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Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

In 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged to cause high viral infectivity and severe respiratory illness in humans (COVID-19). Worldwide, limited pandemic mitigation strategies, including lack of diagnostic test availability, resulted in COVID-19 overrunning health systems and spreading throughout the global population. Currently, proximal respiratory tract (PRT) specimens such as nasopharyngeal swabs are used to diagnose COVID-19 because of their relative ease of collection and applicability in large scale screening. However, localization of SARS-CoV-2 in the distal respiratory tract (DRT) is associated with more severe infection and symptoms. Exhaled breath condensate (EBC) is a sample matrix comprising aerosolized droplets originating from alveolar lining fluid that are further diluted in the DRT and then PRT and collected via condensation during tidal breathing. The COVID-19 pandemic has resulted in recent resurgence of interest in EBC collection as an alternative, non-invasive sampling method for the staging and accurate detection of SARS-CoV-2 infections. Herein, we review the potential utility of EBC collection for detection of SARS-CoV-2 and other respiratory infections. While much remains to be discovered in fundamental EBC physiology, pathogen-airway interactions, and optimal sampling protocols, EBC, combined with emerging detection methods, presents a promising non-invasive sample matrix for detection of SARS-CoV-2.

Blood glucose monitoring (BGM) is the most important part of diabetes management. In classical BGM, glucose measurement by test strips involves invasive finger pricking. We present results of a clinical study that focused on a non-invasive approach based on volatile organic compounds (VOCs) in exhaled breath. Main objective was the discovery of markers for prediction of blood glucose levels (BGLs) in diabetic patients. Exhaled breath was measured repeatedly in 60 diabetic patients (30 type 1, 30 type 2) in fasting state and after a standardized meal. Proton transfer reaction time of flight mass spectrometry was used to sample breath every 15 min for a total of 6 h. BGLs were tested in parallel via BGM test strips. VOC signals were plotted against glucose trends for each subject to identify correlations. Exhaled indole (a bacterial metabolite of tryptophan) showed significant mean correlation to BGL (with negative trend) and significant individual correlation in 36 patients. The type of diabetes did not affect this result. Additional experiments of one healthy male subject by ingestion of lactulose and ¹³C-labeled glucose (n = 3) revealed that exhaled indole does not directly originate from food digestion by intestinal microbiota. As indole has been linked to human glucose metabolism, it might be a tentative marker in breath for non-invasive BGM. Clinical studies with greater diversity are required for confirmation of such results and further investigation of metabolic pathways.

Background. Radiation exposure causes oxidative stress, eliciting production of metabolites that are exhaled in the breath as volatile organic compounds (VOCs). We evaluated breath VOCs as potential biomarkers for use in radiation biodosimetry. Methods. Five anesthetized non-human primates receive total body irradiation (TBI) of three daily fractions of 120 cGy per day for three days, resulting in a cumulative dose of 10.8 Gy. Breath samples were collected prior to irradiation and after each radiation fraction, and analyzed with gas chromatography mass spectrometry. Results. TBI elicited a prompt and statistically significant increase in the abundance of several hundred VOCs in the breath, including some that were increased more than five-fold, with100% sensitivity and 100% specificity for radiation exposure. The most significant breath VOC biomarkers of radiation mainly comprised straight-chain n-alkanes (e.g. hexane), as well as methylated alkanes (e.g. 3-methyl-pentane) and alkane derivatives (e.g. 2-butyl-1-octanol), consistent with metabolic products of oxidative stress. An unidentified breath VOC biomarker increased more than ten-fold following TBI, and rose linearly with the total cumulative dose of radiation (R² = 0.92). Conclusions. TBI of non-human primates elicited increased production of breath VOCs consistent with increased oxidative stress. These findings provide a rational basis for further evaluation of breath VOC biomarkers in human radiation biodosimetry.

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as support vector machines, k-nearest neighbours and various variations of neural networks for the detection of diabetes in patient samples and simulated artificial breath samples.

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

Respiratory and metabolic diseases in livestock cost the agriculture sector billions each year, with delayed diagnosis a key exacerbating factor. Previous studies have shown the potential for breath analysis to successfully identify incidence of disease in a range of livestock. However, these techniques typically involve animal handling, the use of nasal swabs or fixing a mask to individual animals to obtain a sample of breath. Using a cohort of 26 cattle as an example, we show how the breath of individual animals within a herd can be monitored using a passive sampling system, where no such handling is required. These benefits come at the cost of the desired breath samples unavoidably mixed with the complex cocktail of odours that are present within the cattle shed. Data were analysed using positive matrix factorisation (PMF) to identify and remove non-breath related sources of volatile organic compounds. In total three breath factors were identified (endogenous-, non-endogenous breath and rumen) and seven factors related to other sources within and around the cattle shed (e.g. cattle feed, traffic, urine and faeces). Simulation of a respiratory disease within the herd showed that the abnormal change in breath composition was captured in the residuals of the ten factor PMF solution, highlighting the importance of their inclusion as part of the breath fraction. Increasing the number of PMF factors to 17 saw the identification of a 'diseased' factor, which coincided with the visits of the three 'diseased' cattle to the breath monitor platform. This work highlights the important role that factor analysis techniques can play in analysing passive breath monitoring data.

Oral halitosis is characterized by a foul, unpleasant breath that emanates from the oral cavity due to local or systemic conditions. Approximately 90% of offensive odors are caused by volatile sulfur compounds (VSCs). L-cysteine, used as a test solution to control bad breath, induces the formation of VSCs and serves as a preliminary rinse. The study aim was to investigate the effectiveness of L-cysteine solution in differentiating the origin of oral halitosis using a gas chromatography apparatus. Methods: In total, 37 patients with an average age of 49.56 years were evaluated and divided into two groups: halimetry before the use of L-cysteine (n = 37) and halimetry after the use of L-cysteine (n = 37). Patients over 18 years of age, without severe systemic health impairment or infectious/contagious diseases, and who did not use medicines that influenced their breath were included. Halimetry was performed using the OralCroma™ device. In the halimetry before the use of L-cysteine group, 5.40%, 5.40%, and 64.86% of the patients had high levels of sulfide, methyl mercaptan, and dimethyl sulfide, respectively. After the use of L-cysteine, 48.64%, 8.10%, and 37.84% of the patients had high levels of sulfide, methyl mercaptan, and dimethyl sulfide. In this study, L-cysteine proved to be important for the assessment of oral halitosis and effective in differentiating the origin of oral halitosis; therefore, this compound could be used for the differential diagnosis of oral halitosis origin using the OralChroma™ device.

A key component of the differential diagnosis of isolated hyperbilirubinemia (HB) is distinguishing between hemolytic and non-hemolytic types. Routine hemolysis screening markers have unsatisfactory sensitivity and specificity. Erythrocyte (RBC) lifespan shortening, the gold standard marker of hemolysis, is seldomly measured due to the cumbersome and protracted nature of standard methods. A new Levitt's CO breath test method may enable simple, rapid RBC lifespan measurement. In this pilot prospective diagnostic study, Levitt's CO breath test was evaluated to discriminate hemolytic from non-hemolytic HB in adults. One hundred and thirty eligible non-smoking adult patients who were aged 18 or older, referred for chronic (>6 months) isolated HB or had a known diagnosis of isolated HB of a rare cause, were recruited, including 77 with non-hemolytic HB and 53 with hemolytic HB. ROC curve analysis was applied to determine the optimal cutoff for discriminating between hemolytic and non-hemolytic HB, and the performance was calculated. Results showed that the mean RBC lifespan in non-hemolytic HB (93 ± 26 d) was reduced (p = 0.001 vs. normal reference value of 126 d), but longer than that in hemolytic HB (36 ± 17 d; p = 0.001). RBC lifespans did not differ significantly between 26 patients with simple hemolytic HB (32 ± 14 d) and 27 patients with a Gilbert syndrome comorbidity (40 ± 18 d). ROC curve analysis revealed an optimal lifespan cutoff for discriminating between hemolytic and non-hemolytic HB of 60 d (AUC = 0.982), with a diagnostic accuracy of 95.4%, 94.3% sensitivity and 96.1% specificity respectively. These results indicate that Levitt's CO breath test seems to be very sensitive and specific for detecting hemolysis in adult patients with chronic isolated HB, and could enable simple, rapid, and reliable differential diagnosis of isolated HB. A large-scale validation study of the method is warranted.

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.

The real-time PCR (RT-PCR) on nasopharyngeal swabs (NPS) is the gold standard for the diagnosis of SARS-CoV-2. The exhaled breath condensate (EBC) is used to perform collection of biological fluid condensed in a refrigerated device from deep airways' exhaled air. We aimed to verify the presence of SARS-CoV-2 virus in the EBC from patients with confirmed SARS-CoV-2 infection by RT-PCR, and to determine if the EBC may represent a valid alternative to the NPS. Seventeen consecutive patients admitted to the Emergency Department of the Policlinico were enrolled in the present study with RT-PCR, clinical and radiological evidence of SARS-CoV-2. Within 24 h from the NPS collection the EBC collection was performed on SARS-CoV-2 positive patients. Informed written consent was gathered and the Ethic Committee approved the study. The mean age of patients was 60 years (24–92) and 64.7% (11/17) were male. Patient n.9 and n.17 died. All NPS resulted positive for SARS-CoV-2 at RT-PCR. RT-PCR on EBC resulted negative for all but one patients (patient n.12). In this study we did not find any correlation between positive NPS and the EBC in all but one patients enrolled. Based on these data which greatly differ from previous reports on the topic, this study opens several questions related to small differences in the complex process of EBC collection and how EBC could be really standardized for the diagnosis of SARS-CoV-2 infection. Further studies will be warranted to deepen this topic.

Volatile organic compounds (VOCs) in exhaled breath have the potential to be used as biomarkers for screening and diagnosis of diseases. Clinical studies are often complicated by both modifiable and non-modifiable factors influencing the composition of VOCs in exhaled breath. Small laboratory animal studies contribute in obtaining fundamental insight in alterations in VOC composition in exhaled breath and thereby facilitate the design and analysis of clinical research. However, long term animal experiments are often limited by invasive breath collection methods and terminal experiments. To overcome this problem, a novel device was developed for non-invasive breath collection in mice using glass nose-only restrainers thereby omitting the need of anesthetics. C57Bl/6 J mice were used to test reproducibility and different air sampling settings for air-flow (ml min⁻¹) and time (minutes). Exhaled air was collected on desorption tubes and analysed for VOCs by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). In total 27 compounds were putatively identified and used to assess the variability of the VOC measurements in the breath collections. Best reproducibility is obtained when using an air flow of 185 ml min⁻¹ and a collection time of 20 min. Due to the non-invasive nature of breath collections in murine models, this device has the potential to facilitate VOC research in relation to disturbed metabolism and or disease pathways.

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 lowest p-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.

Clinical assessment of children with asthma is problematic, and non-invasive biomarkers are needed urgently. Monitoring exhaled volatile organic compounds (VOCs) is an attractive alternative to invasive tests (blood and sputum) and may be used as frequently as required. Standardised reproducible breath-sampling is essential for exhaled-VOC analysis, and although the ReCIVA (Owlstone Medical Limited) breath-sampler was designed to satisfy this requirement, paediatric use was not in the original design brief. The efficacy of the ReCIVA at sampling breath from children has been studied, and 90 breath-samples from 64 children (5–15 years) with, and without asthma (controls), were collected with two different ReCIVA units. Seventy samples (77.8%) contained the specified 1 l of sampled-breath. Median sampling times were longer in children with acute asthma (770.2 s, range: 532.2–900.1 s) compared to stable asthma (690.6 s, range: 477.5–900.1 s; p = 0.01). The ReCIVA successfully detected operational faults, in 21 samples. A leak, caused by a poor fit of the face mask seal was the most common (15); the others were USB communication-faults (5); and, a single instance of a file-creation error. Paediatric breath-profiles were reliably monitored, however synchronisation of sampling to breathing-phases was sometimes lost, causing some breaths not to be sampled, and some to be sampled continuously. This occurred in 60 (66.7%) of the samples and was a source of variability. Importantly, multi-variate modelling of untargeted VOC analysis indicated the absence of significant batch effects for eight operational variables. The ReCIVA appears suitable for paediatric breath-sampling. Post-processing of breath-sample meta-data is recommended to assess the quality of sample-acquisition. Further, future studies should explore the effect of pump-synchronisation faults on recovered VOC profiles, and mask sizes to fit all ages will reduce the potential for leaks and importantly, provide higher levels of comfort to children with asthma.

Background. The SARS-CoV-2 pandemic changed the way the society functioned. The race to develop a rapid, non-invasive, widely available test resulted in multiple studies examining the potential of breath to be that 'game changing test'. Breath sampling is a non-invasive point of care test, but SAR-CoV-2 has introduced a level of danger into collection and analysis that requires a change in workflow to keep staff and participants safe. We developed a SARS-CoV 2 breath test work flow for collection and processing of breath samples in an ambulatory care setting and prospectively evaluated the protocol. Protocol development included testing the effect of respiratory filters on the integrity and reproducibility of breath samples. Methods. Prospective, observational study conducted at community COVID-19 testing sites, collecting breath samples from patients presenting for RT-PCR testing. Breath was collected via Tedlar®, and/or BioVOC-2™ as well as an environmental sample for all participants. Samples were transferred to Tenex tubes, dry purged and analyzed using a Centri automated sample introduction machine, GC, and a Bench-ToF-HD. Results. We successfully collected and processed 528 breath samples from 393 participants at community-based ambulatory COVID-19 test sites. The majority of samples were collected before vaccines were available and throughout the emergence of the Delta Variant. No staff member was infected. Conclusion. We demonstrated a safe workflow for the collection, handling, transport, storage, and analysis of breath samples during the pandemic collecting highly infectious SARS-CoV-2 positive breath samples. This was done without filters as they added complexity to the breath matrix, jeopardizing the sample integrity.