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Predictive biomarker modeling of pediatric atopic dermatitis severity based on longitudinal serum collection
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  • Sarah Engle,
  • Ching-Yun Chang,
  • Benjamin Ulrich,
  • Allyson Satterwhite,
  • Tristan Hayes,
  • Kim Robling,
  • Sean Sissons,
  • Jochen Schmitz,
  • Rob Tepper,
  • Mark Kaplan,
  • Jonathan Sims
Sarah Engle
Eli Lilly and Company
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Ching-Yun Chang
Eli Lilly and Company
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Benjamin Ulrich
Indiana University School of Medicine
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Allyson Satterwhite
Eli Lilly and Company
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Tristan Hayes
Indiana University School of Medicine
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Kim Robling
Eli Lilly and Company
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Sean Sissons
Eli Lilly and Company
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Jochen Schmitz
Eli Lilly and Company
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Rob Tepper
Indiana University School of Medicine
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Mark Kaplan
Indiana University School of Medicine
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Jonathan Sims
Eli Lilly and Company
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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.