There is growing interest in blood eosinophil counts in the management of chronic respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD). Despite this, typical blood eosinophil levels in the general population, and the impact of potential confounders on these levels have not been clearly defined.We measured blood eosinophil counts in a random sample of 11 042 subjects recruited from the general population in Austria. We then: 1) identified factors associated with high blood eosinophil counts (>75th percentile); and 2) excluded subjects with these factors to estimate median blood eosinophil counts in a “healthy” sub-population (n=3641).We found that: 1) in the entire cohort, age ≤18 years (OR 2.41), asthma (OR 2.05), current smoking (OR 1.72), positive skin prick test (OR 1.64), COPD (OR 1.56), metabolic syndrome (OR 1.41), male sex (OR 1.36) and obesity (OR 1.16) were significantly (p<0.05) associated with high blood eosinophil counts (binary multivariable logistic regression analysis), and had an additive effect; and 2) after excluding these factors, in those older than 18 years, blood eosinophil counts were higher in males than in females (median 120 (5%–95% CI: 30–330)versus100 (30–310) cells·µL⁻¹, respectively) and did not change with age.Median blood eosinophil counts in adults are considerably lower than those currently regarded as normal, do not change with age beyond puberty, but are significantly influenced by a variety of factors which have an additive effect. These observations will contribute to the interpretation of blood eosinophil levels in clinical practice.

For patients with non-small-cell lung cancer (NSCLC) tumors without currently targetable molecular alterations, standard-of-care treatment is immunotherapy with anti-PD-(L)1 checkpoint inhibitors, alone or with platinum-doublet therapy. However, not all patients derive durable benefit and resistance to immune checkpoint blockade is common. Understanding mechanisms of resistance—which can include defects in DNA damage response and repair pathways, alterations or functional mutations in STK11/LKB1, alterations in antigen-presentation pathways, and immunosuppressive cellular subsets within the tumor microenvironment—and developing effective therapies to overcome them, remains an unmet need. Here the phase 2 umbrella HUDSON study evaluated rational combination regimens for advanced NSCLC following failure of anti-PD-(L)1-containing immunotherapy and platinum-doublet therapy. A total of 268 patients received durvalumab (anti-PD-L1 monoclonal antibody)–ceralasertib (ATR kinase inhibitor), durvalumab–olaparib (PARP inhibitor), durvalumab–danvatirsen (STAT3 antisense oligonucleotide) or durvalumab–oleclumab (anti-CD73 monoclonal antibody). Greatest clinical benefit was observed with durvalumab–ceralasertib; objective response rate (primary outcome) was 13.9% (11/79) versus 2.6% (5/189) with other regimens, pooled, median progression-free survival (secondary outcome) was 5.8 (80% confidence interval 4.6–7.4) versus 2.7 (1.8–2.8) months, and median overall survival (secondary outcome) was 17.4 (14.1–20.3) versus 9.4 (7.5–10.6) months. Benefit with durvalumab–ceralasertib was consistent across known immunotherapy-refractory subgroups. In ATM-altered patients hypothesized to harbor vulnerability to ATR inhibition, objective response rate was 26.1% (6/23) and median progression-free survival/median overall survival were 8.4/22.8 months. Durvalumab–ceralasertib safety/tolerability profile was manageable. Biomarker analyses suggested that anti-PD-L1/ATR inhibition induced immune changes that reinvigorated antitumor immunity. Durvalumab–ceralasertib is under further investigation in immunotherapy-refractory NSCLC.ClinicalTrials.gov identifier: NCT03334617

Background: The clinical context for using blood eosinophil (EOS) counts as treatment–response biomarkers in asthma and COPD requires better understanding of EOS distributions and ranges. We describe EOS distributions and ranges published in asthma, COPD, control (non-asthma/COPD) and general populations.Methods: We conducted a comprehensive literature review and meta-analysis of observational studies (January 2008 to November 2018) that included EOS counts in asthma, severe asthma, COPD, control and general populations. Excluded studies had total sample sizes <200, EOS as inclusion criterion, hospitalised population only and exclusively paediatric participants.Results: Overall, 91 eligible studies were identified, most had total-population-level data available: asthma (39 studies), severe asthma (12 studies), COPD (23 studies), control (seven studies) and general populations (14 studies); some articles reported data for multiple populations. Reported EOS distributions were right-skewed (seven studies). Reported median EOS counts ranged from 157–280 cells·µL⁻¹(asthma, 22 studies); 200–400 cells·µL⁻¹(severe asthma, eight studies); 150–183 cells·µL⁻¹(COPD, six studies); and 100–160 cells·µL⁻¹(controls, three studies); and 100–200 cells·µL⁻¹(general populations, six studies). The meta-analysis showed that observed variability was mostly between studies rather than within studies. Factors reportedly associated with higher blood EOS counts included current smoking, positive skin-prick test, elevated total IgE, comorbid allergic rhinitis, age ≤18 years, male sex, spirometric asthma/COPD diagnosis, metabolic syndrome and adiposity.Conclusion: EOS distribution and range varied by study population, and were affected by clinical factors including age, smoking history and comorbidities, which, regardless of severity, should be considered during treatment decision-making.

The Lung, hEart, sociAl, boDy (LEAD) Study (ClinicalTrials.gov; NCT01727518; http://clinicaltrials.gov) is a longitudinal, observational, population-based Austrian cohort that aims to investigate the relationship between genetic, environmental, social, developmental and ageing factors influencing respiratory health and comorbidities through life. The general working hypothesis of LEAD is the interaction of these genetic, environmental and socioeconomic factors influences lung development and ageing, the risk of occurrence of several non-communicable diseases (respiratory, cardiovascular, metabolic and neurologic), as well as their phenotypic (ie, clinical) presentation. LEAD invited from 2011-2016 a random sample (stratified by age, gender, residential area) of Vienna inhabitants (urban cohort) and all the inhabitants of six villages from Lower Austria (rural cohort). Participants will be followed-up every four years. A number of investigations and measurements were obtained in each of the four domains of the study (Lung, hEart, sociAl, boDy) including data to screen for lung, cardiovascular and metabolic diseases, osteoporosis, and cognitive function. Blood and urine samples are stored in a biobank for future investigations. A total of 11.423 males (47.6%) and females (52.4%), aged 6-80 years have been included in the cohort. Compared to governmental statistics, the external validity of LEAD with respect to age, gender, citizenship, and smoking status was high. In conclusion, the LEAD cohort has been established following high quality standards; it is representative of the Austrian population and offers a platform to understand lung development and ageing as a key mechanism of human health both in early and late adulthood.

Rationale: Respiratory resistance (Rrs) and reactance (Xrs) as measured by oscillometry and their intrabreath changes have emerged as sensitive parameters for detecting early pathological impairments during tidal breathing. Objectives: This study evaluates the prevalence and association of abnormal oscillometry parameters with respiratory symptoms and respiratory diseases in a general adult population. Methods: A total of 7,560 subjects in the Austrian LEAD (Lung, hEart, sociAl, boDy) Study with oscillometry measurements (computed with the Resmon Pro FULL; Restech Srl) were included in this study. The presence of respiratory symptoms and doctor-diagnosed respiratory diseases was assessed using an interview-based questionnaire. Rrs and Xrs at 5 Hz, their inspiratory and expiratory components, the area above the Xrs curve, and the presence of tidal expiratory flow limitation were analyzed. Normality ranges for oscillometry parameters were defined. Measurements and Main Results: The overall prevalence of abnormal oscillometry parameters was 20%. The incidence of abnormal oscillometry increased in the presence of symptoms or diagnoses: 17% (16–18%) versus 27% (25–29%), P < 0.0001. All abnormal oscillometry parameters except Rrs at 5 Hz were significantly associated with respiratory symptoms/diseases. Significant associations were found, even in subjects with normal spirometry, with abnormal oscillometry incidence rates increasing by 6% (4–8%; P < 0.0001) in subjects with symptoms or diagnoses. Conclusions: Abnormal oscillometry parameters are present in one-fifth of this adult population and are significantly associated with respiratory symptoms and disease. Our findings underscore the potential of oscillometry as a tool for detecting and evaluating respiratory impairments, even in individuals with normal spirometry.

Background: In the context of the COVID-19 pandemic, numerous new serological test systems for the detection of anti-SARS-CoV-2 antibodies have become available quickly. However, the clinical performance of many of them is still insufficiently described. Therefore we compared three commercial, CE-marked, SARS-CoV-2 antibody assays side by side. Methods: We included a total of 1,154 specimens from pre-COVID-19 times and 65 samples from COVID-19 patients (≥14 days after symptom onset) to evaluate the test performance of SARS-CoV-2 serological assays by Abbott, Roche, and DiaSorin. Results: All three assays presented with high specificities: 99.2% (98.6-99.7) for Abbott, 99.7% (99.2-100.0) for Roche, and 98.3% (97.3-98.9) for DiaSorin. In contrast to the manufacturers’ specifications, sensitivities only ranged from 83.1% to 89.2%. Although the three methods were in good agreement (Cohen’s Kappa 0.71-0.87), McNemar’s test revealed significant differences between results obtained from Roche and DiaSorin. However, at low seroprevalences, the minor differences in specificity resulted in profound discrepancies of positive predictability at 1% seroprevalence: 52.3% (36.2-67.9), 77.6% (52.8-91.5), and 32.6% (23.6-43.1) for Abbott, Roche, and DiaSorin, respectively. Conclusion: We find diagnostically relevant differences in specificities for the anti-SARS-CoV-2 antibody assays by Abbott, Roche, and DiaSorin that have a significant impact on the positive predictability of these tests.