Establishment and validation of a nomogram predicting the risk of osteoporosis with primary aldosteronism

This study aimed to identify risk factors for osteoporosis (OP) in patients with primary aldosteronism (PA) and to develop a predictive nomogram for estimating OP risk in this population.

We retrospectively enrolled PA patients diagnosed at our hospital between January 2020 and December 2024. The dataset was randomly divided into training (n = 185) and validation (n = 79) sets in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression combined with multivariate logistic regression was used to identify predictive factors for OP and construct the nomogram. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Additional performance assessments included the Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA).

The study included 264 PA patients (mean age 61.2 ± 10.0 years; 110 men, 154 women), with an OP prevalence of 11.4%. LASSO regression identified seven independent predictors: age, sex, body mass index, diabetes history, fasting insulin, plasma aldosterone concentration, and serum creatinine. The nomogram demonstrated strong predictive performance, with AUC values of 0.931 (95% CI: 0.879-0.982) in the training set and 0.842 (95% CI: 0.749-0.935) in the validation set. Calibration curves and DCA confirmed the model's clinical utility.

The developed nomogram effectively predicts OP risk in PA patients, offering valuable clinical utility for early identification of high-risk individuals.