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Predictive biomarker modeling of pediatric atopic dermatitis severity based on longitudinal serum collection
Sarah M Engle1, Ching-Yun Chang1, Benjamin J Ulrich2,3
1Eli Lilly and Company, Indianapolis, IN, USA.
Insights
This study identifies key inflammatory biomarkers in infants with atopic dermatitis (AD). These biomarkers, including IL-13 and TARC/CCL17, predict disease severity and persistence, aiding in understanding AD pathogenesis.
Area of Science:
- Immunology
- Dermatology
- Pediatrics
Background:
- Atopic dermatitis (AD) pathogenesis involves environmental factors, barrier defects, and immune dysregulation.
- Systemic inflammation plays a crucial role in AD.
- Understanding pediatric AD's inflammatory profiles is vital for disease management.
Purpose of the Study:
- To characterize circulating inflammatory profiles in infants at high risk for AD.
- To identify signaling nodes driving AD heterogeneity and progression.
- To correlate inflammatory analytes with AD severity (SCORAD).
Main Methods:
- Analysis of serum and PBMCs from 87 high-risk infants over four years.
- Measurement of 126 unique analytes using multiplex platforms and immunoassays.
- Correlation analysis with SCORAD, flow cytometry, and pathway analysis.
Main Results:
- IL-13 and TARC/CCL17 significantly correlated with SCORAD across all timepoints.
- CD4 T cell involvement in type 2 immune responses was enhanced early in the study.
- A signature of 18 serum analytes at study entry predicted SCORAD change over four years.
Conclusions:
- Identified a pediatric AD biomarker signature linked to disease severity.
- This signature has predictive value for AD persistence in youth.
- Provides utility in defining core systemic inflammatory signals in AD pathogenesis.
Abstract:
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. We analyzed a sample set of 87 infants that were at high risk for atopic disease based on atopic 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. We assessed the correlation of inflammatory analytes with AD severity (SCORAD). Key biomarkers, such as IL-13 (rmcorr=0.47) and TARC/CCL17 (rmcorr=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. 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.
