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. 2025 Aug 6;15:28797. doi: 10.1038/s41598-025-11401-3

Depression mediates the association between lipid accumulation products and overactive bladder

Caijuan Huang 1,#, Ying Xu 1,#, Yibing Guan 2, Jingjing Liu 1, Haiping Yang 1,✉
PMCID: PMC12328613  PMID: 40770007

Abstract

The relationship between lipid accumulation products (LAP) and overactive bladder (OAB) has not been explored. This study aims to investigate the relationship between LAP and OAB and the mediating role of depression. Data from seven cycles of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 were used to analyze the relationship between LAP and OAB. The association between LAP and OAB was estimated using multivariable logistic regression models, and the dose-response relationship was estimated using restricted cubic splines (RCS). In addition, subgroup analysis was also performed. Moreover, important factors associated with OAB were identified by Random Forest (RF). Finally, the extent to which depression mediates the association between LAP and OAB was investigated. A total of 13,861 participants with an average age of 49.65 years were enrolled, including 6,869 females (50%), of which 2,739 were diagnosed with OAB. The age-adjusted prevalence of OAB gradually increased among the quartile groups of LAP. In the fully adjusted model, there was a significant positive association between Ln(LAP) and OAB [adjusted odds ratio (AOR): 1.158, 95% CI: 1.073–1.251, P < 0.001]. In the categorical model of LAP, compared with the lowest quartile group of LAP, the prevalence of OAB in the highest quartile group significantly increased after adjustment (AOR: 1.320, 95% CI: 1.105–1.578, P = 0.002). The trend test (P for trend < 0.001) and RCS (P for nonlinearity = 0.196) consistently indicated a linear relationship between LAP and OAB. Subgroup analysis suggested that female gender was an important interaction variable. RF indicated that LAP was the most important factor associated with OAB other than age. Furthermore, in the mediation analysis, depression explained approximately 10% of the association between LAP and OAB. Our research results indicate that there is a linear positive association between LAP and OAB, and there is a significant interaction between gender and LAP. In addition, depression is an important intermediate factor in the association between LAP and OAB.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-11401-3.

Keywords: Overactive bladder, Lipid accumulation products, Depression, NHANES, Cross-sectional study, Mediation analysis.

Subject terms: Epidemiology, Risk factors

Introduction

Overactive bladder (OAB) represents a prevalent urinary system disorder characterized by symptoms such as urgency and frequency of urination, and nocturia, which seriously affects the quality of life of millions of patients worldwide1,2. Earlier, it was estimated that the prevalence of OAB among both males and females in the U.S. exceeded 16%3. Recent studies have shown that the overall prevalence of OAB among American males has increased significantly from 2005 to 20204. OAB not only causes significant psychological stress and social isolation but also reduces work efficiency and imposes a considerable burden on the medical system5.

Since it is difficult to determine the underlying pathology of OAB development in most patients, it is often labeled as “idiopathic”6. Some researchers have classified OAB based on pathophysiological elements, including Metabolic syndrome (MetS) subtype, affective disorders subtype, sex hormone deficiency subtype, urinary microbiota subtype, functional gastrointestinal disorders subtype, and autonomic nervous system dysfunction subtype7,8. Multiple previous studies have confirmed that obesity indicators in different dimensions, such as body mass index (BMI), weight-adjusted waist index (WWI), body roundness index (BRI), and waist circumference (WC), are related to the prevalence of OAB9–13. Although the specific mechanism has not been revealed yet, abdominal fat accumulation can increase abdominal pressure and exert continuous pressure on the bladder, which may lead to abnormal bladder function and increase the risk of OAB14. However, these indicators are relatively rough and controversial and cannot reflect the details of visceral and subcutaneous fat distribution15–18. Lipid accumulation products (LAP) combines body measurement indicators and blood lipids, which can reflect the anatomical and physiological obesity characteristics of the human body19. LAP has been proven to be a clinical predictor of MetS, the performance of which is superior to that of visceral adiposity index (VAI), triglyceride and glucose index (TyG), and waist triglyceride index (WTI)20. Insulin resistance is known to be a risk factor for OAB, and patients with the Mets subtype of OAB have a poor response to conventional treatment. Therefore, exploring the relationship between LAP and OAB may bring good news to these potential populations21,22. In addition, epidemiological studies support the role of sex hormone deficiency in the occurrence of OAB; androgen, estrogen, and progesterone deficiencies all affect lower urinary tract function to varying degrees23–25. Higher LAP is associated with testosterone deficiency, and LAP is a better predictor of testosterone deficiency than the triglyceride-glucose index (TyG)26. Therefore, from the perspective of sex hormones, LAP is very likely to be an effective predictor of OAB. Emotional strain and a previous history of anxiety or depression could potentially serve as risk elements for developing OAB in females27, and individuals with a higher LAP index may have a greater risk of depression28,29. Based on these research backgrounds, it is speculated that LAP is associated with OAB in multiple aspects of health.

This study aims to deeply explore the association of LAP with OAB by utilizing the NHANES dataset and attempts to reveal the mediating role of depression in this association.

Methods

Study population

NHANES is a continuous national cross-sectional study conducted every two years, the target population of which is the non-institutionalized civilian residents in the United States. All NHANES protocols were approved by the Ethics Review Committee of the National Center for Health Statistics of the United States, and each participant provided written informed consent. All methods were performed in accordance with the relevant guidelines and regulations. In this study, the data were acquired from seven non-repetitive cycles (2005–2018) of NHANES.

The inclusion and exclusion processes are shown in Fig. 1. In the seven cycles, there were a total of 70,190 participants, among whom 28,047 were under 18 years old, 590 were excluded due to pregnancy, 24,143 due to lack of LAP, 2,401 due to lack of OAB, and 1,148 due to lack of covariables. Finally, a total of 13,861 participants met the inclusion criteria for the study.

Fig. 1.

Fig. 1

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Flowchart of participant selection. OAB overactive bladder, LAP lipid accumulation products.

LAP (exposure variable)

The exposure variable in this study was LAP. According to Kahn et al.19, LAP was calculated as (waist circumference [cm] − 65) multiplied by (triglyceride concentration [mmol/L]) for males, and (waist circumference [cm] − 58) multiplied by (triglyceride concentration [mmol/L]) for females.

Definition of OAB (outcome variable)

Consistent with the diagnostic methods of previous NHANES - related studies, OAB was defined as urge urinary incontinence (UUI) and nocturia30. After collecting relevant symptoms, participants were scored according to the Overactive Bladder Symptom Score (OABSS) standard, and those with a score of 3 or above were classified as having OAB31–33. The scoring scheme is detailed in Supplementary Fig. 1.

Depression (mediator variable)

Based on previous research experience, the Patient Health Questionnaire-9 (PHQ-9) was used to diagnose depression34. According to extensive studies on the precision of PHQ-9, the participants with a total score of 10 or above were considered to have depression35.

Definition of covariables

All covariables included demographic, socioeconomic, lifestyle, and health-related characteristics. The following demographic and socioeconomic variables were collected: gender (male/female), age (continuous), race (non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic, and multiracial), educational background (college degree or above and others), marital status (married and others), and poverty income ratio (PIR) (< 1.3, 1.3–3.5, > 3.5, and not available). The following lifestyle and health-related variables were collected: smoking status (yes/no), drinking status (yes/no), physical activity (inactive/moderate/active/not available), dietary health (defined by the Healthy Eating Index − 2015), body mass index (BMI) (< 25 kg/m2, 25–30 kg/m2, > 30 kg/m2), and the concentration of aspartate transaminase (AST) (U/L). Besides, health-related characteristics were considered: cancer (no/yes), diabetes (no/yes/borderline), hypertension (no/yes), chronic kidney disease (CKD) (no/yes), cardiovascular disease (CVD) (no/yes), liver disease (no/yes), and hyperlipidemia (no/yes).

Statistical analysis

For categorical variables, chi-square tests were employed for comparison between groups, while for continuous variables, t-tests or U-tests were used for comparison between groups. The age-standardized prevalence estimates and 95% confidence intervals (CI) of OAB for quartile groups of LAP were calculated. The adjusted odds ratios (OR) and 95% CI were estimated using multivariable logistic regression models to analyze the relationship between LAP and OAB. Four logistic regression models were constructed in this study: The crude model (Model 0) did not adjust for any variables. Model 1 adjusted for age, gender, race, marital status, PIR, and educational level. Model 2 further adjusted for drinking status, smoking status, physical activity, BMI, and HEI-2015 based on Model (1) Model 3 further adjusted for cancer, diabetes, hypertension, CKD, CVD, liver disease, and hyperlipidemia based on Model (2) Stratified analysis was performed to evaluate the effect of LAP on the prevalence of OAB in different subgroups of demographic, socioeconomic, lifestyle, and health-related characteristics. The dose-response relationship between LAP and OAB was estimated by restricted cubic spline (RCS) regression model. To determine the importance of LAP in association with OAB, random forest (RF) analysis was performed. In the RF model, the number of trees was set to 500. When splitting a single tree, the number of random features was taken as the square root of the total number of features. The default value of 1 was retained as the minimum number of samples at leaf nodes to fully capture the data associations, and bootstrap sampling was enabled to enhance the generalization ability. For the remaining parameters (such as the maximum number of nodes), no restrictions were imposed to ensure that the trees could grow freely. Moreover, receiver operating characteristic (ROC) curve was constructed, and the ability of LAP to predict OAB was evaluated by the Area under the Curve (AUC). Finally, a mediation analysis was conducted, during which two logistic regression models, i.e., “Depression ~ Ln(LAP) + covariables” and “OAB ~ Ln(LAP) + depression + covariables”, were constructed. Two algorithms, Bayesian and Bootstrap, were adopted for 1000 simulations respectively to estimate the mediation proportion of depression in the association between LAP and OAB.

Two sensitivity analyses were conducted. In Sensitivity Analysis 1, additional adjustments were made for water intake, caffeine intake, and medication use. It should be noted that for the treatment of OAB, three medications, namely Tolterodine, Solifenacin, and Mirabegron, were taken into account. In Sensitivity Analysis 2, BMI was further divided into four categories (< 18.5 kg/m², 18.5–25 kg/m², 25–30 kg/m², and > 30 kg/m²), for which the model was adjusted.

The statistical analysis in this paper was implemented through the X64 version of R4.3.1.

Results

Baseline characteristics

A total of 13,861 participants over 18 years old were included (Fig. 1), among whom 2,739 were diagnosed with OAB. The average age of all participants was 49.65 years, and there were 6,869 females (50%). Table 1 lists the variable characteristics of the participants according to the presence or absence of OAB. OAB was associated with LAP, gender, age, race, educational level, marital status, economic status, drinking, physical activity, BMI, cancer, diabetes, hypertension, CVD, CKD, hyperlipidemia, and liver problems (P < 0.05 for all). However, Table 1 also shows that there was no significant association between OAB and smoking, HEI-2015, and AST levels (P > 0.05 for all). More specifically, OAB patients had higher LAP levels and were more likely to be older females, black, less educated, unmarried, poorer, non-drinkers, less physically active, and obese. Additionally, OAB patients were also more likely to be diagnosed with cancer, diabetes, hypertension, CVD, CKD, hyperlipidemia, and liver diseases.

Table 1.

Baseline characteristics of the study population(n = 13,861).

Total, N = 13,861 OAB
non-OAB, N = 11,122 OAB, N = 2739 P-value
LAP, Median (Q1,Q3) 42.74(23.76, 73.53) 40.16 (22.3, 70.29) 53.07(31.21, 85.91) < 0.001
ln (LAP), Mean (SD) 3.70 (0.86) 3.65 (0.87) 3.93 (0.78) < 0.001
Sex, n (%) < 0.001
Female 6869 (50) 5277 (47) 1592 (58)
Male 6992 (50) 5845 (53) 1147 (42)
Age, Mean (SD) 49.65 (17.53) 47.17 (17.12) 59.74 (15.43) < 0.001
Race, n (%) < 0.001
Mexican American 2197 (16) 1767 (16) 430 (16)
Non-Hispanic Black 2783 (20) 2048 (18) 735 (27)
Non-Hispanic White 6136 (44) 5011 (45) 1125 (41)
Other Hispanic 1380 (10) 1097 (10) 283 (10)
Other Race 1365 (10) 1199 (11) 166 (6)
Education, n (%) < 0.001
Less than College 6531 (47) 4936 (44) 1595 (58)
College or above 7330 (53) 6186 (56) 1144 (42)
Marry, n (%) < 0.001
No 5461 (39) 4220 (38) 1241 (45)
Yes 8400 (61) 6902 (62) 1498 (55)
Poverty, n (%) < 0.001
<1.3 3822 (28) 2887 (26) 935 (34)
1.3–3.5 4908 (35) 3890 (35) 1018 (37)
>3.5 4047 (29) 3497 (31) 550 (20)
Not available 1084 (8) 848 (8) 236 (9)
Drinking, n (%) < 0.001
No 4122 (30) 3006 (27) 1116 (41)
Yes 9739 (70) 8116 (73) 1623 (59)
Smoke, n (%) 0.226
No 11,009 (79) 8857 (80) 2152 (79)
Yes 2852 (21) 2265 (20) 587 (21)
Physical activity, n (%) < 0.001
<700 2697 (19) 2135 (19) 562 (21)
700–2400 3167 (23) 2658 (24) 509 (19)
>=2400 4616 (33) 3922 (35) 694 (25)
Not available 3381 (24) 2407 (22) 974 (36)
HEI-2015, Median (Q1,Q3) 49.94(40.58, 59.89) 49.84(40.62, 59.85) 50.18(40.5, 59.99) 0.509
Caffeine intake (mg/day), Mean (SD) 151.65 (195.35) 152.08 (189.04) 149.91 (219.11) 0.602
Water intake (g/day), Mean (SD) 1,056.86 (1,138.60) 1,063.80 (1,123.11) 1,028.67 (1,199.23) 0.148
BMI, n (%) < 0.001
<25 kg/m2 4030 (29) 3456 (31) 574 (21)
25 ~ 30 kg/m2 4618 (33) 3815 (34) 803 (29)
>=30 kg/m2 5213 (38) 3851 (35) 1362 (50)
Cancer, n (%) < 0.001
No 12,591 (91) 10,269 (92) 2322 (85)
Yes 1270 (9) 853 (8) 417 (15)
Diabetes, n (%) < 0.001
No 8451 (61) 7264 (65) 1187 (43)
Yes 2880 (21) 1893 (17) 987 (36)
Borderline 2530 (18) 1965 (18) 565 (21)
Hypertension, n (%) < 0.001
No 7997 (58) 6983 (63) 1014 (37)
Yes 5864 (42) 4139 (37) 1725 (63)
CVD, n (%) < 0.001
No 12,364 (89) 10,229 (92) 2135 (78)
Yes 1497 (11) 893 (8) 604 (22)
CKD, n (%) < 0.001
No 11,470 (83) 9580 (86) 1890 (69)
Yes 2391 (17) 1542 (14) 849 (31)
Hyperlipidemia, n (%) < 0.001
No 3869 (28) 3343 (30) 526 (19)
Yes 9992 (72) 7779 (70) 2213 (81)
Liver diseases, n (%) < 0.001
No 13,285 (96) 10,711 (96) 2574 (94)
Yes 576 (4) 411 (4) 165 (6)
AST, Mean (SD) 25.58 (18.69) 25.43 (17.41) 26.18 (23.14) 0.059
Drug use, n (%) < 0.001
No 5848 (42) 5233 (47) 615 (22)
Other drugs 7953 (57) 5867 (53) 2086 (76)
OAB drugs 60 (0) 22 (0) 38 (1)
BMI (four categories), n (%) < 0.001
<18.5 kg/m2 223 (2) 197 (2) 26 (1)
18.5 ~ 25 kg/m2 3807 (27) 3259 (29) 548 (20)
25 ~ 30 kg/m2 4618 (33) 3815 (34) 803 (29)
>=30 kg/m2 5213 (38) 3851 (35) 1362 (50)
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Data were presented as number (percentages) or means ± standard deviation or median (interquartile range). OAB overactive bladder, LAP lipid accumulation products, DM diabetes, BMI body mass index, Kg kilogram, M2 square of meters, CVD cardiovascular diseases, CKD chronic kidney disease, AST aspartate transaminase, SD standard deviation.

Independent association of LAP with OAB

Figure 2 shows the age-standardized prevalence of OAB in the quartiles of LAP. The prevalence of OAB gradually increased among the four groups of LAP, which was 10.85% in the lowest quartile group (Q1) of LAP, 13.41% in Q2 group, 15.38% in Q3 group, and 18.72% in the highest quartile group (Q4).

Fig. 2.

Fig. 2

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Age-adjusted prevalence of OAB in different levels of LAP. Numbers at the top of the bars represent the weighted percentage. Bar whiskers represent the 95% confidence level. OAB Overactive bladder, LAP lipid accumulation products.

Table 2 shows the results of logistic regression of LAP and OAB. Due to the non-normal distribution of LAP, it was transformed by natural logarithm. The results were presented in a way that Ln(LAP) increased by one unit. In the crude model, Ln(LAP) was significantly associated with OAB [OR = 1.485, 95% CI: 1.411–1.563, P < 0.0001]. After adjusting for demographic factors and socioeconomic factors, the association between Ln(LAP) and OAB was weakened [AOR = 1.348, 95% CI: 1.271–1.429, P < 0.0001]. After further adjusting for lifestyle and physical indicators, this association was further weakened (AOR: 1.248, 95% CI: 1.164–1.338, P < 0.0001). In the fully adjusted model, Ln(LAP) was still positively associated with OAB (AOR: 1.158, 95% CI: 1.073–1.251, P < 0.001). To better reveal the changing trend of OAB prevalence with LAP, LAP was divided into four groups. In the fully adjusted model with covariables, compared with Q1 group, there was no significant change in the prevalence of OAB in Q2 group (AOR: 0.996, 95% CI: 0.853–1.164, P = 0.960) and Q3 group (AOR: 1.142, 95% CI: 0.968–1.347, P = 0.116), while the prevalence of OAB in Q4 group significantly increased (AOR: 1.320, 95% CI: 1.105–1.578, P = 0.002).

Table 2.

Association between LAP and OAB.

Model 0 Model 1 Model 2 Model 3
OR [95%CI] P-value OR [95%CI] P-value OR [95%CI] P-value OR [95%CI] P-value
ln [LAP] 1.485[1.411,1.563] < 0.0001 1.348[1.271,1.429] < 0.0001 1.248[1.164,1.338] < 0.0001 1.158[1.073,1.251] < 0.001
LAP Quartiles
Quartile 1 Reference Reference Reference Reference
Quartile 2 1.513[1.324,1.729] < 0.0001 1.128[0.979,1.302] 0.097 1.019[0.876,1.186] 0.810 0.996[0.853,1.164] 0.960
Quartile 3 2.074[1.825,2.359] < 0.0001 1.420[1.237,1.632] < 0.0001 1.206[1.031,1.413] 0.020 1.142[0.968,1.347] 0.116
Quartile 4 2.475[2.183,2.810] < 0.0001 1.856[1.619,2.130] < 0.0001 1.511[1.285,1.779] < 0.0001 1.320[1.105,1.578] 0.002
P for trend < 0.0001 < 0.0001 < 0.0001 < 0.001
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Model 0 does not adjust for any confounding factors; Model 1: Adjust for age, sex, race, marital status, poverty income ratio, educational level. Model 2 further adjusts for drinking status, smoking status, healthy diet, body mass index, and physical activity. Model 3 further adjusts for cancer, diabetes, hypertension, chronic kidney disease, cardiovascular disease, liver disease, hyperlipidemia, and AST level. OR odds ratio, CI confidence interval, OAB overactive bladder, LAP lipid accumulation products, AST aspartate transaminase.

Subgroup analysis

As shown in Fig. 3, there was a significant multiplicative interaction between gender and LAP in the subgroup analysis (P for interaction < 0.001). Specifically, the association between LAP and OAB was stronger in the female subgroup (AOR: 1.206, 95% CI: 1.076–1.352, P = 0.001), whereas it was not significant in the male subgroup (AOR: 0.956, 95% CI: 0.851–1.075, P = 0.457).

Fig. 3.

Fig. 3

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Subgroup analysis and multiplication interaction tests of the association between LAP and OAB. In subgroup analysis, it was adjusted for sex, age, race, educational level, partner status, poverty income ratio, smoking, alcohol consumption, body mass index, weekly exercise level, dietary health, hypertension, cardiovascular disease, chronic kidney disease, liver disease, cancer, diabetes, hyperlipidemia, and AST level. LAP lipid accumulation products, OAB overactive bladder, DM diabetes, BMI body mass index, Kg kilogram, M2 square of meters, CVD cardiovascular diseases, CKD chronic kidney disease, AST aspartate transaminase, OR odds ratio, CI confidence interval.

Dose-response effect between LAP and OAB

To better understand the changing trend of OAB prevalence with increasing LAP, RCS analysis was performed. As shown in Fig. 4, after fixing the median value of LAP as the reference point, the curve showed an overall linear increase (P for nonlinearity = 0.196). Basically consistent with the entire population, the curve for females showed an overall linear increase (P for nonlinearity = 0.510), but it was steeper.

Fig. 4.

Fig. 4

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Nonlinear analysis of the association between LAP and OAB. In the restricted cubic spline analysis, it was adjusted for sex, age, race, educational level, partner status, poverty income ratio, smoking, alcohol consumption, healthy diet, weekly exercise level, dietary health, body mass index, hypertension, cardiovascular disease, chronic kidney disease, liver disease, cancer, diabetes, hyperlipidemia, and AST level. LAP lipid accumulation products, OAB overactive bladder, AST aspartate transaminase, OR odds ratio, CI confidence interval.

Random forest (RF)

In addition, LAP was included together with all covariables in the RF model to determine the factors most closely related to OAB. The results of RF analysis are shown in Fig. 5. In the ranking based on the Gini index, LAP was the most important factor other than age. The accuracy-based ranking results indicated that LAP ranked third. These results suggested that LAP was an important factor in predicting OAB.

Fig. 5.

Fig. 5

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Variable importance according to the random forest analysis.

ROC analysis

Supplementary Fig. 2 demonstrates the accuracy of Ln(LAP) in predicting OAB. In the unadjusted model, the AUC of Ln(LAP) was 0.596 (95% CI: 0.585–0.608; P < 0.001) (Supplementary Fig. 2A); in the model adjusted for demographic factors, the AUC of Ln(LAP) was 0.751 (95% CI: 0.742–0.761; P < 0.001) (Supplementary Fig. 2B); in the model further adjusted for factors such as lifestyle, the AUC of Ln(LAP) was 0.755 (95% CI: 0.745–0.764; P < 0.001) (Supplementary Fig. 2C); and in the fully adjusted model, the AUC of Ln(LAP) was 0.765 (95% CI: 0.755–0.774; P < 0.001) (Supplementary Fig. 2D).

Mediating role of depression between LAP and OAB

The results of the mediation analysis are shown in Fig. 6. After adjusting for confounding factors, Ln(LAP) was positively associated with the prevalence of depression (P < 0.0001); after adjusting for Ln(LAP) and other confounding factors, depression was significantly positively associated with OAB. Figure 6A shows that the mediation proportion of depression estimated according to the Bayesian algorithm was 9.25% (P < 0.0001). Figure 6B shows that the mediation proportion of depression estimated according to the Bootstrap algorithm was 11.4% (P = 0.004).

Fig. 6.

Fig. 6

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Mediation analysis of depression in the association between LAP and OAB. (A) Bayesian algorithms was used to estimate the proportion of mediation effect. (B) Bootstrap was used to estimate the proportion of mediation effect. LAP lipid accumulation products, OAB overactive bladder.

Sensitivity analysis

In Model 3 of Sensitivity Analysis 1, further adjustments were made for water intake, caffeine intake, and medication use. As shown in Supplementary Table 1, Ln(LAP) was independently and positively associated with OAB (OR: 1.145, 95% CI: 1.060–1.237, P < 0.001). In Model 3 of Sensitivity Analysis 2, BMI was further classified into four categories. As shown in Supplementary Table 2, Ln(LAP) was still independently and positively associated with OAB (OR: 1.112, 95% CI: 1.030–1.200, P = 0.007). Overall, the sensitivity analyses further enhanced the reliability of our conclusions.

Discussion

In this cross-sectional study, a total of 13,861 participants were included, and the association between LAP and OAB was evaluated among non-institutionalized residents in the United States. The age-adjusted prevalence of OAB showed a gradient increase with the increase of LAP. After fully adjusting for confounding factors such as demographic characteristics, lifestyle, and comorbidities, there was a linear positive association between LAP and the prevalence of OAB. In addition, our subgroup analysis showed that the association between LAP and OAB was stronger in the female subgroup. In RF analysis, LAP was the most important factor for OAB other than age. Furthermore, our mediation analysis also showed that depression was a significant mediating factor in the association between LAP and OAB.

The occurrence of OAB involves multiple mechanisms, including but not limited to abnormal detrusor function, urothelial dysfunction, autonomic nervous system dysfunction, metabolic syndrome, sex hormone deficiency, changes in the urinary microbiota, and so on6. Therefore, some researchers have classified OAB based on pathophysiological factors, including the MetS subtype, the affective disorders subtype, the sex hormone deficiency subtype, etc7,8. In the myogenic hypothesis, detrusor overactivity can be attributed to abnormal electrical coupling of smooth muscle cells. The urotheliogenic hypothesis attributes urgency to the abnormal sensory function of the urothelium. The neurogenic hypothesis suggests that the disorder of ascending afferent signals from the bladder and the dysfunction of the control area of micturition reflex jointly lead to the occurrence of OAB36.

Obese people usually have multiple physiological changes that may affect lower urinary tract function. There is direct evidence revealing the relationship between obesity and OAB. Multiple obesity indicators, including BMI, WWI, BRI, and WC, are related to the prevalence of OAB9–13. Although the specific mechanism has not been revealed yet, abdominal fat accumulation can increase abdominal pressure and exert continuous pressure on the bladder, which may lead to abnormal bladder function and increase the risk of OAB14. An earlier study analyzed four anthropometric indicators and found that LAP was a better predictor for identifying individuals with MetS in the adult American population in a clinical setting20. Many studies have demonstrated the association between MetS and OAB, although initially it was believed that this association was attributed to benign prostatic hyperplasia or chronic prostatitis37. However, more and more evidence shows that the incidence of OAB is the same in males and females with MetS37,38. In MetS patients, OAB might possess its unique pathophysiology, which could be due to the increase in sensory afferents to the trigone and bladder neck. Additionally, it could also be attributed to oxidative stress, systemic inflammation, and insulin resistance. These factors can contribute to chronic pelvic ischemia and urothelial dysfunction37,38. Affective disorders may also serve as a bridge between LAP and OAB. Two existing studies have pointed out that individuals with a higher LAP index may have a greater risk of depression28,29. Emotional strain and a previous history of anxiety or depression could potentially serve as risk elements for developing OAB in females27. In our mediation analysis, depression explained approximately 10% of the association between LAP and OAB, which is not unexpected considering that the limbic region of the brain centrally processes afferent impulses. Researchers have conducted a comprehensive assessment of the temporal connection between emotional symptoms and OAB-related manifestations, uncovering the influence of emotional strain and affective disorders on the natural course of OAB27. Furthermore, it is highly likely that there is a bidirectional relationship between affective disorders and OAB. Common underlying biological mechanisms likely drive the simultaneous occurrence of both diseases. Corticotropin-releasing factor (CRF) could potentially be a common pathophysiological element for OAB and depression39. In an animal experiment, after using a type 1 CRF receptor antagonist, a decrease in serum CRF levels was observed, along with an improvement in depression-induced OAB40. Serotonin depletion is hypothesized to be another common pathophysiological candidate factor for depression and OAB. Given the well-established role of serotonin in affective disorders, some experiments have shown that a decrease in serotonin levels in the central nervous system is accompanied by increased frequency of urination and detrusor overactivity41,42. Sex hormone levels may also be an intermediate medium between LAP and OAB. It has been confirmed that LAP has excellent predictive ability for testosterone deficiency43. Experimental studies have shown that testosterone can reduce detrusor excitability, improve bladder wall fibrosis, and may have an impact on the release of urothelial mediators24,25,44. Besides, testosterone replacement therapy has been proven to be able to optimize the lower urinary tract symptoms of male patients45.

However, the strength of the association between LAP and OAB is inconsistent between males and females, which increases among female participants. In fact, the relationship between OAB and sex hormone deficiency is more definite in females. In clinical practice, nearly 70% of incontinence episodes in female patients are related to their last menstruation23. Estrogen deficiency may mediate female urethral symptoms. Studies have shown that estrogen deficiency activates Rho kinase, which in turn increases detrusor contraction23,46. Intravaginal estrogen intervention may improve urinary incontinence, especially UUI47. A randomized controlled trial showed that estradiol has a similar therapeutic effect as oxybutynin in postmenopausal OAB patients48. Studies have shown that estrogen can also reduce the breakdown of abdominal fat and inhibit adipogenesis by inhibiting the expression of adipocyte marker genes, thereby reducing the accumulation of abdominal fat49. In a menopausal rat model, estrogen deficiency leads to weight gain and abdominal fat accumulation, which can be improved by estrogen treatment50. LAP, calculated using triglyceride and WC, has become an emerging indicator for evaluating obesity and may be affected by estrogen. In the future, it is necessary to conduct more in-depth research on this topic.

Strengths and limitations

As far as we know, this is the first study to explore the association between LAP and OAB in the American population. In this study, a large sample size was used, confounding factors were comprehensively considered in multiple dimensions, and a stratified analysis was conducted on participants. In addition, a mediation analysis was also carried out, which to some extent revealed the mechanism of the association between LAP and OAB. Finally, this study also explored the dose-response relationship and identified LAP as an important predictor of OAB through RF.

Nevertheless, some limitations of this study need to be acknowledged. First, as this is a cross-sectional study, the causal relationship between OAB and LAP cannot be determined. Longitudinal studies are necessary to verify the effectiveness of LAP as a predictive marker for OAB. Second, the diagnosis of OAB depends on OABSS, and the collection of symptoms through questionnaires will inevitably be affected by recall bias. Third, the application of the mediation model in cross-sectional studies indeed has limitations in terms of its effectiveness, but it is not entirely without value. The mediation model can help researchers explore potential action pathways by quantifying the relationships between variables. Even in the absence of a clear temporal sequence, it can provide an important basis for generating hypotheses and clues for subsequent research. From a statistical perspective, mediation analysis evaluates the role of the mediator variable in the relationship between the independent variable and the dependent variable based on regression models after controlling for other covariates. In this study, although the temporal sequence among LAP, depression, and OAB cannot be determined, mediation analysis has allowed for an understanding of the extent to which depression may play a role in the relationship between LAP and OAB, providing a reference for analyzing the associative mechanism among the three. Fourth, reverse causality is indeed possible in this study. OAB may indeed affect the prevalence of depression in reverse. Some studies have shown that nearly one-fourth of OAB patients suffer from depression. Moreover, OAB patients with depression have reported more severe urinary incontinence symptoms, greater distress, and a more significant impact on their quality of life compared with those without depression. Therefore, there may be a common pathogenic mechanism between the two51,52. Clinical workers have also been aware of the need for a comprehensive treatment approach from both the psychological and urinary system dimensions53,54. Fifth, unmeasured confounding factors are highly likely to interfere with the research results. For example, chronic stress, as an unmeasured factor, may lead to a continuous increase in the cortisol levels in individuals, thereby triggering metabolic disorders, emotional disorders, and abnormal bladder function. Besides, genetic factors cannot be ignored. Certain genes may simultaneously affect an individual’s metabolic characteristics, psychological traits, and bladder physiological function. Finally, since the current study only included American participants, the research results need to be cautiously generalized to other countries and populations.

Conclusion

In conclusion, our research shows that LAP is linearly positively associated with OAB, and depression is an important link in the association between LAP and OAB. Nevertheless, our findings need to be verified in longitudinal studies in the future.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (481.3KB, docx)

Acknowledgements

Thanks to the organizers and participants of NHANES.

Author contributions

Caijuan Huang: Funding and conception; Ying Xu: methodology, writing, statistics, graphing, data cleaning; Yibing Guan: polish the language and review; Jingjing Liu: review and writing. Haiping Yang: Funding and conception.

Funding

No funding was received for this work.

Data availability

The data used in this study are available at https://wwwn.cdc.gov/nchs/nhanes/.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Informed consent was obtained from each participant in this study. The National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES data collection procedures.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Caijuan Huang and Ying Xu.

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Associated Data

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Supplementary Materials

Supplementary Material 1 (481.3KB, docx)

Data Availability Statement

The data used in this study are available at https://wwwn.cdc.gov/nchs/nhanes/.

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