上千种痕量组分的定性定量分析,使得呼出气分析成为可能。此文总结了呼出气分析的技术发展:样品采集手段,预富集技术和定性定量分析技术。评价了呼出气生物标识物在糖尿病,乳腺癌,以及肺癌等疾病中的临床诊断前景。最后讨论了呼出气分析的技术在国内的发展现状及其面临的主要问题。     

Pilot Study on Exhaled Breath Analysis for a Healthy Adult Population in Hawaii

Fast diagnostic results using breath analysis are an anticipated possibility for disease diagnosis or general health screenings. Tests that do not require sending specimens to medical laboratories possess capabilities to speed patient diagnosis and protect both patient and healthcare staff from unnecessary prolonged exposure. The objective of this work was to develop testing procedures on an initial healthy subject cohort in Hawaii to act as a range-finding pilot study for characterizing the baseline of exhaled breath prior to further research. Using comprehensive two-dimensional gas chromatography (GC×GC), this study analyzed exhaled breath from a healthy adult population in Hawaii to profile the range of different volatile organic compounds (VOCs) and survey Hawaii-specific differences. The most consistently reported compounds in the breath profile of individuals were acetic acid, dimethoxymethane, benzoic acid methyl ester, and n-hexane. In comparison to other breathprinting studies, the list of compounds discovered was representative of control cohorts. This must be considered when implementing proposed breath diagnostics in new locations with increased interpersonal variation due to diversity. Further studies on larger numbers of subjects over longer periods of time will provide additional foundational data on baseline breath VOC profiles of control populations for comparison to disease-positive cohorts.     

Human exhaled air analytics: biomarkers of diseases

Over the last few years, breath analysis for the routine monitoring of metabolic disorders has attracted a considerable amount of scientific interest, especially since breath sampling is a non-invasive technique, totally painless and agreeable to patients. The investigation of human breath samples with various analytical methods has shown a correlation between the concentration patterns of volatile organic compounds (VOCs) and the occurrence of certain diseases. It has been demonstrated that modern analytical instruments allow the determination of many compounds found in human breath both in normal and anomalous concentrations. The composition of exhaled breath in patients with, for example, lung cancer, inflammatory lung disease, hepatic or renal dysfunction and diabetes contains valuable information. Furthermore, the detection and quantification of oxidative stress, and its monitoring during surgery based on composition of exhaled breath, have made considerable progress. This paper gives an overview of the analytical techniques used for sample collection, preconcentration and analysis of human breath composition. The diagnostic potential of different disease-marking substances in human breath for a selection of diseases and the clinical applications of breath analysis are discussed.     

Ultrafast gas chromatography coupled to electronic nose to identify volatile biomarkers in exhaled breath from chronic obstructive pulmonary disease patients: A pilot study

An analytical method to identify volatile organic compounds (VOCs) in the exhaled breath from patients with a diagnosis of chronic obstructive pulmonary disease (COPD) using a ultrafast gas chromatography system equipped with an electronic nose detector (FGC eNose) has been developed. A prospective study was performed in 23 COPD patients and 33 healthy volunteers; exhalation breathing tests were performed with Tedlar bags. Each sample was analyzed by FCG eNose and the identification of VOCs was based on the Kovats index. Raw data were reduced by principal component analysis (PCA) and canonical discriminant analysis [canonical analysis of principal coordinates (CAP)]. The FCG eNose technology was able to identify 17 VOCs that distinguish COPD patients from healthy volunteers. At all stages of PCA and CAP the discrimination between groups was obvious. Chemical prints were correctly classified up to 82.2%, and were matched with 78.9% of the VOCs detected in the exhaled breath samples. Receiver operating characteristic curve analysis indicated the sensitivity and specificity to be 96% and 91%, respectively. This pilot study demonstrates that FGC eNose is a useful tool to identify VOCs as biomarkers in exhaled breath from COPD patients. Further studies should be performed to enhance the clinical relevance of this quick and ease methodology for COPD diagnosis.     

The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66–0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.     

Quantification of Volatile Aldehydes Deriving from In Vitro Lipid Peroxidation in the Breath of Ventilated Patients

Exhaled aliphatic aldehydes were proposed as non-invasive biomarkers to detect increased lipid peroxidation in various diseases. As a prelude to clinical application of the multicapillary column–ion mobility spectrometry for the evaluation of aldehyde exhalation, we, therefore: (1) identified the most abundant volatile aliphatic aldehydes originating from in vitro oxidation of various polyunsaturated fatty acids; (2) evaluated emittance of aldehydes from plastic parts of the breathing circuit; (3) conducted a pilot study for in vivo quantification of exhaled aldehydes in mechanically ventilated patients. Pentanal, hexanal, heptanal, and nonanal were quantifiable in the headspace of oxidizing polyunsaturated fatty acids, with pentanal and hexanal predominating. Plastic parts of the breathing circuit emitted hexanal, octanal, nonanal, and decanal, whereby nonanal and decanal were ubiquitous and pentanal or heptanal not being detected. Only pentanal was quantifiable in breath of mechanically ventilated surgical patients with a mean exhaled concentration of 13 ± 5 ppb. An explorative analysis suggested that pentanal exhalation is associated with mechanical power—a measure for the invasiveness of mechanical ventilation. In conclusion, exhaled pentanal is a promising non-invasive biomarker for lipid peroxidation inducing pathologies, and should be evaluated in future clinical studies, particularly for detection of lung injury.     

Hybrid Volatolomics and Disease Detection

This Review presents a concise, but not exhaustive, didactic overview of some of the main concepts and approaches related to “volatolomics”—an emerging frontier for fast, risk‐free, and potentially inexpensive diagnostics. It attempts to review the source and characteristics of volatolomics through the so‐called volatile organic compounds (VOCs) emanating from cells and their microenvironment. It also reviews the existence of VOCs in several bodily fluids, including the cellular environment, blood, breath, skin, feces, urine, and saliva. Finally, the usefulness of volatolomics for diagnosis from a single bodily fluid, as well as ways to improve these diagnostic aspects by “hybrid” approaches that combine VOC profiles collected from two or more bodily fluids, will be discussed. The perspectives of this approach in developing the field of diagnostics to a new level are highlighted.     

The Impact of a Graded Maximal Exercise Protocol on Exhaled Volatile Organic Compounds: A Pilot Study

Exhaled volatile organic compounds (VOCs) are of interest due to their minimally invasive sampling procedure. Previous studies have investigated the impact of exercise, with evidence suggesting that breath VOCs reflect exercise-induced metabolic activity. However, these studies have yet to investigate the impact of maximal exercise to exhaustion on breath VOCs, which was the main aim of this study. Two-litre breath samples were collected onto thermal desorption tubes using a portable breath collection unit. Samples were collected pre-exercise, and at 10 and 60 min following a maximal exercise test (VO_2MAX). Breath VOCs were analysed by thermal desorption-gas chromatography-mass spectrometry using a non-targeted approach. Data showed a tendency for reduced isoprene in samples at 10 min post-exercise, with a return to baseline by 60 min. However, inter-individual variation meant differences between baseline and 10 min could not be confirmed, although the 10 and 60 min timepoints were different (p = 0.041). In addition, baseline samples showed a tendency for both acetone and isoprene to be reduced in those with higher absolute VO_2MAX scores (mL(O₂)/min), although with restricted statistical power. Baseline samples could not differentiate between relative VO_2MAX scores (mL(O₂)/kg/min). In conclusion, these data support that isoprene levels are dynamic in response to exercise.     

VOCs Are Relevant Biomarkers of Elicitor-Induced Defences in Grapevine

Grapevine is susceptible to fungal diseases generally controlled by numerous chemical fungicides. Elicitors of plant defence are a way of reducing the use of these chemicals, but still provide inconsistent efficiency. Easy-to-analyse markers of grapevine responses to elicitors are needed to determine the best conditions for their efficiency and position them in protection strategies. We previously reported that the elicitor sulphated laminarin induced the emission of volatile organic compounds (VOCs) by grapevine leaves. The present study was conducted to characterise and compare VOC emissions in response to other elicitors. Bastid^® was first used to test the conditions of VOC collection and analysis. Using SBSE-GC-MS, we detected several VOCs, including the sesquiterpene α-farnesene, in a time-dependent manner. This was correlated with the induction of farnesene synthase gene expression, in parallel with stilbene synthesis (another defence response), and associated to resistance against downy mildew. The other elicitors (Redeli^®, Romeo^®, Bion^®, chitosan, and an oligogalacturonide) induced VOC emission, but with qualitative and quantitative differences. VOC emission thus constitutes a response of grapevine to elicitors of various chemical structures. Therefore, VOC analysis is relevant for studying the impact of environmental factors on grapevine defence responses and optimising the performance of elicitors in vineyards.     

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

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.     

Effects of a Volatile Organic Compound Filter on Breath Profiles Measured by Secondary Electrospray High-Resolution Mass Spectrometry

Environmental volatile organic compounds (VOCs) from the ambient air potentially influence on-line breath analysis measurements by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). The aim of this study was to investigate how inhaling through a VOC filter affects the detected breath profiles and whether it is feasible to integrate such filters into routine measurements. A total of 24 adult participants performed paired breath analysis measurements with and without the use of an activated carbon filter for inspiration. Concordance correlation coefficients (CCCs) and the Bland–Altman analysis were used to assess the agreement between the two methods. Additionally, the effect on a selection of known metabolites and contaminants was analyzed. Out of all the detected features, 78.3% showed at least a moderate agreement before and after filter usage (CCC > 0.9). The decrease in agreement of the remaining m/z features was mostly associated with reduced signal intensities after filter usage. Although a moderate-to-substantial concordance was found for almost 80% of the m/z features, the filter still had an effect by decreasing signal intensities, not only for contaminants, but also for some of the studied metabolites. Operationally, the use of the filter complicated and slowed down the conductance of measurements, limiting its applicability in clinical studies.     

Breathing Rhythm Variations during Wash-In Do Not Influence Exhaled Volatile Organic Compound Profile Analyzed by an Electronic Nose

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in.     

质子转移反应质谱对肺癌患者呼气中特征性VOCs的筛选及研究

利用质子转移反应质谱(PTR-MS)对40名肺癌患者、32名健康志愿者呼出气体中的挥发性有机物(VOCs)进行检测,Mann-Whitney秩和检验与多因素logistic回归的结果表明,呼气中VOC 33、VOC 39、VOC 45可能为肺癌人群的呼气标志物,同时VOC 45在小细胞肺癌和非小细胞肺癌患者之间存在显著差异。以肺癌人群的呼气标志物作为自变量,采用二元logistic回归分析和Fisher判别分析分别建立肺癌预测模型。logistic回归模型的受试者工作曲线下面积(AUC)达到0.878,灵敏度和特异性分别为85.5%和63.5%。Fisher判别模型的受试者工作曲线下面积(AUC)达到0.822,灵敏度和特异性分别为82.5%和62.5%。两种模型对肺癌的预测均具有统计学意义。     

Determination of volatile organic compounds in human breath for Helicobacter pylori detection by SPME-GC/MS

Helicobacter pylori living in the human stomach release volatile organic compounds (VOCs) that can be detected in expired air. The aim of the study was the application of breath analysis for bacteria detection. It was accomplished by determination of VOCs characteristic for patients with H. pylori and the analysis of gases released by bacteria in suspension. Solid-phase microextraction was applied as a selective technique for preconcentration and isolation of analytes. Gas chromatography coupled with mass spectrometry was used for the separation and identification of volatile analytes in breath samples and bacterial headspace. For data calculation and processing, discriminant and factor analyses were used. Endogenous substances such as isobutane, 2-butanone and ethyl acetate were detected in the breath of persons with H. pylori in the stomach and in the gaseous mixture released by the bacteria strain but they were not identified in the breath of healthy volunteers. The canonical analysis of discrimination functions showed a strong difference between the three examined groups. Knowledge of substances emitted by H. pylori with the application of an optimized breath analysis method might become a very useful tool for noninvasive detection of this bacterium.     

Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles

Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs’ latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction.     

高血糖人群呼气标志物的质子转移反应质谱研究

利用自制的质子转移反应质谱(PTR-MS)探索了高血糖人群呼气特征挥发性有机物(VOCs),利用改进的呼气分析系统对70名血糖含量正常志愿者与22名高血糖志愿者的呼气进行实时在线分析,数据统计分析采用Mann-Whitney U非参数检验和线性判别分析法。实验结果表明两组受试者呼气存在5种差异VOCs,该5种VOCs的质荷比(m/z)分别为61、31、120、104、82。其线性判别模型的受试者工作曲线(Re-ceiver operating curve,ROC)下面积达到0.926,灵敏度和特异度分别为86.4%和84.3%,该模型有一定的诊断价值。考察了两组志愿者呼气中丙酮含量的分布情况,统计分析结果显示两组的丙酮含量无显著性差异,丙酮含量与血糖浓度间也无显著相关性。     

Breath Figure Arrays: Unconventional Fabrications,Functionalizations, and Applications

A breath figure (BF) is the water droplet array that forms when moisture comes in contact with a cold substrate. This water droplet array has been widely utilized in the past two decades as a versatile soft template for the fabrication of polymeric porous films. Accordingly, the ordered pores on the polymer films formed with such a method are named a breath figure array (BFA).The BF templating technique is undergoing rapid development. Several unconventional BF processes have been established to prepare porous films with unique morphologies or primary materials, and various newly developed functionalization techniques have significantly improved the performance of polymeric films with BFA, leading to novel applications, including templates, biosensors, and separation membranes. These recent achievements will be described in this Minireview.     

Exploratory Study Using Urinary Volatile Organic Compounds for the Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) biomarkers are lacking in clinical practice. We therefore explored the pattern and composition of urinary volatile organic compounds (VOCs) in HCC patients. This was done in order to assess the feasibility of a potential non-invasive test for HCC, and to enhance our understanding of the disease. This pilot study recruited 58 participants, of whom 20 were HCC cases and 38 were non-HCC cases. The non-HCC cases included healthy individuals and patients with various stages of non-alcoholic fatty liver disease (NAFLD), including those with and without fibrosis. Urine was analysed using gas chromatography–ion mobility spectrometry (GC–IMS) and gas chromatography–time-of-flight mass spectrometry (GC–TOF-MS). GC–IMS was able to separate HCC from fibrotic cases with an area under the curve (AUC) of 0.97 (0.91–1.00), and from non-fibrotic cases with an AUC of 0.62 (0.48–0.76). For GC-TOF-MS, a subset of samples was analysed in which seven chemicals were identified and tentatively linked with HCC. These include 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene (2TMS derivative), 2-butanone, 2-hexanone, benzene, 1-ethyl-2-methyl-, 3-butene-1,2-diol, 1-(2-furanyl)-, bicyclo(4.1.0)heptane, 3,7,7-trimethyl-, [1S-(1a,3β,6a)]-, and sulpiride. Urinary VOC analysis using both GC–IMS and GC-TOF-MS proved to be a feasible method of identifying HCC cases, and was also able to enhance our understanding of HCC pathogenesis.