Breath Profiling Research Articles

Selection of consistent breath biomarkers of abnormal liver function using feature selection: a pilot study

PurposeBreath profiling has gained importance in recent years as it is a non-invasive technique to identify biomarkers for various diseases. Breath profiling of abnormal liver function in individuals for identifying potential biomarkers in exhaled breath could be a useful diagnostic tool. The objective of this study was to identify potential biomarkers in exhaled breath that remain stable and consistent during different physiological states, including rest and brief workouts, intending to develop a non-invasive diagnostic tool for detecting abnormal liver function.MethodOur study employed a gas chromatography and mass-spectrometer quantified dataset for analysis. Machine learning techniques, including feature selection and model training, were used to rank and evaluate potential biomarkers' contributions to the model's performance. Statistical methods were applied to filter significant and consistent biomarkers. The final selected biomarkers were iterated for all possible combinations using machine learning algorithms to determine their accuracy range. Furthermore, classification models were used to evaluate the performance metrics of the biomarkers and compare models.ResultThe final selected biomarkers, including 2-Myristynoyl Pantetheine, Pterin-6 Carboxylic Acid, Methyl Mercaptan, N-Acetyl Cysteine, and Butyric Acid, exhibited stable levels in exhaled breath during different physiological states. They showed high accuracy and precision in detecting abnormal liver function. Our machine learning models achieved an accuracy rate ranging from 0.7 to 0.95 in all conditions, with precision, recall, prediction probability, and a 95% confidence interval ranging from 0.84 to 0.94, using various combinations of these biomarkers.ConclusionOur statistical and machine learning analysis identified significant and potential biomarkers that contribute to the detection of abnormal liver function. These biomarkers were consistent across different physiological states of the body in both patient and healthy groups. The use of breath samples and feature selection machine learning methods proved to be an accurate and reliable approach for identifying these biomarkers. Our findings provide valuable insights for future research in this field and can inform the development of non-invasive and cost-effective diagnostic tests for liver disease.

Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study

Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16%probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

Profiling of exhaled volatile organics in the screening scenario of a COVID-19 test center

Breath volatile organics (VOCs) may provide immediate information on infection mechanisms and host response. We conducted real-time mass-spectrometry based breath profiling in 708 non-preselected consecutive subjects in the screening scenario of a COVID-19 test center. Recruited subjects were grouped based on PCR confirmed infection status and presence or absence of flu like symptoms. Exhaled VOC profiles of SARS-CoV-2 positive cases (n=36) differed from healthy (n=256) and those with other respiratory infections (n=416). Concentrations of most VOCs were supressed in COVID-19. VOC concentrations also differed between symptomatic and asymptomatic cases. Breath markers mirror effects of infections onto host’s cellular metabolism and microbiome. Down-regulation of specific VOCs were attributed to suppressive effects of SARS-CoV-2 onto gut or pulmonary microbial metabolism. Breath analysis holds potential for monitoring SARS-CoV-2 infections rather than for primary diagnosis. Breath profiling offers unconventional insight into host-virus cross-talk and infection microbiology and enables non-invasive assessment of disease manifestation.

Effects of long-term vegan diet on breath composition

The composition of exhaled breath derives from an intricate combination of normal and abnormal physiological processes that are modified by the consumption of food and beverages, circadian rhythms, bacterial infections, and genetics as well as exposure to xenobiotics. This complexity, which results wide intra- and inter-individual variability and is further influenced by sampling conditions, hinders the identification of specific biomarkers and makes it difficult to differentiate between pathological and nominally healthy subjects. The identification of a ‘normal’ breath composition and the relative influence of the aforementioned parameters would make breath analyses much faster for diagnostic applications. We thus compared, for the first time, the breath composition of age-matched volunteers following a vegan and a Mediterranean omnivorous diet in order to evaluate the impact of diet on breath composition. Mixed breath was collected from 38 nominally healthy volunteers who were asked to breathe into a 2 l handmade Nalophan bag. Exhalation flow rate and carbon dioxide values were monitored during breath sampling. An aliquot (100 ml) of breath was loaded into a sorbent tube (250 mg of Tenax GR, 60/80 mesh) before being analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Breath profiling using TD-GC-MS analysis identified five compounds (methanol, 1-propanol, pentane, hexane, and hexanal), thus enabling differentiation between samples collected from the different group members. Principal component analysis showed a clear separation between groups, suggesting that breath analysis could be used to study the influence of dietary habits in the fields of nutrition and metabolism. Surprisingly, one Italian woman and her brother showed extremely low breath isoprene levels (about 5 pbv), despite their normal lipidic profile and respiratory data, such as flow rate and pCO2. Further investigations to reveal the reasons behind low isoprene levels in breath would help reveal the origin of isoprene in breath.

Development of a Sensing Array for Human Breath Analysis Based on SWCNT Layers Functionalized with Semiconductor Organic Molecules.

A sensor array based on heterojunctions between semiconducting organic layers and single walled carbon nanotube (SWCNT) films is produced to explore applications in breathomics, the molecular analysis of exhaled breath. The array is exposed to gas/volatiles relevant to specific diseases (ammonia, ethanol, acetone, 2-propanol, sodium hypochlorite, benzene, hydrogen sulfide, and nitrogen dioxide). Then, to evaluate its capability to operate with real relevant biological samples the array is exposed to human breath exhaled from healthy subjects. Finally, to provide a proof of concept of its diagnostic potential, the array is exposed to exhaled breath samples collected from subjects with chronic obstructive pulmonary disease (COPD), an airway chronic inflammatory disease not yet investigated with CNT-based sensor arrays, and breathprints are compared with those obtained from of healthy subjects. Principal component analysis shows that the sensor array is able to detect various target gas/volatiles with a clear fingerprint on a 2D subspace, is suitable for breath profiling in exhaled human breath, and is able to distinguish subjects with COPD from healthy subjects based on their breathprints. This classification ability is further improved by selecting the most responsive sensors to nitrogen dioxide, a potential biomarker of COPD.

Exhaled breath profiling by electronic nose enabled discrimination of allergic rhinitis and extrinsic asthma

Aim: To assess whether an e-nose could discriminate between subjects affected by allergic rhinitis with and without concomitant extrinsic asthma, as well as from healthy controls, in terms of exhaled VOC-profile.Methods: Fourteen patients with Extrinsic Asthma and Allergic Rhinitis (AAR), 14 patients with Allergic Rhinitis without asthma (AR) and 14 healthy controls (HC) participated in a cross-sectional study. Exhaled breath was collected by a standardized method and sampled by an e-nose (Cyranose 320). Raw data were reduced by Principal component analysis and analyzed by canonical discriminant analysis. Cross-validation accuracy (CVA) and Receiver Operating Characteristic(ROC)-curves were calculated. External validation in newly recruited patients (7 AAR, 7 AR and 7 HC) was tested using the previous training model.Results: Breathprints of patients with AR clustered from those with AAR (CVA = 85.7%), as well as HC (CVA = 82.1%). Breathprints from AAR were also separated from those of HC (CVA = 75.0%). External validation confirmed the above findings.Conclusions: An e-nose can discriminate exhaled breath from subjects with allergic rhinitis with and without extrinsic asthma, which represent two different diseases with partly overlapping features. This supports the view of using breath profiling to diagnose asthma also in patients with allergic rhinitis.

Real-time breath gas analysis of CO and CO2 using an EC-QCL

Real-time breath gas analysis is a promising, non-invasive tool in medical diagnostics, and well-suited to investigate the physiology of carbon monoxide (CO), a potential biomarker for oxidative stress and respiratory diseases. A sensor for precise, breath-cycle resolved, simultaneous detection of exhaled CO (eCO) and carbon dioxide (eCO2) was developed based on a continuous wave, external-cavity quantum cascade laser (EC-QCL), a low-volume multi-pass cell and wavelength modulation spectroscopy. The system achieves a noise-equivalent (1σ) sensitivity of 8.5 × 10−8 cm−1 Hz−1/2 and (2σ) detection limits of 9 ± 2 ppbv and 650 ± 7 ppmv at 0.14 s spectrum acquisition time for CO and CO2, respectively. Integration over 15 s yields a precision of 0.6 ppbv for CO. The fact that the eCO2 expirograms measured by capnography and laser spectroscopy have essentially identical shape confirms true real-time detection. It is found that the individual eCO exhalation profiles from healthy non-smokers have a slightly different shape than the eCO2 profiles and exhibit a clear dependence on exhalation flow rate and breath-holding time. Detection of indoor air CO and broadband breath profiling across the 93 cm−1 mode-hop-free tuning range of the EC-QCL are also demonstrated.

Electronic Nose Technology in Respiratory Diseases.

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.

Exhaled Breath Metabolomics for the Diagnosis of Pneumonia in Intubated and Mechanically-Ventilated Intensive Care Unit (ICU)-Patients.

The diagnosis of hospital-acquired pneumonia remains challenging. We hypothesized that analysis of volatile organic compounds (VOCs) in exhaled breath could be used to diagnose pneumonia or the presence of pathogens in the respiratory tract in intubated and mechanically-ventilated intensive care unit patients. In this prospective, single-centre, cross-sectional cohort study breath from mechanically ventilated patients was analysed using gas chromatography-mass spectrometry. Potentially relevant VOCs were selected with a p-value < 0.05 and an area under the receiver operating characteristics curve (AUROC) above 0.7. These VOCs were used for principal component analysis and partial least square discriminant analysis (PLS-DA). AUROC was used as a measure of accuracy. Ninety-three patients were included in the study. Twelve of 145 identified VOCs were significantly altered in patients with pneumonia compared to controls. In colonized patients, 52 VOCs were significantly different. Partial least square discriminant analysis classified patients with modest accuracy (AUROC: 0.73 (95% confidence interval (CI): 0.57–0.88) after leave-one-out cross-validation). For determining the colonization status of patients, the model had an AUROC of 0.69 (95% CI: 0.57–0.82) after leave-one-out cross-validation. To conclude, exhaled breath analysis can be used to discriminate pneumonia from controls with a modest to good accuracy. Furthermore breath profiling could be used to predict the presence and absence of pathogens in the respiratory tract. These findings need to be validated externally.

Exhaled breath profiling in patients with COPD and OSA overlap syndrome: a pilot study

The analysis of volatile organic compounds (VOCs) by an electronic nose (e-nose) is a groundbreaking method that provides distinct exhaled molecular patterns in several respiratory and systemic diseases. It has been shown that an e-nose can detect obstructive sleep apnea (OSA) as well as chronic obstructive pulmonary disease (COPD). OSA and COPD are sometimes associated into the so-called overlap syndrome (OVS). In this pilot study we hypothesized that an e-nose could discriminate the exhaled breath of patients with OVS from that of subjects with OSA and COPD alone.Thirteen patients with OSA, 15 patients with COPD and 13 with OVS participated in a cross-sectional study. Exhaled breath was collected by a formerly validated method and sampled by using an electronic nose (Cyranose 320). Raw data were analyzed by canonical discriminant analysis on principal component reduction. Cross-validation accuracy (CVA) and ROC-curves were calculated. External validation in newly recruited patients (6 OSA, 6 OVS and 6 COPD) was tested using the previous training set.Breathprints of patients with OSA clustered distinctly from those with OVS (CVA = 96.2%) as well as those with COPD (CVA = 82.1%). Breathprints from OVS were not significantly separated from those of COPD (CVA = 67.9%). External validation confirmed the above findings.The e-nose can distinguish with high accuracy the exhaled VOC-profile of patients with OSA from those with OVS as well as those with COPD. This warrants future studies to confirm the potential of this technique in the non-invasive detection of sleep apnea.

Exhaled breath profiling using broadband quantum cascade laser-based spectroscopy in healthy children and children with asthma and cystic fibrosis

Exhaled breath analysis is a potential non-invasive tool for diagnosing and monitoring airway diseases. Gas chromatography–mass spectrometry and electrochemical sensor arrays are the main techniques to detect volatile organic compounds (VOC) in exhaled breath. We developed a broadband quantum cascade laser spectroscopy technique for VOC detection and identification.The objective of this study was to assess the repeatability of exhaled breath profiling with broadband quantum cascade laser-based spectroscopy and to explore the clinical applicability by comparing exhaled breath samples from healthy children with those from children with asthma or cystic fibrosis (CF).Healthy children and children with stable asthma or stable CF, aged 6–18 years, were included. Two to four exhaled breath samples were collected in Tedlar bags and analyzed by quantum cascade laser spectroscopy to detect VOCs with an absorption profile in the wavenumber region between 832 and 1262.55 cm−1.We included 35 healthy children, 39 children with asthma and 15 with CF. Exhaled breath VOC profiles showed poor repeatability (Spearman’s rho = 0.36 to 0.46) and agreement of the complete profiles. However, we were able to discriminate healthy children from children with stable asthma or stable CF and identified VOCs that were responsible for this discrimination.Broadband quantum cascade laser-based spectroscopy detected differences in VOC profiles in exhaled breath samples between healthy children and children with asthma or CF. The combination of a relatively easy and fast method and the possibility of molecule identification makes broadband quantum cascade laser-based spectroscopy attractive to investigate the diagnostic and prognostic potential of volatiles in exhaled breath.

SAT-472 - Exhaled Breath Profiling by Electronic Nose as a Novel Non-Invasive Method for Assessment of Chronic Liver Disease: Proof of Principle Study

SAT-472 - Exhaled Breath Profiling by Electronic Nose as a Novel Non-Invasive Method for Assessment of Chronic Liver Disease: Proof of Principle Study

Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study.

Distinguishing between the inflammatory bowel diseases [IBD], Crohn's disease [CD] and ulcerative colitis [UC], is important for determining management and prognosis. Selected ion flow tube mass spectrometry [SIFT-MS] may be used to analyse volatile organic compounds [VOCs] in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified. The aim of this pilot study was to identify, quantify, and analyse VOCs present in the breath of IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. SIFT-MS breath profiling of 56 individuals [20 UC, 18 CD, and 18 healthy controls] was undertaken. Multivariate analysis included principal components analysis and partial least squares discriminant analysis with orthogonal signal correction [OSC-PLS-DA]. Receiver operating characteristic [ROC] analysis was performed for each comparative analysis using statistically significant VOCs. OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs [dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal, and nonanal] gave integrated areas under the curve of 0.86 [CD vs healthy controls], 0.74 [UC vs healthy controls], and 0.83 [CD vs UC]. Exhaled breath VOC profiling was able to distinguish IBD patients from controls, as well as to separate UC from CD, using both multivariate and univariate statistical techniques.

OC-011 Exhaled volatile organic compound breath analysis in inflammatory bowel disease

<h3>Introduction</h3> Distinguishing between Crohn’s disease (CD) and ulcerative colitis (UC) is important for determining management and prognosis. Selected ion flow tube mass spectrometry (SIFT-MS) may be used to analyse volatile organic compounds (VOCs) in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified.¹A recent paediatric study used SIFT-MS to distinguish IBD patients from healthy controls (HC).²The aim of this pilot study was to identify, quantify and analyse VOCs present in the breath of adult IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. <h3>Method</h3> SIFT-MS breath profiling of 56 individuals (20 UC, 18 CD and 18 healthy controls) was undertaken. Multivariate analysis included principal components analysis (PCA) and partial least squares discriminant analysis with orthogonal signal correction (OSC-PLS-DA). Receiver Operator Characteristic (ROC) analysis was performed for each comparative analysis using statistically significant VOCs. <h3>Results</h3> OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs (dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal and nonanal) gave integrated areas under the curve (AUC) of 0.86 (CD vs HC), 0.74 (UC vs HC) and 0.83 (CD vs UC). <h3>Conclusion</h3> SIFT-MS breath profiling was able to distinguish IBD patients from controls, as well as separate UC from CD, using both multivariate and univariate statistical techniques. The specific VOCs characterising the breath in IBD relate to bacterial dysbiosis (sulphur compounds and ammonia) and oxidative stress (aldehydes)³– both mechanisms implicated in disease pathogenesis. <h3>Disclosure of interest</h3> None Declared. <h3>References</h3> Popov TA. Human exhaled breath analysis. Ann Allergy Asthma Immunol. 2011;106(6):451–6 Patel N, Alkhouri N, Eng K, <i>et al</i>. Metabolomic analysis of breath volatile organic compounds reveals unique breathprints in children with inflammatory bowel disease: a pilot study. Aliment Pharmacol Ther. 2014;40(5):498–507 Bos LD, Sterk PJ, Schultz MJ. Volatile metabolites of pathogens: a systematic review. PLoS Pathog. 2013;9(5):e1003311

Analysis of human breath samples using a modified thermal desorption: gas chromatography electrospray ionization interface

A two-stage thermal desorption/secondary electrospray ionization/time-of-flight mass spectrometry for faster targeted breath profiling has been studied. A new secondary electrospray ionization (SESI) source was devised to constrain the thermal desorption plume and promote efficient mixing in the ionization region. Further, a chromatographic pre-separation stage was introduced to suppress interferences from siloxanes associated with thermal desorption profiles of exhaled breath samples.In vitro tests with 5-nonanone indicated an increased sensitivity and a lowered limit-of-detection, both by a factor of ~4, the latter to an on-trap mass of 14.3 ng, equivalent to a sampled breath concentration of 967 pptv. Analysis of the mass spectrometric responses from 20 breath samples acquired sequentially from a single participant indicated enhanced reproducibility (reduced relative standard deviations (RSD) for 5-nonanone, benzaldehyde and 2-butanone were 28 %, 16% and 14% respectively. The corresponding values for an open SESI source were that 5-nonanone was not detected, with %RSD of 39% for benzaldehyde and 31% for 2-butanone).The constrained source with chromatographic pre-separation resulted in an increase in the number of detectable volatile organic compounds (VOCs) from 260 mass spectral peaks with an open SESI source to 541 peaks with the constrained source with pre-separation. Most of the observed VOCs were present at trace levels, at less than 2.5% of the intensity of the base peak.Seventeen 2.5 dm3 distal breath samples were collected from asthma patients and healthy controls respectively, and subjected to comparative high-throughput screening using thermal desorption/SESI/time-of-flight mass spectrometry (TD-SESI-ToFMS). Breath metabolites were detected by using a background siloxane ion (hexamethylcyclotrisiloxane m/z 223.0642) as an internal lockmass. Eleven breath metabolites were selected from the breath research literature and successfully targeted. These data reinforce the proposition that TD-SESI-MS has potential for development as a rapid screening method for disease stratification and targeted metabolism profiling.

Exhaled breath profiling for diagnosing acute respiratory distress syndrome.

BackgroundThe acute respiratory distress syndrome (ARDS) is a common, devastating complication of critical illness that is characterized by pulmonary injury and inflammation. The clinical diagnosis may be improved by means of objective biological markers. Electronic nose (eNose) technology can rapidly and non–invasively provide breath prints, which are profiles of volatile metabolites in the exhaled breath. We hypothesized that breath prints could facilitate accurate diagnosis of ARDS in intubated and ventilated intensive care unit (ICU) patients.MethodsProspective single-center cohort study with training and temporal external validation cohort. Breath of newly intubated and mechanically ventilated ICU-patients was analyzed using an electronic nose within 24 hours after admission. ARDS was diagnosed and classified by the Berlin clinical consensus definition. The eNose was trained to recognize ARDS in a training cohort and the diagnostic performance was evaluated in a temporal external validation cohort.ResultsIn the training cohort (40 patients with ARDS versus 66 controls) the diagnostic model for ARDS showed a moderate discrimination, with an area under the receiver–operator characteristic curve (AUC–ROC) of 0.72 (95%–confidence interval (CI): 0.63-0.82). In the external validation cohort (18 patients with ARDS versus 26 controls) the AUC–ROC was 0.71 [95%–CI: 0.54 – 0.87]. Restricting discrimination to patients with moderate or severe ARDS versus controls resulted in an AUC–ROC of 0.80 [95%–CI: 0.70 – 0.90]. The exhaled breath profile from patients with cardiopulmonary edema and pneumonia was different from that of patients with moderate/severe ARDS.ConclusionsAn electronic nose can rapidly and non–invasively discriminate between patients with and without ARDS with modest accuracy. Diagnostic accuracy increased when only moderate and severe ARDS patients were considered. This implicates that breath analysis may allow for rapid, bedside detection of ARDS, especially if our findings are reproduced using continuous exhaled breath profiling.Trial registrationNTR2750, registered 11 February 2011.

Predicting steroid responsiveness in patients with asthma using exhaled breath profiling

Exhaled breath contains disease-dependent volatile organic compounds (VOCs), which may serve as biomarkers distinguishing clinical phenotypes in asthma. Their measurement may be particularly beneficial in relation to treatment response. Our aim was to compare the performance of electronic nose (eNose) breath analysis with previously investigated techniques (sputum eosinophils, exhaled nitric oxide (FeNO) and airway hyperresponsiveness) to discriminate asthma from controls and identify steroid responsiveness in steroid-free patients. Trial registration ACTRN12613000038796. Twenty-five patients with mild/moderate asthma had their inhaled steroid treatment discontinued until loss of control or 28days. They were subsequently treated with oral prednisone 30mg/day for 14days. Steroid responsiveness was defined as an increase of either >12% FEV1 or >2 doubling doses PC20 AMP. Steroid-free assessment of sputum eosinophils, FeNO and exhaled breath VOCs were used to construct algorithms predicting steroid responsiveness. Performance characteristics were compared by ROC analysis. The eNose discriminated between asthma and controls (area under the curve=0.766±0.14; P=0.002) with similar accuracy to FeNO (0.862±0.12; P<0.001) and sputum eosinophils (0.814±0.15; P<0.001). Steroid responsiveness was predicted with greater accuracy by VOC-analysis (AUC=0.883±0.16; P=0.008) than FeNO (0.545±0.28; P=0.751) or sputum eosinophils (0.610±0.29; P=0.441). Breath analysis by eNose can identify asthmatic patients and may be used to predict their response to steroids with greater accuracy than sputum eosinophils or FeNO. This implies a potential role for breath analysis in the tailoring of treatment for asthma patients.

Exhaled breath profiling in diagnosing wheezy preschool children

Although wheeze is common in preschool children, the underlying pathophysiology has not yet been disentangled. Volatile organic compounds (VOCs) in exhaled breath may serve as noninvasive markers of early wheeze. We aimed to assess the feasibility of VOC collection in preschool children, and to study whether a VOC profile can differentiate between children with and without recurrent wheeze. We included children (mean (range) age 3.3 (1.9-4.5) yrs) with (n=202) and without (n=50) recurrent wheeze. Exhaled VOCs were analysed by gas chromatography-time-of-flight mass spectrometry. VOC profiles were generated by ANOVA simultaneous component analysis (ASCA) and sparse logistic regression (SLR). Exhaled breath collection was possible in 98% of the children. In total, 913 different VOCs were detected. The signal-to-noise ratio improved after correction for age, sex and season using ASCA pre-processing. An SLR model with 28 VOCs correctly classified 83% of the children (84% sensitivity, 80% specificity). After six-fold cross-validation, 73% were correctly classified (79% sensitivity, 50% specificity). Assessment of VOCs in exhaled breath is feasible in young children. VOC profiles are able to distinguish children with and without recurrent wheeze with a reasonable accuracy. This proof of principle paves the way for additional research on VOCs in preschool wheezing.

Detection of gastro-oesophageal reflux disease (GORD) in patients with obstructive lung disease using exhaled breath profiling

Gastro-oesophageal reflux disease (GORD) has been implicated in the worsening of several respiratory disorders. Current methods of diagnosis lack accuracy, are invasive and can be costly. Recently, novel methods of analysing lung pathophysiology have been developed including the use of an electronic nose and analysis of components of exhaled breath condensate (EBC). We hypothesised that these methods would distinguish patients with GORD from those without GORD in the common obstructive lung diseases and healthy controls. In a cross-sectional study, exhaled breath was analysed using the Cyranose 320 electronic nose, using principal components and canonical discriminant analyses. EBC pH and pepsin were quantified using a pH meter and an enzyme-linked immunosorbent assay, respectively. A standardized reflux disease questionnaire (RDQ) was used to assess reflux symptoms. The Cyranose 320 distinguished exhaled breath profiles of obstructive lung disease patients without GORD from obstructive lung disease patients with GORD (p = 0.023, accuracy 67.6%), asthmatic patients with reflux from asthmatics without GORD (85%, p = < 0.015, interclass M distance > 2.8), but did not produce as robust a profile for patients with COPD and COPD with GORD (p = 0.047, accuracy 64%). Patients with obstructive lung disease and GORD had significantly higher levels of EBC pepsin (9.81 ± interquartile range (IQR) 4.38 ng ml−1) than those without GORD (4.6 ± IQR 6.95 ng ml−1), as well as healthy controls (3.44 ± IQR 7.87 ng ml−1; p = < 0.013). EBC pH was not significantly related to the presence of GORD in any group. The RDQ results correlated significantly with the presence of EBC pepsin. This pilot study has shown that exhaled breath profiling can be used for detecting GORD in obstructive lung diseases. While the electronic nose was useful in asthma, EBC pepsin was more helpful in COPD. In this study, several different confounders could potentially have affected results and larger prospective interventional studies are needed.

Non-invasive phenotyping using exhaled volatile organic compounds in asthma

BackgroundBreath volatile organic compounds (VOCs) may be useful for asthma diagnosis and phenotyping, identifying patients who could benefit from personalised therapeutic strategies. The authors aimed to identify specific patterns of...