Laser spectroscopy for breath analysis : towards clinical implementation

Detection and analysis of volatile compounds in exhaled breath represents an attractive tool for monitoring the metabolic status of a patient and disease diagnosis, since it is non-invasive and fast. Numerous studies have already demonstrated the benefit of breath analysis in clinical settings/applications and encouraged multidisciplinary research to reveal new insights regarding the origins, pathways, and pathophysiological roles of breath components. Many breath analysis methods are currently available to help explore these directions, ranging from mass spectrometry to laser-based spectroscopy and sensor arrays. This review presents an update of the current status of optical methods, using near and mid-infrared sources, for clinical breath gas analysis over the last decade and describes recent technological developments and their applications. The review includes: tunable diode laser absorption spectroscopy, cavity ring-down spectroscopy, integrated cavity output spectroscopy, cavity-enhanced absorption spectroscopy, photoacoustic spectroscopy, quartz-enhanced photoacoustic spectroscopy, and optical frequency comb spectroscopy. A SWOT analysis (strengths, weaknesses, opportunities, and threats) is presented that describes the laser-based techniques within the clinical framework of breath research and their appealing features for clinical use.Peer reviewe

Bedside breath tests in children with abdominal pain: a prospective pilot feasibility study

Background: There is no definitive method of accurately diagnosing appendicitis before surgery. We evaluated the feasibility of collecting breath samples in children with abdominal pain and gathered preliminary data on the accuracy of breath tests. Methods: We conducted a prospective pilot study at a large tertiary referral paediatric hospital in the UK. We recruited 50 participants with suspected appendicitis, aged between 5 and 15 years. Five had primary diagnosis of appendicitis. The primary outcome was the number of breath samples collected. We also measured the number of samples processed within 2 h and had CO2 ≥ 3.5%. Usability was assessed by patient-reported pain pre- and post-sampling and user-reported sampling difficulty. Logistic regression analysis was used to predict appendicitis and evaluated using the area under the receiver operator characteristic curve (AUROC). Results: Samples were collected from all participants. Of the 45 samples, 36 were processed within 2 h. Of the 49 samples, 19 had %CO2 ≥ 3.5%. No difference in patient-reported pain was observed (p = 0.24). Sampling difficulty was associated with patient age (p = 0.004). The logistic regression model had AUROC = 0.86. Conclusions: Breath tests are feasible and acceptable to patients presenting with abdominal pain in clinical settings. We demonstrated adequate data collection with no evidence of harm to patients. The AUROC was better than a random classifier; more specific sensors are likely to improve diagnostic performance. Trial registration: ClinicalTrials.gov, NCT03248102. Registered 14 Aug 2017

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy

BACKGROUND: Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma. OBJECTIVE: We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma. METHODS: Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics. RESULTS: Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics. CONCLUSION: eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping

Sputum microbiome profiles identify severe asthma phenotypes of relative stability at 12-18 months

BACKGROUND: Asthma is a heterogeneous disease characterized by distinct phenotypes with associated microbial dysbiosis. OBJECTIVES: To identify severe asthma phenotypes based on sputum microbiome profiles and assess their stability after 12-18 months. Furthermore, to evaluate clusters' robustness after inclusion of an independent mild-to-moderate asthmatics. METHODS: In this longitudinal multicenter cohort study, sputum samples were collected for microbiome profiling from a subset of the U-BIOPRED adult patient cohort at baseline and after 12-18 months of follow-up. Unsupervised hierarchical clustering was performed using the Bray-Curtis β-diversity measure of microbial profiles. For internal validation, partitioning around medoids, consensus cluster distribution, bootstrapping and topological data analysis were applied. Follow-up samples were studied to evaluate within-patient clustering stability in severe asthmatics. Cluster robustness was evaluated by an independent mild-moderate asthma cohort. RESULTS: Data were available for 100 severe asthma subjects (median age: 55 yrs, 42% males). Two microbiome-driven clusters were identified, characterized by differences in asthma onset, smoking status, residential locations, percentage of blood and/or sputum neutrophils and macrophages, lung spirometry, and concurrent asthma medications (all p-values <.05). Cluster 2 patients displayed a commensal-deficient bacterial profile which was associated with worse asthma outcomes compared to cluster 1. Longitudinal clusters revealed high relative stability after 12-18 months in the severe asthmatics. Further inclusion of 24 independent mild-to-moderate asthmatics was consistent with the clustering assignments. CONCLUSION: Unbiased microbiome-driven clustering revealed two distinct robust severe asthma phenotypes, which exhibited relative overtime stability. This suggests that the sputum microbiome may serve as a biomarker for better characterizing asthma phenotypes