Analysis Of Exhaled Volatile Organic Compounds Research Articles

Identification of exhaled volatile organic compounds that characterize asthma phenotypes: A J-VOCSA study

BackgroundAsthma is characterized by phenotypes of different clinical, demographic, and pathological characteristics. Identifying the profile of exhaled volatile organic compounds (VOCs) in asthma phenotypes may facilitate establishing biomarkers and understanding asthma background pathogenesis. This study aimed to identify exhaled VOCs that characterize severe asthma phenotypes among patients with asthma. MethodsThis was a multicenter cross-sectional study of patients with severe asthma in Japan. Clinical data were obtained from medical records, and questionnaires were collected. Exhaled breath was sampled and subjected to thermal desorption gas chromatography-mass spectrometry (GC/MS). ResultsUsing the decision tree established in the previous nationwide asthma cohort study, 245 patients with asthma were divided into five phenotypes and subjected to exhaled VOC analysis with 50 healthy controls (HCs). GC/MS detected 243 VOCs in exhaled breath samples, and 142 frequently detected VOCs (50% of all samples) were used for statistical analyses. Cluster analysis assigning the groups with similar VOC profile patterns showed the highest similarities between phenotypes 3 and 4 (early-onset asthma phenotypes), followed by the similarities between phenotypes 1 and 2 (late-onset asthma phenotypes). Comparisons between phenotypes 1–5 and HC revealed 19 VOCs, in which only methanesulfonic anhydride showed p < 0.05 adjusted by false discovery rate (FDR). Comparison of these phenotypes yielded several VOCs showing different trends (p < 0.05); however, no VOCs showed p < 0.05 adjusted by FDR. ConclusionsExhaled VOC profiles may be useful for distinguishing asthma and asthma phenotypes; however, these findings need to be validated, and their pathological roles should be clarified.

The Utility of Breath Analysis in the Diagnosis and Staging of Parkinson's Disease.

The analysis of volatile organic compounds (VOCs) collected in breath samples has the potential to be a rapid, non-invasive test to aid in the clinical diagnosis and tracking of chronic conditions such as Parkinson's disease (PD). To assess the feasibility and utility of breath sample analysis done, both at point of collection in clinic and when sent away to be analyzed remotely, to diagnose, stratify and monitor disease course in a moderately large cohort of patients with PD. Breath samples were collected from 177 people with PD and 37 healthy matched control individuals followed over time. Standard clinical data (MDS-UPDRS & cognitive assessments) from the PD patients were collected at the same time as the breath sample was taken, these measures were then correlated with the breath test analysis of exhaled VOCs. The breath test was able to distinguish patients with PD from healthy control participants and correlated with disease stage. The off-line system (remote analysis) gave good results with overall classification accuracies across a range of clinical measures of between 73.6% to 95.6%. The on-line (in clinic) system showed comparable results but with lower levels of correlation, varying between 33.5% to 82.4%. Chemical analysis identified 29 potential molecules that were different and which may relate to pathogenic pathways in PD. Breath analysis shows potential for PD diagnostics and monitoring. Both off-line and on-line sensor systems were easy to do and provided comparable results which will enable this technique to be easily adopted in clinic if larger studies confirm our findings.

Chemical signature of colorectal cancer: case-control study for profiling the breath print.

BackgroundEffective screening for colorectal cancer can reduce mortality by early detection of tumours and colonic polyps. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a potential non‐invasive diagnostic tool for detection of cancer. The aim of this study was to evaluate the reliability of breath‐testing for colorectal cancer screening and early diagnosis using an advanced breath sampler.MethodsThe exhaled breath of patients with colorectal cancer and non‐cancer controls with negative findings on colonoscopy was collected using the ReCIVA® Breath Sampler. This portable device is able to capture the alveolar breath fraction without environmental contamination. VOCs were desorbed thermally and analysed by gas chromatography–mass spectrometry. The discriminatory ability of VOCs in detecting colorectal cancer was evaluated by receiver operating characteristic (ROC) curve analysis for each VOC, followed by cross‐validation by the leave‐one‐out method, and by applying stepwise logistic regression analysis.ResultsThe study included 83 patients with colorectal cancer and 90 non‐cancer controls. Fourteen VOCs were found to have significant discriminatory ability in detecting patients with colorectal cancer. The model with the diagnosis of cancer versus no cancer resulted in a statistically significant likelihood of discrimination of 173·45 (P < 0·001), with an area under the ROC curve of 0·979. Cross‐validation of the model resulted in a true predictive value for colorectal cancer of 93 per cent overall. Reliability of the breath analysis was maintained irrespective of cancer stage.ConclusionThis study demonstrated that analysis of exhaled VOCs can discriminate patients with colorectal cancer from those without. This finding may eventually lead to the creation of a smart online sensory device, capable of providing a binary answer (cancer/no cancer) and directing to further screening.

Exhaled breath compositions under varying respiratory rhythms reflects ventilatory variations: translating breathomics towards respiratory medicine

Control of breathing is automatic and its regulation is keen to autonomic functions. Therefore, involuntary and voluntary nervous regulation of breathing affects ventilatory variations, which has profound potential to address expanding challenges in contemporary pulmonology. Nonetheless, the fundamental attributes of the aforementioned phenomena are rarely understood and/or investigated. Implementation of unconventional approach like breathomics may leads to a better comprehension of those complexities in respiratory medicine. We applied breath-resolved spirometry and capnometry, non-invasive hemodynamic monitoring along with continuous trace analysis of exhaled VOCs (volatile organic compounds) by means of real-time mass-spectrometry in 25 young and healthy adult humans to investigate any possible mirroring of instant ventilatory variations by exhaled breath composition, under varying respiratory rhythms. Hemodynamics remained unaffected. Immediate changes in measured breath compositions and corresponding variations occurred when respiratory rhythms were switched between spontaneous (involuntary/unsynchronised) and/or paced (voluntary/synchronised) breathing. Such changes in most abundant, endogenous and bloodborne VOCs were closely related to the minute ventilation and end-tidal CO2 exhalation. Unprecedentedly, while preceded by a paced rhythm, spontaneous rhythms in both independent setups became reproducible with significantly (P-value ≤ 0.005) low intra- and inter-individual variation in measured parameters. We modelled breath-resolved ventilatory variations via alveolar isoprene exhalation, which were independently validated with unequivocal precision. Reproducibility i.e. attained via our method would be reliable for human breath sampling, concerning biomarker research. Thus, we may realize the actual metabolic and pathophysiological expressions beyond the everlasting in vivo physiological noise. Consequently, less pronounced changes are often misinterpreted as disease biomarker in cross-sectional studies. We have also provided novel information beyond conventional spirometry and capnometry. Upon clinical translations, our findings will have immense impact on pulmonology and breathomics as they have revealed a reproducible pattern of ventilatory variations and respiratory homeostasis in endogenous VOC exhalations.

Human volatilome analysis using eNose to assess uncontrolled asthma in a clinical setting.

Analyses of exhaled volatile organic compounds (VOCs) have shown promising results when distinguishing individuals with asthma. Currently, there are no biomarkers for uncontrolled asthma. Therefore, we aimed to assess, in a real-life clinical setting, the ability of the exhaled VOC analysis, using an electronic nose (eNose), to identify individuals with uncontrolled asthma. A cross-sectional study was conducted, and breath samples from 199 participants (130 females, aged 6-78, 66% with asthma) were analysed using an eNose. A multivariate unsupervised cluster analysis, using the resistance data from 32 sensors, could distinguish three clusters of VOC patterns in the training and testing groups. Comparisons between the clusters were performed using the one-way ANOVA, Kruskal-Wallis and chi-squared tests. In the training set (n=121), three different clusters covering asthma, lung function, symptoms in the previous 4weeks and age were identified. The pairwise comparisons showed significant differences with respect to chest tightness during exercise, dyspnoea and gender. These findings were confirmed in the testing set (n=78) where the training model identified three clusters. The participants who reported fewer respiratory symptoms (dyspnoea and night-time awakenings) were grouped into one cluster, while the others comprised participants who showed similar poor control over symptoms with the distribution of the individuals with asthma being significantly different between them. In a clinical setting, the analysis of the exhaled VOC profiles using an eNose could be used as a fast and noninvasive complementary assessment tool for the detection of uncontrolled asthma symptoms.

Exhaled volatile substances in children suffering from type 1 diabetes mellitus: results from a cross-sectional study

Monitoring metabolic adaptation to type 1 diabetes mellitus in children is challenging. Analysis of volatile organic compounds (VOCs) in exhaled breath is non-invasive and appears as a promising tool. However, data on breath VOC profiles in pediatric patients are limited. We conducted a cross-sectional study and applied quantitative analysis of exhaled VOCs in children suffering from type 1 diabetes mellitus (T1DM) (n = 53) and healthy controls (n = 60). Both groups were matched for sex and age. For breath gas analysis, a very sensitive direct mass spectrometric technique (PTR-TOF) was applied. The duration of disease, the mode of insulin application (continuous subcutaneous insulin infusion vs. multiple daily insulin injection) and long-term metabolic control were considered as classifiers in patients. The concentration of exhaled VOCs differed between T1DM patients and healthy children. In particular, T1DM patients exhaled significantly higher amounts of ethanol, isopropanol, dimethylsulfid, isoprene and pentanal compared to healthy controls (171, 1223, 19.6, 112 and 13.5 ppbV vs. 82.4, 784, 11.3, 49.6, and 5.30 ppbV). The most remarkable differences in concentrations were found in patients with poor metabolic control, i.e. those with a mean HbA1c above 8%. In conclusion, non-invasive breath testing may support the discovery of basic metabolic mechanisms and adaptation early in the progress of T1DM.

Breathomics and treatable traits for chronic airway diseases.

The long-term management goals of the inflammatory airway diseases asthma and chronic obstructive pulmonary disease (COPD) are similar and focus on symptom control and reduction of exacerbation frequency and severity. Treatable traits have recently been postulated as a management concept which complements the traditional diagnostic labels 'asthma' and 'COPD', thereby focusing on therapy targeted to a patients' individual disease-associated characteristics. Exhaled volatile organic compounds (VOCs) may be utilized as noninvasive biomarker for disease activity or manifestation in asthma and COPD. In this review, we provide an overview of the current achievements concerning exhaled breath analysis in the field of uncontrolled chronic airways diseases. Monitoring of (airway) inflammation and identification of (molecular) phenotypic characteristics in asthma and COPD through exhaled VOC analysis by either mass spectrometry (MS) based or sensor-driven electronic nose technology (eNose) seems to be feasible, however pending confirmation could hamper the valorization of breathomics into clinical tests. Exhaled VOC analysis and the management of asthma and COPD through the concept of pulmonary treatable traits are an interesting match. To develop exhaled breath analysis into an added value for pulmonary treatable traits, multicentre studies are required following international standards for study populations, sampling methods and analytical strategies enabling external validation.

Confounding effect of benign pulmonary diseases in selecting volatile organic compounds as markers of lung cancer

Lung cancer (LC) is a leading cause of cancer-related morbidity and mortality globally, and exhaled breath testing has been considered as a fast, convenient and non-invasive way to diagnose LC in its early stages. Volatile organic compounds (VOCs), as markers of LC in exhaled breath, have been widely investigated for cancer diagnosis. However, few studies have reported on the interference of benign pulmonary diseases (BPD) in the selection of VOC markers for LC. During this study, 207 samples were analyzed using thermal desorption instrumentation/gas chromatography/mass spectrometry (TD-GCMS) to detect C6−C30 VOCs, and all samples were divided into four groups: LC group, BPD group, lung disease (LD) group (including LC group and BPD group) and healthy group. To make up for the deficiency of detecting low carbon hydrocarbons (<C6), 277 samples were analyzed using solid-phase micro-extraction/gas chromatography/mass spectrometry (SPME-GCMS), divided among the four groups. VOC markers were selected by reference to the receiver operating characteristics curve. With the comparisons among the LC group, BPD group and healthy group from TD-GCMS and SPME-GCMS results, we found that exhaled VOCs are capable of discriminating LC group versus healthy group and BPD group versus healthy group with a consistency of 70%–80%. However, no VOCs can be selected with good discrimination capability between the LC group and BPD group, indicating that BPD interferes significantly in VOC marker selection for LC. To discriminate breath samples from the LD group and healthy group, 11 VOCs, including ten selected from TD-GCMS and one from SPME-GCMS, were chosen as markers for LD diagnosis. The sensitivity, specificity and overall accuracy of the diagnostic model established using ten VOCs were 80.8%, 84% and 82.7%, and those of the model established by using one VOC were 75.6%, 78.9% and 76.7%. These results validate that LD patients can be effectively discriminated and diagnosed using exhaled VOC analysis. (Clinical trial registration number: ChiCTR-DCD-15007106.)

Validation of exhaled volatile organic compounds analysis using electronic nose as index of COPD severity.

AimSix-minute walking test distance (6MWD) and body mass index, obstruction, dyspnea and exercise (BODE) index are measures of functional status in COPD patients, but require space, time and patient’s compliance. Exhaled volatile organic compounds (VOCs) analysis via electronic nose is a quick and easy method that has already been used to discriminate COPD phenotypes. The aim of this study is to evaluate whether VOCs analysis can predict functional status and its variation over time in COPD patients.MethodsA monocentric prospective study with 1 year of follow-up was carried out. All patients underwent pulmonary function tests, arterial gas analysis, bioimpedance analysis, 6-minute walking test, and VOCs collection. Exhaled breath was collected with Pneumopipe® and analyzed using BIONOTE electronic nose. Outcomes prediction was performed by k-fold cross-validated partial least square discriminant analysis: accuracy, sensitivity and specificity as well as Cohen’s kappa for agreement were calculated.ResultsWe enrolled 63 patients, 60.3% men, with a mean age of 71 (SD: 8) years, median BODE index of 1 (interquartile range: 0–3) and mean 6MWD normalized by squared height (n6MWD) of 133.5 (SD: 42) m/m2. The BIONOTE predicted baseline BODE score (dichotomized as BODE score <3 or ≥3) with an accuracy of 86% and quartiles of n6MWD with an accuracy of 79%. n6MWD decline more than the median value after 1 year was predicted with an accuracy of 86% by BIONOTE, 52% by Global Initiative for Chronic Obstructive Lung Disease (GOLD) class and 78% by combined BIONOTE and GOLD class.ConclusionExhaled VOCs analysis identifies classes of BODE and n6MWD quartiles, and outperforms GOLD classification in predicting n6MWD variation.

Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology: A pilot study.

ObjectivesNew point of care diagnostics are urgently needed to reduce the over-prescription of antimicrobials for bacterial respiratory tract infection (RTI). We performed a pilot cross sectional study to assess the feasibility of gas-capillary column ion mobility spectrometer (GC-IMS), for the analysis of volatile organic compounds (VOC) in exhaled breath to diagnose bacterial RTI in hospital inpatients.Methods71 patients were prospectively recruited from the Acute Medical Unit of the Royal Liverpool University Hospital between March and May 2016 and classified as confirmed or probable bacterial or viral RTI on the basis of microbiologic, biochemical and radiologic testing. Breath samples were collected at the patient’s bedside directly into the electronic nose device, which recorded a VOC spectrum for each sample. Sparse principal component analysis and sparse logistic regression were used to develop a diagnostic model to classify VOC spectra as being caused by bacterial or non-bacterial RTI.ResultsSummary area under the receiver operator characteristic curve was 0.73 (95% CI 0.61–0.86), summary sensitivity and specificity were 62% (95% CI 41–80%) and 80% (95% CI 64–91%) respectively (p = 0.00147).ConclusionsGC-IMS analysis of exhaled VOC for the diagnosis of bacterial RTI shows promise in this pilot study and further trials are warranted to assess this technique.

Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study.

RationaleMalignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell’s metabolism.ObjectivesThe goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose).MethodsBreath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose.ResultsUsing GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively.ConclusionThis study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening.

Non-invasive exhaled volatile organic biomarker analysis to detect inflammatory bowel disease (IBD)

IntroductionEarly inflammatory bowel disease (IBD) diagnosis remains a clinical challenge. Volatile organic compounds (VOCs) have shown distinct patterns in Crohn's disease (CD) and ulcerative colitis (UC). VOC production, reflecting gut fermentome metabolites, is perturbed in IBD. VOC sampling is non-invasive, with various compounds identified from faecal, breath and urine samples. This study aimed to determine if FAIMS (field asymmetric ion mobility spectroscopy) analysis of exhaled VOCs could distinguish IBD from controls. MethodsSeventy-six subjects were recruited, 54 established IBD (25 CD, 29 UC) and 22 healthy controls. End expiratory breath was captured using a Warwick device and analysed by FAIMS. Data were pre-processed using wavelet transformation, and classification performed in a 10-fold cross-validation. Feature selection was performed using Wilcoxon rank sum test, and sparse logistic regression gave class predictions, to calculate sensitivity and specificity. ResultsFAIMS breath VOC analysis showed clear separation of IBD from controls, sensitivity: 0.74 (0.65–0.82), specificity: 0.75 (0.53–0.90), AUROC: 0.82 (0.74–0.89), p-value 6.2×10−7. IBD subgroup analysis distinguished UC from CD: sensitivity of 0.67 (0.54–0.79), specificity: 0.67 (0.54–0.79), AUROC: 0.70 (0.60–0.80), p-value 9.23×10−4. ConclusionThis confirms the utility of exhaled VOC analysis to distinguish IBD from healthy controls, and UC from CD. It conforms to other studies using different technology, whilst affirming exhaled VOCs as biomarkers for diagnosing IBD.

Detection of exhaled breath olfactory signature of pah with artificial nose: From proof-of-concept to clinical validation

Novel tools for the non-invasive detection of pulmonary arterial hypertension (PAH) are needed in order to facilitate early diagnosis, particularly in high-risk groups. Volatile organic compounds (VOCs) in the exhaled breath are potential specific biomarkers of disease pathophysiology. We hypothesized that PAH can be detected by exhaled VOCs analysis using a novel artificial nose based on sensor arrays. In the proof-of-concept (PoC) study, we performed a case-control study in 25 PAH patients and 25 healthy controls to assess the diagnostic performance of the artificial nose. Exhaled breath were collected and analysed using an “offline” method. Predictive models were developed using discriminant factor analysis (DFA). Canonical score derived from DFA model was able to discriminate PAH from controls with high discriminative power with receiver operator curve analysis AUC = 0.97, P P

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

There is an increasing interest in the potential of exhaled biomarkers, such as volatile organic compounds (VOCs), to improve accurate diagnoses and management decisions in pulmonary diseases. The objective of this manuscript is to systematically review the current knowledge on exhaled VOCs with respect to their potential clinical use in asthma, lung cancer, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and respiratory tract infections. A systematic literature search was performed in PubMed, EMBASE, Cochrane database, and reference lists of retrieved studies. Controlled, clinical, English-language studies exploring the diagnostic and monitoring value of VOCs in asthma, COPD, CF, lung cancer and respiratory tract infections were included. Data on study design, setting, participant characteristics, VOCs techniques, and outcome measures were extracted. Seventy-three studies were included, counting in total 3,952 patients and 2,973 healthy controls. The collection and analysis of exhaled VOCs is non-invasive and could be easily applied in the broad range of patients, including subjects with severe disease and children. Various research groups demonstrated that VOCs profiles could accurately distinguish patients with a pulmonary disease from healthy controls. Pulmonary diseases seem to be characterized by a disease specific breath-print, as distinct profiles were found in patients with dissimilar diseases. The heterogeneity of studies challenged the inter-laboratory comparability. In conclusion, profiles of VOCs are potentially able to accurately diagnose various pulmonary diseases. Despite these promising findings, multiple challenges such as further standardization and validation of the diverse techniques need to be mastered before VOCs can be applied into clinical practice.

Exhaled methyl nitrate as a noninvasive marker of hyperglycemia in type 1 diabetes

Recent technical advances allow detection of several hundred volatile organic compounds (VOCs) in human exhaled air, many of which reflect unidentified endogenous pathways. Our group has previously estimated plasma glucose levels in healthy adults during a standard oral glucose tolerance test via exhaled VOC analysis. As a result of the metabolic characteristics of hyperglycemia in the diabetic (low insulin and increased free fatty acids and ketones), we hypothesized that different exhaled VOC profiles may be present in children with type 1 diabetes mellitus (T1DM) during spontaneous hyperglycemia. Exhaled methyl nitrate strongly correlated specifically with the acute, spontaneous hyperglycemia of T1DM children. Eighteen experiments were conducted among 10 T1DM children. Plasma glucose and exhaled gases were monitored during either constant euglycemia (n = 5) or initial hyperglycemia with gradual correction (n = 13); all subjects received i.v. insulin and glucose as needed. Gas analysis was performed on 1.9-liter breath samples via gas chromatography using electron capture, flame ionization, and mass selective detection. Among the approximately 100 measured exhaled gases, the kinetic profile of exhaled methyl nitrate, commonly present in room air in the range of 5-10 parts per trillion, was most strongly statistically correlated with that of plasma glucose (P = 0.003-0.001). Indeed, the kinetic profiles of the two variables paralleled each other in 16 of 18 experiments, including repeat subjects who at different times displayed either euglycemia or hyperglycemia.