8280186 Biomarkers for Occupational Hand Eczema: Improving Diagnostic Differentiation

<h3>Objective</h3> Contact dermatitis (CD), also known as contact eczema, is one of the most common occupational skin diseases, resulting from direct exposure to irritants or allergens. CD is generally classified as either irritant contact dermatitis (ICD) or allergic contact dermatitis (ACD). Occupational ICD is most frequently caused by wet work, which is highly prevalent among healthcare workers, hairdressers, construction workers, cleaners, and food industry employees. Common occupational allergens include preservatives (e.g., isothiazolinones), rubber additives, metals such as nickel and chromium, acrylates, epoxy resins, and hair dyes. Professions at highest risk for ACD include hairdressers, dentists, metalworkers, and healthcare workers. Due to overlapping clinical features, distinguishing between CD subtypes remains challenging. Accurate diagnosis is crucial for effective treatment, prevention strategies, and eligibility for compensation. This study aimed to identify biomarkers that differentiate between ICD and ACD. <h3>Material and Methods</h3> We analyzed a broad panel of immune and skin barrier mediators in patients with CD who underwent patch testing with allergens and irritants as part of routine diagnostics, as well as in workers with hand dermatitis. Skin samples were collected using tape stripping, a simple and non-invasive method. <h3>Results</h3> Preliminary findings suggest that while ICD and ACD share many immune markers, certain Th-cell mediated markers appear more specific to ACD. Skin barrier biomarkers, especially cholesterol derivatives and filaggrin breakdown products (natural moisturizing factors), showed distinct profiles between the two, likely reflecting irritant-induced damage, which is less pronounced in ACD. <h3>Conclusions</h3> We hypothesize that integrating immune and skin biomarkers will improve the ability to distinguish CD subtypes more effectively than using single markers alone. A predictive model will be developed using machine learning and validated in samples from workers with hand dermatitis.