Developmental trajectories of atopic dermatitis with multiomics approaches in the infant gut: COCOA birth cohort.

Background: An understanding of the phenotypes and endotypes of atopic dermatitis (AD) is essential for developing precision therapies. Recent studies have demonstrated evidence for the gut-skin axis in AD.
Objective: We sought to determine the natural course and clinical characteristics of AD phenotypes and investigate their mechanisms on the basis of multiomics analyses.
Methods: Latent class trajectory analysis was used to classify AD phenotypes in 2247 children who were followed until age 9 years from the COhort for Childhood Origin of Asthma and allergic diseases birth cohort study. Multiomics analyses (microbiome, metabolites, and gut epithelial cell transcriptome) using stool samples collected at age 6 months were performed to elucidate the underlying mechanisms of AD phenotypes.
Results: Five AD phenotypes were classified as follows: never/infrequent, early-onset transient, intermediate transient, late-onset, and early-onset persistent. Early-onset persistent and late-onset phenotypes showed increased risks of food allergy and wheezing treatment ever, with bronchial hyperresponsiveness evident only in the early-onset persistent phenotype. Multiomics analyses revealed a significantly lower relative abundance of Ruminococcus gnavus and a decreased gut acetate level in the early-onset persistent phenotype, with potential associations to ACSS2, Janus kinase-signal transducer and activator of transcription signaling, and systemic T _H 2 inflammation. The early-onset transient phenotype was associated with adenosine monophosphate-activated protein kinase (AMPK) and/or chemokine signaling regulation, whereas the late-onset phenotype was linked with IL-17 and barrier dysfunction.
Conclusions: Multiomics profiling in early life may offer insights into different mechanisms underlying AD phenotypes in children.
Competing Interests: Disclosure statement This study was funded by the Research Program of the Korea National Institute of Health, South Korea (grant nos. 2008-E33030-00, 2009-E33033-00, 2011-E33021-00, 2012-E33012-00, 2013-E51003-00, 2014-E51004-00, 2014-E51004-01, 2014-E51004-02, 2017-E67002-00, 2017-E67002-01, 2017-E67002-02, 2020E670200, 2020E670201, and 2020E670202) and by the National Research Foundation funded by the Korean government (Ministry of Science and Information and communication Technology, MSIT) (grant nos. 2017M3A9F3043834 and 2021R1A2C2095664). Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest.
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