Association Between Oxidative Stress Biomarkers And Clinical Severity Of Alopecia Areata: A Systematic Review
DOI:
https://doi.org/10.67440/ahj.v21i5s.1374Abstract
Background: Alopecia areata (AA) is a chronic autoimmune disorder characterized by non-scarring hair loss with a variable clinical course. The identification of reliable biomarkers is essential for assessing disease severity, monitoring progression, and predicting treatment response. Emerging evidence indicates that inflammatory cytokines, oxidative stress markers, and biochemical parameters play significant roles in the pathogenesis of AA. This narrative review provides a comprehensive overview of established inflammatory and biochemical biomarkers associated with disease severity in alopecia areata.
Methodology: This systematic review was conducted according to the PRISMA 2020 guidelines to evaluate the association between oxidative stress biomarkers and clinical severity of alopecia areata (AA). PubMed/MEDLINE, Scopus, Web of Science, Embase, Cochrane Library, and Google Scholar were searched from database inception to June 2026. Human observational studies assessing oxidative stress biomarkers and AA severity were included. Two reviewers independently screened studies, extracted data using a standardized form, and assessed risk of bias. Of 450 identified records, 50 studies met the eligibility criteria and were included in the qualitative synthesis.
Results: Among the 10 included studies (2020–2024), research originated from eight countries, with Egypt and India contributing two studies each. Participants had a mean age of 29.9–38.1 years, and 51.3% were male. MDA was the most frequently evaluated biomarker, while the SALT score was the predominant method for assessing alopecia areata severity.
Conclusion: This systematic review indicates that oxidative stress is strongly associated with alopecia areata severity, with elevated oxidant and reduced antioxidant biomarkers correlating with higher SALT scores. Standardized, large-scale prospective studies are needed for clinical validation.

