Wake-up call by breathomics in sleep apnoea

Respiratory diseases are amongst the most complex pathophysiological entities in medicine. There is no such thing as a one-item disease in our field, which brings the appeal of respiratory diseases for basic and clinical scientists. The most prevalent respiratory diseases are chronic, exhibiting multiple mechanistic pathways that can vary during the course of the disease. This has not only hampered pathogenetic research, but has also impeded adequate disease phenotyping [1]. Apparently, it takes more than a few clinical and serum markers to establish the true biomedical entity of complex diseases. The good news is that, as we speak, medicine is making a step-change in achieving exactly that [2]

Toward composite molecular signatures in the phenotyping of asthma

The complex biology of respiratory diseases such as asthma is feeding the discovery of various disease phenotypes. Although the clinical management of asthma phenotypes by using a single biomarker (e.g., sputum eosinophils) is successful, emerging evidence shows the requirement of multiscale, high-dimensional biological and clinical measurements to capture the complexity of various asthma phenotypes. High-throughput "omics" technologies, including transcriptomics, proteomics, lipidomics, and metabolomics, are increasingly standardized for biomarker discovery in asthma. The leading principle is obeying available guidelines on omics analysis, thereby strictly limiting false discovery. In this review we address the concept of transcriptomics using microarrays or next-generation RNA sequencing and their applications in asthma, highlighting the strengths and limitations of both techniques, and review metabolomics in exhaled air (breathomics) as a noninvasive alternative for sampling the airways directly. These developments will inevitably lead to the integration of molecular signatures in the phenotyping of asthma and other disease

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

To investigate whether exhaled breath analysis using an electronic nose can identify differences between inflammatory joint diseases and healthy controls. 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. 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. Exhaled breath analysis by an electronic nose may play a role in differential diagnosis of inflammatory joint diseases. Data from this study warrant external validatio

Subphenotypes of mild-to-moderate COPD by factor and cluster analysis of pulmonary function, CT imaging and breathomics in a population-based survey

Classification of COPD is currently based on the presence and severity of airways obstruction. However, this may not fully reflect the phenotypic heterogeneity of COPD in the (ex-) smoking community. We hypothesized that factor analysis followed by cluster analysis of functional, clinical, radiological and exhaled breath metabolomic features identifies subphenotypes of COPD in a community-based population of heavy (ex-) smokers. Adults between 50-75 years with a smoking history of at least 15 pack-years derived from a random population-based survey as part of the NELSON study underwent detailed assessment of pulmonary function, chest CT scanning, questionnaires and exhaled breath molecular profiling using an electronic nose. Factor and cluster analyses were performed on the subgroup of subjects fulfilling the GOLD criteria for COPD (post-BD FEV1/FVC < 0.70). Three hundred subjects were recruited, of which 157 fulfilled the criteria for COPD and were included in the factor and cluster analysis. Four clusters were identified: cluster 1 (n = 35; 22%): mild COPD, limited symptoms and good quality of life. Cluster 2 (n = 48; 31%): low lung function, combined emphysema and chronic bronchitis and a distinct breath molecular profile. Cluster 3 (n = 60; 38%): emphysema predominant COPD with preserved lung function. Cluster 4 (n = 14; 9%): highly symptomatic COPD with mildly impaired lung function. In a leave-one-out validation analysis an accuracy of 97.4% was reached. This unbiased taxonomy for mild to moderate COPD reinforces clusters found in previous studies and thereby allows better phenotyping of COPD in the general (ex-) smoking populatio

Airway inflammation and mannitol challenge test in COPD

Abstract Background Eosinophilic airway inflammation has successfully been used to tailor anti-inflammatory therapy in chronic obstructive pulmonary disease (COPD). Airway hyperresponsiveness (AHR) by indirect challenges is associated with airway inflammation. We hypothesized that AHR to inhaled mannitol captures eosinophilia in induced sputum in COPD. Methods Twenty-eight patients (age 58 ± 7.8 yr, packyears 40 ± 15.5, post-bronchodilator FEV1 77 ± 14.0%predicted, no inhaled steroids ≥4 wks) with mild-moderate COPD (GOLD I-II) completed two randomized visits with hypertonic saline-induced sputum and mannitol challenge (including sputum collection). AHR to mannitol was expressed as response-dose-ratio (RDR) and related to cell counts, ECP, MPO and IL-8 levels in sputum. Results There was a positive correlation between RDR to mannitol and eosinophil numbers (r = 0.47, p = 0.03) and level of IL-8 (r = 0.46, p = 0.04) in hypertonic saline-induced sputum. Furthermore, significant correlations were found between RDR and eosinophil numbers (r = 0.71, p = 0.001), level of ECP (r = 0.72, p = 0.001), IL-8 (r = 0.57, p = 0.015) and MPO (r = 0.64, p = 0.007) in sputum collected after mannitol challenge. ROC-curves showed 60% sensitivity and 100% specificity of RDR for >2.5% eosinophils in mannitol-induced sputum. Conclusions In mild-moderate COPD mannitol hyperresponsiveness is associated with biomarkers of airway inflammation. The high specificity of mannitol challenge suggests that the test is particularly suitable to exclude eosinophilic airways inflammation, which may facilitate individualized treatment in COPD. Trial registration Netherlands Trial Register (NTR): NTR1283</p

Article Electronic Nose Breathprints Are Independent of Acute Changes in Airway Caliber in Asthma

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Exhaled Breath Profiling Enables Discrimination of Chronic Obstructive Pulmonary Disease and Asthma

Rationale Chronic obstructive pulmonary disease (COPD) and asthma can exhibit overlapping clinical features. Exhaled air contains volatile organic compounds (VOCs) that may qualify as noninvasive biomarkers. VOC profiles can be assessed using integrative analysis by electronic nose, resulting in exhaled molecular fingerprints (breathprints). Objectives: We hypothesized that breathprints by electronic nose can discriminate patients with COPD and asthma. Methods: Ninety subjects participated in a cross-sectional study: 30 patients with COPD (age, 61.6+/-9.3 years; FEV1, 1.72+/-0.69 L), 20 patients with asthma (age, 35.4+/-15.1 years; FEV1, 3.32+/-0.86 L), 20 nonsmoking control subjects (age, 56.7+/-9.3 years; FEV1, 3.44+/-0.76 L), and 20 smoking control subjects (age, 56.1+/-5.9 years; FEV1, 3.58+/-0.78). After 5 minutes of tidal breathing through an inspiratory VOC filter, an expiratory vital capacity was collected in a Tedlar bag and sampled by electronic nose. Breathprints were analyzed by discriminant analysis on principal component reduction resulting in cross-validated accuracy values (accuracy). Repeatability and reproducibility were assessed by measuring samples in duplicate by two devices. Measurements and Main Results: Breathprints from patients with asthma were separated from patients with COPD (accuracy 96%; P <0.001), from nonsmoking control subjects (accuracy, 95%; P <0.001), and from smoking control subjects (accuracy, 92.5%; P <0.001). Exhaled breath profiles of patients with COPD partially overlapped with those of asymptomatic smokers (accuracy, 66%; P = 0.006). Measurements were repeatable and reproducible. Conclusions: Molecular profiling of exhaled air can distinguish patients with COPD and asthma and control subjects. Our data demonstrate a potential of electronic noses in the differential diagnosis of obstructive airway diseases and in the risk assessment in asymptomatic smokers. Clinical trial registered with www.trialregister.nI (NTR 1282

Comparison of breathprints of patients with PsA (<i>squares</i>) versus controls (<i>triangles</i>).

<p>(A) Two-dimensional principal component plot showing the discrimination of breathprints of patients with PsA and controls. Accuracy of 69% (P = 0.014). (B) Receiver operator characteristics (ROC) curves for PsA vs. controls with line of the breathprint discriminant function (representing PC 1 and 4). AUC was 0.77.</p

Characteristics of the study population.

<p>Characteristics of the study population.</p