Biological clustering supports both "Dutch'' and "British'' hypotheses of asthma and chronic obstructive pulmonary disease

BACKGROUND: Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous diseases. OBJECTIVE: We sought to determine, in terms of their sputum cellular and mediator profiles, the extent to which they represent distinct or overlapping conditions supporting either the "British" or "Dutch" hypotheses of airway disease pathogenesis. METHODS: We compared the clinical and physiological characteristics and sputum mediators between 86 subjects with severe asthma and 75 with moderate-to-severe COPD. Biological subgroups were determined using factor and cluster analyses on 18 sputum cytokines. The subgroups were validated on independent severe asthma (n = 166) and COPD (n = 58) cohorts. Two techniques were used to assign the validation subjects to subgroups: linear discriminant analysis, or the best identified discriminator (single cytokine) in combination with subject disease status (asthma or COPD). RESULTS: Discriminant analysis distinguished severe asthma from COPD completely using a combination of clinical and biological variables. Factor and cluster analyses of the sputum cytokine profiles revealed 3 biological clusters: cluster 1: asthma predominant, eosinophilic, high TH2 cytokines; cluster 2: asthma and COPD overlap, neutrophilic; cluster 3: COPD predominant, mixed eosinophilic and neutrophilic. Validation subjects were classified into 3 subgroups using discriminant analysis, or disease status with a binary assessment of sputum IL-1β expression. Sputum cellular and cytokine profiles of the validation subgroups were similar to the subgroups from the test study. CONCLUSIONS: Sputum cytokine profiling can determine distinct and overlapping groups of subjects with asthma and COPD, supporting both the British and Dutch hypotheses. These findings may contribute to improved patient classification to enable stratified medicine

A Polymorphism Affecting MYB Binding within the Promoter of the PDCD4 Gene is Associated with Severe Asthma in Children

A previous genome-wide association study in asthma revealed putative associations that merit further investigation. In this study, the genome-wide significant associations of SNPs at the 5% false discovery rate were examined in independent groups of severe asthmatics. The panel consisted of 397 severe asthmatic adults, 116 severe asthmatic children, and a collection of 207 family-trios with an asthmatic proband. Three SNPs in the PDCD4 gene (rs6585018:G>A, rs1322997:C>A, and rs34104444:G>A) were significantly associated with severe childhood asthma (P values: 0.003, 0.002, 0.004) and total immunoglobulin E (IgE) levels (P values: 0.034, 0.041, 0.052). In an independent group of 234 asthmatic children and 652 controls, PDCD4 SNPs rs1407696:T>G and rs11195360:T>C were associated with total IgE levels (P values: 0.006, 0.014). In silico analysis of PDCD4 locus showed that rs6585018:G>A had the potential to affect MYB transcription factor binding, shown to act as a PDCD4-transcription inducer. Electromobility shift assays and reporter assays revealed that rs6585018:G>A alters MYB binding thereby influencing the expression of PDCD4. SNPs within MYB itself confer susceptibility to eosinophilia and asthma. Our association between a variant MYB binding site in PDCD4 and the severest form of childhood asthma therefore suggests that PDCD4 is a novel molecule of importance to asthmatic inflammatory responses

Corticosteroid responsiveness following mepolizumab in severe eosinophilic asthma—a randomized, placebo-controlled crossover trial (MAPLE)

Mepolizumab inhibits interleukin-5 (IL-5) activity, reduces exacerbation frequency and maintenance oral corticosteroid (OCS) dosage in patients with severe eosinophilic asthma (SEA). Some patients remain dependent on OCS despite anti-IL-5 treatment suggesting residual corticosteroid responsive mechanisms. We aimed to determine the clinical and anti-inflammatory effects of OCS in patients with SEA on mepolizumab. We conducted a randomised, triple-blind, placebo-controlled crossover trial of prednisolone (0.5mg/kg/day, maximum 40mg/day, for 14±2 days) in adults with SEA after ≥12 weeks of mepolizumab. We compared change in asthma symptoms, quality of life, lung function measured by spirometry and airwave oscillometry, FeNO, and blood and sputum eosinophil cell count after prednisolone and placebo. 27 patients completed the study. Prednisolone did not improve ACQ-5 (mean difference in change for prednisolone vs placebo -0.23, 95% CI -0.58 to 0.11), mini-AQLQ (0.03, 95% CI -0.26 to 0.42), SGRQ (0.24, 95% CI -3.20 to 3.69) or VAS scores for overall asthma symptoms (0.11, 95% CI -0.58 to 0.80). The mean difference for FEV in favour of prednisolone was 105ml (95% CI -4 to 213 ml); FEF 484ml/s (95% CI 151 to 816 ml/s); FeNO reduction 41% (95% CI 25 to 54%); blood eosinophil count reduction 49% (95% CI 31 to 62%); and percentage of sputum eosinophil reduction 71% (95% CI 26 to 89%). OCS improved small airway obstruction and reduced biomarkers of type 2 inflammation but had no significant effect on symptoms or quality of life in patients with severe eosinophilic asthma receiving treatment with mepolizumab. [Abstract copyright: Copyright © 2022. Published by Elsevier Inc.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation:A Multi-center, Multi-vendor, and Multi-disease Study

Background: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.Purpose: Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center.Study type: Retrospective.Population: A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6–85); 42% females) and 31 healthy participants (median age (range): 34 (23–76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.Field Strength/Sequence: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI.Assessment: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.Statistical Tests: Kruskal–Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland–Altman analyses assessed agreement with manually derived lung volumes. A P value of &lt;0.05 was considered statistically significant.Results: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880–0.987), Average HD of 1.63 mm (0.65–5.45) and XOR of 0.079 (0.025–0.240) on the testing set and a DSC of 0.973 (0.866–0.987), Average HD of 1.11 mm (0.47–8.13) and XOR of 0.054 (0.026–0.255) on external validation data.Data Conclusion: The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.Evidence Level: 4.Technical Efficacy: Stage 1.</p

Exome-wide analysis of rare coding variation identifies novel associations with COPD and airflow limitation in MOCS3, IFIT3 and SERPINA12.

Several regions of the genome have shown to be associated with COPD in genome-wide association studies of common variants.To determine rare and potentially functional single nucleotide polymorphisms (SNPs) associated with the risk of COPD and severity of airflow limitation.3226 current or former smokers of European ancestry with lung function measures indicative of Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 COPD or worse were genotyped using an exome array. An analysis of risk of COPD was carried out using ever smoking controls (n=4784). Associations with %predicted FEV1 were tested in cases. We followed-up signals of interest (p<10(-5)) in independent samples from a subset of the UK Biobank population and also undertook a more powerful discovery study by meta-analysing the exome array data and UK Biobank data for variants represented on both arrays.Among the associated variants were two in regions previously unreported for COPD; a low frequency non-synonymous SNP in MOCS3 (rs7269297, pdiscovery=3.08×10(-6), preplication=0.019) and a rare SNP in IFIT3, which emerged in the meta-analysis (rs140549288, pmeta=8.56×10(-6)). In the meta-analysis of % predicted FEV1 in cases, the strongest association was shown for a splice variant in a previously unreported region, SERPINA12 (rs140198372, pmeta=5.72×10(-6)). We also confirmed previously reported associations with COPD risk at MMP12, HHIP, GPR126 and CHRNA5. No associations in novel regions reached a stringent exome-wide significance threshold (p<3.7×10(-7)).This study identified several associations with the risk of COPD and severity of airflow limitation, including novel regions MOCS3, IFIT3 and SERPINA12, which warrant further study

Determining the contribution of IL33 and IL1RL1 polymorphisms to clinical and immunological features of asthma

Rationale: IL33 (9p24.1) and the IL33 receptor (IL1RL, 2q12) have been reproducibly identified as asthma susceptibility genes. However, the variants driving genetic associations are not yet fully defined. Using a population based birth cohort of 1059 children (Manchester Asthma and Allergy Study-(MAAS)) and 2536 adults with asthma (Genetics of Asthma Severity and Phenotypes- (GASP)) cohort we aimed to define genetic variants associated with clinical and immunological features of asthma. Methods: MAAS samples were genotyped using the Illumina 610 Quad array and imputed using 1000G reference panel. GASP samples were genotyped using two custom designed Affymetrix arrays (UK BiLEVE/UK Biobank array). Datasets were quality controlled for gender mismatches, outliers and relatedness. Data was generated for the IL33/IL1RL1 regions consisting of the genes and surrounding regions (chr9:5715785−6757983 & chr2:102427961−103468497) on the following traits: asthma diagnosis (MAAS), atopy, FEV1 (GASP) and FEV1/FVC (MAAS and GASP) as well as total blood eosinophil counts and serum total IgE levels (GASP). Variables for blood eosinophils and total IgE were log10 transformed. Analysis was carried out in PLINK using linear or logistic regression modelling including appropriate covariates for each trait. Results: In the MAAS cohort, we replicated the association of the IL33 locus with asthma diagnosis, identifying potentially two independent novel signals in that locus (rs10975398; P=1.70E-05; B= -1.519; MAF=0.32 and rs2890697; P=1.10E-04; B= -1.573; MAF=0.43). This association survived a Bonferroni correction for multiple testing. Although not surviving correction, an association was also identified for atopy in the IL1RL1 locus for MAAS (P=1.08E-04; MAF=0.48). In GASP we identified modest associations not in known LD with published loci (P-value range: 5.00E-02 – 7.60E-04) for FEV1, FEV1/FVC, atopy, blood eosinophils and total IgE in both the IL33 and IL1RL1 loci. Multiple SNPs presented nominal association (P<0.01) with more than one trait such as atopy & total IgE, providing supporting evidence for association. Conclusion: We replicated the association of IL33 region SNPs with asthma diagnosis in MAAS, highlighting the role of this locus in childhood asthma. Although trait association signals did not survive correction for multiple testing, nominal association across multiple phenotypes in GASP provides suggestive evidence of the role of the IL33/IL1RL1 genetic polymorphisms in determining clinical and immunological features of asthma

Systems medicine and integrated care to combat chronic noncommunicable diseases

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems