Related Experiment Video
Updated: Aug 21, 2026
Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
Published on: September 26, 2019
Non-Invasive Biomarker Models for Objective Severity Assessment and Detection of Subclinical Inflammation in
Robert A Byers1,2, Paul V Andrew1, Sura Sahib1,3,4,5
1Sheffield Dermatology Research, School of Medicine & Population Health, The University of Sheffield Medical School, Beech Hill Road, Sheffield, UK.
Background:
Atopic dermatitis (AD) is a chronic, fluctuating inflammatory skin disease in which severity assessment relies on subjective clinical scoring. However, these approaches do not capture subclinical inflammation that may precede disease worsening or follow disease resolution. Furthermore, comprehensive research typically requires invasive methods such as skin biopsy, limiting routine use.
Objectives:
This study developed and evaluated non-invasive, multimodal biomarker models to objectively assess AD severity and detect subclinical disease activity.
Methods:
We conducted a cross-sectional observational study (NCT04295824) involving 80 participants aged 11-60, including healthy controls and individuals with mild to severe AD. Clinical severity and patient-reported outcomes were assessed, and 32 biomarkers spanning invasive to non-invasive modalities were collected from lesional and non-lesional skin. These included structural (e.g. optical coherence tomography), biophysical (e.g. transepidermal water loss), molecular (e.g. Fourier-transform infrared spectroscopy) and metabolite (biomarkers measured in skin cells and blood) markers. Lasso regression was used to build predictive models of AD severity and classify subclinical disease in clinically non-lesional skin.
Results:
A multivariable model comprised of non-invasive OCT imaging derived biomarkers predicted local AD severity outcomes with good accuracy (r=0.82). Global severity and patient reported outcomes could also be predicted with high accuracy (r=0.95,0.76 respectively), providing information regarding the extent of AD was provided to the model. Multimodal classification models distinguished healthy from clinically non-lesional AD skin with excellent performance (AUC = 0.94, 95% CI: 0.88-0.98), suggesting sensitivity to subclinical inflammation.
Conclusions:
A comprehensive, patient-centered skin assessment approach can reliably determine AD severity and detect subclinical activity that traditional scoring methods may overlook. Robust OCT-derived metrics highlight the potential of integrating non-invasive, objective tools into clinical workflows to complement standard evaluations and improve treatment strategies. These methods also offer a more objective assessment of disease activity in clinical trials and practice.
