Allergy. 2021 Nov 28. doi: 10.1111/all.15192. Online ahead of print.
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 life quality. 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 T cell activation 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 biological data led to a deeper understanding of the mechanisms underlying UC phenotype.
PMID:34839541 | DOI:10.1111/all.15192
