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dc.creatorDelgado Dolset, María Isabel-
dc.creatorObeso Montero, David-
dc.creatorEscribese Alonso, María Marta-
dc.creatorBarber Hernández, Domingo-
dc.creatorVillaseñor Solis, Alma Cristina-
dc.creatorBarbas Arribas, Coral.-
dc.creatorRodríguez Coira Villanueva, Juan-
dc.creatorTarin, Carlos-
dc.creatorTan, Ge-
dc.creatorCumplido, José A.-
dc.creatorCabrera, Ana-
dc.creatorAngulo Díaz-Parreño, Santiago-
dc.creatorSokolowska, Milena-
dc.creatorCarrillo, Teresa-
dc.date2021-
dc.date.accessioned2022-02-02T05:00:59Z-
dc.date.available2022-02-02T05:00:59Z-
dc.date.issued2021-11-02-
dc.identifier000000726083-
dc.identifier.urihttp://hdl.handle.net/10637/13376-
dc.descriptionEn: Allergy e-ISSN:1398-9995, 2021, nov. 28, 14 pp-
dc.description.abstractBackground: 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.en_EN
dc.formatapplication/pdf-
dc.language.isoen-
dc.relationMinisterio de Ciencia, Innovación y Universidades, Grant/Award Number: PCI2018-092930; ISCIII, Grant/Award Number: PI18/01467 and PI19/00044; Ministerio de Ciencia, Innovación y Universidades, Grant/Award Number: RTI2018-095166- B- I00; Swiss National Science Foundation (SNFS), Grant/Award Number: 310030_189334/1-
dc.relationCRUE/UNIRIS - Fundacion Universitaria San Pablo CEU-
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es-
dc.subjectAllergy.en_EN
dc.subjectAsthma.en_EN
dc.subjectMachine learning.en_EN
dc.subjectMetabolomics.en_EN
dc.subjectProteomics.en_EN
dc.titleUnderstanding uncontrolled severe allergic asthma by integration of omic and clinical data.-
dc.typeArtículo-
dc.identifier.doihttps://doi.org/10.1111/all.15192-
dc.centroUniversidad San Pablo-CEU-
Aparece en las colecciones: Facultad de Farmacia




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