Serum-Proteomic Profiling Reveals Distinct Atopic Dermatitis Severity-Linked Signatures.

INTRODUCTION: Atopic Dermatitis (AD) is a chronic inflammatory skin disease characterized by complex pathogenesis, variable clinical phenotypes, and broad severity spectrum. We utilized a serum-proteomic approach integrated with machine learning (ML) to identify novel biomarkers that distinguish mild from severe AD. METHODS: Serum from sixty-seven AD adults, stratified by Eczema Area and Severity Index/EASI and Rajka-Langeland/RJL into mild (≤ 7 and ≤ 4, respectively; n = 33) or severe (≥ 20 and ≥ 8, respectively; n = 34), was analyzed with Olink Explore 3072. Differentially expressed proteins (DEPs) were identified using t-tests and False Discovery Rate correction (FDR ≤ 0.05). Tissue enrichment analysis was conducted using HPAStainR. Pearson correlations were performed between DEPs and clinical variables, serum lactate dehydrogenase/LDH and biomarkers for Th-pathways. ML (TMLE/SuperLearner, Boruta, MUVR) was used to identify top severity biomarkers. RESULTS: 469 DEPs distinguished severe vs. mild AD. DEPs were significantly enriched for epithelial proteins (e.g., skin, tonsil, and esophagus epithelium). Subsets correlated strongly with LDH (68 DEPs) and/or Th2/Th22 markers (90 DEPs; r ≥ 0.6, FDR ≤ 0.05). Nine serum proteins (CCL17, CCL22, DEFB4A/B, EZR, GPR15L, IL22, PRSS53, SERPINB8, SETMAR) overlapped across three ML analyses as severity discriminating biomarkers (cross-validated AUC = 0.989; 95% CI: 0.974-1.00). CONCLUSIONS: Severe AD exhibits a proteomic footprint enriched in epithelial-associated proteins that correlate with LDH-associated tissue injury and/or Th2/Th22 pathways. In this cross-sectional cohort, ML identified biomarkers that discriminated mild/severe AD groups with high cross-validated accuracy. Longitudinal and external validation studies, including healthy controls, are needed to determine specificity, generalizability, and prognostic utility.