ArticleEuropean journal of medical research2025
U‑shaped association between relative fat mass (RFM) and stress urinary incontinence: a cross‑sectional study.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Varying In-Person Coaching: Effects on Weight and Urinary Incontinence in Women with Obesity in a 20-Week Randomized Controlled Trial.International urogynecology journal · 2026Article
- Beyond BMI: Visceral Fat and Comorbidities as Major Risk Factors for Urinary Incontinence in Elderly Women-A Case-Control Study.Health science reports · 2026Article
- Association Between Non-HDL/HDL Cholesterol Ratio and Urinary Incontinence in US Women:NHANES 2005-2018.International journal of women's health · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to investigate the relationship between relative fat mass (RFM) and stress urinary incontinence (SUI).
methodsThis 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.
resultsAmong 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.
conclusionA 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.
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