The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.
Data-driven asthma endotypes defined from blood biomarker and gene expression data
George, B. J., Reif, D. M., Gallagher, J. E., Williams-DeVane, C. R., Heidenfelder, B. L., Hudgens, E. E., Jones, W., Neas, L., Hubal, E. A. C., & Edwards, S. W. (2015). Data-driven asthma endotypes defined from blood biomarker and gene expression data. PLoS One, 10(2). https://doi.org/10.1371/journal.pone.0117445
Abstract
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