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

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

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).

A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.

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 ppbvvalues 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.

BACKGROUND: As a chronic occupational disease, silicosis could cause irreversible and incurable impair to the lung. The current diagnosis of silicosis relies on imaging of X-ray or CT, but these methods cannot detect lung lesions in the early stage of silicosis.
OBJECTIVE: To establish a regular screening and early diagnosis methods for silicosis, which could be helpful for the prevention and treatment of silicosis.
METHODS: A total of 161 subjects were enrolled in the study, including 69 patients with silicosis (SILs) and 92 healthy controls. The exhaled breath samples of the subjects were collected with breath sampler and Tedlar bag. The analysis of volatile organic compounds (VOCs) in exhaled breath was performed by solid-phase microextraction (SPME) combined with gas chromatography mass spectrometry (GC-MS).
RESULTS: After excluding the pollutants from sampling bags and instruments, 86 VOCs have been identified in the exhaled breath. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed for the screening of potential biomarkers of silicosis. Those components that related to smoking were also excluded from the biomarkers. Finally, nine possible biomarkers for silicosis were screened out, including 2,3-butanedione, ethyl acetate, chlorobenzene, o-cymene, 4-ethylhex-2-ynal, 3,5-dimethyl-3-heptanol, hydroquinone, phthalic anhydride and 5-(2-methylpropyl)nonane. Based on these biomarkers screened, a predicted model for silicosis was generated with the accuracy of 89.61%.
CONCLUSIONS: The nine biomarkers in exhaled breath were preliminarily screened out for the early diagnosis of silicosis, which can be helpful to the establishment of a noninvasive screening method for silicosis. Follow-up studies should be conducted to further verify these markers.

Isoprene is one of the most abundant and most frequently evaluated volatile organic compounds in exhaled breath. Recently, several individuals with background levels of exhaled isoprene have been identified. Here, case study data are provided for an individual, identified from a previous study, with this low prevalence phenotype. It is hypothesized that the individual will illustrate low levels of exhaled isoprene at rest and during exercise. At rest, the subject (7.1 ppb) shows background (μ= 14.2 ± 7.0 ppb) levels of exhaled isoprene while the control group illustrates significantly higher quantities (μ= 266.2 ± 72.3 ppb) via proton transfer reaction mass spectrometry (PTR-MS). The result, background levels of isoprene at rest, is verified by thermal desorption gas chromatography mass spectrometry (TD-GC-MS) collections with the individual showing -3.6 ppb exhaled isoprene while the room background containedμ= -4.1 ± 0.1 ppb isoprene. As isoprene has been shown previously to increase at the initiation of exercise, exercise bike experiments were performed with the individual identified with low isoprene, yielding low and invariant levels of exhaled isoprene (μ= 6.6 ± 0.1 ppb) during the exercise while control subjects illustrated an approximate 2.5-fold increase (preμ= 286.3 ± 43.8 ppb, exerciseμ= 573.0 ± 147.8 ppb) in exhaled isoprene upon exercise start. Additionally, exhaled breath bag data showed a significant decrease in isoprene (delta post/pre, p = 0.0078) of the control group following the exercise regimen. Finally, TD-GC-MS results for exhaled isoprene from the individual\'s family (mother, father, sister and maternal grandmother) illustrated that the mother and father exhibited isoprene values (28.5 ppb, 77.2 ppb) below control samples 95% confidence interval (μ= 166.8 ± 43.3 ppb) while the individual\'s sister (182.0 ppb) was within the control range. These data provide evidence for a large dynamic range in exhaled isoprene in this family. Collectively, these results provide additional data surrounding the existence of a small population of individuals with background levels of exhaled isoprene.

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

Electronic nose devices (EN) have been developed for detecting volatile organic compounds (VOCs). This study aimed to assess the ability of the MENT-EGAS prototype-based EN to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. This study was performed on a dairy farm using 11 (n = 11) multiparous Holstein-Friesian cows. The cows were divided into two groups housed in two different barns: group I included six lactating cows fed with a lactating diet (LD), and group II included 5 non-lactating late pregnant cows fed with a far-off diet (FD). Each group was offered 250 g of their respective diet; 10 min later, exhalated breath was collected for VOC determination. After this sampling, 4 cows from each group were offered 250 g of pellet concentrates. Ten minutes later, the exhalated breath was collected once more. VOCs were also measured directly from the feed\'s headspace, as well as from the environmental backgrounds of each. Principal component analyses (PCA) were performed and revealed clear discrimination between the two different environmental backgrounds, the two different feed headspaces, the exhalated breath of groups I and II cows, and the exhalated breath within the same group of cows before and after the feed intake. Based on these findings, we concluded that the MENT-EGAS prototype can recognize several error sources with accuracy, providing a novel EN technology that could be used in the future in precision livestock farming.

Cystic fibrosis (CF) is characterized by chronic respiratory infections which progressively decrease lung function over time. Affected individuals experience episodes of intensified respiratory symptoms called pulmonary exacerbations (PEx), which in turn accelerate pulmonary function decline and decrease survival rate. An overarching challenge is that there is no standard classification for PEx, which results in treatments that are heterogeneous. Improving PEx classification and management is a significant research priority for people with CF. Previous studies have shown volatile organic compounds (VOCs) in exhaled breath can be used as biomarkers because they are products of metabolic pathways dysregulated by different diseases. To provide insights on PEx classification and other CF clinical factors, exhaled breath samples were collected from 18 subjects with CF, with some experiencing PEx and others serving as a baseline. Exhaled breath was collected in Tedlar bags during tidal breathing and cryotransferred to headspace vials for VOC analysis by solid phase microextraction coupled to gas chromatography-mass spectrometry. Statistical significance testing between quantitative and categorical clinical variables displayed percent-predicted forced expiratory volume in one second (FEV1pp) was decreased in subjects experiencing PEx. VOCs correlating with other clinical variables (body mass index, age, use of highly effective modulator treatment (HEMT), and the need for inhaled tobramycin) were also explored. Two volatile aldehydes (octanal and nonanal) were upregulated in patients not taking the HEMT. VOCs correlating to potential confounding variables were removed and then analyzed by regression for significant correlations with FEV1pp measurements. Interestingly, the VOC with the highest correlation with FEV1pp (3,7-dimethyldecane) also gave the lowestp-value when comparing subjects at baseline and during PEx. Other VOCs that were differentially expressed due to PEx that were identified in this study include durene, 2,4,4-trimethyl-1,3-pentanediol 1-isobutyrate and 5-methyltridecane. Receiver operator characteristic curves were developed and showed 3,7-dimethyldecane had higher ability to classify PEx (area under the curve (AUC) = 0.91) relative to FEV1pp values at collection (AUC = 0.83). However, normalized ΔFEV1pp values had the highest capability to distinguish PEx (AUC = 0.93). These results show that VOCs in exhaled breath may be a rich source of biomarkers for various clinical traits of CF, including PEx, that should be explored in larger sample cohorts and validation studies.