The blood proteomic signature of early-onset pediatric atopic dermatitis shows systemic inflammation and is distinct from adult long-standing disease

医学 全身炎症 免疫学 特应性皮炎 斯科拉德 炎症 疾病 内科学 皮肤科生活质量指数
作者
Patrick M. Brunner,Helen He,Ana B. Pavel,Tali Czarnowicki,Rachel Lefferdink,Taylor Erickson,Talia Canter,Neha Puar,Stephanie M. Rangel,Kunal Malik,Yeriel Estrada,James G. Krueger,Emma Guttman‐Yassky,Amy S. Paller
出处
期刊:Journal of The American Academy of Dermatology [Elsevier BV]
卷期号:81 (2): 510-519 被引量:107
标识
DOI:10.1016/j.jaad.2019.04.036
摘要

Despite increasing evidence that adults with long-standing atopic dermatitis (AD) have systemic inflammation, little is known about systemic inflammation in recent-onset early pediatric AD.To analyze blood inflammatory proteins of early pediatric AD.Using high-throughput proteomics (proximity extension assay), we assessed 257 inflammatory and cardiovascular risk proteins in the blood of 30 children with moderate to severe AD younger than 5 years of age (within 6 months of onset) compared with age-matched pediatric control individuals and adult patients with AD.In pediatric AD blood, T helper (Th) type 2 (CCL13, CCL22) and Th17 (peptidase inhibitor-3/elafin) markers were increased, together with markers of tissue remodeling (matrix metalloproteinases 3/9/10, urokinase receptor), endothelial activation (E-selectin), T-cell activation (IL2RA), neutrophil activation (myeloperoxidase), lipid metabolism (FABP4), and growth factors (FGF21, transforming growth factor-α). Total numbers of dysregulated proteins were smaller in pediatric AD (n = 22) than in adult AD (n = 61). Clinical severity scores were positively correlated with receptors for interleukins 33 and 36 and inversely correlated with some Th1 markers (interferon gamma, CXCL11).Different baseline expression levels in healthy pediatric vs adult samples.Within months of pediatric AD onset, systemic immune activation is present, with Th2/Th17 skewing but otherwise different proteomic patterns from adult AD. Future correlation of proteomic patterns with disease course, comorbidity development, and drug response may yield predictive biomarkers.
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