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. 2025 May 11;18(5):489–502. doi: 10.1159/000546104

The Association between Obesity and Wet Overactive Bladder: Results from 2005 to 2020 National Health and Nutrition Examination Survey

Jiahao Zhang a, Wanru Chen b, Zhicheng Tang a,✉, Xuqing Lin c, Xitong Wan d, Shuqiang Huang e, Hongcheng Luo a, Yuxin Qian a, Zhaohui He a,✉, Fucai Tang a,✉
PMCID: PMC12176402  PMID: 40349698

Abstract

Introduction

The association between obesity and wet overactive bladder (wet OAB) was few and was also inconsistent and controversial in sex differences. This study aimed to ascertain the specific association between obesity and wet OAB for females and males using five obesity measures.

Methods

This cross-sectional study included 29,041 participants from the 2005–2020 National Health and Nutrition Examination Survey database. The outcome was the risk of wet OAB. Five obesity measures were exposure using quintile, including body mass index (BMI), waist-to-stature ratio (WSR), body fat percentage (BFP), fat mass, and lean mass. The associations were elucidated using weighted logistic regression models and restricted cubic spline (RCS) analysis.

Results

The elevated BMI, WSR, BFP, and fat mass were positively associated with the risk of wet OAB in both sexes, which was evident in females. Interestingly, lean mass was adversely associated with the risk of wet OAB in both sexes. In RCS analysis about BMI, WSR, BFP, and fat mass, monotonically increasing nonlinear associations were found in males, while positive linear associations were found in females. Besides, negative linear and nonlinear relationships were shown between lean mass and the risk of wet OAB in females and males, respectively.

Conclusions

Obesity was positively associated with the risk of wet OAB in both sexes. Controlling BMI, WSR, BFP, fat mass, and strengthening lean mass may help prevent the development of wet OAB.

Keywords: Overactive bladder, Body mass index, Waist-to-stature ratio, Fat mass, Lean mass

Introduction

Overactive bladder (OAB) was defined as a storage symptom syndrome characterized by urgency, with or without urgency urinary incontinence (UUI), usually with increased daytime frequency and nocturia [1]. OAB is divided into two subtypes based on whether accompanied by UUI: dry OAB without UUI and wet OAB with UUI [2, 3]. The prevalence of OAB was a little discrepancy in different nations, such as 16.5% in the USA, 27.4% in Pakistan, and 11.8% in the other five countries [4–6]. Besides, the risk factors of OAB include age, smoking, economic status, educational level, and body mass index (BMI) [7]. And weight management was considered one of the behavioral therapies that alleviated these symptoms in patients with OAB [8]. However, the specific associations between obesity and wet OAB with more severe symptoms have not been researched completely, so it is necessary to investigate their associations.

Obesity is a global health issue, usually defined by a BMI over 30 kg/m2, and its prevalence is rising annually among US adults [9]. However, BMI was not the ideal indicator of adiposity, and lean and fat mass should be further considered [10]. Additionally, while explaining obesity-related health risks, waist-to-stature ratio (WSR) and body fat percentage (BFP) were the meaningful indexes [11, 12]. Furthermore, to accurately describe the health risks associated with obesity, the above various obesity measures should be combined. Besides, it is inconsistent and controversial to determine the relationship between different obesity indexes and OAB. Zhang et al. [13] study found that an increased body roundness index was associated with an increased risk of OAB in America. In a small cross-sectional study (n = 1,932), the BFP and visceral fat area were also positively associated with the risk of OAB in females [14]. A Korean study showed that BMI was a risk factor for dry OAB, but it had nothing to do with wet OAB [15]. Moreover, it has sex differences between obesity indexes and the risk of OAB. Link et al. [16] reported that the prevalence of OAB had no association with the waist-to-hip ratio and was associated positively with the waist, hip circumference, and BMI in females and negatively in males in Boston. Lai et al. [17] found that central or general obesity had a positive association with the OAB in females and was not significant in males. Some studies on OAB and obesity have been mainly performed in females and limited areas, and also less research on wet OAB [16, 18, 19]. Considering the differences and controversies of the above obesity indexes and gender in OAB, our study wishes to further understand these issues and associations between wet OAB and obesity through big populations based on the National Health and Nutrition Examination Survey (NHANES).

Therefore, our study further explored the association between wet OAB and obesity measures combined with the five obesity-related indexes mentioned above (BMI, WSR, BFP, fat mass, and lean mass) based on the NHANES database representing the US large samples. Our study will further supplement the comprehensive understanding between obesity and wet OAB, and provide the population with wet OAB for important guidance clarifying the associations of BMI, WSR, BFP, fat mass, lean mass, and wet OAB.

Methods

Study Population

Participants were enrolled from January 2005 to March 2020 in the NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm). NHANES is a nationally representative and population-based survey to study health and nutritional status conducted by the US Centers for Disease Control and Prevention, which was performed based on complex systematic sampling and sampling weight. A total of 29,041 participants were included after performing inclusion and exclusion criteria. Inclusion criteria were as follows: (1) participants responded to the Kidney Conditions-Urology questionnaire; (2) participants had body measure data and complete data. Exclusion criteria were as follows: (1) exclude missing data based on outcome-related information (n = 39,061), exposures (n = 1,197), and all covariates (n = 5,273); (2) exclude other diseases that influenced the diagnosis of wet OAB (n = 1,274); (3) exclude pregnant people (n = 650). And the detailed following of this study is shown in Figure 1 (created with BioGDP.com) [20]. The process of inclusion and exclusion was described in online supplementary Figure 1 (for all online suppl. material, see https://doi.org/10.1159/000546104).

Fig. 1.

Fig. 1.

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Detailed road map of this study.

Assessment of Outcome and Exposures

The outcome of this study was the risk of wet OAB. Combined with the definition and symptoms of the OAB, UUI is a representative urgent manifestation, more severe than urgent urination, and is a primary characteristic of wet OAB [3, 21]. In the NHANES, participants who answered “yes” to the question “During the past 12 months, have you leaked or lost control of even a small amount of urine with an urge or pressure to urinate and you couldn’t get to the toilet fast enough?” can be considered UUI [22, 23]. And nocturia was defined as urinating more than twice at night. And the OAB symptom score (OABSS) was applied based on the frequency of UUI and urine at night from the studies of Xiao et al. [24] and Zhu et al. [25]. The OABSS was summed from the UUI scores and nocturnal scores. The detailed OABSS was shown in online supplementary Table 1. When one participant had OABSS ≥3 with UUI after excluding some disease-influenced diagnoses, that one can be diagnosed with wet OAB [26]. The exclusion diseases were as follows: (1) dysuria, non-micturition emptiness, and benign prostate hypertrophy; (2) a history of the urinary tract infection; (3) cancer history of the urinary system, reproductive system, and adjacent organ; (4) relevant neurologic disease, including stroke and nervous system tumor. The specific diagnosis process has been shown in online supplementary Figure 2.

Five obesity-related indexes (BMI, WSR, BFP, fat mass, and lean mass) were exposures using quintile. BMI was obtained indirectly from body measure data of the NHANES database, which was calculated as weight (kg)/square of height (m2). The WSR was calculated as waist circumference (cm) divided by height (cm). The BFP was calculated using a published equation by Javier et al. [27] study. Fat mass and lean mass were calculated using a new comprehensive equation by Liu et al. [28] study. The detailed equations of obesity measures can be obtained in online supplementary Table 2. Five obesity measures were categorized using quintile, and their specific ranges were stored in online supplementary Table 3.

Confounding Variable Evaluation

Based on the potential confounding variables being widely recognized in previous related studies, demographic variables, physical activity, smoking status, alcohol consumption, sleep time, and energy intake were included to adjust models in this study [13, 25, 26]. To clarify the confounding variables between obesity and wet OAB, a directed acyclic graph was utilized to show a causal hypothesis based on the above references and expertise (online suppl. Fig. 3). Demographic variables included personally reported age (<60 years old, ≥60 years old), race/ethnicity (blacks, whites, other Hispanics, Mexican Americans, others), marital status (never married, married/living with partner, widowed/divorced/separated), poverty income ratio (<3.5, ≥3.5), and education level (≤high school, >high school). Alcohol use was graded as never/rarely, former, and current. Smoke status was categorized into less than and more than 100 cigarettes in one’s life. Physical activity level was divided into inactive, low active, and high active. Relevant descriptions and classification of variables were detailed in online supplementary Table 2.

Statistical Analysis

All analyses used the weights of complex sampling design in NHANES to derive nationally representative estimates. Continuous data and categorical data are expressed as interquartile ranges and weighted percentages (standard errors), respectively. The t test, Wilcoxon, and chi-square tests were used to compare normally or near-normally distributed continuous variables, non-normally distributed continuous variables, and categorical variables, respectively. The total effects of these associations were estimated based on the weighted binomial logistic regression models with an odds ratio (OR) and 95% confidence interval (95% CI) by using complex sampling weights. The crude model 1 was adjusted without other covariates except for fat mass and lean mass models. Besides, fat mass and lean mass were mutually adjusted in each model [28]. The partially adjusted model 2 was adjusted for age, race/ethnicity, marital status, educational levels, and poverty income ratio. The fully adjusted model 3 added adjustments for physical activity level, smoking status, alcohol use, sleep time, and energy intake based on model 2. Five obesity-related indexes were grouped using quintiles, and their quintile medians were used to calculate trend significance (online suppl. Table 3). Restricted cubic splines (RCSs) with 3 knots (10th, 50th, and 90th centiles of five obesity indexes) and the Wald test were used to explore potential nonlinear relationships based on the fully adjusted model 3.

These associations were further checked for their stability by three sensitivity analyses based on the model 3, as follows: (1) included participants with missing data of all covariates; (2) added to adjust for cycles years; (3) added to adjust for eGFR; (4) excluded participants with diabetes, cardiovascular disease, and menopause. All two-tailed p values <0.05 were regarded as significant values. This study was analyzed via R software (version 4.3.2).

Results

Demographic Characteristics

A total of 29,041 participants were included in the analysis with 14,527 females (weighted, 50.5%) and 3,095 participants with wet OAB (weighted, 8.3%), which can represent about 170 million Americans after weighting. The prevalence of wet OAB was 11.6% for females, 4.9% for males, and 13.2% for the blacks after weighting in the 2005–2020 US population. Both females and males with wet OAB tended to be older, black individuals, widowed/divorced/separated, former alcohol users, smoking more than 100 cigarettes in their lifetime, and have low poverty income ratio, low education levels, inactive physical level (overall p < 0.05, Table 1). Energy intake was lower in males with wet OAB, where there were no significant differences in females. No significant differences existed between sleep time and wet OAB in people distribution. In obesity measures, participants with wet OAB were more likely to have higher BMI, WSR, BFP, and fat mass in both sexes (overall p < 0.001). The weighted median (interquartile range) of lean mass was higher in females with wet OAB (p < 0.001), but no significant difference was detected in males (Table 1).

Table 1.

The baseline characteristics of participants with wet OAB status in 2005–2020 NHANES

Variable Female (n = 14,527) p value Male (n = 14,514) p value
no yes no yes
Agea <0.001 <0.001
 <60 years 9,032 (76.7) 948 (49.9) 9,715 (80.0) 344 (41.3)
 ≥60 years 3,424 (23.3) 1,123 (50.1) 3,775 (20.0) 680 (58.7)
Race/ethnicitya <0.001 <0.001
 Black 2,565 (10.3) 605 (16.8) 2,721 (9.3) 307 (15.8)
 White 5,508 (70.2) 841 (66.4) 6,063 (69.6) 429 (67.3)
 Other Hispanic 1,216 (5.2) 211 (5.5) 1,185 (5.4) 90 (5.0)
 Mexican American 1,828 (7.2) 310 (7.2) 2,051 (8.7) 147 (7.7)
 Other 1,339 (7.1) 104 (4.2) 1,470 (7.1) 51 (4.3)
Marital statusa <0.001 <0.001
 Never married 2,363 (17.1) 294 (11.0) 2,680 (20.7) 115 (9.8)
 Married/living with partner 6,974 (61.8) 935 (52.7) 8,870 (67.5) 666 (71.1)
 Widowed/divorced/separated 3,119 (21.1) 842 (36.3) 1,940 (11.8) 243 (19.1)
Poverty income ratioa <0.001
 <3.5 8,360 (54.6) 1,620 (68.5) 8,781 (52.1) 749 (62.0)
 ≥3.5 4,096 (45.4) 451 (31.5) 4,709 (47.9) 275 (38.0)
Education levela <0.001 <0.001
 ≤High school 5,046 (33.3) 1,106 (46.1) 6,291 (38.6) 571 (49.4)
 >High school 7,410 (66.7) 965 (53.9) 7,199 (61.4) 453 (50.6)
Physical activity levela <0.001 <0.001
 Inactive 3,288 (22.0) 806 (35.8) 2,438 (14.4) 295 (25.8)
 Low active 6,931 (57.6) 1,031 (51.7) 7,091 (52.8) 550 (54.5)
 High active 2,237 (20.4) 234 (12.5) 3,961 (32.7) 179 (19.6)
Alcohol usea <0.001 <0.001
 Never or rarely 2,246 (12.9) 430 (17.0) 1,009 (6.4) 74 (6.3)
 Former 1,684 (11.2) 399 (17.5) 1,926 (11.2) 242 (21.0)
 Current 8,526 (75.9) 1,242 (65.5) 10,555 (82.4) 708 (72.7)
Smokea 0.023 <0.001
 Less than 100 cigarettes in one’s life 8,041 (61.7) 1,231 (58.2) 6,463 (50.1) 360 (37.1)
 More than 100 cigarettes in one’s life 4,415 (38.3) 840 (41.8) 7,027 (49.9) 664 (62.9)
Sleep time (mean, SE)b, hours 7.29 (0.02) 7.34 (0.04) 0.230 7.10 (0.02) 7.22 (0.07) 0.089
Energy intake (median, IQR)c, kcal 1,747 (1,337, 2,226) 1,725 (1,312, 2,232) 0.425 2,434 (1,835, 3,140) 2,293 (1,761, 2,898) <0.001
BMI (median, IQR)c, kg/m2 27.29 (23.40, 32.70) 31.05 (26.34, 36.90) <0.001 28.00 (24.80, 31.80) 29.36 (25.90, 34.20) <0.001
BFP (median, IQR)c, % 39.94 (33.97, 45.93) 45.20 (40.24, 50.13) <0.001 28.72 (23.75, 33.53) 31.70 (27.68, 36.90) <0.001
WSR (median, IQR)c 0.58 (0.51, 0.65) 0.65 (0.58, 0.72) <0.001 0.57 (0.52, 0.63) 0.61 (0.56, 0.69) <0.001
Fat mass (median, IQR)c, kg 29.28 (22.67, 38.49) 35.60 (27.38, 45.76) <0.001 25.19 (19.39, 31.93) 27.75 (22.23, 36.47) <0.001
Lean mass (median, IQR)c, kg 41.30 (37.34, 46.46) 43.21 (38.11, 49.64) <0.001 60.01 (54.22, 66.87) 59.74 (52.65, 67.14) 0.283
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BMI, body mass index; IQR, interquartile range; SE, standard error.

aCategorical variables are shown by actual sample number (weighted percentage) and tested by the chi-squared test to identify significant differences in distribution, and percentage was calculated based on complex sample weights.

bNormally or nearly normally distributed continuous variables are shown as mean values (SE) and tested by t test.

cNon-normally continuous variables are shown as median values (IQR) and tested by the Wilcoxon test.

Association between BMI and Wet OAB

The multivariable-adjusted models showed strong positive associations between BMI and wet OAB status (p for trend <0.001 in each model, Fig. 2a). In the fully adjusted model 3, compared with the lowest quintile, higher BMI quintiles were associated with significantly increased risk of wet OAB in females, specifically showing OR and 95% CI for quintile 3 of 1.69 (1.37–2.08), quintile 4 of 2.14 (1.71–2.67), and quintile 5 of 3.03 (2.44–3.77) (Fig. 2a). For males, higher BMI was also positively associated with the risk of wet OAB in the model 3 (quintile 4, OR: 1.37, 95% CI: 1.02–1.84; quintile 5, OR: 1.87, 95% CI: 1.42–2.46). In the BMI model, a monotonically increasing linear association was found in females (p for overall <0.001 and p for nonlinear = 0.056, Fig. 2b), and a small J-shaped nonlinear association was found in males with the lowest inflection point of 24.4 kg/m2 BMI (p for nonlinear = 0.007, Fig. 2c).

Fig. 2.

Fig. 2.

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Association between BMI and wet OAB. a Weighted binary logistic regression models between BMI quintiles and wet OAB. b, c RCS analyses based on the fully adjusted model 3 in female group (b) and male group (c).

Association between BFP and Wet OAB

In both sexes, the significantly increasing risks of wet OAB were observed with increasing BFP in all models (p for trend <0.001, Fig. 3a). In the fully adjusted model 3 of females, the likelihood of suffering wet OAB rose from quintiles 2–5 of BFP by 53% (95% CI: 1.11–2.10), 130% (95% CI: 1.74–3.06), 210% (95% CI: 2.31–4.17), 325% (95% CI: 3.23–5.60) versus quintile 1. For males, the risk of wet OAB was associated with the quintile 3 of BFP (OR: 1.52, 95% CI: 1.04–2.23), the quintile 4 of BFP (OR: 1.53, 95% CI: 1.05–2.22), and the quintile 5 of BFP (OR: 2.33, 95% CI: 1.64–3.22) in the model 3. Interestingly, for relationships between BFP and wet OAB, there was a positive linear association in females (p for nonlinear = 0.531, Fig. 3b) and a nonlinear association monotonically increasing in males (p for nonlinear = 0.042, Fig. 3c).

Fig. 3.

Fig. 3.

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Association between BFP and wet OAB. a Weighted binary logistic regression models between BFP quintiles and wet OAB. b, c RCS analyses based on the fully adjusted model 3 in female group (b) and male group (c).

Association between WSR and Wet OAB

A heightened risk of wet OAB was found to be associated with an increase in the WSR, with all p values trending below 0.001 in all models for both sexes, as depicted in Figure 3. In the fully adjusted female model 3, the risk of wet OAB increased from quintiles 2–5 with OR and 95% CI for 1.42 (1.08–1.86), 1.92 (1.52–2.43), 2.35 (1.85–3.00), and 3.71 (2.89–4.76), respectively, when compared to quintile 1 (Fig. 4a). In males, compared to quintile 1, quintiles 3–5 of WSR had a significant risk of wet OAB with OR and 95% CI for 1.49 (1.05–2.12), 1.50 (1.05–2.12), 2.45 (1.74–3.44) in the model 3. Also, there was a positive linear association between WSR and wet OAB in females (p for nonlinear = 0.247, Fig. 4b) and a nonlinear association monotonically increasing in males (p for nonlinear = 0.034, Fig. 4c).

Fig. 4.

Fig. 4.

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Association between WSR and wet OAB. a Weighted binary logistic regression models between WSR quintiles and wet OAB. b, c RCS analyses based on the fully adjusted model 3 in female group (b) and male group (c).

Association between Fat Mass and Wet OAB

Both sexes exhibited increased susceptibility to suffering wet OAB as fat mass increased overall (p for trend <0.01, Fig. 5a). In the model 3 of females, compared to quintile 1, the risk of wet OAB increased from quintiles 2–5 of fat mass by 30% (95% CI: 1.00–1.70), 63% (95% CI: 1.23–2.15), 151% (95% CI: 1.82–1.3.45), and 290% (95% CI: 2.54–6.01), respectively. A significantly increasing relationship with linearity was observed between fat mass and wet OAB in females (p for overall <0.001 and P for nonlinear = 0.089, Fig. 5b). However, only male participants in the highest quintile of fat mass had a significant difference with the risk of wet OAB (OR: 2.37, 95% CI: 1.29–4.35) in model 3 (Fig. 5a). A positive nonlinear association was observed with a gradual increase between fat mass and wet OAB in males (p for nonlinear <0.05, Fig. 5c).

Fig. 5.

Fig. 5.

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Association between fat mass and wet OAB. a Weighted binary logistic regression models between fat mass quintiles and wet OAB. b, c RCS analyses based on the fully adjusted model 3 in female group (b) and male group (c).

Association between Lean Mass and Wet OAB

For females, a significantly negative association between lean mass and the risk of wet OAB was found in all models (Fig. 6a). The fully adjusted model 3 showed a gradual decrease in the risk of wet OAB from quintiles 2 to 5 of lean mass with OR and 95% CI for 0.77 (0.59–0.99), 0.74 (0.58–0.93), 0.70 (0.54–0.91), and 0.60 (0.40–0.90), respectively (Fig. 6a). In the RCS analysis, a negative linear association was also shown between lean mass and the risk of wet OAB in females (p for overall <0.001 and p for nonlinear = 0.161, Fig. 6b).

Fig. 6.

Fig. 6.

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Association between lean mass and wet OAB. a Weighted binary logistic regression models between lean mass quintiles and wet OAB. b, c RCS analyses based on the fully adjusted model 3 in female group (b) and male group (c).

For males, the fully adjusted model 3 also showed a gradual decrease in the risk of wet OAB from quintiles 2 to 5 of lean mass with OR and 95% CI for 0.67 (0.49–0.91), 0.53 (0.38–0.73), 0.47 (0.32–0.68), and 0.47 (0.32–0.68), respectively (Fig. 6a). A negative nonlinear association between lean mass and the risk of wet OAB was discerned in males (p for nonlinear = 0.001, Fig. 6c). In sensitivity analyses based on the model 3, after including all participants with missing data of covariates, adding to adjust for cycle years, adding to adjust for eGFR, or excluding participants with diabetes, cardiovascular disease, and menopause, the above associations between five obesity measures and the risk of wet OAB were still robust in both sexes (online suppl. Tables 4, 5).

Discussion

In this study, the risk of wet OAB increased considerably in both sexes with rising BMI, WSR, BFP, and fat mass, and females had a greater effect. Increasing lean mass was adversely correlated with the risk of wet OAB in both sexes. RCS analyses further proved these trends. Therefore, obesity and shape were positively associated with the risk of wet OAB. A study by Link et al. [16] also showed similar sex-specific differences in associations between obesity and OAB when considering waist circumference, BMI, and hip circumference. It is worth mentioning that Link et al. [16] reported that OAB was associated positively with obesity in females and negatively in males. However, Lai et al. [17] study found that obesity had no significant association with the OAB in males. The results of these differences may come from the limitations of some studies, such as sample size, insufficient control for confounding factors, and geographical coverage. In our study, the five obesity indexes were all significantly with the risk of wet OAB; moreover, these effects were more pronounced in females than in males considering enough sample sizes, adequate control for confounding factors, and broader geographical coverage across the USA.

The pathophysiology linking obesity and OAB is not entirely understood, but several potential mechanisms are proposed. Interestingly, excess body weight may increase abdominal pressure, subsequently elevating bladder pressure and urethral mobility, thereby exacerbating OAB. Furthermore, obesity may bring about chronic strain to stretch and weaken muscles, nerves, and structures of the pelvic floor [29]. Regarding neural mechanisms, adipose tissue may boost autonomic neuronal activity through leptin production, especially noradrenergic sympathetic neurons [30]. Attenuating small-conductance Ca2+-activated K+ channels and large-conductance voltage- and Ca2+-activated K+ (BK) of rats with a high-fat diet and weight increases can increase the excitability and contractility of the detrusor smooth muscle to induce OAB [31, 32]. Besides, the detrusor mitochondrial respiration can be reduced and the nerve-mediated contractions can be strengthened based on a chronic high-fat diet with weight increases [33]. Aging is closely associated with the development of obesity [34]. Bladder aging-related processes have been proposed as potential causes of urinary incontinence in the elderly, such as reduced adaptability in the functional ability to maintain consistency in voiding frequency, uncontrollable detrusor overactivity, structural alterations, and age-related loss of bladder capacity [35, 36]. Additionally, obesity is accompanied by inflammation and oxidative stress [37, 38]. Moreover, chronic suburothelial inflammation and cumulative oxidative stress contribute to the development of OAB [39, 40].

In our study, the intensity of these associations between obesity measures and wet OAB has differed in females and males. Males may be less susceptible because of their stronger pelvic floor anchoring and low odds of occurring prolapse [17]. Notably, females have a higher prevalence of obesity versus males accounting for changes in estrogen levels [41]. In a follow-up cohort, overweight women especially with central obesity had a higher risk of menopause [42]. In females, menopausal symptoms are considered separate risk factors for OAB, which may involve changes in estrogen and emotion [43]. And progestin may negatively affect urinary incontinence by decreasing the muscle tension of the urethra and bladder [44]. Moreover, knocking out Pirt can cause the occurrence of OAB by influencing the P2X3, and the Pirt deficiency induces obesity and glucose intolerance in female mice but not male mice [45–47]. Therefore, female obesity increases OAB risk more than males, which was also confirmed in our study.

This study is the first large-scale survey to comprehensively examine the association between five obesity indices and wet OAB in a nationally representative sample of Americans. Besides, the time scale was over 15 years from 2005 to 2020 in this study. Our study also included various obesity indexes and confounding factors based on the directed acyclic graph. However, our study has some inevitable limitations. First, this study used a cross-sectional design, which cannot provide enough evidence to establish causality between obesity and wet OAB. Second, respondent honesty and recalling bias may impact the quality of study data and underestimate the population with wet OAB. Third, although the directed acyclic graph was used to select the confounding variables in order to make the total effects of the model more stable, it may still miss some important confounding variables. Therefore, further cohort studies and mechanistic studies are needed to verify the association between obesity indices and wet OAB in the future.

Conclusions

The risk of wet OAB was positively associated with increasing BMI, WSR, BFP, and fat mass in both sexes, especially stronger in females. In males, when BMI >24.4 kg/m2, the increased BMI was stable and significant with an elevated risk of wet OAB. The lean mass was negatively correlated with the risk of wet OAB in both sexes. Therefore, treating wet OAB symptoms should include weight reduction, controlling body shape, and strengthening lean mass, especially in clinical management. These findings could guide targeted interventions and tailor personalized strategies that could potentially lower the risk of wet OAB in public health, such as weight management programs, especially in populations with a higher risk of elevated adiposity indicators. This study can provide a foundation for future cohort studies and mechanistic studies between obesity and wet OAB.

Acknowledgments

We would like to express our gratitude to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People’s Hospital, Fudan University) for developing the nhanesR package, which facilitated our exploration of the NHANES database and the successful completion of this study.

Statement of Ethics

Because this study was performed from an online public database – NHANES – with no human or animals involved directly, the principles of the Helsinki Declaration were followed in lieu of formal Ethics Committee approval. Therefore, as the research unit of the main authors, the Ethical Review Board of the Eighth Affiliated Hospital of Sun Yat-sen University determined that ethical approval was not demanded in this study. Besides, the protocols of every NHANES survey cycle have been approved by the Ethics Review Board of the National Center for Health Statistics with approval protocol numbers #2005-06, #2011-17, and #2018-01 (https://www.cdc.gov/nchs/nhanes/irba98.htm, accessed on December 1, 2024). All participants completed written informed consent forms before participation. Health information collected in the NHANES is kept in strictest confidence. And the consent forms were stated in the NHANES (https://www.cdc.gov/nchs/nhanes/genetics/genetic_participants.htm).

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

This study was funded by the project of the Eighth Affiliated Hospital of Sun Yat-sen University (Zhaohui He, contract number: GCCRCYJ010), the Key Department of Urology, Futian District, Shenzhen, Guangdong Province, China (Zhaohui He, contract number: QZDZK202414 and ZDXKJF008), and Shenzhen Science and Technology Program (Zhaohui He, contract number: ZDSYS20220606100801004). The specific role of the funding organization or sponsor is as follows: design and conduct of the study; collection, analysis, and interpretation of data.

Author Contributions

Jiahao Zhang: conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, validation, visualization, and writing – original draft, review, and editing. Wanru Chen: data curation, methodology, project administration, and writing – original draft, review, and editing. Zhicheng Tang: data curation, formal analysis, visualization, and writing – original draft, review, and editing. Xuqing Lin: visualization and writing – original draft, review, and editing. Xitong Wan: investigation and writing – original draft, review, and editing. Shuqiang Huang, Hongcheng Luo, Yuxin Qian: writing – original draft, review, and editing. Fucai Tang: conceptualization, formal analysis, methodology, supervision, validation, visualization, and writing – review and editing. Zhaohui He: conceptualization, funding acquisition, investigation, methodology, supervision, and writing – review and editing.

Funding Statement

This study was funded by the project of the Eighth Affiliated Hospital of Sun Yat-sen University (Zhaohui He, contract number: GCCRCYJ010), the Key Department of Urology, Futian District, Shenzhen, Guangdong Province, China (Zhaohui He, contract number: QZDZK202414 and ZDXKJF008), and Shenzhen Science and Technology Program (Zhaohui He, contract number: ZDSYS20220606100801004). The specific role of the funding organization or sponsor is as follows: design and conduct of the study; collection, analysis, and interpretation of data.

Data Availability Statement

The original data of this study can be accessed publicly by an open NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm). The detailed regulations of data privacy can be viewed in the NHANES statement (online suppl. Material 2, https://www.cdc.gov/nchs/nhanes/participant/participant-confidentiality.htm). More data details are displayed in supplementary materials and can be provided by contacting the author.

Supplementary Material.

Supplementary Material.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_material-Suppl.1-s1.docx (702.3KB, docx)
Supplementary_material-Suppl.2-s2.pdf (223.6KB, pdf)

Data Availability Statement

The original data of this study can be accessed publicly by an open NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm). The detailed regulations of data privacy can be viewed in the NHANES statement (online suppl. Material 2, https://www.cdc.gov/nchs/nhanes/participant/participant-confidentiality.htm). More data details are displayed in supplementary materials and can be provided by contacting the author.

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