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Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer., (© 2024 IOP Publishing Ltd.)

Real-time measurements of the differences in inhaled and exhaled, unlabeled and fully deuterated acetone concentration levels, at rest and during exercise, have been conducted using proton transfer reaction mass spectrometry. A novel approach to continuously differentiate between the inhaled and exhaled breath acetone concentration signals is used. This leads to unprecedented fine grained data of inhaled and exhaled concentrations. The experimental results obtained are compared with those predicted using a simple three compartment model that theoretically describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds with high blood:air partition coefficients, and hence is appropriate for acetone. An agreement between the predicted and observed concentrations is obtained. Our results highlight that the influence of the upper airways cannot be neglected for volatiles with high blood:air partition coefficients, i.e. highly water soluble volatiles.

The presence of certain volatile organic compounds (VOCs) in the breath of patients with gastric cancer has been reported by a number of research groups; however, the source of these compounds remains controversial. Comparison of VOCs emitted from gastric cancer tissue to those emitted from non-cancerous tissue would help in understanding which of the VOCs are associated with gastric cancer and provide a deeper knowledge on their generation. Gas chromatography with mass spectrometric detection (GC-MS) coupled with head-space needle trap extraction (HS-NTE) as the pre-concentration technique, was used to identify and quantify VOCs released by gastric cancer and non-cancerous tissue samples collected from 41 patients during surgery. Excluding contaminants, a total of 32 VOCs were liberated by the tissue samples. The emission of four of them (carbon disulfide, pyridine, 3-methyl-2-butanone and 2-pentanone) was significantly higher from cancer tissue, whereas three compounds (isoprene, γ-butyrolactone and dimethyl sulfide) were in greater concentration from the non-cancerous tissues (Wilcoxon signed-rank test, p < 0.05). Furthermore, the levels of three VOCs (2-methyl-1-propene, 2-propenenitrile and pyrrole) were correlated with the occurrence of H. pylori; and four compounds (acetonitrile, pyridine, toluene and 3-methylpyridine) were associated with tobacco smoking. Ex vivo analysis of VOCs emitted by human tissue samples provides a unique opportunity to identify chemical patterns associated with a cancerous state and can be considered as a complementary source of information on volatile biomarkers found in breath, blood or urine.

In a recent paper (Unterkofler et al 2015 J. Breath Res. 9 036002) we presented a simple two compartment model which describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds (VOCs) with small Henry constants. In this paper we extend this investigation concerning the influence of inhaled concentrations on exhaled breath concentrations for VOCs with higher Henry constants. To this end we extend our model with an additional compartment which takes into account the influence of the upper airways on exhaled breath VOC concentrations.

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.

Existing methods for the early detection of infections in mechanically ventilated (MV) patients at intensive care units (ICUs) are unsatisfactory. Here we present an exploratory study assessing the feasibility of breath VOC analyses for the non-invasive detection of pathogens in the lower respiratory tract of ventilated patients. An open uncontrolled clinical pilot study was performed by enrolling 28 mechanically ventilated (MV) patients with severe intracranial disease, being at risk for the development of or already with confirmed ventilation-associated pneumonia (VAP). The recently developed sampling technique enabled the collection of breath gas with a maximized contribution of alveolar air directly from the respiratory circuit under continuous capnography control, adsorptive preconcentration and final analysis by means of gas chromatography-mass spectrometry (GC-MS).VAP was confirmed in 22/28 preselected patients (78%). The most common microorganisms were Staphylococcus aureus (5/22 VAP patients), Escherichia coli (5/22 VAP patients) and Candida spp. (5/22 VAP patients). 12/32 metabolites released by S. aureus in our previous in vitro studies were also detected in the end-tidal air of VAP patients infected with this pathogen. A similar overlap was seen in Candida albicans infections (8/29 VOCs). Moreover, the concentration profile of selected compounds correlated with the course of the infection.This prospective pilot study provides proof of the concept that the appearance and the concentration profile of pathogen-derived metabolites (elucidated from in vitro experiments) in the breath of ventilated patients during clinically confirmed VAP correlates with the presence of a particular pathogen.

Breath analysis for the purpose of non-invasive diagnosis of lung cancer has yielded numerous candidate compounds with still questionable clinical relevance. To arrive at suitable volatile organic compounds our approach combined the analysis of different sources: isolated tumor samples compared to healthy lung tissues, and exhaled breath from lung cancer patients and healthy controls. Candidate compounds were further compared to substances previously identified in the comparison of transformed and normal lung epithelial cell lines. For human studies, a breath sampling device was developed enabling automated and CO2-controlled collection of the end-tidal air. All samples were first preconcentrated on multibed sorption tubes and analyzed with gas chromatography mass spectrometry (GC-MS). Significantly (p < 0.05) higher concentrations in all three types of cancer samples studied were observed for ethanol and n-octane. Additional metabolites (inter alia 2-methylpentane, n-hexane) significantly released by lung cancer cells were observed at higher levels in cancer lung tissues and breath samples (compared to respective healthy controls) with statistical significance (p < 0.05) only in breath samples. The results obtained confirmed the cancer-related origin of volatile metabolites, e.g. ethanol and octane that were both detected at significantly (p < 0.05) elevated concentrations in all three kinds of cancer samples studied. This work is an important step towards identification of volatile breath markers of lung cancer through the demonstration of cancer-related origin of certain volatile metabolites.

This paper introduces the advanced breath analysis (ABA) platform, an innovative scientific research platform for the entire breath research domain. Within the ABA project, we are investigating novel data management concepts and semantic web technologies to document breath analysis studies for the long run as well as to enable their full automatic reproducibility. We propose several concept taxonomies (a hierarchical order of terms from a glossary of terms), which can be seen as a first step toward the definition of conceptualized terms commonly used by the international community of breath researchers. They build the basis for the development of an ontology (a concept from computer science used for communication between machines and/or humans and representation and reuse of knowledge) dedicated to breath research.

Non-invasive disease monitoring on the basis of volatile breath markers is a very attractive but challenging task. Several hundreds of compounds have been detected in exhaled air using modern analytical techniques (e.g. proton-transfer reaction mass spectrometry, gas chromatography-mass spectrometry) and have even been linked to various diseases. However,the biochemical background for most of compounds detected in breath samples has not been elucidated; therefore, the obtained results should be interpreted with care to avoid false correlations. The major aim of this study was to assess the effects of smoking on the composition of exhaled breath. Additionally, the potential origin of breath volatile organic compounds (VOCs) is discussed focusing on diet, environmental exposure and biological pathways based on other's studies. Profiles of VOCs detected in exhaled breath and inspired air samples of 115 subjects with addition of urine headspace derived from 50 volunteers are presented. Samples were analyzed with GC-MS after preconcentration on multibed sorption tubes in case of breath samples and solid phase micro-extraction (SPME) in the case of urine samples. Altogether 266 compounds were found in exhaled breath of at least 10% of the volunteers. From these, 162 compounds were identified by spectral library match and retention time (based on reference standards). It is shown that the composition of exhaled breath is considerably influenced by exposure to pollution and indoor-air contaminants and particularly by smoking. More than 80 organic compounds were found to be significantly related to smoking, the largest group comprising unsaturated hydrocarbons (29 dienes, 27 alkenes and 3 alkynes). On the basis of the presented results, we suggest that for the future understanding of breath data it will be necessary to carefully investigate the potential biological origin of volatiles, e.g., by means of analysis of tissues, isolated cell lines or other body fluids. In particular, VOCs linked to smoking habit or being the results of human exposure should be considered with care for clinical diagnosis since small changes in their concentration profiles(typically in the ppt(v)–ppb(v) range) revealing that the outbreak of certain disease might be hampered by already high background.

The approach for breath-VOCs' collection and preconcentration by applying needle traps was developed and optimized. The alveolar air was collected from only a few exhalations under visual control of expired CO(2) into a large gas-tight glass syringe and then warmed up to 45 °C for a short time to avoid condensation. Subsequently, a specially constructed sampling device equipped with Bronkhorst® electronic flow controllers was used for automated adsorption. This sampling device allows time-saving collection of expired/inspired air in parallel onto three different needle traps as well as improvement of sensitivity and reproducibility of NT-GC-MS analysis by collection of relatively large (up to 150 ml) volume of exhaled breath. It was shown that the collection of alveolar air derived from only a few exhalations into a large syringe followed by automated adsorption on needle traps yields better results than manual sorption by up/down cycles with a 1 ml syringe, mostly due to avoided condensation and electronically controlled stable sample flow rate. The optimal profile and composition of needle traps consists of 2 cm Carbopack X and 1 cm Carboxen 1000, allowing highly efficient VOCs' enrichment, while injection by a fast expansive flow technique requires no modifications in instrumentation and fully automated GC-MS analysis can be performed with a commercially available autosampler. This optimized analytical procedure considerably facilitates the collection and enrichment of alveolar air, and is therefore suitable for application at the bedside of critically ill patients in an intensive care unit. Due to its simplicity it can replace the time-consuming sampling of sufficient breath volume by numerous up/down cycles with a 1 ml syringe.

We analysed breath and inhaled room air samples from 39 healthy volunteers (28 non-smokers, 8 smokers and 3 ex-smokers) by SPME-GC-MS. Mixed expiratory and indoor air samples were collected in freshly cleaned Tedlar bags. Eighteen millilitres of each sample were transferred into sealed, evacuated glass vials, preconcentrated by solid-phase microextraction (SPME, carboxen/polydimethylsiloxane) and investigated by gas chromatography with mass spectrometric detection (GC-MS). For the unequivocal identification of potential marker compounds, pure calibration mixtures of reference compounds (depending on commercial availability) were prepared to determine the retention time and mass spectra with respect to our analytical setting. Applying the adapted SPME-GC/MS method with limit of detection in the high ppb range (0.05-15.00 ppb), we succeeded in identifying altogether 38 compounds with concentrations in exhaled breath being at least 50% higher than concentration in inhaled air. From these 38 compounds, 31 were identified not only by the spectral library match but also by retention time of standards. A comparison of retention times and spectrum obtained for standards and determined compounds was performed. We found hydrocarbons (isoprene, 2-pentene, 2-methyl-1-pentene, benzene, toluene, p-cymene, limonene, 2,4-dimethylheptane, n-butane), ketones (acetone, hydroxypropanone, methylvinyl ketone), ethers (dimethyl ether, 1,3-dioxolane), esters (ethyl acetate), aldehydes (propanal, hexanal, heptanal, acrolein) and alcohols (ethanol, 2-metoxyethanol, isopropyl alcohol, 2,2,3,3- tetramethylcyclopropanemethanol, 3,4-dimethylcyclohexanol). Proper identification of compounds in different cohorts of patients and volunteers is the base for further investigation of origin, biochemical background and distribution of potential breath biomarkers.