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Int Neurourol J > Volume 29(2); 2025 > Article
Luo, Kim, Xu, Lin, and Shin: Association Between Lipid Accumulation Product and Stress Urinary Incontinence in Women in the United States: The Mediating Role of Oxidative Stress

ABSTRACT

Purpose

The relationship between lipid accumulation product (LAP) and stress urinary incontinence (SUI) in women remains unclear, and the potential mediating roles of oxidative stress and inflammatory factors in this process have not been extensively studied. This analysis aimed to explore the association between elevated LAP indices and SUI in women, specifically examining the mediating effects of oxidative stress and inflammatory factors.

Methods

Data from 4,292 participants across 6 consecutive cycles of the National Health and Nutrition Examination Survey were analyzed. Logistic regression and subgroup analyses, adjusted for various covariates, were performed to investigate the relationship between LAP and the prevalence of SUI in adult women. Additionally, mediation analysis was conducted to evaluate the contributions of oxidative stress and inflammatory factors to this association.

Results

The prevalence of SUI among women in the United States was 46.02%. After adjustment for confounding factors, the odds ratio for LAP and SUI was 1.003 (95% confidence interval, 1.001–1.004; P=0.002), indicating a positive association. Gamma-glutamyl transferase mediated this relationship, explaining 40.0% of the effect (P=0.036).

Conclusions

This study’s findings indicate a positive association between LAP and the prevalence of SUI in women, with oxidative stress potentially acting as a mediator in this relationship.

INTRODUCTION

Stress urinary incontinence (SUI) refers to the unintentional loss of urine triggered by activities that increase intra-abdominal pressure [1]. Due to a shorter urethral length, weaker periurethral support tissues, and factors such as childbirth and postmenopausal hormonal decline, women are more frequently affected by this condition. The reported prevalence of SUI among women varies significantly, ranging from 12% to 46% [2, 3]. It is estimated that annual expenditures related to SUI exceed 7 billion euros in European countries such as the United Kingdom, Spain, and Italy [4]. In South Korea, annual medical costs associated with SUI among women aged 40–50 reach as high as $15–20 million [5]. Unfortunately, many women lack awareness of SUI, often perceiving it as a natural consequence of childbirth and aging rather than a health issue [6]. Koch and colleagues demonstrated that less than 38% of women seek assistance for SUI symptoms [7]. This reluctance significantly impacts their social activities, sexual function, and reproductive health. A comprehensive understanding of SUI risk factors is thus critical for raising public awareness, developing preventive healthcare strategies, and enhancing women’s reproductive health.
Obesity is a major contributor to the onset and progression of SUI. It directly increases abdominal, bladder, and pelvic floor pressure, thereby promoting SUI [8]. Additionally, obesity contributes to the accumulation of lipid droplets intracellularly and extracellularly, elevates free fatty acids, and enhances leptin secretion. This process activates interleukin (IL)-6 and oxidative stress factors while decreasing adiponectin secretion, subsequently inhibiting the repair of damaged vascular endothelium [9]. Consequently, oxidative stress imbalances damage pelvic floor muscles, urethral tissues, and pelvic floor nerve function, elevating the risk of SUI [10]. The lipid accumulation product (LAP), calculated using waist circumference (WC) and plasma triglyceride (TG) levels, is a reliable marker of excessive lipid storage in the body. Initially developed to identify cardiovascular risk in American adults, LAP has been strongly correlated with cardiovascular diseases [11].
However, there is limited research examining the relationship between LAP and female SUI, particularly regarding the mediating roles of oxidative stress and inflammatory factors. This study primarily investigates the relationship between LAP and SUI prevalence among women using large-scale population data from a United States public database. Moreover, it evaluates the potential mediating effects of oxidative stress and inflammatory factors, providing additional insights into the role of LAP in the pathogenesis of SUI in women.

MATERIALS AND METHODS

Survey Description

The data utilized in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database. The NHANES employs periodic surveys and scientifically designed sampling methods, providing researchers with valuable opportunities to examine health trends and disease prevalence comprehensively. Ethical approval for this study was granted by the National Center for Health Statistics Research Ethics Review Board. Additional details regarding the NHANES data can be found at https://www.cdc.gov/nchs/nhanes/index.htm.

Study Population

This study analyzed data from 6 consecutive cycles of the NHANES database, collected between 2007 and 2018. To clarify the association between LAP and SUI in adult women, participants meeting any of the following criteria were excluded: (1) absence of information regarding SUI diagnosis; (2) missing WC and TG measurements necessary for calculating LAP; (3) male participants, female participants under the age of 20, and pregnant women; and (4) missing information on covariates such as hysterectomy status, the number of natural deliveries, and water intake. After applying these criteria, a total of 4,292 participants were included in the final analysis (Fig. 1).

Definition of LAP and SUI

The LAP index for women is calculated using WC and TG levels. WC was measured in centimeters, and TG was reported in mmol/L. For female participants, LAP was computed according to the following formula provided by reference [11]:
Females: LAP=(WC−58)×TG
Participants were asked whether they had experienced involuntary urine leakage during the past 12 months triggered by activities increasing intra-abdominal pressure, such as coughing, lifting heavy objects, or physical exercise. An affirmative response classified participants as having SUI [12].

Covariate Definitions

According to previous studies [12-15], the covariates adjusted for in this analysis included several demographic factors: age, race, and educational level. Educational levels were categorized into 3 groups: less than a high school diploma, a high school diploma or equivalent, and greater than a high school diploma. Additionally, marital status and family poverty income ratio (PIR) were included, with PIR divided into 3 categories: less than 1.3, between 1.3 and 3.49, and 3.5 or greater. Furthermore, additional variables were considered, such as body mass index (BMI), alcohol consumption (defined as consuming more than 1 alcoholic drink per day on drinking days) [16], smoking status, and hypertension status. Other factors accounted for included diabetes status, physical activity level, average daily water intake, number of vaginal deliveries, and hysterectomy status. In the mediation analysis, gamma-glutamyl transferase (GGT) and serum bilirubin were included as markers of oxidative stress, while white blood cell count and serum alkaline phosphatase were employed as markers of chronic inflammation [13, 17].

Statistical Methods

Continuous variables were presented as means with their corresponding standard deviations, while categorical variables were expressed as frequencies (n) and percentages. Multivariate logistic regression models were used to investigate the association between LAP scores and SUI prevalence. Three distinct regression models were constructed: Model 1 examined the LAP-SUI relationship without adjustments; model 2 adjusted for demographic factors such as age, race, educational level, marital status, and PIR; and model 3 incorporated additional covariates including BMI, smoking status, alcohol consumption, diabetes and hypertension status, physical activity level, water intake, and biochemical markers (blood urea nitrogen, creatinine, and uric acid). Model 3 also accounted for the number of vaginal deliveries and hysterectomy status. Age, BMI, biochemical markers, and number of vaginal deliveries were treated as continuous variables, while the remaining variables were treated as categorical. Mediation analysis was conducted to evaluate the potential mediating effects of oxidative stress factors and inflammatory mediators on the LAP-SUI relationship [13, 18]. Statistical analyses were performed using R software (ver. 4.4.2, R Foundation for Statistical Computing) and Empower Stats 4.2 (StataCorp LLC), with statistical significance established at a 2-tailed P-value <0.05.

RESULTS

Population Characteristics

This study included a total of 4,292 participants with a mean age of 52.53±16.31 years. Among them, 1,975 individuals were diagnosed with SUI, corresponding to a prevalence rate of 46.02%. Compared to the non-SUI group, participants with SUI showed significantly higher age, a higher frequency of being married, greater alcohol consumption, increased smoking prevalence, higher rates of diabetes and hypertension, higher BMI, and increased average water intake, as well as elevated urea nitrogen and uric acid levels (P<0.05). Regarding reproductive health, the SUI group also exhibited a greater number of vaginal deliveries and higher rates of hysterectomy (P<0.05). Additionally, the LAP was significantly higher in the SUI group compared to the non-SUI group (64.81±55.17 vs. 49.99±41.13, P<0.05) (Table 1).

Association Between LAP and SUI

Multivariate logistic regression analyses revealed a positive association between LAP and the prevalence of SUI (Table 2). Three models were established for analysis. Model 1, unadjusted for potential confounding factors, showed a significant positive association (odds ratio [OR], 1.010; 95% confidence interval [CI], 1.008–1.012; P<0.001). In model 2, after adjusting for demographic variables, the association between LAP and SUI remained statistically significant (OR, 1.008; 95% CI, 1.006–1.010; P<0.001). Model 3, which included additional covariates, also showed a persistent significant positive relationship (OR, 1.012; 95% CI, 1.008–1.017; P=0.002). Furthermore, when LAP was categorized into tertiles in model 3, the prevalence of SUI in the highest tertile (T3) was 1.294 times higher compared to the lowest tertile (T1) (95% CI, 1.059–1.581; P=0.012). This increasing trend in SUI prevalence across LAP tertiles was statistically significant in all 3 models (P for trend <0.05).
Fig. 2 and Table 3 further illustrate the smooth curve fitting results and threshold effect analysis of the LAP-SUI relationship. After adjusting for covariates included in model 3, the relationship between LAP and SUI demonstrated a non-linear correlation with a breakpoint at LAP=12.277. On the left side of this breakpoint, each unit increase in LAP was associated with a 7.8% increase in the prevalence of SUI (OR, 1.078; 95% CI, 1.002–1.160; P=0.044). Conversely, this association became weaker on the right side of the breakpoint (OR, 1.002; 95% CI, 1.001–1.004; P=0.005). The log-likelihood ratio test supported this non-linear relationship (P=0.045).

Subgroup Analysis

Subgroup analysis was performed to evaluate the robustness of the association between LAP and SUI across different population segments (Fig. 3). Interaction tests demonstrated that the positive relationship between LAP and SUI was consistent across various subgroups defined by age, race, education level, marital status, PIR, alcohol consumption, and smoking status (all P for interaction >0.05).

Mediation Analysis

Mediation analysis explored the potential roles of oxidative stress markers (GGT and serum bilirubin) and inflammation markers (white blood cell count and alkaline phosphatase) in mediating the association between LAP and SUI. After adjusting for all covariates included in this study, only GGT demonstrated significant mediation, accounting for 40.03% of the association (P=0.036). Specifically, the direct effect of LAP on SUI was 0.023 (P=0.016), and the indirect effect mediated by GGT was 0.015 (P=0.036). In contrast, the mediation effects of serum bilirubin, white blood cell count, and alkaline phosphatase were not statistically significant (P>0.05) (Fig. 4).

DISCUSSION

In this study, we conducted a retrospective analysis of data from 4,292 adult American women derived from the NHANES dataset spanning 2007–2018. The findings revealed a positive correlation between LAP and SUI. Specifically, as LAP values increased, the prevalence of SUI also increased, with the association remaining significant after adjustments for relevant confounding factors. The smooth-fitting curve identified a nonlinear relationship between LAP and SUI, and subgroup analysis confirmed that this positive association remained consistent across different demographic and lifestyle groups. Mediation analysis indicated that GGT partially mediated this relationship. Considering the often overlooked prevalence of SUI in women, monitoring LAP and controlling GGT levels could represent a simple yet effective method for early intervention in SUI in women.
Over the past decade, scholarly attention toward LAP as an indicator of lipid metabolism has markedly increased, with previous studies examining associations between LAP and various health conditions across different populations. In a 10-year prospective study involving 2,020 participants from the Mediterranean region, Kyrou et al. [19] observed that, after adjusting for multiple established cardiovascular disease risk factors, each 10-unit increment in LAP was associated with an 11% increased risk of developing cardiovascular disease. Similarly, Qiao et al. [20] reported that individuals with type 2 diabetes and a LAP >65 had a 6.03-fold greater risk of cardiovascular events compared to those with a LAP <26. Besides predicting cardiovascular disease risk, LAP has been established as a valuable marker for early diabetes identification. In a 12-year longitudinal study conducted in Korea, 608 (14.2%) initially nonobese participants developed type 2 diabetes during the follow-up period [21]. Despite these findings, the potential link between LAP and SUI had remained unexplored prior to our current investigation.
Considerable research has explored the association of SUI with various risk factors, including obesity-related indicators, lipid profiles, blood glucose levels, and lifestyle behaviors. Sun performed an extensive analysis examining the independent association between weight-adjusted waist index (WWI) and SUI, reporting a 38% increase in SUI prevalence per unit increase in WWI [22]. Chen et al. [23] conducted a study involving 7,973 adult women diagnosed with SUI, finding a positive relationship between serum TG levels and both the prevalence and severity of SUI. Zhu et al. [24] obtained similar results in a study that further explored the combined effects of hypertriglyceridemia and obesity on SUI prevalence among obese women under 50 years old. Zhu et al. [24] reported that this synergistic interaction accounted for approximately 67% of SUI cases, highlighting the potential for developing targeted preventive strategies for specific populations. Ying et al. [25] analyzed data spanning 10 years and discovered a link between blood glucose levels and SUI prevalence. After adjusting for confounding factors such as age, race, hypertension, and blood urea nitrogen, individuals with blood glucose levels exceeding 98 mg/dL had a 15.2% higher prevalence of SUI compared to those with blood glucose levels below 86 mg/dL. Additionally, Di et al. [26] noted that women who spent over 7 hours per day engaging in sedentary activities had a higher SUI prevalence. The breadth of these findings underscores the complex and multifaceted nature of SUI, as well as its potential risk factors. Introducing LAP as a metabolic indicator in this study provides valuable insight into the pathogenesis of SUI, contributing to the development of effective preventive and therapeutic strategies.
The exact mechanisms through which LAP is associated with SUI remain unclear. Abnormal lipid metabolism resulting from elevated LAP partially explains this relationship, potentially mediated by oxidative stress [27]. It is well established that obesity, a contributing factor to elevated LAP, increases intra-abdominal pressure on pelvic floor muscles and connective tissues, thereby increasing the likelihood of urinary incontinence [28]. Such mechanical pressure adversely affects bladder function and weakens urethral sphincter control. Additionally, visceral adipocytes release various adipokines, including leptin and adiponectin, which activate oxidative stress and inflammatory responses. These responses lead to vascular endothelial damage, impaired angiogenesis, and injury to the urethral epithelium, muscles, pelvic nerves, blood vessels, and ligaments [29]. In vitro experiments involving muscle-derived stem cells from young female Zucker Fatty (ZF) rats—a model for obesity/type 2 diabetes with SUI—showed that incubation with biotinylated antibodies (ZF4-SC) and exposure to lipid factors such as cholesterol and sodium palmitate induced apoptosis in these cells. Elevated lipid exposure also influenced multiple microRNAs related to myostatin, resulting in muscle-derived stem cell injury and subsequent alterations in muscle mass [30]. Wang et al. [31] conducted immunofluorescence staining of urethral tissues in transgenic aged female rats induced with severe obesity, finding increased lipid deposition within striated muscle fibers, thinning and atrophy of striated muscles, impaired urethral sphincter function, increased urinary frequency, reduced bladder capacity, frequent contractions, and urine overflow.
The process of urination in females results from the coordinated action of the parasympathetic, sympathetic, and pudendal nerves, which regulate contraction and relaxation of the detrusor muscle, urethral smooth muscle, and external urethral sphincter. Adipose tissue secretes inflammatory cytokines, such as tumor necrosis factor-alpha, IL-1, IL-6, and IL-8, which can damage nerve fibers and axons, leading to pelvic nerve injury and dysregulation of urinary function [32, 33]. Lipid metabolism disorders associated with obesity can activate oxidative stress, potentially promoting the activity of matrix metalloproteinases, leading to collagen degradation and compromising the structural integrity and elasticity of pelvic floor tissues [34]. Additional research suggests that lipid peroxidation is often accompanied by an imbalance between oxidation and antioxidation, where elevated oxidative stress factors inhibit collagen synthesis and impair pelvic support structures [35]. GGT plays a critical role in glutathione metabolism, functioning as a key regulator of the cellular antioxidant system. By breaking down extracellular glutathione into its constituent components—glutamate, cysteine, and glycine—GGT enables cells to reuse these precursors to synthesize new glutathione, thereby maintaining intracellular antioxidant capacity. However, under certain pathological conditions, increased demand for antioxidant defense leads to elevated GGT levels. When reactive oxygen species production exceeds the clearance capacity of glutathione, the compensatory effect of GGT becomes insufficient, resulting in an oxidative-antioxidative imbalance. Upon stimulation by factors secreted from adipocytes, excessive GGT may further exacerbate oxidative stress, leading to oxidative damage to DNA, proteins, and lipids. This oxidative damage impairs cellular structure and function, contributing to muscle weakness and reduced elasticity of connective tissues. Petermann-Rocha et al. [36] reported significantly elevated GGT levels in patients with sarcopenia, highlighting the detrimental impact of GGT on muscle tissues. Similarly, Hong et al. [37] demonstrated an association between elevated GGT and sarcopenic obesity, suggesting that oxidative stress induced by GGT might contribute to muscle damage. In this study, mediation analysis indicated that GGT, as a biomarker of oxidative stress, significantly mediated the association between LAP and SUI. This finding partially clarifies the potential mechanism linking LAP to SUI. However, the analysis did not identify serum bilirubin, white blood cells, or alkaline phosphatase as mediators. Nevertheless, this does not preclude the possibility that other oxidative stress markers or inflammatory factors may contribute to this relationship. Excessive visceral fat is often associated with chronic low-grade inflammation, characterized by elevated inflammatory markers such as C-reactive protein, interleukin-6, and tumor necrosis factor-alpha. These factors could influence the development of SUI through multiple pathways and warrant further investigation. This study utilized a large-scale population sample to investigate the relationship between LAP and SUI in women, as well as the potential mediating role of GGT, thereby providing insights into the underlying mechanisms of SUI. Based on these results, LAP could potentially serve as a screening tool for identifying individuals at high risk for SUI in clinical practice, facilitating targeted early interventions. For public health policymakers, promoting healthy lifestyles, maintaining optimal body weight, and reducing LAP levels could assist high-risk women in recognizing and effectively managing SUI at an earlier stage. Such strategies may contribute significantly to improved prevention and management of SUI in vulnerable populations. Despite identifying a positive association between LAP and SUI, this study's cross-sectional design inherently limits the ability to establish causality. Thus, it remains unclear whether LAP contributes to SUI risk via oxidative stress or if SUI might indirectly impact lipid metabolism, resulting in elevated LAP levels. Future longitudinal studies are necessary to investigate the temporal sequence and potential causal pathways among these variables. Additionally, excluding samples with missing variables might have introduced selection bias. Although potential confounding variables were extensively adjusted for, caution is still advised when interpreting the LAPSUI association, since not all confounding variables could be controlled. Furthermore, the mediation analysis incorporated a limited set of indicators, precluding a comprehensive exploration of the roles of oxidative stress and inflammatory factors. While GGT is an established marker of oxidative stress, its sensitivity and specificity for reflecting oxidative stress specifically related to SUI remain uncertain. Prospective clinical studies are warranted to further elucidate the relationship between LAP and SUI and to clarify the potential roles of oxidative stress and inflammatory factors.
In summary, this study demonstrates a positive association between elevated LAP levels and an increased prevalence of SUI among adult women in the United States. Additionally, the mediation analysis highlighted oxidative stress as a significant mediator in this association. The incorporation of LAP assessments into public health policy could therefore have substantial implications for enhancing women’s reproductive health.

NOTES

Grant/Fund Support
This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF), which is funded by the Korean government (MIST) (No. RS-2023-00236157).
Research Ethics
The portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki and were approved by the National Center for Health Statistics (NCHS) Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
Conflict of Interest
SJK, a member of the Editorial Board of International Neurourology Journal, is the co-first author of this article. However, she played no role whatsoever in the editorial evaluation of this article or the decision to publish it. Except for that, no potential conflict of interest relevant to this article was reported.
AUTHOR CONTRIBUTION STATEMENT
· Conceptualization: YSS, CL
· Data curation: ZL, SJK, JX
· Formal analysis: SJK, CL, JX
· Funding acquisition: YSS
· Methodology: JX
· Project administration: YSS
· Visualization: JX
· Writing - Original Draft: ZL, SJK
· Writing - Review & Editing: CL, YSS

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Fig. 1.
Flowchart of the participants selection from the National Health and Nutrition Examination Survey (NHANES) 2007 to 2018. SUI, stress urinary incontinence; TG, triglyceride; WC, waist circumference.
inj-2448460-230f1.jpg
Fig. 2.
Smooth curve fitting for LAP and SUI. LAP, lipid accumulation product; SUI, stress urinary incontinence.
inj-2448460-230f2.jpg
Fig. 3.
Forest plot of the subgroup analysis investigating the associations between lipid accumulation product and stress urinary incontinence. OR, odds ratio; CI, confidence interval; GED, general educational development; PIR, poverty income ratio.
inj-2448460-230f3.jpg
Fig. 4.
Mediation analysis of the associations between LAP and SUI. (A) The mediating role of gamma-glutamyl transferase. (B) The mediating role of bilirubin. (C) The mediating role of leukocyte. (D) The mediating role of alkaline phosphatase. CI, confidence interval; LAP, lipid accumulation product; SUI, stress urinary incontinence.
inj-2448460-230f4.jpg
Table 1.
Baseline characteristics of non-SUI group versus the SUI group
Characteristic All (N = 4,292) Non-SUI (N = 2,317) SUI (N = 1,975) P-value
Age (yr) 52.53 ± 16.31 50.88 ± 17.04 54.47 ± 15.18 < 0.001
Race (%) < 0.001
 Mexican American 635 (14.79) 310 (13.38) 325 (16.46)
 Other Hispanic 454 (10.58) 245 (10.57) 209 (10.58)
 Non-Hispanic White 1,938 (45.15) 940 (40.57) 998 (50.53)
 Non-Hispanic Black 890 (20.74) 607 (26.20) 283 (14.33)
 Other Race 375 (8.74) 215 (9.28) 160 (8.10)
Education level (%) 0.081
 Below high school 985 (22.95) 502 (21.67) 483 (24.46)
 High school/GED or equivalent 990 (23.07) 551 (23.78) 439 (22.23)
 Above high school 2,317 (58.48) 1,264 (54.55) 1,053 (53.32)
Marital status (%) 0.002
 Married or with partner 2,510 (58.48) 1,304 (56.28) 1,206 (61.06)
 Single 1,782 (41.52) 1,013 (43.72) 769 (38.94)
PIR 0.186
 < 1.3 1,425 (33.20) 795 (34.31) 630 (31.90)
 1.3–3.49 1,656 (38.53) 889 (38.37) 767 (38.84)
 ≥ 3.5 1,211 (28.22) 633 (27.32) 578 (29.27)
Drinking (%) 0.027
 Yes 2,816 (65.61) 1,486 (64.13) 1,330 (67.34)
 No 1,476 (34.39) 831 (35.87) 645 (32.66)
Smoking (%) 0.002
 Yes 1,673 (38.98) 854 (36.86) 819 (41.47)
 No 2,619 (61.02) 1,463 (63.14) 1,156 (58.53)
Hypertension (%) < 0.001
 Yes 1,763 (41.08) 861 (37.16) 902 (45.67)
 No 2,529 (58.92) 1,456 (62.84) 1,073 (54.33)
Diabetes (%) < 0.001
 Yes 616 (14.35) 281 (12.13) 335 (16.96)
 No 3,676 (85.65) 2,036 (87.87) 1,640 (83.04)
Vigorous activity (%) 0.676
 Yes 568 (13.23) 302 (13.03) 266 (13.47)
 No 3,724 (86.77) 2,015 (86.97) 1,709 (86.53)
Moderate activity (%) 0.085
 Yes 1,474 (34.34) 769 (33.19) 705 (35.70)
 No 2,818 (65.66) 1,548 (66.81) 1,270 (64.30)
Hysterectomy (%) < 0.001
 Yes 1,111 (25.89) 526 (22.70) 585 (29.62)
 No 3,181 (74.11) 1,791 (77.30) 1,390 (70.38)
BMI (kg/m2) 29.87 ± 7.28 29.02 ± 7.03 30.87 ± 7.44 < 0.001
Urea nitrogen (mg/dL) 13.26 ± 6.00 13.09 ± 6.02 13.46 ± 5.97 0.004
Creatinine (mg/dL) 0.78 ± 0.30 0.79 ± 0.35 0.77 ± 0.24 0.576
Uric acid (mg/dL) 4.98 ± 1.31 4.92 ± 1.29 5.05 ± 1.33 0.002
Vaginal deliveries 1.72 ± 1.79 1.54 ± 1.81 1.95 ± 1.74 < 0.001
Water consumption (g) 2,526.21 ± 1,065.01 2,437.90 ± 1,036.31 2,629.80 ± 1,088.89 < 0.001
Leukocyte (1,000/μL) 6.76 ± 2.13 6.63 ± 2.02 6.91 ± 2.25 < 0.001
Neutrophil (1,000/μL) 3.95 ± 1.64 3.85 ± 1.63 4.06 ± 1.64 < 0.001
Lymphocyte (1,000/μL) 2.08 ± 0.85 2.06 ± 0.68 2.09 ± 1.03 0.767
Alkaline phosphatase (IU/L) 71.83 ± 24.96 70.06 ± 24.03 73.91 ± 25.86 < 0.001
γ-glutamyl transferase (U/L) 26.27 ± 38.15 24.44 ± 33.57 28.42 ± 42.81 < 0.001
Bilirubin (mg/dL) 0.61 ± 0.27 0.62 ± 0.27 0.61 ± 0.26 0.172
LAP 56.35 ± 58.27 49.99 ± 41.13 64.81 ± 55.17 < 0.001

Values are presented as mean±standard deviation or number (%).

SUI, stress urinary incontinence; GED, general educational development; PIR, poverty income ratio; BMI, body mass index; LAP, lipid accumulation product.

Table 2.
The relationship between LAP and SUI
Variable Model 1 P-value Model 2 P-value Model 3 P-value
SUI
LAP 1.007 (1.006–1.009) < 0.001 1.006 (1.005–1.008) < 0.001 1.003 (1.001–1.004) 0.002
Categories
 T1 (1.847–30.802) 1.0 1.0 1.0
 T2 (30.857–61.600) 1.353 (1.165–1.571) < 0.001 1.307 (1.120–1.527) < 0.001 1.045 (0.883–1.238) 0.607
 T3 (61.635–789.737) 2.148 (1.849–2.494) < 0.001 1.952 (1.668–2.285) < 0.001 1.294 (1.059–1.581) 0.012
P for trend < 0.001 < 0.001 0.010

Values are presented as odds ratio (95% confidence interval).

LAP, lipid accumulation product; SUI, stress urinary incontinence; model 1, no covariates were adjusted; model 2, adjusted for age, race, education level, marital status, family poverty income ratio; model 3, based on model 2 by additionally adjusting for factors such as drinking, smoking, hypertension status, diabetes status, vigorous activity, moderate activity, urea nitrogen, creatinine, uric acid, water consumption, vaginal deliveries, hysterectomy; T1, lowest tertile; T2, middle tertile; T3, highest tertile.

Table 3.
Threshold effect analysis of LAP and SUI
SUI Adjusted OR (95% CI) P-value
LAP
 Linear regression model 1.003 (1.001–1.004) 0.004
Two-segment piecewise linear regression model
 Inflection point 12.277 -
 LAP < Inflection point 1.078 (1.002–1.160) 0.044
 LAP > Inflection point 1.002 (1.001–1.004) 0.005
 Log-likelihood ratio 0.045

LAP, lipid accumulation product; SUI, stress urinary incontinence; OR, odds ratio; CI, confidence interval.

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