Development and validation of a predictive nomogram for osteoporosis in patients with psoriasis

Psoriasis is a chronic inflammatory disease associated with increased risk of osteoporosis. However, practical tools for identifying high-risk patients in clinical settings remain limited. This study aimed to develop and validate a nomogram for predicting osteoporosis in patients with psoriasis.

A retrospective cohort study was conducted including 639 psoriasis patients from January 2023 to October 2025. Patients were randomly divided into training (n = 447) and validation (n = 192) sets at a 7:3 ratio. Osteoporosis was defined as dual-energy X-ray absorptiometry (DXA) T-score ≤-2.5. Univariate analysis and multivariate logistic regression were performed to identify independent risk factors. A nomogram was constructed based on the final model and evaluated using C-index, calibration curves, and decision curve analysis (DCA).

The overall prevalence of osteoporosis was 11.7% (75/639). Six independent risk factors were identified: advanced age (OR = 4.054, 95%CI 2.281-7.237), disease duration (OR = 1.068, 95%CI 1.026-1.11), psoriatic arthritis (OR = 2.178, 95%CI 1.243-3.796), vitamin D deficiency (OR = 5.148, 95%CI 1.986-17.672), systemic corticosteroid use (OR = 3.501, 95%CI 1.756-6.826), male sex (OR = 0.399, 95%CI 0.229-0.686). The nomogram demonstrated good discrimination with AUC of 0.824 in the training set and 0.771 in the validation set. Calibration curves showed excellent agreement between predicted and observed probabilities. DCA confirmed the clinical utility of the model.

We developed and validated a practical nomogram incorporating psoriasis-specific and traditional risk factors for predicting osteoporosis risk. This tool may facilitate early identification of high-risk patients and guide clinical decision-making for DXA screening and preventive interventions.