Age- and Phenotype-Specific Patterns of Atopic and Autoimmune Comorbidities in Alopecia Areata: A Cross-Sectional Study.

Authors

Ruxin Ji, Mutande Chisanga, Jia Jian, Min Li, Tian Tian, Shiwei Zhang, Ke Yang, Fangfen Liu, Zhixiang Zhao, Ji Li, Wei Shi, Jundong Huang, Yan Tang

Published in

Journal of cutaneous medicine and surgery. Pages 12034754261480647. Sep 07, 2026. Epub Sep 07, 2026.

Abstract

Alopecia areata (AA) is frequently associated with atopic and autoimmune comorbidities, yet comprehensive data in Chinese populations remain scarce. We aimed to characterize the comorbidity landscape and identify predictors of the total comorbidity count in AA patients.
In this cross-sectional study of 1008 AA patients, we assessed the prevalence of atopic and autoimmune comorbidities across different age groups and clinical subtypes. Predictors of the total comorbidity count were identified using multivariable negative binomial regression.
Overall, 21.1% of patients presented with at least 1 comorbidity (14.7% atopic, 8.2% autoimmune). The comorbidity profile of AA exhibits significant age-specific distribution variations: younger patients are more frequently affected by atopic conditions, whereas autoimmune comorbidities progressively increase with advancing age, reflecting general population immunological trends. Furthermore, autoimmune comorbidities were significantly associated with the alopecia totalis subtype (22.5% vs 8.2% in the overall cohort; P = .013). Multivariable regression identified female sex (aIRR = 1.507, P = .003), pediatric age (aIRR = 1.553, P = .006), and notably, mild disease severity (Severity of Alopecia Tool <25; aIRR = 1.854, P = .011) as independent predictors of the total comorbidity count.
The comorbidity profile of AA is defined by a striking age dichotomy: younger patients are predominantly predisposed to atopic conditions, whereas autoimmune comorbidities progressively emerge with advancing age. This age-specific transition strongly advocates for age-stratified, targeted screening strategies.

PMID:
42704199
Bibliographic data and abstract were imported from PubMed on 07 Sep 2026.

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