Factors that influence the volatile organic compound content in human breath

Diagnosis of Lung Cancer Through Exhaled Breath: A Comprehensive Study

OBJECTIVES

Exhaled breath analysis is an attractive lung cancer diagnostic tool. However, various factors that are not related to the disease status, comorbidities, and other diseases must be considered to obtain a reliable diagnostic model.

METHODS

Exhaled breath samples from 646 individuals including 273 patients with lung cancer (LC), 90 patients with cancer of other localizations (OC), 150 patients with noncancer lung diseases (NLD), and 133 healthy controls (HC) were analyzed using gas chromatography-mass spectrometry (GC-MS). The samples were collected in Tedlar bags. Volatile organic compounds (VOCs) were preconcentrated on Tenax TA sorbent tubes with subsequent two-stage thermal desorption followed by GC-MS analysis. The influence of age, gender, smoking status, time since last food consumption, and comorbidities on exhaled breath were evaluated. Also, the effect of histology, TNM, tumor localization, treatment status, and the presence of a tumor on VOC profile of patients with lung cancer were assessed. Intergroup statistics were estimated, diagnostic models were created using artificial neural networks (ANNs) and gradient boosted decision trees (GBDTs).

RESULTS

Smoking status and food consumption affect exhaled breath VOC profile: benzene, ethylbenzene, toluene, 1,3-pentadiene 1,4-pentadiene acetonitrile, and some ratios are significantly different in exhaled breath of smokers and nonsmokers; the ratios 2,3-butandione/2-pentanone, 2,3-butandione/dimethylsulfide, and 2-butanone/2-pentanone are affected by time since last food consumption. Exhaled breath of LC is affected by the form of the disease and comorbidities. One-pentanol and 2-butanone were different in exhaled breath of patients with various tumor localization; 2-butanone was different in exhaled breath of patients before and during treatment. Diabetes as a comorbidity affects the pentanal level in exhaled breath; obesity affects the ratios of 2,3-butanedione/dimethylsulfide and 2-butanone/isoprene. Sensitivity and specificity of diagnostic models aimed to discriminate LC and HC, OC, and NLD were 78.7% and 51.0%, 62.2% and 53.4%, and 60.4% and 58.0%, respectively. HC and patients, regardless of the disease, can be classified with sensitivity of 76.6% and specificity of 68.2%.

CONCLUSIONS

The models created to diagnose lung cancer can also classify OC and NLD as patients with lung cancer. Additionally, the influence of comorbidities and factors not related to the disease status must be considered before the creation of diagnostic models to avoid false results.

Fulfilling the Promise of Breathomics: Considerations for the Discovery and Validation of Exhaled Volatile Biomarkers

The exhaled breath represents an ideal matrix for noninvasive biomarker discovery, and exhaled metabolomics have the potential to be clinically useful in the era of precision medicine. In this concise translational review, we specifically address volatile organic compounds in the breath, with a view toward fulfilling the promise of these as actionable biomarkers, in particular, for lung diseases. We review the literature paying attention to seminal work linked to key milestones in breath research; discuss potential applications for breath biomarkers across disease areas and healthcare systems, including the perspectives of industry; and outline critical aspects of study design that will need to be considered for any pivotal research going forward if breath analysis is to provide robust validated biomarkers that meet the requirements for future clinical implementation.

Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients

OBJECTIVES

Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested.

METHODS

Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques.

RESULTS

Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs.

CONCLUSIONS

These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening.

CLINICAL REGISTRATION NUMBER

ClinicalTrials.gov Identifier: NCT06034730.

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Diagnosis of chondrosarcoma in a noninvasive way using volatile organic compounds in exhaled breath: a pilot study

Aim: Aim of this explorative pilot study was to evaluate the capability of an electronic nose (aeoNose, the eNose Company) to classify healthy individuals and patients with chondrosarcoma, based on their volatile organic compound profiles in exhaled breath.Materials & methods: Fifty-seven patients (25 healthy controls, 24 chondrosarcoma and 8 different benign lesions) were included in the study from 2018 to 2023. An artificial neural network was used as classifier.Results: The developed model had a sensitivity of 75%, and a specificity of 65% with an AUC of 0.66.Conclusion: Results show that there is not enough evidence to include the aeoNose as diagnostic biomarker for chondrosarcoma in daily practice. However, the aeoNose might play an additional role alongside MRI, in questionable chondrosarcoma cases.

Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.

Recapitulation of Structure-Function-Regulation of Blood-Brain Barrier under (Patho)Physiological Conditions

The blood-brain barrier (BBB) is a remarkable and intricate barrier that controls the exchange of molecules between the bloodstream and the brain. Its role in maintaining the stability of the central nervous system cannot be overstated. Over the years, advancements in neuroscience and technology have enabled us to delve into the cellular and molecular components of the BBB, as well as its regulation. Yet, there is a scarcity of comprehensive reviews that follow a logical framework of structure-function-regulation, particularly focusing on the nuances of BBB regulation under both normal and pathological conditions. This review sets out to address this gap by taking a historical perspective on the discovery of the BBB and highlighting the major observations that led to its recognition as a distinct brain barrier. It explores the intricate cellular elements contributing to the formation of the BBB, including endothelial cells, pericytes, astrocytes, and neurons, emphasizing their collective role in upholding the integrity and functionality of the BBB. Furthermore, the review delves into the dynamic regulation of the BBB in physiological states, encompassing neural, humoral, and auto-regulatory mechanisms. By shedding light on these regulatory processes, a deeper understanding of the BBB's response to various physiological cues emerges. This review also investigates the disruption of the BBB integrity under diverse pathological conditions, such as ischemia, infection, and toxin exposure. It elucidates the underlying mechanisms that contribute to BBB dysfunction and explores potential therapeutic strategies that aim to restore the BBB integrity and function. Overall, this recapitulation provides valuable insights into the structure, functions, and regulation of the BBB. By integrating historical perspectives, cellular elements, regulatory mechanisms, and pathological implications, this review contributes to a more comprehensive understanding of the BBB and paves the way for future research and therapeutic interventions.

Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity

INTRODUCTION

Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media.

METHODS

Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation.

RESULTS

The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature.

CONCLUSION

This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

A systematic review on the detection of volatile organic compounds in exhaled breath in experimental animals in the context of gastrointestinal and hepatic diseases

BACKGROUND

Analysis of volatile organic compounds (VOCs) in exhaled breath has the potential to serve as an accurate diagnostic tool for gastro-intestinal diseases. Animal studies could be instrumental as a preclinical base and subsequent clinical translation to humans, as they are easier to standardize and better equipped to relate specific VOCs to metabolic and pathological processes. This review provides an overview of the study design, characteristics and methodological quality of previously published animal studies on analysis of exhaled breath in gastrointestinal and hepatic diseases. Guidelines are provided for standardization in study design and breath collection methods to improve comparability, avoid duplication of research and reduce discomfort of animals in future studies.

METHODS

PubMed and Embase database were searched for animal studies using exhaled breath analysis to detect gastro-intestinal diseases. Risk of bias was assessed using the SYRCLE's risk of bias tool for animal studies. Information on study design, standardization methods, animal models, breath collection methods and identified VOCs were extracted from the included studies.

RESULTS

10 studies were included (acute liver failure n = 1, non-alcoholic steatohepatitis n = 1, hepatic ischemia n = 2, mesenteric ischemia n = 2, sepsis and peritonitis n = 3, colitis n = 1). Rats were used in most of the studies. Exhaled breath was mostly collected using invasive procedures as tracheal cannulation or tracheostomy. Poor reporting on standardization, breath collection methods, analytical techniques, as well as heterogeneity of the studies, complicate comparison of the different studies.

CONCLUSION

Poor reporting of essential methodological details impaired comprehensive summarizing the various studies on exhaled breath in gastrointestinal and hepatic diseases. Potential pitfalls in study design, and suggestions for improvement of study design are discussed which, when applied, lead to consistent and generalizable results and a reduction in the use of laboratory animals. Refining the methodological quality of animal studies has the potential to improve subsequent clinical trial design.

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

Background

Asthma and COPD are among the most common respiratory diseases. To improve the early detection of exacerbations and the clinical course of asthma and COPD new biomarkers are needed. The development of noninvasive metabolomics of exhaled air into a point-of-care tool is an appealing option. However, risk factors for obstructive pulmonary diseases can potentially introduce confounding markers due to altered volatile organic compound (VOC) patterns being linked to these risk factors instead of the disease. We conducted a systematic review and presented a comprehensive list of VOCs associated with these risk factors.

Methods

A PRISMA-oriented systematic search was conducted across PubMed, Embase and Cochrane Libraries between 2000 and 2022. Full-length studies evaluating VOCs in exhaled breath were included. A narrative synthesis of the data was conducted, and the Newcastle-Ottawa Scale was used to assess the quality of included studies.

Results

The search yielded 2209 records and, based on the inclusion/exclusion criteria, 24 articles were included in the qualitative synthesis. In total, 232 individual VOCs associated with risk factors for obstructive pulmonary diseases were found; 58 compounds were reported more than once and 12 were reported as potential markers of asthma and/or COPD in other studies. Critical appraisal found that the identified studies were methodologically heterogeneous and had a variable risk of bias.

Conclusion

We identified a series of exhaled VOCs associated with risk factors for asthma and/or COPD. Identification of these VOCs is necessary for the further development of exhaled metabolites-based point-of-care tests in these obstructive pulmonary diseases.

Recent innovations in the screening and diagnosis of systemic sclerosis-associated interstitial lung disease

INTRODUCTION

Interstitial lung disease (ILD) is the leading cause of mortality in patients with systemic sclerosis (SSc). Risk of developing progressive ILD is highest among patients with diffuse cutaneous disease, positive anti-topoisomerase I antibody, and elevated acute phase reactants. With the FDA approval of two medications and a pipeline of novel therapeutics in trials, early recognition and intervention is critical. High-resolution computed tomography of the chest is the current gold standard test for diagnosis of ILD. Yet, it is not offered as a screening tool to all patients due to which ILD can be missed in up to a third of patients. There is a need to develop and validate more innovative screening modalities.

AREAS COVERED

In this review, we provide an overview of screening and diagnosis of SSc-ILD, highlighting the recent innovations particularly the role of soluble serologic, radiomic (quantitative lung imaging, lung ultrasound), and breathomic (exhaled breath analysis) biomarkers in the early detection of SSc-ILD.

EXPERT OPINION

There is remarkable progress in the development of new radiomics and serum biomarkers in diagnosing SSc-ILD. There is an urgent need for conceptualizing and testing composite ILD screening strategies that incorporate these biomarkers.

Urinary volatile organic compounds for colorectal cancer screening, a systematic review and meta-analysis

BACKGROUND

The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia.

METHODS

A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database.

RESULTS

Sixteen studies-involving 837 CRC patients and 1618 controls-were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73-91%) and 70% (95% CI 63-77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied.

CONCLUSION

Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes.

Noninvasive detection of soft tissue sarcoma using volatile organic compounds in exhaled breath: a pilot study

Aim: The aim of this pilot study was to assess whether an electronic nose can detect patients with soft tissue sarcoma (STS) based on volatile organic compound profiles in exhaled breath. Patients & methods: In this cross-sectional pilot study, patients with primary STS and healthy controls, matched on sex and age, were included for breath analysis. Machine learning techniques were used to develop the best-fitting model. Results: Fifty-nine breath samples were collected (29 STS and 30 control) from March 2018 to March 2022. The final model yielded a c-statistic of 0.85 with a sensitivity of 83% and specificity of 60%. Conclusion: This study suggests that exhaled volatile organic compound analysis could serve as a noninvasive diagnostic biomarker for the detection of STS with a good performance.

Analysis of Volatile Organic Compounds in Exhaled Breath Following a COMEX-30 Treatment Table

The COMEX-30 hyperbaric treatment table is used to manage decompression sickness in divers but may result in pulmonary oxygen toxicity (POT). Volatile organic compounds (VOCs) in exhaled breath are early markers of hyperoxic stress that may be linked to POT. The present study assessed whether VOCs following COMEX-30 treatment are early markers of hyperoxic stress and/or POT in ten healthy, nonsmoking volunteers. Because more oxygen is inhaled during COMEX-30 treatment than with other treatment tables, this study hypothesized that VOCs exhaled following COMEX-30 treatment are indicators of POT. Breath samples were collected before and 0.5, 2, and 4 h after COMEX-30 treatment. All subjects were followed-up for signs of POT or other symptoms. Nine compounds were identified, with four (nonanal, decanal, ethyl acetate, and tridecane) increasing 33–500% in intensity from before to after COMEX-30 treatment. Seven subjects reported pulmonary symptoms, five reported out-of-proportion tiredness and transient ear fullness, and four had signs of mild dehydration. All VOCs identified following COMEX-30 treatment have been associated with inflammatory responses or pulmonary diseases, such as asthma or lung cancer. Because most subjects reported transient pulmonary symptoms reflecting early-stage POT, the identified VOCs are likely markers of POT, not just hyperbaric hyperoxic exposure.

The accuracy of an electronic nose to diagnose tuberculosis in patients referred to an expert centre

INTRODUCTION

An electronic nose (eNose) device has shown a high specificity and sensitivity to diagnose or rule out tuberculosis (TB) in the past. The aim of this study was to evaluate its performance in patients referred to INERAM.

METHODS

Patients aged ≥15 years were included. A history, physical examination, chest radiography (CRX) and microbiological evaluation of a sputum sample were performed in all participants, as well as a 5-minute breath test with the eNose. TB diagnosis was preferably established by the gold standard and compared to the eNose predictions. Univariate and multivariate logistic regression analyses were performed to assess potential risk factors for erroneous classification results by the eNose.

RESULTS

107 participants with signs and symptoms of TB were enrolled of which 91 (85.0%) were diagnosed with TB. The blind eNose predictions resulted in an accuracy of 50%; a sensitivity of 52.3% (CI 95%: 39.6-64.7%) and a specificity of 36.4% (CI 95%: 12.4-68.4%). Risk factors for erroneous classifications by the eNose were older age (multivariate analysis: OR 1.55, 95% CI 1.10-2.18, p = 0.012) and antibiotic use (multivariate analysis: OR 3.19, 95% CI 1.06-9.66, p = 0.040).

CONCLUSION

In this study, the accuracy of the eNose to diagnose TB in a tertiary referral hospital was only 50%. The use of antibiotics and older age represent important factors negatively influencing the diagnostic accuracy of the eNose. Therefore, its use should probably be restricted to screening in high-risk communities in less complex healthcare settings.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Systematic Review: Contribution of the Gut Microbiome to the Volatile Metabolic Fingerprint of Colorectal Neoplasia

Colorectal cancer (CRC) has been associated with changes in volatile metabolic profiles in several human biological matrices. This enables its non-invasive detection, but the origin of these volatile organic compounds (VOCs) and their relation to the gut microbiome are not yet fully understood. This systematic review provides an overview of the current understanding of this topic. A systematic search using PubMed, Embase, Medline, Cochrane Library, and the Web of Science according to PRISMA guidelines resulted in seventy-one included studies. In addition, a systematic search was conducted that identified five systematic reviews from which CRC-associated gut microbiota data were extracted. The included studies analyzed VOCs in feces, urine, breath, blood, tissue, and saliva. Eight studies performed microbiota analysis in addition to VOC analysis. The most frequently reported dysregulations over all matrices included short-chain fatty acids, amino acids, proteolytic fermentation products, and products related to the tricarboxylic acid cycle and Warburg metabolism. Many of these dysregulations could be related to the shifts in CRC-associated microbiota, and thus the gut microbiota presumably contributes to the metabolic fingerprint of VOC in CRC. Future research involving VOCs analysis should include simultaneous gut microbiota analysis.

Common Strategies and Factors Affecting Off-Line Breath Sampling and Volatile Organic Compounds Analysis Using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS)

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis.

Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: A Systematic Review

BACKGROUND

Remote medical scent detection of cancer and infectious diseases with dogs and rats has been an increasing field of research these last 20 years. If validated, the possibility of implementing such a technique in the clinic raises many hopes. This systematic review was performed to determine the evidence and performance of such methods and assess their potential relevance in the clinic.

METHODS

Pubmed and Web of Science databases were independently searched based on PRISMA standards between 01/01/2000 and 01/05/2021. We included studies aiming at detecting cancers and infectious diseases affecting humans with dogs or rats. We excluded studies using other animals, studies aiming to detect agricultural diseases, diseases affecting animals, and others such as diabetes and neurodegenerative diseases. Only original articles were included. Data about patients' selection, samples, animal characteristics, animal training, testing configurations, and performances were recorded.

RESULTS

A total of 62 studies were included. Sensitivity and specificity varied a lot among studies: While some publications report low sensitivities of 0.17 and specificities around 0.29, others achieve rates of 1 sensitivity and specificity. Only 6 studies were evaluated in a double-blind screening-like situation. In general, the risk of performance bias was high in most evaluated studies, and the quality of the evidence found was low.

CONCLUSIONS

Medical detection using animals' sense of smell lacks evidence and performances so far to be applied in the clinic. What odors the animals detect is not well understood. Further research should be conducted, focusing on patient selection, samples (choice of materials, standardization), and testing conditions. Interpolations of such results to free running detection (direct contact with humans) should be taken with extreme caution. Considering this synthesis, we discuss the challenges and highlight the excellent odor detection threshold exhibited by animals which represents a potential opportunity to develop an accessible and non-invasive method for disease detection.

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.

Volatile organic compounds from exhaled breath in schizophrenia

OBJECTIVES

This study aims to find out whether volatile organic compounds (VOCs) from exhaled breath differ significantly between patients with schizophrenia and healthy controls and whether it might be possible to create an algorithm that can predict the likelihood of suffering from schizophrenia.

METHODS

To test this theory, a group of patients with clinically diagnosed acute schizophrenia as well as a healthy comparison group has been investigated, which have given breath samples during awakening response right after awakening, after 30 min and after 60 min. The VOCs were measured using Proton-Transfer-Reaction Mass Spectrometry.

RESULTS

By applying bootstrap with mixed model analysis (n = 1000), we detected 10 signatures (m/z 39, 40, 59, 60, 69, 70, 74, 85, 88 and 90) showing reduced concentration in patients with schizophrenia compared to healthy controls. These could safely discriminate patients and controls and were not influenced by smoking. Logistic regression forward method achieved an area under the receiver operating characteristic curve (AUC) of 0.91 and an accuracy of 82% and a machine learning approach with bartMachine an AUC of 0.96 and an accuracy of 91%.

CONCLUSION

Breath gas analysis is easy to apply, well tolerated and seems to be a promising candidate for further studies on diagnostic and predictive clinical utility.

Detecting Colorectal Adenomas and Cancer Using Volatile Organic Compounds in Exhaled Breath: A Proof-of-Principle Study to Improve Screening

INTRODUCTION

Early detection of colorectal cancer (CRC) by screening programs is crucial because survival rates worsen at advanced stages. However, the currently used screening method, the fecal immunochemical test (FIT), suffers from a high number of false-positives and is insensitive for detecting advanced adenomas (AAs), resulting in false-negatives for these premalignant lesions. Therefore, more accurate, noninvasive screening tools are needed. In this study, the utility of analyzing volatile organic compounds (VOCs) in exhaled breath in a FIT-positive population to detect the presence of colorectal neoplasia was studied.

METHODS

In this multicenter prospective study, breath samples were collected from 382 FIT-positive patients with subsequent colonoscopy participating in the national Dutch bowel screening program (n = 84 negative controls, n = 130 non-AAs, n = 138 AAs, and n = 30 CRCs). Precolonoscopy exhaled VOCs were analyzed using thermal desorption-gas chromatography-mass spectrometry, and the data were preprocessed and analyzed using machine learning techniques.

RESULTS

Using 10 discriminatory VOCs, AAs could be distinguished from negative controls with a sensitivity and specificity of 79% and 70%, respectively. Based on this biomarker profile, CRC and AA combined could be discriminated from controls with a sensitivity and specificity of 77% and 70%, respectively, and CRC alone could be discriminated from controls with a sensitivity and specificity of 80% and 70%, respectively. Moreover, the feasibility to discriminate non-AAs from controls and AAs was shown.

DISCUSSION

VOCs in exhaled breath can detect the presence of AAs and CRC in a CRC screening population and may improve CRC screening in the future.

Real-Time Monitoring of Breath Biomarkers with A Magnetoelastic Contactless Gas Sensor: A Proof of Concept

In the quest for effective gas sensors for breath analysis, magnetoelastic resonance-based gas sensors (MEGSs) are remarkable candidates. Thanks to their intrinsic contactless operation, they can be used as non-invasive and portable devices. However, traditional monitoring techniques are bound to slow detection, which hinders their application to fast bio-related reactions. Here we present a method for real-time monitoring of the resonance frequency, with a proof of concept for real-time monitoring of gaseous biomarkers based on resonance frequency. This method was validated with a MEGS based on a Metglass 2826 MB microribbon with a polyvinylpyrrolidone (PVP) nanofiber electrospun functionalization. The device provided a low-noise (RMS = 1.7 Hz), fast (<2 min), and highly reproducible response to humidity (Δf = 46−182 Hz for 17−95% RH), ammonia (Δf = 112 Hz for 40 ppm), and acetone (Δf = 44 Hz for 40 ppm). These analytes are highly important in biomedical applications, particularly ammonia and acetone, which are biomarkers related to diseases such as diabetes. Furthermore, the capability of distinguishing between breath and regular air was demonstrated with real breath measurements. The sensor also exhibited strong resistance to benzene, a common gaseous interferent in breath analysis.

Dogs can discriminate between human baseline and psychological stress condition odours

Previous research suggests that dogs can detect when humans are experiencing stress. This study tested whether baseline and stress odours were distinguishable to dogs, using a double-blind, two-phase, three-alternative forced-choice procedure. Combined breath and sweat samples were obtained from participants at baseline, and after a stress-inducing (mental arithmetic) task. Participants’ stress was validated with self-report and physiological measures recorded via a Biopac MP150 system. Thirty-six participants’ samples were presented to four dogs across 36 sessions (16, 11, 7 and 2 sessions, respectively). Each session consisted of 10 Phase One training trials and 20 Phase Two discrimination trials. In Phase One, the dog was presented with a participant’s stress sample (taken immediately post-task) alongside two blanks (the sample materials without breath or sweat), and was required to identify the stress sample with an alert behaviour. In Phase Two, the dog was presented with the stress sample, the same participant’s baseline sample (taken pre-task), and a blank. Which sample (blank, baseline, or stress) the dog performed their alert behaviour on was measured. If dogs can correctly alert on the stress sample in Phase Two (when the baseline sample was present), it suggests that baseline and stress odours are distinguishable. Performance ranged from 90.00% to 96.88% accuracy with a combined accuracy of 93.75% (N trials = 720). A binomial test (where probability of success on a single trial was 0.33, and alpha was 0.05) showed that the proportion of correct trials was greater than that expected by chance (p < 0.001). Results indicate that the physiological processes associated with an acute psychological stress response produce changes in the volatile organic compounds emanating from breath and/or sweat that are detectable to dogs. These results add to our understanding of human-dog relationships and could have applications to Emotional Support and Post Traumatic Stress Disorder (PTSD) service dogs.

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma

Simple Summary

Malignant pleural mesothelioma (MPM) is an incurable asbestos-related thoracic cancer for which early-stage diagnosis remains a major challenge. Volatile organic compounds (VOCs), which are metabolites present in exhaled breath, have proven to be promising non-invasive biomarkers for MPM. However, without the necessary validation in an independent group of individuals, clinical implementation is hampered. Therefore, we performed external validation of a VOC-based prediction model for MPM, which initially revealed a poor performance and thus poor generalisability of the model. However, subsequent updating of the model improved its performance in the validation cohort, resulting in a more generalisable model with a screening potential, which could significantly impact MPM management.

Abstract

During the past decade, volatile organic compounds (VOCs) in exhaled breath have emerged as promising biomarkers for malignant pleural mesothelioma (MPM). However, as these biomarkers lack external validation, no breath test for MPM has been implemented in clinical practice. To address this issue, we performed the first external validation of a VOC-based prediction model for MPM. The external validation cohort was prospectively recruited, consisting of 47 MPM patients and 76 asbestos-exposed (AEx) controls. The predictive performance of the previously developed model was assessed by determining the degree of agreement between the predicted and actual outcome of the participants (patient/control). Additionally, to optimise the performance, the model was updated by refitting it to the validation cohort. External validation revealed a poor performance of the original model as the accuracy was estimated at only 41%, indicating poor generalisability. However, subsequent updating of the model improved the differentiation between MPM patients and AEx controls significantly (73% accuracy, 92% sensitivity, and 92% negative predictive value), substantiating the validity of the original predictors. This updated model will be more generalisable to the target population and exhibits key characteristics of a potential screening test for MPM, which could significantly impact MPM management.

Sampling and Analysis of Low-Molecular-Weight Volatile Metabolites in Cellular Headspace and Mouse Breath

Volatile compounds, abundant in breath, can be used to accurately diagnose and monitor a range of medical conditions. This offers a noninvasive, low-cost approach with screening applications; however, the uptake of this diagnostic approach has been limited by conflicting published outcomes. Most published reports rely on large scale screening of the public, at single time points and without reference to ambient air. Here, we present a novel approach to volatile sampling from cellular headspace and mouse breath that incorporates multi-time-point analysis and ambient air subtraction revealing compound flux as an effective proxy of active metabolism. This approach to investigating breath volatiles offers a new avenue for disease biomarker discovery and diagnosis. Using gas chromatography mass spectrometry (GC/MS), we focus on low molecular weight, metabolic substrate/by-product compounds and demonstrate that this noninvasive technique is sensitive (reproducible at ~1 µg cellular protein, or ~500,000 cells) and capable of precisely determining cell type, status and treatment. Isolated cellular models represent components of larger mammalian systems, and we show that stress- and pathology-indicative compounds are detectable in mice, supporting further investigation using this methodology as a tool to identify volatile targets in human patients.

Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis

IMPORTANCE

There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath.

OBJECTIVE

To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer.

DATA SOURCES

An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021).

STUDY SELECTION

Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples.

DATA EXTRACTION AND SYNTHESIS

PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed.

MAIN OUTCOMES AND MEASURES

Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve.

RESULTS

This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath.

CONCLUSIONS AND RELEVANCE

Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.

Volatile Organic Compounds Frequently Identified after Hyperbaric Hyperoxic Exposure: The VAPOR Library

Diving or hyperbaric oxygen therapy with increased partial pressures of oxygen (pO2) can have adverse effects such as central nervous system oxygen toxicity or pulmonary oxygen toxicity (POT). Prevention of POT has been a topic of interest for several decades. One of the most promising techniques to determine early signs of POT is the analysis of volatile organic compounds (VOCs) in exhaled breath. We reanalyzed the data of five studies to compose a library of potential exhaled markers for the early detection of POT. GC-MS data from five hyperbaric hyperoxic studies were collected. Wilcoxon signed-rank tests were used to compare baseline- and postexposure measurements; all ion fragments that significantly varied were compared by similarity using the National Institute of Standards and Technology (NIST) library. All identified molecules were cross-referenced with open-source databases and other scientific publications on VOCs to exclude compounds that occurred as a result of contamination, and to identify the compounds most likely to occur due to hyperbaric hyperoxic exposure. After identification and removal of contaminants, 29 compounds were included in the library. This library of hyperbaric hyperoxic-related VOCs can help to advance the development of an early noninvasive marker of POT. It enables validation by others who use more targeted MS-related techniques, instead of full-scale GC-MS, for their exhaled VOC research.

Exhaled Breath Volatile Organic Compound Analysis for the Detection of Lung Cancer- A Systematic Review

A rapid and effective diagnostic method is essential for lung cancer since it shows symptoms only at its advanced stage. Research is being carried out in the area of exhaled breath analysis for the diagnosis of various pulmonary diseases including lung cancer. In this method exhaled breath volatile organic compounds (VOC) are analyzed with various techniques such as gas chromatography-mass spectrometry, ion mobility spectrometry, and electronic noses. The VOC analysis is suitable for lung cancer detection since it is non-invasive, fast, and also a low-cost method. In addition, this technique can detect primary stage nodules. This paper presents a systematic review of the various method employed by researchers in the breath analysis field. The articles were selected through various search engines like EMBASE, Google Scholar, Pubmed, and Google. In the initial screening process, 214 research papers were selected using various inclusion and exclusion criteria and finally, 55 articles were selected for the review. The results of the reviewed studies show that detection of lung cancer can be effectively done using the VOC analysis of exhaled breath. The results also show that this method can be used for detecting the different stages and histology of lung cancer. The exhaled breath VOC analysis technique will be popular in the future, bypassing the existing imaging techniques. This systematic review conveys the recent research opportunities, obstacles, difficulties, motivations, and suggestions associated with the breath analysis method for lung cancer detection.

A low cost, easy-to-assemble, open-source modular mobile sampler design for thermal desorption analysis of breath and environmental VOCs

Exhaled breath vapor contains hundreds of volatile organic compounds (VOCs), which are the byproducts of health and disease metabolism, and they have clinical and diagnostic potential. Simultaneous collection of breath VOCs and background environmental VOCs is important to ensure analyses eliminate exogenous compounds from clinical studies. We present a mobile sampling system to extract gaseous VOCs onto commercially available sorbent-packed thermal desorption tubes. The sampler can be connected to a number of commonly available disposable and reusable sampling bags, in the case of this study, a Tedlar bag containing a breath sample. Alternatively, the inlet can be left open to directly sample room or environmental air when obtaining a background VOC sample. The system contains a screen for the operator to input a desired sample volume. A needle valve allows the operator to control the sample flow rate, which operates with an accuracy of -1.52 ± 0.63% of the desired rate, and consistently generated that rate with 0.12 ± 0.06% error across repeated measures. A flow pump, flow sensor and microcontroller allow volumetric sampling, as opposed to timed sampling, with 0.06 ± 0.06% accuracy in the volume extracted. Four samplers were compared by sampling a standard chemical mixture, which resulted in 6.4 ± 4.7% error across all four replicate modular samplers to extract a given VOC. The samplers were deployed in a clinical setting to collect breath and background/environmental samples, including patients with active SARS-CoV-2 infections, and the device could easily move between rooms and can undergo required disinfection protocols to prevent transmission of pathogens on the case exterior. All components required for assembly are detailed and are made publicly available for non-commercial use, including the microcontroller software. We demonstrate the device collects volatile compounds, including use of chemical standards, and background and breath samples in real use conditions.

Breathing new life into clinical testing and diagnostics: perspectives on volatile biomarkers from breath

Human breath offers several benefits for diagnostic applications, including simple, noninvasive collection. Breath is a rich source of clinically-relevant biological information; this includes a volatile fraction, where greater than 1,000 volatile organic compounds (VOCs) have been described so far, and breath aerosols that carry nucleic acids, proteins, signaling molecules, and pathogens. Many of these factors, especially VOCs, are delivered to the lung by the systemic circulation, and diffusion of candidate biomarkers from blood into breath allows systematic profiling of organismal health. Biomarkers on breath offer the capability to advance early detection and precision medicine in areas of global clinical need. Breath tests are noninvasive and can be performed at home or in a primary care setting, which makes them well-suited for the kind of public screening program that could dramatically improve the early detection of conditions such as lung cancer. Since measurements of VOCs on breath largely report on metabolic changes, this too aids in the early detection of a broader range of illnesses and can be used to detect metabolic shifts that could be targeted through precision medicine. Furthermore, the ability to perform frequent sampling has envisioned applications in monitoring treatment responses. Breath has been investigated in respiratory, liver, gut, and neurological diseases and in contexts as diverse as infectious diseases and cancer. Preclinical research studies using breath have been ongoing for some time, yet only a few breath-based diagnostics tests are currently available and in widespread clinical use. Most recently, tests assessing the gut microbiome using hydrogen and methane on breath, in addition to tests using urea to detect Helicobacter pylori infections have been released, yet there are many more applications of breath tests still to be realized. Here, we discuss the strengths of breath as a clinical sampling matrix and the technical challenges to be addressed in developing it for clinical use. Historically, a lack of standardized methodologies has delayed the discovery and validation of biomarker candidates, resulting in a proliferation of early-stage pilot studies. We will explore how advancements in breath collection and analysis are in the process of driving renewed progress in the field, particularly in the context of gastrointestinal and chronic liver disease. Finally, we will provide a forward-looking outlook for developing the next generation of clinically relevant breath tests and how they may emerge into clinical practice.

Breath Sensor Technology for the Use in Mechanical Lung Ventilation Equipment for Monitoring Critically Ill Patients

Background: The need for mechanical lung ventilation is common in critically ill patients, either with COVID-19 infection or due to other causes. Monitoring of patients being ventilated is essential for timely and improved management. We here propose the use of a novel breath volatile organic compound sensor technology to be used in a mechanical lung ventilation machine for this purpose; the technology was evaluated in critically ill COVID-19 patients on mechanical lung ventilation. Methods: Based on the consistency results of our study data, the breath sensor device with metal oxide gas sensors and environment-controlling sensors was mounted on the ventilation exhaust port of the ventilation machine; this allowed to ensure additional safety since the device was placed outside the contour between the patient and equipment. Results: The sensors allowed stable registration of the signals for up to several weeks for 10 patients in total, depending on the storage amount; a proportion of patients were intubated or received tracheostoma during the evaluation period. Future studies are on the way to correlate sensor readings to other parameters characterizing the severity of the patient condition and outcome. Conclusions: We suppose that such technology will allow patient monitoring in real-time for timely identification of deterioration, potentially requiring some change of management. The obtained results are preliminary and further studies are needed to examine their clinical significance.

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.

Non-invasive breath collection in murine models using a newly developed sampling device

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/6J mice were used to test reproducibility and different air sampling settings for air-flow (ml/min) 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 and a collection time of 20 minutes. 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.

The use of exhaled air analysis in discriminating interstitial lung diseases: a pilot study

BACKGROUND

Fibrotic Interstitial lung diseases (ILD) are a heterogeneous group of chronic lung diseases characterized by diverse degrees of lung inflammation and remodeling. They include idiopathic ILD such as idiopathic pulmonary fibrosis (IPF), and ILD secondary to chronic inflammatory diseases such as connective tissue disease (CTD). Precise differential diagnosis of ILD is critical since anti-inflammatory and immunosuppressive drugs, which are beneficial in inflammatory ILD, are detrimental in IPF. However, differential diagnosis of ILD is still difficult and often requires an invasive lung biopsy. The primary aim of this study is to identify volatile organic compounds (VOCs) patterns in exhaled air to non-invasively discriminate IPF and CTD-ILD. As secondary aim, the association between the IPF and CTD-ILD discriminating VOC patterns and functional impairment is investigated.

METHODS

Fifty-three IPF patients, 53 CTD-ILD patients and 51 controls donated exhaled air, which was analyzed for its VOC content using gas chromatograph- time of flight- mass spectrometry.

RESULTS

By applying multivariate analysis, a discriminative profile of 34 VOCs was observed to discriminate between IPF patients and healthy controls whereas 11 VOCs were able to distinguish between CTD-ILD patients and healthy controls. The separation between IPF and CTD-ILD could be made using 16 discriminating VOCs, that also displayed a significant correlation with total lung capacity and the 6 min' walk distance.

CONCLUSIONS

This study reports for the first time that specific VOC profiles can be found to differentiate IPF and CTD-ILD from both healthy controls and each other. Moreover, an ILD-specific VOC profile was strongly correlated with functional parameters. Future research applying larger cohorts of patients suffering from a larger variety of ILDs should confirm the potential use of breathomics to facilitate fast, non-invasive and proper differential diagnosis of specific ILDs in the future as first step towards personalized medicine for these complex diseases.

Developing GLAD Parameters to Control the Deposition of Nanostructured Thin Film

In this paper, we describe the device developed to control the deposition parameters to manage the glancing angle deposition (GLAD) process of metal-oxide thin films for gas-sensing applications. The GLAD technique is based on a set of parameters such as the tilt, rotation, and substrate temperature. All parameters are crucial to control the deposition of nanostructured thin films. Therefore, the developed GLAD controller enables the control of all parameters by the scientist during the deposition. Additionally, commercially available vacuum components were used, including a three-axis manipulator. High-precision readings were tested, where the relative errors calculated using the parameters provided by the manufacturer were 1.5% and 1.9% for left and right directions, respectively. However, thanks to the formula developed by our team, the values were decreased to 0.8% and 0.69%, respectively.

UV Illumination as a Method to Improve the Performance of Gas Sensors Based on Graphene Field-Effect Transistors

The ability to detect and recognize airborne chemical species is essential to enable applications in security, health, and environmental monitoring. Here, we report a sensing platform based on graphene field-effect transistor (GFET) devices combined with optical illumination for the detection of volatile compounds. We compare the change in resistance of GFET sensors upon exposure to analytes such as ethanol, dimethyl methylphosphonate (DMMP), and water vapors with and without the presence of a local illuminating ultraviolet (UV) light-emitting diode (LED). Our results show that UV illumination acts as a control knob for the electronic transport properties of graphene, increasing the device's response to ethanol, water, and DMMP, up to a factor of 54, and enabling ppb-level detection of DMMP at 800 ppb without chemical functionalization of the graphene layer. The sensing response can be optimized to reveal an analyte-specific interplay between the induced changes in carrier concentration and mobility of the GFET. These findings provide a pathway to enhancing the sensitivity of GFET sensors and a differentiation channel to improve their selectivity.

Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

BACKGROUND AND AIMS

In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.

MATERIALS AND METHODS

A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.

RESULTS

Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI_95%: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.

CONCLUSIONS

Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.

The variability of volatile organic compounds in the indoor air of clinical environments

The development of clinical breath-analysis is confounded by the variability of background volatile organic compounds (VOCs). Reliable interpretation of clinical breath-analysis at individual, and cohort levels requires characterisation of clinical-VOC levels and exposures. Active-sampling with thermal-desorption/gas chromatography-mass spectrometry recorded and evaluated VOC concentrations in 245 samples of indoor air from three sites in a large National Health Service (NHS) provider trust in the UK over 27 months. Data deconvolution, alignment and clustering isolated 7344 features attributable to VOC and described the variability (composition and concentration) of respirable clinical VOC. 328 VOC were observed in more than 5% of the samples and 68 VOC appeared in more than 30% of samples. Common VOC were associated with exogenous and endogenous sources and 17 VOC were identified as seasonal differentiators. The presence of metabolites from the anaesthetic sevoflurane, and putative-disease biomarkers in room air, indicated that exhaled VOC were a source of background-pollution in clinical breath-testing activity. With the exception of solvents, and waxes associated with personal protective equipment (PPE), exhaled VOC concentrations above 3µg m^-3are unlikely to arise from room air contamination, and in the absence of extensive survey-data, this level could be applied as a threshold for inclusion in studies, removing a potential environmental confounding-factor in developing breath-based diagnostics.

Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Background

The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC).

Methods

Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning.

Results

Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86–90%.

Conclusions

Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01862-z.

Cheap and easy human breath collection system for trace volatile organic compounds screening using thermal desorption - gas chromatography mass spectrometry

•

By analyzing the VOCs presents in our breath, we could identify if some components should not be present in our bodies, or their concentration is higher or lower than normal.

•

To collect breath samples for VOC analysis, we looked into the current available methodologies and, due to their high prices, tried to develop our own easy and cheap device. A simple single use Minigrip LDPE plastic bag was used in this work and its efficiency and performance were tested.

•

After breath collection, samples were analyzed using Thermal Desorption (TD) system, coupled with Gas Chromatography Mass Spectrometer (GC-MS).

The electronic nose as a rule-out test for tuberculosis in an indigenous population

INTRODUCTION

To end the tuberculosis (TB) epidemic, efficient diagnostic tools are needed. In a previous calibration study, a portable 'point of care' electronic nose device (Aeonose^TM ) proved to be a promising tool in a hospital setting. We evaluated this technology to detect TB in an indigenous population in Paraguay.

METHODS

A total of 131 participants were enrolled. eNose results were compared with anamnesis, physical examinations, chest radiography and mycobacterial cultures in individuals with signs and symptoms compatible with TB. The eNose analysis was performed in two stages: first, the training with a combination of a previous study population plus 47 participants from the new cohort (total n = 153), and second, the 'blind prediction' of 84 participants.

RESULTS

21% of all participants (n = 131) showed symptoms and/or chest radiography abnormalities suspicious of TB. No sputum samples resulted culture positive for Mycobacterium tuberculosis complex. Only one patient had a positive smell print analysis. In the training model, the specificity was 92% (95% confidence interval (CI): 85%-96%) and the negative predictive value (NPV) was 95%. In the blind prediction model, the specificity and the NPV were 99% (95% CI: 93%-99%) and 100%, respectively. Although the sensitivity and positive predictive value of the eNose could not be assessed in this cohort due to the small sample size, no active TB cases were found during a one year of follow-up period.

CONCLUSION

The eNose showed promising specificity and negative predictive value and might therefore be developed as a rule-out test for TB in vulnerable populations.

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

Selected ion flow tube mass spectrometry for targeted analysis of volatile organic compounds in human breath

The analysis of volatile organic compounds (VOCs) within breath for noninvasive disease detection and monitoring is an emergent research field that has the potential to reshape current clinical practice. However, adoption of breath testing has been limited by a lack of standardization. This protocol provides a comprehensive workflow for online and offline breath analysis using selected ion flow tube mass spectrometry (SIFT-MS). Following the suggested protocol, 50 human breath samples can be analyzed and interpreted in <3 h. Key advantages of SIFT-MS are exploited, including the acquisition of real-time results and direct compound quantification without need for calibration curves. The protocol includes details of methods developed for targeted analysis of disease-specific VOCs, specifically short-chain fatty acids, aldehydes, phenols, alcohols and alkanes. A procedure to make custom breath collection bags is also described. This standardized protocol for VOC analysis using SIFT-MS is intended to provide a basis for wider application and the use of breath analysis in clinical studies.

Exhaled volatile organic compounds analysis in clinical pediatrics: a systematic review

BACKGROUND

Measured exhaled volatile organic compounds (VOCs) in breath also referred to as exhaled volatilome have been long claimed as a potential source of non-invasive and clinically applicable biomarkers. However, the feasibility of using exhaled volatilome in clinical practice remains to be demonstrated, particularly in pediatrics where the need for improved non-invasive diagnostic and monitoring methods is most urgent. This work presents the first formal evidence-based judgment of the clinical potential of breath volatilome in the pediatric population.

METHODS

A rigorous systematic review across Web of Science, SCOPUS, and PubMed databases following the PRISMA statement guidelines. A narrative synthesis of the evidence was conducted and QUADAS-2 was used to assess the quality of selected studies.

RESULTS

Two independent reviewers deemed 22 out of the 229 records initially found to satisfy inclusion criteria. A summary of breath VOCs found to be relevant for several respiratory, infectious, and metabolic pathologies was conducted. In addition, we assessed their associated metabolism coverage through a functional characterization analysis.

CONCLUSION

Our results indicate that current research remains stagnant in a preclinical exploratory setting. Designing exploratory experiments in compliance with metabolomics practice should drive forward the clinical translation of VOCs breath analysis.

IMPACT

What is the key message of your article? Metabolomics practice could help to achieve the clinical utility of exhaled volatilome analysis. What does it add to the existing literature? This work is the first systematic review focused on disease status discrimination using analysis of exhaled breath in the pediatric population. A summary of the reported exhaled volatile organic compounds is conducted together with a functional characterization analysis. What is the impact? Having noted challenges preventing the clinical translation, we summary metabolomics practices and the experimental designs that are closer to clinical practice to create a framework to guide future trials.

Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.