Big asthma data: getting bigger and more beautiful?

‘Big data’ is on trend and the term is used in equal measure to reflect both one of the greatest challenges and likeliest solutions to future scientific advances from fundamental understanding in astrophysics, climate change, economics, health and disease. Like many trends, it means different things to different people. In medicine, it is used to describe the data derived from large populations in epidemiology studies, high fidelity multiscale ‘omic datasets across spatial scales within individuals or sometimes a combination of the two. Big data will often capture information at a single time point. Typically, it does not address temporal scales of chronic disease including day-to-day variability, response to perturbations such as intercurrent infection, decompensation of the disease or response to therapeutic interventions and is rarely obtained over a life course. Observations will therefore always be limited by what is measured, when and in whom and will only ever provide estimates of what is ‘real’ within the larger group from which the sample is taken. Big data that includes large populations makes interpretations more robust and generalisable. Indeed, as the population studied or sample size approaches a majority, or at least a sizeable minority, of the whole population then the observations begin to no longer be estimates, but simply a description of the population

New and emerging drug treatments for severe asthma

Asthma is a common chronic inflammatory condition of the airways affecting over 300 million people world-wide. In 5%-10% of cases, it is severe, with disproportionate healthcare resource utilization including costs associated with frequent exacerbations and the long-term health effects of systemic steroids. Characterization of inflammatory pathways in severe asthma has led to the development of targeted biological and small molecule therapies which aim to achieve disease control while minimizing corticosteroid-associated morbidity. Herein, we review currently licensed agents and those in development, and speculate how drug therapy for severe asthma might evolve and impact on clinical outcomes in the near future

Assessing causal treatment effect estimation when using large observational datasets

Background: Recently, there has been a heightened interest in developing and evaluating different methods for analysing observational data. This has been driven by the increased availability of large data resources such as Electronic Health Record (EHR) data alongside known limitations and changing characteristics of randomised controlled trials (RCTs). A wide range of methods are available for analysing observational data. However, various, sometimes strict, and often unverifiable assumptions must be made in order for the resulting effect estimates to have a causal interpretation. In this paper we will compare some common approaches to estimating treatment effects from observational data in order to highlight the importance of considering, and justifying, the relevant assumptions prior to conducting an observational analysis. Methods: A simulation study was conducted based upon a small cohort of patients with chronic obstructive pulmonary disease. Two-stage least squares instrumental variables, propensity score, and linear regression models were compared under a range of different scenarios including different strengths of instrumental variable and unmeasured confounding. The effects of violating the assumptions of the instrumental variables analysis were also assessed. Sample sizes of up to 200,000 patients were considered. Results: Two-stage least squares instrumental variable methods can yield unbiased treatment effect estimates in the presence of unmeasured confounding provided the sample size is sufficiently large. Adjusting for measured covariates in the analysis reduces the variability in the two-stage least squares estimates. In the simulation study, propensity score methods produced very similar results to linear regression for all scenarios. A weak instrument or strong unmeasured confounding led to an increase in uncertainty in the two-stage least squares instrumental variable effect estimates. A violation of the instrumental variable assumptions led to bias in the two-stage least squares effect estimates. Indeed, these were sometimes even more biased than those from a naïve linear regression model. Conclusions: Instrumental variable methods can perform better than naïve regression and propensity scores. However, the assumptions need to be carefully considered and justified prior to conducting an analysis or performance may be worse than if the problem of unmeasured confounding had been ignored altogether

Neutrophil elastase as a biomarker for bacterial infection in COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is predominantly associated with neutrophilic inflammation. Active neutrophil elastase (NE) is a serine proteinase, secreted by neutrophils, in response to inflammation and pathogen invasion. We sought to investigate if NE could be used as a biomarker for bacterial infection in patients with COPD. METHODS: NE was quantified using ProteaseTag® Active NE Immunoassay (ProAxsis, Belfast) from the sputum of COPD subjects at stable state, exacerbation and 2 weeks post treatment visit. RESULTS: NE was measured in 90 samples from 30 COPD subjects (18 males) with a mean (range) age of 65 (45-81) years and mean (SD) FEV1 of 47% (18). The geometric mean (95%CI) of NE at stable state was 2454 ng/mL (1460 to 4125 ng/mL). There was a significant increase in NE levels at an exacerbation (p = 0.003), and NE levels were higher in a bacterial-associated exacerbation (NE log difference 3.873, 95% CI of log difference 1.396 to 10.740, p = 0.011). NE was an accurate predictor of a bacteria-associated exacerbation (area (95%CI) under the receiver operator characteristic curve 0.812 (0.657 to 0.968). CONCLUSION: NE is elevated during exacerbations of COPD. NE may be a viable biomarker for distinguishing a bacterial exacerbation in patients with COPD. TRIAL REGISTRATION: Leicestershire, Northamptonshire and Rutland ethics committee (reference number: 07/H0406/157)

Long COVID research: an update from the PHOSP-COVID Scientific Summit

Correspondence</p

The stability of blood Eosinophils in chronic obstructive pulmonary disease

Blood eosinophils are a predictive biomarker of inhaled corticosteroid response in chronic obstructive pulmonary disease (COPD). We investigated blood eosinophil stability over 1 year using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019 thresholds of < 100, 100- < 300 and ≥ 300 eosinophils/μL in 225 patients from the COPDMAP cohort. Blood eosinophils showed good stability (rho: 0.71, p < 0.001, ICC 0.84), and 69.3% of patients remained in the same eosinophil category at 1 year. 85.3% of patients with eosinophils < 100 cells/μL had stable counts. The majority of blood eosinophil counts remain stable over 1 year using the GOLD 2019 thresholds

Sputum Moraxella catarrhalis strains exhibit diversity within and between COPD subjects.

Purpose: Moraxella catarrhalis is implicated in the pathogenesis of some COPD exacerbations. We sought to investigate whether the M. catarrhalis strain is variable between COPD subjects; that an exacerbation is associated with acquisition of a new strain and that certain strains are more commonly associated with exacerbations. Patients and methods: Sputum samples were collected at stable and exacerbation visits from COPD subjects from a single center as part of the COPDMAP consortium. Samples identified as M. catarrhalis positive by qPCR were recultured in liquid cultures grown to extract genomic DNA; underwent Illumina MiSeq and bacterial genome sequences were de novo assembled and Multi Locus Sequence Type (MLST) was determined. Results: Thirty-five samples were obtained from 18 subjects. These included 13 stable and 22 exacerbation samples. The diversity between samples was very large with 25 different M. catarrhalis MLSTs being identified out of the 35 samples of which 12 MSLTs have not been described previously. Change and persistence of M. catarrhalis strain were observed between stable visits, from stable to exacerbation and vice-a-versa, and between exacerbation visits. Conclusion: Sputum M. catarrhalis strains exhibit marked diversity within and between COPD subjects. Acquisition of a new strain is common between stable and exacerbation events such that no strain is specifically associated with an exacerbation

Differences in hospital admissions for acute exacerbations of COPD during the COVID-19 pandemic stratified by stable-state blood eosinophil count

Hospital admission for exacerbations of COPD fell only in non-T2-high patients during the COVID-19 pandemic and only in non-eosinophilic admissions. Phenotyping of AECOPD, including at time of exacerbation, is needed for personalised management. </p

Genome-wide Association Study Combining UK Biobank and GASP Consortium Highlights Novel Loci Associated with Moderate-Severe Asthma

The genetic architecture of asthma to date has been described by the discovery of around 20 loci from genome-wide association studies (GWAS), primarily with cases covering mild-to-moderate asthma. We hypothesised that moderate-to-severe asthma, which is currently difficult to treat, may have a specific genetic architecture, however there have not been large GWAS of moderate-to-severe asthma.Accordingly, we selected 5,135 European ancestry moderate-severe asthma cases (British Thoracic Society criteria 3 or above) and 25,675 controls free from lung disease, allergic rhinitis and atopic dermatitis, from UK Biobank and the Genetics of Asthma Severity & Phenotypes (GASP) cohort (cases only). We tested 33,771,858 SNPs and indels genome-wide (imputation against combined UK10K and 1000 genomes phase 3 panels) for association with moderate-severe asthma.We identified 23 independent signals associated with moderate-to-severe asthma (P -8), including novels signals in or near GATA3, RIC1, ZNF652, RPAP3 and MUC5AC, highlighting regions that harbour variants that effect gene expression or genes that play a role in respiratory disease and immune response. Previously described asthma loci where replicated including signals in or near D2HGDH, CD247, HLA-DQB1, HLA-DQA1, TSLP/WDR36, IL1RL1/IL18R1, CLEC16A, GATA3, IL33, SMAD3, SLC22A5/IL13, C11orf30, ZBTB10, IKZF3-ORMDL3 and IKZF4.This largest GWAS of moderate-severe asthma to date and highlights novel loci that may provide new biological insights relevant to treatment of severe asthma.</p

Face mask sampling reveals antimicrobial resistance genes in exhaled aerosols from patients with chronic obstructive pulmonary disease and healthy volunteers.

Introduction: The degree to which bacteria in the human respiratory tract are aerosolised by individuals is not established. Building on our experience sampling bacteria exhaled by individuals with pulmonary tuberculosis using face masks, we hypothesised that patients with conditions frequently treated with antimicrobials, such as chronic obstructive pulmonary disease (COPD), might exhale significant numbers of bacteria carrying antimicrobial resistance (AMR) genes and that this may constitute a previously undefined risk for the transmission of AMR. Methods: Fifteen-minute mask samples were taken from 13 patients with COPD (five paired with contemporaneous sputum samples) and 10 healthy controls. DNA was extracted from cell pellets derived from gelatine filters mounted within the mask. Quantitative PCR analyses directed to the AMR encoding genes: blaTEM (β-lactamase), ErmB (target methylation), mefA (macrolide efflux pump) and tetM (tetracycline ribosomal protection protein) and six additional targets were investigated. Positive signals above control samples were obtained for all the listed genes; however, background signals from the gelatine precluded analysis of the additional targets. Results: 9 patients with COPD (69%), aerosolised cells containing, in order of prevalence, mefA, tetM, ErmB and blaTEM, while three healthy controls (30%) gave weak positive signals including all targets except blaTEM. Maximum estimated copy numbers of AMR genes aerosolised per minute were mefA: 3010, tetM: 486, ErmB: 92 and blaTEM: 24. The profile of positive signals found in sputum was not concordant with that in aerosol in multiple instances. Discussion: We identified aerosolised AMR genes in patients repeatedly exposed to antimicrobials and in healthy volunteers at lower frequencies and levels. The discrepancies between paired samples add weight to the view that sputum content does not define aerosol content. Mask sampling is a simple approach yielding samples from all subjects and information distinct from sputum analysis. Our results raise the possibility that patient-generated aerosols may be a significant means of AMR dissemination that should be assessed further and that consideration be given to related control measures