To develop and validate a nomogram for predicting osteoporosis in patients with psoriasis.
Approach:
Study Design: A retrospective cohort study including 639 psoriasis patients, divided into training (n = 447) and validation (n = 192) sets.
Osteoporosis Definition: Defined as dual-energy X-ray absorptiometry (DXA) T-score ≤−2.5.
Statistical Analysis: Univariate analysis and multivariate logistic regression were used to identify independent risk factors.
Nomogram Construction: A nomogram was constructed based on the final model and evaluated using C-index, calibration curves, and decision curve analysis (DCA).
Key Findings:
Overall prevalence of osteoporosis was 11.7% (75/639).
Identified six independent risk factors: 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).
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.
Interpretation:
The developed nomogram incorporates psoriasis-specific and traditional risk factors for predicting osteoporosis risk.
Limitations:
Retrospective design may introduce bias.
Findings may not be generalizable to all psoriasis populations.
Conclusion:
The nomogram may assist in identifying high-risk psoriasis patients for DXA screening and preventive interventions.
Investigational inhibitor was not associated with treatment-related serious adverse events and produced biomarker changes consistent with pathway inhibition in healthy volunteers.
