LAUSR

Introduction: Asthma is a complex heterogeneous disease that likely comprises several distinct disease phenotypes. Clustering approach has been used to classify heterogeneous asthma population into distinct phenotypes. Method: We evaluated 110 children with asthma aged 5 to 17 years. For all children we performed pulmonaryfunction tests, nasal smears for eosinophil counts, drew blood for IgE against inhalation and food allergies antibodies detection and ACQ. Differences in clinical indices including ΔFEV1(pre- and postbronchodilator) and nasal and blood biomarkers at enrollment among clusters were analyzed. Results: Five distinct phenotypes were determined. Cluster 1 (n = 13): (non-atopic) the lowest IgE level, very low ACQ, median age of diagnosis and IgE levels. Cluster 2 (n = 45): (mixed) the highest BMI with the latest age of diagnosis and high ACQ and BDR levels. Cluster 3 (n = 33) (atopic) early diagnosis, highest BDR, highest ACQ score, highest total and specific IgE levels among the clusters. Cluster 4 (n = 12): (atopic) the highest Immunocap result, relatedly high BMI and IgE with median ACQ score among clusters. Cluster 5 (n = 7): (non-atopic) the earliest age for diagnosis, with the lowest BMI, the lowest ACQ score, and Immunocap result, with high BDR and median level of IgE among clusters. The patients with severe asthma were over 65% of the patients in cluster 3. Only one severe asthma patient was in cluster 2, and none in the other three clusters. Conclusion: We identified asthma phenotypes in Bulgarian children according IgE levels, ACQ score, BDR and age of diagnosis. Almost all of the severe asthma patients were in cluster 3 – with early diagnosis and highest IgE but not highest Immunocap results