Volatile Organic Compounds In Exhaled Air Research Articles

Early diagnosis of lung cancer using a sensor gas analysis complex: case report

Background. Currently, low-dose computed tomography (LDCT) is the only screening test that reduces the risk of death from lung cancer. However, there are a number of disadvantages, such as lack of widespread use, high cost, high false-positive rate and the need to conduct studies only in high-risk groups, which significantly limit mass screening. exhaled breath analysis, which uses sensitive breath sensors, is a promising method to improve early diagnosis of lung cancer. Cancer Research Institute of Tomsk National Research Medical Center together with Tomsk State University and Tomsk Polytechnic Research Institute has developed a gas analysis complex capable of analyzing the gas composition of exhaled air with remote sampling from bags. during the study, data obtained by digitizing signals from gas analysis system sensors and patient metadata are recorded in a database for subsequent automated processing and analysis using a neural network. Case description. A 48-year-old female patient with a long history of smoking came to the clinic of the Cancer Research Institute for consultation with suspected pathological infiltration around the celiac trunk detected by abdominal CT. As a clinical trial of the developed gas analytical complex for cancer detection, a sample of exhaled air was taken, and the comparison of the composition of volatile organic compounds (VOCs) with that in the control group (healthy individuals) revealed abnormalities characteristic of lung cancer. the patient underwent a chest CT scan, which revealed stage IIB peripheral cancer of the lower lobe of the left lung. the original sensor gas analysis complex, which has no analogues in Russia, was used for the first time in the detection of lung cancer. the data obtained allowed us to suspect the presence of lung tumor in the patient and perform radical surgical treatment. the composition of VOCs in exhaled air was assessed on day 10 after surgery, and no significant changes in the composition of exhaled air were observed. Conclusion. Machine learning algorithms are actively used to diagnose socially significant diseases. the platforms being developed based on arrays of chemical sensors with data analysis using a neural network are promising candidates for implementation in screening activities.

Application of Volatile Organic Compound Analysis in a Nutritional Intervention Study: Differential Responses during Five Hours Following Consumption of a High- and a Low-Fat Dairy Drink.

ScopeExhaled volatile organic compounds (VOCs) are a possible relevant target for noninvasive assessment of metabolic responses. Using a breathomics approach, it is aimed to explore whether lipid intake influences VOC profiles in exhaled air, and to obtain insight in intra‐ and interindividual variations.Methods and resultsThree human interventions are performed. In the first, 12 males consume a high‐fat drink on three study days. In the second, 12 males receive a high‐ and a low‐fat drink on 6 days. In the third, three volunteers consume the high‐fat drink again for tentative compound identification. Participants are asked to exhale, for 5 h postprandial with 15–20 min intervals, into a proton‐transfer‐reaction mass spectrometer, and VOCs in exhaled air are measured. Consumption of a drink alters the VOC profile, with considerable interindividual variation and quantitative intraindividual differences between days. Consumption of two different drinks results in a distinct VOC profile, caused by several specific m/z values. Most of these compounds are identified as being related to ketone body formation and lipid oxidation, showing an increase in high‐ versus low‐fat drink.ConclusionExhaled VOCs have the potential to assess differences in metabolic responses induced by nutrition, especially when day‐to‐day variation can be minimized.

Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air

BackgroundSeveral classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients.MethodsBreath samples were analyzed for VOC profiles by gas chromatography–mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes.ResultsWe identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics.ConclusionThis study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-014-0136-8) contains supplementary material, which is available to authorized users.

Glucose prediction by analysis of exhaled metabolites - a systematic review.

BackgroundIn critically ill patients, glucose control with insulin mandates time– and blood–consuming glucose monitoring. Blood glucose level fluctuations are accompanied by metabolomic changes that alter the composition of volatile organic compounds (VOC), which are detectable in exhaled breath. This review systematically summarizes the available data on the ability of changes in VOC composition to predict blood glucose levels and changes in blood glucose levels.MethodsA systematic search was performed in PubMed. Studies were included when an association between blood glucose levels and VOCs in exhaled air was investigated, using a technique that allows for separation, quantification and identification of individual VOCs. Only studies on humans were included.ResultsNine studies were included out of 1041 identified in the search. Authors of seven studies observed a significant correlation between blood glucose levels and selected VOCs in exhaled air. Authors of two studies did not observe a strong correlation. Blood glucose levels were associated with the following VOCs: ketone bodies (e.g., acetone), VOCs produced by gut flora (e.g., ethanol, methanol, and propane), exogenous compounds (e.g., ethyl benzene, o–xylene, and m/p–xylene) and markers of oxidative stress (e.g., methyl nitrate, 2–pentyl nitrate, and CO).ConclusionThere is a relation between blood glucose levels and VOC composition in exhaled air. These results warrant clinical validation of exhaled breath analysis to monitor blood glucose levels.

Current breathomics—a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, ‘which VOCs are discriminatory?’, remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

Volatile organic compounds analyzed by gas chromatography-deep ultraviolet spectroscopy

Exhaled breath contains thousands of volatile organic compounds (VOCs) of which the composition varies depending on status of the individual and the environment. Different metabolic processes within the body produce volatile substances that are released into the blood. When the blood reaches the lungs the products are released into lung tissue and airways. Also, chronic inflammation and/or oxidative stress can result in the excretion of volatile compounds that generate unique VOC patterns. Therefore, measuring the presence of VOCs in exhaled air (breathomics), for clinical diagnosis and monitoring purposes has gained increased interest over the last years. This paper describes one methodology based on gas chromatography (GC) and deep ultraviolet (DUV) spectroscopy. Spectra of compounds found in exhaled breath are presented.

The versatile use of exhaled volatile organic compounds in human health and disease

Exhaled breath contains thousands of volatile organic compounds (VOCs) of which the composition varies depending on health status. Various metabolic processes within the body produce volatile products that are released into the blood and will be passed on to the airway once the blood reaches the lungs. Moreover, the occurrence of chronic inflammation and/or oxidative stress can result in the excretion of volatile compounds that generate unique VOC patterns. Consequently, measuring the total amount of VOCs in exhaled air, a kind of metabolomics also referred to as breathomics, for clinical diagnosis and monitoring purposes gained increased interest over the last years. This paper describes the currently available methodologies regarding sampling, sample analysis and data processing as well as their advantages and potential drawbacks. Additionally, different application possibilities of VOC profiling are discussed. Until now, breathomics has merely been applied for diagnostic purposes. Exhaled air analysis can, however, also be applied as an analytical or monitoring tool. Within the analytic perspective, the use of VOCs as biomarkers of oxidative stress, inflammation or carcinogenesis is described. As monitoring tool, breathomics can be applied to elucidate the heterogeneity observed in chronic diseases, to study the pathogen(s) responsible for occurring infections and to monitor treatment efficacy.

Gas sensor using noble metal-loaded TiO2 nanotubes for detection of large-sized volatile organic compounds

Detection of volatile organic compounds (VOCs) has attracted considerable attention for indoor air quality and human breath analysis. Here, resistive-type gas sensors with porous films made of Au-loaded TiO2 nanotubes were developed for the detection of large-sized VOCs. Well-dispersed Au nanoparticles (10 to 20 nm) were photo-chemically deposited on TiO2 nanotubes (80 nm in diameter, 700 nm in length) prepared by a hydrothermal method. The device using the Au-loaded TiO2 nanotubes showed improved sensor responses to ethanol and toluene (50 ppm) in air at 500°C. This suggested that Au nanoparticles deposited on the surface of TiO2 nanotubules without aggregation. We also demonstrated that the device could be used for the detection of large-sized VOC molecule, i.e., 2,6-diisopropylphenol (propofol), an intravenously administered hypnotic drug for induction and maintenance of anesthesia. The Au-loaded TiO2 nanotubes would offer a way to continuously monitor a change in the concentration of VOCs in exhaled air for medical diagnostics.

Volatile organic compounds in exhaled breath as a diagnostic tool for asthma in children

The correct diagnosis of asthma in young children is often hard to achieve, resulting in undertreatment of asthmatic children and overtreatment in transient wheezers. To develop a new diagnostic tool that better discriminates between asthma and transient wheezing and that leads to a more accurate diagnosis and hence less undertreatment and overtreatment. A first stage in the development of such a tool is the ability to discriminate between asthmatic children and healthy controls. The integrative analysis of large numbers of volatile organic compounds (VOC) in exhaled breath has the potential to discriminate between various inflammatory conditions of the respiratory tract. Breath samples were obtained and analysed for VOC by gas chromatography-mass spectrometry from asthmatic children (n=63) and healthy controls (n=57). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate diseased from healthy children. A set of samples from both asthmatic and healthy children was selected to construct a model that was subsequently used to predict the asthma or the healthy status of a test group. In this way, the predictive value of the model could be tested. The discriminant analyses demonstrated that asthma and healthy groups are distinct from one another. A total of eight components discriminated between asthmatic and healthy children with a 92% correct classification, achieving a sensitivity of 89% and a specificity of 95%. Conclusion The results show that a limited number of VOC in exhaled air can well be used to distinguish children with asthma from healthy children.