Breath Profiles Research Articles

Exhaled breath profiles to detect lung infection with Staphylococcus aureus in children with cystic fibrosis

BackgroundAn electronic nose (eNose) can be used to detect volatile organic compounds (VOCs). Exhaled breath contains numerous VOCs and individuals’ VOCs mixtures create distinct breath profiles. Previous reports have shown that eNose can detect lung infections. Whether eNose can detect Staphylococcus aureus airway infections in breath of children with cystic fibrosis (CF) is currently unclear. MethodsIn this cross-sectional observational study, a cloud-connected eNose was used for breath profile analysis of clinically stable paediatric CF patients with airway microbiology cultures positive or negative for CF pathogens. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant and receiver operating characteristics (ROC) analyses. ResultsBreath profiles from 100 children with CF (median predicted FEV1 91%) were obtained and analysed. CF patients with positive airway cultures for any CF pathogen were distinguishable from no CF pathogens (no growth or usual respiratory flora) with accuracy of 79.0% (AUC-ROC 0.791; 95% CI: 0.669–0.913) and between patients positive for Staphylococcus aureus (SA) only and no CF pathogen with accuracy of 74.0% (AUC-ROC 0.797; 95% CI: 0.698–0.896). Similar differences were seen for Pseudomonas aeruginosa (PA) infection vs no CF pathogens (78.0% accuracy, AUC-ROC 0.876, 95% CI: 0.794–0.958). SA- and PA-specific signatures were driven by different sensors in the SpiroNose suggesting pathogen-specific breath signatures. ConclusionsBreath profiles of CF patients with SA in airway cultures are distinct from those with no infection or PA infection, suggesting the utility of eNose technology in the detection of this early CF pathogen in children with CF.

Effects of a Volatile Organic Compound Filter on Breath Profiles Measured by Secondary Electrospray High-Resolution Mass Spectrometry.

Environmental volatile organic compounds (VOCs) from the ambient air potentially influence on-line breath analysis measurements by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). The aim of this study was to investigate how inhaling through a VOC filter affects the detected breath profiles and whether it is feasible to integrate such filters into routine measurements. A total of 24 adult participants performed paired breath analysis measurements with and without the use of an activated carbon filter for inspiration. Concordance correlation coefficients (CCCs) and the Bland−Altman analysis were used to assess the agreement between the two methods. Additionally, the effect on a selection of known metabolites and contaminants was analyzed. Out of all the detected features, 78.3% showed at least a moderate agreement before and after filter usage (CCC > 0.9). The decrease in agreement of the remaining m/z features was mostly associated with reduced signal intensities after filter usage. Although a moderate-to-substantial concordance was found for almost 80% of the m/z features, the filter still had an effect by decreasing signal intensities, not only for contaminants, but also for some of the studied metabolites. Operationally, the use of the filter complicated and slowed down the conductance of measurements, limiting its applicability in clinical studies.

Comparative Analysis of Pre- and Post-Surgery Exhaled Breath Profiles of Volatile Organic Compounds of Patients with Lung Cancer and Benign Tumors

Comparative Analysis of Pre- and Post-Surgery Exhaled Breath Profiles of Volatile Organic Compounds of Patients with Lung Cancer and Benign Tumors

Variation of volatile organic compound levels within ambient room air and its impact upon the standardisation of breath sampling

The interest around analysis of volatile organic compounds (VOCs) within breath has increased in the last two decades. Uncertainty remains around standardisation of sampling and whether VOCs within room air can influence breath VOC profiles. To assess the abundance of VOCs within room air in common breath sampling locations within a hospital setting and whether this influences the composition of breath. A secondary objective is to investigate diurnal variation in room air VOCs. Room air was collected using a sampling pump and thermal desorption (TD) tubes in the morning and afternoon from five locations. Breath samples were collected in the morning only. TD tubes were analysed using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS). A total of 113 VOCs were identified from the collected samples. Multivariate analysis demonstrated clear separation between breath and room air. Room air composition changed throughout the day and different locations were characterized by specific VOCs, which were not influencing breath profiles. Breath did not demonstrate separation based on location, suggesting that sampling can be performed across different locations without affecting results.

Investigation of everyday influencing factors on the variability of exhaled breath profiles in healthy subjects

Abstract Introduction: The human breath is an accurate but complex read-out of many physiological processes in the organism that can be monitored via volatile organic compounds (VOCs) in the exhaled air. However, there are many confounding variables that limit the transfer and application of breath analysis to become a clinical procedure. Method: This work aims to establish a systematic procedure for sampling and characterization of various everyday influences of healthy subjects using proton transfer reaction-mass spectrometry (PTR-MS). In order to limit the influencing factors on the breath profile, a standard analysis procedure for sampling and evaluation of the exhaled breath samples was developed. The correlations between the selected experimental conditions and the resulting VOC profiles were investigated using a non-parametric Wilcoxon rank sum test. Results: In addition to the relevant influence of methodological experimental parameters, interesting insights into the effect of everyday factors on the exhalat gas were obtained and discussed. Furthermore, subject and condition-specific differences were found in the exhaled air of male and female subjects. Conclusion: With a more robust, standardized and reproducible breath sampling protocol, breath analysis is a promising non-invasive tool towards a system-wide understanding and personalized diagnosis and treatment of a wide range of diseases.

Exhaled Breath Analysis for Investigating the Use of Inhaled Corticosteroids and Corticosteroid Responsiveness in Wheezing Preschool Children.

Exhaled breath analysis has great potential in diagnosing various respiratory and non-respiratory diseases. In this study, we investigated the influence of inhaled corticosteroids (ICS) on exhaled volatile organic compounds (VOCs) of wheezing preschool children. Furthermore, we assessed whether exhaled VOCs could predict a clinical steroid response in wheezing preschool children. We performed a crossover 8-week ICS trial, in which 147 children were included. Complete data were available for 89 children, of which 46 children were defined as steroid-responsive. Exhaled VOCs were measured by GC-tof-MS. Statistical analysis by means of Random Forest was used to investigate the effect of ICS on exhaled VOCs. A set of 20 VOCs could best discriminate between measurements before and after ICS treatment, with a sensitivity of 73% and specificity of 67% (area under ROC curve = 0.72). Most discriminative VOCs were branched C11H24, butanal, octanal, acetic acid and methylated pentane. Other VOCs predominantly included alkanes. Regularised multivariate analysis of variance (rMANOVA) was used to determine treatment response, which showed a significant effect between responders and non-responders (p < 0.01). These results show that ICS significantly altered the exhaled breath profiles of wheezing preschool children, irrespective of clinical treatment response. Furthermore, exhaled VOCs were capable of determining corticosteroid responsiveness in wheezing preschool children.

FRI075 - Unbiased clustering of exhaled breath profiles in metabolic dysfunction-associated fatty liver disease identifies a patient phenotype at higher risk of disease progression

FRI075 - Unbiased clustering of exhaled breath profiles in metabolic dysfunction-associated fatty liver disease identifies a patient phenotype at higher risk of disease progression

Abstract 666: A phase 1 study to detect adverse events after SBRT and immunotherapy by electronic nose in advanced NSCLC

Abstract It is increasingly understood that cancers can be recognized by the immune system and that inflammation relates to response. Combining stereotactic radiotherapy (SBRT) to increase release of cancer cell antigens with an anti-CTLA-4 and PD-L1 inhibitor may lead to increased response rates. Due to tumor heterogeneity it may be important to irradiate the primary tumor (addressing trunk mutations), instead of its metastasis (branch mutations). No solid predictive biomarker for response and/or immunotherapy related adverse events (TRAEs) is available. Electronic nose (eNose) technology measures the complete mixture of volatile organic compounds in exhaled breath and can detect lung cancer based on pattern recognition of the breath profile. We hypothesize that eNose is able to predict TRAEs and response in patients with NSCLC. Methods: In 3 sequential cohorts, immunotherapy regimes combined with SBRT were studied in stage IIIB/IV NSCLC patients progressing on chemotherapy. All patients were irradiated on the primary tumor (1x20 Gy on 9cc) 1 week after the 1st dose of immunotherapy. The 1st cohort (n=3) received durvalumab. The 2nd and 3rd cohort (both n=6) received a combination of durvalumab and tremelimumab followed by durvalumab monotherapy. Duplicate eNose measurements were performed by using the SpiroNose that contains 7 metal oxide semiconductor sensors. TRAEs were categorized using NCI CTCAE version 4.3. Descriptive statistics were used to summarize baseline characteristics and TRAEs. The relationship between breath profiles and response and TRAEs was analyzed with advanced signal processing, ambient correction and Mann-Whitney U test. Linear discriminant and receiver operating characteristics analysis followed. Pearson correlation and regression assessed duration of response. Findings: Fifteen patients were included as described above. Baseline characteristics of the groups were comparable. Median progression free survival was 2 months, overall survival 10 months (immature). No statistical difference was found in (duration of) response. There was 1 low grade TRAE (CTC 1-2) in cohort 1 and 10 in cohort 2/3. High grade TRAEs (CTC 3) were only present in cohort 2/3 (n=3) and 1 patient discontinued treatment. There was one dose limiting toxicity. At baseline, 12/15 patients performed eNose measurements; 7 with TRAEs and 5 without. The TRAE group had a significant higher sensor 7 signal compared to those without TRAE (p=.042). The cross-validated accuracy for detecting TRAE was 67%. The ROC-AUC was .857 [.638-1]. Interpretation: eNose breath profiles at baseline may predict which patients will develop TRAEs. The small sample size increases the risk of overfitting, therefore another cohort of 34 immuno-monotherapy patients is being analyzed (15 with and 19 without TRAEs, pending). We demonstrated no new safety data. Disclosure: This study was sponsored by a research grant from AstraZeneca. Citation Format: M Benthe Muntinghe-Wagenaar, Hanneke Kievit, Lucie B. Hijmering-Kappelle, Birgitta I. Hiddinga, J Fred Ubbels, Robin Wijsman, Mechteld F. Brasz, Gitte Slingers, Rianne de Vries, Milou M. Schuurbiers, Harry J. Groen, Huib A. Kerstjens, Anthonie J. van der Wekken, Michel M. van den Heuvel, T Jeroen Hiltermann. A phase 1 study to detect adverse events after SBRT and immunotherapy by electronic nose in advanced NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 666.

Modelling of Breath and Various Blood Volatilomic Profiles-Implications for Breath Volatile Analysis.

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.

Infrared cavity ring-down spectroscopy for detecting non-small cell lung cancer in exhaled breath

Early diagnosis of lung cancer greatly improves the likelihood of survival and remission, but limitations in existing technologies like low-dose computed tomography have prevented the implementation of widespread screening programs. Breath-based solutions that seek disease biomarkers in exhaled volatile organic compound (VOC) profiles show promise as affordable, accessible and non-invasive alternatives to traditional imaging. In this pilot work, we present a lung cancer detection framework using cavity ring-down spectroscopy (CRDS), an effective and practical laser absorption spectroscopy technique that has the ability to advance breath screening into clinical reality. The main aims of this work were to (1) test the utility of infrared CRDS breath profiles for discriminating non-small cell lung cancer (NSCLC) patients from controls, (2) compare models with VOCs as predictors to those with patterns from the CRDS spectra (breathprints) as predictors, and (3) present a robust approach for identifying relevant disease biomarkers. First, based on a proposed learning curve technique that estimated the limits of a model’s performance at multiple sample sizes (10–158), the CRDS-based models developed in this work were found to achieve classification performance comparable or superior to like mass spectroscopy and sensor-based systems. Second, using 158 collected samples (62 NSCLC subjects and 96 controls), the accuracy range for the VOC-based model was 65.19%–85.44% (51.61%–66.13% sensitivity and 73.96%–97.92% specificity), depending on the employed cross-validation technique. The model based on breathprint predictors generally performed better, with accuracy ranging from 71.52%–86.08% (58.06%–82.26% sensitivity and 80.21%–88.54% specificity). Lastly, using a protocol based on consensus feature selection, three VOCs (isopropanol, dimethyl sulfide, and butyric acid) and two breathprint features (from a local binary pattern transformation of the spectra) were identified as possible NSCLC biomarkers. This research demonstrates the potential of infrared CRDS breath profiles and the developed early-stage classification techniques for lung cancer biomarker detection and screening.

Effects of COVID-19 protective face masks and wearing durations on respiratory haemodynamic physiology and exhaled breath constituents.

BackgroundWhile assumed to protect against coronavirus transmission, face masks may have effects on respiratory–haemodynamic parameters. Within this pilot study, we investigated immediate and progressive effects of FFP2 and surgical masks on exhaled breath constituents and physiological attributes in 30 adults at rest.MethodsWe continuously monitored exhaled breath profiles within mask space in older (age 60–80 years) and young to middle-aged (age 20–59 years) adults over the period of 15 and 30 min by high-resolution real-time mass-spectrometry. Peripheral oxygen saturation (SpO2) and respiratory and haemodynamic parameters were measured (noninvasively) simultaneously.ResultsProfound, consistent and significant (p≤0.001) changes in SpO2 (≥60_FFP2-15 min: 5.8±1.3%↓, ≥60_surgical-15 min: 3.6±0.9%↓, <60_FFP2-30 min: 1.9±1.0%↓, <60_surgical-30 min: 0.9±0.6%↓) and end-tidal carbon dioxide tension (PETCO2) (≥60_FFP2-15 min: 19.1±8.0%↑, ≥60_surgical-15 min: 11.6±7.6%↑, <60_FFP2- 30 min: 12.1±4.5%↑, <60_surgical- 30 min: 9.3±4.1%↑) indicate ascending deoxygenation and hypercarbia. Secondary changes (p≤0.005) to haemodynamic parameters (e.g. mean arterial pressure (MAP) ≥60_FFP2-15 min: 9.8±10.4%↑) were found. Exhalation of bloodborne volatile metabolites, e.g. aldehydes, hemiterpene, organosulfur, short-chain fatty acids, alcohols, ketone, aromatics, nitrile and monoterpene mirrored behaviour of cardiac output, MAP, SpO2, respiratory rate and PETCO2. Exhaled humidity (e.g. ≥60_FFP2-15 min: 7.1±5.8%↑) and exhaled oxygen (e.g. ≥60_FFP2-15 min: 6.1±10.0%↓) changed significantly (p≤0.005) over time.ConclusionsBreathomics allows unique physiometabolic insights into immediate and transient effects of face mask wearing. Physiological parameters and breath profiles of endogenous and/or exogenous volatile metabolites indicated putative cross-talk between transient hypoxaemia, oxidative stress, hypercarbia, vasoconstriction, altered systemic microbial activity, energy homeostasis, compartmental storage and washout. FFP2 masks had a more pronounced effect than surgical masks. Older adults were more vulnerable to FFP2 mask-induced hypercarbia, arterial oxygen decline, blood pressure fluctuations and concomitant physiological and metabolic effects.

A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning

Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject’s breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.

Relationship between untreated obstructive sleep apnea and breath hydrogen and methane after glucose load.

Patients with sleep disturbances have gastrointestinal symptoms. Breath hydrogen (H2) and methane (CH4) indicating small intestinal bacterial overgrowth (SIBO) might be related with these symptoms. The study was conducted to assess the link between breath profiles and untreated obstructive sleep apnea (OSA). : This prospective study enrolled consecutive patients with OSA using polysomnography. Heart rate variability (HRV) was used as a measurement for the balance of autonomic nervous system during polysomnography. Glucose breath test (GBT) to evaluate breath H2 and CH4 and bowel symptom questionnaire to investigate associated intestinal symptoms were performed. Among 52 patients with OSA, 16 (30.8%) showed positivity to GBT. Although no significant difference was shown in GBT positivity between patients with healthy controls and patients with OSA (13.3% vs 30.8%, P = 0.109), breath H2 and CH4 levels in the OSA group were significantly higher than those in controls (P < 0.05). Flatulence was significantly common in OSA groups with GBT positivity than those without GBT positivity. Multivariate analysis demonstrated that waist-to-hip ratio (odds ratio = 12.889; 95% confidence interval (CI): 1.257-132.200; P = 0.031) and low-to-high-frequency ratio of HRV (odds ratio = 1.476; 95% CI: 1.013-2.151, P = 0.042) are independently related to GBT positivity in patients with OSA. : Elevated breath H2 or CH4 after glucose load might not be an uncommon finding in patients with untreated OSA. Abdominal obesity and autonomic imbalance dysfunction are significantly associated with GBT positivity in OSA patients. SIBO could be considered as target for therapeutic management in OSA patients.

Early iDEntification of Pancreatic cancer: a study to evaluate the utility of pancreatic exocrine insufficiency as a scrEening tool in high-risk iNdiviDuals (The DEPEND study)

Introduction: 13C-stable isotope breath testing is an established test for pancreatic exocrine insufficiency (PEI). It is not known whether 13C-stable isotope breath testing can be used to detect the presence of pancreatic ductal adenocarcinoma PDAC whilst it is still suitable for resection. The aim of this study was to investigate if individuals with resectable PDAC had a noticeably different 13C-stable breath profile compared to healthy volunteers.

Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities.

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Noninvasive real-time breath test for controlling hormonal background of the human body: detection of serotonin and melatonin with quantum point-contact sensors

Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains of Helicobacter pylori and selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient’s breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.

Combination of Exhaled Breath Analysis with Parallel Lung Function and FeNO Measurements in Infants.

Breath analysis by secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) offers the possibility to measure comprehensive metabolic profiles. The technology is currently being deployed in several clinical settings in Switzerland and China. However, patients are required to exhale directly into the device located in a dedicated room. Consequently, clinical implementation in patients incapable of performing necessary exhalation maneuvers (e.g., infants) or immobile (e.g., too weak, elderly, or in intensive care) remains a challenge. The aim of this study was to develop a method to extend such breath analysis capabilities to this subpopulation of patients by collecting breath samples remotely (offline) and promptly (within 10 min) transfer them to SESI-HRMS for chemical analysis. We initially assessed the method in adults by comparing breath mass spectra collected offline with Nalophan bags against spectra of breath samples collected in real time. In total, 13 adults provided 176 pairs of real-time and offline measurements. Lin's concordance correlation coefficient (CCC) was used to estimate the agreement between offline and real-time analyses. Here, 1249 mass spectral features (55% of total detected) exhibited Lin's CCC > 0.6. Subsequently, the method was successfully deployed to analyze breath samples from infants (n = 16), obtaining as a result SESI-HRMS breath profiles. To demonstrate the clinical feasibility of the method, we measured in parallel other clinical variables: (i) lung function, which characterizes the breathing patterns, and (ii) nitric oxide, which is a surrogate marker of airway inflammation. As a showcase, we focused our analysis on the exhaled oxidative stress marker 4-hydroxynonenal and its association with nitric oxide and minute ventilation.

Diagnostic Performance of Electronic Nose Technology in Sarcoidosis

BackgroundDiagnosing sarcoidosis can be challenging, and a noninvasive diagnostic method is lacking. The electronic nose (eNose) technology profiles volatile organic compounds in exhaled breath and has potential as a point-of-care diagnostic tool.Research QuestionCan eNose technology be used to distinguish accurately between sarcoidosis, interstitial lung disease (ILD), and healthy control subjects, and between sarcoidosis subgroups?Study Design and MethodsIn this cross-sectional study, exhaled breath of patients with sarcoidosis and ILD and healthy control subjects was analyzed by using an eNose (SpiroNose). Clinical characteristics were collected from medical files. Partial least squares discriminant and receiver-operating characteristic analyses were applied to a training and independent validation cohort.ResultsThe study included 252 patients with sarcoidosis, 317 with ILD, and 48 healthy control subjects. In the validation cohorts, eNose distinguished sarcoidosis from control subjects with an area under the curve (AUC) of 1.00 and pulmonary sarcoidosis from other ILD (AUC, 0.87; 95% CI, 0.82-0.93) and hypersensitivity pneumonitis (AUC, 0.88; 95% CI, 0.75-1.00). Exhaled breath of sarcoidosis patients with and without pulmonary involvement, pulmonary fibrosis, multiple organ involvement, pathology-supported diagnosis, and immunosuppressive treatment revealed no distinctive differences. Breath profiles differed between patients with a slightly and highly elevated soluble IL-2 receptor level (median cutoff, 772.0 U/mL; AUC, 0.78; 95% CI, 0.64-0.92).InterpretationPatients with sarcoidosis can be distinguished from ILD and healthy control subjects by using eNose technology, indicating that this method may facilitate accurate diagnosis in the future. Further research is warranted to understand the value of eNose in monitoring sarcoidosis activity.

ZeNose/GO hybrid composite for detection of clinically relevant VOCs in lower respiratory tract (Case study using Carene)

The exhaled human breath profile offers immense potential for non-invasive disease diagnosis and biomarker discovery as it provides mechanistic insights of endogenously produced volatile organic compounds and metabolic pathways affected due to disease prognosis. We have developed a ZIF-8/GO hybrid composite for sensing carene vapor levels, which vary because of respiratory infections. The developed electrochemical sensor platform uses ZIF-8/GO for detection of the target analyte and offers advantage for gas sensing due to high electrochemical activity, surface area and porosity for diffusion. The ZIF-8/GO-modified sensor platform demonstrates high sensitivity and specificity for carene detection up to 100 parts-per-billion. The results led to the development of breathomics-enabled electrochemical strategy that can be used for Point-of-Care diagnostics. We envision that our platform for sensing carene can be used for the detection of lower respiratory tract-related infections such as ventilator associated pneumonia.