Search

Background: Fractional exhaled nitric oxide (FeNO) is a common biomarker used in the diagnosis and monitoring of asthma in children. Little is known about the variability of FeNO beyond one week in healthy or asthmatic children. We investigated the medium-term variability of FeNO in children with and without asthma. Methods: Children from the Manchester Asthma and Allergy Study attended follow-up at the age of 11 years on two occasions. Skin prick testing was completed. FeNO and spirometry were measured on each visit. Clinically significant FeNO variability (∆FeNO) was defined according to ATS 2011 recommendations. Results: Of the 425 children who had FeNO measured on two occasions [median (IQR) 20 (9-47) days apart], 11% had current asthma and 33% were atopic. Mean (SD) FeNO in children with current asthma [37.5 (31.6) ppb] or atopy [34.6 (32.2) ppb] was higher than those without [16.7 (21.0) ppb and 10.9 (9.4) ppb, respectively, p Conclusion: Increased medium-term variability of FeNO were observed in children with current wheeze and atopy compared to those without. History of hayfever, asthma medication use and atopy were independent confounding factors for clinically significant variability in FeNO.

Rationale: Paediatric severe asthma is a heterogeneous disease suggesting that there may be discrete underlying clinical phenotypes. Objective: To generate and validate unbiased paediatric clusters in U-BIOPRED. Methods: Cross-sectional analysis of baseline data from the European U-BIOPRED study. Demography, medical history, medication, environmental risk factors, asthma control and quality of life data were collected. External validation utilised a Dutch community paediatric-asthma cohort (PACMAN). Clinical variables were first condensed by factor analysis, followed by subsampled (10000x, proportion: 90%) partition-around-medoid clustering and stability testing by consensus distributions and Calinski & Harabasz index. Finally Classification and Regression Tree Analysis (CART) was applied to generate a prediction model. Results: 250 patients (147 male, 7.9 ±4.8 yrs) with a complete data set of 34 clinical variables were included generating 7 stable clusters (figure) with unique clinical characteristics. Clustering of the PACMAN cohort using the same variables and application of CART-model delivered 3 phenotypes, in agreement with the U-BIOPRED less severe phenotypes (P2,P3,P6). Conclusion: Unsupervised analysis based on clinical parameters revealed 7 stable clusters of paediatric asthma. Part of these clusters could be replicated in an external cohort, suggesting broad pathophysiological and clinical relevance.

Background: We have previously reported altered gene expression in adults with asthma compared to healthy controls from the U-BIOPRED study (Bigler, 2016). Altered transcripts may define dysregulated biological pathways and identify novel therapeutic targets. We hypothesised that similar dysregulation would be seen in children with asthma. Aim: To compare blood transcriptomic profiles of children and adults with asthma. Methods: Affymetrix blood transcriptomic profiles of severe asthmatic adult non-smokers, n=152, were compared to mild moderate asthmatics, n=50 (Shaw, 2015; Fleming, 2015). Profiles of school-aged children with severe asthma, n=75, were compared to mild moderate asthmatics, n=37, and in the preschool age group severe wheeze, n=62, was compared to mild moderate wheeze, n=42. Differentially expressed genes (DEG) were identified as probe sets with maximum median group intensity >log2 5, with a significant (raw P≤0.01) change ≥ 20%. Overlapping genes were determined and pathway analysis performed. Results: We found 1887 DEG comparing severe and mild moderate asthmatic adults. Only 28 DEG were found between the severe wheeze and mild pre-school age children, with a larger signature (569 DEG) in the school aged children. 480 genes were specific to school-aged children and 1801 specific to adults, with 89 DEG in common between the adults and school-aged children. Conclusions: Preschool age children were poorly defined in terms of blood transcriptomics by the clinical definitions used. While the school-aged children showed some DEG overlap with the adults, they were distinct in many DEG and pathways indicating that childhood and adult asthma may be mechanistically different.