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Rationale: Knowledge about the clinical importance of patient-reported outcome measures (PROMs) in severe asthma is limited., Objectives: To assess whether and to what extent asthma exacerbations affect changes in PROMS over time and asthma-specific PROMs can predict exacerbations in adult patients with severe asthma in usual care., Methods: Data of 421 patients with severe asthma (62% female; mean age 51.9 ± 13.4 years; mean FEV 1 67.5 ± 21.3%pred) from the U-BIOPRED cohort were analyzed. The included PROMs were: Asthma Control Questionnaire (ACQ5); Asthma Quality of Life Questionnaire (AQLQ); Hospital Anxiety and Depression scale (HADS); Epworth Sleepiness Scale (ESS); Medication Adherence Report Scale (MARS); Sino-Nasal Outcomes Test (SNOT20). Participants were assessed at baseline and after 12-18 months of usual care., Results: PROMs showed very weak to weak correlations with clinical characteristics such as age, body mass index, FEV 1 , FeNO and eosinophilic cell count. Patients presenting no exacerbations during follow-up showed a statistically significant improvement in all PROMs (except for MARS), whereas individuals experiencing > 2 exacerbations showed a deterioration. Baseline ACQ5 was a predictor of exacerbations with an AUC of 0.590 (95%CI 0.514-0.666)., Conclusions: The association of PROMs with clinical measures was poor in severe asthmatics. Moreover, PROMs were prone to changes in usual care, with exacerbations playing a key role. PROMs need to be systematically evaluated in severe asthma to improve clinical care based on specific patient's needs., Competing Interests: Declarations. Ethics approval and consent to participate: The medical ethics committee of each participating center of the U-BIOPRED study approved this study. All participants gave written and signed informed consent. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Lung quantitative computed tomography (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways, using radiomultiomics, integrating qCT, multiomics and machine learning/artificial intelligence., Methods: We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort. The same qCT measurements were used to replicate qCT clusters in a subsample of the ATLANTIS asthma cohort (n=97). We performed integrated enrichment analysis using blood, sputum, bronchial biopsies, bronchial brushings and nasal brushings transcriptomics and blood and sputum proteomics to characterise radiomultiomic-associated clusters (RACs)., Results: qCT clusters and clinical features in U-BIOPRED were replicated in the matched ATLANTIS cohort. In the U-BIOPRED cohort, RAC1 (n=30) was predominantly female with elevated body mass index, mild airflow limitation, decreased CT lung volume and increased lung density and upregulation of the complement pathway. RAC2 (n=34) subjects had airway wall thickness and a mild degree of airflow limitation, with upregulation of proliferative pathways including neurotrophic receptor tyrosine kinase 2/tyrosine kinase receptor B, and downregulation of semaphorin pathways. RAC3 (n=41) showed increased lung attenuation area and air trapping, severe airflow limitation, hyperinflation, and upregulation of cytokine signalling and signalling by interleukin pathways, and matrix metallopeptidase 1, 2 and 9., Conclusions: U-BIOPRED severe asthma qCT clusters were replicated in a matched independent asthmatic cohort and associated with specific molecular pathways. Radiomultiomics might represent a novel strategy to identify new molecular pathways in asthma pathobiology., Competing Interests: Conflict of interest: K.F. Chung reports grants from the MRC, EPSRC, GSK, Merck and NIEHS, payment or honoraria for lectures, presentations, manuscript writing or educational events from GSK, Novartis and AstraZeneca, and participation on a data safety monitoring board or advisory board with GSK, AstraZeneca, Novartis, Roche, Merck, Trevi, Rickett-Beckinson, Nocion, Shionogi and Clean Breathing Institute supported by Haleon. C. Auffray reports support for the present study from the Innovative Medicines Initiative. Y. Badi reports support for the present study from the Innovative Medicines Initiative. P. Bakke reports payment or honoraria for lectures, presentations, manuscript writing or educational events from AstraZeneca, GSK and Sanofi. C. Brightling reports support for the present study from the ATLANTIS study (Chiesi grant) and Leicester NIHR BRC, grants from 4D Pharma, Areteia, AstraZeneca, Chiesi, Genentech, GSK, Mologic, Novartis, Regeneron Pharmaceuticals, Roche and Sanofi, and consultancy fees from 4D Pharma, Areteia, AstraZeneca, Chiesi, Genentech, GSK, Mologic, Novartis, Regeneron Pharmaceuticals, Roche and Sanofi. P. Chanez reports grants from ALK, AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Menarini, Novartis and Sanofi Aventis, consultancy fees from ALK, AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Menarini, Novartis and Sanofi Aventis, payment or honoraria for lectures, presentations, manuscript writing or educational events from ALK, AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Menarini, Novartis and Sanofi Aventis, and support for attending meetings from ALK, AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Menarini, Novartis and Sanofi Aventis. B. De Meulder reports support for the present study from the Innovative Medicines Initiative. R. Djukanovic reports consultancy fees from Synairgen plc, GSK, ZenasBio and Celltrion, leadership role as Chair of the European Respiratory Society's clinical research collaboration on severe asthma (SHARP), and stock (or stock options) with Synairgen plc. L. Fabbri reports consultancy fees from Chiesi Farmaceutici and AstraZeneca, payment or honoraria for lectures, presentations, manuscript writing or educational events from Chiesi Farmaceutici, AstraZeneca, GSK, Alfasigma and Novartis, support for attending meetings from Chiesi Farmaceutici, GSK and Novartis, and participation on a data safety monitoring board or advisory board with Novartis and Chiesi. S.J. Fowler reports grants from the NIHR, and payment or honoraria for lectures, presentations, manuscript writing or educational events from Boehringer Ingelheim. P. Howarth reports employment with GSK. J. Kolmert reports consultancy fees from Gesynta Pharma AB and Lipum AB. M. Kraft reports grants from the NIH, American Lung Association, Synairgen, Janssen, AstraZeneca and Sanofi, consultancy fees from AstraZeneca, Sanofi, Chiesi, GSK, Kinaset and Genentech, payment or honoraria for lectures, presentations, manuscript writing or educational events from Chiesi, support for attending meetings from the European Respiratory Society, one patent issued and tiled filed to development of therapeutics for inflammatory lung disease as CoFounder and Chief Medical Officer, RaeSedo, Inc., participation on a data safety monitoring board or advisory board with ALung, leadership role with the National Heart, Lung and Blood Advisory Council, stock (or stock options) with RaeSedo, Inc. (equity ownership), and the following financial (or non-financial) interests: Section Editor of UpToDate. A.H. Maitland-van der Zee reports grants from Health Holland, GSK, Boehringer Ingelheim and Vertex, consultancy fees from Boehringer Ingelheim and AstraZeneca, payment or honoraria for lectures, presentations, manuscript writing or educational events from GSK, and participation on a data safety monitoring board or advisory board for a study on BPD in neonates. A. Papi reports grants from Chiesi, AstraZeneca, GSK and Sanofi, consultancy fees from Chiesi, AstraZeneca, GSK, Novartis, Sanofi, Iqvia, Avillion, Elpen Pharmaceuticals, Moderna and Roche, payment or honoraria for lectures, presentations, manuscript writing or educational events from Chiesi, AstraZeneca, GSK, Menarini, Zambon, Mundipharma, Sanofi, Edmond Pharma, Iqvia, Avillion, Sanofi and Regeneron, participation on a data safety monitoring board or advisory board with Chiesi, AstraZeneca, GSK, Novartis, Sanofi, Iqvia, Avillion, Elpen Pharmaceuticals and Moderna, receipt of equipment, materials, drugs, medical writing, gifts or other services from Consorzio Futuro in Ricerca. K. Rabe reports payment or honoraria for lectures, presentations, manuscript writing or educational events from AstraZeneca, Boehringer Ingelheim, Chiesi Pharmaceuticals, Novartis, Sanofi/Regeneron, GSK, Berlin Chemie and Roche Pharma, participation on a data safety monitoring board or advisory board with AstraZeneca, Boehringer Ingelheim, Sanofi/Regeneron and CSL Behring, and leadership roles with the German Center for Lung Research (DZL), German Chest Society (DGP) and American Thoracic Society. D.E. Shaw reports consultancy fees from GSK, AstraZeneca and Novartis, payment or honoraria for lectures, presentations, manuscript writing or educational events from GSK and AstraZeneca, and support for attending meetings from GSK. D. Singh reports consultancy fees from Aerogen, AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, CSL Behring, EpiEndo, Genentech, GSK, Glenmark, Gossamer Bio, Kinaset Therapeutics, Menarini, Novartis, Orion, Pulmatrix, Sanofi, Synairgen, Teva, Theravance, Biopharma and Verona Pharma. M. van Den Berge reports grants from GSK, Roche, Genentech and Novartis. Y. Guo reports support for the present study from the Innovative Medicines Initiative. S. Wagers reports consultancy fees from King's College Hospital NHS Foundation Trust, Academic Medical Research, AMC Medical Research BV, Asthma UK, Athens Medical School, Boehringer Ingelheim International GmbH, CHU de Toulouse, CIRO, DS Biologicals Ltd, École Polytechnique Fédérale De Lausanne, European Respiratory Society, FISEVI, Fluidic Analytics Ltd, Fraunhofer IGB, Fraunhofer ITEM, GSK Research & Development Ltd, Holland & Knight, Karolinska Institutet Fakturor, KU Leuven, Longfonds, National Heart and Lung Institute, Novartis Pharma AG, Owlstone Medical Limited, PExA AB, UCB Biopharma SPRL, Umeå University, University Hospital Southampton NHS Foundation Trust, Università Campus Bio-Medico di Roma, Universita Cattolica Del Sacro Cuore, Universität Ulm, University of Bern, University of Edinburgh, University of Hull, University of Leicester, University of Loughborough, University of Luxembourg, University of Manchester, University of Nottingham, Vlaams Brabant, Dienst Europa, Imperial College London, Boehringer Ingelheim, Breathomix, Gossamer Bio, AstraZeneca, CIBER, OncoRadiomics, University of Leiden, University of Wurzburg, Chiesi Pharmaceutical, University of Liege, Teva Pharmaceuticals, Sanofi, Pulmonary Fibrosis Foundation and Three Lakes Foundation. P.J. Sterk reports consultancy fees from SME Breathomix, and stock (or stock options) with SME Breathomix. S-E. Dahlen reports grants from AstraZeneca, GSK and Sanofi, consultancy fees from AstraZeneca, Cayman Chemicals, GSK and Regeneron, and payment or honoraria for lectures, presentations, manuscript writing or educational events from AstraZeneca, GSK and Sanofi. I.M. Adcock reports support for the present study from the Innovative Medicines Initiative, grants from GSK, MRC, EPSRC and Sanofi, consultancy fees from GSK, Sanofi, Chiesi and Kinaset, payment or honoraria for lectures, presentations, manuscript writing or educational events from AstraZeneca, Sanofi, Eurodrug and Sunovion, and support for attending meetings from AstraZeneca. S. Siddiqui consultancy fees from AstraZeneca, GSK, CSL Behring, Areteia Therapeutics, ERT Medical, Chiesi and Roche, and payment or honoraria for lectures, presentations, manuscript writing or educational events from GSK, Chiesi and AstraZeneca. P. Montuschi reports support for the present study from the Innovative Medicines Initiative. The remaining authors have no potential conflicts of interest to disclose., (Copyright ©The authors 2024.)

Background: The anti-IgE monoclonal antibody omalizumab is widely used for severe asthma. This study aimed to identify biomarkers that predict clinical improvement during 1 year of omalizumab treatment. Methods: One-year open-label Study of Mechanisms of action of Omalizumab in Severe Asthma (SoMOSA) involving 216 patients with severe (Global Initiative for Asthma step 4/5) uncontrolled atopic asthma (at least two severe exacerbations in the previous year) taking high-dose inhaled corticosteroids and long-acting β-agonists with or without maintenance oral corticosteroids. It had two phases: 0-16 weeks, to assess early clinical improvement by Global Evaluation of Therapeutic Effectiveness (GETE); and 16-52 weeks, to assess late responses based on ⩾50% reduction in exacerbations or mOCS dose. All participants provided samples (exhaled breath, blood, sputum, urine) before and after 16 weeks of omalizumab treatment. Measurements and Main Results: A total of 191 patients completed phase 1; 63% had early improvement. Of 173 who completed phase 2, 69% had reduced exacerbations by ⩾50% and 57% (37 of 65) taking mOCSs had reduced their dose by ⩾50%. The primary outcomes 2,3-dinor-11-β-PGF2α, GETE score, and standard clinical biomarkers (blood and sputum eosinophils, exhaled nitric oxide, serum IgE) did not predict either clinical response. Five volatile organic compounds and five plasma lipid biomarkers strongly predicted the ⩾50% reduction in exacerbations (receiver operating characteristic areas under the curve of 0.780 and 0.922, respectively) and early responses (areas under the curve of 0.835 and 0.949, respectively). In an independent cohort, gas chromatography/mass spectrometry biomarkers differentiated between severe and mild asthma. Conclusions: This is the first discovery of omics biomarkers that predict improvement in asthma with biologic agent treatment. Prospective validation and development for clinical use is justified.

Purpose: Asthma is a clinical syndrome with various underlying pathomechanisms and clinical phenotypes. Genetic, ethnic, and geographic factors may influence the differences in clinical presentation, severity, and prognosis. We compared the characteristics of asthma based on the geographical background by analyzing representative cohorts from the United States, Europe, South America, and Asia using the Severe Asthma Research Program (SARP), Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED), Program for Control of Asthma in Bahia (ProAR), and Cohort for Reality and Evolution of Adult Asthma in Korea (COREA), respectively., Methods: The clinical characteristics and medications for the SARP (n = 669), U-BIOPRED (n = 509), ProAR (n = 996), and COREA (n = 3,748) were analyzed. Subgroup analysis was performed for severe asthma., Results: The mean age was highest and lowest in the COREA and SARP, respectively. The asthma onset age was lowest in the ProAR. The mean body mass index was highest and lowest in the SARP and COREA, respectively. Baseline pulmonary function was lowest and highest in the U-BIOPRED and COREA, respectively. The number of patients with acute exacerbation in the previous year was highest in U-BIOPRED. The mean blood eosinophil count was highest in COREA. The total immunoglobulin E was highest in the ProAR. The frequency of atopy was highest in the SARP. The principal component analysis plot revealed differences among all cohorts., Conclusions: The cohorts from 4 different continents exhibited different clinical and physiological characteristics, probably resulting from the interplay between genetic susceptibility and geographical factors., Competing Interests: There are no financial or other issues that might lead to conflict of interest., (Copyright © 2024 The Korean Academy of Asthma, Allergy and Clinical Immunology • The Korean Academy of Pediatric Allergy and Respiratory Disease.)

Background: Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD., Research Question: Can eNose technology be used for prospective detection of early lung cancer in patients with COPD?, Study Design and Methods: BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis., Results: Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95)., Interpretation: Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Rationale: Patients with severe asthma are dependent upon treatment with high doses of inhaled corticosteroids (ICS) and often also oral corticosteroids (OCS). The extent of endogenous androgenic anabolic steroid (EAAS) suppression in asthma has not previously been described in detail. The objective of the present study was to measure urinary concentrations of EAAS in relation to exogenous corticosteroid exposure., Methods: Urine collected at baseline in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease outcomes) study of severe adult asthmatics (SA, n=408) was analysed by quantitative mass spectrometry. Data were compared to that of mild-to-moderate asthmatics (MMA, n=70) and healthy subjects (HC, n=98) from the same study., Measurements and Main Results: The concentrations of urinary endogenous steroid metabolites were substantially lower in SA than in MMA or HC. These differences were more pronounced in SA patients with detectable urinary OCS metabolites. Their dehydroepiandrosterone sulfate (DHEA-S) concentrations were <5% of those in HC, and cortisol concentrations were below the detection limit in 75% of females and 82% of males. The concentrations of EAAS in OCS-positive patients, as well as patients on high-dose ICS only, were more suppressed in females than males (p<0.05). Low levels of DHEA were associated with features of more severe disease and were more prevalent in females (p<0.05). The association between low EAAS and corticosteroid treatment was replicated in 289 of the SA patients at follow-up after 12-18 months., Conclusion: The pronounced suppression of endogenous anabolic androgens in females might contribute to sex differences regarding the prevalence of severe asthma., Competing Interests: Conflict of interest: V. Yasinska reports participation in advisory boards for AZ and GSK, and lecture honoraria from Sanofi and GSK. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: J. Kolmert has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: A. Pohanka has nothing to disclose. Conflict of interest: A. James reports personal grant from Swedish Heart-Lung Foundation. Conflict of interest: L.I. Andersson has nothing to disclose. Conflict of interest: M. Sparreman-Mikus has nothing to disclose. Conflict of interest: A.R. Sousa reports employment and stocks or stock options from GSK. Conflict of interest: J.H. Riley has nothing to disclose. Conflict of interest: S. Bates has nothing to disclose. Conflict of interest: P.S. Bakke reports lecture honoraria from AstraZeneca and Boehringer Ingelheim. Conflict of interest: N. Zounemat Kermani has nothing to disclose. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez reports participation in advisory boards, honoraria for consultancy, lectures fees and support for attending and/or travel from ALK, Almirall, AZ, Chiesi, GSK, Menarini, Novartis and Sanofi. Conflict of interest: S.J. Fowler has nothing to disclose. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: P.H. Howarth reports employment and stocks or stock options from GSK. Conflict of interest: I. Horváth reports participation on an advisory board for AZ and Chiesi, honoraria for lectures from Chiesi and Roche, and support for attending and/or travel from Roche. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: M. Sanak has nothing to disclose. Conflict of interest: A. Behndig has nothing to disclose. Conflict of interest: D.E. Shaw has nothing to disclose. Conflict of interest: R.G. Knowles has nothing to disclose. Conflict of interest: B. Dahlén reports grant from GSK and Novartis. Conflict of interest: A-H. Maitland-van der Zee reports grants from BI, Vertex Innovation Award, Dutch Lung Foundation, Stichting Astma Bestrijding, IMI/3TR, EU grant ONELAB and EUROSTARS grant with Respiq, consulting fees from AZ and BI, and lecture honoraria from GSK. Conflict of interest: P.J. Sterk reports a grant from the Innovative Medicines Initiative. Conflict of interest: R. Djukanovic reports consulting fees from Synairgen, lecture honoraria from Regeneron, GSK and Kymab, and an advisory board for Synairgen. Conflict of interest: I.M. Adcock reports grant from EU-IMI, grants from GSK, MRK, EPSRC and Sanofi, consulting fees from GSK, Sanofi, Chiesi and Kinaset, lecture honoraria from AZ, Sanofi and Eurodrug, and payment for an educational event from Sunovion. Conflict of interest: K.F. Chung reports lectures honoraria from Novartis, AZ and Merck, advisory boards for GSK, AZ, Novartis, Roche, Merck, Rickett-Beckinson, Nocion and Shionogi, the Scientific Advisory Board of the Clean Breathing Institute supported by Haleon, grants from GSK, MRC and EPSRC, and support for travel from AZ. Conflict of interest: C.E. Wheelock has nothing to disclose. Conflict of interest: S-E. Dahlén reports research grants, consulting fees or lecture honoraria from AZ, Cayman Chemicals, GSK, Regeneron, Sanofi and Teva. Conflict of interest: E. Wikström Jonsson reports a research grant and expert assignment by Region Stockholm, and an expert appointment by the Swedish Medical Product Agency., (Copyright ©The authors 2023.)

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis β-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus , Veillonella , Rothia , and Haemophilus . The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV 1 % predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-β (transforming growth factor-β) (highest in the Veillonella cluster) and Wnt/β-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Background: Growing evidence indicates high comorbid anxiety and depression in patients with asthma. However, the mechanisms underlying this comorbid condition remain unclear. The aim of this study was to investigate the role of inflammation in comorbid anxiety and depression in three asthma patient cohorts of the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project., Methods: U-BIOPRED was conducted by a European Union consortium of 16 academic institutions in 11 European countries. A subset dataset from subjects with valid anxiety and depression measures and a large blood biomarker dataset were analysed, including 198 non-smoking patients with severe asthma (SAn), 65 smoking patients with severe asthma (SAs), 61 non-smoking patients with mild-to-moderate asthma (MMA), and 20 healthy non-smokers (HC). The Hospital Anxiety and Depression Scale was used to measure anxiety and depression and a series of inflammatory markers were analysed by the SomaScan v3 platform (SomaLogic, Boulder, Colo). ANOVA and the Kruskal-Wallis test were used for multiple-group comparisons as appropriate., Results: There were significant group effects on anxiety and depression among the four cohort groups (p < 0.05). Anxiety and depression of SAn and SAs groups were significantly higher than that of MMA and HC groups (p < 0.05. There were significant differences in serum IL6, MCP1, CCL18, CCL17, IL8, and Eotaxin among the four groups (p < 0.05). Depression was significantly associated with IL6, MCP1, CCL18 level, and CCL17; whereas anxiety was associated with CCL17 only (p < 0.05)., Conclusions: The current study suggests that severe asthma patients are associated with higher levels of anxiety and depression, and inflammatory responses may underlie this comorbid condition., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Professor Hou sits on the ECNP Scientific Advisory Panel and currently holds a grant from Asthma Allergy Inflammation Research charity. Prof. Shaw receives consulting fees from Adherium, Nuvoair, Astra Zeneca and Chiesi. He also receives honoraria from Astra Zeneca and Chiesi and travel support from Chiesi and GSK. Professor Sven-Eric Dahlén declares consulting fees from Astra Zenica, Cayman Chemicals, GSK, Novartis, Regeneron, Sanofi and Teva and honorarium from Sanofi. Dr Barbro Dahlén is in receipt of grants from GSK and Novartis and declares consulting fees from Novartis, Astra Zeneca and Sanofi. She is on the advisory board for Astra Zeneca and Sanofi. Prof. Fowler receives a grant from Boehringer Ingelheim and an honorarium from Chiesi. Prof. Sandstrom received payment for the Boehringer Ingelheim lecture (paid to his institution). Dr Auffrey and Dr De Meulder have both received support for the manuscript from the Innovative Medicines Initiative. Prof. Adcock has received grants from GSK, MRC and EPSRC. He also declares consulting fees from GSK, Sanofi, Chiesi and Kinaset. He has received honoraria from Astra Zeneca, Sanofi, Eurodrug, and Sunovion. He has also received payment for expert testimony from Chiesi and travel support from Astra Zeneca. Prof. Chung is in receipt of grants from MRC, EPSRC and GSK and honoraria from Astra Zeneca and Novartis. He is on the advisory board of Astra Zeneca, GSK, Roche and Novartis. Prof. Sterk received a grant from Innovative Medicines Initiative and has a non-substantial interest in SME Breathomix. Prof. Skipp has a grant from EU UBIOPRED IMI FP and is a shareholder in TopMD Precision Medicine Ltd. Prof. Djukanovic receives consulting fees from Synairgen and honoraria from Regeneron, GSK and is on the advisory board for Synairgen. He also holds stock in Synairgen. Dr Ye, Dr Cheng, Dr Bakke, Dr Caruso, Dr Horvárth, Prof. Howarth, Dr Krug, Dr Montuschi, Dr Sanak and Dr Schofield report no potential conflict of interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features., Objective: We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls., Methods: Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes., Results: Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts., Conclusion: We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator., (Copyright © 2023 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Rationale: There is a major unmet need for improving the care of children and adolescents with severe asthma and wheeze. Identifying factors contributing to disease severity may lead to improved diagnostics, biomarkers, or therapies. The airway microbiota may be such a key factor. Objectives: To compare the oropharyngeal airway microbiota of children and adolescents with severe and mild/moderate asthma/wheeze. Methods: Oropharyngeal swab samples from school-age and preschool children in the European U-BIOPRED (Unbiased BIOmarkers in the PREDiction of respiratory disease outcomes) multicenter study of severe asthma, all receiving severity-appropriate treatment, were examined using 16S ribosomal RNA gene sequencing. Bacterial taxa were defined as amplicon sequence variants. Results: We analyzed 241 samples from four cohorts: A) 86 school-age children with severe asthma; B) 39 school-age children with mild/moderate asthma; C) 65 preschool children with severe wheeze; and D) 51 preschool children with mild/moderate wheeze. The most common bacteria were Streptococcus (mean relative abundance, 33.5%), Veillonella (10.3%), Haemophilus (7.0%), Prevotella (5.9%), and Rothia (5.5%). Age group (school-age vs. preschool) was associated with the microbiota in β-diversity analysis ( F  = 3.32, P  = 0.011) and in a differential abundance analysis (28 significant amplicon sequence variants). Among all children, we found no significant difference in the microbiota between children with severe and mild/moderate asthma/wheeze in univariable β-diversity analysis ( F  = 1.99, P  = 0.08, N  = 241), but a significant difference in a multivariable model ( F  = 2.66, P  = 0.035), including the number of exacerbations in the previous year. Age was also significant when expressed as a microbial maturity score (Spearman Rho, 0.39; P  = 4.6 × 10 -10 ); however, this score was not associated with asthma/wheeze severity. Conclusions: There was a modest difference in the oropharyngeal airway microbiota between children with severe and mild/moderate asthma/wheeze across all children but not in individual age groups, and a strong association between the microbiota and age. This suggests the oropharyngeal airway microbiota as an interesting entity in studying asthma severity, but probably without the strength to serve as a biomarker for targeted intervention.

Asthma symptoms are often exacerbated by the common-cold-causing rhinovirus (RV). In this study, we characterized the temporal behavior of circulating exosomal microRNAs (ExoMiRNAs) in a longitudinal bi-phasic case-control study of mild asthmatics ( n = 12) and matched non-atopic healthy controls ( n = 12) inoculated with rhinovirus. We aimed to define clinical and immunologic characteristics associated with differentially expressed (DE) miRNAs. In total, 26 DE ExoMiRNAs, including hsa-let-7f-5p, hsa-let-7a-5p, hsa-miR-122-5p, hsa-miR-101-3p, and hsa-miR-126-3p, were identified between asthmatic and healthy subjects after inoculation with RV. Time series clustering identified a unique Cluster of Upregulated DE ExoMiRNAs with augmenting mean expression and a distinct Cluster of Downregulated DE ExoMiRNAs with mean expression decline in asthmatic subjects upon RV challenge. Notably, the Upregulated Cluster correlated with Th1 and interferon-induced cytokines/chemokines (IFN-γ and IFN-γ-inducible protein-10) and interleukin-10 (IL-10). Conversely, the Downregulated Cluster correlated with IL-13, a Th2 cytokine, pulmonary function measurements (FVC%, FEV1%, and PEF%), and inflammatory biomarkers (FeNO, eosinophil%, and neutrophil%). Key ExoMiRNA-target gene and anti-viral defense mechanisms of the Upregulated and Downregulated Clusters were identified by network and gene enrichment analyses. Our findings provide insight into the regulatory role of ExoMiRNAs in RV-induced asthma.

Introduction: Asthma is a heterogeneous disease with poorly defined phenotypes. Patients with severe asthma often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication., Methods: Baseline urine was collected prospectively from healthy participants (n=100), patients with mild-to-moderate asthma (n=87) and patients with severe asthma (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from patients with severe asthma (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods., Results: A total of 90 metabolites were identified, with 40 significantly altered (p<0.05, false discovery rate <0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and patients with mild-to-moderate asthma differed significantly from those in patients with severe asthma (p=2.6×10 -20 ), OCS-treated asthmatic patients differed significantly from non-treated patients (p=9.5×10 -4 ), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings., Conclusions: This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the need to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma., Competing Interests: Conflict of interest: S.N. Reinke reports grants from Canadian Institutes of Health Research, during the conduct of the study. Conflict of interest: S. Naz has nothing to disclose. Conflict of interest: R. Chaleckis has nothing to disclose. Conflict of interest: H. Gallart-Ayala has nothing to disclose. Conflict of interest: J. Kolmert reports personal fees for consultancy from Gesynta Pharma AB, outside the submitted work. Conflict of interest: N.Z. Kermani has nothing to disclose. Conflict of interest: A. Tiotiu has nothing to disclose. Conflict of interest: D.I. Broadhurst has nothing to disclose. Conflict of interest: A. Lundqvist has nothing to disclose. Conflict of interest: H. Olsson is an employee and shareholder of AstraZeneca. Conflict of interest: M. Ström has nothing to disclose. Conflict of interest: Å.M. Wheelock has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: A.R. Sousa has nothing to disclose. Conflict of interest: J.H. Riley works for and own shares in GlaxoSmithKline. Conflict of interest: S. Bates is an employee of Johnson & Johnson and has previously worked for and holds stock in GlaxoSmithKline. Conflict of interest: J. Scholfield reports grants from Innovative Medicines Initiative, during the conduct of the study; and is director and employee of TopMD Precision Medicine Ltd. Conflict of interest: M. Loza is an employee of and owns stock in Johnson & Johnson. Conflict of interest: F. Baribaud is a shareholder of Johnson & Johnson and a current employee of Bristol Myers Squibb. Conflict of interest: P.S. Bakke reports personal fees for advisory board work and lectures from AstraZeneca, and personal fees for lectures from Novartis and Boehringer Ingelheim, outside the submitted work. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez reports grants and personal fees from AstraZeneca, ALK, Boehringer Ingelheim, Chiesi, Sanofi-Aventis, Novartis and GlaxoSmithKline, outside the submitted work. Conflict of interest: S.J. Fowler reports personal fees from AstraZeneca, Novartis, TEVA and Chiesi, outside the submitted work. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: P. Howarth has nothing to disclose. Conflict of interest: I. Horvath has nothing to disclose. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: A. Behndig has nothing to disclose. Conflict of interest: F. Singer reports personal fees from Vertex Pharmaceuticals (CH) and Novartis, outside the submitted work. Conflict of interest: J. Musial has nothing to disclose. Conflict of interest: D.E. Shaw has nothing to disclose. Conflict of interest: B. Dahlén reports personal fees for advisory board work and lectures from AstraZeneca, TEVA and Sanofi, and grants from Novartis and GlaxoSmithKline, outside the submitted work. Conflict of interest: S. Hu has nothing to disclose. Conflict of interest: J. Lasky-Su has nothing to disclose. Conflict of interest: P.J. Sterk reports a public private grant from the Innovative Medicines Initiative (IMI) covered by the EU and EFPIA, during the conduct of the study. Conflict of interest: K.F. Chung has received honoraria for participating in advisory board meetings of GlaxoSmithKline, AstraZeneca, Roche, Novartis, Merck, Nocion and Shionogi regarding treatments for asthma, COPD and chronic cough and has also been remunerated for speaking engagements. Conflict of interest: R. Djukanovic reports receiving fees for lectures at symposia organised by Novartis, AstraZeneca and TEVA, consultation for TEVA and Novartis as member of advisory boards, and participation in a scientific discussion about asthma organised by GlaxoSmithKline; and is a co-founder and current consultant, and has shares in Synairgen, a University of Southampton spin out company. Conflict of interest: S-E. Dahlén reports personal fees for consultancy from AstraZeneca, Cayman Chemical, GlaxoSmithKline, Novartis, Merck, Regeneron, Sanofi and TEVA, outside the submitted work. Conflict of interest: I.M. Adcock has nothing to disclose. Conflict of interest: C.E. Wheelock has nothing to disclose., (Copyright ©The authors 2022.)

Background: Defective translocation of the translational repressor TIAR (T-cell internal antigen receptor) in bronchial epithelial cells (BECs) from asthma patients underlies epithelial hyperresponsiveness, reflected by an exaggerated production of a select panel of inflammatory cytokines such as CXCL-8, interleukin (IL)-6, granulocyte colony-stimulating factor, CXCL-10, upon exposure to tumour necrosis factor (TNF) and IL-17A. With this study we aimed to clarify whether epithelial hyperresponsiveness is a consistent finding, is changed upon in vivo exposure to rhinovirus (RV)-A16 and applies to the bronchoconstrictor endothelin-1., Methods: BECs were obtained from asthma patients (n=18) and healthy individuals (n=11), 1 day before and 6 days post-RV-A16 exposure. BECs were cultured and stimulated with TNF and IL-17A and inflammatory mediators were analysed. The bronchoalveolar lavage fluid (BALF) was obtained in parallel with BECs to correlate differential cell counts and inflammatory mediators with epithelial hyperresponsiveness., Results: Epithelial hyperresponsiveness was confirmed in sequential samples and even increased in BECs from asthma patients after RV-A16 exposure, but not in BECs from healthy individuals. Endothelin-1 tended to increase in BECs from asthma patients collected after RV-A16 exposure, but not in BECs from healthy individuals. In vitro CXCL-8 and endothelin-1 production correlated. In vivo relevance for in vitro CXCL-8 and endothelin-1 production was shown by correlations with forced expiratory volume in 1 s % predicted and CXCL-8 BALF levels., Conclusion: Epithelial hyperresponsiveness is an intrinsic defect in BECs from asthma patients, which increases upon viral exposure, but not in BECs from healthy individuals. This epithelial hyperresponsiveness also applies to the bronchoconstrictor endothelin-1, which could be involved in airway obstruction., Competing Interests: Conflict of interest: A. Ravi has nothing to disclose. Conflict of interest: S. Chowdhury has nothing to disclose. Conflict of interest: A. Dijkhuis has nothing to disclose. Conflict of interest: B.S. Dierdorp has nothing to disclose. Conflict of interest: T. Dekker has nothing to disclose. Conflict of interest: R. Kruize has nothing to disclose. Conflict of interest: Y.S. Sabogal Piñeros has nothing to disclose. Conflict of interest: C.J. Majoor has nothing to disclose. Conflict of interest: P.J. Sterk has nothing to disclose. Conflict of interest: R. Lutter reports support for the present manuscript from the Netherlands Asthma Foundation (now Netherlands Lung Foundation; grant numbers 3-2-07-012 and 3.2.10.069) and GlaxoSmithKline (CRT 114696)., (Copyright ©The authors 2022.)

Background: Exacerbation-prone asthma is a feature of severe disease. However, the basis for its persistency remains unclear., Objectives: To determine the clinical and transcriptomic features of frequent exacerbators (FEs) and persistent FEs (PFEs) in the U-BIOPRED cohort., Methods: We compared features of FE (≥2 exacerbations in past year) to infrequent exacerbators (IE, <2 exacerbations) and of PFE with repeat ≥2 exacerbations during the following year to persistent IE (PIE). Transcriptomic data in blood, bronchial and nasal epithelial brushings, bronchial biopsies and sputum cells were analysed by gene set variation analysis for 103 gene signatures., Results: Of 317 patients, 62.4% had FE, of whom 63.6% had PFE, while 37.6% had IE, of whom 61.3% had PIE. Using multivariate analysis, FE was associated with short-acting beta-agonist use, sinusitis and daily oral corticosteroid use, while PFE was associated with eczema, short-acting beta-agonist use and asthma control index. CEA cell adhesion molecule 5 (CEACAM5) was the only differentially expressed transcript in bronchial biopsies between PE and IE. There were no differentially expressed genes in the other four compartments. There were higher expression scores for type 2, T-helper type-17 and type 1 pathway signatures together with those associated with viral infections in bronchial biopsies from FE compared to IE, while there were higher expression scores of type 2, type 1 and steroid insensitivity pathway signatures in bronchial biopsies of PFE compared to PIE., Conclusion: The FE group and its PFE subgroup are associated with poor asthma control while expressing higher type 1 and type 2 activation pathways compared to IE and PIE, respectively., (© 2022 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Rationale: Asthma phenotyping requires novel biomarker discovery., Objectives: To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs)., Methods: An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED., Results: In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-β and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation., Conclusions: The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2-independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA., Competing Interests: Conflict of interest: M. Sparreman Mikus has nothing to disclose. Conflict of interest: J. Kolmert reports personal fees from Gesynta Pharma AB. Conflict of interest: L.I. Andersson has nothing to disclose. Conflict of interest: J. Östling has nothing to disclose. Conflict of interest: R.G. Knowles has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: J-O. Thörngren has nothing to disclose. Conflict of interest: P. Emami Khoonsari has nothing to disclose. Conflict of interest: B. Dahlén reports personal fees from AstraZeneca, Teva, Sanofi and grants from Novartis and GlaxoSmithKline outside the submitted work. Conflict of interest: M. Kupczyk has nothing to disclose. Conflict of interest: B. De Meulder report grants from the Innovative Medicines Initiative during the conduct of the study. Conflict of interest: C. Auffray report grants from the Innovative Medicines Initiative during the conduct of the study. Conflict of interest: P.S. Bakke reports personal fees from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim and Chiesi outside the submitted work. Conflict of interest: B. Beghe reports personal fees from AstraZeneca, Boehringer Ingelheim, Menarini and GlaxoSmithKline outside the submitted work. Conflict of interest: E.H. Bel reports grants and personal fees from GlaxoSmithKline, AstraZeneca, Novartis, TEVA, Sanofi/Regeneron, Chiesi, and Sterna outside the submitted work. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez has nothing to disclose. Conflict of interest: B. Chawes has nothing to disclose. Conflict of interest: S.J. Fowler has nothing to disclose. Conflict of interest: M. Gaga reports grants and personal fees from Novartis, Menarini, Merck Sharp & Dohme, BMS, Galapagos, and AstraZeneca outside the submitted work. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: M. Gjomarkaj has nothing to disclose. Conflict of interest: I. Horváth reports grants from EFPIA during the conduct of the study, and personal fees from AstraZeneca, GlaxoSmithKline, Novartis, Boehringer-Ingelheim, Sandoz, Teva and Chiesi outside the submitted work. Conflict of interest: P.H. Howarth has nothing to disclose. Conflict of interest: S.L. Johnston reports personal fees from Virtus Respiratory Research, Myelo Therapeutics GmbH, Concert Pharmaceuticals, Bayer, Synairgen, Novartis, Boehringer Ingelheim, Chiesi, Gerson Lehrman Group, resTORbio, Bioforce, Materia Medical Holdings, PrepBio Pharma, Pulmotect, Virion Health, Lallemand Pharma and AstraZeneca outside the submitted work. In addition, Sebastian L. Johnston also has three patents (Anti-virus therapy for respiratory diseases, UK patent application No. GB 0405634.7, Interferon-Beta for Anti-Virus Therapy for Respiratory Diseases, International Patent Application No. PCT/ GB05/50031 and Interferon Lambda therapy for the treatment of respiratory disease, UK patent application No.6779645.9). Conflict of interest: G. Joos reports grants and personal fees from AstraZeneca, Bayer, Chiesi, Eureca vzw, GlaxoSmithKline and Teva outside the submitted work. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: J. Musial has nothing to disclose. Conflict of interest: E. Niżankowska-Mogilnicka has nothing to disclose. Conflict of interest: H.K. Olsson reports other support from AstraZeneca outside the submitted work. Conflict of interest: A. Papi reports grants, personal fees, non-financial support and others from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Chiesi, Teva, Mundipharma, Zambon, Novartis, Menarini, Sanofi/Regeneron, Roche, Fondazione Salvatore Maugeri, Chiesi and Edmond pharma outside the submitted work. Conflict of interest: K.F. Rabe reports grants and personal fees from AstraZeneca, Boehringer Ingelheim, Sanofi Aventis, MERCK SHARP & DOHME, Novartis, Orion Cooperation, Berlin Chemie, Roche, Chiesi and grants for research from the Ministry of Education and Science, Germany. Conflict of interest: T. Sandström has nothing to disclose. Conflict of interest: D.E. Shaw reports personal fees and non-financial support from GlaxoSmithKline, Novartis and AstraZeneca outside the submitted work. Conflict of interest: N.M. Siafakas has nothing to disclose. Conflict of interest: M. Uhlen has nothing to disclose. Conflict of interest: J.H. Riley is an employee and shareholder in GlaxoSmithKline. Conflict of interest: S. Bates is an employee and shareholder in GlaxoSmithKline. Conflict of interest: R.J.M. Middelveld reports grants from the Swedish Strategic Research Foundation, AstraZeneca, the Swedish Heart Lung Foundation and the Swedish Asthma and Allergy Association outside the submitted work. Conflict of interest: C.E. Wheelock has nothing to disclose. Conflict of interest: K.F. Chung reports grants and personal fees from GlaxoSmithKline, AstraZeneca, Novartis, Merck, Boehringer Ingelheim, Roche and Shionogi outside the submitted work. Conflict of interest: I.M. Adcock has nothing to disclose. Conflict of interest: P.J. Sterk reports grants to Amsterdam UMC from the public private Innovative Medicines Initiative (IMI) covered by the European Union (EU) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) during the conduct of the study. Conflict of interest: R. Djukanovic reports receiving fees for lectures at symposia organised by Novartis, GlaxoSmithKline, AstraZeneca and Teva, consultation fees from Teva and Novartis; he is a co-founder and current consultant and has shares in Synairgen. Conflict of interest: P. Nilsson has nothing to disclose. Conflict of interest: S-E. Dahlén reports personal fees from AstraZeneca, Cayman Chemicals, GlaxoSmithKline, Novartis, Regeneron, Sanofi and Teva outside the submitted work. Conflict of interest: A. James has nothing to disclose., (Copyright ©The authors 2022.)

Rationale: Mast cells (MCs) play a role in inflammation and both innate and adaptive immunity, but their involvement in severe asthma (SA) remains undefined. Objectives: We investigated the phenotypic characteristics of the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes) asthma cohort by applying published MC activation signatures to the sputum cell transcriptome. Methods: Eighty-four participants with SA, 20 with mild/moderate asthma (MMA), and 16 healthy participants without asthma were studied. We calculated enrichment scores (ESs) for nine MC activation signatures by asthma severity, sputum granulocyte status, and three previously defined sputum molecular phenotypes or transcriptome-associated clusters (TACs) 1, 2, and 3 using gene set variation analysis. Measurements and Main Results: MC signatures except unstimulated, repeated FcεR1-stimulated and IFN-γ-stimulated signatures were enriched in SA. A FcεR1-IgE-stimulated and a single-cell signature from asthmatic bronchial biopsies were highly enriched in eosinophilic asthma and in the TAC1 molecular phenotype. Subjects with a high ES for these signatures had elevated sputum amounts of similar genes and pathways. IL-33- and LPS-stimulated MC signatures had greater ES in neutrophilic and mixed granulocytic asthma and in the TAC2 molecular phenotype. These subjects exhibited neutrophil, NF-κB (nuclear factor-κB), and IL-1β/TNF-α (tumor necrosis factor-α) pathway activation. The IFN-γ-stimulated signature had the greatest ES in TAC2 and TAC3 that was associated with responses to viral infection. Similar results were obtained in an independent ADEPT (Airway Disease Endotyping for Personalized Therapeutics) asthma cohort. Conclusions: Gene signatures of MC activation allow the detection of SA phenotypes and indicate that MCs can be induced to take on distinct transcriptional phenotypes associated with specific clinical phenotypes. IL-33-stimulated MC signature was associated with severe neutrophilic asthma, whereas IgE-activated MC was associated with an eosinophilic phenotype.

Background: Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases., Objective: We sought to determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma and whether a transcriptomic signature for patients with AD who respond clinically to anti-IL-22 (fezakinumab [FZ]) is enriched in severe asthma., Methods: An AD disease signature was obtained from analysis of differentially expressed genes between AD lesional and nonlesional skin biopsies. Differentially expressed genes from lesional skin from therapeutic superresponders before and after 12 weeks of FZ treatment defined the FZ-response signature. Gene set variation analysis was used to produce enrichment scores of AD and FZ-response signatures in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes asthma cohort., Results: The AD disease signature (112 upregulated genes) encompassing inflammatory, T-cell, T H 2, and T H 17/T H 22 pathways was enriched in the blood and sputum of patients with asthma with increasing severity. Patients with asthma with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (P < .05). The FZ-response signature (296 downregulated genes) was enriched in asthmatic blood (P < .05) and particularly in neutrophilic and mixed granulocytic sputum (P < .05). These data were confirmed in sputum of the Airway Disease Endotyping for Personalized Therapeutics cohort. IL-22 mRNA across tissues did not correlate with FZ-response enrichment scores, but this response signature correlated with T H 22/IL-22 pathways., Conclusions: The FZ-response signature in AD identifies severe neutrophilic asthmatic patients as potential responders to FZ therapy. This approach will help identify patients for future asthma clinical trials of drugs used successfully in other chronic diseases., (Copyright © 2021 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Pseudomonas aeruginosa (PA) is an important respiratory pathogen for cystic fibrosis (CF) patients. Routine microbiology surveillance is time-consuming, and is best performed on expectorated sputum. As alternative, volatile organic compounds (VOCs) may be indicative of PA colonisation. In this study, we aimed to identify VOCs associated with PA in literature and perform targeted exhaled breath analysis to recognize PA positive CF patients non-invasively., Methods: This study consisted of 1) a literature review to select VOCs of interest, and 2) a cross-sectional CF study. Definitions used: A) PA positive, PA culture at visit/chronically; B) PA free, no PA culture in ≥12 months. Exhaled VOCs were identified via quadrupole MS. The primary endpoint was the area under the receiver operating characteristics curve (AUROCC) of individual VOCs as well as combined VOCs against PA culture., Results: 241 VOCs were identified in literature, of which 56 were further evaluated, and 13 could be detected in exhaled breath in our cohort. Exhaled breath of 25 pediatric and 28 adult CF patients, PA positive (n=16) and free (n=28) was available. 3/13 VOCs were significantly (p<0.05) different between PA groups in children; none were in adults. Notably, a composite model based on 5 or 1 VOC(s) showed an AUROCC of 0.86 (CI 0.71-1.0) and 0.87 (CI 0.72-1.0) for adults and children, respectively., Conclusions: Targeted VOC analysis appears to discriminate children and adults with and without PA positive cultures with clinically acceptable sensitivity values., Competing Interests: Declaration of Competing Interests AHM and PB are supported by an innovation grant from Vertex Pharmaceuticals B.V. PJS is scientific advisor and has a formally inconsiderable interest in the SME Breathomix AHM reports grants and personal fees from GSK, Boehringer Ingelheim, and AstraZeneca, and grants from Chiesi, outside the submitted work., (Copyright © 2021. Published by Elsevier B.V.)

Background: Asthma is a heterogeneous disease with differences in onset, severity, and inflammation. Bronchial epithelial cells (BECs) contribute to asthma pathophysiology., Objective: We determined whether transcriptomes of BECs reflect heterogeneity in inflammation and severity in asthma, and whether this was affected in BECs from patients with severe asthma after their regeneration by bronchial thermoplasty., Methods: RNA sequencing was performed on BECs obtained by bronchoscopy from healthy controls (n = 16), patients with mild asthma (n = 17), patients with moderate asthma (n = 5), and patients with severe asthma (n = 17), as well as on BECs from treated and untreated airways of the latter (also 6 months after bronchial thermoplasty) (n = 23). Lipidome and metabolome analyses were performed on cultured BECs from healthy controls (n = 7); patients with severe asthma (n = 9); and, for comparison, patients with chronic obstructive pulmonary disease (n = 7)., Results: Transcriptome analysis of BECs from patients showed a reduced expression of oxidative phosphorylation (OXPHOS) genes, most profoundly in patients with severe asthma but less profoundly and more heterogeneously in patients with mild asthma. Genes related to fatty acid metabolism were significantly upregulated in asthma. Lipidomics revealed enhanced levels of lipid species (phosphatidylcholines, lysophosphatidylcholines. and bis(monoacylglycerol)phosphate), whereas levels of OXPHOS metabolites were reduced in BECs from patients with severe asthma. BECs from patients with mild asthma characterized by hyperresponsive production of mediators implicated in neutrophilic inflammation had decreased expression of OXPHOS genes compared with that in BECs from patients with mild asthma with normoresponsive production. BECs obtained after thermoplasty had significantly increased expression of OXPHOS genes and decreased expression of fatty acid metabolism genes compared with BECs obtained from untreated airways., Conclusion: BECs in patients with asthma are metabolically different from those in healthy individuals. These differences are linked with inflammation and asthma severity, and they can be reversed by bronchial thermoplasty., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment., Materials and Methods: This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis., Results: In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89-1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91-1.00)., Conclusion: Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: In all chronic airway diseases, the dynamics of airway function are influenced by underlying airway inflammation and bronchial hyperresponsiveness along with limitations in reversibility owing to airway and lung remodeling as well as mucous plugging. The relative contribution of each component translates into specific clinical patterns of symptoms, quality of life, exacerbation risk, and treatment success., Objective: We aimed to evaluate whether subgrouping of patients with obstructive airway diseases according to patterns of fluctuation in lung function allows identification of specific phenotypes with distinct clinical characteristics., Methods: We applied the novel method of fluctuation-based clustering (FBC) to twice-daily FEV 1 measurements recorded over a 1-year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or chronic obstructive pulmonary disease from the European BIOAIR cohort., Results: Independently of clinical diagnosis, FBC divided patients into 4 fluctuation-based clusters with progressively increasing alterations in lung function that corresponded to patterns of increasing clinical severity, risk of exacerbation, and lower quality of life. Clusters of patients with airway disease with significantly elevated levels of biomarkers relating to remodeling (osteonectin) and cellular senescence (plasminogen activator inhibitor-1), accompanied by a loss of airway reversibility, pulmonary hyperinflation, and loss of diffusion capacity, were identified. The 4 clusters generated were stable over time and revealed no differences in levels of markers of type 2 inflammation (blood eosinophils and periostin)., Conclusion: FBC-based phenotyping provides another level of information that is complementary to clinical diagnosis and unrelated to eosinophilic inflammation, which could identify patients who may benefit from specific treatment strategies or closer monitoring., (Copyright © 2021. Published by Elsevier Inc.)

Background: Early detection/prediction of flare-ups in asthma, commonly triggered by viruses, would enable timely treatment. Previous studies on exhaled breath analysis by electronic nose (eNose) technology could discriminate between stable and unstable episodes of asthma, using single/few time-points. To investigate its monitoring properties during these episodes, we examined day-to-day fluctuations in exhaled breath profiles, before and after a rhinovirus-16 (RV16) challenge, in healthy and asthmatic adults., Methods: In this proof-of-concept study, 12 atopic asthmatic and 12 non-atopic healthy adults were prospectively followed thrice weekly, 60 days before, and 30 days after a RV16 challenge. Exhaled breath profiles were detected using an eNose, consisting of 7 different sensors. Per sensor, individual means were calculated using pre-challenge visits. Absolute deviations (|%|) from this baseline were derived for all visits. Within-group comparisons were tested with Mann-Whitney U tests and receiver operating characteristic (ROC) analysis. Finally, Spearman's correlations between the total change in eNose deviations and fractional exhaled nitric oxide (FeNO), cold-like symptoms, and pro-inflammatory cytokines were examined., Results: Both groups had significantly increased eNose fluctuations post-challenge, which in asthma started 1 day post-challenge, before the onset of symptoms. Discrimination between pre- and post-challenge reached an area under the ROC curve of 0.82 (95% CI = 0.65-0.99) in healthy and 0.97 (95% CI = 0.91-1.00) in asthmatic adults. The total change in eNose deviations moderately correlated with IL-8 and TNFα (ρ ≈ .50-0.60) in asthmatics., Conclusion: Electronic nose fluctuations rapidly increase after a RV16 challenge, with distinct differences between healthy and asthmatic adults, suggesting that this technology could be useful in monitoring virus-driven unstable episodes in asthma., (© 2021 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)

Background: Although estimates of suboptimal adherence to oral corticosteroids in asthma range from 30% to 50%, no ideal method for measurement exists; the impact of poor adherence in severe asthma is likely to be particularly high., Research Questions: What is the prevalence of suboptimal adherence detected by self-reporting and direct measures? Is suboptimal adherence associated with disease activity?, Study Design and Methods: Data were included from individuals with severe asthma taking part in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study and prescribed daily oral corticosteroids. Participants completed the Medication Adherence Report Scale, a five-item questionnaire used to grade adherence on a scale from 1 to 5, and provided a urine sample for analysis of prednisolone and metabolites by liquid chromatography-mass spectrometry., Results: Data from 166 participants were included in this study: mean (SD) age, 54.2 (± 11.9) years; FEV 1 , 65.1% (± 20.5%) predicted; female, 58%; 37% completing the Medication Adherence Report Scale reported suboptimal adherence; and 43% with urinary corticosteroid data did not have detectable prednisolone or metabolites in their urine. Good adherence by both methods was detected in 49 of the 142 (35%) of participants in whom both methods were performed; adherence detection did not match between methods in 53%. Self-reported high adherers had better asthma control and quality of life, whereas directly measured high adherers had lower blood eosinophil levels., Interpretation: Low adherence is a common problem in severe asthma, whether measured directly or self-reported. We report poor agreement between the two methods, suggesting some disassociation between self-assessment of medication adherence and regular oral corticosteroid use, which suggests that each approach may provide complementary information in clinical practice., (Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.)

Introduction: Recent clinical trials with immune checkpoint inhibitors (ICIs) have shown that a subgroup of patients with malignant pleural mesothelioma (MPM) could benefit from these agents. However, there are no accurate biomarkers to predict who will respond. The aim of this study was to assess the accuracy of exhaled breath analysis using electronic technology (eNose) for discriminating between responders to ICI and non-responders., Methods: This proof-of-concept prospective observational study was part of an intervention study (INITIATE) in patients with recurrent MPM who were treated with nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA-4). At baseline and after six weeks of treatment, breath profiles were collected by an eNose. Modified Response Evaluation Criteria in Solid Tumors were used to assess efficacy at 6-month follow-up. For data processing and statistics, we used independent t-test analyses followed by linear discriminant and receiver-operating characteristic (ROC) analysis., Results: Exhaled breath data of 31 MPM patients who received nivolumab plus ipilimumab were available at baseline. There were 16 with and 15 without a response after 6 months of treatment. At baseline, breath profiles significantly differed between responders and non-responders, with a cross validation value of 71%. The ROC-AUC after internal cross-validation was 0.90 (confidence interval: 0.80-1.00)., Conclusion: An eNose is able to discriminate at baseline between responders and non-responders to nivolumab plus ipilimumab in MPM, thereby potentially identifying a subgroup of patients that will benefit from ICI treatment., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: R.d.V. receives personal fees and has a substantial interest in the start-up company Breathomix BV, which produces eNoses linked to an online cloud solution. P.J.S. is a scientific adviser and has an officially non-substantial interest in the start-up company Breathomix BV that produces eNoses and accompanying cloud algorithms. P.B. reports grants from BMS, grants from MSD, outside the submitted work. All remaining authors declared no conflicts of interest., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver-operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66-0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43-0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49-0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50-0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.