U‑shaped association between relative fat mass (RFM) and stress urinary incontinence: a cross‑sectional study

Abstract

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.

Introduction

Stress urinary incontinence (SUI) is a prevalent condition that is often closely associated with pelvic floor dysfunction. The International Continence Society (ICS) defines SUI as the involuntary leakage of urine from the urethra during activities that increase intra-abdominal pressure, such as sneezing, coughing, or physical exertion (e.g., sporting activities) [1]. Epidemiological data indicate that SUI is the most typical kind of urinary incontinence, and the occurrence of urinary incontinence is notably higher in women compared to men [2]. As the severity of SUI increases, patients may experience associated issues such as the physical health of patients and significantly impair their psychological stability, social interactions, and life satisfaction [3]. Furthermore, with the global aging population and the growing demand for higher quality of life, the healthcare costs related to urinary incontinence are expected to rise [4]. Therefore, identifying potential risk factors for SUI and implementing early clinical interventions and treatments are crucial.

Multiple established risk factors for SUI include advanced age, elevated BMI, diabetes mellitus, reproductive history (e.g., parity and childbirth experiences), and modifiable lifestyle factors (e.g., smoking and alcohol consumption) [5]. However, these factors collectively account for only a fraction of SUI cases, suggesting the involvement of additional mechanisms. Various studies have proven a strong relationship between obesity and SUI, indicating that both overweight and obesity serve as independent risk factors for the occurrence and progression of SUI. Moderate weight reduction in overweight or obese women leads to a substantial decrease in the frequency of urinary incontinence [6].

Obesity is a chronic metabolic condition arising from the interaction of multiple mechanisms. Although conventional obesity measurement parameters, such as BMI and waist circumference (WC), are commonly used in clinical practice, they do not accurately distinguish the elements of body composition [7]. Considering the shortcomings of BMI and WC, further research is essential to examine innovative obesity approaches [8]. In recent years, researchers at Cedars-Sinai Medical Center have introduced a new obesity index—relative fat mass (RFM). RFM is calculated based on the height-to-waist circumference ratio and is a more precise approach for evaluating body fat percentage in adults. RFM has been demonstrated to calculate body fat percentage in both men and women more accurately than BMI and WC [9].

Furthermore, an increasing amount of research indicates that RFM is closely linked to various diseases, including hypertension, type 2 diabetes (T2DM), coronary artery disease (CAD), and heart failure (HF). As a result, RFM is considered an essential tool for assessing the overall health risks associated with obesity and related diseases [10,11,12]. Although existing studies have clearly established associations between RFM and various diseases, the evidence regarding the potential link between RFM and SUI remains limited and inconclusive. Consequently, this research intends to analyze the cross-sectional association between RFM and SUI, utilizing the 2005–2018 NHANES data to provide new tools for the timely recognition of individuals at high risk.

Methods

Study population

The data for this research were acquired from the 2005–2018 NHANES, which adopted a cross-sectional design. Trained interviewers conducted standardized in-person interviews, while certified technicians performed physical examinations, physiological assessments, and laboratory tests. Initially, 70,190 participants were enrolled in this study. The following exclusion criteria were applied: (A) participants aged under 20 years (n = 30,441); (B) pregnant participants (n = 708); (C) participants with missing SUI data (n = 5,246); and (D) participants with missing RFM data (n = 1,201). Ultimately, data from 32,594 participants were incorporated into the final analysis, meeting the criteria for a complete case analysis, as shown in Fig. 1.

Fig. 1

The participant flow diagram

Measurement of the RFM

The exposure variable considered in this study is RFM, calculated by the ratio of the participant's height to waist circumference. The RFM calculation is defined by the following formula:

$${\text{RFM }} = {64 } - \, \left( {{2}0 \, \times {\text{ height}}/{\text{waist}} {\text{circumference}}} \right) \, + \, \left( {{12 } \times {\text{ sex}}} \right)$$

Sex equals 0 for men and 1 for women [9]. Height and waist circumference were measured by a health professional in the Mobile Examination Center (MEC). MEC had an extraordinary height measuring device on which the participants stood together, barefoot, with their backs to the board and their heads level, and then took the measurements. At the end of normal breathing, the line above the iliac crest in the mid-axillary line was the waist circumference, and measurements were accurate to within 0.1 cm [13]. Specifically, the RFM formula adjusts for body shape differences by using the ratio of height to waist circumference, with waist circumference serving as an indicator of abdominal fat distribution. The gender adjustment term (12 × sex) further refines the formula to account for physiological differences between males and females in fat distribution and body composition.

Measurement of SUI

SUI was assessed through self-reported data from the specific survey, Kidney Conditions Urology Form KIQ042. Participants were asked, "In the past 12 months, have you experienced urine leakage or loss of control, even if it was a small amount, due to coughing, lifting heavy objects, or exercising?" Participants who answered "Yes" were diagnosed with a history of SUI. Specifically, before completing the questionnaires, the researchers provided participants with a detailed explanation of the definition, symptoms, and standardized diagnostic criteria for SUI. During questionnaire completion, participants were asked to respond based on their symptom descriptions, with specific examples included in the questionnaire to assist them in understanding each question more accurately. Furthermore, all questionnaires were designed using a standardized procedure and rigorously reviewed to ensure the accuracy of the SUI diagnosis. These measures were implemented to minimize subjective bias to the greatest extent possible while striving to ensure both the accuracy of the SUI diagnosis and the reliability of the data.

Covariates

This study includes several potential confounding factors as covariates to improve the model's reliability. Key variables assessed include age, sex, race, education level, marital status, PIR, BMI, smoking status, alcohol use, diabetes, hypertension, vigorous activity, moderate activity, number of vaginal deliveries, history of cesarean section, and use of female hormones. Data for all variables used in this study were obtained from the NHANES database, ensuring comprehensive and consistent data.

Statistical analysis

Considering the intricate, multi-stage sampling procedure design of NHANES, continuous variables are expressed as mean ± standard deviation and categorical variables as percentages. Weighted t-tests and chi-square tests were used for group comparisons. We applied multivariate logistic regression models to evaluate the RFM, the different RFM tertile groups, and the independent relationship between RFM and SUI. Different adjustment levels were incorporated for potential confounding factors: in Model 1, covariates were left unadjusted. Model 2 with adjustments for sex, age, and race, and Model 3 was designed considering all the enrolled variables. Threshold effect analysis and smooth curve fitting assessed the non-linear link between RFM and SUI.

Additionally, the variability across various subgroups was examined through subgroup analyses and interaction tests to validate the findings' robustness. The diagnostic ability of RFM, BMI, and WC for SUI was assessed through ROC analysis. The statistical analyses used R software (version 4.4.2) and EmpowerStats (version 2.0). P-values < 0.05 were considered statistically significant.

Results

Characteristics of the participants

This study included 32,594 participants. The average age was 49.81 ± 17.63 years. Among the participants, 50.23% were male and 49.77% were female. Participants were grouped according to the RFM quartiles, and Table 1 displays the essential demographic characteristics and additional covariates for each group. A total of 22.94% of participants self-reported a history of SUI. Participants in higher RFM categories exhibited a progressively elevated prevalence of SUI. Extended analysis revealed that participants in the highest RFM quartile were more inclined to be white, have higher educational levels, be divorced or separated, drink alcohol, not have diabetes, have elevated blood pressure, engage in less physical activity, have had more vaginal deliveries, have a history of cesarean section, and never have used female hormones. Table 2 indicates the results of subgroup analyses based on self-reported SUI status. The presence of SUI was found to be significantly associated with age, gender, race, marital status, PIR, smoking, alcohol use, hypertension, diabetes, physical activity, number of vaginal deliveries, history of cesarean section, use of female hormones, BMI, WC, and RFM (P < 0.05).

Table 1 Basic characteristics of participants by RFM among US adults

Table 2 Base characteristics of participants with and without SUI in the NHANES 2005–2018 cycles

Association between RFM and SUI

In this study, we applied multivariable logistic regression analysis to assess the association between RFM and SUI, considering multiple potential confounding variables. This method assumes that the dependent variable SUI is a binary variable and that the effects of the predictor variables on SUI are independent, thereby avoiding multicollinearity issues. To ensure the robustness of the model, we adjusted for all potential confounders to control for confounding variables and estimate the independent effect of RFM on the occurrence of SUI. Table 3 displays the findings of the weighted multivariable logistic regression models, which were utilized to evaluate the connection between RFM and SUI. The models were categorized into three distinct categories. Across all three models, a statistically significant positive association between RFM and SUI was consistently observed (P < 0.0001). Preliminary analysis without adjustment indicates that per one-unit increment in the RFM index, the likelihood of SUI occurrence increases by 15% (OR = 1.15; 95% CI 1.14–1.15, P < 0.0001). Model 3, which included all adjustments, the positive correlation remained substantial (OR = 1.05; 95% CI 1.05–1.06, P < 0.0001), demonstrating that with a one-unit increase in the RFM index, the likelihood of SUI occurrence increases by 5%. Additionally, when RFM was segmented into quartiles, the analysis revealed that participants in the highest quartile had a markedly higher risk of developing SUI (OR = 2.42; 95% CI 2.05–2.86; P < 0.0001), with a 142% higher risk of SUI than participants in the lowest quartile.

Table 3 Associations between RFM and Stress Urinary Incontinence

In this study, we further analyzed the relationship between BMI, WC, and SUI using weighted multivariable logistic regression models for comparison with RFM. As shown in supplementary Table 1 and supplementary Table 2, the fully adjusted model (Model 3) indicates that both BMI and WC are positively correlated with SUI (OR = 1.04, 95% CI 1.03–1.04, P < 0.0001; OR = 1.02, 95% CI 1.01–1.02, P < 0.0001, respectively). When BMI and WC were categorized by quartiles, participants in the highest quartile had a 91% increased risk of SUI compared to those in the lowest quartile (OR = 1.91, 95% CI 1.75–2.09, P < 0.0001 for BMI; OR = 1.98, 95% CI 1.81–2.17, P < 0.0001 for WC). Compared to BMI and WC, RFM demonstrated a more significant positive association with SUI. By including a detailed analysis of the relationship between BMI, WC, and SUI and comparing the three indicators, we found that RFM is more strongly associated with SUI, providing further comprehensive evidence to support the superior association of RFM with SUI in this study. All models demonstrated statistically significant trends (P for trend < 0.01).

Non-linear and saturation effect analysis of RFM and SUI

To explore the non-linear relationship between RFM and SUI, we employed smooth curve fitting, a non-parametric method based on the Generalized Additive Model (GAM). This approach allows for flexible estimation of the relationship between predictor and outcome variables, making it particularly suitable for uncovering potential correlations between RFM and SUI without relying on predefined functional forms. Smooth curve fitting does not overfit the data, making it ideal for revealing complex patterns of association. The results of the smooth curve fitting validated a non-linear correlation between the RFM and SUI, with a U-shaped association (Fig. 2). To further clarify this relationship, a threshold effect analysis was performed (Table 4). The log-likelihood ratio test (P < 0.001) significantly differed between two segmented linear models. In the relationship between RFM and SUI, a threshold point of 21.89 was identified. The relationship was negative on the left side of the threshold (OR = 0.87, 95% CI 0.82–0.92). A positive correlation was observed on the right side (OR = 1.06, 95% CI 1.05–1.06). This suggests that the impact of RFM on the prevalence of SUI differs on either side of this threshold. Interestingly, when the smooth curve fitting was stratified by sex, a U-shaped relationship between RFM and SUI was found only in males, whereas females exhibited a positive correlation (Fig. 3).

Fig. 2

The non-linear associations between RFM and stress urinary incontinence. RFM relative fat mass

Table 4 Threshold effect analysis of RFM on SUI using a linear regression model

Fig. 3

The association between RFM and stress urinary incontinence by sex

Subgroup analysis

Subgroup analyses and interaction tests were conducted to investigate whether covariates influence the relationship between RFM and SUI. The findings presented in Fig. 4 reveal distinct interactions in the following subgroups: sex (P for interaction < 0.0001), marital status (P for interaction = 0.02), smoking status (P for interaction = 0.0417), and number of vaginal deliveries (P for interaction = 0.0005), with all interaction P-values being < 0.05. Specifically, gender significantly influences the relationship between RFM and SUI. We hypothesize that this may be attributed to physiological factors such as the anatomical structure, hormonal levels, and pelvic floor muscle function in women. Marital status also exerts a significant impact on the relationship between RFM and SUI. Among married women, especially those who have experienced childbirth, the correlation between RFM and SUI is stronger. In contrast, unmarried women are less affected by this relationship. We believe that pregnancy and childbirth following marriage may be key factors contributing to this phenomenon. In the smoking group, the relationship between RFM and SUI is more pronounced. Smoking may exacerbate the occurrence of SUI by affecting weight distribution, increasing intra-abdominal pressure, and damaging the pelvic floor muscles. Additionally, the correlation between RFM and SUI is more significant in women who have undergone multiple vaginal deliveries. Vaginal deliveries may cause pelvic floor muscle damage and decrease urethral support, thereby increasing the risk of SUI. These subgroup analysis results suggest that, in clinical practice, women with high RFM, particularly those who are married, smoke, and have experienced vaginal deliveries, should be closely monitored for the occurrence of SUI, with targeted preventive and intervention measures implemented.

Fig. 4

Subgroup analysis of the association between RFM and SUI

In stratified analysis, in addition to the factors mentioned above, the relationship between RFM and SUI remained stable (P for interaction > 0.05). There was no statistically significant interaction and no effect on the positive association between RFM and SUI, with no statistical significance observed (P for interaction > 0.05).

Predictive value of RFM for the SUI

ROC curves analysis is illustrated in Fig. 5. Compared to BMI and WC (AUC: BMI = 0.578; WC = 0.541), RFM demonstrated a stronger association with the risk of SUI (AUC: RFM = 0.788). The AUC value is a commonly used metric for assessing the accuracy of diagnostic tools, with a range from 0 to 1. The closer the AUC value is to 1, the better the model's classification performance. Generally, an AUC value between 0.7 and 0.8 indicates good classification ability. Therefore, an AUC value of 0.788 for RFM suggests strong diagnostic capability in assessing the risk of SUI.

Fig. 5

ROC curve analysis compares the predictive power between RFM and BMI, WC for SUI

Conspicuously, the RFM index outperformed BMI and WC in assessing SUI risk, suggesting that RFM is likely to be a precious tool for evaluating the risk of SUI. Based on this result, RFM can provide valuable reference for clinical screening and help physicians identify patients at different risk levels, enabling timely interventions. Thus, RFM holds significant potential clinical value for the early diagnosis and management of SUI.

Interpretation of the random forest analysis

This study further employs the Random Forest (RF) method to assess the predictive ability of RFM for SUI. In the application of random forest, we utilized the Random Forest Variable Importance plot and SHAP Dependence Plot to analyze the role of RFM in predicting SUI.

The Random Forest Variable Importance plot (Supplementary Fig. 1) displays the top 10 most important variables in predicting SUI risk. The variable importance in the plot is measured by the percentage increase in mean squared error (MSE) (%IncMSE), reflecting the contribution of each variable to the model's prediction accuracy. According to the plot ranking, RFM is highlighted in red as the most crucial variable. These results indicate that RFM contributes the most to predicting SUI risk, followed by BMI and WC, which are also important indicators influencing SUI risk and are closely related to incontinence risk.

The SHAP Dependence Plot (Supplementary Fig. 2) provides significant insights into the positive correlation between SUI risk and RFM, emphasizing the existence of a substantial non-linear relationship (the non-linear relationship test yields a p-value < 2e-16). The black curve indicates a typical U-shaped relationship between RFM values and SUI risk, while the red dashed line highlights that when RFM reaches 30, the SHAP value sharply increases, representing a significant impact of body fat increase on SUI risk. This finding may have important clinical implications, suggesting that moderate increases in body fat may be significantly associated with an increased risk of SUI.

Discussion

This research utilized NHANES data from 2005 to 2018 to examine the relationship between relative fat mass RFM and the prevalence of SUI in individuals aged 20 and older. The adjusted model outcomes demonstrated a positive association between RFM and SUI prevalence in all three models using continuous variables. A robust positive correlation remained evident in the categorical models, where RFM was divided into quartiles (Q1–Q4). Smooth curve fitting analysis indicated a non-linear relationship between RFM and SUI, displaying a U-shaped curve. Furthermore, gender-stratified smooth curve fitting revealed that RFM may be an essential marker for urinary incontinence, especially in females, where a significant positive correlation was observed. In males, a U-shaped relationship was noted, suggesting that SUI prevalence significantly decreases when RFM is within an optimal range. However, when RFM exceeds a certain threshold, the likelihood of increased SUI prevalence becomes considerably higher. These findings have profound implications for clinical interventions and management of urinary incontinence in male patients. Subgroup analyses and interaction tests confirmed this association's consistency and the results' robustness. Finally, ROC curve analysis demonstrated that RFM outperforms diagnostic efficiency for SUI compared to BMI and WC.

Therefore, this study suggests that RFM is a convenient and accurate measurement method for investigating the relationship between obesity and SUI. Several anthropometric measures are currently used to assess obesity levels, with BMI being the most widely used parameter. However, studies have shown that BMI does not account for differences in fat distribution between males and females, nor does it accurately reflect the decline in muscle mass related to aging. It also fails to distinguish between central and peripheral obesity [14]. Romero-Corral et al. demonstrated that when BMI is ≥ 30 kg/m², its sensitivity for diagnosing obesity is low, and individuals with typical fat content may be incorrectly classified as overweight [15]. Therefore, the limitations of using BMI to assess obesity are undeniable. WC is a simple indicator for evaluating central obesity. A survey conducted in Korea demonstrated that WC is a more sensitive measure than BMI for diagnosing obesity-related urinary incontinence in the elderly population [16]. However, as a standalone measure, WC may be biased for individuals with different body types and does not fully reflect an individual's overall degree of obesity or fat distribution. As such, WC should be combined with other indicators and supplemented with relevant imaging techniques for a multidimensional obesity assessment [17]. Overall, RFM, as a novel metric incorporating height and waist circumference, offers a simple calculation formula, providing robust evidence for analyzing the relationship between obesity and SUI, and is readily applicable in clinical practice.

Previous studies have constantly proven a statistically significant association between obesity and both the incidence and intensity of SUI, with SUI patients showing a higher propensity for obesity. Many mechanisms have been put forward to account for the impact of obesity on the incidence of SUI, likely involving multiple factors. An increasing number of studies have increasingly focused on the relationship between obesity and SUI. For example, Shang et al. conducted a large population-based study and found that overweight and obesity significantly increase the risk of urinary incontinence in middle-aged populations [18]. In obese individuals, fat accumulation in central body regions increases intra-abdominal pressure, leading to excessive stretching of the pelvic floor muscles, thinning of connective tissues, and worsening of muscle atrophy and distortion, which results in pelvic floor weakness and dysfunction. At the same time, the continuous increase in abdominal pressure raises bladder pressure, causing hyperactivity of the urethra and bladder neck, which worsens SUI symptoms [19, 20].

Recent studies have begun to link obesity-induced systemic metabolic disturbances with SUI. Obesity is linked to insulin resistance in diabetes, activation of oxidative stress pathways, and inflammatory responses, all of which contribute to pelvic muscle atrophy, weakened nerve innervation, increased damage to pelvic arteries, and reduced pelvic floor strength. The urethral sphincter may relax, increasing the risk of involuntary urinary leakage with bouts of coughing or sneezing, thereby enhancing the likelihood of SUI [21, 22]. Insulin is involved in oxidative stress responses and the processes of increased cellular damage, which may trigger oxidative damage in the bladder and urethral sphincter, potentially altering lower urinary tract function. Elevated oxidative stress and reduced bladder blood flow can also lead to bladder nerve dysfunction and damage to urethral smooth muscle cells, ultimately contributing to the development of SUI [23]. Furthermore, obesity also impairs the endocrine regulation of visceral fat, resulting in increased levels of free fatty acids and promoting the release of inflammatory cytokines. These factors may contribute to the collection of atherosclerotic plaques in the bladder wall arteries, resulting in bladder epithelial dysfunction, bladder wall ischemia, and other bladder dysfunctions, further increasing the risk of SUI [24, 25].

We found that the relationship between RFM and SUI exhibits significant differences based on gender. Specifically, the prevalence of urinary incontinence is higher in females than in males, which may be related to anatomical factors, menopausal hormonal changes, and reproductive factors, particularly with vaginal delivery [21]. In pelvic anatomy, the pelvic floor muscles and urethral structure in women differ from those in men. Women tend to have a more comprehensive pelvic structure, a larger subpubic angle, a shorter urethra, and a downward displacement of the proximal urethra. As age increases, the relaxation of pelvic floor muscles and ligaments weakens their supportive function. The synergistic effects of these factors impair the pelvic floor's ability to effectively resist increased intra-abdominal pressure [26]. Elevated intra-abdominal pressure leads to diminished motor function of the pelvic floor nerves and the pudendal nerve motor fibers, which can trigger SUI and accelerate its progression. Additionally, hormonal levels are also an important factor contributing to gender differences. During pregnancy and menopause, fluctuations in estrogen levels can significantly affect the elasticity and function of the pelvic floor muscles. The expression and distribution of estrogen receptors in the pelvic floor muscles are influenced by various factors, including hormonal environment and structural and functional changes in the pelvic floor muscle effectors. When estrogen receptors are downregulated, or pelvic floor muscle damage occurs, it can lead to muscle atrophy and reduced urethral support, thereby increasing the risk of SUI [27]. The study by Post, Wilke M et al. focuses on the molecular processes involved in the pathophysiology of SUI and confirms that abdominal obesity, through alterations in estrogen levels and estrogen receptor expression, leads to abnormal pelvic floor collagen metabolism and decreased muscle tone, significantly increasing the risk of SUI. Animal models have shown that SUI is closely associated with inflammation and oxidative stress pathways, which cause a reduction in the elasticity of the pelvic floor muscles and impairment of the urethral sphincter function [28].

Furthermore, after menopause, the decline in estrogen levels causes atrophy of the urethral mucosa, reduced function of the urethral sphincter, and a decrease in both the size and number of smooth and urethral striated muscle fibers, thereby lowering the maximum urethral closure pressure (MUCP) [29]. Finally, lifestyle factors also play a role in gender differences. During childbirth, stretching or compression of the bladder and urethral nerves by the fetus as it passes through the birth canal can cause pelvic floor muscle fiber damage, further weakening the pelvic floor strength and increasing the risk of SUI [30]. After pregnancy and childbirth, women may experience varying degrees of pelvic floor muscle damage, which can persist for years after delivery and increase the risk of SUI. In addition to the aforementioned lifestyle factors, women are more likely to experience obesity and a sedentary lifestyle, which may exacerbate SUI symptoms or accelerate its onset [31].

In males, radical prostatectomy is the most common cause of postprostatectomy stress urinary incontinence (PPI-SUI). Risk factors contributing to PPI-SUI include advanced age, obesity, and large prostate volume. In obese patients, increased periprostatic fat may heighten the chance of damage to nerves and blood vessels during surgery, affecting postoperative urinary recovery [32]. Moreover, Sabine Rohrmann et al. have proposed that men with obesity are at an increased likelihood of developing benign prostatic hyperplasia and lower urinary tract symptoms compared to men with average body weight, thereby exacerbating the symptoms of male SUI [33].

Therefore, the relationship between RFM and SUI shows significant gender differences, which can be explained by anatomy, hormonal levels, and lifestyle factors. These factors should be thoroughly considered in clinical practice to develop personalized prevention and treatment strategies.

This study primarily focuses on the relationship between RFM and SUI. However, the occurrence of SUI is not solely influenced by body fat levels; various other factors, including lifestyle and socioeconomic status, also play a crucial role. Therefore, in this section, we have expanded the discussion to explore how these factors collectively influence the onset of SUI. Research indicates that lifestyle plays a critical role in the development of SUI. Prolonged sitting, lack of physical activity, and poor dietary habits are all considered significant risk factors for SUI [34]. Additionally, smoking is a known risk factor for SUI. Chronic hypoxia caused by smoking can lead to increased muscle fatigue, which may result in pelvic floor dysfunction [35]. Furthermore, socioeconomic status is another important factor that may influence SUI. Studies have shown that individuals with lower socioeconomic status are more likely to develop SUI, which may be related to factors such as education level, income, and access to healthcare services [36]. Individuals with low income and education levels may fail to receive timely treatment or management due to a lack of relevant health knowledge, resources, and preventive measures, which further exacerbates the relationship between obesity and SUI. In summary, factors such as obesity, lifestyle, and socioeconomic status collectively contribute to the onset and progression of SUI. By further exploring the interactions between these factors, a more comprehensive understanding of the complex etiology of SUI can be achieved, which can help guide clinical treatment.

This study provides evidence supporting RFM as a potential clinical tool for stratifying SUI risk in women, enabling early detection and facilitating the application of effective interventions and preventive strategies to reduce the incidence of SUI. Our findings robustly demonstrate a dose–response relationship between elevated RFM and SUI incidence. Given the association between RFM and SUI, weight management is crucial for improving incontinence symptoms in obese patients. Future studies should investigate the physiological and biological mechanisms underlying the connection between obesity and SUI further to provide insights into the clinical management of SUI.

Strengths and limitations of the study

This study has three key strengths. First, by screening a large sample and employing various statistical methods, this innovative investigation is the first to explore the relationship between RFM and SUI, enhancing our understanding of the link between obesity and urinary incontinence. The relationship between RFM and SUI was assessed through comprehensive analysis. Second, this study validated the consistency of its results by conducting subgroup analyses of the participant population. Additionally, additional confounding variables were adjusted in this study to improve the data's reliability.

This study has inherent limitations that must be acknowledged. First, as a cross-sectional design, it lacks dynamic follow-up data, which precludes the ability to establish causal relationships. A cross-sectional design can only describe associations between variables but cannot infer causal chains between them. Therefore, although a consistent relationship between RFM and SUI is observed, it cannot be determined whether RFM is a cause of SUI or whether SUI, in turn, influences RFM. Furthermore, a bidirectional association between RFM and SUI may exist. While higher RFM may be a risk factor for SUI, the occurrence or worsening of SUI may lead to decreased physical activity, increasing body fat percentage, thus elevating RFM. This hypothesis requires further validation and exploration through long-term longitudinal studies. Second, although confounding factors were strictly controlled for in the multivariable logistic regression and ROC analysis to assess the prevalence of SUI, potential unmeasured confounders could still affect the results. This study's analysis did not include factors such as hormonal levels and dietary habits, which may interact with the relationship between RFM and SUI. Future research should account for additional potential confounders, particularly endocrine and metabolic variables, which may influence both body fat distribution and the occurrence of incontinence. Lastly, this study is primarily based on a US participant population, and whether these findings can be generalized to broader populations remains unclear. Further investigation is needed to address this issue. Therefore, we recommend that future studies adopt a longitudinal cohort design to more accurately delineate the causal relationship between RFM and SUI and to account for a broader range of confounders in multivariate adjustments. Such a design would allow for better control of temporal changes and further validate RFM as a potential predictor of SUI.

Conclusion

This study identifies a U-shaped relationship between RFM and SUI. Additionally, RFM demonstrates significantly superior diagnostic ability for SUI compared to BMI and WC. Lifestyle modifications and weight management have significantly impacted and alleviated the incidence and intensity of SUI. Furthermore, incorporating RFM into large-scale screening programs could facilitate the early identification of high-risk individuals, thereby improving the prevention and treatment of SUI.