Value of serum uric acid in a risk prediction model for postmenopausal osteoporosis

To investigate the potential role of serum uric acid (UA) in risk stratification for postmenopausal osteoporosis (PMOP) and to establish an accessible risk prediction model that can aid in early screening and diagnosis.

We retrospectively enrolled 295 postmenopausal women who underwent dual-energy X-ray absorptiometry (DXA) at Zhuhai Hospital affiliated with Jinan University from July 2021 to July 2023. Participants were divided into a PMOP group (T-score < -2.5; n = 125) and a control group (T-score ≥ -2.5; n = 170). Clinical and laboratory data were collected, including markers of inflammation, renal function, and uric acid levels. Univariable and multivariable logistic regression analyses identified independent risk factors for PMOP. A nomogram was constructed based on the final logistic regression model and evaluated for discrimination and calibration using receiver operating characteristic (ROC) curves, calibration curves, and the concordance index (C-index).

The PMOP group exhibited significantly higher mean values of age, alkaline phosphatase (ALP), neutrophil count (NEU), monocyte count (MO), monocyte-to-lymphocyte ratio (MLR), and the systemic immune-inflammation index (SII), while demonstrating significantly lower lymphocyte counts (LYM), height, OSTA scores, and albumin (ALB). Serum UA values were slightly lower in the PMOP group than in the control group. Multivariable logistic regression yielded a prediction model incorporating ALB, ALP, MLR, and UA. The area under the ROC curve (AUC) for this model was 0.781 (95% CI: 0.682-0.879). The calibration curve aligned well with the ideal reference line, and the C-index was 0.779 (95% CI: 0.728-0.831).

Serum uric acid may have a contributory role in risk stratification for PMOP when combined with key clinical and laboratory markers. This nomogram-based model demonstrates moderate predictive performance; future large-scale multicenter prospective cohorts are warranted to validate these findings and to refine the model by accounting for potential confounding factors such as medication use, dietary intake, and lifestyle habits.