The data for this analysis originated from the National Health and Nutrition Examination Survey (NHANES), conducted between 2005 and 2018. NHANES is a research endeavor administered by the Centers for Disease Control and Prevention (CDC), intended to gather exhaustive information on the health status, dietary habits, and lifestyles of the U.S. populace. It employs a sophisticated, multi-stage stratified sampling technique to collect these data. The protocols employed in NHANES were cleared by the Review Board of the National Center for Health Statistics (NCHS). All participants provided their informed consent. The NHANES database encompasses five key components: demographic information, screening results, dietary records, laboratory test outcomes, and questionnaire responses (15). Additional information on the NHANES database is accessible at: http://www.cdc.gov/nhanes.

In total, 70,190 participants were incorporated into the NHANES dataset spanning from 2005 to 2018. Figure 1 depicts the exclusion criteria, which include: (1) participants with absent OAB data; (2) those lacking information on VAI and LAP; and (3) individuals missing covariate data or below the age of 20. Consequently, a final count of 28,704 samples was retained for the definitive analysis. The procedure for data selection is illustrated in Figure 1.

In this study, VAI and LAP were designated as the exposure variables. NHANES involved the measurement of triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) levels using fasting blood samples, which were collected by healthcare professionals at the Mobile Examination Centers (MECs). At the Mobile Examination Centers (MECs), waist circumference (WC) was assessed by skilled technicians using a tape measure, positioned at the midpoint of the axillary line just above the iliac crest during the end of a normal exhalation, with a precision of 0.1 cm. The calculations for LAP and VAI were gender-specific and based on the following formulas, where TG and HDL were measured in mmol/L, WC in centimeters, and BMI in kilograms per square meter (16).

Overactive bladder (OAB) is a condition primarily characterized by urinary frequency, urgency urinary incontinence (UUI), and nocturia. In this study, standardized questionnaires were administered during face-to-face interviews conducted by trained personnel to collect the necessary data. The severity of UUI was determined by two questions: “In the past 12 months, have you experienced urine leakage or loss of bladder control due to urinary urgency or pressure, and been unable to reach the toilet in time?” and “How frequently does this occur?” Nocturia was assessed on the basis of the following question: “In the past 30 days, how many times did you typically wake up at night to urinate, from the time you went to bed until you got up in the morning?” Ultimately, the severity of OAB for each participant was assessed using the Overactive Bladder Symptom Score (OABSS), where a total score of three or higher was deemed indicative of the presence of OAB (17).

Drawing from the existing literature, the following variables were identified as covariates: gender, age, ethnicity, educational attainment, marital situation, income-to-poverty ratio, smoking habits, alcohol intake, and the occurrence of hypertension or diabetes. Ethnicity was grouped into non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, and other racial categories. Marital situation was categorized as either living alone or being married/in a cohabiting relationship. The educational level is classified into three categories: below high school, high school, or above high school. The poverty-to-income ratios (PIRs) were categorized as <1.5, 1.5–3.5, and >3.5. Smoking and alcohol consumption were determined on the basis of the following questions: “Have you ever smoked at least 100 cigarettes in your lifetime?” and “Do you consume at least 12 alcoholic beverages per year?” (18).

In this study, the NHANES-recommended sample weights were utilized for analysis. Continuous variables were presented as the mean, accompanied by their standard deviations, whereas categorical variables were denoted as percentages. The LAP and VAI data were stratified into quartiles, with the lowest quartile serving as the baseline comparison group. To explore the associations between LAP, VAI, and OAB, multivariable logistic regression models were employed to estimate odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) for each variable. Model 1 served as the unadjusted baseline, without considering any confounding variables. Model 2 incorporated adjustments for sex, age, race, educational level, marital status, and poverty-income ratio (PIR). Model 3 extended these adjustments by further accounting for smoking history, alcohol use, hypertension, and diabetes, in addition to the variables included in Model 2. Subgroup analyses were carried out based on sex, age, race, educational attainment, smoking history, alcohol intake, and diabetes status. All statistical assessments were executed using EmpowerStats version 4.2 software, with all tests conducted on a two-tailed basis. Statistical significance was established at a p-value <0.05.

Table 1 summarizes the baseline characteristics of the participants. On the basis of the data analysis of 28,704 participants who met the inclusion and exclusion criteria, 5,776 (20.12%) were diagnosed with overactive bladder syndrome (OAB), and 22,958 (79.88%) did not have OAB. Among these participants, 62.88% were women, with a mean age of 47.21 years. Compared with participants without OAB, those with OAB were more likely to be female, older, have a larger waist circumference, live alone, have a lower educational level, be diagnosed with diabetes, and have higher LAP and VAI values (all p < 0.05).

The results of the multivariable linear regression analysis are shown in Table 2. The results indicate a notable link between both LAP and VAI and the likelihood of developing OAB. We analyzed three different models and categorized the LAP and VAI indices into four quartiles. According to the unadjusted model (Model 1), higher LAP and VAI values were associated with an increased incidence of OAB. The odds ratios (ORs) for the highest quartile of LAP (OR = 2.407, 95% CI: 2.150, 2.695) and the OR for the highest quartile of VAI (OR = 1.726, 95% CI: 1.539, 1.936) were both significantly greater than those for the lowest quartile, with increases of 141 and 72%, respectively. After fully adjusting for all confounding factors (Model 3), the significant associations between LAP, VAI, and OAB persisted. The OR for the highest quartile of LAP (OR = 1.555, 95% CI: 1.376, 1.758) was 56% higher than that for the lowest quartile, with a trend p-value of <0.0001. The OR for the highest quartile of the VAI (OR = 1.225, 95% CI: 1.084, 1.384) was 22% higher than that for the lowest quartile, with a trend p-value of <0.0001.

To further visualize the relationships among the LAP, VAI, and OAB, we performed smooth curve fitting with Model 3. Figure 2 illustrates the nonlinear associations among the LAP, VAI, and OAB. A threshold effect analysis was subsequently conducted to clarify the associations (Table 3). The turning point for LAP was identified at 86.234, with a likelihood ratio <0.001, indicating that when LAP levels drop below 86.23, each unit increase in LAP is associated with a 0.6% increase in OAB risk. When LAP levels exceeded 86.234, the correlation between LAP and OAB risk diminished, suggesting that further increases in LAP did not lead to a statistically significant increase in OAB risk. The results for the VAI exhibited a similar pattern, with a turning point at 1.49 (likelihood ratio 0.025), suggesting that the VAI had different effects on OAB risk below and above this threshold.

To evaluate the stability of the associations among the VAI, LAP, and OAB, additional subgroup analyses were performed. Subgroup analyses were stratified by sex, age <60 years, education level, smoking status, alcohol consumption, and the presence of diabetes. The results of these subgroup analyses, adjusted for all confounding factors, are presented in Table 4. The associations between LAP, VAI, and OAB were consistent across all subgroups.

It is worth noting that there were notable interactions detected among LAP, VAI, as well as gender and age, with statistical significance at p < 0.05. A statistically significant positive correlation was found in women, but this association was not observed in men, indicating that increased visceral fat accumulation may increase the likelihood of OAB in women. Additionally, significant interactions between LAP, VAI, and age <60 years were found, whereas the results became nonsignificant in participants aged ≥60 years, suggesting that higher levels of LAP and VAI may increase the risk of OAB in individuals aged <60 years.

Our study boasts several notable strengths. Leveraging the NHANES database from the United States, a cross-sectional approach was employed, benefiting from its highly dependable data owing to stringent quality assurance and standardized methodologies for data acquisition. Furthermore, the utilization of LAP and VAI as indicators of visceral fat distribution offers simplicity, cost-effectiveness, and enhanced precision compared to conventional metrics like WC and BMI, which may be prone to inaccuracies. These indices possess the potential to aid physicians in tailoring more individualized treatment strategies for patients, based on their specific LAP and VAI scores.

Although our study has identified a possible correlation between LAP, VAI, and OAB, it is important to acknowledge several constraints. Primarily, the cross-sectional design of the study prevents the establishment of a definitive causal link between LAP, VAI, and OAB. Moreover, this study only examines their statistical correlation and lacks substantive evidence from clinical research to support such associations. Secondly, the diagnosis of OAB was largely reliant on self-reported questionnaire responses, which could potentially introduce bias stemming from recall inaccuracies. Lastly, and crucially, the study’s survey data is predominantly from the U.S. population, necessitating further validation to ascertain the generalizability of these findings to populations in diverse geographical regions.