ATOPIC dermatitis biomarkers accurately predicted disease severity and detected hidden inflammation in clinically clear skin areas.
The findings suggest that noninvasive measurements could complement conventional clinical scoring, which depends partly on subjective assessment and may not detect disease activity beneath skin that appears unaffected.
The cross-sectional observational study included 80 participants aged 11–60 years, comprising healthy controls and people with mild to severe atopic dermatitis. Researchers evaluated clinical severity and patient reported outcomes while collecting 32 biomarkers from lesional and nonlesional skin.
The measurements covered several modalities, including optical coherence tomography (OCT), transepidermal water loss, Fourier transform infrared spectroscopy, and metabolites measured in skin cells and blood. Lasso regression was then used to construct models that predicted disease severity and identified subclinical inflammation.
A multivariable model composed of noninvasive OCT imaging biomarkers predicted local atopic dermatitis severity with good accuracy, producing a correlation coefficient of 0.82.
When the model was provided with information about the extent of atopic dermatitis, it predicted global severity with a correlation coefficient of 0.95. Patient reported outcomes were predicted with a coefficient of 0.76.
Multimodal classification models also distinguished healthy skin from clinically nonlesional skin in participants with atopic dermatitis. Performance was strong, with an area under the curve of 0.94 and a 95% confidence interval of 0.88–0.98.
This ability to classify visibly unaffected skin suggests that the models were sensitive to subclinical inflammation that conventional scoring could overlook. Such disease activity may be present before clinical worsening or remain after visible lesions resolve.
The results highlight OCT derived measurements as particularly promising components of an objective, patient centered assessment. Integrating noninvasive atopic dermatitis biomarkers into clinical workflows could eventually help clinicians characterize disease activity more comprehensively and inform treatment strategies.
However, the observational design evaluated the models at a single point in time. Further research will be needed before these approaches can be adopted routinely. If validated, they could provide objective measures of atopic dermatitis severity for both clinical practice and trials while reducing reliance on invasive skin biopsies.
Reference
Byers RA et al. Non-Invasive Biomarker Models for Objective Severity Assessment and Detection of Subclinical Inflammation in Non-Lesional Atopic Dermatitis. Br J Dermatol. 2026;doi:10.1093/bjd/ljag343.
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