Noninvasive biomarker models for objective severity assessment and detection of subclinical inflammation in nonlesional atopic dermatitis

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 noninvasive, 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 years, including healthy controls and individuals with mild to severe AD. Clinical severity and patient-reported outcomes were assessed, and 32 biomarkers spanning invasive to noninvasive modalities were collected from lesional and nonlesional skin. Biomarkers included those measured by structural [e.g. optical coherence tomography (OCT)], biophysical (e.g. transepidermal water loss), molecular (e.g. Fourier-transform infrared spectroscopy) and metabolite assay (skin cell and blood) methods. Lasso regression was used to build predictive models of AD severity and classify subclinical disease in clinically nonlesional skin.

Results

A multivariable model comprised of noninvasive 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 and r = 0.76, respectively), as long as information regarding the extent of AD was provided to the model. Multimodal classification models distinguished healthy from clinically nonlesional AD skin with excellent performance (area under the curve = 0.94, 95% confidence interval 0.88–0.98), suggesting sensitivity to subclinical inflammation.

Conclusions

A comprehensive, patient-centred 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 noninvasive, 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.