PDF file - 242K, Table S2. Clinical characteristics of patients in the first replication set (UBC). Table S3. Clinical characteristics of patients in the second replication set (Groningen). Figure S1. Volcano plots showing the impact of smoking, COPD, and lung cancer on gene expression in the lung. Figure S2. Comparison of gene expression recovery following smoking cessation between the discovery set (Laval) and UBC. Figure S3. Comparison of gene expression recovery following smoking cessation between the discovery set (Laval) and Groningen. Figure S4. Expression of SERPIND1 in lung parenchyma of smoker and never-smoker by immunochemistry. Figure S5. Enrichment plot for slowly reversible gene in UBC. Figure S6. Enrichment plot for slowly reversible gene in Groningen

<p>PDF file - 589K, Probe sets significantly associated with smoking in the discovery set</p>

Cigarette smoke-induced apoptosis and necrosis contribute to the pathogenesis of chronic obstructive pulmonary disease. The induction of heme oxygenase-1 provides cytoprotection against oxidative stress, and may protect in smoking-related disease. Since mitochondria regulate cellular death, we examined the functional expression and mitochondrial localization of heme oxygenase-1 in pulmonary epithelial cells exposed to cigarette smoke extract (CSE), and its role in modulating cell death. Heme oxygenase-1 expression increased dramatically in cytosolic and mitochondrial fractions of human alveolar (A549), or bronchial epithelial cells (Beas-2b) exposed to either hemin, lipopolysaccharide, or CSE. Mitochondrial localization of heme oxygenase-1 was also observed in a primary culture of human small airway epithelial cells. Furthermore, heme oxygenase activity increased dramatically in mitochondrial fractions, and in whole cell extracts of Beas-2b after exposure to hemin and CSE. The mitochondrial localization of heme oxygenase-1 in Beas-2b was confirmed using immunogold-electron microscopy and immunofluorescence labeling on confocal laser microscopy. CSE caused loss of cellular ATP and rapid depolarization of mitochondrial membrane potential. Apoptosis occurred in Beas-2b at low concentrations of cigarette smoke extract, whereas necrosis occurred at high concentrations. Overexpression of heme oxygenase-1 inhibited CSE-induced Beas-2b cell death and preserved cellular ATP levels. Finally, heme oxygenase-1 mRNA expression was elevated in the lungs of mice chronically exposed to cigarette smoke. We demonstrate the functional compartmentalization of heme oxygenase-1 in the mitochondria of lung epithelial cells, and its potential role in defense against mitochondria-mediated cell death during CSE exposure.

Hyperinflation contributes to dyspnea intensity in COPD. Little is known about the molecular mechanisms underlying hyperinflation and how inhaled corticosteroids (ICS) affect this important aspect of COPD pathophysiology. To investigate the effect of ICS/long-acting β2-agonist (LABA) treatment on both lung function measures of hyperinflation, and the nasal epithelial gene-expression profile in severe COPD. 117 patients were screened and 60 COPD patients entered a 1-month run-in period on low-dose ICS/LABA budesonide/formoterol (BUD/F) 200/6 one inhalation b.i.d. Patients were then randomly assigned to 3-month treatment with either a high dose BDP/F 100/6 two inhalations b.i.d. (n = 31) or BUD/F 200/6 two inhalations b.i.d. (n = 29). Lung function measurements and nasal epithelial gene-expression were assessed before and after 3-month treatment and validated in independent datasets. After 3-month ICS/LABA treatment, residual volume (RV)/total lung capacity (TLC)% predicted was reduced compared to baseline (p < 0.05). We identified a nasal gene-expression signature at screening that associated with higher RV/TLC% predicted values. This signature, decreased by ICS/LABA treatment was enriched for genes associated with increased p53 mediated apoptosis was replicated in bronchial biopsies of COPD patients. Finally, this signature was increased in COPD patients compared to controls in nasal, bronchial and small airways brushings. Short-term ICS/LABA treatment improves RV/TLC% predicted in severe COPD. Furthermore, it decreases the expression of genes involved in the signal transduction by the p53 class mediator, which is a replicable COPD gene expression signature in the upper and lower airways.Trial registration: ClinicalTrials.gov registration number NCT01351792 (registration date May 11, 2011), ClinicalTrials.gov registration number NCT00848406 (registration date February 20, 2009), ClinicalTrials.gov registration number NCT00158847 (registration date September 12, 2005).

BACKGROUND There is no national protocol for the use of light therapy in bipolar depression. AIM The chronotherapy collaboration group of the Foundation for Bipolar Disorders intended to write a protocol for light therapy in bipolar depressive episodes. METHOD Narrative review of several systematic reviews, two clinician’s guides and deliberation with the sub-commission Guidelines of the Dutch Ophthalmologic Society. RESULTS The following indication was established: depressive episode, with or without seasonal features, in bipolar I or II disorder, including subsyndromal (depressive) seasonal complaints. The list of relative contra-indications (pre-existent retinal illnesses, systemic illnesses with effect on the retina and use of photosensitive medication) was shortened. In this case the medical professional discusses the possibility of an ophthalmologic consultation with the patient. Use of a mood stabilizer/antimanic medication in order to prevent mania or a mixed episode is only necessary in a depressive episode in bipolar I, but not in bipolar II disorder. Standard treatment is 10.000 lux white light during 30 minutes in the morning. CONCLUSION There is sufficient evidence to propose light therapy in a bipolar depressive episode with or without seasonal features.

Introduction: While SAD is a feature of asthma, the association of SAD with relevant asthma outcomes deserves further investigation. The ATLANTIS study was designed to identify which combination of physiologic variables best measures the presence and extent of SAD in asthma cross-sectionally and during 1-year follow-up. We created a SAD score as a data-driven combination of many physiologic variables measured and found evidence of SAD in 91% of our asthma population. In the 1-year follow up, we determined which of the physiologic tests associated most strongly with exacerbations. Methods: 773 participants with mild, moderate and severe stable asthma were followed for one year with six-month clinic and three-month telephone follow-ups. Physiologic tests included: spirometry, body plethysmography, impulse oscillometry (IOS), and multiple breath nitrogen washout. We examined associations between physiologic measurements and asthma exacerbations over one year using correlations and Poisson regression. Results: The mean number of exacerbations per patient per year in the longitudinal phase was 0.32. Exacerbations over one year were significantly associated with RV/TLC (r=0.20, p<0.0001) and R5-R20 (r=0.22, p<0.0001). Conclusion: These results suggest SAD as measured by RV/TLC and R5-R20, two accessible tests of small airways function, contributes longitudinally to important clinical outomes in asthma.

Translation of genomic alterations to protein changes in chronic obstructive pulmonary disease (COPD) is largely unexplored. Using integrated proteomic and RNA sequencing analysis of COPD and control lung tissues, we identified a protein signature in COPD characterised by extracellular matrix changes and a potential regulatory role for SUMO2. Furthermore, we identified 61 differentially expressed novel, non-reference, peptides in COPD compared with control lungs. This included two peptides encoding for a new splice variant of SORBS1, of which the transcript usage was higher in COPD compared with control lungs. These explorative findings and integrative proteogenomic approach open new avenues to further unravel the pathology of COPD.

The IL1RL1 (ST2) gene locus is robustly associated with asthma; however, the contribution of single nucleotide polymorphisms (SNPs) in this locus to specific asthma subtypes and the functional mechanisms underlying these associations remain to be defined. We tested for association between IL1RL1 region SNPs and characteristics of asthma as defined by clinical and immunological measures and addressed functional effects of these genetic variants in lung tissue and airway epithelium. Utilizing 4 independent cohorts (Lifelines, Dutch Asthma GWAS [DAG], Genetics of Asthma Severity and Phenotypes [GASP], and Manchester Asthma and Allergy Study [MAAS]) and resequencing data, we identified 3 key signals associated with asthma features. Investigations in lung tissue and primary bronchial epithelial cells identified context-dependent relationships between the signals and IL1RL1 mRNA and soluble protein expression. This was also observed for asthma-associated IL1RL1 nonsynonymous coding TIR domain SNPs. Bronchial epithelial cell cultures from asthma patients, exposed to exacerbation-relevant stimulations, revealed modulatory effects for all 4 signals on IL1RL1 mRNA and/or protein expression, suggesting SNP-environment interactions. The IL1RL1 TIR signaling domain haplotype affected IL-33–driven NF-κB signaling, while not interfering with TLR signaling. In summary, we identify that IL1RL1 genetic signals potentially contribute to severe and eosinophilic phenotypes in asthma, as well as provide initial mechanistic insight, including genetic regulation of IL1RL1 isoform expression and receptor signaling.

Citation for published version (APA): Brandsma, C-A., Guryev, V., Timens, W., Ciconelle, A., Postma, D. S., Bischoff, R., Johansson, M., Ovchinnikova, E. S., Malm, J., Marko-Varga, G., Fehniger, T. E., van den Berge, M., & Horvatovich, P. (2020). Integrated proteogenomic approach identifying a protein signature of COPD and a new splice variant of SORBS1. Thorax, 75(2), 180-183. https://doi.org/10.1136/thoraxjnl-2019-213200

Background Small airways dysfunction (SAD) is well recognised in asthma, yet its role in the severity and control of asthma is unclear. This study aimed to assess which combination of biomarkers, physiological tests, and imaging markers best measure the presence and extent of SAD in patients with asthma. Methods In this baseline assessment of a multinational prospective cohort study (the Assessment of Small Airways Involvement in Asthma [ATLANTIS] study), we recruited participants with and without asthma (defined as Global Initiative for Asthma severity stages 1-5) from general practices, the databases of chest physicians, and advertisements at 29 centres across nine countries (Brazil, China, Germany, Italy, Spain, the Netherlands, the UK, the USA, and Canada). All participants were aged 18-65 years, and participants with asthma had received a clinical diagnosis of asthma more than 6 months ago that had been confirmed by a chest physician. This diagnosis required support by objective evidence at baseline or during the past 5 years, which could be: positive airway hyperresponsiveness to methacholine, positive reversibility (a change in FEV 1 >= 12% and >= 200 mL within 30 min) after treatment with 400 mu g of salbutamol in a metered-dose inhaler with or without a spacer, variability in peak expiratory flow of more than 20% (measured over 7 days), or documented reversibility after a cycle (eg, 4 weeks) of maintenance anti-asthma treatment. The inclusion criteria also required that patients had stable asthma on any previous regular asthma treatment (including so-called rescue beta 2-agonists alone) at a stable dose for more than 8 weeks before baseline and had smoked for a maximum of 10 pack-years in their lifetime. Control group participants were recruited by advertisements; these participants were aged 18-65 years, had no respiratory symptoms compatible with asthma or chronic obstructive pulmonary disease, normal spirometry, and normal airways responsiveness, and had smoked for a maximum of 10 pack-years. We assessed all participants with spirometry, body plethysmography, impulse oscillometry, multiple breath nitrogen washout, CT (in selected participants), and questionnaires about asthma control, asthma-related quality of life (both in participants with asthma only), and health status. We applied structural equation modelling in participants with asthma to assess the contribution of all physiological and CT variables to SAD, from which we defined clinical SAD and CT SAD scores. We then classified patients with asthma into SAD groups with model-based clustering, and we compared asthma severity, control, and health-care use during the past year by SAD score and by SAD group. This trial is registered with ClinicalTrials.gov, number NCT02123667. Findings Between June 30, 2014, and March 3, 2017, we recruited and evaluated 773 participants with asthma and 99 control participants. All physiological measures contributed to the clinical SAD model with the structural equation modelling analysis. The prevalence of SAD in asthma was dependent on the measure used; we found the lowest prevalence of SAD associated with acinar airway ventilation heterogeneity (S-acin), an outcome determined by multiple breath nitrogen washout that reflects ventilation heterogeneity in the most peripheral, pre-acinar or acinar airways. Impulse oscillometry and spirometry results, which were used to assess dysfunction of small-sized to mid-sized airways, contributed most to the clinical SAD score and differed between the two SAD groups. Participants in clinical SAD group 1 (n= 452) had milder SAD than group 2 and comparable multiple breath nitrogen washout S-acin to control participants. Participants in clinical SAD group 2 (n= 312) had abnormal physiological SAD results relative to group 1, particularly their impulse oscillometry and spirometry measurements, and group 2 participants also had more severe asthma (with regard to asthma control, treatments, exacerbations, and quality of life) than group 1. Clinical SAD scores were higher (indicating more severe SAD) in group 2 than group 1, and we found that these scores were related to asthma control, severity, and exacerbations. We found no correlation between clinical SAD and CT SAD scores. Interpretation SAD is a complex and silent signature of asthma that is likely to be directly or indirectly captured by combinations of physiological tests, such as spirometry, body plethysmography, impulse oscillometry, and multiple breath nitrogen washout. SAD is present across patients with all severities of asthma, but it is particularly prevalent in severe disease. The clinical classification of SAD into two groups (a milder and a more severe group) by use of impulse oscillometry and spirometry, which are easy to use, is meaningful given its association with GINA severity stages, asthma control, quality of life, and exacerbations. Copyright (c) 2019 Elsevier Ltd. All rights reserved.

Thirty percent to 50% of preschool children experience asthma-like symptoms, such as wheezing,1Sears M.R. Predicting asthma outcomes.J Allergy Clin Immunol. 2015; 136: 829-836Abstract Full Text Full Text PDF PubMed Scopus (52) Google Scholar, 2Caudri D. Wijga A. Schipper C.M. Hoekstra M. Postma D.S. Koppelman G.H. et al.Predicting the long-term prognosis of children with symptoms suggestive of asthma at preschool age.J Allergy Clin Immunol. 2009; 124: 903-910Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar, 3Hafkamp-De Groen E. Lingsma H.F. Caudri D. Levie D. Wijga A. Koppelman G.H. et al.Predicting asthma in preschool children with asthma-like symptoms: validating and updating the PIAMA risk score.J Allergy Clin Immunol. 2013; 132: 1303-1310Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar but only approximately 30% of these children will have asthma. Because of the nonspecific symptoms of asthma at preschool age and the lack of a diagnostic test for asthma in this age group, it is difficult to determine which child will have asthma. Several prediction models based on family, personal, and environmental factors have been developed to improve the early diagnosis of asthma,2Caudri D. Wijga A. Schipper C.M. Hoekstra M. Postma D.S. Koppelman G.H. et al.Predicting the long-term prognosis of children with symptoms suggestive of asthma at preschool age.J Allergy Clin Immunol. 2009; 124: 903-910Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar, 3Hafkamp-De Groen E. Lingsma H.F. Caudri D. Levie D. Wijga A. Koppelman G.H. et al.Predicting asthma in preschool children with asthma-like symptoms: validating and updating the PIAMA risk score.J Allergy Clin Immunol. 2013; 132: 1303-1310Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar yet these are of modest clinical value.4Colicino S. Munblit D. Minelli C. Custovic A. Cullinan P. Validation of childhood asthma predictive tools: a systematic review.Clin Exp Allergy. 2019; 49: 410-418Crossref PubMed Scopus (7) Google Scholar In addition, these models are based on children with respiratory symptoms, whereas asthma prediction at a time point when no clinical symptoms have occurred might identify children at risk for asthma to start early preventative measures. It has been proposed that genetics might improve asthma prediction.4Colicino S. Munblit D. Minelli C. Custovic A. Cullinan P. Validation of childhood asthma predictive tools: a systematic review.Clin Exp Allergy. 2019; 49: 410-418Crossref PubMed Scopus (7) Google Scholar Recently, 2 consortiums published the results of large meta-analyses of genome-wide association studies (GWASs), which doubled the number of genetic variants that are associated with asthma.5Demenais F. Margaritte-Jeannin P. Barnes K.C. Cookson W.O.C. Altmüller J. Ang W. et al.Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks.Nat Genet. 2018; 50: 42-50Crossref PubMed Scopus (54) Google Scholar, 6Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (55) Google Scholar The Trans-National Asthma Genetic Consortium (TAGC) described 18 loci to be associated with asthma in a multiancestry meta-analysis in 142,000 subjects,5Demenais F. Margaritte-Jeannin P. Barnes K.C. Cookson W.O.C. Altmüller J. Ang W. et al.Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks.Nat Genet. 2018; 50: 42-50Crossref PubMed Scopus (54) Google Scholar with 5 additional loci specifically related to pediatric asthma. Moreover, the SHARE consortium discovered 136 independent genetic variants to be associated with allergic disease (asthma, hay fever, or eczema) in 360,000 subjects, with almost all variants contributing to either disease.6Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (55) Google Scholar These asthma-associated variants from the TAGC and SHARE consortiums offer the opportunity to investigate asthma prediction based on genetic risk scores (GRSs). We generated a prediction model for asthma in the first 8 years of life based on the combination of family, perinatal, environmental, and genetic risk factors, with the aim to investigate the added value of genetics at predicting childhood asthma determined by easily available factors known in the first year of life. Asthma definition was based on asthma ever from age 3 to 8 years, in which cases had 1 or more of the following 3 criteria: (1) having 1 or more attacks of wheeze in the last 12 months, (2) having 1 or more events of shortness of breath (dyspnea) in the last 12 months, or (3) having inhaled steroids for respiratory or lung problems prescribed by a doctor in the last 12 months. We used data from the Prevalence and Incidence of Asthma and Mite Allergy (PIAMA) birth cohort7Wijga A.H. Kerkhof M. Gehring U. de Jongste J.C. Postma D.S. Aalberse R.C. et al.Cohort profile: the prevention and incidence of asthma and mite allergy (PIAMA) birth cohort.Int J Epidemiol. 2014; 43: 527-535Crossref PubMed Scopus (64) Google Scholar with inclusion of 1968 children (see the Methods section in this article's Online Repository at www.jacionline.org). With univariate and multivariate logistic regression analysis, familial, perinatal, and environmental risk scores were made based on variables that previously predicted asthma in children experiencing respiratory symptoms.2Caudri D. Wijga A. Schipper C.M. Hoekstra M. Postma D.S. Koppelman G.H. et al.Predicting the long-term prognosis of children with symptoms suggestive of asthma at preschool age.J Allergy Clin Immunol. 2009; 124: 903-910Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar We selected independent single nucleotide polymorphisms (SNPs) and calculated weighted GRSs based on the TAGC and SHARE consortium data (see the Methods section in this article's Online Repository).5Demenais F. Margaritte-Jeannin P. Barnes K.C. Cookson W.O.C. Altmüller J. Ang W. et al.Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks.Nat Genet. 2018; 50: 42-50Crossref PubMed Scopus (54) Google Scholar, 6Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (55) Google Scholar Receiver operating characteristics (ROC) analysis was performed to test the added value of the GRSs to familial, perinatal, and environmental scores. Because predictors of the non-GRSs were generated from PIAMA birth cohort data, we tested for optimism caused by overfitting with use of an internal bootstrap validation approach (using the R package ‘rms’). Predicted probabilities of separate risk scores were categorized into deciles to analyze the discriminative performance of each score. Replication of the models obtained in the PIAMA birth cohort was performed in the Children/Barn, Allergy, Milieu, Stockholm, Epidemiology (BAMSE; n = 427), a Swedish birth cohort with a design comparable to that of the PIAMA birth cohort.8Kull I. Melen E. Alm J. Hallberg J. Svartengren M. van Hage M. et al.Breast-feeding in relation to asthma, lung function, and sensitization in young schoolchildren.J Allergy Clin Immunol. 2010; 125: 1013-1019Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar Of the 1968 children with genotype data in our study, 1858 children had information on the presence of asthma in the first 8 years of life. Of these, 42.6% (n = 792) had asthma ever in the first 8 years of life (Table I).Table IGeneral characteristics of the study population and univariate analysis on asthma ever∗In the PIAMA birth cohort asthma is defined as having 2 or more of the following 3 criteria: (1) having 1 or more attacks of wheeze in the last 12 months, (2) having 1 or more events of shortness of breath (dyspnea) in the last 12 months, and (3) having inhaled steroids for respiratory or lung problems prescribed by a doctor in the last 12 months. Because no data on shortness of breath were available in the BAMSE cohort, we used an adjusted asthma definition in which asthma was defined as having 1 or more of the following 2 criteria: (1) having 1 or more more attacks of wheeze in the last 12 months and (2) having inhaled steroids for respiratory or lung problems prescribed by a doctor in the last 12 months. at age 8 yearsCandidate predictorPIAMA birth cohortBAMSE cohortAll children (n = 1858)OR (95% CI)P valueAll children (n = 427)OR (95% CI)P valueFamilial factors, % (no. total) Parental allergy†In the PIAMA birth cohort parental allergy is based on parental asthma ever and/or current house dust mite allergy and/or pet allergy and/or hay fever. In the BAMSE cohort it is based on a mother and/or father with a doctor's diagnosis of asthma and/or a doctor's diagnosis of hay fever in combination with pollen allergy at baseline.55.5 (1858)1.87 (1.55-2.26)6.50 × 10−1128.6 (423)2.74 (1.76-4.28)8.32 × 10−6 Parental asthma16.1 (1848)1.88 (1.47-2.42)7.35 × 10−726.2 (423)5.01 (3.04-8.26)2.52 × 10−10 Parental allergy house dust mite34.0 (1835)1.77 (1.46-2.15)9.93 × 10−9NANANA Parental allergy to pets30.6 (1836)1.91 (1.56-2.33)2.83 × 10−1037.1 (423)2.83 (1.87-4.26)7.36 × 10−7 Parental hay fever41.8 (1832)1.51 (1.25-1.82)1.9 × 10−547.5 (423)2.41 (1.63-3.56).00001 Parental inhaled medication16.2 (1835)2.20 (1.71-2.83)9.72 × 10−1030.5 (423)4.65 (2.93-7.39)7.64 × 10−11 Low parental education‡In the PIAMA birth cohort parental education is defined as an education less than the level of a Bachelor's/Master's degree (HBO/University in Dutch system) for at least 1 of the parents. In the BAMSE cohort it is defined as an education level less than university grade for both of the parents.26.7 (1840)1.25 (1.02-1.54).03451.1 (427)1.89 (1.28-2.77).001Allergic siblings§In the PIAMA birth cohort a sibling with allergy is based on a sibling with asthma ever and/or eczema and/or hay fever. In the BAMSE cohort it is based on allergy to furred animals or pollen.20.5 (1845)1.52 (1.21-1.90)3.12 × 10-410.4 (222)2.45 (0.97-6.21).06 Asthmatic siblings4.6 (1856)2.88 (1.82-4.58)7 × 10−66.6 (427)3.91 (1.55-9.86).004 Sibling with eczema17.8 (1846)1.36 (1.07-1.73).01220.2 (223)1.02 (0.53-1.97).95 Siblings with hay fever1.98 (1851)2.11 (1.04-4.26).0388.1 (223)0.97 (0.37-2.55).95Perinatal factors, % (no. total) Male sex51.1 (1858)1.46 (1.21-1.75)6.9 × 10−556.2 (427)2.18 (1.47-3.21).0001 Low birth weight, <2500 g3.1 (1855)2.20 (1.28-3.78).0042.4 (424)4.04 (0.85-19.3).08 Any breast-feeding84.4 (1849)1.07 (0.83-1.37).61998.3 (424)3.06 (0.61-15.3).174 Breast-feeding <16 wk63.4 (1849)1.35 (1.12-1.64).00218.4 (423)1.77 (1.07-2.92).03 DeliveryTerm (≥37-≤42 wk)92.0 (1855)ReferenceReference89.2 (427)ReferenceReferencePreterm (<37 wk)4.7 (1855)1.55 (1.01-2.39).0477.0 (427)2.05 (0.94-4.50).073Postterm (>42 wk)3.3 (1855)1.17 (0.70-1.96).5453.8 (427)0.80 (0.29-2.19).662 Born by caesarian section8.5 (1841)1.07 (0.77-1.49).69616.7 (427)1.43 (0.85-2.39).174Environmental factors, % (no. total) Pets at home during pregnancy44.4 (1856)1.22 (1.01-1.47).03612.7 (427)0.76 (0.43-1.35).35 Smoking mother during pregnancy‖In the PIAMA birth cohort smoking during pregnancy is defined as smoking at least the first 4 weeks of pregnancy. In the BAMSE cohort it is defined as smoking at least 1 cigarette per day in any point of time during pregnancy.15.5 (1842)1.50 (1.16-1.93).00211.0 (427)2.30 (1.20-4.38).01 Older siblings living in home51.7 (1858)1.18 (0.98-1.41).08552.2 (424)1.03 (0.70-150).89NA, Not applicable.∗ In the PIAMA birth cohort asthma is defined as having 2 or more of the following 3 criteria: (1) having 1 or more attacks of wheeze in the last 12 months, (2) having 1 or more events of shortness of breath (dyspnea) in the last 12 months, and (3) having inhaled steroids for respiratory or lung problems prescribed by a doctor in the last 12 months. Because no data on shortness of breath were available in the BAMSE cohort, we used an adjusted asthma definition in which asthma was defined as having 1 or more of the following 2 criteria: (1) having 1 or more more attacks of wheeze in the last 12 months and (2) having inhaled steroids for respiratory or lung problems prescribed by a doctor in the last 12 months.† In the PIAMA birth cohort parental allergy is based on parental asthma ever and/or current house dust mite allergy and/or pet allergy and/or hay fever. In the BAMSE cohort it is based on a mother and/or father with a doctor's diagnosis of asthma and/or a doctor's diagnosis of hay fever in combination with pollen allergy at baseline.‡ In the PIAMA birth cohort parental education is defined as an education less than the level of a Bachelor's/Master's degree (HBO/University in Dutch system) for at least 1 of the parents. In the BAMSE cohort it is defined as an education level less than university grade for both of the parents.§ In the PIAMA birth cohort a sibling with allergy is based on a sibling with asthma ever and/or eczema and/or hay fever. In the BAMSE cohort it is based on allergy to furred animals or pollen.‖ In the PIAMA birth cohort smoking during pregnancy is defined as smoking at least the first 4 weeks of pregnancy. In the BAMSE cohort it is defined as smoking at least 1 cigarette per day in any point of time during pregnancy. Open table in a new tab NA, Not applicable. The combined familial, perinatal, and environmental risk score included parental allergy (odd ratio [OR], 1.38; 95% CI, 1.08-1.7), parents allergic to pets (OR, 1.43; 95% CI, 1.10-1.85), parental inhaled medication (OR, 1.54; 95% CI, 1.17-2.0), siblings with asthma (OR, 2.46; 95% CI, 1.52-3.99), low parental education (OR, 1.33; 95% CI, 1.07-1.65), male sex (OR, 1.44l 95% CI, 1.20-1.74), breast-feeding for less than 16 weeks (1.32 (1.09-1.60), low birth weight of less than 2500 g (OR, 2.15; 95% CI, 1.24-3.70), pets at home during pregnancy (OR, 1.21; 95% CI, 1.00-1.46), smoking mother during pregnancy (OR, 1.45; 95% CI, 1.13-1.88), and older siblings living at home (OR, 1.20; 95% CI, 1.00-1.45; see Table E1 in this article's Online Repository at www.jacionline.org). Association analyses with asthma separately for familial, perinatal, and environmental scores and GRSs indicated that the familial risk score had the strongest prediction (PIAMA: OR, 1.25; P = 3.17 × 10−19; BAMSE: OR, 1.46; P = 3.17 × 10−13; see Table E2 in this article's Online Repository at www.jacionline.org). The combined model of familial, perinatal, and environmental factors showed moderate discrimination (area under the receiver operating characteristic curve (AUC) = 0.65), with similar predictive properties of this model in the BAMSE cohort (AUC = 0.67). The optimism-corrected AUC in the PIAMA birth cohort was 0.65, which indicates no overfitting of data. In the PIAMA birth cohort, inclusion of the TAGC consortium GRS in models with familial, perinatal, and environmental risk scores resulted in an AUC of 0.66, whereas inclusion of the SHARE consortium GRS had an AUC of 0.65 (Fig 1, A and B). There was no improvement over the risk prediction based on familial, perinatal, and environmental factors (AUC difference between familial, perinatal, and environmental factors solely and combined with GRSs: TAGC consortium, z = −0.55 and P = .29; SHARE consortium, z = 0.0 and P = .5). Replication analyses in the BAMSE cohort showed similar results, with AUCs of 0.69 (AUC difference between familial, perinatal, and environmental factors solely and combined with GRSs: TAGC consortium, z = −0.55 and P = .29; SHARE consortium, z = −0.83 and P = .2; Fig 1, C and D). Discriminative analysis showed the best predictive probability for the familial risk score (see Figs E1 and E2 in this article's Online Repository at www.jacionline.org). In the PIAMA birth cohort the results did not change when we used a more specific asthma diagnosis as the outcome, doctor's diagnosed asthma at age 8 years, which will exclude transient wheezers (model of familial, perinatal, and environmental factors; AUC = 0.64) combined with TAGC consortium GRSs (AUC = 0.64; SHARE consortium GRS, AUC = 0.64). It has been suggested that genetic risk prediction can improve when adding additional SNPs that are associated with the disease, although not at a genome-wide significance threshold. To investigate this possibility, we performed additional predictive analysis of the GRSs from the SHARE consortium in the PIAMA birth cohort by using more liberal P value thresholds of 1 × 10−6 and 1 × 10−4. However, this did not improve genetic risk prediction for asthma ever in the first 8 years of life with combined familial, perinatal, and environmental score and GRS AUC values of 0.65 and 0.65, respectively (see the Results section in this article's Online Repository at www.jacionline.org). Identifying children at high risk for asthma development is important for prevention and installation of early treatment. However, the GRSs based on SNPs from the largest asthma GWAS did not improve asthma prediction over familial, perinatal, and environmental factors. Asthma in childhood is a highly heterogeneous disease, with different genes being related to different subtypes of asthma. The fact that we used a more common asthma definition with the selection of all children with respiratory symptoms in the first 8 years of life and no selection on disease specificity could have influenced our results, although using a more strict definition (ie, doctor-diagnosed asthma) led to the same conclusion. For calculation of TAGC consortium GRSs, we added loci specifically related to pediatric asthma. In addition to this, an even more specific (subtype-related) selection of SNPs could be beneficial for generating GRSs to improve the prediction of asthma subtypes. Asthma has a strong genetic contribution. However, based on the most recent insights in asthma genetics, genetic variants have no added value in predicting nonspecific asthma. This can be explained in several ways. The known heritability of asthma is due to common SNPs of modest effect, resulting in many children carrying risk alleles but not having asthma. Second, although the number of risk SNPs has increased considerably in the past years, these SNPs still explain only a small fraction of asthma heritability. We also acknowledge that a substantially larger study might have yielded a significant but small increase in AUC values after inclusion of the GRS. We show in our article that variation in P value thresholds for GRS SNP selection made no difference in asthma prediction. This is underlined by the findings of Zhang et al,9Zhang Y. Qi G. Park J.H. Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits.Nat Genet. 2018; 50: 1318-1326Crossref PubMed Scopus (74) Google Scholar who propose subsequently to focus more on effect size distributions than P values for selection of SNPs for disease prediction. These novel methodological approaches might benefit future genetic risk prediction in asthmatic patients. Better prediction might also depend on our ability to define different subtypes of asthma with shared causes. Moreover, better modeling of potential interactions between genes and environmental factorsE1Bønnelykke K. Ober C. Leveraging gene-environment interactions and endotypes for asthma gene discovery.J Allergy Clin Immunol. 2016; 137: 667-679Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar might be needed to accurately predict asthma in future studies. The PIAMA birth cohort is a multicenter birth cohort initiated in 1996. Seven thousand eight hundred sixty-two women (2779 with allergy and 5083 without allergy) were invited to participate in the study; 3963 live-born children participated in the study (1327 with a mother with allergy were defined as high risk, and 2726 children with a mother without allergy were defined as low risk). Questionnaires for parental completion, partly based on the International Study of Asthma and Allergies in Childhood core questionnaires, were sent to the parents during pregnancy when the children were aged 3 and 12 months, yearly thereafter up to age 8 years, and at age 11, 14, 16, and 17 years. All 1327 high-risk children and a random sample of 663 low-risk children were selected for an extensive medical examination at age 4 and 8 years. Blood or a buccal brush was used for DNA extraction in the group undergoing an extensive medical examination at age 4 years and in all children at age 8 years. At age 8 years, 92% of the baseline population was still in the study, and therefore our study focused on the first 8 years of life. Combined phenotypic and genotypic data for this study were available for 1968 children. The study protocol was approved by the Medical Ethical Committees of the participating university hospitals, and all participants provided written informed consent. A detailed description of the cohort outline has been published previously.E2Wijga A.H. Kerkhof M. Gehring U. de Jongste J.C. Postma D.S. Aalberse R.C. et al.Cohort profile: the prevention and incidence of asthma and mite allergy (PIAMA) birth cohort.Int J Epidemiol. 2014; 43: 527-535Crossref PubMed Scopus (94) Google Scholar Between 1994 and 1996, 4089 newborn infants were recruited in the BAMSE cohort, and questionnaire data on baseline study characteristics were obtained.E3Wickman M. Kull I. Pershagen G. Nordvall S.L. The BAMSE project: presentation of a prospective longitudinal birth cohort study.Pediatr Allergy Immunol. 2002; 13: 11-13Crossref PubMed Scopus (197) Google Scholar The recruitment area included central and northwestern parts of Stockholm. At approximately 1, 2, 4, and 8 years of age, parents completed questionnaires on their children's symptoms related to asthma and other allergic diseases. Response rates were 96%, 94%, 92% and 84%, respectively. At ages 4 and 8 years, blood samples were collected in 2605 (63.7%) and 2470 (60.4%) children, respectively. DNA was extracted from 2033 samples at 8 years after exclusion of samples with too little blood, lack of questionnaire data, or if parental consent to genetic analysis of the sample was not obtained. From these samples, all children with a doctor's diagnosis of asthma ever were selected as cases (n = 273), and a random sample of children with no history of asthma or other allergic diseases were selected as control subjects (n = 273). After quality control (QC), a total of 239 cases and 246 control subjects, all of white ancestry, were retained for genetic analyses.E4Melén E. Granell R. Kogevinas M. Strachan D. Gonzalez J.R. Wjst M. et al.Genome-wide association study of body mass index in 23 000 individuals with and without asthma.Clin Exp Allergy. 2013; 43: 463-474Crossref PubMed Scopus (58) Google Scholar Children from the PIAMA birth cohort were genotyped on 3 different platforms. One thousand three hundred and seventy-seven children were genotyped with the Illumina Omni Express Exome chip, whereas 288 children were genotyped with the Illumina Omni Express chip (Illumina, San Diego, Calif), both with use of an Illumina BeadArray Reader and Iscan at the Genomics Facility of the University Medical Center Groningen (Groningen, The Netherlands). DNA of 404 children were genotyped with the Illumina Human610 (HM610) quad array and use of the Illumina BeadArray reader and Iscans at the Centre National de Génotypage (CNG, Evry, France) as part of the GABRIEL consortium.E5Moffatt M.F. Gut I.G. Demenais F. Strachan D.P. Bouzigon E. Heath S. et al.A large-scale, consortium-based genomewide association study of asthma.N Engl J Med. 2010; 363: 1211-1221Crossref PubMed Scopus (1338) Google Scholar QC inclusion measures per chip on the subjects included a missing genotype call rate of less than 0.03, identical by state (IBS) of less than 0.1875, and a heterozygosity rate deviating of less than 4 SDs from the mean. Male subjects with greater than 1% heterozygote SNPs on chromosome X were excluded. Ethnicity was assessed by using principal component analyses with HapMap CEU, CHB+JPT, and YRI reference panels; only white subjects were included.E6Purcell S. Neale B. Todd-Brown K. Thomas L. Ferreira M.A.R. Bender D. et al.PLINK: a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (16842) Google Scholar QC measures per SNP included a missing genotype call rate of less than 0.05, a minor allele frequency (MAF) of greater than 0.05, and a Hardy-Weinberg equilibrium P value of greater than 10−6. SNPs that were greater than 1% heterozygous in male subjects on chromosome X were excluded. Base pair positions of SNPs on the HM610 chip were converted to genome build 37, in accordance with the Omni Express Exome chip and the Omni Express chip. The strand was determined of each SNP and on the different platforms and, if necessary, converted to the positive strand. SNPs with unknown strand orientation were removed. Discordant genotypes of duplicate SNPs were set to missing. SNPs that showed large differences in allele frequencies between platforms (>15%) were either recoded (ie, alleles were swapped) in case of an A/T or C/G SNP (and rechecked) or removed in other cases. Duplicate subjects between the platforms were considered sampling errors, and both subjects were removed. Single chips were matched to the 1000 Genomes reference set with respect to base pair positions Resemblance between the chip and the 1000 Genomes European panel (EUR) of rs numbers, alleles, and allele frequencies of SNPs on the autosomal chromosomes were checked and deleted if discrepant. After QC, a total of 1968 subjects remained, with the presence of 873 (44.4%) high-risk children. Imputation was performed per platform by using IMPUTE 2.0E7Howie B. Fuchsberger C. Stephens M. Marchini J. Abecasis G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.Nat Genet. 2012; 44: 955-959Crossref PubMed Scopus (1090) Google Scholar against the reference data set of the ALL panel of 1000 Genomes (version 3, March 2012).E8Consortium 1000 Genomes Project Abecasis G.R. Auton A. Brooks L.D. DePristo M.A. Durbin R.M. et al.An integrated map of genetic variation from 1,092 human genomes.Nature. 2012; 491: 56-65Crossref PubMed Scopus (5172) Google Scholar After imputation, only SNPs of high quality (information score IMPUTE ≥ 0.7) were selected per chip. We removed SNPs that showed discrepancy between chips in allele frequency (>15%, n = 1795). Rs numbers and insertions or deletions were separately merged by using GTOOL (http://www.well.ox.ac.uk/∼cfreeman/software/gwas/gtool.html) because of potential localization at the same base pair position. Obtained files were combined into 1 data set (SNPs: n = 11,713,219) that was used for further analyses. Genotyping was done on the Illumina Human610 Quad platform at the Centre National de Génotypage in Evry, France, under the GABRIEL project framework.E5Moffatt M.F. Gut I.G. Demenais F. Strachan D.P. Bouzigon E. Heath S. et al.A large-scale, consortium-based genomewide association study of asthma.N Engl J Med. 2010; 363: 1211-1221Crossref PubMed Scopus (1338) Google Scholar For imputation, genotyped SNPs were filtered at a call rate of greater than 95%, a Hardy-Weinberg P value of greater than 1 × 10−6, and an MAF of greater than 0.01 and a sample call rate of greater than 95%, and 515,445 SNPs remained after QC. These were imputed by using MiniMac release stamp 2012-11-16 and the GIANT ALL reference panel, phase 1 v3.20101123, onto 30,061,897 variants. Resultant SNPs were filtered for imputation quality threshold at an R2 value of 0.3 or greater. The primary outcome variable of this study is based on asthma ever at age 8 years, in which asthma is defined by the following characteristics: 1 or more attacks of wheeze in the last 12 months, 1 or more events of shortness of breath (dyspnea) in the last 12 months, or inhaled corticosteroids for respiratory or lung problems prescribed by a doctor in the last 12 months. A child who had 1 or more of these characteristics was categorized as having “asthma.” A child who had none of these characteristics was categorized as “not having asthma.” At 1 and 2 years of age, data on shortness of breath are not available, and data on steroids use are limited. Therefore our outcome variable is based on asthma ever from age 3 until age 8 years. We acknowledge that with our asthma ever definition, we select all children with respiratory symptoms in the first 8 years of life and that some included children will not have asthma but will have respiratory symptoms. Therefore we performed our analyses as well on a the PIAMA birth cohort variable doctor's diagnosis of asthma at age 8 years, which is defined as asthma diagnosed ever by a doctor and asthma in the last 12 months at the age of 8 years. In the BAMSE cohort no data were available on 1 or more events of shortness of breath (dyspnea) in the last 12 months. Therefore we used an adjusted diagnosis of asthma ever at age 8 years based on (1) 1 or more attacks of wheeze in the last 12 months or (2) inhaled corticosteroids for respiratory or lung problems prescribed by a doctor in the last 12 months. A child who had at least 1 of these characteristics was categorized as having “asthma.”

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted.

To the editor, Asthma, one of the most common chronic diseases in childhood, is caused by interactions between genes and environmental factors. The mainstay of treatment is daily use of inhaled corticosteroids (ICS), which are the most effective medication for controlling asthma symptoms and preventing (severe) exacerbations. ICS use reduces both hospitalizations and mortality rates1 and improves asthma control; reflected in forced expiratory volume in 1 second (FEV1) levels and fraction of exhaled nitric oxide (FeNO). These effects are particularly observed in asthma patients with eosinophilic, type 2 airway inflammation.2 However, responses to ICS are heterogeneous, which while controversial, possibly reflect genetic associations.3, 4 Genome-wide association studies (GWAS) have reproducibly found the Interleukin 1 receptor like 1 (IL1RL1, ST2) gene to be associated with asthma susceptibility.5 IL1RL1 single-nucleotide polymorphisms (SNPs) and IL1RL1 expression levels have been associated with blood eosinophils and markers of Th2 type inflammation.6, 7 However, the influence of IL1RL1 SNPs on the effectiveness of asthma treatment has not been investigated. Since the IL-33/IL1RL1 pathway has been associated with eosinophilic, type 2, inflammation, we hypothesized that IL1RL1 SNPs may affect corticosteroid treatment response in asthma patients. Since IL1RL1-a functions as a decoy receptor to dampen IL-33-induced signaling, genetically determined low levels of IL1RL1-a may predispose to enhanced IL-33-induced inflammation with consequently more exacerbations. In the current study, we investigated whether IL1RL1 gene variants are associated with asthma exacerbations (based on ER visits/hospitalizations and courses of oral corticosteroid [OCS] use), questionnaire-based asthma control and FeNO levels in asthma patients using ICS. Furthermore, we aimed to identify whether there is a pharmacogenetic effect of IL1RL1 variants on change in FeNO levels and FEV1% predicted in asthma patients after 4-6 weeks of ICS treatment. After close inspection of the Linkage Disequilibrium structure of IL1RL1, we selected 6 IL1RL1 SNPs that tag important LD blocks in IL1RL1 (r2 > .8) with SNPs previously found to be associated with asthma5; rs13431828, rs1041973, rs1420101, rs1946131, rs1921622, and rs10204137 (Table S1). Cross-sectional IL1RL1 SNP discovery analysis was performed in ICS treated asthmatic children, mainly of European ancestry, from the Pharmacogenetics of Asthma Medication in Children: Medication with Anti-inflammatory effects (PACMAN) cohort (N = 820) using logistic and linear regression models. We replicated FDR corrected significant findings (P < .05) in four different cohorts collaborating within the Pharmacogenomics in Childhood Asthma (PiCA) consortium,8 one Hispanic/Latino study; Genes-Environment and Admixture in Latino Americans (GALA II, N = 876) study, one African American population; Study of African Americans, Asthma, Genes, and Environments (SAGE, N = 525), and two European studies (≥96% European ancestry); the Effectiveness and Safety of Treatment with Asthma Therapy in children (ESTATe, N = 197) and SLOVENIA (N = 104). In addition, we performed a meta-analysis (N = 2412). The longitudinal effect of IL1RL1 on FeNO levels and FEV1% predicted upon ICS treatment in asthmatic children and adults was assessed in the SLOVENIA cohort. Conditional analysis was performed in PACMAN to assess the independent effects of the IL1RL1 SNPs. A detailed representation of the included cohorts and the allele frequencies of the IL1RL1 SNPs are provided in Tables S2 and S3, respectively. In PACMAN, we found a significant association between four of the six SNPs (rs13431828, rs1420101, rs1921622, and rs10204137) with ER visits and "any exacerbation" (Table 1A-C), which were selected for the replication study. Sensitivity analyses on Dutch ethnicity, atopy, and medication adherence did not change these results. We did not observe an association with questionnaire-based asthma control or FeNO measurements (Table S4A-B). In GALA II, we replicated our findings with significant results with the same direction of effect for rs13431828, rs1420101, and rs1921622 on ER visits/hospitalizations and "any exacerbation." Rs10204137 showed a significant association with "any exacerbation" (Table 1A-C). In SAGE, rs1921622 was associated with "any exacerbation" but the direction of the effect differed when compared to PACMAN. No association between IL1RL1 and questionnaire-based asthma control was found. In the smaller SLOVENIA and ESTATe studies, no significant cross-sectional or longitudinal associations were found (Table S5). Meta-analysis of the 4 IL1RL1 SNPs carried through to replication showed statistically significant results for rs13431828. The C allele of rs13431828 was associated with ER visits/hospitalizations (OR = 1.32, P = .02) and increased risk of "any exacerbations" (1.31, P = .02; Table 1A-C, Figure 1). No evidence of heterogeneity was found (Q = 3.6, P = .33). Conditional analysis in PACMAN on rs13431828, rs142010, rs1921622, and rs10204137 for "any exacerbation" indicated that rs13431828 was the most independently associated SNP (Table S6). These results provide new evidence that children and adolescents with the IL1RL1 risk alleles are prone to more exacerbations than children with the protective genotypes, while using ICS. This extends previous findings that SNPs in IL1RL1 are important in different asthma phenotypes, with more prominent effect in studies investigating childhood-onset asthma.5 Rs1420101 has been specifically linked to the type 2-high asthma phenotype,6 as well as to increased eosinophil numbers in peripheral blood.9 We observed replicable associations of the same IL1RL1 risk alleles in the Caucasian (PACMAN) and Hispanic/Latino (GALA II) population, but not in the African American study population (SAGE). This could be due to differences in ethnicity between study groups and LD patterns in this gene, suggested by the observed differences in allele frequency between the cohorts (see Table S3). It is possible that our results may have been influenced by factors other than currently included in the model such as inhalation technique or respiratory infections, but as such data were not available in all cohorts these were not considered. Different mechanisms may explain our findings. Firstly, IL1RL1 SNPs may modify the asthma phenotype into a more severe phenotype, with more severe exacerbations, which are insufficiently treated with the ICS dosages prescribed to the children in this study. The risk alleles described in our study for rs13431828 (C), rs1420101 (T), rs1921622 (A), and rs10204137 (A) were previously associated with lower IL1RL1 blood methylation levels and lower serum IL1RL1-a levels,7 indicating that the associated SNPs are important for regulation of IL1RL1 expression. Another mechanism to explain our results is that IL1RL1 may have a direct pharmacogenetic interaction with steroids resulting in reduced efficacy of the steroids. Rs10204137 is a missense mutation and has been associated with increased IL1RL1-a expression, which induces IL-33 expression and enhances IL-33 responsiveness.10 Moreover, rs10204137 tags an LD block that contains 5 nonsynonymous coding SNPs that result in changes to four amino acids in the intracellular domain of IL1RL1-b. These coding changes affect the Toll/interleukin-1 receptor (TIR) domain of the intracellular part of the IL1RL1 protein, which plays an important role in IL-33 induced signal transduction by IL1RL1. This triggers a signaling cascade that eventually results in the activation of downstream mitogen-activated protein kinases and transcription factors, such as nuclear factor kB (NF-kB) and activator protein-1.5 Through this pathway, asthmatic children carrying the risk allele of rs10204137 may be more sensitive to IL-33. As IL1RL1 is expressed on effector cells of the type-2 immune response such as mast cells, eosinophils, basophils, Th2 cells and ILC2 cells,11 an increased sensitivity to IL33 will contribute to an exaggerated type-2 inflammatory response after viral or allergen exposure. Secondly, IL1RL1 may have a direct pharmacogenetic interaction with steroids resulting in reduced efficacy of the steroids. A recent study on ulcerative colitis found an association between dexamethasone and upregulation of soluble IL1RL1 transcription mediated via interaction of the steroid with the glucocorticoid-responsive element in the IL1RL1 promotor patients carrying polymorphisms.12 To gain more insight into the mechanism underlying our finding, future studies should be performed in larger cohorts or with the use of biobank data. This study shows that an IL1RL1 SNP effect is present in asthmatic children using ICS. This highlights the potential investigating if novel treatment strategies targeting the IL33/IL1RL1 pathway could be used as add-on asthma treatment in patients using ICS. We would like to thank the participants and their parents of the studied cohorts for their participation. We also would like to acknowledge the field workers, data managers, and scientific collaborators dedicated to these cohorts. The authors acknowledge the GALA II and SAGE investigators (Kelley Meade, Harold J. Farber, Pedro C. Avila, Denise Serebrisky, Shannon M. Thyne, Emerita Brigino-Buenaventura, William Rodriguez-Cintron, Saunak Sen, Rajesh Kumar, Michael Lenoir, Luisa N. Borrell, and Jose R. Rodriguez-Santana), the recruiters, participants, and the study coordinator Sandra Salazar. We thank as well the ESTATe investigators (Pharmo: Ron Herings, Annemarie Janse, Jettie Overbeek, Josine Kuiper. IPCI: Katia Verhamme, Hettie Janssens, Johan de Jongste, Miriam Sturkenboom). N. Hernandez-Pacheco received a grant from Instituto de Salud Carlos III (ISCIII) and was co-funded by the European Social Funds from the European Union (ESF) "ESF invests in your future." MC Nawijn received a grant from GSK during the conduct of this study and outside the submitted work. ME Engelkes received a grant from Zonmw. K. M. Verhamme received a grant from ZonMw and she works for a research group who in the past received unconditional grants from: Yamanouchi, Pfizer/Boehringer Ingelheim, Novartis and GSK. M Pino-Yanes received a grant from the Spanish Ministry of Economy, Industry and Competitiveness and a grant from Instituto de Salud Carlos III (ISCIII). AH Maitland-van der Zee received an unrestricted research grant from GSK, and Boehringer Ingelheim. She also received a grant from ERANET ERACOSYSMED, and she participated in an advisory board for Astra Zeneca. DS Postma declares that the University of Groningen has received money for DS Postma regarding a grant for research from Astra Zeneca, Chiesi, Genentec, GSK and Roche. Fees for consultancies were given to the University of Groningen by Astra Zeneca, Chiesi, and GSK. GH Koppelman received grants from the Lung Foundation of the Netherlands, the Ubbo Emmius Foundation, during the conduct of the study; and he received grants from Lung Foundation of the Netherlands, GSK, Tetri Foundation, Vertex, TEVA the Netherlands, outside the submitted work. GHK participated in an advisory board meeting of GSK. The rest of the authors declare that they have no relevant conflict of interests. The PACMAN study was supported by an unrestricted grant from GlaxoSmithKline (GSK), whereas genetic analysis for the present study was supported by a Lung Foundation of the Netherlands grant no. AF3.2.09.081JU. FND was supported by the Ubbo Emmius Foundation. The GALA II and SAGE studies were funded by the Sandler Family Foundation, the American Asthma Foundation, the RWJF Amos Medical Faculty Development Program, Harry Wm. and Diana V. Hind Distinguished Professor in Pharmaceutical Sciences II, National Institutes of Health (1R01HL117004, R01Hl128439, R01HL135156, and 1X01HL134589), National Institute of Health and Environmental Health Sciences (R01ES015794 and R21ES24844), the National Institute on Minority Health and Health Disparities (1P60MD006902, U54MD009523, and 1R01MD010443), and the Tobacco-Related Disease Research Program under Award Number 24RT-0025 to EGB. This work was also funded by Instituto de Salud Carlos III (AC15/00015), through Strategic action for Health Research (AES) and European Community (EC) within the Active and Assisted Living (ALL) Programme framework, and by the SysPharmPedia grant from the ERACoSysMed first joint Transnational cCall from the European Union under the Horizon 2020. NH-P was funded by a fellowship (FI16/00136) from Instituto de Salud Carlos III (ISCIII) and co-funded by the European Social Funds from the European Union (ESF) "ESF invests in your future" and MP-Y was supported by the Ramón y Cajal Program (RYC-2015-17205) by the Spanish Ministry of Economy, Industry, and Competitiveness. For the SLOVENIA study, the authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P3-0067) and from SysPharmPedia grant, co-financed by Ministry of Education, Science and Sport of the Republic of Slovenia. The ESTATe project was supported by a ZonMw Grant No 113201006. The PiCA study was overall supported by ERACoSysMed 1st Joint Transnational Call (SysPharmPedia). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Sex strongly affects the gene expression profile in lung tissue, and overall reflects the activity of different biological pathways that probably play a role in the susceptibility to develop COPD with smokinghttp://bit.ly/31drVkX

OBJECTIVES:We aimed to investigate the role of genetics in the respiratory response of asthmatic children to air pollution, with a genome-wide level analysis of gene by nitrogen dioxide (NO2) and carbon monoxide (CO) interaction on lung function and to identify biological pathways involved. METHODS:We used a two-step method for fast linear mixed model computations for genome-wide association studies, exploring whether variants modify the longitudinal relationship between 4-month average pollution and post-bronchodilator FEV1 in 522 Caucasian and 88 African-American asthmatic children. Top hits were confirmed with classic linear mixed-effect models. We used the improved gene set enrichment analysis for GWAS (i-GSEA4GWAS) to identify plausible pathways. RESULTS:Two SNPs near the EPHA3 (rs13090972 and rs958144) and one in TXNDC8 (rs7041938) showed significant interactions with NO2 in Caucasians but we did not replicate this locus in African-Americans. SNP-CO interactions did not reach genome-wide significance. The i-GSEA4GWAS showed a pathway linked to the HO-1/CO system to be associated with CO-related FEV1 changes. For NO2-related FEV1 responses, we identified pathways involved in cellular adhesion, oxidative stress, inflammation, and metabolic responses. CONCLUSION:The host lung function response to long-term exposure to pollution is linked to genes involved in cellular adhesion, oxidative stress, inflammatory, and metabolic pathways.

INTRODUCTION:Asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) is a heterogenous condition with clinical features shared by both asthma and COPD.OBJECTIVES:This online global survey of respiratory/allergy specialists and primary care practitioners (PCPs) was performed to understand current clinical approaches to the differential diagnosis and management of asthma, COPD and ACO.METHODS:Respondents were recruited through: (a) a global online physician respondent community (49,980 PCPs and 7205 specialists); (b) market research agents; (c) experts; (d) professional societies; (e) colleague invitation. Respondents were presented with a survey including hypothetical clinical scenarios of diagnostic uncertainty to identify management approaches.RESULTS:891 responses (447 PCPs and 444 specialists) were collected across 13 countries. Reported features used for diagnosis of asthma and COPD were consistent with practice guidelines, but there was variability in those selected for ACO diagnosis. Features typically selected by specialists focused on spirometry/history, while PCPs focused on previous treatment/symptoms. Most respondents could correctly diagnose patients with features of ACO; however, features selected for theoretical diagnosis were often different to those selected in the case scenarios. Additionally, treatment selection was often inconsistent with guidelines, with over half of respondents not recommending inhaled corticosteroids in a patient with ACO and dominant features of asthma.CONCLUSION:While most PCPs and respiratory/allergy specialists can reach a working diagnosis of ACO, there remains uncertainty around which diagnostic features are most important and what constitutes optimal management. It is imperative that clinical studies including patients with ACO are initiated, allowing the generation of evidence-based management strategies.

MvdB reports grants paid to the University from Astra Zeneca, TEVA, GSK, and Chiesi. IMB reports consultancy fees paid to the University from GSK. MCN reports grants paid to the University from GSK. PGW reports a grant from Medimmune and consultancy fees from Astra Zeneca, Regeneron, Sanofi, Genentech, Novartis, and Glemmark. SAC reports a grant from Medimmune, fees from Astra Zeneca and nonfinancial support from Genentech. PH is involved in development and design of the Twincer high-dose dry powder inhaler, and his employer receives royalties from the sales of the Novolizer and Genuair. HWF has a patent WO2003/000325 with royalties paid to Astra-Zeneca and his employer receives royalties from the sales of Genuair products. DFC is an employee of Genentech and has submitted patents for methods for the diagnosis and treatment of respiratory disease patients. DSP reports grants paid to the University from Astra Zeneca, Chiesi, Genentech, GSK, and Roche and reports consultancy fees paid to the University by Astra Zeneca, Chiesi, and GSK. JF reports grants from Health Research Council of New Zealand, AstraZeneca, GSK, Fisher & Paykel, and Genentech; fees from AstraZeneca and Boehringer-Ingelheim and nonfinancial support from Novartis and Boehringer-Ingelheim. RB reports grants from Health Research Council of New Zealand, Astra Zeneca, GSK, and Genentech and fees from Astra Zeneca. AL, AF, CAC, SB, SS, SJV, UB, MW, and VG have nothing to disclose. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.