Yang Jiaqing, Du Yuanzhuo, Guo Ju
Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330000, Jiangxi, China.
Jiangxi Institute of Urology, Nanchang, 330000, Jiangxi, China.
Eur J Med Res. 2025 Apr 7;30(1):256. doi: 10.1186/s40001-025-02481-y.
This study aimed to investigate the relationship between relative fat mass (RFM) and stress urinary incontinence (SUI).
This cross-sectional study employed data from the National Health and Nutrition Examination Survey (NHANES), collected from 2005 to 2018. Weighted logistic regression and smooth curve fitting were employed to evaluate the association between RFM and SUI. Subgroup analyses and interaction tests were performed to validate the robustness of the findings. The predictive effect was evaluated using receiver operating characteristic (ROC) curves. Finally, we analyzed the role of RFM in predicting SUI using the Random Forest Variable Importance plot and SHAP Dependence Plot.
Among 32,594 participants aged 20 years and older, 22.94% were diagnosed with SUI. The fully adjusted multivariable model indicated that a higher RFM was associated with an increased risk of developing SUI (OR = 2.42; 95% CI 2.05-2.86). Subgroup analysis and interaction tests were performed to validate this association further. Smoothing curve fitting revealed a U-shaped relationship between RFM and SUI. The ROC curve demonstrated that RFM (AUC = 0.788, 95% CI 0.782-0.793) is a good predictor of SUI. Lastly, the Random Forest Variable Importance plot and SHAP Dependence Plot effectively identified the positive correlation and non-linear relationship between SUI and RFM.
A non-linear correlation was observed between elevated RFM and the incidence of SUI. Especially within the female population, an increase in RFM is related to a higher likelihood of SUI, indicating that RFM could be a possible tool for identifying SUI.
本研究旨在探讨相对脂肪量(RFM)与压力性尿失禁(SUI)之间的关系。
这项横断面研究采用了2005年至2018年期间美国国家健康与营养检查调查(NHANES)收集的数据。采用加权逻辑回归和平滑曲线拟合来评估RFM与SUI之间的关联。进行亚组分析和交互检验以验证研究结果的稳健性。使用受试者工作特征(ROC)曲线评估预测效果。最后,我们使用随机森林变量重要性图和SHAP依赖图分析了RFM在预测SUI中的作用。
在32,594名20岁及以上的参与者中,22.94%被诊断为SUI。完全调整后的多变量模型表明,较高的RFM与发生SUI的风险增加相关(OR = 2.42;95% CI 2.05 - 2.86)。进行亚组分析和交互检验以进一步验证这种关联。平滑曲线拟合显示RFM与SUI之间呈U形关系。ROC曲线表明RFM(AUC = 0.788,95% CI 0.782 - 0.793)是SUI的良好预测指标。最后,随机森林变量重要性图和SHAP依赖图有效地识别了SUI与RFM之间的正相关和非线性关系。
观察到RFM升高与SUI发病率之间存在非线性相关性。特别是在女性人群中,RFM的增加与SUI的可能性更高相关,这表明RFM可能是识别SUI的一种潜在工具。