J.J.B.N. van Berkel

A profile of volatile organic compounds in breath discriminates COPD patients from controls.

BACKGROUND Chronic obstructive pulmonary disease (COPD) is an inflammatory condition characterized by oxidative stress and the formation of volatile organic compounds (VOCs) secreted via the lungs. We recently developed a methodological approach able to identify profiles of VOCs in breath unique for patient groups. Here we applied this recently developed methodology regarding diagnosis of COPD patients. METHODS Fifty COPD patients and 29 controls provided their breath and VOCs were analyzed by gas chromatography-mass spectrometry to identify relevant VOCs. An additional 16 COPD patients and 16 controls were sampled in order to validate the model, and 15 steroid naïve COPD patients were sampled to determine whether steroid use affects performance. FINDINGS 1179 different VOCs were detected, of which 13 were sufficient to correctly classify all 79 subjects. Six of these 13 VOCs classified 92% of the subjects correctly (sensitivity: 98%, specificity: 88%) and correctly classified 29 of 32 subjects (sensitivity: 100%, specificity: 81%) from the independent validation population. Fourteen out of 15 steroid naïve COPD patients were correctly classified thus excluding treatment influences. INTERPRETATION This is the first study distinguishing COPD subjects from controls solely based on the presence of VOCs in breath. Analysis of VOCs might be highly relevant for diagnosis of COPD.

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

Background The correct diagnosis of asthma in young children is often hard to achieve, resulting in undertreatment of asthmatic children and overtreatment in transient wheezers.

Breath analysis as a potential diagnostic tool for tuberculosis

SETTING Cape Town, South Africa. OBJECTIVES We investigated the potential of breath analysis by gas chromatography-mass spectrometry (GC-MS) to discriminate between samples collected prospectively from patients with suspected tuberculosis (TB). DESIGN Samples were obtained in a TB-endemic setting in South Africa, where 28% of culture-proven TB patients had Ziehl-Neelsen (ZN) negative sputum smear. A training set of breath samples from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS. We used support vector machine analysis for classification of the patient samples into TB and non-TB. RESULTS A classification model with seven compounds had a sensitivity of 72%, a specificity of 86% and an accuracy of 79% compared with culture. The classification model was validated with breath samples from a different set of 21 TB and 50 non-TB patients from the same area, giving a sensitivity of 62%, a specificity of 84% and an accuracy of 77%. CONCLUSION This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath samples even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis. We conclude that breath analysis by GC-MS merits further research.

Exhaled volatile organic compounds predict exacerbations of childhood asthma in a 1-year prospective study

The hypothesis was that prediction of asthma exacerbations in children is possible by profiles of exhaled volatile organic compounds (VOCs), a noninvasive measure of airway inflammation. The aims of the present study were to determine: 1) whether VOCs in exhaled breath are able to predict asthma exacerbations; and 2) the time course and chemical background of the most predictive VOCs. A prospective study was performed in 40 children with asthma over 1 year. At standard 2-month intervals, exhaled nitric oxide fraction (FeNO), VOC profiles in exhaled breath samples, lung function and symptoms were determined in a standardised way. VOC profiles were analysed by gas chromatography–time-of-flight mass spectrometry. 16 out of 40 children experienced an exacerbation. With support vector machine analysis, the most optimal model of baseline measurements versus exacerbation within patients was based on six VOCs (correct classification 96%, sensitivity 100% and specificity 93%). The model of baseline values of patients with compared to those without an exacerbation consisted of seven VOCs (correct classification 91%, sensitivity 79% and specificity 100%). FeNO and lung function were not predictive for exacerbations. This study indicates that a combination of different exhaled VOCs is able to predict exacerbations of childhood asthma.

Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography-mass spectrometry

The identification of specific volatile organic compounds (VOCs) produced by microorganisms may assist in developing a fast and accurate methodology for the determination of pulmonary bacterial infections in exhaled air. As a first step, pulmonary bacteria were cultured and their headspace analyzed for the total amount of excreted VOCs to select those compounds which are exclusively associated with specific microorganisms. Development of a rapid, noninvasive methodology for identification of bacterial species may improve diagnostics and antibiotic therapy, ultimately leading to controlling the antibiotic resistance problem. Two hundred bacterial headspace samples from four different microorganisms (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumoniae) were analyzed by gas chromatography-mass spectrometry to detect a wide array of VOCs. Statistical analysis of these volatiles enabled the characterization of specific VOC profiles indicative for each microorganism. Differences in VOC abundance between the bacterial types were determined using ANalysis of VAriance-principal component analysis (ANOVA-PCA). These differences were visualized with PCA. Cross validation was applied to validate the results. We identified a large number of different compounds in the various headspaces, thus demonstrating a highly significant difference in VOC occurrence of bacterial cultures compared to the medium and between the cultures themselves. Additionally, a separation between a methicillin-resistant and a methicillin-sensitive isolate of S. aureus could be made due to significant differences between compounds. ANOVA-PCA analysis showed that 25 VOCs were differently profiled across the various microorganisms, whereas a PCA score plot enabled the visualization of these clear differences between the bacterial types. We demonstrated that identification of the studied microorganisms, including an antibiotic susceptible and resistant S. aureus substrain, is possible based on a selected number of compounds measured in the headspace of these cultures. These in vitro results may translate into a breath analysis approach that has the potential to be used as a diagnostic tool in medical microbiology.