Li Bingquan, Ye Yongheng, Li Jianfeng
Zhuhai People's Hospital, Zhuhai Clinical Medical College of Jinan University, 79 Kangning Road, Zhuhai, 519000, Guangdong, China.
Arch Gynecol Obstet. 2025 Aug 9. doi: 10.1007/s00404-025-08119-y.
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.
探讨血清尿酸（UA）在绝经后骨质疏松症（PMOP）风险分层中的潜在作用，并建立一种可用于早期筛查和诊断的便捷风险预测模型。
我们回顾性纳入了2021年7月至2023年7月在暨南大学附属珠海医院接受双能X线吸收法（DXA）检查的295名绝经后女性。参与者被分为PMOP组（T值＜-2.5；n = 125）和对照组（T值≥-2.5；n = 170）。收集临床和实验室数据，包括炎症标志物、肾功能和尿酸水平。单变量和多变量逻辑回归分析确定了PMOP的独立危险因素。基于最终的逻辑回归模型构建了列线图，并使用受试者工作特征（ROC）曲线、校准曲线和一致性指数（C指数）对其判别能力和校准情况进行评估。
PMOP组的年龄、碱性磷酸酶（ALP）、中性粒细胞计数（NEU）、单核细胞计数（MO）、单核细胞与淋巴细胞比值（MLR）以及全身免疫炎症指数（SII）的平均值显著更高，而淋巴细胞计数（LYM）、身高、OSTA评分和白蛋白（ALB）则显著更低。PMOP组的血清UA值略低于对照组。多变量逻辑回归得出了一个包含ALB、ALP、MLR和UA的预测模型。该模型的ROC曲线下面积（AUC）为0.781（95%CI：0.682 - 0.879）。校准曲线与理想参考线拟合良好，C指数为0.779（95%CI：0.728 - 0.831）。
血清尿酸与关键临床和实验室标志物联合应用时，可能在PMOP风险分层中发挥作用。这种基于列线图的模型显示出中等的预测性能；未来有必要开展大规模多中心前瞻性队列研究以验证这些发现，并通过考虑药物使用、饮食摄入和生活习惯等潜在混杂因素来完善该模型。
