Sjögren's disease & overlap diseases
Sex disparities in the phenotype at diagnosis of Sjögren's disease: artificial intelligence-driven characterisation in 17,416 patients
Authors' conclusion
Male SjD patients present a distinct, more systemic phenotype at diagnosis.
Study context
- Population
- Patients worldwide fulfilling the 2002/2016 classification criteria for sjögren disease (SjD) (Sjögren Big Data Registry)mean age 51.11 years
- Participants
- 17,416 participants
- Exposure
- Men vs women with SjD at diagnosis
- Menn = 1,161
- Womenn = 16,255reference
- Outcome
- Sex-based differences in clinical phenotype at diagnosis of SjD and predictors of patient sex
- Follow-up
- Diagnosis
Methodological detailsinternational registry (Sjögren Big Data Registry), multivariate logistic regression adjusted for age and ethnicity, generative AI (GPT-4o)-assisted analysis
- International registry (Sjögren Big Data Registry)
- Multivariate logistic regression adjusted for age and ethnicity
- Generative AI (GPT-4o)-assisted analysis
Main finding
As reported in the abstract
Phenotype at diagnosis by sex
Mean age at diagnosis (t-test)
t=6.08
P < 0.0001
Men
Mean ESSDAI score (t-test)
t=7.91
P < 0.0001
Men
High activity frequency (χ²)
χ² = 81.15
P < 0.0001
Men
Observational data: associations, not effects.
Further findings
Predictors of female sex (logistic regression adjusted for age and ethnicity; pseudo R-squared 0.026)
- Age: coefficient =0.009, p=0.024; each additional year increased likelihood of being female by 1.4%
- Ethnicity: coefficient=0.579, HR=1.78, p=0.004
- Ocular dryness: coefficient=−0.607, HR=0.54, p<0.001
- Glandular ESSDAI domain activity: coefficient=0.359, HR=1.43, p=0.006
- Pulmonary ESSDAI domain activity: coefficient=0.445, HR=1.56, p=0.004
Original publication
Sex disparities in the phenotype at diagnosis of Sjögren's disease: artificial intelligence-driven characterisation in 17,416 patients. Clin Exp Rheumatol. Published 18 December 2025. doi:10.
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