Differences in clinical outcomes between baseline and follow-up for each cluster.

<p>Pre Bronchodilator FEV1 % predicted and Blood eosinophil count statistics are Mean (+/−SD). Exacerbation frequency outcomes are median (IQR) and percentage on oral steroids are percentages.</p

Cluster memberships at baseline compared to classification membership at follow up.

<p>Cluster memberships at baseline compared to classification membership at follow up.</p

Clinical characteristics of the original BTS severe refractory asthma clusters.

<p>Data represented as ^‡Mean (SD) tested with ANOVA test, ^†Median (IQR) tested with Kruskal-Wallis test, *(%) tested with Chi squared test.</p

Cluster specific differences over time for clinically significant outcomes of clusters.

<p>Data represented as ^‡Mean (SD) tested with paired t-test for each cluster, ^†Median (IQR) tested with paired sample Wilcoxon signed rank test, * (%) tested with McNemar's test.</p

Cluster and Classification membership compared at baseline.

<p>Cluster and Classification membership compared at baseline.</p

Clinical characteristics for new BTSsevere refractory asthma dataset using the classifier.

<p>Data represented as ^‡Mean (SD) tested with ANOVA test, ^†Median (IQR) tested with Kruskal-Wallis test, *(%) tested with Chi squared test.</p

Breathomics for the Clinician: The use of volatile organic compounds in respiratory diseases

Introduction: Exhaled breath analysis has the potential to provide valuable insight on the status of various metabolic pathways taking place in the lungs locally and other vital organs, via systemic circulation. For years, volatile organic compounds (VOCs) have been proposed as feasible alternative diagnostic and prognostic biomarkers for different respiratory pathologies. Methods: We reviewed the currently published literature on the discovery of exhaled breath volatile organic compounds and their utilisation in various respiratory diseases Results: Key barriers in the development of clinical breath tests include the lack of unified consensus for breath collection and analysis and the complexity of understanding the relationship between the exhaled VOCs and the underlying metabolic pathways. We present a comprehensive overview, in light of published literature and our experience from co-ordinating a national breathomics centre, of the progress made to date and some of the key challenges in the field and ways to overcome them. We particularly focus on the relevance of breathomics to clinicians and the valuable insights it adds to diagnostics and disease monitoring. Conclusions: Breathomics holds great promise and our findings merit further large-scale multicentre diagnostic studies using standardised protocols to help position this novel technology at the centre of respiratory disease diagnostics.</div

Multi-ancestry genome-wide association study improves resolution of genes, pathways and pleiotropy for lung function and chronic obstructive pulmonary disease

Lung function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry GWAS meta-analysis of lung function to date, comprising 580,869 participants, 1020 independent association signals identified 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score (GRS) showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies (PheWAS) for selected associated variants, and trait and pathway-specific GRS to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.</p

Resistome analyses of sputum from COPD and healthy subjects reveals bacterial load-related prevalence of target genes

Background Antibiotic resistance is a major global threat. We hypothesised that the chronic obstructive pulmonary disease (COPD) airway is a reservoir of antimicrobial resistance genes (ARGs) that associate with microbiome-specific COPD subgroups. Objective To determine the resistance gene profiles in respiratory samples from COPD patients and healthy volunteers. Methods Quantitative PCR targeting 279 specific ARGs was used to profile the resistomes in sputum from subjects with COPD at stable, exacerbation and recovery visits (n=55; COPD-BEAT study), healthy controls with (n=7) or without (n=22) exposure to antibiotics in the preceding 12 months (EXCEED study) and in bronchial brush samples from COPD (n=8) and healthy controls (n=7) (EvA study). Results ARG mean (SEM) prevalence was greater in stable COPD samples (35.2 (1.6)) than in healthy controls (27.6 (1.7); p=0.004) and correlated with total bacterial abundance (r 2 =0.23; p<0.001). Prevalence of ARG positive signals in individuals was not related to COPD symptoms, lung function or their changes at exacerbation. In the COPD subgroups designated High Î 3Proteobacteria and High Firmicutes, ARG prevalence was not different at stable state but significantly declined from stable through exacerbation to recovery in the former (p=0.011) without changes in total bacterial abundance. The ARG patterns were similar in COPD versus health, COPD microbiome-subgroups and between sputum and bronchoscopic samples independent of antibiotic exposure in the last 12 months. Conclusions ARGs are highly prevalent in sputum, broadly in proportion to bacterial abundance in both healthy and COPD subjects. Thus, COPD appears to be an ARG reservoir due to high levels of bacterial colonisation

Visualisation of exhaled breath metabolites reveals distinct diagnostic signatures for acute cardiorespiratory breathlessness

Breath analysis can be a useful noninvasive way to detect disease. Here, Ibrahim et al. studied the volatile organic compound (VOC) signatures associated with acute cardiorespiratory diseases in patients presenting breathlessness. Using two-dimensional gas chromatography and mass spectrometry, the authors found clusters of VOCs associated with acute heart failure, asthma, chronic obstructive pulmonary disease, and pneumonia. These breath biomarkers correlated with blood-based biomarkers. An acute disease VOC score based on a 101-biomarker panel was associated with 2-year all-cause mortality. This study demonstrates how breathomics can help diagnose disease and further our understanding of metabolic subgroups