Please use this identifier to cite or link to this item: http://hdl.handle.net/10637/13376

Understanding uncontrolled severe allergic asthma by integration of omic and clinical data.

Title: Understanding uncontrolled severe allergic asthma by integration of omic and clinical data.
Authors : Delgado Dolset, María Isabel
Obeso Montero, David
Escribese Alonso, María Marta
Barber Hernández, Domingo
Villaseñor Solis, Alma Cristina
Barbas Arribas, Coral.
Rodríguez Coira Villanueva, Juan
Tarin, Carlos
Tan, Ge
Cumplido, José A.
Cabrera, Ana
Angulo Díaz-Parreño, Santiago
Sokolowska, Milena
Carrillo, Teresa
Keywords: Allergy.Asthma.Machine learning.Metabolomics.Proteomics.
Abstract: Background: Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features. Methods: Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms. Results: Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy. Conclusions: UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype.
Description: En: Allergy e-ISSN:1398-9995, 2021, nov. 28, 14 pp
URI: http://hdl.handle.net/10637/13376
Rights : http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
Issue Date: 2-Nov-2021
Center : Universidad San Pablo-CEU
Appears in Collections:Facultad de Farmacia





Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.