Paul Brinkman

An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls

BACKGROUND Malignant Pleural Mesothelioma (MPM) is a tumour of the surface cells of the pleura that is highly aggressive and mainly caused by asbestos exposure. Electronic noses capture the spectrum of exhaled volatile organic compounds (VOCs) providing a composite biomarker profile (breathprint). OBJECTIVE We tested the hypothesis that an electronic nose can discriminate exhaled air of patients with MPM from subjects with a similar long-term professional exposure to asbestos without MPM and from healthy controls. METHODS 13 patients with a histology confirmed diagnosis of MPM (age 60.9±12.2 year), 13 subjects with certified, long-term professional asbestos exposure (age 67.2±9.8), and 13 healthy subjects without asbestos exposure (age 52.2±16.2) participated in a cross-sectional study. Exhaled breath was collected by a previously described method and sampled by an electronic nose (Cyranose 320). Breathprints were analyzed by canonical discriminant analysis on principal component reduction. Cross-validated accuracy (CVA) was calculated. RESULTS Breathprints from patients with MPM were separated from subjects with asbestos exposure (CVA: 80.8%, sensitivity 92.3%, specificity 85.7%). MPM was also distinguished from healthy controls (CVA: 84.6%). Repeated measurements confirmed these results. CONCLUSIONS Molecular pattern recognition of exhaled breath can correctly distinguish patients with MPM from subjects with similar occupational asbestos exposure without MPM and from healthy controls. This suggests that breathprints obtained by electronic nose have diagnostic potential for MPM.

Exhaled breath analysis by electronic nose in airways disease. Established issues and key questions

Exhaled air contains many volatile organic compounds (VOCs) that are the result of normal and disease‐associated metabolic processes anywhere in the body. Different omics techniques can assess the pattern of these VOCs. One such omics technique suitable for breath analysis is represented by electronic noses (eNoses), providing fingerprints of the exhaled VOCs, called breathprints. Breathprints have been shown to be altered in different disease states, including in asthma and COPD. This review describes the current status on clinical validation and application of breath analysis by electronic noses in the diagnosis and monitoring of chronic airways diseases. Furthermore, important methodological issues including breath sampling, modulating factors and incompatibility between eNoses are raised and discussed. Next steps towards clinical application of electronic noses are provided, including further validation in suspected disease, assessment of the influence of different comorbidities, the value in longitudinal monitoring of patients with asthma and COPD and the possibility to predict treatment responses. Eventually, a Breath Cloud may be constructed, a large database containing disease‐specific breathprints. When collaborative efforts are put into optimization of this technique, it can provide a rapid and non‐invasive first line diagnostic test.

Breathomics in Lung Disease

Volatile organic compounds (VOCs) are produced by virtually all metabolic processes of the body. As such, they have potential to serve as noninvasive metabolic biomarkers. Since exhaled VOCs are either derived from the respiratory tract itself or have passed the lungs from the circulation, they are candidate biomarkers in the diagnosis and monitoring of pulmonary diseases in particular. Good examples of the possibilities of exhaled volatiles in pulmonary medicine are provided by the potential use of VOCs to discriminate between patients with lung cancer and healthy control subjects and to noninvasively diagnose infectious diseases and the association between VOCs and markers of disease activity that has been established in obstructive lung diseases. Several steps are, however, required prior to implementation of breath-based diagnostics in daily clinical practice. First, VOCs should be studied in the intention-to-diagnose population, because biomarkers are likely to be affected by multiple (comorbid) conditions. Second, breath collection and analysis procedures need to be standardized to allow pooling of data. Finally, apart from probabilistic analysis for diagnostic purposes, detailed examination of the nature of volatile biomarkers not only will improve our understanding of the pathophysiologic origins of these markers and the nature of potential confounders but also can enable the development of sensors that exhibit maximum sensitivity and specificity toward specific applications. By adhering to such an approach, exhaled biomarkers can be validated in the diagnosis, monitoring, and treatment of patients in pulmonary medicine and contribute to the development of personalized medicine.

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

Breath tests cover the fraction of nitric oxide in expired gas (FENO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for FENO, 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 FENO, 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. ERS technical standard: exhaled biomarkers in lung disease http://ow.ly/mAjr309DBOP

Combined sputum hypermethylation and eNose analysis for lung cancer diagnosis

Aims The aim of this study is to explore DNA hypermethylation analysis in sputum and exhaled breath analysis for their complementary, non-invasive diagnostic capacity in lung cancer. Methods Sputum samples and exhaled breath were prospectively collected from 20 lung cancer patients and 31 COPD controls (Set 1). An additional 18 lung cancer patients and 8 controls only collected exhaled breath as validation set (Set 2). DNA hypermethylation of biomarkers RASSF1A, cytoglobin, APC, FAM19A4, PHACTR3, 3OST2 and PRDM14 was considered, and breathprints from exhaled breath samples were created using an electronic nose (eNose). Results Both DNA hypermethylation markers in sputum and eNose were independently able to distinguish lung cancer patients from controls. The combination of RASSF1A and 3OST2 hypermethylation had a sensitivity of 85% with a specificity of 74%. eNose had a sensitivity of 80% with a specificity of 48%. Sensitivity for lung cancer diagnosis increased to 100%, when RASSF1A hypermethylation was combined with eNose, with specificity of 42%. Both methods showed to be complementary to each other (p≤0.011). eNose results were reproducible in Set 2. Conclusions When used in concert, RASSF1A hypermethylation in sputum and exhaled breath analysis are complementary for lung cancer diagnosis, with 100% sensitivity in this series. This finding should be further validated.

Exhaled breath profiles in the monitoring of loss of control and clinical recovery in asthma

Asthma is a chronic inflammatory airway disease, associated with episodes of exacerbations. Therapy with inhaled corticosteroids (ICS) targets airway inflammation, which aims to maintain and restore asthma control. Clinical features are only modestly associated with airways inflammation. Therefore, we hypothesized that exhaled volatile metabolites identify longitudinal changes between clinically stable episodes and loss of asthma control.

Smelling the Diagnosis: The Electronic Nose as Diagnostic Tool in Inflammatory Arthritis. A Case-Reference Study

Objective To investigate whether exhaled breath analysis using an electronic nose can identify differences between inflammatory joint diseases and healthy controls. Methods In a cross-sectional study, the exhaled breath of 21 rheumatoid arthritis (RA) and 18 psoriatic arthritis (PsA) patients with active disease was compared to 21 healthy controls using an electronic nose (Cyranose 320; Smiths Detection, Pasadena, CA, USA). Breathprints were analyzed with principal component analysis, discriminant analysis, and area under curve (AUC) of receiver operating characteristics (ROC) curves. Volatile organic compounds (VOCs) were identified by gas chromatography and mass spectrometry (GC-MS), and relationships between breathprints and markers of disease activity were explored. Results Breathprints of RA patients could be distinguished from controls with an accuracy of 71% (AUC 0.75, 95% CI 0.60–0.90, sensitivity 76%, specificity 67%). Breathprints from PsA patients were separated from controls with 69% accuracy (AUC 0.77, 95% CI 0.61–0.92, sensitivity 72%, specificity 71%). Distinction between exhaled breath of RA and PsA patients exhibited an accuracy of 69% (AUC 0.72, 95% CI 0.55–0.89, sensitivity 71%, specificity 72%). There was a positive correlation in RA patients of exhaled breathprints with disease activity score (DAS28) and number of painful joints. GC-MS identified seven key VOCs that significantly differed between the groups. Conclusions Exhaled breath analysis by an electronic nose may play a role in differential diagnosis of inflammatory joint diseases. Data from this study warrant external validation.

Doxycycline for outpatient-treated acute exacerbations of COPD: a randomised double-blind placebo-controlled trial

BACKGROUND Antibiotics do not reduce mortality or short-term treatment non-response in patients receiving treatment for acute exacerbations of COPD in an outpatient setting. However, the long-term effects of antibiotics are unknown. The aim of this study was to investigate if the antibiotic doxycycline added to the oral corticosteroid prednisolone prolongs time to next exacerbation in patients with COPD receiving treatment for an exacerbation in the outpatient setting. METHODS In this randomised double-blind placebo-controlled trial, we recruited a cohort of patients with COPD from outpatient clinics of nine teaching hospitals and three primary care centres in the Netherlands. Inclusion criteria were an age of at least 45 years, a smoking history of at least 10 pack-years, mild-to-severe COPD (Global Initiative of Chronic Obstructive Lung Disease [GOLD] stage 1-3), and at least one exacerbation during the past 3 years. Exclusion criteria were poor mastery of the Dutch language, poor cognitive functioning, known allergy to doxycycline, pregnancy, and a life expectancy of shorter than 1 month. If a participant had an exacerbation, we randomly assigned them (1:1; with permuted blocks of variable sizes [ranging from two to ten]; stratified by GOLD stage 1-2 vs 3) to a 7 day course of oral doxycycline 100 mg daily (200 mg on the first day) or placebo. Exclusion criteria for randomisation were fever, admission to hospital, and current use of antibiotics or use within the previous 3 weeks. Patients in both groups received a 10 day course of 30 mg oral prednisolone daily. Patients, investigators, and those assessing outcomes were masked to treatment assignment. The primary outcome was time to next exacerbation in all randomly allocated patients except for those incorrectly randomly allocated who did not meet the inclusion criteria or met the exclusion criteria. This trial is registered with the Netherlands Trial Register, number NTR2499. FINDINGS Between Dec 22, 2010, and Aug 6, 2013, we randomly allocated 305 (34%) patients from the cohort of 887 patients to doxycycline (152 [50%]) or placebo (153 [50%]), excluding four (1%) patients (two [1%] from each group) who were incorrectly randomly allocated from the analysis. 257 (85%) of 301 patients had a next exacerbation (131 [87%] of 150 in the doxycycline group vs 126 [83%] of 151 in the placebo group). Median time to next exacerbation was 148 days (95% CI 95-200) in the doxycycline group compared with 161 days (118-211) in the placebo group (hazard ratio 1·01 [95% CI 0·79-1·31]; p=0·91). We did not note any significant differences between groups in the frequency of adverse events during the first 2 weeks after randomisation (47 [31%] of 150 in the doxycycline group vs 53 [35%] of 151 in the placebo group; p=0·54) or in serious adverse events during the 2 years of follow-up (42 [28%] vs 43 [29%]; p=1). INTERPRETATION In patients with mild-to-severe COPD receiving treatment for an exacerbation in an outpatient setting, the antibiotic doxycycline added to the oral corticosteroid prednisolone did not prolong time to next exacerbation compared with prednisolone alone. These findings do not support prescription of antibiotics for COPD exacerbations in an outpatient setting. FUNDING Netherlands Organization for Health Research and Development.

Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label

Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath. Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set. This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression. Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R2=0.581) and neutrophilic (R2=0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set. Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner. Breathomics may qualify for rapid clinical/inflammatory phenotyping of chronic airway disease at the point of care http://ow.ly/E16p30gE1Cl

Breathomics from exhaled volatile organic compounds in pediatric asthma

Asthma is the most common chronic disease in children, and is characterized by airway inflammation, bronchial hyperresponsiveness, and airflow obstruction. Asthma diagnosis, phenotyping, and monitoring are still challenging with currently available methods, such as spirometry, FENO or sputum analysis. The analysis of volatile organic compounds (VOCs) in exhaled breath could be an interesting non‐invasive approach, but has not yet reached clinical practice. This review describes the current status of breath analysis in the diagnosis and monitoring of pediatric asthma. Furthermore, features of an ideal breath test, different breath analysis techniques, and important methodological issues are discussed. Although only a (small) number of studies have been performed in pediatric asthma, of which the majority is focusing on asthma diagnosis, these studies show moderate to good prediction accuracy (80‐100%, with models including 6‐28 VOCs), thereby qualifying breathomics for future application. However, standardization of procedures, longitudinal studies, as well as external validation are needed in order to further develop breathomics into clinical tools. Such a non‐invasive tool may be the next step toward stratified and personalized medicine in pediatric respiratory disease.