Association between systemic immune-inflammation index and urinary incontinence: Evidence from the National Health and Nutrition Examination Survey

Abstract

Objective

This study examined the association between the systemic immune-inflammation index and urinary incontinence subtypes using data from National Health and Nutrition Examination Survey 2007–2016.

Methods

This cross-sectional study included 21,569 adults. Systemic immune-inflammation index was calculated and log-transformed. Survey-weighted logistic regression analyses, smoothed curve fitting, exploratory threshold analyses, and subgroup analyses stratified by demographic and clinical characteristics were conducted to evaluate its associations with stress urinary incontinence, urgency urinary incontinence, and mixed urinary incontinence.

Results

Higher log₁₀ systemic immune-inflammation index levels were significantly associated with an increased prevalence of stress urinary incontinence. Associations with urgency urinary incontinence and mixed urinary incontinence were weaker, and quartile trends were not statistically significant. Smoothed curve fitting demonstrated monotonic increases for stress urinary incontinence and mixed urinary incontinence, whereas a nonlinear pattern was observed for urgency urinary incontinence, suggesting a potential threshold at a log₁₀ systemic immune-inflammation index of approximately 2.5.

Conclusion

Elevated systemic immune-inflammation index levels were significantly associated with stress urinary incontinence, whereas associations with urgency urinary incontinence and mixed urinary incontinence were weaker and exhibited nonlinear patterns. Longitudinal studies are required to clarify the temporal relationships underlying these associations.

Keywords

Urinary incontinence systemic immune-inflammation index National Health and Nutrition Examination Survey nonlinear

Introduction

Urinary incontinence (UI), encompassing stress urinary incontinence (SUI), urgency urinary incontinence (UUI), and mixed urinary incontinence (MUI), is a prevalent health condition, particularly among women and older adults, and it substantially impairs quality of life.^1,2 The etiology of UI is multifactorial.^3,4 In addition to traditional risk factors such as age,⁵ body mass index (BMI),⁶ pelvic floor dysfunction,^7,8 and hormonal changes,⁹ inflammation has gained increasing attention in recent years as an important contributing factor to UI.¹⁰ For example, significantly higher levels of C-reactive protein (CRP) have been reported in individuals with urinary incontinence compared with those in the general population.¹¹ Nevertheless, reliance on a single inflammatory marker such as CRP is insufficient, and current research has not fully captured the dynamic balance of neutrophil-, lymphocyte-, and platelet-mediated immune regulation and its relationship with inflammation in UI.

The systemic immune-inflammation index (SII), an integrative indicator reflecting the interplay between inflammatory and immune responses,^12,13 has been widely investigated for its association with multiple chronic diseases, including cardiovascular diseases,^14,15 diabetes,¹⁶ and cancer.^17,18 Elevated SII levels have been shown to predict worse outcomes in these conditions. However, the association between SII and UI has received limited attention. Given the pathophysiological complexity of UI, exploring the relationship between SII and different UI subtypes may provide prospective therapeutic targets for this prevalent condition.

In other chronic diseases, inflammatory markers often demonstrate nonlinear associations characterized by threshold effects and subgroup heterogeneity. It remains unclear whether similar patterns exist in UI.^19–21 Investigating these potential dynamics is essential for understanding variability in risk and for informing prevention and management strategies.

This study aimed to delineate the relationship between log₁₀ SII and the risk of different UI subtypes, namely SUI, UUI, and MUI. A cross-sectional analysis was conducted using data from the 2007–2016 National Health and Nutrition Examination Survey (NHANES). Associations between SII and UI were evaluated using logistic regression, threshold effect analysis, and smoothed curve fitting. Subgroup analyses were performed to assess the consistency of these associations across population characteristics, including sex, age, BMI, and comorbid conditions. By identifying subtype-specific patterns and potential threshold effects, this study aims to provide insights into the inflammatory mechanisms underlying UI and to inform personalized intervention strategies.

Methods

Study population

The NHANES, conducted by the Centers for Disease Control and Prevention (CDC), provided the data for this study. The study protocol was reviewed and approved by the Ethics Review Board of the National Center for Health Statistics (NCHS), and informed consent was obtained from all participants at the time of recruitment. Data were obtained from multiple NHANES cycles conducted between 2007 and 2016 and initially included 50,588 individuals aged 20 years and older. These survey cycles were selected because they contained complete laboratory data required for SII calculation and included the UI questionnaire necessary for outcome assessment. Participants were sequentially excluded according to the following criteria: 25,134 individuals lacked kidney questionnaire data, 10,016 participants had missing SII data, and 113 participants had incomplete information on variables such as alcohol consumption, diabetes, hypertension, kidney function, or smoking status. In addition, 1952 individuals were excluded because of missing covariate data. After these exclusions, 21,569 participants were included in the final analysis (Figure 1). To minimize potential bias, strict inclusion and exclusion criteria were applied to ensure a representative study population. Furthermore, survey weight adjustments were used to enhance the accuracy and national representativeness of the estimates.

Figure 1.

Flowchart of the participants included.

SII

The SII was calculated using blood samples that were rigorously tested in laboratories according to standardized protocols to ensure data accuracy and comparability. Blood collection was typically conducted in mobile survey units or designated sampling locations, with subsequent processing and analysis performed in certified laboratories. The SII was calculated using the following formula: SII = platelet count × neutrophil count/lymphocyte count. This index is used in NHANES to assess the inflammatory status of participants.

Assessment of UI

UI was evaluated using two NHANES questions that specifically queried about urine leakage within the past 12 months. SUI was defined by an affirmative response to the question: “During the past 12 months, have you leaked or lost control of even a small amount of urine with activities like coughing, lifting, or exercise?” UUI was defined by a positive response to the question: “During the past 12 months, have you leaked or lost control of even a small amount of urine with an urge or pressure to urinate, and you could not get to the toilet fast enough?” Participants who answered “Yes” to both questions were classified as having MUI. To maintain consistency with established research practice, this categorization follows operational definitions widely adopted in previous NHANES-based epidemiologic studies, in which stress-, urgency-, and mixed-type incontinence are derived directly from these two questionnaire items rather than clinical diagnostic criteria.^22,23

Covariables

In this study, covariates were selected based on their potential influence on both the SII and related outcomes. These covariates included demographic characteristics such as sex, race/ethnicity, age, and marital status as well as socioeconomic factors such as the poverty-to-income ratio (PIR) and educational level. Lifestyle variables, such as alcohol consumption and smoking behavior, were also considered. Key health-related factors, including diabetes, hypertension, and BMI, were incorporated. Additionally, biological measures, such as estimated glomerular filtration rate (eGFR), were included to account for potential confounders and enhance the validity of the analysis.

Statistical analysis

Demographic data of participants were analyzed using SII quartiles, with chi-square tests applied to categorical variables and unpaired Student’s t-tests used for continuous variables. Weighted multivariable linear regression and logistic regression models were applied to evaluate the linear association between log₁₀ SII and UI. All analyses incorporated NHANES sampling weights, stratification, and primary sampling units to account for the complex multistage survey design. The fully adjusted model (Model 3) included the following covariates: age, sex, race/ethnicity, marital status, educational level, PIR smoking status, alcohol consumption, BMI, eGFR, diabetes, and hypertension. SII was segmented into quartiles from its continuous format, and a trend analysis was employed to examine its linear association with UI. Subgroup analyses were performed to further explore the relationship between UI and log₁₀ SII within specific subpopulations stratified by BMI, sex, educational level, kidney function, and diabetes status. Tests for interaction were conducted to assess whether these associations differed significantly across subgroups. In addition, smoothing curve fitting was applied to explore potential nonlinear associations between UI and log₁₀ SII. An apparent inflection near log₁₀ SII ≈ 2.5 was identified based on visual inspection of the restricted cubic spline curve and was considered exploratory rather than a formally estimated statistical threshold. All statistical analyses were performed using R software (version 4.2) and EmpowerStats (version 4.2), with statistical significance defined as a two-tailed p-value <0.05. Given the cross-sectional design of NHANES, the results should be interpreted as associations rather than causal relationships.

Results

Baseline characteristics of participants

Among the 21,569 participants, the mean age (standard error (SE)) ranged from 46.66 (0.37) years in the lowest SII quartile (Q1) to 48.76 (0.36) years in the highest quartile (Q4), indicating that older age was more commonly observed among individuals with elevated SII levels (p < 0.001). The mean BMI (SE) also increased progressively across quartiles, from 28.07 (0.12) kg/m² in Q1 to 30.03 (0.14) kg/m² in Q4 (p < 0.001). Conversely, eGFR (mL/min/1.73 m²) showed a downward trend, with a mean (SE) of 95.37 (0.50) in Q1, decreasing to 93.04 (0.50) in Q4. Participants in higher SII quartiles were more frequently female (Q1: 45.93%; Q4: 57.31%) and non-Hispanic white (Q1: 61.89%; Q4: 74.48%). They also exhibited higher rates of diabetes (Q1: 8.35%; Q4: 11.71%), hypertension (Q1: 30.65%; Q4: 35.89%), and a history of smoking (Q1: 42.33%; Q4: 49.08%) compared with those in the lowest quartile (all p < 0.001). Regarding UI, individuals with higher SII levels were more likely to experience SUI (Q1: 18.84%; Q4: 27.06%), UUI (Q1: 18.76%; Q4: 21.57%; p = 0.009), and MUI (Q1: 8.00%; Q4: 10.84%; p < 0.001). As shown in Table 1, higher SII levels were associated with several adverse health characteristics, including a higher prevalence of UI, reduced kidney function, and an increased burden of comorbid conditions such as hypertension and diabetes.

Table 1.

Weighted baseline characteristics of US adults categorized by the SII.

Characteristics	SII				p-value
Characteristics	Q1 (N = 5392)	Q2 (N = 5392)	Q3 (N = 5392)	Q4 (N = 5393)	p-value
Age (years)	46.66 ± 0.37	46.53 ± 0.33	47.33 ± 0.37	48.76 ± 0.36	<0.001
Family PIR	2.98 ± 0.05	3.05 ± 0.05	3.05 ± 0.05	2.94 ± 0.06	0.031
BMI (kg/m²)	28.07 ± 0.12	28.47 ± 0.13	29.37 ± 0.13	30.03 ± 0.14	<0.001
eGFR (mL/min/1.73 m²)	95.37 ± 0.50	94.75 ± 0.40	94.76 ± 0.51	93.04 ± 0.50
Sex, (%)					<0.001
Male	54.07	51.43	48.51	42.69
Female	45.93	48.57	51.49	57.31
Race/ethnicity, (%)					<0.001
Non-Hispanic White	61.89	69.66	71.46	74.48
Non-Hispanic Black	17.81	9.22	7.74	6.76
Mexican American	7.97	8.12	8.50	7.80
Other races	12.33	13.00	12.30	10.96
Educational level, (%)					<0.001
<High school	16.46	14.42	15.27	16.53
High school	21.51	20.58	22.52	23.28
>High school	62.02	65.00	62.21	60.19
Marital status, (%)					<0.001
Living alone	35.44	33.46	36.48	38.96
Married or living with partner	64.56	66.54	63.52	61.04
Drinking alcohol, (%)					0.061
Ever	76.81	78.67	78.86	76.94
Never	23.19	21.33	21.14	23.06
Diabetes, (%)					<0.001
Yes	8.35	8.15	9.23	11.71
No	91.65	91.85	90.77	88.29
Smoking, (%)					<0.001
Yes	42.33	43.53	44.67	49.08
No	57.67	56.47	55.33	50.92
Blood pressure, (%)					<0.001
Yes	30.65	30.05	31.56	35.89
No	69.35	69.95	68.44	64.11
SUI, (%)					<0.001
Yes	18.84	22.56	24.69	27.06
No	81.16	77.44	75.31	72.94
UUI, (%)					0.009
Yes	18.76	18.66	19.04	21.57
No	81.24	81.34	80.96	78.43
MUI, (%)					<0.001
Yes	8.00	8.33	9.57	10.84
No	92.00	91.67	90.43	89.16

Continuous variables are expressed as mean ± SE, with p values determined using a weighted linear regression model. Categorical variables are presented as percentages (%), and their p values were obtained using the weighted chi-square test.

PIR: poverty-to-income ratio; BMI: body mass index; eGFR: estimated glomerular filtration rate; Q: quartile; SII:systemic immune-inflammation index; SUI: stress urinary incontinence; UUI: urgency urinary incontinence; MUI: mixed urinary incontinence.

Relationship between SII and UI

The correlations between SII and distinct categories of UI, including SUI, UUI, and MUI, are presented in Table 2. Given the non-normal distribution of SII, a logarithmic transformation (log₁₀ SII) was applied to improve statistical precision. In the crude model (Model 1), each 1-unit increase in log₁₀ SII (equivalent to a 10-fold increase in the original SII value) was significantly associated with higher risks of SUI (odds ratio (OR) = 2.12 (1.76, 2.55)), UUI (OR = 1.26 (1.03, 1.56)), and MUI (OR = 1.67 (1.29, 2.16)). These associations remained significant in the minimally adjusted model (Model 2), which accounted for sex, age, and race/ethnicity. In fully adjusted analyses (Model 3), higher log₁₀ SII was associated with SUI (OR = 1.57, 95% confidence interval (CI): 1.26–1.96), whereas associations with UUI (OR = 1.34, 95% CI: 1.06–1.68) and MUI (OR = 1.35, 95% CI: 1.04–1.76) were weaker; importantly, quartile-based trends were not statistically significant for UUI and MUI (p for trend = 0.773 and 0.345, respectively). A visually observable change in slope was noted at a log₁₀ SII of 2.5 on the restricted cubic spline curve, suggesting a possible inflection point rather than a statistically established threshold.

Table 2.

Associations between SII and urinary incontinence types (SUI, UUI, and MUI) in different weighted models.

Log₁₀ SII	SUI	UUI	MUI
	OR (95% CI)	OR (95% CI)	OR (95% CI)
Crude model (Model 1)
Continuous	2.12 (1.76, 2.55)	1.26 (1.03, 1.56)	1.67 (1.29, 2.16)
Categories
Quartile 1	1 (ref)	1 (ref)	1 (ref)
Quartile 2	1.26 (1.11, 1.42)	0.99 (0.88, 1.12)	1.04 (0.87, 1.25)
Quartile 3	1.41 (1.24, 1.61)	1.02 (0.89, 1.17)	1.22 (1.00, 1.48)
Quartile 4	1.60 (1.40, 1.82)	1.20 (1.05, 1.35)	1.40 (1.18, 1.65)
p for trend	<0.001	0.014	<0.001
Minimally adjusted model (Model 2)
Continuous	1.57 (1.26, 1.96)	1.15 (0.92, 1.43)	1.35 (1.04, 1.76)
Categories
Quartile 1	1 (ref)	1 (ref)	1 (ref)
Quartile 2	1.24 (1.08, 1.42)	1.04 (0.91, 1.19)	1.05 (0.87, 1.26)
Quartile 3	1.33 (1.15, 1.54)	1.03 (0.88, 1.20)	1.17 (0.95, 1.44)
Quartile 4	1.32 (1.13, 1.53)	1.12 (0.97, 1.28)	1.23 (1.03, 1.47)
p for trend	<0.001	0.181	0.011
Fully adjusted model (Model 3)
Continuous	1.34 (1.06, 1.68)	0.96 (0.78, 1.17)	1.06 (0.83, 1.36)
Categories
Quartile 1	1 (ref)	1 (ref)	1 (ref)
Quartile 2	1.21 (1.06, 1.39)	1.03 (0.90, 1.18)	1.04 (0.86, 1.25)
Quartile 3	1.26 (1.09, 1.47)	0.97 (0.84, 1.12)	1.11 (0.90, 1.36)
Quartile 4	1.20 (1.03, 1.40)	1.00 (0.88, 1.14)	1.07 (0.91, 1.27)
p for trend	0.034	0.773	0.345

Model 1: no covariates adjusted. Model 2: adjusted for age, sex, and race/ethnicity. Model 3: adjusted for age, sex, race/ethnicity, educational level, PIR, BMI, eGFR, alcohol consumption, diabetes, blood pressure, smoking, and marital status.

SII: systemic immune-inflammation index; SUI: stress urinary incontinence; UUI: urgency urinary incontinence; MUI: mixed urinary incontinence; OR, odds ratio; CI: confidence interval; PIR: poverty-to-income ratio; BMI: body mass index; eGFR: estimated glomerular filtration rate; Q: quartile.

Smoothed curve fitting and threshold effect analyses of log₁₀ SII and UI subtypes

The analysis of different UI subtypes revealed that log₁₀ SII was positively associated with the risks of SUI and MUI. Specifically, the risks of SUI and MUI increased steadily with higher log₁₀ SII, showing a monotonic upward trend without noticeable inflection points or significantly nonlinear characteristics. Although the fitted spline curves showed visually smooth monotonic patterns for SUI and MUI (and a shallow U-shape for UUI), the 95% CIs were wide and substantially overlapped across the exposure range. This indicates limited statistical precision of the smoothed estimates and explains why the quartile-based p-for-trend tests did not reach significance despite the visual trends of the spline curves. These patterns suggest that higher log₁₀ SII levels may contribute cumulatively to the risks of SUI and MUI. In contrast, the UUI curve exhibited marked nonlinearity. Segmented (piecewise) logistic regression identified a breakpoint at log₁₀ SII ≈ 2.5; below this threshold, log₁₀ SII was inversely associated with UUI (OR = 0.45, 95% CI: 0.25–0.82; p = 0.010), whereas at or above the threshold, it was positively associated (OR = 1.65, 95% CI: 1.26–2.19; p = 0.001), with the fitted curve flattening thereafter (Figure 2; Table 3). Taken together, these results support a threshold-like response for UUI, in contrast to the near-linear increases observed for SUI and MUI.

Figure 2.

Survey-weighted restricted cubic spline curves showing the association between log₁₀ SII and the predicted probability of (a) SUI, (b) UUI, and (c) MUI. SII: systemic immune-inflammation index; SUI: stress urinary incontinence; UUI: urge urinary incontinence; MUI: mixed urinary incontinence.

Table 3.

Threshold effect analysis of log₁₀ SII on UUI risk using segmented regression.

Threshold range	Estimate	OR (95% CI)	p-value	RR	RD
Log₁₀ SII<2.5	−2.63	0.45 (0.25, 0.82)	0.010	0.51	−0.139
Log₁₀ SII ≥ 2.5	0.51	1.65 (1.26, 2.19)	0.001	1.476	0.091

SI: systemic immune-inflammation index; UUI: urgency urinary incontinence; OR: odds ratio; CI: confidence interval; RR: risk ratio; RD: risk difference.

Subgroup analyses

As shown in Figure 3, subgroup analyses stratified by population characteristics, including age, BMI, sex, diabetes status, and chronic kidney disease (CKD) status, were performed to examine whether the association between log₁₀ SII and different UI subtypes varied across these groups. Regarding sex, a significant association was observed between log₁₀ SII and SUI among females (OR = 1.37 (1.08, 1.74)) but not males (OR = 1.16 (0.68, 1.97)), although the interaction test did not indicate a significant difference between sexes (p for interaction = 0.547). Regarding age, the association between log₁₀ SII and UUI was significant in individuals aged ≥60 years (OR = 1.31 (1.04, 1.66)) but nonsignificant for those aged <60 years (OR = 0.87 (0.68, 1.12)), with no significant interaction observed between age subgroups (p for interaction = 0.201). Similar trends were observed for other UI subtypes, with subgroup-specific variations in effect estimates but no statistically significant interactions across BMI categories, diabetes status, or CKD status. These findings suggest that although the relationship between log₁₀ SII and UI is generally modest across subgroups, some variations in effect estimates may exist, particularly for age and sex.

Figure 3.

Stratified analysis examining the relationship between log₁₀ SII and various types of UI. Age, sex, race, educational level, PIR, BMI, eGFR, alcohol consumption, diabetes, blood pressure, smoking, and marital status were adjusted. All stratified models were adjusted for covariates, excluding the stratification factor itself. These covariates included sex, age, race/ethnicity, educational level, marital status, PIR, smoking habits, alcohol consumption, BMI, eGFR, blood pressure, and diabetes. For CKD, eGFR ≥60 was classified as “No” and eGFR <60 as “Yes.” SII: systemic immune-inflammation index; UI: urinary incontinence; PIR: poverty-to-income ratio; BMI: body mass index; eGFR: estimated glomerular filtration rate; CKD: chronic kidney disease.

Discussion

In this nationally representative cohort, higher SII levels were associated with an increased likelihood of SUI, whereas the relationships with UUI and MUI were weaker and less consistent. These subtype differences suggest that systemic inflammatory burden may not influence all UI phenotypes uniformly. Although the spline curves displayed smooth visual patterns, these features should be interpreted cautiously, as restricted cubic splines can produce gradual shapes even when statistical precision is limited and formal trend tests are nonsignificant.

Several previous studies have reported associations between inflammatory indicators and UI, including findings based on CRP²⁴ and various interleukins.²⁵ However, most of these investigations examined single biomarkers and did not differentiate among UI subtypes.^26,27 Because SII incorporates information from neutrophils, lymphocytes, and platelets, it provides a more comprehensive measure of systemic inflammatory activity. In the present study, elevated SII was linked to higher UI risk and exhibited subtype-specific patterns. Additionally, although previous studies have primarily focused on overall UI prevalence,²⁸ our threshold modeling suggested a potential nonlinear relationship between log₁₀ SII and UUI, providing exploratory evidence that broader inflammatory profiles may be related to UI differently across subtypes.

Consistent with the smoothed curve fitting analyses (Figure 3), increases in log₁₀ SII were associated with higher probabilities of SUI and MUI. The association with SUI aligns with the well-recognized mechanism in which stress leakage occurs when intra-abdominal pressure exceeds urethral closure forces.²⁹ Prior research has suggested that inflammatory activity—particularly pathways involving neutrophil elastase,³⁰ matrix metalloproteinase activity,³¹ and impaired collagen or endothelial integrity³²—may weaken pelvic support structures. These changes may be further compounded in women, as age- or hormone-related reductions in pelvic floor resilience reduce the ability to counter increases in abdominal pressure.³³ Taken together, these inflammation-related alterations^34,35 may help contextualize why stronger associations were observed for SUI and, to a lesser extent, MUI.

Unlike SUI, which is closely related to urethral support failure, UUI is generally attributed to detrusor overactivity and disturbances in neural regulatory pathways.³⁶ In our survey-weighted and fully adjusted analyses, log₁₀ SII did not show a significant linear relationship with UUI. Although exploratory spline and segmented models suggested a slight elevation in UUI risk when log₁₀ SII was below approximately 2.5, this observation should be interpreted cautiously, as it reflects a visually inferred feature of the curve rather than a formally derived threshold (Figure 3). Limited evidence from prior studies indicates that inflammation-related alterations in afferent signaling or neurotransmitter modulation may influence urgency mechanisms,³⁷ but such interpretations remain speculative and require validation through longitudinal research.

For MUI, which integrates symptoms from both stress and urgency domains, heterogeneity within the phenotype may attenuate observable associations. Systemic inflammation could still contribute to connective-tissue changes relevant to the stress component,^30,31 whereas its influence on urgency-related pathways appears less clearly defined, potentially explaining the weaker and inconsistent results observed.

The subgroup findings indicate that demographic factors may shape the relationship between SII and UI. The comparatively stronger association between SII and SUI in women may reflect sex-specific inflammatory patterns and estrogen-related maintenance of pelvic connective tissues.³³ In older individuals, modestly elevated UUI patterns could be influenced by accumulated inflammatory exposure together with age-related alterations in bladder or neural function.^34,35 These variations highlight that the impact of systemic inflammation on UI may differ across population groups.

Despite the strengths of this study, several limitations should be acknowledged. First, because NHANES collects exposure and outcome information simultaneously, the temporal sequence between inflammation and UI cannot be established, raising the possibility of reverse causation. Second, although SII reflects systemic inflammatory burden, it does not capture localized inflammatory activity that may also contribute to UI development. Third, reliance on self-reported UI measures may introduce misclassification, particularly in subgroups where symptom reporting varies. In addition, the subtle turning point observed in the spline curve should be interpreted cautiously, as it represents a visually inferred feature rather than a statistically estimated or validated threshold. Finally, although we adjusted for a wide range of demographic, lifestyle, and health-related covariates, unmeasured or incompletely captured factors may still bias the observed associations, and residual confounding cannot be ruled out.

Overall, the subtype-specific patterns observed in this study indicate that systemic inflammation may be related to UI phenotypes in different ways. The more continuous trend for SUI contrasts with the greater variability observed in UUI and MUI, suggesting that inflammatory burden may not operate uniformly across symptom domains. These observations are exploratory and should be interpreted in the context of subtype heterogeneity, with confirmation needed from longitudinal research.

Conclusion

Elevated log₁₀ SII was consistently associated with SUI after adjustment for demographic and clinical factors, whereas associations with UUI and MUI were weaker and did not exhibit clear linear trends. Exploratory spline analyses suggested a potential UUI threshold near log₁₀ SII ≈ 2.5, although these findings should be interpreted cautiously. Overall, the results improve understanding of the inflammatory correlates of UI and underscore the need for longitudinal studies to clarify temporal relationships and underlying mechanisms.

Footnotes

Acknowledgments

We extend our gratitude to all individuals who participated in this study.

Author contributions

Ting-ting Tao, Yue Duan, and Hao Zhang designed the research. Feike Ma and Zhi-cheng Cong collected and analyzed the data, and Yihan Chen drafted the manuscript. Yilun Cui revised the manuscript. All authors contributed to the development of this article and approved the final submitted version.

Clinical trial number

Not applicable.

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration of conflicting interests

Not applicable.

Funding

This work was supported by grants from the Zhejiang Provincial Basic Public Welfare Program (LGD22H290001) and Departmental Urology and Andrology Laboratory.

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki, with ethical approval granted by the NCHS Ethics Review Board for all aspects involving human participants, materials, or data. All participants provided written informed consent prior to participation.

ORCID iDs

Yue Duan

Ting-Ting Tao

References

Vaughan

Markland

AD.

Urinary incontinence in women. Ann Intern Med 2020; 172: ITC17–ITC32.

Gacci

Sakalis

Karavitakis

, et al. European association of urology guidelines on male urinary incontinence. Eur Urol 2022; 82: 387–398.

Whiting

Shane

Pope

, et al. Female urinary incontinence in sub-Saharan Africa. BJU Int 2022; 130: 543–549.

Govender

Gabriel

Minassian

, et al. The current evidence on the association between the urinary microbiome and urinary incontinence in women. Front Cell Infect Microbiol 2019; 9: 133.

Hagen

Bugge

Dean

, et al. Basic versus biofeedback-mediated intensive pelvic floor muscle training for women with urinary incontinence: the OPAL RCT. Health Technol Assess 2020; 24: 1–144.

Fuselier

Hanberry

Margaret Lovin

, et al. Obesity and stress urinary incontinence: impact on pathophysiology and treatment. Curr Urol Rep 2018; 19: 10.

Yang

Wang

Gao

, et al. The anatomical pathogenesis of stress urinary incontinence in women. Medicina (Kaunas) 2022; 59: 5.

Urinary incontinence, pelvic floor dysfunction, exercise and sport. Sports Med 2004; 34: 451–464.

Molinet Coll

Martinez Franco

Altimira Queral

, et al. Hormonal influence in stress urinary incontinence during pregnancy and postpartum. Reprod Sci 2022; 29: 2190–2199.

10.

Doumouchtsis

Loganathan

Pergialiotis

The role of obesity on urinary incontinence and anal incontinence in women: a review. BJOG 2022; 129: 162–170.

11.

Post

Ruiz-Zapata

Grens

, et al. Genetic variants and expression changes in urgency urinary incontinence: a systematic review. Neurourol Urodyn 2020; 39: 2089–2110.

12.

Cheng

, et al. Higher systemic immune-inflammation index and systemic inflammation response index levels are associated with stroke prevalence in the asthmatic population: a cross-sectional analysis of the NHANES 1999–2018. Front Immunol 2023; 14: 1191130.

13.

Luo

Liu

Jiao

, et al. The associations of two novel inflammation indexes, systemic immune-inflammation index (SII) and system inflammation response index (SIRI), with periodontitis: evidence from NHANES 2009-2014. Clin Oral Investig 2024; 28: 129.

14.

Mahemuti

Jing

Zhang

, et al. Association between systemic immunity-inflammation index and hyperlipidemia: a population-based study from the NHANES (2015-2020). Nutrients 2023; 15: 1177.

15.

Cao

Zhang

, et al. Association of systemic immune inflammatory index with all-cause and cause-specific mortality in hypertensive individuals: results from NHANES. Front Immunol 2023; 14: 1087345.

16.

Guo

Song

Sun

, et al. Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: evidence from NHANES 2011-2018. Front Endocrinol (Lausanne) 2022; 13: 1071465.

17.

Yang

, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 2014; 20: 6212–6222.

18.

Nost

Alcala

Urbarova

, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol 2021; 36: 841–848.

19.

Chen

Wen

, et al. Association between systemic immune-inflammation index and chronic kidney disease: a population-based study. PLoS One 2024; 19: e0292646.

20.

Mattisson

Bogren

Horstmann

, et al. Risk factors for depressive disorders in the Lundby cohort – a 50 year prospective clinical follow-up. J Affect Disord 2009; 113: 203–215.

21.

Yuan

Xiao

Zhang

, et al. Inverted U-shaped relationship between systemic immune-inflammation index and pulmonary function: a large population-based study in US adults. Int J Chron Obstruct Pulmon Dis 2024; 19: 1971–1987.

22.

Jiang

Chen

Tang

, et al. Urinary phenols and parabens exposure in relation to urinary incontinence in the US population. BMC Public Health 2024; 24: 515.

23.

Malviya

Cheung

Kim

, et al. The Relationship Between Urinary Incontinence, Mortality, and Frailty: Insights From a Large Cohort Study. Neurourol Urodyn 2025; 44: 1149–1155.

24.

Kupelian

Rosen

Roehrborn

, et al. Association of overactive bladder and C-reactive protein levels. Results from the Boston area community health (BACH) survey. BJU Int 2012; 110: 401–407.

25.

Jiang

Jhang

, et al. Urine biomarker could be a useful tool for differential diagnosis of a lower urinary tract dysfunction. Tzu Chi Med J 2024; 36: 110–119.

26.

Tang

Song

Liang

, et al. The relationship between serum alpha-klotho levels and urinary incontinence in middle-aged and older females: insights from NHANES. Sci Rep 2024; 14: 28667.

27.

Sun

Huang

Tang

, et al. Association between weight-adjusted-waist index and urge urinary incontinence: a cross-sectional study from NHANES 2013 to 2018. Sci Rep 2024; 14: 478.

28.

Milsom

Gyhagen

The prevalence of urinary incontinence. Climacteric 2019; 22: 217–222.

29.

Brandt

Lorenzato

Nobrega

, et al. Intra-abdominal pressure measurement during ultrasound assessment of women with stress urinary incontinence: a novel model. Acta Cir Bras 2006; 21: 237–241.

30.

Yin

Jia

, et al. Skeletal muscle atrophy: from mechanisms to treatments. Pharmacol Res 2021; 172: 105807.

31.

Liu

Wang

, et al. Dimethyl fumarate ameliorates stress urinary incontinence by reversing ECM remodeling via the Nrf2-TGF-beta1/Smad3 pathway in mice. Int Urogynecol J 2022; 33: 1231–1242.

32.

Gardella

Scatigno

Belli

, et al. Aging of pelvic floor in animal models: a sistematic review of literature on the role of the extracellular matrix in the development of pelvic floor prolapse. Front Med (Lausanne) 2022; 9: 863945.

33.

Rahn

Carberry

Sanses

; Society of Gynecologic Surgeons Systematic Review Groupet al. Vaginal estrogen for genitourinary syndrome of menopause: a systematic review. Obstet Gynecol 2014; 124: 1147–1156.

34.

Sima

Xiao

, et al. Exosomes derived from mesenchymal stromal cells: a promising treatment for pelvic floor dysfunction. Hum Cell 2023; 36: 937–949.

35.

Chen

BMSCs-EVs alleviate pelvic floor dysfunction in mice by reducing inflammation and promoting tissue regeneration. In Vivo 2024; 38: 2680–2687.

36.

Smith

Jaffe

Wang

, et al. Detrusor overactivity leak point pressure in women with urgency incontinence. Int Urogynecol J 2012; 23: 443–446.

37.

Payne

CK.

Epidemiology, pathophysiology, and evaluation of urinary incontinence and overactive bladder. Urology 1998; 51: 3–10.

Association between systemic immune-inflammation index and urinary incontinence: Evidence from the National Health and Nutrition Examination Survey

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Methods

Study population

SII

Assessment of UI

Covariables

Statistical analysis

Results

Baseline characteristics of participants

Relationship between SII and UI

Smoothed curve fitting and threshold effect analyses of log10 SII and UI subtypes

Subgroup analyses

Discussion

Conclusion

Footnotes

Acknowledgments

Author contributions

Clinical trial number

Data availability statement

Declaration of conflicting interests

Funding

Ethics approval and consent to participate

ORCID iDs

References

Smoothed curve fitting and threshold effect analyses of log₁₀ SII and UI subtypes