Association between body fat distribution and female stress urinary incontinence: a cross-sectional study with the NHANES 2011–2018

Introduction

Stress urinary incontinence (SUI) is a prevalent pelvic floor disorder characterized by involuntary urine leakage. It typically occurs during physical activities that lead to temporary increases in intra-abdominal pressure, such as sneezing or coughing (1). Epidemiological studies have shown that SUI affects a substantial number of women globally (2,3). The condition significantly impacts quality of life and imposes considerable socioeconomic costs (4,5). Identifying modifiable risk factors through rigorous research is critical for developing targeted SUI prevention strategies. Addressing modifiable contributors, such as through lifestyle changes and medical treatment, can help lower the likelihood of SUI development. For instance, as this study suggests, if the android to gynoid ratio (A/G ratio) contributes to SUI through specific fat distribution patterns, it could potentially be improved through diet, exercise, or focused interventions to reduce SUI risk. Strategies may benefit individual health and help lessen the broader burden of the disease.

Abdominal obesity is a well-established risk factor for SUI (4). The underlying mechanisms involve excessive mechanical loading on pelvic floor structures and compromised musculofascial integrity, collectively predisposing to SUI development (6). While body mass index (BMI) has been historically employed to assess the association between obesity and SUI (7,8), its inability to distinguish regional fat distribution reduces its clinical relevance. This limitation may partly account for inconsistent BMI and SUI across studies (7). These shortcomings point to the need for more precise indicators of body fat distribution, such as the A/G ratio, to improve SUI risk assessment.

The A/G ratio has emerged as a robust anthropometric biomarker for quantifying regional adiposity patterns (9,10). This metric utilizes dual-energy X-ray absorptiometry (DXA)-based compartmental analysis to quantify abdominal (Android) versus gluteofemoral (Gynoid) fat depots (11). Unlike BMI, the A/G ratio better distinguishes between central and peripheral fat (12). DXA provides several advantages over computed tomography (CT) or magnetic resonance imaging (MRI) for body composition analysis, including non-invasive methodology, minimal radiation exposure, and cost-effectiveness (11). In recent years, research into the A/G ratio has increasingly shown its association with various metabolic conditions. Studies have demonstrated that the A/G ratio is closely related to metabolic diseases such as diabetic retinopathy (13), arterial stiffness in type 2 diabetes (14), non-alcoholic fatty liver and liver fibrosis (15), which further confirms its potential in assessing health risks. However, the association between the A/G ratio and SUI remains unexplored, representing a critical gap in pelvic floor disorder research.

This study hypothesizes a positive relationship between the A/G ratio and the prevalence of SUI in women. Therefore, our objective is to investigate the association between the A/G ratio and SUI in female populations. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-166/rc).

Methods

Data sources and participants

The National Health and Nutrition Examination Survey (NHANES) is a nationally representative study conducted by the National Center for Health Statistics (NCHS) to assess the health and nutritional status of the U.S. population. All data in this study were derived from the NHANES database. De-identified data for this study are available on the official NHANES website (http://www.cdc.gov/nchs/NHANEs/). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Data from four consecutive NHANES cycles [2011–2018] were analyzed. Exclusion criteria were as follows: (I) participants with incomplete data on SUI or A/G ratio; (II) male participants; (III) participants with outliers falling below the 1st percentile or above the 99th percentile (Figure 1).

Figure 1 Flow diagram of study cohort selection. A/G ratio, android to gynoid ratio; NHANES, National Health and Nutrition Examination Survey.

Definitions of exposure and outcome variables

The A/G ratio, derived from whole-body DXA scans, served as the primary exposure variable. During the DXA examination, subjects were excluded if they were pregnant, had used barium contrast agents within the past 7 days, weighed over 450 pounds, or were taller than 6 feet 5 inches.

In the “Kidney Conditions-Urology” questionnaire of the NHANES database, participants were classified as having SUI if they responded with “yes” to the question “During the past 12 months, leaked or lost control of even a small amount of urine with an activity like coughing, lifting or exercise?”.

Covariates

Based on past research findings and clinical practice experience, this study incorporated several covariates that could potentially influence the study’s outcomes. The covariates were: age, race (Mexican American, other Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian and other race), education level (less than high school, high school or equivalent and more than high school), marital status (cohabiting, solitude), household poverty income ratio (PIR) (<1.3, 1.3–<3.5, ≥3.5 or missing) (16), hypertension (yes, no), diabetes (yes, no), hypercholesterolemia (yes, no), BMI (<25.0, ≥25.0 kg/m²) (17), number of vaginal deliveries (0, 1–2, 3–4, ≥5) (18), cesarean deliveries (yes, no), and menopause (yes, no), smoking status (never, former, and current), alcohol consumption (yes, no), vigorous recreational activity (yes, no), moderate recreational activity (yes, no). To preserve statistical power missing values for household PIR (n=409) and alcohol consumption status (n=548) were retained as separate categorical levels, while observations with missing data in other covariates were excluded through complete-case analysis to ensure methodological rigor. Participants fulfilling any of the subsequent criteria were regarded with hypertension: (I) having four blood pressure measurements on two different occasions and the average of the four blood pressure measurements ≥140/90 mmHg (19); (II) diagnosed with hypertension by a physician; (III) taking anti-hypertensive medications. Individuals who satisfied any of the three criteria were regarded as having diabetes: (I) diagnosed with diabetes by a physician; (II) glycated hemoglobin >6.5%; (III) fasting blood glucose ≥7.0 mmol/L (20). Participants with any of the three characteristics were regarded as having hypercholesterolemia: (I) diagnosed with hypercholesterolemia by a doctor; (II) total cholesterol level ≥240 mg/dL; (III) taking cholesterol-lowering medications (21). Vaginal deliveries included both live births and stillbirths, with multiple gestations (twins or more) counted as a single delivery event. Cesarean deliveries also included both live births and stillbirths. A count of 0 indicated no cesarean history, while a count of ≥1 indicated a history of cesarean delivery. Smoking status was categorized as follows: never-smokers (fewer than 100 cigarettes smoked in a lifetime), former-smokers (more than 100 cigarettes smoked but not currently smoking), and current-smokers (22). Alcohol consumption was defined as drinking on ≥12 occasions per year (23). Vigorous recreational activity was defined as any activity causing substantial increases in breathing or heart rate, lasting at least 10 minutes per session, including activities such as running or basketball. Moderate recreational activity referred to physical activity that caused moderate increases in breathing or heart rate.

Statistical analysis

Sample weights, as outlined in the NHANES analytical guidelines were applied to account for the complex survey design and to correct for nonresponse bias. Categorical variables were reported as weighted proportions with corresponding 95% confidence intervals (95% CIs), and differences between SUI and non-SUI groups were assessed using the weighted Chi-squared test. Continuous variables were presented as weighted mean ± standard deviation (SD), and group comparisons for continuous outcomes were conducted using weighted linear regression models. SUI prevalence was stratified by A/G ratio quartiles for comparative analysis. Weighted multivariable logistic regression was used to evaluate the association between the A/G ratio and SUI, treating the A/G ratio both as a continuous variable and as a categorical variable divided into quartiles. Model 1 was unadjusted for covariates. Model 2 adjusted for age, race, education level, marital status, and family PIR. Model 3 included adjustments for age, race, education level, marital status, family PIR, hypertension, diabetes, hypercholesterolemia, vigorous recreational activity, moderate recreational activity, vaginal delivery, cesarean delivery, and menopausal status. In addition, for Model 3, we employed the weighted restricted cubic splines (RCS) to explore potential nonlinear relationships between the A/G ratio and SUI. Meanwhile, subgroup analyses were carried out based on smoking status, hypertension, diabetes, hypercholesterolemia, vigorous activity, moderate activity, vaginal delivery, cesarean section, menopausal status, and BMI, and the likelihood ratio test was used to add interaction terms to test the heterogeneity between subgroups. The predictive ability of the A/G ratio female SUI was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). All statistical analyses were performed using Empower software (http://www.empowerstats.com) and R version 4.4.2 (https://www.r-project.org/). A two-sided P<0.05 was deemed statistically significant.

Results

Characteristics of participants

The final analytical sample included 5,309 women from NHANES 2011–2018 cycles, who met the inclusion criteria. Among these, 1,958 reported having SUI, while 3,351 did not. This study found that the A/G ratio value in the SUI group was higher than that of the non-SUI group (0.930±0.004 vs. 0.894±0.004, P<0.001). The age in the SUI group was older in the two groups (43.731±0.409 vs. 37.635±0.336, P<0.001). Non-Hispanic Whites constituted the largest racial group in both cohorts (67.640%, 58.773%). No significant intergroup differences were observed for PIR (P=0.09), moderate activity (P=0.47), and cesarean deliveries (P=0.37) (Table 1).

Correlation between A/G ratio and SUI

First, the A/G ratio was divided into quartiles. The prevalence of SUI was then calculated for each group. From the statistical results, the prevalence of each group exhibited a significant dose-response gradient, specifically, Quartile 4 > Quartile 3 > Quartile 2 > Quartile 1, indicating that there are significant differences in the prevalence among different groups (P<0.001) (Table 2, Figure 2). Next, weighted univariate and multivariate Logistic regression analyses were conducted to examine the relationship between the A/G ratio and SUI. When treated as a continuous variable in the univariate model, each one-unit increase of one unit in the A/G ratio was associated with a 4.464-fold rise in SUI risk [odds ratio (OR) =5.464, 95% confidence interval (CI): 3.511–8.502]. After adjusting for covariates, the A/G ratio remained positively linked with SUI (Model 2: OR =4.015, 95% CI: 2.450–6.581; Model 3: OR =3.640, 95% CI: 2.150–6.162). When the A/G ratio was analyzed by quartiles, individuals in the highest quartile had a 64.9% higher risk of SUI compared to those in the lowest quartile (OR =1.649, 95% CI: 1.298–2.095) (Table 3).

Figure 2 Prevalence of SUI among participants with different A/G ratio. A/G ratio, android to gynoid ratio; SUI, stress urinary incontinence.

Nonlinear correlation between A/G ratio with SUI

To further investigate the association between A/G ratio and SUI, weighted RCS analysis was performed, adjusting for all covariates in Model 3. The RCS curve (Figure 3) showed no statistically significant nonlinear association (P for nonlinear =0.25), but a clear monotonic upward trend was evident across range of the A/G ratio (P for overall <0.001).

Figure 3 RCS curve of A/G ratio and SUI. A/G ratio, android to gynoid ratio; OR, odds ratio; RCS, restricted cubic splines; SUI, stress urinary incontinence.

Subgroup analysis

Subgroup analyses suggested potential interactions between the A/G ratio and the number of vaginal deliveries, cesarean deliveries, and BMI (likelihood ratio test for adding interaction P<0.05). Within subgroups defined by hypertension, diabetes, hypercholesterolemia, moderate activity, and menopausal status, higher A/G ratio consistently showed a positive association with the incidence of SUI. In the smoking status analysis, increased SUI risk was observed in both never-smokers (OR =3.611, 95% CI: 1.739–7.497) and current-smokers (OR =4.720, 95% CI: 1.311–16.997). Among physical activity subgroups, the strongest association between A/G ratio and SUI was observed in participants not engaging in vigorous activity (OR =4.752, 95% CI: 2.386–9.506) (Figure 4).

Figure 4 Forest plot of subgroup analysis on the relationship between A/G ratio and SUI. A/G ratio, android to gynoid ratio; BMI, body mass index; CI, confidence interval; OR, odds ratio; SUI, stress urinary incontinence.

The predictive ability of A/G ratio for SUI

ROC analysis demonstrated significant discriminative capacity of the A/G ratio for SUI prediction. The AUC was 0.758, and the optimal diagnostic cut-off point for the A/G ratio was 0.319, at which the sensitivity was 0.727 and the specificity was 0.637 (Figure 5).

Figure 5 Diagnostic performance of A/G ratio index on SUI prevalence. A/G ratio, android to gynoid ratio; AUC area under the receiver operating characteristic curve; ROC, receiver operating characteristic; SUI, stress urinary incontinence.

Discussion

The results showed that as the A/G ratio increased, the likelihood of developing SUI also rose. When the A/G ratio quartiles were analyzed as categorical variables, the positive association remained consistent. Subgroup analysis indicated that the number of vaginal deliveries, cesarean section, and BMI interacted with the A/G ratio. In other subgroups, specifically those defined by hypertension, diabetes, hypercholesterolemia, menopausal status, and moderate activity, the association between higher A/G ratios and increased SUI prevalence was statistically significant across all categories.

The A/G ratio reflects patterns of fat distribution, where a higher value suggests greater abdominal fat relative to gluteofemoral fat. Accumulation of visceral fat raises intra-abdominal pressure through increased mechanical load (24). Chronic pressure can cause progressive weakening and degeneration of pelvic floor muscles and connective tissues, reducing their structural support (25). From a pathophysiological standpoint, SUI results from impaired support to the bladder and urethra or from defective urethral closure (4). The biomechanical disruptions offer a plausible mechanism linking fat distribution to SUI risk. In addition to mechanical effects, obesity induced inflammation can damage components of the peripheral nervous system. Inflammatory processes may affect the dorsal root ganglia and sensory nerve endings lacking a blood-nerve barrier, as well as the perineurium and endothelial cells of microvessels nerve function (26). Urinary routes are innervated by autonomic (sympathetic and parasympathetic nerves) and somatic nerves (pudendal nerve) (27). The sympathetic system releases norepinephrine, which activates β-adrenergic receptors in the bladder’s detrusor muscle (28), while the parasympathetic system uses acetylcholine to mediate bladder contraction (28). The somatic nerve innervates the pelvic floor muscles, including the external urethral sphincter (27). Damage to any of these peripheral nerves may impair their function, disrupting bladder and, urethral control, and thereby increasing SUI risk (29). Schlesinger et al. identified CCL7, CXCL10, and DNER as key inflammatory mediators linking abdominal obesity to distal sensorimotor polyneuropathy (DSPN) (30). Although no direct evidence currently connect DSPN to SUI, it is plausible that similar neuropathological mechanisms could contribute to bladder dysfunction and raise SUI risk. Several studies have shown that Gynoid fat distribution is linked to lower metabolic risk and is associated with protective health effects (12,31). An elevation in the A/G ratio might signify that the detrimental factors start to prevail over the protective factors, thereby augmenting the likelihood of SUI.

During vaginal delivery, the passage of the fetus through the birth canal results in direct mechanical stretching and damage to the pelvic floor tissues. Multiparity exacerbates this damage, compromising vesicourethral support structures and altering bladder neck position/urethral angle, thereby increasing SUI risk (32). Additionally, in the context of preexisting pelvic floor impairment, A/G ratio-associated intra-abdominal hypertension further predisposes to SUI. In our analysis, the association between the A/G ratio and SUI appeared weaker in women with ≥5 vaginal deliveries (with a wide 95% CI), likely due to limited statistical power from the smaller sample size in this subgroup. While cesarean delivery avoid the direct mechanical trauma to the pelvic floor seen with vaginal birth it does not eliminate SUI risk, pregnancy-related hormonal changes, particularly increases in progesterone and relaxin during late gestation, facilitate cervical dilation and pubic symphysis relaxation (33), while downregulation of estrogen receptors impairs elastic fiber regeneration, contributing to pelvic floor weakening (34). Postpartum visceral adiposity increases independently of total body fat mass (35), suggesting pregnancy-induced adiposity redistribution interacts with A/G ratio to influence SUI pathogenesis. Combined high BMI and elevated A/G ratio augment intra-abdominal pressure and compromise pelvic floor innervation (6). A study by Xiao et al. reported that higher BMI is linked to increased insulin resistance (36), with obesity (BMI ≥30 kg/m²) driving macrophage infiltration into adipose tissue and stimulating the secretion of pro-inflammatory cytokines. These cytokines, including TNF-α, IL-1β, IL-6, and IL-18 into adipose tissue and stimulating the insulin resistance (37). In addition, obesity related hepatic fat accumulation contributes to the synthesis of sn-1,2-diacylglycerol (DAG), a bioactive lipid intermediate. DAG acts as a signaling molecule that activates protein kinase Cε (PKCε), which interferes with insulin signaling and promotes insulin resistance progression (38). Insulin resistance has been shown to correlate positively with SUI, particularly in females (39), suggesting that the combined influence of BMI and the A/G ratio may contribute to female SUI risk. In the smoking status subgroup, current smokers demonstrated a significant A/G ratio-SUI association, potentially due to cigarette smoke-induced oxidative stress and inflammation disrupting coagulation and microvascular function (40). Chronic vascular damage from smoking promotes systemic arteriosclerosis, impairing urinary tract microcirculation and causing detrusor muscle sensitization via hypoxia and metabolite accumulation (41). The impact of smoking cessation varies depending on both the time since quitting and the quantity of smoking prior to cessation Studies have linked both factors to subclinical markers of cardiovascular damage (42). However, in this study, the dataset for former smokers lacked detailed information on cessation duration and prior smoking volume. This may be the reason why our research results failed to find the connection between A/G ratio and SUI in this group. Notably, previous research has found a higher prevalence of SUI among individuals engaging in frequent vigorous physical activity, particularly competitive athletes (43,44), suggesting that vigorous activity could be a contributing factor for SUI. However, our subgroup analysis failed to detect a significant association in the physically active group. Indeed, vigorous exercise strengthens pelvic floor muscles (45), potentially counteracting A/G ratio-associated SUI risk. Additionally, vigorous exercise modulates adiposity distribution, reducing BMI/waist circumference and A/G ratio.

This study represents the first investigation into the association between the A/G ratio and SUI in women. Our research has certain advantages. First, we utilized the nationally representative NHANES, which features a large sample encompassing diverse age, racial, and ethnic groups. This design enables robust generalizability of our findings to the U.S. female population, and supports a broad understanding of the A/G ratio-SUI relationship. Second, our stratified analyses identified effect modification by multiple factors (parity, smoking status, physical activity), yielding nuanced insights into SUI pathogenesis. These subgroup findings help illustrate how demographic, behavioral, and metabolic factors interact with fat distribution patterns to influence SUI risk.

However, this study has several methodological limitations. First, as a cross-sectional analysis, it cannot determine the temporal sequence or establish causality between the A/G ratio and SUI in women. It also cannot assess the A/G ratio’s ability to predict future disease occurrence. While our findings are consistent with existing mechanistic hypotheses and epidemiological data, confirmation through prospective cohort studies or interventional trials is necessary to clarify causality and predictive relevance. Second, outcome and covariates data were collected via self-report, an approach with inherent limitations. Recall bias cannot be excluded, and self-reported data may introduce measurement error, potentially compromising the precision of our estimates. These limitations highlight the need for future studies incorporating objective measures (direct adiposity assessments, urodynamic testing) to validate our findings.

Conclusions

Our results show that women with higher A/G ratio have greater odds of experiencing SUI. These findings suggest that the A/G ratio may serve as a clinically actionable biomarker for SUI risk stratification in female populations. However, large-scale, longitudinal studies incorporating mechanistic evaluations are warranted to elucidate the pathophysiological pathways linking adiposity distribution patterns to SUI. Future research should also aim to develop targeted strategies that address both metabolic dysfunction and pelvic floor health, potentially through a combination of lifestyle interventions and pharmacological treatment. Such studies would provide critical evidence to guide precision medicine strategies for this prevalent condition.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-166/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-166/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-166/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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