Abstract
Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.
Highlights
Asthma is a complex disorder of a still not completely known pathobiology, characterized by reversible airway obstruction, airway hyperresponsiveness to specific and non-specific stimuli, and a chronic inflammation in the airways
There are no recommendations as to treatment failure identification and changes recommended towards the treatment of choice or only general choice recommendations are made
The main phenotype variable discriminatory for the response clusters according to Decision tree classification (DTC) was Maximal Expiratory Flow at 50% (MEF50) predicted at baseline, followed by the use of reliever medication (SABA) which is a parameter incorporated in asthma control assessment, use of combination treatment (ICS + Long-acting beta agonists (LABA)) which indicates poorer disease control; High-sensitivity C-reactive protein (hsCRP), Fraction of exhaled nitric oxide (FENO) at baseline, neutrophil blood count which reflect the type and level of inflammation, and total Immunoglobulin E (IgE) which corresponds to the atopy status and sensitization levels, these variables were not significantly different between clusters in the cluster statistics
Summary
Asthma is a complex disorder of a still not completely known pathobiology, characterized by reversible airway obstruction, airway hyperresponsiveness to specific and non-specific stimuli, and a chronic inflammation in the airways. Most of them have identified age of onset- early onset vs late onset disease presentation [3,4,5,6,7]; gender [8]; atopy status [3, 9], obesity [5, 6] and type of inflammation- eosinophil, neutrophil, mixed type, Th2 high/low [4, 8, 10, 11] as main discriminants in distinguishing specific clusters (phenotypes) These studies identified several distinct phenotypes, the vast disease heterogeneity has still most likely been a major hindrance in the development of targeted therapies in asthma so far [12]. Few phenotyping studies to date have focused on treatment success as a study outcome despite the evident issues in treatment efficacy in asthma [9, 15]
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