Predictive biomarker modeling of pediatric atopic dermatitis severity
based on longitudinal serum collection
Abstract
Background: The pathogenesis of atopic dermatitis (AD) results from
complex interactions between environmental factors, barrier defects, and
immune dysregulation resulting in systemic inflammation. Therefore, we
sought to characterize circulating inflammatory profiles in pediatric AD
patients and identify potential signaling nodes which drive disease
heterogeneity and progression. Methods: We analyzed a population of 87
infants that were at high risk for atopic disease based on dermatitis
diagnoses. Clinical parameters, serum, and peripheral blood mononuclear
cells (PBMCs) were collected upon entry, and at one and four years
later. Within patient serum, 126 unique analytes were measured using a
combination of multiplex platforms and ultrasensitive immunoassays.
Results: We assessed the correlation of inflammatory analytes with AD
severity (SCORAD). Key biomarkers, such as IL-13 (corr=0.47) and TARC
(corr=0.37), among other inflammatory signals, significantly correlated
with SCORAD across all timepoints in the study. Flow cytometry and
pathway analysis of these analytes implies that CD4 T cell involvement
in type 2 immune responses were enhanced at the earliest time point
(year 1) relative to the end of study collection (year 5). Importantly,
forward selection modeling identified 18 analytes in infant serum at
study entry which could be used to predict change in SCORAD four years
later. Conclusions: We have identified a pediatric AD biomarker
signature linked to disease severity which will have predictive value in
determining AD persistence in youth and provide utility in defining core
systemic inflammatory signals linked to pathogenesis of atopic disease.