Evening and morning exhaled volatile compound patterns are different in obstructive sleep apnoea assessed with electronic nose

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.

Current and future strategies for diagnostic and management of obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA) is a common sleep disorder with multiple comorbidities including hypertension, diabetes, and cardiovascular disorders. Detected based on an overnight sleep study is called polysomnography (PSG); OSA still remains undiagnosed in majority of the population mainly attributed to lack of awareness. To overcome the limitations posed by PSG such as patient discomfort and overnight hospitalization, newer technologies are being explored. In addition, challenges associated with current management of OSA using continuous positive airway pressure (CPAP), etc. presents several pitfalls.

AREAS COVERED

Conventional and modern detection/management techniques including PSG, CPAP, smart wearable/pillows, bio-motion sensors, etc., have both pros and cons. To fulfill the limitations in OSA diagnostics, there is an imperative need for new technology for screening of symptomatic and more importantly asymptomatic OSA patients to reduce the risk of several associated life-threatening comorbidities. In this line, molecular marker-based diagnostics have shown great promises.

EXPERT OPINION

A detailed overview is presented on the OSA management and diagnostic approaches and recent advances in the molecular screening methods. The potentials of biomarker-based detection and its limitations are also portrayed and a comparison between the standard, current modern approaches, and promising futuristic technologies for OSA diagnostics and management is set forth.ABBREVIATIONS AHI: Apnea hypopnea index; AI: artificial intelligence; CAM: Cell adhesion molecules; CPAP: Continuous Positive Airway Pressure; COVID-19: Coronavirus Disease 2019; CVD: Cardiovascular disease; ELISA: Enzyme linked immunosorbent assay; HSAT: Home sleep apnea testing; IR-UWB: Impulse radio-ultra wideband; MMA: maxillomandibular advancement; PSG: Polysomnography; OSA: Obstructive sleep apnea; SOD: Superoxide dismutase; QD: Quantum dot.

Continuous positive airway pressure affects mitochondrial function and exhaled PGC1-α levels in obstructive sleep apnea

Purpose: Subjects with obstructive sleep apnea (OSA) exhibit systemic and upper airway oxidative stress and inflammation, which cause mitochondrial dysfunction. The intend of this study is to estimate mitochondrial function (mitochondrial DNA/nuclear DNA [Mt/N] ratio) and protein levels of peroxisome proliferator-coactivated receptor gamma co-activator 1-alpha (PGC1-α) in the exhaled breath condensate (EBC) and plasma before and after continuous positive airway pressure (CPAP) treatment. Materials and methods: Twenty healthy individuals (control) and 40 subjects with severe or moderate OSA were recruited to undergo CPAP treatment and evaluation in a sleep study. The Mt/N ratio in the EBC and blood were assayed by quantitative real-time polymerase chain reaction. Enzyme-linked immunosorbent assay was used to measure the protein concentration of PGC1-α in the EBC and plasma. All experiments were performed after 3 months of CPAP treatment in subjects with OSA. Results: We observed no noteworthy differences between the control and treatment groups. Moreover, there were no differences in the Mt/N ratio in the blood and plasma levels of PGC1-α in subjects with OSA before and after treatment. However, the Mt/N ratio and protein levels of PGC1-α in the EBC of OSA subjects were higher than those in the control group and returned to normal levels after CPAP treatment. Conclusions: We successfully treated subjects with OSA by CPAP, which restored the Mt/N ratio and levels of PGC1-α in the EBC.

The smell of lung disease: a review of the current status of electronic nose technology

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

Validation of breath biomarkers for obstructive sleep apnea

BACKGROUND AND OBJECTIVES

Obstructive sleep apnea (OSA) is an underdiagnosed respiratory disease with negative metabolic and cardiovascular effects. The current gold standard for diagnosing OSA is in-hospital polysomnography, a time-consuming and costly procedure, often inconvenient for the patient. Recent studies revealed evidence for the potential of breath analysis for the diagnosis of OSA based on a disease-specific metabolic pattern. However, none of these findings were validated in a larger and broader cohort, an essential step for its application in clinics.

METHODS

In the present study, we validated a panel of breath biomarkers in a cohort of patients with possible OSA (N = 149). These markers were previously identified in our group by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS).

RESULTS

Here, we could confirm significant differences between metabolic patterns in exhaled breath from OSA patients compared to control subjects without OSA as well as the association of breath biomarker levels with disease severity. Our prediction of the diagnosis for the patients from this completely independent validation study using a classification model trained on the data from the previous study resulted in an area under the receiver operating characteristic curve of 0.66, which is comparable to questionnaire-based OSA screenings.

CONCLUSIONS

Thus, our results suggest that breath analysis by SESI-HRMS might be useful to screen for OSA as an objective measure. However, its true predictive power should be tested in combination with OSA screening questionnaires.

CLINICAL TRIAL

"Mass Spectral Fingerprinting in Obstructive Sleep Apnoea", NCT02810158, www.ClinicalTrials.gov.

Characterization of inflammatory profile by breath analysis in chronic coronary syndromes

AIMS

Exhaled breath contains thousands of volatile organic compounds (VOCs) produced during various metabolic processes both in health and disease.Analysis of breath with electronic nose BIONOTE-V allows modifications of exhaled VOCs to be studied, which are clinically recognized to be a marker for several disorders, including heart failure. New noninvasive tests based on VOCs analysis might be a useful tool for early detection of chronic coronary syndromes (CCS).

METHODS

Exhaled air was collected and measured in individuals with an indication to perform invasive coronary angiography (ICA). All patients' samples were obtained before ICA.

RESULTS

Analysis with BIONOTE-V was performed in a total cohort of 42 patients consecutively enrolled, of whom 19 did not require myocardial revascularization and 23 with indication for myocardial revascularization. BIONOTE-V was able to correctly identify 18 out of 23 patients affected by severe coronary artery disease (sensitivity = 78.3% and specificity = 68.4%). Our predicted model had a tight correlation with SYNTAX score (error of the BIONOTE-V = 15).

CONCLUSION

CCS patients have a distinctive fingerprint of exhaled breath, and analysis by BIONOTE-V has the potential for identifying these patients. Moreover, it seems that this technique can correctly identify patients according to anatomical disease severity at ICA. If the preliminary data of this proof of concept study will be confirmed, this rapid and noninvasive diagnostic tool able to identify CCS might have an impact in routine clinical practice.

A community study of the risk for obstructive sleep apnea and respiratory inflammation in an adult Chinese population

Objectives: We aimed to investigate the relationship between obstructive sleep apnea (OSA) risk and respiratory inflammation evaluated by the exhaled breath condensate (EBC) interleukin-6 (IL-6) and plasma surfactant protein-D (SP-D), based on the Berlin questionnaire (BQ) screening values in an adult, urban community in Beijing, China.Methods: Volunteers aged >40 years were recruited from the Shichahai community of central Beijing (Registration number: NCT04832711). Their general information and disease history were recorded. OSA risk was assessed using the BQ. IL-6 in EBC and plasma SP-D were d etected by enzyme-linked immunoassay through specimens collected while fasting. The differences in IL-6 and SP-D values between high-risk and low-risk groups for OSA were compared, and the factors affecting their values were analyzed.Results: Among 1,239 participants, 18.8% of participants were in the high-risk group. There were more participants with higher body mass index, chronic hypertension, coronary heart disease, and diabetes in the high-risk group than in the low-risk group (P < 0.05). There were no significant differences in EBC IL-6 and plasma SP-D between the high- and low-OSA risk groups (p > 0.05). After adjustment for age, sex and chronic comorbidities, multivariate logistic regression showed that there was no correlation between risk of OSA and IL-6 in EBC. However, the risk of OSA (odds ratio [OR] [95% CI]: 1.69 [1.15,2.48]; β = 0.522) and BMI (OR [95%CI]: 0.94 [0.91,0.98]; β = -0.061) were independently associated with plasma SP-D level (p < 0.05 for both). Stratification analysis showed that OSA risk were independently associated with plasma SP-D levels in participants <65 years, or men, or participants with BMI<25.Conclusion: This study showed that plasma SP-D, an inflammation biomarker, was associated with risk of OSA and BMI in a Chinese central urban community.The relationship between the risk of OSA and respiratory inflammation in community populations needs to be further evaluated.

The Role of Electronic Noses in Phenotyping Patients with Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a common progressive disorder of the respiratory system which is currently the third leading cause of death worldwide. Exhaled breath analysis is a non-invasive method to study lung diseases, and electronic noses have been extensively used in breath research. Studies with electronic noses have proved that the pattern of exhaled volatile organic compounds is different in COPD. More recent investigations have reported that electronic noses could potentially distinguish different endotypes (i.e., neutrophilic vs. eosinophilic) and are able to detect microorganisms in the airways responsible for exacerbations. This article will review the published literature on electronic noses and COPD and help in identifying methodological, physiological, and disease-related factors which could affect the results.

Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms

Non-intrusive, easy-to-use and pragmatic collection of biological processes is warranted to evaluate potential biomarkers of psychiatric symptoms. Prior work with relatively modest sample sizes suggests that under highly-controlled sampling conditions, volatile organic compounds extracted from the human breath (exhalome), often measured by an electronic nose ("e-nose"), may be related to physical and mental health. The present study utilized a streamlined data collection approach and attempted to replicate and extend prior e-nose links to mental health in a standard research setting within large transdiagnostic community dataset (N = 1207; 746 females; 18-61 years) who completed a screening visit at the Laureate Institute for Brain Research between 07/2016 and 05/2018. Factor analysis was used to obtain latent exhalome variables, and machine learning approaches were employed using these latent variables to predict three types of symptoms independent of each other (depression, anxiety, and substance use disorder) within separate training and a test sets. After adjusting for age, gender, body mass index, and smoking status, the best fitting algorithm produced by the training set accounted for nearly 0% of the test set's variance. In each case the standard error included the zero line, indicating that models were not predictive of clinical symptoms. Although some sample variance was predicted, findings did not generalize to out-of-sample data. Based on these findings, we conclude that the exhalome, as measured by the e-nose within a less-controlled environment than previously reported, is not able to provide clinically useful assessments of current depression, anxiety or substance use severity.

Exhaled Breath Analysis in Obstructive Sleep Apnea Syndrome: A Review of the Literature

Background and Objectives: Obstructive sleep apnea syndrome (OSAS) represents an independent risk factor for cardiovascular, metabolic and neurological events. Polysomnography is the gold-standard for the diagnosis, however is expensive and time-consuming and not suitable for widespread use. Breath analysis is an innovative, non-invasive technique, able to provide clinically relevant information about OSAS. This systematic review was aimed to outline available evidence on the role of exhaled breath analysis in OSAS, taking into account the techniques' level of adherence to the recently proposed technical standards. Materials and Methods: Articles reporting original data on exhaled breath analysis in OSAS were identified through a computerized and manual literature search and screened. Duplicate publications, case reports, case series, conference papers, expert opinions, comments, reviews and meta-analysis were excluded. Results: Fractional exhaled Nitric Oxide (FeNO) is higher in OSAS patients than controls, however its absolute value is within reported normal ranges. FeNO association with AHI is controversial, as well as its change after continuous positive airway pressure (C-PAP) therapy. Exhaled breath condensate (EBC) is acid in OSAS, cytokines and oxidative stress markers are elevated, they positively correlate with AHI and normalize after treatment. The analysis of volatile organic compounds (VOCs) by spectrometry or electronic nose is able to discriminate OSAS from healthy controls. The main technical issues regards the dilution of EBC and the lack of external validation in VOCs studies. Conclusions: Exhaled breath analysis has a promising role in the understanding of mechanisms underpinning OSAS and has demonstrated a clinical relevance in identifying individuals affected by the disease, in assessing the response to treatment and, potentially, to monitor patient's adherence to mechanical ventilation. Albeit the majority of the technical standards proposed by the ERS committee have been followed by existing papers, further work is needed to uniform the methodology.

On-Line Analysis of Exhaled Breath Focus Review

On-line analysis of exhaled breath offers insight into a person's metabolism without the need for sample preparation or sample collection. Due to its noninvasive nature and the possibility to sample continuously, the analysis of breath has great clinical potential. The unique features of this technology make it an attractive candidate for applications in medicine, beyond the task of diagnosis. We review the current methodologies for on-line breath analysis, discuss current and future applications, and critically evaluate challenges and pitfalls such as the need for standardization. Special emphasis is given to the use of the technology in diagnosing respiratory diseases, potential niche applications, and the promise of breath analysis for personalized medicine. The analytical methodologies used range from very small and low-cost chemical sensors, which are ideal for continuous monitoring of disease status, to optical spectroscopy and state-of-the-art, high-resolution mass spectrometry. The latter can be utilized for untargeted analysis of exhaled breath, with the capability to identify hitherto unknown molecules. The interpretation of the resulting big data sets is complex and often constrained due to a limited number of participants. Even larger data sets will be needed for assessing reproducibility and for validation of biomarker candidates. In addition, molecular structures and quantification of compounds are generally not easily available from on-line measurements and require complementary measurements, for example, a separation method coupled to mass spectrometry. Furthermore, a lack of standardization still hampers the application of the technique to screen larger cohorts of patients. This review summarizes the present status and continuous improvements of the principal on-line breath analysis methods and evaluates obstacles for their wider application.

Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models’ performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

Breath analysis in respiratory diseases: state-of-the-art and future perspectives

INTRODUCTION

The vast majority of respiratory diseases are associated with the production of volatile organic compounds (VOCs), the analysis of which might improve our knowledge about these disorders and their clinical management. The aim of this narrative review is to provide a comprehensive summary of current evidence supporting the application of breath analysis in the field of respiratory diseases, as well as suggesting potential applications available in the near future. Areas covered: A computerized literature search was performed to identify relevant articles reporting original data on the clinical use of breath analysis in respiratory diseases. Papers focusing on diseases other than respiratory, technical issues of VOC sampling and analysis, in vitro experiments or exogenous compounds were excluded. Expert commentary: Currently available evidence on the application of breath analysis in respiratory diseases is encouraging; however, it is mostly based on single-center studies without external validation. The standardization of the technique, together with multicenter clinical trials with external validation, will ensure it is ready for clinical use. Current and new applications in respiratory diseases may represent a major breakthrough in the field, so much so as to deserve further efforts in outlining the most effective way to apply VOC analysis for clinical purposes.

Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols

Human breath, along with urine and blood, has long been one of the three major biological media for assessing human health and environmental exposure. In fact, the detection of odor on human breath, as described by Hippocrates in 400 BC, is considered the first analytical health assessment tool. Although less common in comparison to contemporary bio-fluids analyses, breath has become an attractive diagnostic medium as sampling is non-invasive, unlimited in timing and volume, and does not require clinical personnel. Exhaled breath, exhaled breath condensate (EBC), and exhaled breath aerosol (EBA) are different types of breath matrices used to assess human health and disease state. Over the past 20 years, breath research has made many advances in assessing health state, overcoming many of its initial challenges related to sampling and analysis. The wide variety of sampling techniques and collection devices that have been developed for these media are discussed herein. The different types of sensors and mass spectrometry instruments currently available for breath analysis are evaluated as well as emerging breath research topics, such as cytokines, security and airport surveillance, cellular respiration, and canine olfaction.

Circulating Survivin Levels in Obstructive Sleep Apnoea

INTRODUCTION

Obstructive sleep apnoea (OSA) is characterised by a low-grade systemic and airway inflammation; however, the regulatory mechanisms of inflammation are poorly explored. Survivin (Birc5) is an anti-apoptotic protein which inhibits Type 1 inflammation; however, this molecule has not been investigated in OSA.

METHODS

Forty-five patients with OSA and 31 non-OSA control subjects were involved. Venous blood was collected for plasma survivin measurements before and after diagnostic overnight polysomnography. Plasma survivin levels were compared between the two groups and correlated to OSA severity and comorbidities.

RESULTS

Plasma survivin levels were lower in OSA in the evening (27.6 ± 89.9 vs. 108.3 ± 161.2 pg/ml, p < 0.01) and in the morning (17.4 ± 48.6 vs. 36.4 ± 69.2 pg/ml, p = 0.02) compared to the control group. This OSA-related decrease was also present when only the non-obese patients were analysed. Significant indirect relationships were observed between plasma survivin levels and measures of OSA severity such as the apnoea-hypopnoea index (r = - 0.45) or oxygen desaturation index (r = - 0.40, both p < 0.01); however, when adjusting to BMI, these became insignificant (p > 0.05). Low plasma survivin concentrations were associated with high BMI (r = - 0.35), high CRP (r = - 0.31), low HDL cholesterol (r = 0.24) and high triglyceride levels (r = - 0.24, all p < 0.05).

CONCLUSION

Plasma survivin levels are reduced in OSA, relate to disease severity, and are associated with high CRP levels. This suggests an impaired immunoregulation in this disorder which needs to be studied in further detail.

Role of lung volume and airway inflammation in obstructive sleep apnea

Obstructive sleep apnea (OSA) is a prevalent disorder that affects not only the upper airways but also the intrathoracic airways. In this review, we summarize the results of studies on lung function and airway inflammation. We provide evidence that the alterations in intrathoracic airways observed in OSA are not purely consequences of mechanical trauma and oxidative stress during apneic events but have a causal role in the structural changes associated with OSA and increasing severity of this disorder.

Screening of Obstructive Sleep Apnea Syndrome by Electronic-Nose Analysis of Volatile Organic Compounds

Obstructive Sleep Apnea Syndrome (OSAS) carries important social and economic implications. Once the suspicion of OSAS has arisen, Polysomnography (PSG) represents the diagnostic gold standard. However, about 45% of people who have undergone PSG are free from OSAS. Thus, efforts should be made to improve the selection of subjects. We verified whether the pattern of Volatile Organic Compounds (VOCs) helps to select patients amenable to PSG. We studied 136 subjects (20 obese non-OSAS, 20 hypoxic OSAS, 20 non-hypoxic OSAS, and 20 non-hypoxic Chronic Obstructive Pulmonary Disease (COPD) vs 56 healthy controls) without any criteria of exclusion for comorbidity to deal with a real-life population. VOCs patterns were analyzed using electronic-nose (e-nose) technology. A Discriminant Analysis (Partial Least Square-Discriminant Analysis) was performed to predict respiratory functions and PSG parameters. E-nose distinguished controls (100% correct classification) from others and identified 60% of hypoxic, and 35% of non-hypoxic OSAS patients. Similarly, it identified 60% of COPD patients. One-by-one group comparison yielded optimal discrimination of OSAS vs controls and of COPD vs controls (100% correct classification). In conclusion, e-nose technology applied to breath-analysis can discriminate non-respiratory from respiratory diseased populations in real-life multimorbid populations and exclude OSAS. If confirmed, this evidence may become pivotal for screening purposes.

A European Respiratory Society technical standard: exhaled biomarkers in lung disease

Breath tests cover the fraction of nitric oxide in expired gas (F_eNO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for F_eNO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and F_eNO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management.Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members.Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised.Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.

Electronic Nose Technology in Respiratory Diseases

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.

Overnight Changes in Lung Function of Obese Patients with Obstructive Sleep Apnoea

PURPOSE

Obstructive sleep apnoea (OSA) is a prevalent disorder, characterised by collapse of the upper airways during sleep. The impact of sleep-disordered breathing on pulmonary function indices is however currently not well described. The aim of the study was to evaluate diurnal change in lung function indices in a cohort of patients with OSA and relate pulmonary function changes to disease severity.

METHODS

42 patients with OSA and 73 healthy control subjects participated in the study. Asthma and COPD were excluded in all volunteers following a clinical and spirometric assessment. Spirometry was then performed in all subjects in the evening and the morning following a polysomnography study.

RESULTS

There was no difference in evening or morning FEV₁ or FVC between patients and control subjects (p > 0.05). Neither FEV₁ nor FVC changed in control subjects overnight (p > 0.05). In contrast, FEV₁ significantly increased from evening (2.18/1.54-4.46/L) to morning measurement (2.26/1.42-4.63/L) in OSA without any change in FVC. The FEV₁ increase in OSA was related to male gender, obesity and the lack of treatment with statins or β-blockers (all p < 0.05). A tendency for a direct correlation was apparent between overnight FEV₁ change and RDI (p = 0.05, r = 0.30).

CONCLUSIONS

Diurnal variations in spirometric indices occur in patients with OSA and FEV₁ appears to increase in subjects with OSA overnight. These changes occur in the absence of change in FVC and are directly related to the severity of OSA. These findings dictate a need to consider time of lung function measurement.

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

Exhaled breath analysis by electronic nose in respiratory diseases

Breath analysis via electronic nose is a technique oriented around volatile organic compound (VOC) profiling in exhaled breath for diagnostic and prognostic purposes. This approach, when supported by methodologies for VOC identification, has been often referred to as metabolomics or breathomics. Although breath analysis may have a substantial impact on clinical practice, as it may allow early diagnosis and large-scale screening strategies while being noninvasive and inexpensive, some technical and methodological limitations must be solved, together with crucial interpretative issues. By integrating a review of the currently available literature with more speculative arguments about the potential interpretation and application of VOC analysis, the authors aim to provide an overview of the main relevant aspects of this promising field of research.

An electronic nose in the discrimination of obese patients with and without obstructive sleep apnoea

Exhaled breath contains thousands of volatile organic compounds (VOCs) in gaseous form, which may be used as markers of airway inflammation and lung disease. Electronic noses enable quick and real-time pattern analysis of VOC spectra. It has been shown that the exhaled breath of patients with obstructive sleep apnoea (OSA) differs from that of non-obese controls. We aimed to assess the influence of obesity in the composition of exhaled VOCs by comparing obese subjects with and without OSA. Moreover, we aimed to identify the discriminant VOCs in the two groups.19 obese patients with established OSA (OO; age 51.2 ± 6.8; body mass index (BMI) 34.3 ± 3.5), 14 obese controls without OSA (ONO; age 46.5 ± 7.6; BMI 33.5 ± 4.1) and 20 non-obese healthy controls (HC; age 41.1 ± 12.6; BMI 24.9 ± 3.8) participated in a cross-sectional study. Exhaled breath was collected by a previously described method and sampled by using an electronic nose (Cyranose 320) and by gas chromatography-mass spectrometry (GC-MS) analysis. Breathprints were analyzed by canonical discriminant analysis on principal component reduction. Cross-validation accuracy (CVA) was calculated. Breathprints from the HC group were separated from those of OO (CVA = 97.4%) and ONO (CVA = 94.1%). Breathprints from OO were moderately separated from those of ONO (CVA = 67.6%).The presence of OSA alters the exhaled VOC pattern in obese subjects. The incomplete separation of breathprints between OO and ONO may be due to the same underlying inflammation caused by obesity.

Expiratory flow rate, breath hold and anatomic dead space influence electronic nose ability to detect lung cancer

Background

Electronic noses are composites of nanosensor arrays. Numerous studies showed their potential to detect lung cancer from breath samples by analysing exhaled volatile compound pattern (“breathprint”). Expiratory flow rate, breath hold and inclusion of anatomic dead space may influence the exhaled levels of some volatile compounds; however it has not been fully addressed how these factors affect electronic nose data. Therefore, the aim of the study was to investigate these effects.

Methods

37 healthy subjects (44 ± 14 years) and 27 patients with lung cancer (60 ± 10 years) participated in the study. After deep inhalation through a volatile organic compound filter, subjects exhaled at two different flow rates (50 ml/sec and 75 ml/sec) into Teflon-coated bags. The effect of breath hold was analysed after 10 seconds of deep inhalation. We also studied the effect of anatomic dead space by excluding this fraction and comparing alveolar air to mixed (alveolar + anatomic dead space) air samples. Exhaled air samples were processed with Cyranose 320 electronic nose.

Results

Expiratory flow rate, breath hold and the inclusion of anatomic dead space significantly altered “breathprints” in healthy individuals (p < 0.05), but not in lung cancer (p > 0.05). These factors also influenced the discrimination ability of the electronic nose to detect lung cancer significantly.

Conclusions

We have shown that expiratory flow, breath hold and dead space influence exhaled volatile compound pattern assessed with electronic nose. These findings suggest critical methodological recommendations to standardise sample collections for electronic nose measurements.