Exploring the association between the ZJU index and overactive bladder: a cross-sectional study based on NHANES 2011–2018

Introduction

Overactive bladder (OAB) is a prevalent lower urinary tract symptom (LUTS) primarily characterized by bothersome voiding-phase symptoms, including urinary urgency, increased frequency, and nocturia, often accompanied by urge incontinence (1). Globally, the prevalence of OAB is estimated at 12–17%, with higher incidence rates observed in women, the elderly, and individuals with chronic conditions such as diabetes and neurological disorders (2). In China, the prevalence of OAB in women increased from 8% before 2006 to 18% in 2016 (3). The etiology of OAB is complex and multifactorial, involving neurogenic factors such as abnormal bladder nerve signaling, structural changes like detrusor overactivity, hormonal influences, and lifestyle or behavioral factors, including fluid intake and dietary habits (4). Despite its high prevalence, the mechanisms underlying OAB remain poorly understood, and treatment outcomes are often suboptimal, leading to significant impairments in patients’ quality of life and psychological well-being (5). In the United States, the annual healthcare expenditure for OAB exceeds billions of dollars, representing a substantial burden on the healthcare system (6). Thus, further research is urgently needed to elucidate the pathophysiology of OAB and develop more effective, personalized prevention and treatment strategies to improve patient outcomes and quality of life.

The ZJU index, developed by a research team at Zhejiang University in 2015, is an innovative metabolic composite index initially designed for assessing metabolic health in non-alcoholic fatty liver disease (NAFLD) (7,8). It integrates multiple metabolic markers, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglycerides (TG), body mass index (BMI), and fasting blood glucose (FBG), providing a comprehensive and objective evaluation of metabolic health (7). The inclusion of ALT/AST ratios reflects liver function, while elevated TG and BMI often indicate obesity, and abnormal FBG levels signal insulin resistance or diabetes. Cohort study shows strong predictive power of ZJU Index in identifying NAFLD in overweight women (8). Subsequent studies have demonstrated the utility of the ZJU index in assessing metabolic conditions such as diabetes (9), insulin resistance (10), gallstones (11), and sarcopenia (12). By combining multiple markers, the ZJU index provides a more holistic measure of metabolic dysfunction compared to traditional single-marker approaches.

Recent epidemiological and clinical studies have highlighted a strong association between OAB and systemic conditions such as obesity, diabetes, inflammatory bowel disease, and hypertension (13-15). Notably, visceral fat accumulation has been positively correlated with OAB risk, although the exact mechanisms linking obesity and OAB remain unclear (16). For instance, women with a BMI ≥30 kg/m² have been reported to have a higher risk of nocturia compared to those with normal BMI (17). Metabolic syndrome, high BMI, low fluid intake, and specific dietary habits have also been identified as risk factors for OAB (18). Additionally, NAFLD, which shares overlapping metabolic pathways with OAB, may contribute to its development (19). While individual components of the ZJU index, such as BMI, FBG, and TG, are associated with OAB risk factors, the predictive accuracy of single markers remains limited. To date, no studies have investigated the relationship between the ZJU index and OAB.

This study aims to bridge this knowledge gap by conducting a cross-sectional analysis of a representative U.S. population using data from the National Health and Nutrition Examination Survey (NHANES). Specifically, it evaluates the feasibility of the ZJU index as a predictive tool for OAB, offering valuable insights into its potential role in early intervention and management strategies for OAB. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-557/rc).

Methods

Data source and study population selection

This study utilized data from the NHANES, a nationally representative cross-sectional survey designed to evaluate the health and nutritional status of the U.S. population. NHANES collects comprehensive data through structured interviews, physical examinations, and laboratory tests, making it an essential resource for epidemiological research. Data from four NHANES cycles spanning 2011 to 2018 were included. Inclusion criteria for this analysis were: (I) participants aged ≥20 years; (II) availability of complete data for the ZJU index and OAB; and (III) availability of data on all relevant covariates. Participants with missing data for these criteria were excluded, resulting in a final analytic sample of 15,873 individuals (Figure 1). The NHANES protocol was approved by the Institutional Review Board of the National Center for Health Statistics. Ethical approval for this secondary analysis was waived due to the public availability of anonymized data, and all participants provided written informed consent (20). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flowchart of participant selection. NHANES, National Health and Nutrition Examination Survey.

Calculation of ZJU index

The ZJU index was calculated using the formula: ZJU index = FBG (mmol/L) + BMI (kg/m²) + TG (mmol/L) + 3 × ALT (U/L)/AST (U/L) ratio (+2 if female). This composite index integrates multiple metabolic markers, offering a comprehensive assessment of metabolic dysfunction [participants were categorized into quartiles based on the ZJU index distribution (Q1–Q4)].

Evaluation of OAB history

OAB was assessed based on the International Continence Society definition, focusing on urgency urinary incontinence (UUI) and nocturia. Trained researchers used standardized questionnaires to evaluate these symptoms. UUI was assessed with two questions: “In the last 12 months, have you leaked or lost control of urine due to an urge or pressure to urinate and were unable to reach the toilet in time?” and “How frequently does this occur?” Nocturia was evaluated with the question: “In the past 30 days, how many times per night did you typically get up to urinate, from the time you went to bed until you got up in the morning?” The severity of OAB was determined using the modified Overactive Bladder Symptom Score (OABSS), calculated by summing scores for UUI and nocturia. Participants with a total OABSS score of ≥3 were classified as having OAB, while those scoring <3 were considered non-OAB, in line with established criteria (Tables S1,S2).

Covariates and confounding factors

To mitigate potential confounding, a wide range of covariates was included in the analysis. Demographic factors included age, gender, and race/ethnicity. Socioeconomic variables encompassed educational attainment, marital status, and the poverty-income ratio (PIR). Behavioral and lifestyle factors, such as alcohol use, smoking, and physical activity (PA), were also considered. PA was quantified using a standardized scale that reflects activity intensity and duration. Dietary intake data included protein, carbohydrate, and fat consumption. Clinical covariates included hypertension, diabetes, and biochemical measures such as BMI, FBG, TG, and the ALT/AST ratio. These covariates were sequentially adjusted in three models to ensure a robust evaluation of the ZJU index’s association with OAB.

Statistical analysis

The ZJU index was divided into quartiles to explore its association with OAB. Continuous variables were presented as medians with interquartile ranges (IQRs), while categorical variables were described as frequencies and percentages. The Kruskal-Wallis test was applied for continuous data comparisons, and the Rao-Scott Chi-squared test was used for categorical data, ensuring population representativeness. Multivariable logistic regression was conducted to evaluate the association between the ZJU index and OAB risk, with findings reported as odds ratios (ORs) with 95% confidence intervals (CIs). Three models were developed: Model 1 (unadjusted), Model 2 (adjusted for demographic and socioeconomic factors), and Model 3 (further adjusted for clinical and lifestyle factors). Restricted cubic splines (RCS) were employed to examine nonlinear associations, with knots placed at the 5th, 35th, 65th, and 95th percentiles. Subgroup analyses by age and gender were conducted to explore effect modifications, and interaction terms were tested to assess significance. All analyses were performed using R software (version 4.2), applying complex sampling weights to enhance generalizability to the U.S. population. Statistical significance was defined as a two-tailed P value <0.05.

Results

Population characteristics

From the NHANES database (2011–2018), a total of 39,156 participants were initially recruited for the investigation. After excluding individuals without OAB symptom data (n=19,613), those with missing ZJU index data (n=1,256), and participants with missing values for other covariates (n=2,414), the final sample size retained for analysis was 15,873 participants (Figure 1). The clinical characteristics of all participants are summarized, categorized by ZJU index quartiles (Q1–Q4: Q1, ≤33.77; Q2, >33.77 and ≤38.675; Q3, >38.675 and ≤44.473; Q4, >44.473) (Table 1). Higher ZJU index levels correlated with increasing risk factors and metabolic disturbances. Median age rose from 40 years (Q1) to 48 years (Q4, P<0.001), while males peaked at 55.7% in Q3 before decreasing to 47.9% in Q4 (P<0.001). Mexican American representation increased from 4.6% (Q1) to 11.1% (Q4), while non-Hispanic Whites declined from 70.2% to 65.1% (P<0.001). College-educated participants decreased from 69.9% (Q1) to 60.5% (Q4), while high school graduates increased (P<0.001). PIR <1.3 was most common in Q4 (24.1%, P<0.001). Health metrics worsened with higher ZJU levels. Hypertension prevalence rose from 16.2% (Q1) to 48.8% (Q4), and diabetes increased from 1.9% to 24.7% (P<0.001). Sleep difficulties were more frequent in Q4 (38.0%) compared to Q1 (26.1%, P<0.001). Lifestyle trends included decreased alcohol consumption (32.5% in Q1 to 19.7% in Q4, P<0.001), steady smoking rates (peaking at ~46.5% in Q3/Q4, P<0.001), and reduced PA (150 minutes/week in Q1 to 0 in Q4, P<0.001). Protein and fat intake increased slightly, while carbohydrate intake remained stable (P<0.001). ALT/AST levels and BMI rose significantly, with BMI increasing from 22 (Q1) to 37 (Q4, P<0.001). Fasting plasma glucose and TG nearly doubled from Q1 to Q4 (P<0.001). OAB prevalence climbed steadily with ZJU quartiles, rising from 10.9% in Q1 to 23.7% in Q4 (P<0.001). These results highlight the association between higher ZJU index levels and worsening metabolic health, characterized by hypertension, diabetes, OAB, lower PA, and increased BMI.

Correlation between ZJU index and OAB

The relationship between the ZJU index and OAB symptoms is summarized in Table 2. In the unadjusted model (Model 1), higher ZJU quartiles were significantly associated with increased OAB risk. Compared to Q1, the ORs were 1.29 (95% CI: 1.14–1.45, P<0.001) for Q2, 1.76 (95% CI: 1.57–1.98, P<0.001) for Q3, and 2.52 (95% CI: 2.25–2.82, P<0.001) for Q4, showing a clear dose-response relationship (P for trend <0.001). After adjusting for demographic and socioeconomic factors, including age, gender, race, education, marital status, and PIR (Model 2), the association remained significant for Q3 (OR =1.45, 95% CI: 1.28–1.65, P<0.001) and Q4 (OR =2.23, 95% CI: 1.97–2.52, P<0.001), while Q2 showed no significant association (OR =1.08, 95% CI: 0.95–1.23, P=0.23). In the fully adjusted model (Model 3), which accounted for additional factors such as hypertension, diabetes, PA, smoking, drinking, and macronutrient intake, the association remained robust. The ORs for Q3 and Q4 were 1.31 (95% CI: 1.16–1.49, P<0.001) and 1.79 (95% CI: 1.57–2.04, P<0.001), respectively, while Q2 (OR =1.04, 95% CI: 0.92–1.19, P=0.53) remained nonsignificant. The P for trend remained consistent across all models (P<0.001). These findings confirm a strong and consistent association between higher ZJU index levels and increased OAB risk, particularly in Q3 and Q4. The results suggest that the ZJU index is a valuable marker of metabolic and lifestyle factors contributing to bladder dysfunction.

Nonlinear link of ZJU index to OAB

The nonlinear relationship between the ZJU index and OAB symptoms was analyzed using segmented logistic regression and RCS modeling. Segmented logistic regression identified an inflection point at a ZJU index of 40, dividing the relationship into two distinct phases. Below this threshold (<40), the OR for OAB increased gradually, with an OR of 1.18 per standard deviation (SD) increase (95% CI: 1.12–1.25, P<0.001). Above the threshold (≥40), the OR increased more steeply, with a slightly higher value of 1.28 per SD (95% CI: 1.22–1.35, P<0.001), indicating that the ZJU index is a significant predictor of OAB risk in both phases, with a greater impact when the index exceeds 40 (Table 3). All ORs were strictly adjusted for multiple potential confounders including household income ratio, PIR, age, sex, education level, marital status, hypertension, diabetes, current drinking and smoking status, protein intake, carbohydrate intake, fat intake, PA, and ethnicity (Figures S1-S3). The RCS model provides further visualization of the nonlinear trend (P value <0.001, P-nonlinear =0.044) (Figure 2). The curve demonstrates a gradual rise in OAB risk for ZJU index values below 40, followed by a steeper increase above this threshold. The inflection point at 40 highlights a shift in the association between ZJU levels and OAB risk, as supported by the significant P value for nonlinearity (P=0.044). The findings indicate that while the risk of OAB increases at all levels of the ZJU index, the relationship is more pronounced after the threshold of 40. This suggests that higher ZJU index levels are strongly linked to OAB, reflecting metabolic and lifestyle factors that contribute to bladder dysfunction. These results underscore the utility of the ZJU index as a predictive marker for OAB, particularly in populations with higher scores.

Figure 2 Nonlinear correlation between ZJU index and OAB with the RCS function. Model with 4 knots located at 5th, 35th, 65th and 95th percentiles. CI, confidence interval; OAB, overactive bladder; RCS, restricted cubic splines.

Subgroup analysis

We carried out thorough subgroup analyses to evaluate the reliability and consistency of the association between the ZJU index and OAB incidence across a range of demographic categories. The results revealed significant variations across age and gender subgroups (Table 4). Among the overall population (n=15,873), the ZJU index was positively associated with OAB risk, with an adjusted OR of 1.02 per unit increase (95% CI: 1.02–1.03, P<0.001). In the age-specific analysis, individuals aged <50 years (n=7,973) exhibited a stronger association (OR =1.04, 95% CI: 1.03–1.05, P<0.001) compared to those aged ≥50 years (n=7,900; OR =1.02, 95% CI: 1.02–1.03, P<0.001), with a significant interaction (P for interaction =0.001). Similarly, the gender analysis revealed no significant association among males (n=7,816; OR =1.01, 95% CI: 1.00–1.01, P=0.15), while females (n=8,057) demonstrated a stronger association (OR =1.03, 95% CI: 1.02–1.04, P<0.001), with a significant interaction (P for interaction =0.001). These findings highlight the stronger association between the ZJU index and OAB risk among younger individuals (<50 years) and females (Figures 3,4). The clear dose-response relationship across ZJU index quartiles in these subgroups underscores their heightened susceptibility to ZJU-related metabolic and lifestyle factors. These results suggest the importance of considering demographic differences when designing interventions to mitigate OAB risks associated with elevated ZJU index levels.

Figure 3 Subgroup analysis. CI, confidence interval; OR, odds ratio; PA, physical activity; PIR, poverty-income ratio.

Figure 4 Association of ZJU with OAB in total population and stratified by race and ethnicity. Q1, ≤33.77; Q2, >33.77 and ≤38.675; Q3, >38.675 and ≤44.473; Q4, >44.473. OAB, overactive bladder.

Discussion

OAB has a global prevalence of 12% to 17% and is clinically manifested by urinary urgency, typically accompanied by increased frequency and nocturia with or without urge incontinence, in the absence of urinary tract infections or other evident pathologies (2). The risk factors for OAB include metabolic syndrome and hormone deficiencies, among others; however, the path-ophysiology of OAB remains incompletely understood and is often classified as an “idiopathic” condition (4,21). Chronic, refractory OAB can lead to complications such as urinary tract infections, urinary incontinence, depression, falls, and fractures, significantly impairing quality of life and imposing substantial economic burden (22,23). The ZJU index integrates BMI, FBG, TG, and liver enzymes (ALT, AST) to comprehensively assess metabolic dysfunction (7). It has been effectively applied in conditions such as NAFLD, gallstones, and diabetes (10,11). Given its strong association with metabolic syndrome and related factors, the ZJU index holds promise in evaluating the metabolic contributions to the development of OAB.

This study provides new insights by examining the relationship between the ZJU index and OAB in a nationally representative U.S. population. Analysis of data from 15,873 participants revealed a significant association between the ZJU index and the risk of developing OAB. Specifically, there was a positive correlation between the ZJU index and increased risk of OAB, even after adjusting for demographic, socioeconomic, and lifestyle factors (OR =1.79, P<0.001). Additionally, logistic regression and RCS analysis indicated a significant nonlinear relationship between the ZJU index and the occurrence of OAB. Below the inflection point of 40, the risk increase was gradual, whereas it significantly accelerated beyond a ZJU index of 40. Previous studies have similarly identified a nonlinear association between the ZJU index and other conditions such as gallstones and sarcopenia. For example, studies on gallstones (11), have suggested a ZJU index threshold of 40.6, while a threshold of 33 was significant for sarcopenia (12). Beyond these points, the slope of the curve increased, indicating a higher disease risk. Our findings support the utility of the ZJU index in predicting OAB risk and recommend its use in clinical settings.

Although direct evidence linking the ZJU index and OAB is lacking, the ZJU index comprehensively incorporates BMI, FBG, TG, and liver enzymes (ALT/AST), all of which are closely related to the occurrence of OAB. This explains the increased likelihood of OAB in individuals with higher ZJU levels. Multiple previous studies have demonstrated the connection between metabolic syndrome and OAB, particularly highlighting the relationships among obesity, elevated FBG, and high-risk OAB (24,25). Elevated BMI and TG are often indicators of obesity, which significantly increases the risk of nocturia and urinary urgency. A cohort study by Zacche et al. found that obesity (OR =1.09, 95% CI: 1.05–1.13) and a BMI ≥30 kg/m² (OR =1.5, 95% CI: 1.1–2.1) are independent risk factors for OAB (26). The exact mechanisms linking obesity, overweight, and OAB remain unclear; however, some studies suggest that abdominal obesity leads to the accumulation of fat and visceral fat within the abdominal cavity, increasing pressure on the abdomen and bladder, resulting in chronic pelvic ischemia and subsequent reduced bladder blood flow. This can lead to bladder dysfunction and OAB (27,28). Coincidentally, the current β3-adrenergic receptor agonist, mirabegron, used to treat OAB, was initially designed as an anti-obesity drug (29,30), further validating obesity as a risk factor for OAB. Additionally, elevated FBG levels often indicate diabetes and insulin resistance, which are also important factors in OAB. Liu et al. investigated the relationship between type 2 diabetes and OAB and reported that the duration of diabetes is a predictor of OAB (31). In a prospective study by Baytaroglu et al. [2021] involving outpatient patients, higher FBG was identified as a predictor of OAB (OR =1.229, P=0.001), with OAB participants having significantly higher FBG levels (25). Animal studies have shown that glucose metabolism abnormalities lead to pathological changes in the bladder. Hyperglycemia can induce oxidative stress, neuropathy, and vascular damage, impairing detrusor muscle contractility and compliance. Insulin resistance may disrupt the autonomic nervous system, further damaging bladder function (32). Moreover, elevated ALT and AST levels indicate liver dysfunction, and a higher ALT/AST ratio is independently associated with increased risks of NAFLD and liver fibrosis (33,34). Previous research has found that NAFLD and OAB may share common metabolic pathways, where liver dysfunction exacerbates metabolic disturbances, thereby increasing the risk of OAB (19). Therefore, these findings highlight the potential role of the ZJU index in identifying high-risk populations for OAB, such as those with metabolic syndrome and liver dysfunction, and in developing preventive and interventional strategies.

Subgroup analysis revealed that females exhibited a significantly higher risk (OR =1.03, P<0.001), whereas no significant association was found among males (n=7,816; OR =1.01, P=0.15). This suggests that females may be more susceptible to bladder dysfunction associated with metabolic disturbances, consistent with existing epidemiological evidence. In the EpiLUTS (Epidemiology of Lower Urinary Tract Symptoms) survey, participants aged ≥40 years showed a higher prevalence of OAB in females compared to males, with prevalence rates of 27–46% in females and 26–33% in males (35). A 2018 study on adults over 40 years in China reported an overall OAB prevalence of 23.9%, with 21.4% in males and 26.4% in females, increasing with age (36). Factors such as female obesity, menopause, vaginal deliveries, and multiple deliveries contribute to pelvic muscle relaxation, increasing the prevalence of OAB (37). In contrast, diagnosing and treating OAB in males is relatively challenging as its symptoms often overlap with those of benign prostatic hyperplasia (38). Early detection and treatment are particularly important for female patients.

Based on the NHANES cross-sectional study, we reported a significant association between the ZJU index and OAB, confirming that obesity, high BMI, diabetes, and liver disease are risk factors for OAB, with higher prevalence in females. In line with The American Urological Association (AUA) guidelines, behavioral therapies for OAB, such as fluid management, caffeine reduction, PA/exercise, dietary modifications, and mindfulness, offer patients with OAB efficacy, excellent safety, and minimal adverse effects (2). For healthy populations, we recommend that physically inactive individuals engage in more standing and walking to control BMI and high lipid levels, thereby preventing obesity and related diseases (39). For individuals with obesity, diabetes, or liver disease, we recommend controlling weight, blood glucose, and protecting liver function (39). For postpartum women, early pelvic muscle training is advisable. For those already diagnosed with OAB, timely urination diaries, pharmacological or surgical treatments are necessary, along with psychological adjustments (23).

Advantages and limitations of the study

This study has several notable strengths. It is the first to investigate the link between the ZJU index and OAB in a U.S. population, providing new insights into the metabolic factors associated with OAB. The study also demonstrates the ZJU index’s value as a comprehensive tool for metabolic health assessment, showing its superiority over traditional single-indicator methods in identifying OAB risk. The robustness of the findings is further supported by consistent associations across various models and subgroup analyses, even after adjusting for multiple confounders. Additionally, the large, nationally representative sample size and use of complex sampling weights enhance the generalizability and statistical reliability of the results.

However, several limitations exist. The cross-sectional design restricts the ability to establish causality, emphasizing the need for longitudinal studies. As the participants were all from the U.S., the findings may not be applicable to other ethnic, cultural, or healthcare settings. The reliance on self-reported OAB symptoms, without clinical confirmation, introduces potential recall and reporting biases. Lastly, unmeasured confounders, such as urinary tract infections, medication use, or other comorbidities, were not accounted for, which could influence the associations observed. Despite these limitations, the study provides a strong foundation for future research and highlights the potential of the ZJU index as a predictive tool for OAB risk.

Conclusions

This study demonstrated that the prevalence of OAB increases with higher ZJU index levels, with the trend being more pronounced in women. The ZJU index may serve as a useful predictor for assessing OAB risk. Clinicians can leverage health education and targeted interventions to promote self-regulation, guiding patients in preventing metabolic syndrome. Encouraging regular PA, managing weight and BMI, controlling blood lipids, preventing obesity, regulating blood sugar, and protecting liver function can help reduce the risk of OAB. To further validate these findings, additional prospective studies, randomized controlled trials, and research into the underlying mechanisms are needed.

Acknowledgments

We extend our heartfelt gratitude to the staff at the National Center for Health Statistics, a division of the Centers for Disease Control and Prevention (CDC), for their dedicated efforts in designing, collecting, and compiling the NHANES data. Their commitment to creating and maintaining the public database has been invaluable to this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-557/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-557/prf

Funding: This research was supported by the Key Research Projects of the Ministry of Science and Technology, China (No. 2022YFC3602905) and the National Natural Science Foundation of China (Nos. 82170784 and 82370775).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-557/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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