Real-time Breath Analysis Research Articles

Real-time breath analysis towards a healthy human breath profile.

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

Preservation of exhaled breath samples for analysis by off-line SESI-HRMS: proof-of-concept study

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) is an established technique in the field of breath analysis characterized by its short analysis time, as well as high levels of sensitivity and selectivity. Traditionally, SESI-HRMS has been used for real-time breath analysis, which requires subjects to be at the location of the analytical platform. Therefore, it limits the possibilities for an introduction of this methodology in day-to-day clinical practice. However, recent methodological developments have shown feasibility on the remote sampling of exhaled breath in Nalophan® bags prior to measurement using SESI-HRMS. To further explore the range of applications of this method, we conducted a proof-of-concept study to assess the impact of the storage time of exhaled breath in Nalophan® bags at different temperatures (room temperature and dry ice) on the relative intensities of the compounds. In addition, we performed a detailed study of the storage effect of 27 aldehydes related to oxidative stress. After 2 h of storage, the mean of intensity of all m/z signals relative to the samples analyzed without prior storage remained above 80% at both room temperature and dry ice. For the 27 aldehydes, the mean relative intensity losses were lower than 20% at 24 h of storage, remaining practically stable since the first hour of storage following sample collection. Furthermore, the mean relative intensity of most aldehydes in samples stored at room temperature was higher than those stored in dry ice, which could be related to water vapor condensation issues. These findings indicate that the exhaled breath samples could be preserved for hours with a low percentage of mean relative intensity loss, thereby allowing more flexibility in the logistics of off-line SESI-HRMS studies.

A benchmark study of data normalisation methods for PTR-TOF-MS exhaled breath metabolomics

Volatilomics is the branch of metabolomics dedicated to the analysis of volatile organic compounds in exhaled breath for medical diagnostic or therapeutic monitoring purposes. Real-time mass spectrometry (MS) technologies such as proton transfer reaction (PTR) MS are commonly used, and data normalisation is an important step to discard unwanted variation from non-biological sources, as batch effects and loss of sensitivity over time may be observed. As normalisation methods for real-time breath analysis have been poorly investigated, we aimed to benchmark known metabolomic data normalisation methods and apply them to PTR-MS data analysis. We compared seven normalisation methods, five statistically based and two using multiple standard metabolites, on two datasets from clinical trials for COVID-19 diagnosis in patients from the emergency department or intensive care unit. We evaluated different means of feature selection to select the standard metabolites, as well as the use of multiple repeat measurements of ambient air to train the normalisation methods. We show that the normalisation tools can correct for time-dependent drift. The methods that provided the best corrections for both cohorts were probabilistic quotient normalisation and normalisation using optimal selection of multiple internal standards. Normalisation also improved the diagnostic performance of the machine learning models, significantly increasing sensitivity, specificity and area under the receiver operating characteristic (ROC) curve for the diagnosis of COVID-19. Our results highlight the importance of adding an appropriate normalisation step during the processing of PTR-MS data, which allows significant improvements in the predictive performance of statistical models. Clinical trials: VOC-COVID-Diag (EudraCT 2020-A02682-37); RECORDS trial (EudraCT 2020-000296-21).

Development and integration of a hierarchical Pd/WO3 acetone-sensing device for real-time exhaled breath monitoring with disposable face mask

This study introduces an inventive acetone-sensing device seamlessly integrated into a disposable face mask, enabling real-time continuous breath monitoring. The sensor demonstrates exceptional sensitivity, registering a response of 8.22 at 1 ppm and an impressive sensor response of 57.33 at 100 ppm of acetone concentration. Particularly noteworthy is the remarkable lower limit of detection (LOD) of 0.076 ppm within the concentration range of 0.1–0.8 ppm, underscored by a robust R2 value of 0.994. To validate practicality, the Pd/WO3 sensor was fabricated onto cellulose paper and utilized for real-time breath analysis, yielding a substantial sensor response of 1.70 at 8 vol% (equivalent to a single exhale breath volume). The unique design incorporates a built-in disposable face mask, facilitating dependable and convenient real-time breath analysis. Additionally, this research explores the profound impact of introducing acetone and Pd atoms on the energy levels and dipole moments. The species elucidated through density functional theory (DFT) investigations encompassing WO3, WO3-acetone, Pd-WO3, and Pd-WO3-acetone species. This work presents an innovative and cost-effective approach for developing a portable, non-invasive, and highly sensitive acetone-sensing device, effectively integrated into a disposable face mask for real-time breath analysis. This pioneering technology holds immense potential for various applications in healthcare and beyond.

Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents.

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19.

Although rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mass spectrometry breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19. In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata. We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After using machine learning algorithms to process breath analysis data and further enhancing the model using patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants with a sensitivity of 98%, a specificity of 74%, a negative predictive value of 98%, a positive predictive value of 72% and an area under the receiver operating characteristic curve of 0.961. The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by COVID-19 vaccination status. Real-time, noninvasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening.

UHPLC-MS/MS-Based Identity Confirmation of Amino Acids Involved in Response to and Side Effects from Antiseizure Medications.

Real-time breath analysis using secondary electrospray ionization coupled with high-resolution mass spectrometry is a fast and noninvasive method to access the metabolic state of a person. However, it lacks the ability to unequivocally assign mass spectral features to compounds due to the absence of chromatographic separation. This can be overcomed by using exhaled breath condensate and conventional liquid chromatography-mass spectrometry (LC-MS) systems. In this study, to the best of our knowledge, we confirm for the first time the presence of six amino acids (GABA, Oxo-Pro, Asp, Gln, Glu, and Tyr) previously reported to be involved in response to and side effects from antiseizure medications in exhaled breath condensate and by extension in exhaled human breath. Raw data are publicly available at MetaboLights with the accession number MTBLS6760.

"Did you eat a MOLEcule today?" An Interactive Demonstration of Molecular Weight with Real-Time Breath Analysis Using Mass Spectrometry for All Ages.

Although mass spectrometry is a widely used analytical tool, age-appropriate, interactive outreach activities for laboratory visitors, especially children, are lacking. The presented interactive demonstration, "Did you eat a MOLEcule today?", introduces all ages to molecular weight concepts and mass spectrometry in a research laboratory, while connecting the concepts to real-world applications. Through real-time breath analysis, participants explore the concepts of molecular weight, electrostatic field manipulation of charged molecules, and analyte identification by mass analysis. This module is rapid and highly adaptable for outreach activities but also includes age- or classroom-appropriate variations to decrease or increase difficulty levels. The presented interactive demonstration has repeatedly been implemented, with over 2300 participants during six annual "Take Our Daughters & Sons to Work Day" and two corporate "Family Day" outreach activities, successfully engaging, exciting, and educating both kids and parents.

MXene/MWCNT electronic fabric with enhanced mechanical robustness on humidity sensing for real-time respiration monitoring

Humidity sensing-based wearable respiration monitoring systems have promising application prospects in providing the critical health information. Currently, due to the inherent flexibility and compatibility, fabric-based wearables have attracted tremendous interest in humidity sensing. Herein, we proposed a humidity sensor made from fabric coated with MXene and multi-walled carbon nanotubes (MWCNT). The sensor showed 265% response at 90% relative humidity (RH) and great response stability under deformation. The humidity response of MXene/MWCNT fabric (MC-fabric) sensor only varied 7% under stretch, which is greatly alleviated compared with pristine MXene fabric sensor (35% under stretch). The MC-fabric sensor, integrated into a mask, can accurately identify diverse human respiration patterns despite the deformation caused by motions. This work not only offers a facile modification strategy to enhance the sensing robustness under deformation, but also provides a feasible method for real-time breath analysis.

Comparison of Plasma Ionization- and Secondary Electrospray Ionization- High-resolution Mass Spectrometry for Real-time Breath Analysis

Real-time breath analysis by high-resolution mass spectrometry (HRMS) is a promising method to noninvasively retrieve relevant biochemical information. In this work, we conducted a head-to-head comparison of two ionization techniques: Secondary electrospray ionization (SESI) and plasma ionization (PI), for the analysis of exhaled breath. Two commercially available SESI and PI sources were coupled to the same HRMS device to analyze breath of two healthy individuals in a longitudinal study. We analyzed 58 breath specimens in both platforms, leading to 2,209 and 2,296 features detected by SESI-HRMS and by PI-HRMS, respectively. 60% of all the mass spectral features were detected in both platforms. However, remarkable differences were noted in terms of the signal-to-noise ratio (S/N), whereby the median (interquartile range, IQR) S/N ratio for SESI-HRMS was 115 (IQR = 408), whereas for PI-HRMS it was 5 (IQR = 5). Differences in the mass spectral profiles for the same samples make the inter-comparability of both techniques problematic. Overall, we conclude that both techniques are excellent for real-time breath analysis because of the very rich mass spectral fingerprints. However, further work is needed to fully understand the exact metabolic insights one can gather using each of these platforms.

Personalised therapeutic management of epileptic patients guided by pathway-driven breath metabolomics

BackgroundTherapeutic management of epilepsy remains a challenge, since optimal systemic antiseizure medication (ASM) concentrations do not always correlate with improved clinical outcome and minimal side effects. We tested the feasibility of noninvasive real-time breath metabolomics as an extension of traditional therapeutic drug monitoring for patient stratification by simultaneously monitoring drug-related and drug-modulated metabolites.MethodsThis proof-of-principle observational study involved 93 breath measurements of 54 paediatric patients monitored over a period of 2.5 years, along with an adult’s cohort of 37 patients measured in two different hospitals. Exhaled breath metabolome of epileptic patients was measured in real time using secondary electrospray ionisation–high-resolution mass spectrometry (SESI–HRMS).ResultsWe show that systemic ASM concentrations could be predicted by the breath test. Total and free valproic acid (VPA, an ASM) is predicted with concordance correlation coefficient (CCC) of 0.63 and 0.66, respectively. We also find (i) high between- and within-subject heterogeneity in VPA metabolism; (ii) several amino acid metabolic pathways are significantly enriched (p < 0.01) in patients suffering from side effects; (iii) tyrosine metabolism is significantly enriched (p < 0.001), with downregulated pathway compounds in non-responders.ConclusionsThese results show that real-time breath analysis of epileptic patients provides reliable estimations of systemic drug concentrations along with risk estimates for drug response and side effects.

Novel barium hexaferrite based highly selective and stable trace ammonia sensor for detection of renal disease by exhaled breath analysis

Exhaled human breath bears the fingerprints of multifarious pathophysiological conditions, and diseases. In this paper we report for the first time a highly sensitive, selective, and stable barium hexaferrite based sensor for the detection of trace ammonia vapor in exhaled human breath, the biomarker for renal diseases. Barium hexaferrite nanoparticles were synthesized by a facile solid-state reaction route. The as prepared nanopowder and the sensor film were well characterized by using XRD, FESEM, TEM, EDX, XPS, BET, and I-V measurements. The fabricated sensor delineates p-type behavior and the capability to detect ammonia down to 0.2 ppm with reasonably high response of ∼1.46 folds. Further, the sensor showed remarkable response of ∼2.34 folds towards 1 ppm ammonia. The sensor is practically insensitive towards similar concentrations of other major interfering breath volatiles, viz. acetone, ethanol, and saturated moisture. Also, the sensor exhibited fast response (∼2.88 s), and recovery (∼39.4 s) times ensuring real time breath analysis. Finally, long-term stability of the sensor for more than three months renders it suitable for commercial applications.

Real-time breath analysis of exhaled compounds upon peppermint oil ingestion by secondary electrospray ionization-high resolution mass spectrometry: technical aspects

Breath analysis by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) has potential for clinical diagnosis and drug monitoring. However, there is still a lack of benchmarking data that shows the capability of this technique and allows comparability with other breath analysis techniques. In this regard, the goal of this study was the identification of volatile compounds upon ingestion of a specific peppermint oil capsule to get benchmark data for real-time breath analysis with SESI-HRMS. This was done in the framework of a consortium set up by the International Association of Breath Research (IABR), aimed at comparing several analytical instruments for breath analysis.Breath temporal profiles of two subjects were analyzed with SESI-HRMS before and after ingestion of a peppermint oil capsule. The measurements were performed at two different locations using identical SESI-HRMS platforms to allow for comparability and benchmarking. Remarkably, along with the four major compounds (monoterpenes/cineole, menthone, menthofuran and menthol) reported by other members of the consortium, we detected 57 additional features significantly associated (ρ > 0.8) with the peppermint oil capsule, suggesting that this relatively simple intervention might trigger a more complex metabolic cascade than initially expected. This observation was made on both sites. Additional replicate experiments for one of the subjects suggested that a core of 35–40 unique molecules are consistently detected in exhaled breath upon ingestion of the capsule. In addition, we illustrate the analytical capabilities of real-time SESI-HRMS/MS to assist in the identification of unknown compounds. The results outlined herein showcase the performance of SESI-HRMS and enable comparison with other breath analysis techniques. Along with that, they strengthen the potential of this analytical technique for non-invasive drug monitoring and clinical diagnostic purposes.

Non-Invasive Assessment of Metabolic Adaptation in Paediatric Patients Suffering from Type 1 Diabetes Mellitus.

An analysis of exhaled volatile organic compounds (VOC) may deliver systemic information quicker than available invasive techniques. Metabolic aberrations in pediatric type 1 diabetes (T1DM) are of high clinical importance and could be addressed via breathomics. Real-time breath analysis was combined with continuous glucose monitoring (CGM) and blood tests in children suffering from T1DM and age-matched healthy controls in a highly standardized setting. CGM and breath-resolved VOC analysis were performed every 5 minutes for 9 hours and blood was sampled at pre-defined time points. Per participant (n = 44) food intake and physical activity were identical and a total of 22 blood samples and 93 minutes of breath samples were investigated. The inter-individual variability of glucose, insulin, glucagon, leptin, and soluble leptin receptor relative to food intake differed distinctly between patients and controls. In T1DM patients, the exhaled amounts of acetone, 2-propanol, and pentanal correlated to glucose concentrations. Of note, the strength of these correlations strongly depended on the interval between food intake and breath sampling. Our data suggests that metabolic adaptation through postprandial hyperglycemia and related oxidative stress is immediately reflected in exhaled breath VOC concentrations. Clinical translations of our findings may enable point-of-care applicability of online breath analysis towards personalized medicine.

An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N2O.

A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) was demonstrated for simultaneous measurements of exhaled carbon monoxide (CO) and nitrous oxide (N2O). The sampling pressure was optimized to improve the sensitivity, the optimal pressure was determined to be 150 mbar based on an optical density analysis of simulated and measured absorption spectra. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network was developed and proposed for real-time exhaled breath analysis in order to perform fast and high precision on-line measurements. The detection limits (1σ) of 1.14 ppb and 1.12 ppb were experimentally achieved for CO and N2O detection, respectively. Typical concentrations of exhaled CO and N2O from smokers and non-smokers were analyzed. The experimental results indicated that the state-of-the-art CW-QCL based sensor has a great potential for non-invasive, on-line identification and quantification of biomarkers in human breath.

Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry

Despite the attractiveness of breath analysis as a non-invasive means to retrieve relevant metabolic information, its introduction into routine clinical practice remains a challenge. Among all the different analytical techniques available to interrogate exhaled breath, secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) offers a number of advantages (e.g., real-time, yet wide, metabolome coverage) that makes it ideal for untargeted and targeted studies. However, so far, SESI-HRMS has relied mostly on lab-built prototypes, making it difficult to standardize breath sampling and subsequent analysis, hence preventing further developments such as multi-center clinical studies. To address this issue, we present here a number of new developments. In particular, we have characterized a new SESI interface featuring real-time readout of critical exhalation parameters such as CO2, exhalation flow rate, and exhaled volume. Four healthy subjects provided breath specimens over a period of 1 month to characterize the stability of the SESI-HRMS system. A first assessment of the repeatability of the system using a gas standard revealed a coefficient of variation (CV) of 2.9%. Three classes of aldehydes, namely 4-hydroxy-2-alkenals, 2-alkenals and 4-hydroxy-2,6-alkedienals―hypothesized to be markers of oxidative stress―were chosen as representative metabolites of interest to evaluate the repeatability and reproducibility of this breath analysis analytical platform. Median and interquartile ranges (IQRs) of CVs for CO2, exhalation flow rate, and exhaled volume were 3.2% (1.5%), 3.1% (1.9%), and 5.0% (4.6%), respectively. Despite the high repeatability observed for these parameters, we observed a systematic decay in the signal during repeated measurements for the shorter fatty aldehydes, which eventually reached a steady state after three/four repeated exhalations. In contrast, longer fatty aldehydes showed a steady behavior, independent of the number of repeated exhalation maneuvers. We hypothesize that this highly molecule-specific and individual-independent behavior may be explained by the fact that shorter aldehydes (with higher estimated blood-to-air partition coefficients; approaching 100) mainly get exchanged in the airways of the respiratory system, whereas the longer aldehydes (with smaller estimated blood-to-air partition coefficients; approaching 10) are thought to exchange mostly in the alveoli. Exclusion of the first three exhalations from the analysis led to a median CV (IQR) of 6.7 % (5.5 %) for the said classes of aldehydes. We found that such intra-subject variability is in general much lower than inter-subject variability (median relative differences between subjects 48.2%), suggesting that the system is suitable to capture such differences. No batch effect due to sampling date was observed, overall suggesting that the intra-subject variability measured for these series of aldehydes was biological rather than technical. High correlations found among the series of aldehydes support this notion. Finally, recommendations for breath sampling and analysis for SESI-HRMS users are provided with the aim of harmonizing procedures and improving future inter-laboratory comparisons.Graphical abstract

Molecular breath analysis supports altered amino acid metabolism in idiopathic pulmonary fibrosis.

Diagnosis of idiopathic pulmonary fibrosis (IPF) is complex and its pathogenesis is poorly understood. Recent findings indicate elevated levels of proline and other amino acids in lung tissue of IPF patients which may also be of diagnostic value. Following these findings, we hypothesized that such altered metabolic profiles would be mirrored in exhaled breath and could therefore be captured non-invasively in real time. We aimed to validate these results using real-time exhaled breath analysis by secondary electrospray ionization-mass spectrometry, which can provide a non-invasive, painless and fast insight into the metabolism. Breath analysis was performed in a matched 1:1 case-control study involving 21 patients with IPF and 21 control subjects. We found significantly (P < 0.05) elevated levels of proline, 4-hydroxyproline, alanine, valine, leucine/isoleucine and allysine in breath of IPF patients, whereas pyroglutamic acid and phenylalanine did not show significant differences. This coincides with the amino acid's abundance in pulmonary tissue indicating that our observations reflect progressing fibrosis. In addition, amino acid levels correlated across subjects, further supporting a common underlying pathway. We were able to obtain a cross-validated area under the curve of 0.86, suggesting that these increased amino acid levels in exhaled breath have the potential to be used as biomarkers for IPF. We could validate previous findings of elevated lung tissue amino acid levels in IPF and show that online breath analysis might be a practical tool for a rapid screening for IPF.

Real-Time Breath Analysis Reveals Specific Metabolic Signatures of COPD Exacerbations

Exacerbations of COPD are defined by acute worsening of respiratory symptoms leading to a change in therapy. Identifying altered metabolic processes in patients at risk for future exacerbations is desirable for treatment optimization, the development of new therapeutic strategies, and perhaps diagnostic value. We aimed to identify affected pathways using the profiles of volatile organic compounds in exhaled breath from patients with COPD with and without frequent exacerbations (≥ 2 exacerbations within the past 12months). In this matched cohort study, exhaled breath profiles from patients with COPD and frequent exacerbations ("frequent exacerbators") and without frequent exacerbations ("nonfrequent exacerbators") were analyzed during an exacerbation-free interval using real-time secondary electrospray ionization high-resolution mass spectrometry. We analyzed exhaled breath from 26 frequent exacerbators and 26 nonfrequent exacerbators that were matched in terms of age, sex, and smoking history. To obtain new pathophysiological insights, we investigated significantly altered metabolites, which can be assigned to specific pathways. Metabolites were identified by using a Wilcoxon rank-sum test. Metabolite levels from the ω-oxidation pathway, namely ω-hydroxy, ω-oxo, and dicarboxylic acids, were consistently decreased in frequent exacerbators. Additionally, several new nitro-aromatic metabolites, which were significantly increased in frequent exacerbators, were identified. Real-time breath analysis by secondary electrospray high-resolution mass spectrometry allows molecular profiling of exhaled breath, providing insights about ongoing biochemical processes in patients with COPD at risk for exacerbations. ClinicalTrials.gov; No.: NCT02186639; URL: www.clinicaltrials.gov.

Positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath; however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (~30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers; (ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MS-based breath analysis.

Hepatobiliary MRI: Signal intensity based assessment of liver function correlated to 13C-Methacetin breath test

Gadoxetic acid (Gd-EOB-DTPA) is a paramagnetic MRI contrast agent with raising popularity and has been used for evaluation of imaging-based liver function in recent years. In order to verify whether liver function as determined by real-time breath analysis using the intravenous administration of 13C-methacetin can be estimated quantitatively from Gd-EOB-DTPA-enhanced MRI using signal intensity (SI) values. 110 patients underwent Gd-EOB-DTPA-enhanced 3-T MRI and, for the evaluation of liver function, a 13C-methacetin breath test (13C-MBT). SI values from before (SIpre) and 20 min after (SIpost) contrast media injection were acquired by T1-weighted volume-interpolated breath-hold examination (VIBE) sequences with fat suppression. The relative enhancement (RE) between the plain and contrast-enhanced SI values was calculated and evaluated in a correlation analysis of 13C-MBT values to SIpost and RE to obtain a SI-based estimation of 13C-MBT values. The simple regression model showed a log-linear correlation of 13C-MBT values with SIpost and RE (p < 0.001). Stratified by 3 different categories of 13C-MBT readouts, there was a constant significant decrease in both SIpost (p ≤ 0.002) and RE (p ≤ 0.033) with increasing liver disease progression as assessed by the 13C-MBT. Liver function as determined using real-time 13C-methacetin breath analysis can be estimated quantitatively from Gd-EOB-DTPA-enhanced MRI using SI-based indices.