Breathomics for the clinician: the use of volatile organic compounds in respiratory diseases

Targeted improvement of narrow micropores in porous carbon for enhancing trace benzene vapor removal: Revealing the adsorption mechanism via experimental and molecular simulation

Porous carbon materials are highly desirable for removing benzene due to their low energy for capture and regeneration. Research has demonstrated that narrow microporous volume is crucial for effective adsorption of benzene at ultra-low concentration. Unfortunately, achieving directional increase in the narrow microporous volume in porous carbon remains a challenge. Here, nitrogen-doped hydrothermal carbon was prepared using urea-assisted hydrothermal method, and then porous carbon (PUC800) was prepared by KOH activation. The resulting material had 180 % higher pore volume and 179 % higher surface area compared to non-nitrogen activation methods. Then, using mechanochemical (mechanical compaction and KOH activation) approach to produce PUC800-3T, which had a 30 % increase in pore volume and a 33 % increase in surface area compared to PUC800. PUC800-3T showed benzene adsorption capacity of 4.2 mmol g-1 at 1 Pa and 5.8 mmol g-1 at 5 Pa. Experimental and molecular simulation indicate that the benzene adsorption at 1 and 5 Pa is determined by pore volume of less than 0.8 and 0.9 nm, respectively. Density functional theory calculations provided insight into the CH⋯X (X = N/O) interactions drive benzene adsorption on the carbon framework. This work provides valuable theoretical and experimental support for designing, preparing, and applying adsorbents for trace removal of benzene vapor.

Exhaled breath analyses for bronchial thermoplasty in severe asthma patients

BACKGROUND

Bronchial 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.

METHODS

Exhaled 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.

RESULTS

Data 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).

CONCLUSIONS

This 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.

New perspectives on 'Breathomics': metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. "Breathomics" as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

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 (adjustedP-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.

Tuning phosphorene and MoS2 2D materials for detecting volatile organic compounds associated with respiratory diseases

Efficient identification of volatile organic compounds (VOCs) is essential for the rapid diagnostication of respiratory diseases. By detecting specific biomarkers associated with different pathologies one may distinguish between tuberculosis, nosocomial pneumonia, Aspergillus fumigatus, influenza and SARS-CoV-2 virus infections. Phosphorene and MoS2 are potential candidates from the class of 2D graphene-like materials, which can be used as active layers for sensing elements. However, as the target molecules poorly adhere to the pristine layers, binding centers are created by introducing substitutional impurities. The adsorbed VOCs induce modifications in the electrical properties of the customized active layers. For each biomarker and a sequence of substitutional impurities, a pattern of conductivities is obtained, which enables the detection of an unknown test specimen. Exploring multiple biosensor configurations we find an optimal design yielding a considerable selectivity for the five biomarker compounds.

Detection of medically relevant volatile organic compounds with graphene field-effect transistors and separated by low-frequency spectral and time signatures

Exhaled human breath contains a mixture of gases including nitrogen, oxygen, carbon dioxide, water vapour and low molecular weight volatile organic compounds (VOCs). Different VOCs detected in human breath condensate have been recently related to several metabolic processes occurring inside body tissues in the pathological state, as candidate biomarkers for monitoring conditions such as lung injury, airway inflammation, immunity dysfunction, infection, and cancer. Current techniques for detecting these compounds include several types of mass spectroscopy, which are highly costly, time-consuming and dependent on trained personnel for sample analysis. The need for fast and label-free biosensors is paving the way towards the design of novel and portable electronic devices for point-of-care diagnosis with VOCs such as E-noses, and based on the measurement of signal signatures derived from their chemical composition. In this paper, we propose a device for VOC detection that was tested inside a controlled gas flow setup, resorting to graphene field-effect transistors (GFETs). Electrical measurements from graphene directly exposed to nitrogen plus VOC vapours involved cyclic measurements for the variation of graphene's resistance and low-frequency spectral noise in order to obtain distinctive signatures of the tested compounds in the time and frequency domains related, respectively, to Gutmann's theory for donor-acceptor chemical species and spectral sub-band analysis.

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 allm/zsignals 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.

Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies

Fibrosis is the concluding pathological outcome and major cause of morbidity and mortality in a number of common chronic inflammatory, immune-mediated and metabolic diseases. The progressive deposition of a collagen-rich extracellular matrix (ECM) represents the cornerstone of the fibrotic response and culminates in organ failure and premature death. Idiopathic pulmonary fibrosis (IPF) represents the most rapidly progressive and lethal of all fibrotic diseases with a dismal median survival of 3.5 years from diagnosis. Although the approval of the antifibrotic agents, pirfenidone and nintedanib, for the treatment of IPF signalled a watershed moment for the development of anti-fibrotic therapeutics, these agents slow but do not halt disease progression or improve quality of life. There therefore remains a pressing need for the development of effective therapeutic strategies. In this article, we review emerging therapeutic strategies for IPF as well as the pre-clinical and translational approaches that will underpin a greater understanding of the key pathomechanisms involved in order to transform the way we diagnose and treat pulmonary fibrosis.

Changes in exhaled volatile organic compounds following indirect bronchial challenge in suspected asthma

BACKGROUND

Inhaled mannitol provokes bronchoconstriction via mediators released during osmotic degranulation of inflammatory cells, and, hence represents a useful diagnostic test for asthma and model for acute attacks. We hypothesised that the mannitol challenge would trigger changes in exhaled volatile organic compounds (VOCs), generating both candidate biomarkers and novel insights into their origin.

METHODS

Participants with a clinical diagnosis of asthma, or undergoing investigation for suspected asthma, were recruited. Inhaled mannitol challenges were performed, followed by a sham challenge after 2 weeks in participants with bronchial hyper-responsiveness (BHR). VOCs were collected before and after challenges and analysed using gas chromatography-mass spectrometry.

RESULTS

Forty-six patients (mean (SD) age 52 (16) years) completed a mannitol challenge, of which 16 (35%) were positive, and 15 of these completed a sham challenge. Quantities of 16 of 51 identified VOCs changed following mannitol challenge (p<0.05), of which 11 contributed to a multivariate sparse partial least square discriminative analysis model, with a classification error rate of 13.8%. Five of these 16 VOCs also changed (p<0.05) in quantity following the sham challenge, along with four further VOCs. In patients with BHR to mannitol distinct postchallenge VOC signatures were observed compared with post-sham challenge.

CONCLUSION

Inhalation of mannitol was associated with changes in breath VOCs, and in people with BHR resulted in a distinct exhaled breath profile when compared with a sham challenge. These differentially expressed VOCs are likely associated with acute airway inflammation and/or bronchoconstriction and merit further investigation as potential biomarkers in asthma.

Recent Advances in Gas Detection Methodologies with a Special Focus on Environmental Sensing and Health Monitoring Applications─A Critical Review

With the expansion of the Internet-of-Things (IoT), the use of gas sensors in the field of wearable technology, smart devices, and smart homes has increased manifold. These gas sensors have two key applications─one is the detection of gases present in the environment and the other is the detection of Volatile Organic Compounds (VOCs) that are found in the breath. In this review, we focus systematically on the advancements in the field of various spectroscopic methods such as mass spectrometry-based analysis and point-of-care approach to detect VOCs and gases for environmental monitoring and disease diagnosis. Additionally, we highlight the development of smart sensors that work on the principle of electrochemical detection and provide examples of the same through an extensive literature review. At the end of this review, we highlight various challenges and future perspectives.

Revealing the Adsorption Mechanisms of Methanol on Lithium-Doped Porous Carbon through Experimental and Theoretical Calculations

Previous reports have shown that it is difficult to improve the methanol adsorption performance of nitrogen and oxygen groups due to their low polarity. Here, we first prepared porous carbon with a high specific surface area and large pore volume using benzimidazole as a carbon precursor and KOH as an activating agent. Then, we improved the surface polarity of the porous carbon by doping with Lithium (Li) to enhance the methanol adsorption performance. The results showed that the methanol adsorption capacity of Li-doped porous carbon reached 35.4 mmol g-1, which increased by 57% compared to undoped porous carbon. Molecular simulation results showed that Li doping not only improved the methanol adsorption performance at low pressure, but also at relatively high pressure. This is mainly because Li-modified porous carbon has higher surface polarity than nitrogen and oxygen-modified surfaces, which can generate stronger electrostatic interactions. Furthermore, through density functional theory (DFT) calculations, we determined the adsorption energy, adsorption distance, and charge transfer between Li atom and methanol. Our results demonstrate that Li doping enhances the adsorption energy, reduces the adsorption distance, and increases the charge transfer in porous carbon. The mechanism of methanol adsorption by Li groups was revealed through experimental and theoretical calculations, providing a theoretical basis for the design and preparation of methanol adsorbents.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Physiology and Biomarkers for Surveillance of Occupational Lung Disease

Respiratory surveillance is the process whereby a group of exposed workers are regularly tested (or screened) for those lung diseases which occur as a result of a specific work exposure. Surveillance is performed by assessing various measures of biological or pathological processes (or biomarkers) for change over time. These traditionally include questionnaires, lung physiological assessments (especially spirometry), and imaging. Early detection of pathological processes or disease can enable removal of a worker from a potentially harmful exposure at an early stage. In this article, we summarize the physiological biomarkers currently used for respiratory surveillance, while commenting on differences in interpretative strategies between different professional groups. We also briefly review the many new techniques which are currently being assessed for respiratory surveillance in prospective research studies and which are likely to significantly broaden and enhance this field in the near future.

A Systematic Review and Meta-Analysis: Volatile Organic Compound Analysis in the Detection of Hepatobiliary and Pancreatic Cancers

BACKGROUND

Hepatobiliary cancers are notoriously difficult to detect, frequently leading to diagnosis in later stages of disease when curative treatment is not an option. The currently used biomarkers such as AFP (alpha-fetoprotein) and CA19.9 lack sensitivity and specificity. Hence, there is an unmet need for an alternative biomarker.

AIM

To evaluate the diagnostic accuracy of volatile organic compounds (VOCs) for the detection of hepatobiliary and pancreatic cancers.

METHODS

A systematic review of VOCs' use in the detection of hepatobiliary and pancreatic cancers was performed. A meta-analysis was performed using the software R. Heterogeneity was explored through meta-regression analysis.

RESULTS

A total of 18 studies looking at 2296 patients were evaluated. Pooled sensitivity and specificity of VOCs for the detection of hepatobiliary and pancreatic cancer were 0.79 (95% CI, 0.72-0.85) and 0.81 (97.5% CI, 0.76-0.85), respectively. The area under the curve was 0.86. Meta-regression analysis showed that the sample media used contributed to heterogeneity. Bile-based VOCs showed the highest precision values, although urine and breath are preferred for their feasibility.

CONCLUSIONS

Volatile organic compounds have the potential to be used as an adjunct tool to aid in the early diagnosis of hepatobiliary cancers.

Detecting latent tuberculosis infection with a breath test using mass spectrometer: A pilot cross-sectional study

Mycobacterium tuberculosis (M.tb) infects a quarter of the world's population and may progress to active tuberculosis (ATB). There is no gold standard for diagnosing latent tuberculosis infection (LTBI). Some immunodiagnostic tests are recommended to detect LTBI but can not distinguish ATB from LTBI. The breath test is useful for diagnosing ATB compared to healthy subjects but was never studied for LTBI. This proof-of-concept study (Chinese Clinical Trials Registry number: ChiCTR2200058346) was the first to explore a novel, rapid, and simple LTBI detection method via breath test on high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). The case group of LTBI subjects (n = 185) and the control group (n = 250), which included ATB subgroup (n = 121) and healthy control (HC) subgroup (n = 129), were enrolled. The LTBI detection model indicated that a breath test via HPPI-TOFMS could distinguish LTBI from the control with a sensitivity of 80.0% (95% CI: 67.6%, 92.4%) and a specificity of 80.8% (95% CI: 71.8%, 89.9%). Nevertheless, further intensive studies with a larger sample size are required for clinical application.

Exhaled breath condensate as bioanalyte: from collection considerations to biomarker sensing

Since the SARS-CoV-2 pandemic, the potential of exhaled breath (EB) to provide valuable information and insight into the health status of a person has been revisited. Mass spectrometry (MS) has gained increasing attention as a powerful analytical tool for clinical diagnostics of exhaled breath aerosols (EBA) and exhaled breath condensates (EBC) due to its high sensitivity and specificity. Although MS will continue to play an important role in biomarker discovery in EB, its use in clinical setting is rather limited. EB analysis is moving toward online sampling with portable, room temperature operable, and inexpensive point-of-care devices capable of real-time measurements. This transition is happening due to the availability of highly performing biosensors and the use of wearable EB collection tools, mostly in the form of face masks. This feature article will outline the last developments in the field, notably the novel ways of EBA and EBC collection and the analytical aspects of the collected samples. The inherit non-invasive character of the sample collection approach might open new doors for efficient ways for a fast, non-invasive, and better diagnosis.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Visualization of exhaled breath metabolites reveals distinct diagnostic signatures for acute cardiorespiratory breathlessness

Acute cardiorespiratory breathlessness accounts for one in eight of all emergency hospitalizations. Early, noninvasive diagnostic testing is a clinical priority that allows rapid triage and treatment. Here, we sought to find and replicate diagnostic breath volatile organic compound (VOC) biomarkers of acute cardiorespiratory disease and understand breath metabolite network enrichment in acute disease, with a view to gaining mechanistic insight of breath biochemical derangements. We collected and analyzed exhaled breath samples from 277 participants presenting acute cardiorespiratory exacerbations and aged-matched healthy volunteers. Topological data analysis phenotypes differentiated acute disease from health and acute cardiorespiratory exacerbation subtypes (acute heart failure, acute asthma, acute chronic obstructive pulmonary disease, and community-acquired pneumonia). A multibiomarker score (101 breath biomarkers) demonstrated good diagnostic sensitivity and specificity (≥80%) in both discovery and replication sets and was associated with all-cause mortality at 2 years. In addition, VOC biomarker scores differentiated metabolic subgroups of cardiorespiratory exacerbation. Louvain clustering of VOCs coupled with metabolite enrichment and similarity assessment revealed highly specific enrichment patterns in all acute disease subgroups, for example, selective enrichment of correlated C5-7 hydrocarbons and C3-5 carbonyls in heart failure and selective depletion of correlated aldehydes in acute asthma. This study identified breath VOCs that differentiate acute cardiorespiratory exacerbations and associated subtypes and metabolic clusters of disease-associated VOCs.

Measurement of Exhaled Volatile Organic Compounds as a Biomarker for Personalised Medicine: Assessment of Short-Term Repeatability in Severe Asthma

The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions.

Recent insights in the role of biomarkers in severe asthma management

Contemporary asthma management requires a proactive and individualized approach, combining precision diagnosis and personalized treatment. The introduction of biologic therapies for severe asthma to everyday clinical practice, increases the need for specific patient selection, prediction of outcomes and monitoring of these costly and long-lasting therapies. Several biomarkers have been used in asthma in disease identification, prediction of asthma severity and prognosis, and response to treatment. Novel advances in the area of personalized medicine regarding disease phenotyping and endotyping, encompass the development and application of reliable biomarkers, accurately quantified using robust and reproducible methods. The availability of powerful omics technologies, together with integrated and network-based genome data analysis, and microbiota changes quantified in serum, body fluids and exhaled air, will lead to a better classification of distinct phenotypes or endotypes. Herein, in this review we discuss on currently used and novel biomarkers for the diagnosis and treatment of asthma.

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

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

Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.

End-to-end design of wearable sensors

Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.

Influence of Home Indoor Dampness Exposure on Volatile Organic Compounds in Exhaled Breath of Mothers and Their Infants: The NELA Birth Cohort

Currently, the effect of exposure to indoor air contaminants and the presence of dampness at home on respiratory/atopic health is of particular concern to physicians. The measurement of volatile organic compounds (VOCs) in exhaled breath is a useful approach for monitoring environmental exposures. A great advantage of this strategy is that it allows the study of the impact of pollutants on the metabolism through a non-invasive method. In this paper, the levels of nine VOCs (acetone, isoprene, toluene, p/m-xylene, o-xylene, styrene, benzaldehyde, naphthalene, and 2-ethyl-1-hexanol) in the exhaled breath of subjects exposed and not exposed to home dampness were assessed. Exhaled breath samples were collected from 337 mother–child pairs of a birth cohort and analysed by gas-chromatography–mass-spectrometry. It was observed that the levels of 2-ethyl-1-hexanol in the exhaled breath of the mothers were significantly influenced by exposure to household humidity. In the case of the infants, differences in some of the VOC levels related to home dampness exposure; however, they did not reach statistical significance. In addition, it was also found that the eosinophil counts of the mothers exposed to home dampness were significantly elevated compared to those of the non-exposed mothers. To our knowledge, these findings show, for the first time, that exposure to home dampness may influence VOC patterns in exhaled breath.

Haze Air Pollution Health Impacts of Breath-Borne VOCs

Here, we investigated the use of breath-borne volatile organic compounds (VOCs) for rapid monitoring of air pollution health effects on humans. Forty-seven healthy college students were recruited, and their exhaled breath samples (n = 235) were collected and analyzed for VOCs before, on, and after two separate haze pollution episodes using gas chromatography-ion mobility spectrometry (GC-IMS). Using a paired t-test and machine learning model (Gradient Boosting Machine, GBM), six exhaled VOC species including propanol and isoprene were revealed to differ significantly among pre-, on-, and post-exposure in both haze episodes, while none was found between clean control days. The GBM model was shown capable of differentiating between pre- and on-exposure to haze pollution with a precision of 90-100% for both haze episodes. However, poor performance was detected for the same model between two different clean days. In addition to gender and particular haze occurrence influences, correlation analysis revealed that NH₄⁺, NO₃^-, acetic acid, mesylate, CO, NO₂, PM_2.5, and O₃ played important roles in the changes in breath-borne VOC fingerprints following haze air pollution exposure. This work has demonstrated direct evidence of human health impacts of haze pollution while identifying potential breath-borne VOC biomarkers such as propanol and isoprene for haze air pollution exposure.

Metabolomics in pulmonary medicine - extracting the most from your data

The metabolome enables unprecedented insight into biochemistry, providing an integrated signature of the genome, transcriptome, proteome and exposome. Measurement requires rigorous protocols combined with specialised data analysis to achieve its promise.https://bit.ly/3yPiYkQ

Polyurethane-Based Porous Carbons Suitable for Medical Application

The main aim of the study was to synthesize and analyze spectral data to determine the structure and stereometry of the carbon-based porous material internal structure. Samples of a porous biomaterial were synthesized through anionic polymerization following our own patent and then carbonized. The samples were investigated using MALDI ToF MS, FTIR ATR spectroscopy, optic microscopy, SEM, confocal laser scanning microscopy and CMT imaging. The analysis revealed the chemical and stereological structure of the obtained porous biomaterial. Then, the parameters characterizing the pore geometry and the porosity of the samples were calculated. The developed material can be used to collect adsorption of breathing phase samples to determine the parity composition of exhaled air.

Volatile compounds in human breath: critical review and meta-analysis

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.

Biomarkers of asthma

Biomarkers may be diagnostic of asthma, they may predict or reflect response to therapy or they may identify patients at risk of asthma exacerbation. A biomarker is most often measured in biologic fluids that are sampled using relatively non-invasive sampling techniques such as blood, sputum, urine or exhaled breath. Biomarkers should be stable, readily quantifiable and their measurement should be reproducible and not confounded by other host factors, or the presence of comorbidities. However, asthma comprises multiple molecular endotypes and single, sensitive, specific, biomarkers reflecting these endotypes may not exist. Combining biomarkers may improve their predictive capability in asthma. The most well-established endotypes are those described as Type2 and non-Type2 asthma. Clinical trials established the fraction of exhaled nitric oxide (FeNO) and blood eosinophil counts as key biomarkers of response to corticosteroid or targeted anti-inflammatory therapy in Type2 asthma. However, these biomarkers may have limited value in the management of asthma in real-life settings or routine clinical practise. Biomarkers for Type2 asthma are not well described or validated and more research is needed. Breathomics has provided evidence to propose a number of exhaled volatile organic compounds (VOCs) as surrogate biomarkers for airway inflammatory phenotypes, disease activity and adherence to therapy. Analysis of urinary eicosanoids has identified eicosanoids related to Type2 and non-Type2 inflammation. Future clinical trials will be important in determining how exhaled VOCs or urinary eicosanoid profiles can be used to direct precision treatments. Their future clinical use will also depend on developing simplified instrumentation for biomarker analysis at the point-of-care.

Diagnosis of COVID-19 by exhaled breath analysis using gas chromatography-mass spectrometry

BACKGROUND

The ongoing coronavirus disease 2019 (COVID-19) pandemic has claimed over two and a half million lives worldwide so far. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is perceived to be seasonally recurrent, and a rapid noninvasive biomarker to accurately diagnose patients early on in their disease course will be necessary to meet the operational demands for COVID-19 control in the coming years.

OBJECTIVE

The aim of this study was to evaluate the role of exhaled breath volatile biomarkers in identifying patients with suspected or confirmed COVID-19 infection, based on their underlying PCR status and clinical probability.

METHODS

A prospective, real-world, observational study was carried out, recruiting adult patients with suspected or confirmed COVID-19 infection. Breath samples were collected using a standard breath collection bag, modified with appropriate filters to comply with local infection control recommendations, and samples were analysed using gas chromatography-mass spectrometry (TD-GC-MS).

RESULTS

81 patients were recruited between April 29 and July 10, 2020, of whom 52 out of 81 (64%) tested positive for COVID-19 by reverse transcription-polymerase chain reaction (RT-PCR). A regression analysis identified a set of seven exhaled breath features (benzaldehyde, 1-propanol, 3,6-methylundecane, camphene, beta-cubebene, iodobenzene and an unidentified compound) that separated PCR-positive patients with an area under the curve (AUC): 0.836, sensitivity: 68%, specificity: 85%.

CONCLUSIONS

GC-MS-detected exhaled breath biomarkers were able to identify PCR-positive COVID-19 patients. External replication of these compounds is warranted to validate these results.

Breathing Rhythm Variations during Wash-In Do Not Influence Exhaled Volatile Organic Compound Profile Analyzed by an Electronic Nose

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in.