Exhaled Volatile Organic Compounds for Asthma Control Classification in Children with Moderate to Severe Asthma: Results from the SysPharmPediA Study.

Rationale: The early identification of children with poorly controlled asthma is imperative for optimizing treatment strategies. The analysis of exhaled volatile organic compounds (VOCs) is an emerging approach to identify prognostic and diagnostic biomarkers in pediatric asthma. Objectives: To assess the accuracy of gas chromatography-mass spectrometry-based exhaled metabolite analysis to differentiate between controlled and uncontrolled pediatric asthma. Methods: This study encompassed discovery (SysPharmPediA [Systems Pharmacology Approach to Uncontrolled Paediatric Asthma]) and validation (U-BIOPRED [Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes] and PANDA [Paediatric-Asthma-Non-Invasive-Diagnostic-Approaches]) phases. First, exhaled VOCs that discriminated degrees of asthma control were identified. Subsequently, outcomes were validated in two independent cohorts. Patients were classified as controlled or uncontrolled on the basis of asthma control test scores and the number of severe attacks in the past year. In addition, the potential of VOCs to predict two or more future severe asthma attacks in SysPharmPediA was evaluated. Measurements and Main Results: Complete data were available for 196 children (SysPharmPediA, n = 100; U-BIOPRED, n = 49; PANDA, n = 47). In SysPharmPediA, after randomly splitting the population into training (n = 51) and test (n = 49) sets, three compounds (acetophenone, ethylbenzene, and styrene) distinguished between patients with uncontrolled and controlled asthma. The areas under the receiver operating characteristic curves (AUROCCs) for training and test sets were, respectively, 0.83 (95% confidence interval [CI], 0.65-1.00) and 0.77 (95% CI, 0.58-0.96). Combinations of these VOCs resulted in AUROCCs of 0.74 ± 0.06 (U-BIOPRED) and 0.68 ± 0.05 (PANDA). Attack prediction tests resulted in AUROCCs of 0.71 (95% CI, 0.51-0.91) and 0.71 (95% CI, 0.52-0.90) for the training and test sets. Conclusions: Exhaled metabolite analysis might enable asthma control classification in children. This should stimulate the further development of exhaled metabolite-based point-of-care tests in asthma.

Dried Blood Spot Method Development and Clinical Validation for the Analysis of Elexacaftor, Elexacaftor-M23, Tezacaftor, Tezacaftor-M1, Ivacaftor, Ivacaftor Carboxylate, and Hydroxymethyl Ivacaftor Using LC-MS/MS.

BACKGROUND: The highly effective Cystic Fibrosis Transmembrane conductance Regulator (CFTR) modulator, elexacaftor-tezacaftor-ivacaftor, is now widely being used by people with cystic fibrosis. However, few independent studies have detailed the pharmacokinetics (PK) of CFTR modulators. Blood collection by venipuncture is the gold standard for PK measurements, but it is invasive. The aim of this study was to develop and clinically validate a quantification method for elexacaftor, tezacaftor, ivacaftor, and their main metabolites in dried blood spots (DBSs) using liquid chromatography with tandem mass spectrometry. METHODS: Linearity, accuracy, precision, stability, hematocrit (Hct), spot-to-spot carryover, spot volume, and extraction efficiency were validated in DBS for all analytes. The clinical validation of elexacaftor-tezacaftor-ivacaftor in patients was performed by comparing 21 DBS samples with matched plasma samples. RESULTS: The preset requirements for linearity, within-run and between-run accuracy, precision, Hct, spot volume, and extraction efficiency were met. Puncher carryover was observed and resolved by punching 3 blanks after each sample. The samples remained stable and showed no notable degradation across the tested temperatures and time intervals. Corrected DBS values with the Passing-Bablok regression equation showed good agreement in Bland-Altman plots, and acceptance values were within 20% of the mean for a minimum of 67% of the repeats, according to the EMA guidelines. CONCLUSIONS: A quantification method for the analysis of elexacaftor, tezacaftor, ivacaftor, and their main metabolites was developed and clinically validated in DBS. This method could be valuable in both clinical care and research to address unanswered PK questions regarding CFTR modulators.

Oropharyngeal Microbiota Clusters in Children with Asthma or Wheeze Associate with Allergy, Blood Transcriptomic Immune Pathways, and Exacerbation Risk.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis ß-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-ß (transforming growth factor-ß) (highest in the Veillonella cluster) and Wnt/ß-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate-to-severe asthma.

BACKGROUND: Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics; however, its association with asthma control has not been explored. We aimed to investigate whether the gastrointestinal microbiome can be used to discriminate between uncontrolled and controlled asthma in children. METHODS: 143 and 103 feces samples were obtained from 143 children with moderate-to-severe asthma aged 6 to 17 years from the SysPharmPediA study. Patients were classified as controlled or uncontrolled asthmatics, and their microbiome at species level was compared using global (alpha/beta) diversity, conventional differential abundance analysis (DAA, analysis of compositions of microbiomes with bias correction), and machine learning [Recursive Ensemble Feature Selection (REFS)]. RESULTS: Global diversity and DAA did not find significant differences between controlled and uncontrolled pediatric asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 86% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples. CONCLUSION: Controlled and uncontrolled children with asthma can be differentiated based on their gastrointestinal microbiome using machine learning, specifically REFS. Our results show an association between asthma control and the gastrointestinal microbiome. This suggests that the gastrointestinal microbiome may be a potential biomarker for treatment responsiveness and thereby help to improve asthma control in children.

Primary ciliary dyskinesia in Volendam: Diagnostic and phenotypic features in patients with a CCDC114 mutation.

Primary ciliary dyskinesia (PCD) is a heterogeneous disease, with impaired mucociliary clearance causing respiratory tract infections. A founding CCDC114 mutation has led to a relatively homogeneous and large Dutch PCD population in Volendam. Our aim was to describe their phenotype. Therefore, all Volendam PCD patients seen at the Amsterdam UMC were included in this study. Data were collected on lung function, microbiology, radiology, and ear-nose-throat (ENT) symptoms. A mixed effects model estimated lung function decline in %point per year (95% confidence interval [CI]). Thirty-three (60%) out of approximately 56 Volendam PCD patients were treated at our center and included in this study. Only 30% of patients had situs inversus. FEV1 declined in children (-1.43%/year, CI: -1.80/-1.05), but not in adults (0.01%/year, CI: -0.36/0.38). Pseudomonas aeruginosa was cultured in 21% of children and 60% of adults, respectively. Patients who have been infected at some point with P. aeruginosa had a steeper decline in FEV1 as compared to patients that have never been infected. Neonatal symptoms (79%) and ENT problems (94%) were common; fertility issues however, were not (11%) common. Compared to other PCD cohorts, the Volendam/CCDC114 patients have a moderately severe phenotype with lung function decline predominantly occurring in childhood.

Sputum microbiome profiles identify severe asthma phenotypes of relative stability at 12 to 18 months.

BACKGROUND: Asthma is a heterogeneous disease characterized by distinct phenotypes with associated microbial dysbiosis. OBJECTIVES: Our aim was to identify severe asthma phenotypes based on sputum microbiome profiles and assess their stability after 12 to 18 months. A further aim was to evaluate clusters' robustness after inclusion of an independent cohort of patients with mild-to-moderate asthma. METHODS: In this longitudinal multicenter cohort study, sputum samples were collected for microbiome profiling from a subset of the Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adult patient cohort at baseline and after 12 to 18 months of follow-up. Unsupervised hierarchical clustering was performed by using the Bray-Curtis ß-diversity measure of microbial profiles. For internal validation, partitioning around medoids, consensus cluster distribution, bootstrapping, and topological data analysis were applied. Follow-up samples were studied to evaluate within-patient clustering stability in patients with severe asthma. Cluster robustness was evaluated by using an independent cohort of patients with mild-to-moderate asthma. RESULTS: Data were available for 100 subjects with severe asthma (median age 55 years; 42% males). Two microbiome-driven clusters were identified; they were characterized by differences in asthma onset, smoking status, residential locations, percentage of blood and/or sputum neutrophils and macrophages, lung spirometry results, and concurrent asthma medications (all P values < .05). The cluster 2 patients displayed a commensal-deficient bacterial profile that was associated with worse asthma outcomes than those of the cluster 1 patients. Longitudinal clusters revealed high relative stability after 12 to 18 months in those with severe asthma. Further inclusion of an independent cohort of 24 patients with mild-to-moderate asthma was consistent with the clustering assignments. CONCLUSION: Unbiased microbiome-driven clustering revealed 2 distinct robust phenotypes of severe asthma that exhibited relative overtime stability. This suggests that the sputum microbiome may serve as a biomarker for better characterizing asthma phenotypes.

Increased day-to-day fluctuations in exhaled breath profiles after a rhinovirus challenge in asthma.

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

Genome-wide association studies of exacerbations in children using long-acting beta2-agonists.

BACKGROUND: Some children with asthma experience exacerbations despite long-acting beta2-agonist (LABA) treatment. While this variability is partly caused by genetic variation, no genome-wide study until now has investigated which genetic factors associated with risk of exacerbations despite LABA use in children with asthma. We aimed to assess whether genetic variation was associated with exacerbations in children treated with LABA from a global consortium. METHODS: A meta-analysis of genome-wide association studies (meta-GWAS) was performed in 1,425 children and young adults with asthma (age 6-21 years) with reported regular use of LABA from six studies within the PiCA consortium using a random effects model. The primary outcome of each study was defined as any exacerbation within the past 6 or 12 months, including at least one of the following: 1) hospital admissions for asthma, 2) a course of oral corticosteroids or 3) emergency room visits because of asthma. RESULTS: Genome-wide association results for a total of 82 996 common single nucleotide polymorphisms (SNPs, MAF ≥1%) with high imputation quality were meta-analysed. Eight independent variants were suggestively (P-value threshold ≤5 × 10-6 ) associated with exacerbations despite LABA use. CONCLUSION: No strong effects of single nucleotide polymorphisms (SNPs) on exacerbations during LABA use were identified. We identified two loci (TBX3 and EPHA7) that were previously implicated in the response to short-acting beta2-agonists (SABA). These loci merit further investigation in response to LABA and SABA use.

Quantitative Method for the Analysis of Ivacaftor, Hydroxymethyl Ivacaftor, Ivacaftor Carboxylate, Lumacaftor, and Tezacaftor in Plasma and Sputum Using Liquid Chromatography With Tandem Mass Spectrometry and Its Clinical Applicability.

BACKGROUND: The novel cystic fibrosis transmembrane conductance regulator (CFTR) modulators, ivacaftor, lumacaftor, and tezacaftor, are the first drugs directly targeting the underlying pathophysiological mechanism in cystic fibrosis (CF); however, independent studies describing their pharmacokinetics are lacking. The aim of this study was to develop a quantification method for ivacaftor and its 2 main metabolites, lumacaftor and tezacaftor, in plasma and sputum using liquid chromatography with tandem mass spectrometry. METHODS: The developed method used a small sample volume (20 µL) and simple pretreatment method; protein precipitation solution and internal standard were added in one step to each sample. Liquid chromatography with tandem mass spectrometry was performed for a total run time of 6 minutes. The method was validated by assessing selectivity, carryover, linearity, accuracy and precision, dilution, matrix effects, and stability. RESULTS: The selectivity was good as no interference from matrices was observed. In the concentration range from 0.01 to 10.0 mg/L, calibration curves were linear with a correlation coefficient >0.9997 for all compounds. The within-run and between-run accuracy were between 99.7% and 116% at the lower limit of quantitation (LLOQ) and between 95.8% and 112.9% for all concentrations above LLOQ for all analytes in plasma and sputum. Within-run and between-run precisions were <12.7% for LLOQ and <6.7% for the higher limit of quantitation. Samples were stable, with no significant degradation at examined temperatures and time points. Clinical applicability was revealed by analyzing samples from 2 patients with CF. CONCLUSIONS: The presented method enables simultaneous quantification of ivacaftor, lumacaftor, and tezacaftor in plasma and sputum and is an improvement over previous methods because it uses smaller sample volumes, a simple pretreatment protocol, and includes tezacaftor. In future studies, it can be applied for examining pharmacokinetics characteristics of new CF transmembrane conductance regulator modulators.

The Impact of Short-Term Exposure to Air Pollution on the Exhaled Breath of Healthy Adults.

Environmental factors, such as air pollution, can affect the composition of exhaled breath, and should be well understood before biomarkers in exhaled breath can be used in clinical practice. Our objective was to investigate whether short-term exposures to air pollution can be detected in the exhaled breath profile of healthy adults. In this study, 20 healthy young adults were exposed 2-4 times to the ambient air near a major airport and two highways. Before and after each 5 h exposure, exhaled breath was analyzed using an electronic nose (eNose) consisting of seven different cross-reactive metal-oxide sensors. The discrimination between pre and post-exposure was investigated with multilevel partial least square discriminant analysis (PLSDA), followed by linear discriminant and receiver operating characteristic (ROC) analysis, for all data (71 visits), and for a training (51 visits) and validation set (20 visits). Using all eNose measurements and the training set, discrimination between pre and post-exposure resulted in an area under the ROC curve of 0.83 (95% CI = 0.76-0.89) and 0.84 (95% CI = 0.75-0.92), whereas it decreased to 0.66 (95% CI = 0.48-0.84) in the validation set. Short-term exposure to high levels of air pollution potentially influences the exhaled breath profiles of healthy adults, however, the effects may be minimal for regular daily exposures.

eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy.

BACKGROUND: Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma. OBJECTIVE: We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma. METHODS: Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics. RESULTS: Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics. CONCLUSION: eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping.

The crosstalk between microbiome and asthma: Exploring associations and challenges.

With the advancement of high-throughput DNA/RNA sequencing and computational analysis techniques, commensal bacteria are now considered almost as important as pathological ones. Understanding the interaction between these bacterial microbiota, host and asthma is crucial to reveal their role in asthma pathophysiology. Several airway and/or gut microbiome studies have shown associations between certain bacterial taxa and asthma. However, challenges remain before gained knowledge from these studies can be implemented into clinical practice, such as inconsistency between studies in choosing sampling compartments and/or sequencing approaches, variability of results in asthma studies, and not taking into account medication intake and diet composition especially when investigating gut microbiome. Overcoming those challenges will help to better understand the complex asthma disease process. The therapeutic potential of using pro- and prebiotics to prevent or reduce risk of asthma exacerbations requires further investigation. This review will focus on methodological issues regarding setting up a microbiome study, recent developments in asthma bacterial microbiome studies, challenges and future therapeutic potential.

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.

Validation of volatile metabolites of pulmonary oxidative injury: a bench to bedside study.

Background: Changes in exhaled volatile organic compounds (VOCs) can be used to discriminate between respiratory diseases, and increased concentrations of hydrocarbons are commonly linked to oxidative stress. However, the VOCs identified are inconsistent between studies, and translational studies are lacking. Methods: In this bench to bedside study, we captured VOCs in the headspace of A549 epithelial cells after exposure to hydrogen peroxide (H2O2), to induce oxidative stress, using high-capacity polydimethylsiloxane sorbent fibres. Exposed and unexposed cells were compared using targeted and untargeted analysis. Breath samples of invasively ventilated intensive care unit patients (n=489) were collected on sorbent tubes and associated with the inspiratory oxygen fraction (F IO2 ) to reflect pulmonary oxidative stress. Headspace samples and breath samples were analysed using gas chromatography and mass spectrometry. Results: In the cell, headspace octane concentration was decreased after oxidative stress (p=0.0013), while the other VOCs were not affected. 2-ethyl-1-hexanol showed an increased concentration in the headspace of cells undergoing oxidative stress in untargeted analysis (p=0.00014). None of the VOCs that were linked to oxidative stress showed a significant correlation with F IO2 (Rs range: -0.015 to -0.065) or discriminated between patients with F IO2 ≥0.6 or below (area under the curve range: 0.48 to 0.55). Conclusion: Despite a comprehensive translational approach, validation of known and novel volatile biomarkers of oxidative stress was not possible in patients at risk of pulmonary oxidative injury. The inconsistencies observed highlight the difficulties faced in VOC biomarker validation, and that caution is warranted in the interpretation of the pathophysiological origin of discovered exhaled breath biomarkers.

Medication use in uncontrolled pediatric asthma: Results from the SysPharmPediA study.

BACKGROUND: Uncontrolled pediatric asthma has a large impact on patients and their caregivers. More insight into determinants of uncontrolled asthma is needed. We aim to compare treatment regimens, inhaler techniques, medication adherence and other characteristics of children with controlled and uncontrolled asthma in the: Systems Pharmacology approach to uncontrolled Paediatric Asthma (SysPharmPediA) study. MATERIAL AND METHODS: 145 children with moderate to severe doctor-diagnosed asthma (91 uncontrolled and 54 controlled) aged 6-17 years were enrolled in this multicountry, (Germany, Slovenia, Spain, and the Netherlands) observational, case-control study. The definition of uncontrolled asthma was based on asthma symptoms and/or exacerbations in the past year. Patient-reported adherence and clinician-reported medication use were assessed, as well as lung function and inhalation technique. A logistic regression model was fitted to assess determinants of uncontrolled pediatric asthma. RESULTS: Children in higher asthma treatment steps had a higher risk of uncontrolled asthma (OR (95%CI): 3.30 (1.56-7.19)). The risk of uncontrolled asthma was associated with a larger change in FEV1% predicted post and pre-salbutamol (OR (95%CI): 1.08 (1.02-1.15)). Adherence and inhaler techniques were not associated with risk of uncontrolled asthma in this population. CONCLUSION: This study showed that children with uncontrolled moderate-to-severe asthma were treated in higher treatment steps compared to their controlled peers, but still showed a higher reversibility response to salbutamol. Self-reported adherence and inhaler technique scores did not differ between controlled and uncontrolled asthmatic children. Other determinants, such as environmental factors and differences in biological profiles, may influence the risk of uncontrolled asthma in this moderate to severe asthmatic population.

Epigenome-Wide Association Studies of the Fractional Exhaled Nitric Oxide and Bronchodilator Drug Response in Moderate-to-Severe Pediatric Asthma.

Asthma is the most prevalent pediatric chronic disease. Bronchodilator drug response (BDR) and fractional exhaled nitric oxide (FeNO) are clinical biomarkers of asthma. Although DNA methylation (DNAm) contributes to asthma pathogenesis, the influence of DNAm on BDR and FeNO is scarcely investigated. This study aims to identify DNAm markers in whole blood associated either with BDR or FeNO in pediatric asthma. We analyzed 121 samples from children with moderate-to-severe asthma. The association of genome-wide DNAm with BDR and FeNO has been assessed using regression models, adjusting for age, sex, ancestry, and tissue heterogeneity. Cross-tissue validation was assessed in 50 nasal samples. Differentially methylated regions (DMRs) and enrichment in traits and biological pathways were assessed. A false discovery rate (FDR) < 0.1 and a genome-wide significance threshold of p < 9 × 10-8 were used to control for false-positive results. The CpG cg12835256 (PLA2G12A) was genome-wide associated with FeNO in blood samples (coefficient= -0.015, p = 2.53 × 10-9) and nominally associated in nasal samples (coefficient = -0.015, p = 0.045). Additionally, three CpGs were suggestively associated with BDR (FDR < 0.1). We identified 12 and four DMRs associated with FeNO and BDR (FDR < 0.05), respectively. An enrichment in allergic and inflammatory processes, smoking, and aging was observed. We reported novel associations of DNAm markers associated with BDR and FeNO enriched in asthma-related processes.

Precision Medicine for More Oxygen (P4O2)-Study Design and First Results of the Long COVID-19 Extension.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40-65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3-6 and 12-18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient's home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3-6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.