Volatile Organic Compounds In Exhaled Breath Research Articles

Detection of Clostridioides difficile infection by assessment of exhaled breath volatile organic compounds

Clostridioides difficile infection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity and Clostridioides difficile polymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negative C.difficile testing (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions.

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers. To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers. We collected exhaled breath from histologically proven PDAC patients, radiological diagnosis IPMN, and healthy volunteers using the ReCIVA® device between 10/2021-11/2022. VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups. A total of 156 participants (44% male, mean age 62.6 ± 10.6) were enrolled (54 PDAC, 42 IPMN, and 60 controls). Among the nine VOCs identified, two VOCs that showed differences between groups were dimethyl sulfide [0.73 vs 0.74 vs 0.94 arbitrary units (AU), respectively; P = 0.008] and acetone dimers (3.95 vs 4.49 vs 5.19 AU, respectively; P < 0.001). After adjusting for the imbalance parameters, PDAC showed higher dimethyl sulfide levels than the control and IPMN groups, with adjusted odds ratio (aOR) of 6.98 (95%CI: 1.15-42.17) and 4.56 (1.03-20.20), respectively (P < 0.05 both). Acetone dimer levels were also higher in PDAC compared to controls and IPMN (aOR: 5.12 (1.80-14.57) and aOR: 3.35 (1.47-7.63), respectively (P < 0.05 both). Acetone dimer, but not dimethyl sulfide, performed better than CA19-9 in PDAC diagnosis (AUROC 0.910 vs 0.796). The AUROC of acetone dimer increased to 0.936 when combined with CA19-9, which was better than CA19-9 alone (P < 0.05). Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants. Additional prospective studies are required to validate these findings.

Exhaled breath analyses for bronchial thermoplasty in severe asthma patients

BackgroundBronchial thermoplasty (BT) is a bronchoscopic treatment for severe asthma. Although multiple trials have demonstrated clinical improvement after BT, optimal patient selection remains a challenge and the mechanism of action is incompletely understood.The aim of this study was to examine whether exhaled breath analysis can contribute to discriminate between BT-responders and non-responders at baseline and to explore pathophysiological insights of BT. MethodsExhaled breath was collected from patients at baseline and six months post-BT. Patients were defined as responders or non-responders based on a half point increase in asthma quality of life questionnaire scores. Gas chromatography-mass spectrometry was used for volatile organic compounds (VOCs) detection and analyses. Analytical workflow consisted of: 1) detection of VOCs that differentiate between responders and non-responders and those that differ between baseline and six months post-BT, 2) identification of VOCs of interest and 3) explore correlations between clinical biomarkers and VOCs. ResultsData was available from 14 patients. Nonanal, 2-ethylhexanol and 3-thujol showed a significant difference in intensity between responders and non-responders at baseline (p = 0.04, p = 0.01 and p = 0.03, respectively). After BT, no difference was found in the compound intensity of these VOCs. A negative correlation was observed between nonanal and IgE and BALF eosinophils (r = −0.68, p < 0.01 and r = −0.61, p = 0.02 respectively) and 3-thujol with BALF neutrophils (r = −0.54, p = 0.04). ConclusionsThis explorative study identified discriminative VOCs in exhaled breath between BT responders and non-responders at baseline. Additionally, correlations were found between VOC's and inflammatory BALF cells. Once validated, these findings encourage research in breath analysis as a non-invasive easy to apply technique for identifying airway inflammatory profiles and eligibility for BT or immunotherapies in severe asthma.

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.

Metabolic insights at the finish line: deciphering physiological changes in ultramarathon runners through breath VOC analysis

Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjusted P-value < .05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath

BackgroundPathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. MethodsExhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). ResultsThe proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. ConclusionsExhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Direct Online Monitoring of Mouse Exhaled Breath for Diabetes Assessment Using Corona Ionization Mass Spectrometry

Since certain types of diabetes are reversible and curable in the early stages, early detection is crucial for effective treatment. Exhaled breath analysis, a noninvasive technique, is increasingly recognized for its potential in health monitoring, providing insights into an organism’s metabolic state more directly than blood glucose tests. This work developed an in situ method utilizing corona ion source mass spectrometry for real-time direct monitoring of volatile organic compounds (VOCs) in mouse exhaled breath. The method was used to track breath changes in two mouse models with streptozotocin-induced diabetes throughout disease progression. Notably, variations in certain VOCs in exhaled breath correlated with blood glucose levels (BGLs) during diabetes development. For instance, in the type 1 diabetes model, acetone levels remained stable within 8 days after hyperglycemia (not significant, n.s.) compared to those in controls but showed a significant increase after 10 days (p < 0.001). In the type 2 diabetes model, isopropanol levels decreased (p < 0.001) during the prediabetes phase, when BGL changes were not significant (n.s.) compared to those in the healthy state. These results indicate that specific substances in exhaled breath change prior to noticeable BGL alterations. This study thus established the viability of direct online mass spectrometry for breath analysis in early diabetes detection and showed that this technique has potential for identifying clear clinical markers for early diabetes screening.

Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics.

Volatile organic compounds (VOCs) have been studied as possible biomarkers in several diseases, including lung cancer. Early detection of cancer can improve long-term survival rates and the quality of life, so the study of VOCs in exhaled breath and urine has been increasing in recent years. This study aimed to assess the urinary VOCs that are modified after chemotherapy to identify those with the potential to be lung cancer biomarkers that can be monitored during treatment. Three urine samples from 10 men with stage IV lung adenocarcinoma were collected, as well as urine samples from 14 men with other types of cancer (control group). All samples were analyzed by headspace-solid phase microextraction gas chromatography coupled with mass spectrometry. A total of 21 urinary VOCs were found with different levels after the administration of chemotherapy, with 2-pentanone being one of those that significantly decreased. Furthermore, 2-pentanone and 3-hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate showed statistically significant differences with the control group. Chemotherapy administered to patients with advanced lung adenocarcinoma modified the volatile profile of urine. 2-Pentanone, a final product of the increased rate of fatty acid oxidation and protein hypermetabolism, significantly decreased after chemotherapy. Therefore, monitoring its urinary excretion could be very useful since its decrease over time could indicate an adequate response to chemotherapy and arrest of cancer development. Another VOC that could be a potential lung cancer biomarker is 3 hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate, whose origin may be due to inhibition of the propanoic acid metabolic pathway or increased aldehyde dehydrogenase activity.

Solid-Phase Microextraction (SPME) and Gas Chromatographic/Mass Spectrometry in Chronic Obstructive Pulmonary Disease (COPD): A Chemometric Approach

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition that often goes undiagnosed despite the availability of spirometry for diagnosis, and its exact prevalence remains uncertain. Exhaled breath has been proposed as a source of relevant health information, particularly Volatile Organic Compounds (VOCs), which can be easily obtained and applied in clinical practice. In this study, exhaled breath samples were collected from patients diagnosed with COPD of varying severity during their stable condition using specialized RTubeVOC tubes. Volatile compounds from the air samples were extracted using a 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber and the analysis was performed using gas chromatography/mass spectrometry (GC/MS) technique. The patients were divided into two groups based on their history of exacerbations, and the aim was to identify VOCs associated with the risk of future COPD exacerbation, thus allowing for more personalized and objective COPD treatment. Blood eosinophil content was also taken into consideration. A panel of distinguishing mass-spectral features was identified between the two patient groups. The discriminating exhaled molecules were heptane 2,2,4,6,6-pentamethyl, gamma-terpinene, 2-ethylhexanol, and undecane demonstrating the potential of analyzing VOCs in exhaled breath for the detection and management of COPD, offering a promising avenue to improve COPD management and treatment approaches.

Non-Invasive Method of Human Exhaled Breath Analysis for Diabetes Detection Using Bidirectional Long-Short-Term Memory Algorithm

Volatile organic compounds (VOCs) have the potential to be used as biomarkers for pathophysiological and physical abnormalities associated with several disorders. A promising non-invasive metabolic monitoring method is the Analysis of VOCs in exhaled breath. It may also be used to monitor the development of certain diseases and their early detection. Diabetes is a metabolic disease and a complicated syndrome. The relationship between oxidative stress, inflammatory syndrome, hypertension, and diabetes is complicated. This study describes the creation of an Internet of Things (IoT) based breath analyzer to identify and track diseases using exhaled breath. Diabetic breath biomarkers and breath analysis are the main topics of discussion. A group of 25 diabetic patients and 15 non-diabetic individuals were tested using this system. Data is initially gathered using the wired module and the Cool Term software. The system is created for both wired and wireless devices. A deep learning algorithm analyses the disease characteristics after data collection. It clearly distinguishes between samples with diabetes and those without with 84% accuracy. This technology could detect a non-transmissible or transmissible disease early, preventing infection to others.

Exhaled Breath Analysis Using Selected Ion Flow Tube Mass Spectrometry and Disease Severity in Heart Failure.

Exhaled breath volatile organic compounds (VOCs) are elevated in heart failure (HF). The ability of VOCs to predict long term cardiovascular mortality and morbidity has not been independently verified. In 55 patients admitted with acute decompensated heart failure (ADHF), we measured exhaled breath acetone and pentane levels upon admission and after 48 h of diuresis. In a separate cohort of 51 cardiac patients undergoing cardiopulmonary exercise testing (CPET), we measured exhaled breath acetone and pentane levels before and at peak exercise. In the ADHF cohort, admission acetone levels correlated with lower left ventricular ejection fraction (LVEF, r = -0.297, p = 0.035). Greater weight loss with diuretic therapy correlated with a greater reduction in both acetone levels (r = -0.398, p = 0.003) and pentane levels (r = -0.309, p = 0.021). In patients with above-median weight loss (≥4.5 kg), patients demonstrated significantly greater percentage reduction in acetone (59% reduction vs. 7% increase, p < 0.001) and pentane (23% reduction vs. 2% reduction, p = 0.008). In the CPET cohort, admission acetone and pentane levels correlated with higher VE/VCO2 (r = 0.39, p = 0.005), (r = 0.035, p = 0.014). However, there were no significant correlations between baseline or peak exercise acetone and pentane levels and peak VO2. In longitudinal follow-up with a median duration of 33 months, patients with elevated exhaled acetone and pentane levels experienced higher composite adverse events of death, ventricular assist device implantation, or orthotopic heart transplantation. In patients admitted with ADHF, higher exhaled breath acetone levels are associated with lower LVEF and poorer outcomes, and greater reductions in exhaled breath acetone and pentane tracked with greater weight loss. Exhaled acetone and pentane may be novel biomarkers in heart failure worthy of future investigation.

Detection of Volatile Organic Compounds (Voc) in COPD Patients and Its Correlation with Serum Leukotriene B4 and Neutrophil

In last few decades, the development of non-invasive method for detecting lung disease has emerged, such as Volatile Organic Compounds (VOC) examination. This study aims to analyze the difference of VOC in exhaled breath of stable COPD and control subjects, also the correlation between VOC with serum Leukotriene B4 and neutrophil.

Food and lifestyle impact on breath VOCs using portable mass spectrometer—pilot study across European countries

In the modern world, many people are changing old dietary and lifestyle habits to improve the quality of their living—to treat or just prevent possible diseases. The main goal of this pilot study was to assess the food and lifestyle impact on exhaled breath volatile organic compounds (VOCs) in various population groups. It was done by employing a recently validated portable membrane-inlet mass spectrometer—MIMS. Thus, the obtained results would also represent the additional confirmation for the employment of the new instrument in the breath analysis. The pilot study involved 151 participants across Europe, including people with overweight, obesity, type 2 diabetes mellitus, cardiovascular disease, people with poor-quality diet and professional athletes. Exhaled breath acetone, ethanol, isoprene, and n-pentane levels were determined in samples before the meal, and 120 min after the meal. Obtained basal ppbv values were mainly in accordance with previously reported, which confirms that MIMS instrument can be used in the breath analysis. Combining the quantified levels along with the information about the participants’ lifestyle habits collected via questionnaire, an assessment of the food and lifestyle impact was obtained. Notable alteration in examined VOC levels upon meal consumption was detected in more than 70% of all participants, with exception for isoprene, which was affected in about half of participants. Lifestyle parameters impact was examined using statistical analysis of variance (ANOVA) on ranks test. Statistically significant differences in basal breath VOC levels were observed among all examined population groups. Also, n-pentane and ethanol levels significantly differed in people of different ages, as well as acetone levels in people with different physical activity habits. These findings are promising for further, more focused research using MIMS technique in breath analysis.

Exhaled volatile organic compounds associated with risk factors for obstructive pulmonary diseases: a systematic review.

Asthma and COPD are among the most common respiratory diseases. To improve the early detection of exacerbations and the clinical course of asthma and COPD new biomarkers are needed. The development of noninvasive metabolomics of exhaled air into a point-of-care tool is an appealing option. However, risk factors for obstructive pulmonary diseases can potentially introduce confounding markers due to altered volatile organic compound (VOC) patterns being linked to these risk factors instead of the disease. We conducted a systematic review and presented a comprehensive list of VOCs associated with these risk factors. A PRISMA-oriented systematic search was conducted across PubMed, Embase and Cochrane Libraries between 2000 and 2022. Full-length studies evaluating VOCs in exhaled breath were included. A narrative synthesis of the data was conducted, and the Newcastle-Ottawa Scale was used to assess the quality of included studies. The search yielded 2209 records and, based on the inclusion/exclusion criteria, 24 articles were included in the qualitative synthesis. In total, 232 individual VOCs associated with risk factors for obstructive pulmonary diseases were found; 58 compounds were reported more than once and 12 were reported as potential markers of asthma and/or COPD in other studies. Critical appraisal found that the identified studies were methodologically heterogeneous and had a variable risk of bias. We identified a series of exhaled VOCs associated with risk factors for asthma and/or COPD. Identification of these VOCs is necessary for the further development of exhaled metabolites-based point-of-care tests in these obstructive pulmonary diseases.

Impact of breast density on classification of infrared spectroscopy for breath-based breast cancer screening.

e13558 Background: While mammography is currently the standard of care for breast cancer screening, dense breast tissue can significantly degrade results. Alternatively, infrared spectroscopy analysis of breath offers a highly sensitive method for identifying exhaled volatile organic compounds (VOCs), which may circumvent issues with breast density. Methods: Alveolar breath samples were collected onto Tenax TA sorbent tubes using a SohnoXB™ breath sampler. Using four desorb temperatures (75, 150, 225 and 300°C), absorption spectra were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a technique for measuring absorption coefficients due to VOCs in exhaled breath. Missing values in the absorption spectra were backfilled using interpolation, and the spectrum was min-max normalized and quadratic detrended. First and second derivatives of the preprocessed absorption spectra were used as features for a support vector machine machine-learning model. Features were ranked based on minimum redundancy maximum relevance (mRMR). The top 20 ranked features were selected to limit the potential for overfitting and then optimized. Model performance was validated using non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV to provide optimistic and pessimistic results, respectively. Results: Absorption spectra from 111 participants (71 positive, 40 control) were used. Of the positive subjects, 30 had low- and 31 had high-density breast tissue (measures missing for 10). Model performance is outlined. A subgroup analysis compared model performance for subjects with low- and high-density breast tissue for both non-nested and nested LOOCV models. Breast density data was not captured for control subjects thus they were not included in the subgroup analysis. Fisher’s exact test was performed to assess for significant difference between model performance for those with low- vs high-density breast tissue, resulting in a p-value of 1.00. Conclusions: Our results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer that is independent of breast density. Breath analysis may therefore become a new alternative or corroborative to mammography to support clinical decision-making. [Table: see text]

Breath Prints for Diagnosing Asthma in Children.

Electronic nose (e-nose) is a new technology applied for the identification of volatile organic compounds (VOC) in breath air. Measuring VOC in exhaled breath can adequately identify airway inflammation, especially in asthma. Its noninvasive character makes e-nose an attractive technology applicable in pediatrics. We hypothesized that an electronic nose could discriminate the breath prints of patients with asthma from controls. A cross-sectional study was conducted and included 35 pediatric patients. Eleven cases and seven controls formed the two training models (models A and B). Another nine cases and eight controls formed the external validation group. Exhaled breath samples were analyzed using Cyranose 320, Smith Detections, Pasadena, CA, USA. The discriminative ability of breath prints was investigated by principal component analysis (PCA) and canonical discriminative analysis (CDA). Cross-validation accuracy (CVA) was calculated. For the external validation step, accuracy, sensitivity and specificity were calculated. Duplicate sampling of exhaled breath was obtained for ten patients. E-nose was able to discriminate between the controls and asthmatic patient group with a CVA of 63.63% and an M-distance of 3.13 for model A and a CVA of 90% and an M-distance of 5.55 for model B in the internal validation step. In the second step of external validation, accuracy, sensitivity and specificity were 64%, 77% and 50%, respectively, for model A, and 58%, 66% and 50%, respectively, for model B. Between paired breath sample fingerprints, there were no significant differences. An electronic nose can discriminate pediatric patients with asthma from controls, but the accuracy obtained in the external validation was lower than the CVA obtained in the internal validation step.

Exhaled Volatile Organic Compounds for Early Prediction of Bronchopulmonary Dysplasia in Infants Born Preterm

To investigate the predictive performances of exhaled breath volatile organic compounds (VOCs) for development of bronchopulmonary dysplasia (BPD) in infants born preterm. Exhaled breath was collected from infants born <30 weeks' gestation at days 3 and 7 of life. Ion-fragments detected by gas-Chromatography-mass-spectrometry analysis were used to derive and internally validate a VOC prediction model for moderate or severe BPD at 36 weeks postmenstrual age. We tested the predictive performance of the National Institute of Child Health and Human Development (NICHD) clinical BPD prediction model with and without VOCs. Breath samples were collected from 117 infants (mean gestation 26.8 [±1.5] weeks). Thirty-three percent of the infants developed moderate or severe BPD. The VOC model showed a c-statistic of 0.89 (95% confidence interval [CI] 0.80-0.97) and 0.92 (95% CI 0.84-0.99)) for the prediction of BPD at days 3 and 7, respectively. Adding the VOCs to the clinical prediction model in non-invasive supported infants resulted in significant improvement in discriminative power on both days (day 3: c-statistic 0.83 versus 0.92, p-value 0.04; day 7: c-statistic 0.82 versus 0.94, p-value 0.03). This study showed that VOC profiles in exhaled breath of preterm infants on non-invasive support in the first week of life differ between those developing and not developing BPD. Adding VOCs to a clinical prediction model significantly improved its discriminative performance.

Analysis of a Broad Range of Carbonyl Metabolites in Exhaled Breath by UHPLC-MS.

Analysis of volatile organic compounds (VOCs) in exhaled breath (EB) has shown great potential for disease detection including lung cancer, infectious respiratory diseases, and chronic obstructive pulmonary disease. Although many breath sample collection and analytical methods have been developed for breath analysis, analysis of metabolic VOCs in exhaled breath is still a challenge for clinical application. Many carbonyl compounds in exhaled breath are related to the metabolic processes of diseases. This work reports a method of ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) for the analysis of a broad range of carbonyl metabolites in exhaled breath. Carbonyl compounds in the exhaled breath were captured by a fabricated silicon microreactor with a micropillar array coated with 2-(aminooxy)ethyl-N,N,N-trimethylammonium (ATM) triflate. A total of six subgroups consisting of saturated aldehydes and ketones, hydroxy-aldehydes, and hydroxy-ketones, unsaturated 2-alkenals, and 4-hydroxy-2-alkenals were identified in the exhaled breath. The combination of a silicon microreactor for the selective capture of carbonyl compounds with UHPLC-MS analysis may provide a quantitative method for the analysis of carbonyls to identify disease markers in exhaled breath.

P235 Patients with inflammatory ileal pouch anal anastomosis (IPAA) disorders are characterized by a distinct breath volatile organic compounds (VOC) metabolome profile

Abstract Background Following creation of an IPAA in patients with ulcerative colitis (UC), more than 60% of subjects develop inflammatory complications. The current objective assessment for inflammation of the pouch is limited to surrogate stool and blood biomarkers or endoscopy. The development of non-invasive and accurate biomarkers for the assessment of IPAA inflammation is an area of unmet need. Measurement of exhaled breath volatile organic metabolome compounds (VOCs) has shown promise as a biomarker for the diagnosis and monitoring of inflammatory disorders. We here aimed to characterize the pattern of VOCs in the exhaled breath of patients with an IPAA and assess whether VOC analysis is able to discriminate patients with endoscopically active IPAA inflammation from patients without IPAA inflammation. Methods This is a cross-sectional study of patients with an IPAA created for the management of UC. Exhaled breath samples were collected at time of endoscopic evaluation of the pouch and 97 VOC metabolites assessed via selective ion flow tube mass spectrometry (SIFT-MS). The IPAA cohort was dichotomized using the endoscopic pouch disease activity index (PDAI) into endoscopic PDAI of &amp;gt;= 4 (severe inflammation), or endoscopic PDAI score &amp;lt;= 1 (mild or no inflammation). Principle component analysis (PCA) was conducted to reveal the VOCs with the strongest discriminatory capability and principle component regression (PCR) was performed to assess the association of exhaled breath VOC analysis in differentiating the groups. Results Exhaled breath metabolome analysis was performed on 10 subjects with PDAI &amp;gt;=4 and 7 subjects with PDAI &amp;lt;=1. Demographics are provided in Table 1. PCA indicated robust discrimination of the two groups based on breath VOCs (Figure 1). 10 out of 97 VOCs were up-regulated in the PDAI &amp;gt;=4 group compared to control, including ammonia and hydrogen sulfide. Isopropenyltoluene, tetrachloroethylene, and 3-pentanone provided the highest contribution to differentiate between the cohorts (Figure 2). Receiver operative curve (ROC) analysis of the PCR model indicated an area under the curve (AUC) of 0.81 (0.61-99), suggesting a strong association of breath VOCs with inflammation of the pouch (Figure 1). Conclusion VOC exhaled breath metabolome analysis shows a strong ability to discriminate patients with severe endoscopic pouch inflammation from patients with minimal to no endoscopic inflammation. The differences in reported VOCs point toward metabolic differences in bacterial fermentation, lipid and carbohydrate metabolism, and an increase in reactive oxygen species in patients with endoscopic pouch inflammation. Validation studies addressing the role of VOC analysis for non-invasive disease assessment patients with IPAA are ongoing.

Membrane inlet mass spectrometry method for food intake impact assessment on specific volatile organic compounds in exhaled breath

This research work describes the development of a novel bioanalytical method for the assessment of food impact on selected exhaled breath volatile organic compounds (VOCs) using a fast and portable screening VOC prototype sensor based on membrane inlet mass spectrometry (MIMS). Method and sensor prototype functionality was verified by obtaining good response times, linearity in the examined concentration ranges, and sensitivity and repeatability for several breath VOCs—acetone, ethanol, n-pentane, and isoprene. A new VOC sensor prototype was also proven to be sensitive enough for selected breath VOC quantification with limits of detection at low part per billion (ppb) levels—5 ppb for n-pentane, 10 ppb for acetone and ethanol, and 25 ppb for isoprene. Food impact assessment was accomplished by tracking the levels of acetone, ethanol, n-pentane, and isoprene in exhaled breath samples collected from 50 healthy participants before the meal and 60 min and 120 min after the meal. For acetone, isoprene, and n-pentane, a larger impact was noticed 120 min after the meal, while for ethanol, it was after 60 min. Obtained VOC levels were in the expected concentration ranges. Mean values at all time points were ~ 500–900 ppb for acetone and ~ 400–600 ppb for ethanol. Most of the results for n-pentane were below 5 ppb, but the mean value for those which were detected was ~ 30 ppb. Along with samples, data about participants’ lifestyle were collected via a short questionnaire, which were compared against obtained VOC levels in order to reveal some significant correlations between habits of participants and their breath VOC levels.Graphical abstractPortable MS: monitoring of food impact on the levels of selected VOCs from exhaled breath