Decoding Gut Microbiome Dysbiosis as a Non-Invasive Diagnostic Biomarker for Alopecia Areata

Alopecia areata (AA) is a chronic autoimmune disease characterized by non-scarring hair loss, where pathogenesis is closely linked to the collapse of hair follicle immune privilege and dysregulated T-cell responses. Increasing evidence suggests that gut dysbiosis may contribute to systemic immune alterations relevant to autoimmune disorders, yet its role in AA remains largely unexplored. In this study, we aimed to characterize the gut microbiota composition of AA patients and evaluate its potential as a biomarker for disease discrimination. Fecal samples from patients with AA and healthy controls were analyzed by 16S rRNA sequencing and processed through QIIME2 and MicrobiomeAnalyst platforms. Diversity metrics, differential abundance, and microbial network correlations were assessed, and supervised machine learning models were developed to classify AA versus control profiles. Our results revealed distinct microbial signatures in AA, with enrichment of pro-inflammatory genera such as Methanobrevibacter, Collinsella, and Ruminococcus gnavus, and depletion of immunoregulatory commensals, including Faecalibacterium and Eubacterium eligens group. Network analyses showed more complex microbial interactions in AA, and Random Forest models achieved 92% accuracy in discriminating AA from controls. These findings indicate that gut dysbiosis may play a role in AA pathogenesis, providing potential diagnostic biomarkers and supporting microbiota-targeted interventions as future therapeutic strategies.