Identification of Pre-Disease State in NC/Nga Mice with Atopic Dermatitis-Like Symptoms Using Dynamical Network Biomarkers.

Abstract

Atopic dermatitis (AD) is a common skin disease worldwide that is characterized by itchy eczema that undergoes cycles of exacerbation and remission. Although several biomarkers reflect AD severity, reliable markers for predicting disease onset and recurrence remain unclear. The dynamical network biomarker theory detects pre-disease states or pre-symptomatic states by analyzing characteristic network fluctuations just before phase transitions from healthy states to disease states. To investigate the applicability of dynamical network biomarker theory to AD, we conducted blood transcriptome analysis in NC/Nga mice before and after the onset of AD-like dermatitis. We identified 356 dynamical network biomarker genes whose temporal fluctuations enabled the detection of the pre-disease state preceding AD-like dermatitis onset. These genes were significantly enriched in pathways related to endoplasmic reticulum stress and apoptosis; fluctuations in these pathways may be associated with the onset of AD. Our study indicates that the dynamical network biomarker theory may be applicable to AD and that identifying a pre-disease state may enable early intervention and disease prevention. Collectively, this approach could provide valuable insights into maintaining long-term remission and improving AD management strategies.