4.6 Article

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

期刊

ALLERGY
卷 77, 期 6, 页码 1772-1785

出版社

WILEY
DOI: 10.1111/all.15192

关键词

allergy; asthma; machine learning; metabolomics; proteomics

资金

  1. Ministerio de Ciencia, Innovacion y Universidades [RTI2018-095166-B-I00]
  2. ISCIII [PI18/01467, PI19/00044]
  3. Swiss National Science Foundation (SNFS) [310030_189334/1]
  4. Swiss National Science Foundation (SNF) [310030_189334] Funding Source: Swiss National Science Foundation (SNF)

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Asthma is a complex disease often linked with sensitization to house dust mites. Some patients do not respond to available treatments, showing higher exacerbations and worse quality of life. Metabolic and immunologic routes underlying poor asthma control were elucidated, revealing a unique pro-inflammatory fingerprint in uncontrolled patients.
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 IFN gamma) 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.

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