European journal of medical research · 2025
U‑shaped association between relative fat mass (RFM) and stress urinary incontinence: a cross‑sectional study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yang Jiaqing, Du Yuanzhuo, Guo Ju
Аннотация
BACKGROUND: This study aimed to investigate the relationship between relative fat mass (RFM) and stress urinary incontinence (SUI).
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.