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Mustafa Abdo,1 Frederik Trinkmann,2,3 Anne-Marie Kirsten,4 Heike Biller,1 Frauke Pedersen,1,4 Benjamin Waschki,1 Erika Von Mutius,5 Matthias Kopp,6,7 Gesine Hansen,8 Klaus F Rabe,1 Thomas Bahmer,1,9,&ast; Henrik Watz4,&ast; On behalf of the ALLIANCE study group1LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 2Department of Pneumology and Critical Care Medicine, Thoraxklinik, University of Heidelberg, Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany; 3Department of Biomedical Informatics, Heinrich-Lanz-Center, University Medical Center Mannheim, Heidelberg University, Heidelberg, Germany; 4Pulmonary Research Institute at the LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 5Dr von Hauner Children’s Hospital, Ludwig Maximilians University of Munich, Comprehensive Pneumology Center Munich, German Center for Lung Research (DZL), and Institute of Asthma and Allergy Prevention, Helmholtz Centre, Both Munich, Germany; 6Department of Pediatric Respiratory Medicine, Inselspital, University Children’s Hospital of Bern, University of Bern, Bern, Switzerland; 7Division of Pediatric Pneumology & Allergology, University Hospital Schleswig-Holstein-Campus Luebeck, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Luebeck, Germany; 8Department of Paediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Biomedical Research in Endstage and Obstructive Lung Disease (BREATH), German Center for Lung Research (DZL), Hannover, Germany; 9University Hospital Schleswig-Holstein-Campus Kiel, department for Internal Medicine I, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Kiel, Germany&ast;These authors contributed equally to this workCorrespondence: Mustafa AbdoLungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Wöhrendamm 80, Grosshansdorf, 22927, GermanyTel +49 4102 601 2412Email m.abdo@lungenclinic.deRationale: Small airway dysfunction (SAD) is a frequent feature of asthma that has been linked to disease severity and poor symptom control. However, little is known about the role of SAD in nocturnal asthma.Objective: To study the association between the severity of SAD and frequency of nocturnal symptoms compared to conventional lung function testing.Methods: We assessed the frequency of self-reported nocturnal symptoms through the asthma control test. We studied the impact of nocturnal asthma using the Asthma Quality of Life Questionnaire (AQLQ) and the Multidimensional Fatigue Inventory (MFI-20). We assessed the lung function using spirometry, body plethysmography, impulse oscillometry, single and multiple inert gas washout and measured markers of T2-inflammation (blood and sputum eosinophils; fractional exhaled nitric oxide (FeNo)). We stratified the patients according to the presence and frequency of nocturnal asthma.Results: A total of 166 asthma patients were enrolled in the analysis. Eighty-seven patients (52%) reported to have nocturnal symptoms at least once in the last four weeks. The odds ratio of nocturnal asthma correlated with the severity of all non-spirometric measures of SAD, yet neither with airflow obstruction (FEV1 and FEV/FVC) nor with large airway resistance (R20). Patients with frequent nocturnal asthma (n = 29) had a numerical increase of T2 markers and more severe SAD, as indicated by all non-spirometric measures of SAD (all p-values < 0.05), worse overall asthma control, increased fatigue and reduced quality of life (all p-values < 0.01) compared to patients with infrequent nocturnal asthma (n = 58) or patients without nocturnal asthma (n = 79). We identified 63 patients without airflow obstruction, nearly 43% of them (n = 27) had nocturnal asthma. In this subgroup, only markers of air trapping and ventilation heterogeneity were significantly elevated and correlated with the frequency of nocturnal symptoms: LCI (Spearman’s coefficient = − 0.42, p < 0.001), RV% (− 0.32, p = 0.02).Conclusion: SAD is closely associated to asthma with nocturnal symptoms. Spirometry might underestimate the broad spectrum of distal lung function impairments in this population of patients.Keywords: small airway dysfunction, nocturnal asthma, ventilation heterogeneity, air trapping

Mustafa Abdo,1 Benjamin Waschki,2 Anne-Marie Kirsten,3 Frederik Trinkmann,4,5 Heike Biller,1 Christian Herzmann,6 Erika von Mutius,7 Matthias Kopp,8,9 Gesine Hansen,10 Klaus F Rabe,1 Thomas Bahmer,1,11,* Henrik Watz3,* On behalf of the ALLIANCE study group1LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 2Department of Cardiology and Pneumology at Hospital Itzehoe, Itzehoe, Germany; 3Pulmonary Research Institute at the LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 4Department of Pneumology and Critical Care Medicine, Thoraxklinik, University of Heidelberg, Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany; 5Department of Biomedical Informatics, Heinrich-Lanz-Center, University Medical Center Mannheim, Heidelberg University, Heidelberg, Germany; 6Research Center Borstel, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Borstel, Germany; 7Dr von Hauner Children’s Hospital, Ludwig Maximilians University of Munich, Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany; 8Department of Pediatric Respiratory Medicine, Inselspital, University Children’s Hospital of Bern, University of Bern, Bern, Switzerland; 9Division of Pediatric Pneumology & Allergology, University Hospital Schleswig-Holstein-Campus Luebeck, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Luebeck, Germany; 10Department of Paediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Biomedical Research in Endstage and Obstructive Lung Disease (BREATH), German Center for Lung Research (DZL), Hannover, Germany; 11University Hospital Schleswig-Holstein-Campus Kiel, Department for Internal Medicine I, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Kiel, Germany*These authors contributed equally to this workCorrespondence: Mustafa AbdoLungenClinic Grosshansdorf GmbH, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Wöhrendamm 80, Großhansdorf, 22927, GermanyTel +49 4102 601 2412Email m.abdo@lungenclinic.deRationale: Asthma, obesity and physical activity (PA) are interrelated. However, longitudinal data with objective PA measures and direct assessment of body composition are still lacking.Objective: To study the impact of symptom control on PA and body composition.Methods: In a longitudinal cohort study of the German Center for Lung Research, we assessed the body composition of 233 asthma patients and 84 healthy controls using bioelectrical impedance analysis. PA (ie average daily steps and time of at least moderate activity, steps/min) was measured by accelerometry for one week. Asthma control was assessed by ACT score, ACQ-5 score and history of severe exacerbations. After two years of follow-up, we studied changes in physical activity and body composition in relation to asthma control.Results: Patients with uncontrolled asthma had increased fat mass and decreased muscle mass compared to patients with controlled asthma or healthy controls. Both fat mass and muscle mass correlated better with asthma control than the body mass index (BMI). In multivariate regressions adjusted for age and sex, asthma control and physical activity were independent predictors of body composition (R2 = 0.61, p&thinsp

Mustafa Abdo,1 Frauke Pedersen,1,2 Frederik Trinkmann,3,4 Felix JF Herth,3 Klaus F Rabe,1 Anne-Marie Kirsten,2 Henrik Watz2 1LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 2Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 3Pneumology and Critical Care Medicine, Thoraxklinik at University Hospital Heidelberg, Translational Lung Research Center Heidelberg (TLRC), Member of German Center for Lung Research (DZL), Heidelberg, Germany; 4Department of Biomedical Informatics (DBMI) at the Center for Preventive Medicine and Digital Health Baden-Württemberg (CPD-BW), University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, GermanyCorrespondence: Mustafa Abdo, LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Wöhrendamm 80, Grosshansdorf, 22927, Germany, Email m.abdo@lungenclinic.de

BackgroundFew studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support.MethodsThe study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions.ResultsIn phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (pConclusionA collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone.

BackgroundImpulse oscillometry (IOS) allows an effort-independent evaluation of small airway function in asthma. Unfortunately, well-determined minimal clinically important differences (MCIDs) for IOS measures are lacking. Here, we provide MCIDs for frequently used IOS measures, namely frequency dependence of resistance (FDR) and area of reactance (AX), in patients with asthma.MethodsWe performed IOS at baseline and 1 year later in adult patients with mild-to-severe asthma (n=235). In a two-step approach, we first applied a distribution-based method to statistically determine the MCID. Next, we validated the proposed MCID according to patient-reported outcome measures (PROMs): Asthma Quality of Life Questionnaire (AQLQ), Asthma Control Questionnaire-7 (ACQ-7) and Asthma Control Test (ACT). We used multivariable analyses to investigate the proposed MCIDs as predictors for improvements in PROMs compared with the established MCID of forced expiratory volume in 1 s (FEV1).ResultsThe proposed MCID was a decline of ≥0.06 kPa·L−1·s−1and ≥0.65 kPa·L−1for FDR and AX, respectively. Patients who had changes beyond the MCIDs for both FDR and AX showed greater improvements in all PROMs than those who had not. The mean improvements in PROMs were beyond the established MCIDs for ACQ-7 and AQLQ, and approximated the MCID for ACT. Multivariable analyses demonstrated the MCIDs for both FDR and AX as independent predictors for the MCIDs of all PROMs. The MCID for FDR was a stronger predictor of all PROMs than the MCID for FEV1.ConclusionsThis study provides MCIDs for IOS-derived measures in adult patients with asthma and emphasises that small airway function is a distinguished end-point beyond the conventional measure of FEV1.

Introduction: There is an ongoing increase in the number of COVID-19 cases worldwide, and further restrictions are necessary to contain the pandemic. The implications of restrictive measures like social-distancing and mouth-nose protection on patients with chronic respiratory diseases have hardly been investigated. Methods: We surveyed 282 participants within the All Age Asthma Cohort (ALLIANCE), a multicenter longitudinal observational study. We assessed the effects of COVID-19 on the course of the disease, quality of life and the mental health of asthma patients. Results: We observed a high rate of problems associated with using facemasks and a significant reduction in using routine medical care. In addition to unsettling impacts, like an increase in depression symptoms in adults, an astonishing striking effect was that preschool children experienced an improvement in disease condition during the lockdown. Conclusion: COVID-19 measures have a positive and negative influence on the course of disease in asthmatics. The observed improvement can be contributed to a significant reduction in exposure to viral infections. Long-term observation of this side-effect may help improve our understanding of the influence of viral infections on asthma in early childhood.

RationaleIn adults, personalised asthma treatment targets patients with type 2 (T2)-high and eosinophilic asthma phenotypes. It is unclear whether such classification is achievable in children.ObjectivesTo define T2-high asthma with easily accessible biomarkers and compare resulting phenotypes across all ages.MethodsIn the multicentre clinical All Age Asthma Cohort (ALLIANCE), 1125 participants (n=776 asthmatics, n=349 controls) were recruited and followed for 2 years (1 year in adults). Extensive clinical characterisation (questionnaires, blood differential count, allergy testing, lung function and sputum induction (in adults)) was performed at baseline and follow-ups. Interleukin (IL)-4, IL-5 and IL-13 were measured after stimulation of whole blood with lipopolysaccharide (LPS) or anti-CD3/CD28.Measurements and main resultsBased on blood eosinophil counts and allergen-specific serum IgE antibodies, patients were categorised into four mutually exclusive phenotypes: “atopy-only”, “eosinophils-only”, “T2-high” (eosinophilia + atopy) and “T2-low” (neither eosinophilia nor atopy). The T2-high phenotype was found across all ages, even in very young children in whom it persisted to a large degree even after 2 years of follow-up. T2-high asthma in adults was associated with childhood onset, suggesting early origins of this asthma phenotype. In both children and adults, the T2-high phenotype was characterised by excessive production of specific IgE to allergens (pConclusionsUsing easily accessible biomarkers, patients with T2-high asthma can be identified across all ages delineating a distinct phenotype. These patients may benefit from therapy with biologicals even at a younger age.

Comprehensive studies investigated the role of T-cells in asthma which led to personalised treatment options targeting severe eosinophilic asthma. However, little is known about the contribution of B-cells to this chronic inflammatory disease. In this study we investigated the contribution of various B-cell populations to specific clinical features in asthma.In the All Age Asthma Cohort (ALLIANCE), a subgroup of 154 adult asthma patients and 28 healthy controls were included for B-cell characterisation by flow cytometry. Questionnaires, lung function measurements, blood differential counts and allergy testing of participants were analysed together with comprehensive data on B-cells using association studies and multivariate linear models.Patients with severe asthma showed decreased immature B-cell populations while memory B-cells were significantly increased compared with both mild-moderate asthma patients and healthy controls. Furthermore, increased frequencies of IgAWith this study we demonstrate for the first time a significant association of increased IgA

BackgroundEchinococcus multilocularis is a causative agent of human alveolar echinococcosis (AE). AE leads to cirrhosis in several organs, such as the liver, triggering severe conditions, including hepatic failure and encephalopathy. The main purpose of this study is to explore the interaction between treated hepatic stellate cells and AE-protoscoleces (AE-PSCs). The results of this study will be provided experimental basis for revealing the mechanisms of hepatic fibrosis after AE infection.MethodsWe investigated the role of alveolar echinococcosis-protoscoleces (AE-PSCs) in liver fibrosis and structural changes and liver fibrosis-related protein expression in a coculture of PSCs and human hepatic stellate cells (HSCs). Structural changes were detected by transmission electron microscopy, whereas liver fibrosis-related proteins, collagen I, alpha-smooth muscle actin, and osteopontin levels were measured by western blotting and enzyme-linked immunosorbent assay. ResultsPSCs exhibited morphological changes, specifically changes in shape, and showed slight changes in the cytoplasmic membrane, whereas structural modifications were observed in HSCs. Additionally, western blotting and enzyme-linked immunosorbent assay revealed that PSCs treated in vitro with HSC-LX2 showed significantly increased collagen-Ⅰ, α-smooth muscle actin, and osteopontin expression levels after 3–4 days of incubation in a coculture system. AE-PSCs induced liver fibrosis by inducing extracellular matrix expression and HSC activation.Conclusions These results provide insight into the pathogenesis of echinococcosis- induced hepatic fibrosis and introduce therapeutic targets and diagnostic criteria for managing echinococcosis-dependent liver fibrosis.

Frauke Pedersen,1,2 Frederik Trinkmann,3 Mustafa Abdo,2 Anne-Marie Kirsten,1 Klaus F Rabe,2 Henrik Watz,1 Simonetta Baraldo,4 Marina Saetta,4 Jens M Hohlfeld,5,6 Olaf Holz5 1Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 2LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 3Pneumology and Critical Care Medicine, Thoraxklinik at University Hospital Heidelberg, Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany; 4Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Respiratory Diseases Clinic, University of Padova, Padova, Italy; 5Fraunhofer ITEM, Clinical Airway Research - Biomedical Research in End-Stage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany; 6Department of Respiratory Medicine, Hannover Medical School (MHH), Biomedical Research in End-Stage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, GermanyCorrespondence: Frauke PedersenPulmonary Research Institute at LungenClinic Grosshansdorf, Wöhrendamm 80, Grosshansdorf, D-22927, GermanyTel +49-4102-6016845Fax +49-4102-8881114Email f.pedersen@pulmoresearch.dePurpose: We recently introduced a sputum cell quality score to rate how cell morphology, cellular debris and squamous cell contamination influence inflammatory cell identification during microscopic evaluation. However, sputum cell quality is generally not considered for the interpretation of sputum fluid phase biomarkers. Therefore, we compared the soluble protein concentrations between sputum samples with different cell quality. The impact of cell quality was compared to other factors potentially affecting soluble biomarker concentrations.Methods: A comprehensive sputum dataset from 154 clinically stable COPD patients was used to analyse the differences and the variability of sputum supernatant concentrations for 23 proteins between low, medium, and high sputum cell quality samples. A model was developed and tested to compare the impact of different factors on sputum supernatant protein levels.Results: Mean percentages of sputum macrophages, neutrophils, eosinophils, monocytes and lymphocytes showed no significant differences between low, medium and high cell quality levels. The mean percentage of squamous cells were lower, while total cell count/mL sputum and cell viability were significantly higher in sputum samples with higher cell quality. The concentrations of Interleukin-6, Interleukin-8 and Tumor Necrosis Factor Receptor 2 were significantly increased in sputum samples of higher cell quality. The variability of most protein concentrations declined with increasing cell quality levels. Sixteen proteins showed significantly negative correlations with the percentage of squamous cells. For 14 proteins we observed a positive correlation with cell number/mL sputum. Multiple regression analysis shows that generally less than 30% of the protein variability can be explained by the included factors.Conclusion: Sputum cell quality has a significant impact on some soluble biomarker concentrations in sputum supernatant. Sputum samples with low sputum cell quality show a higher variability of fluid phase proteins in comparison to medium and high sputum cell quality levels.Keywords: sputum cell quality score, soluble biomarkers, sputum supernatant

“Breathomics” in asthma is a rapidly growing area of significant scientific interest, as indicated by a recently published review, two research articles, and their accompanying editorials in high impact pneumology journals [1–5]. The repeatedly observed associations between breath volatile organic compounds (VOCs) and sputum or blood inflammatory cells [2, 3] suggest that breathomics are on the brink of introduction as a valuable clinically tool. However, there are also major concerns about unresolved methodological issues and a general paucity of high-quality data [1, 6]. In this letter we detail our concerns with breathomics based on data from a cohort of adult asthma patients with a broad spectrum of clinical phenotypes., Despite recent publications, we are not close to finding a clinically valuable breath VOC biomarker for asthma or asthma phenotypes https://bit.ly/3heTgtK

BACKGROUND Little is known about the role of small airway dysfunction (SAD) and its complex relation with asthma control and physical activity (PA). OBJECTIVE To investigate the interrelations among SAD, risk factors for asthma severity, symptom control, and PA. METHODS We assessed SAD by impulse oscillometry and other sophisticated lung function measures including inert gas washout in adults with asthma (mild to moderate, n = 140; severe, n = 128) and 69 healthy controls from the All Age Asthma Cohort. We evaluated SAD prevalence and its interrelation with risk factors for asthma severity (older age, obesity, and smoking), type 2 inflammation (sputum and blood eosinophils, fractional exhaled nitric oxide), systemic inflammation (high-sensitivity C-reactive protein), asthma control (AC), and PA (accelerometer for 1 week). We applied a clinical model based on structural equation modeling that integrated causal pathways among these clinical variables. RESULTS The prevalence of SAD ranged from 75% to 90% in patients with severe asthma and from 53% to 64% in mild to moderate asthma. Severe SAD was associated with poor AC and low PA. Structural equation modeling indicated that age, obesity, obesity-related systemic inflammation, T2 inflammation, and smoking are independent predictors of SAD. Small airway dysfunction was the main determinant factor of AC, which in turn affected PA. Obesity affected AC directly and through its contribution to SAD and low PA. In addition, PA had bidirectional associations with obesity, SAD, and AC. Structural equation modeling also indicated interrelations among distal airflow limitation, air trapping, and ventilation heterogeneity. CONCLUSIONS Small airway dysfunction is a highly prevalent key feature of asthma that interrelates a spectrum of distal lung function abnormalities with risk factors for asthma severity, asthma control, and physical activity.

MicroRNAs belong to a newly identified class of small non-coding RNAs that have been widely implicated in the fine-tuning of many physiological processes such as the pathogenesis of type 2 diabetes (T2D) and obesity. Microarray studies have highlighted an altered profile of miRNA expression in insulin target tissues in diabetic and obese models. Emerging evidences suggest that miRNAs play significant roles in insulin production, secretion and actions, as well as in diverse aspects of glucose homeostasis and adipocyte differentiation. The identification of tissue-specific miRNAs implicated in T2D and obesity might be useful for the future development of effective strategies for early diagnosis and therapeutic intervention of obesity-related medical complications.