Primary cicatricial alopecia (PCA) is a rare, scarring, hair loss disorder. Due to its low incidence, little is known about endocrine and metabolic comorbidities in patients with PCA. Thus, we aimed to investigate the association between PCA and endocrine and metabolic disorders. This nationwide, population-based, cross-sectional study included patients diagnosed with PCA or non-cicatricial alopecia (NCA) and normal individuals without history of alopecia registered in the Korean National Health Insurance Service database between January 1, 2011, and December 31, 2020. We calculated the odds ratios of endocrine and metabolic comorbidities of patients with PCA compared to all patients or age- and sex-matched patients with NCA or normal individuals using multivariable logistic regression models. A total of 3 021 483 individuals (mean age [SD], 38.7 [15.0] years, 1 607 380 [53.2%] men), including 11 956 patients with PCA, 601 852 patients with NCA, and 2 407 675 normal participants, were identified. Patients with PCA had an increased risk for dyslipidemia (adjusted odds ratio [aOR] 1.14, 95% confidence interval [CI] 1.06-1.24), diabetes (aOR 1.38, 95% CI 1.24-1.53), and hypertension (aOR 1.10, 95% CI 1.02-1.19) compared to matched patients with NCA. Regarding PCA subtypes, lichen planopilaris/frontal fibrosing alopecia was positively associated with hypothyroidism (aOR 2.03, 95% CI 1.44-2.86) compared to NCA. Folliculitis decalvans and dissecting cellulitis were positively associated with dyslipidemia (aOR 1.16, 95% CI 1.05-1.28 and aOR 1.16, 95% CI 1.04-1.29, respectively), diabetes (aOR 1.38, 95% CI 1.20-1.58 and aOR 1.52, 95% CI 1.32-1.74, respectively), and hypertension (aOR 1.10, 95% CI 1.00-1.20 and aOR 1.14, 95% CI 1.02-1.27, respectively). Similar trends were observed when each PCA subgroup was compared with the normal control group. This study demonstrates that patients with PCA are more likely to have endocrine and metabolic comorbidities than patients without PCA. Further research on these comorbidities may improve the understanding of PCA.
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