Diagnostic Models for Atopic Dermatitis Based on Serum Microbial Extracellular Vesicle Metagenomic Analysis: A Pilot Study

Purpose: Associations between a wide variety of diseases and the microbiome have been extensively verified. Recently, there has been a rising interest in the role the microbiome plays in atopic dermatitis (AD). Furthermore, metagenomic analysis of microbe-derived extracellular vesicles (EVs) has revealed the importance and relevance of microbial EVs in human health. Methods: We compared the diversity and proportion of microbial EVs in the sera of 24 AD patients and 49 healthy controls, and developed a diagnostic model. After separating microbial EVs from serum, we specifically targeted the V3-V4 hypervariable regions of the 16S rDNA gene for amplification and subsequent sequencing. Results: Alpha and beta diversity between controls and AD patients both differed, but only the difference in beta diversity was significant. Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla in healthy controls and AD patients, accounting for over 85% of the total serum bacterial EVs. Also, Proteobacteria, Firmicutes, Actinobacteria, Verrucomicrobia, and Cyanobacteria relative abundances were significantly different between the AD and control groups. At the genus level, the proportions of Escherichia-Shigella, Acinetobacter, Pseudomonas, and Enterococcus were drastically altered between the AD and control groups. AD diagnostic models developed using biomarkers selected on the basis of linear discriminant analysis effect size from the class to genus levels all yielded area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of value 1.00. Conclusions: In summary, microbial EVs demonstrated the potential in their use as novel biomarkers for AD diagnosis. Therefore, future work should investigate larger case and control groups with cross-sectional or longitudinal clinical data to explore the utility and validity of serum microbiota EV-based AD diagnosis.