Abstract
Objective:
The relationship between cardiovascular health (CVH) and overactive bladder (OAB) remains incompletely elucidated. We aimed to examine the association between CVH, assessed using the Life’s Essential 8 (LE8) score, and the presence and severity of OAB, as well as its related symptoms, in a nationally representative sample.
Methods:
This cross-sectional study included 6042 participants from the National Health and Nutrition Examination Survey. CVH was categorized as low (0–49), moderate (50–79), or high (80–100) based on LE8 scores. Multivariable-adjusted logistic and linear regression models were used to assess associations with OAB, urgency urinary incontinence (UUI), and nocturia. Restricted cubic splines and segmented regression models were employed to evaluate nonlinear relationships. Mediation analyses were conducted to explore potential biological pathways.
Results:
A pronounced inverse gradient was observed between LE8 scores and OAB outcomes. In fully adjusted models, participants with high CVH had significantly lower odds of OAB [odds ratio (OR) 0.59, 95% confidence interval (CI): 0.38–0.91; P-trend < 0.001] compared to the low CVH group. Each 10-point increase in LE8 score was associated with a 13% reduction in OAB odds (OR 0.87, 95% CI: 0.80–0.95). Nonlinear analyses revealed threshold effects at LE8 scores around 73 for OAB and UUI, beyond which protective associations plateaued. Causal mediation analyses indicated that systemic inflammation (LnSII) and plasma osmolality mediated 4.8 and 12.8% of the association, respectively. Among LE8 components, BMI and sleep health were the strongest contributors to urological outcomes.
Conclusion:
Better CVH, as measured by LE8, is nonlinearly and inversely associated with OAB and related symptoms, partially mediated by inflammation and osmolality. Metabolic and sleep factors emerged as dominant contributors, highlighting potential targets for prevention and intervention.
Keywords: cardiovascular health, cross-sectional study, mediation analysis, overactive bladder, United States, urology
Introduction
Overactive bladder (OAB) is a prevalent and burdensome urological syndrome. It is characterized by urinary urgency, frequency, and nocturia, which significantly impair quality of life[1]. Established risk factors for OAB include age, sex, and comorbidities such as diabetes. However, growing evidence suggests that broader cardiometabolic and lifestyle factors may also influence urological health[2–4]. Emerging evidence further suggests that modifiable lifestyle and cardiometabolic factors may significantly influence urological health, supporting the potential utility of integrated health metrics in OAB risk stratification and prevention[3,4].
The American Heart Association’s Life’s Essential 8 (LE8) provides a comprehensive metric for assessing cardiovascular health (CVH), encompassing behavioral and clinical components[5]. Previous studies have reported an inverse relationship between LE8 scores and OAB[6,7]. However, the nature of this association – whether linear or nonlinear – remains inconsistent and incompletely characterized. Furthermore, the underlying biological mechanisms have not been sufficiently explored through in-depth analyses.
We hypothesize that systemic inflammation and plasma osmolality may serve as key mediators in this association. Systemic inflammation, a consequence of poor CVH, is implicated in neuromuscular dysfunction and tissue remodeling within the bladder[8,9]. Similarly, suboptimal CVH – often characterized by poor metabolic control – can lead to elevated plasma osmolality[10], which may directly exacerbate urinary urgency and frequency through osmotic and neuroendocrine pathways. Investigating these factors as mediators could provide insight into the pathophysiological links between CVH and OAB.
Therefore, utilizing a nationally representative sample, this study aimed to: (1) evaluate the association between LE8 scores and the prevalence and severity of OAB and its symptoms; (2) explore potential nonlinear relationships and threshold effects; (3) assess consistency across key subgroups; (4) examine the mediating roles of systemic inflammation (using the systemic inflammatory index) and plasma osmolality; and (5) quantify the relative contributions of individual LE8 components to urological health.
HIGHLIGHTS
In a large national cohort, better cardiovascular health (assessed by Life’s Essential 8) demonstrated a strong, graded inverse association with overactive bladder and related urinary symptoms.
A nonlinear threshold effect was identified, with maximal protective benefits observed at Life’s Essential 8 (LE8) scores above 73, suggesting a target value for clinical intervention.
Mediation analysis revealed that plasma osmolality and systemic inflammation partially explained the association, highlighting potential mechanistic pathways.
Body mass index (BMI) was the dominant component of LE8, contributing to urological health and emphasizing the importance of metabolic management.
These findings advocate for the integration of OAB prevention into primary cardiovascular disease prevention frameworks. Prioritizing improvements in BMI and sleep health in individuals with LE8 scores below 73 may represent a cost-effective strategy for mitigating OAB symptoms, leveraging the well-established benefits of these interventions for both cardiometabolic and urological health.
Methods
The research was reported in line with the STROCSS guidelines[11,12]. Secondary analyses were performed on publicly available and de-identified information from the National Health and Nutrition Examination Survey (NHANES) using a cross-sectional, national approach. Therefore, no additional institutional review board approval or informed consent was required.
Study design and population
NHANES is conducted annually by the National Center for Health Statistics, which operates within the Centers for Disease Control and Prevention. It is designed to collect data from a nationally representative sample of the civilian, non-institutionalized population in the United States. The National Center for Health Statistics Disclosure Review Board reviews and approves the survey. For more information about ethical clearance and informed consent procedures, contact the National Center for Health Statistics[13]. Extensive information about NHANES design, methods, and weighting has been published previously[14]. Between 2005 and 2020, NHANES used a stratified, complex, multi-stage sampling technique to select households from randomly selected clusters. The survey included questions about health, lifestyle risk factors, common diseases, and other relevant health topics. Trained researchers conducted face-to-face interviews to collect the necessary data[14]. The present study utilized data from NHANES conducted between 2005 and 2020. Initially, 76 496 participants aged 20 years or older were included. We applied a series of sequential exclusions to ensure data completeness and minimize potential bias. Specifically, we excluded 39 061 participants with missing data related to OAB scores, 22 427 with missing LE8 scores, and 8338 with incomplete demographic information, including age, sex, race and ethnicity, marital status, PIR, and educational level. An additional 628 individuals were excluded due to missing data on other covariates of interest. After these exclusions, a total of 6042 participants comprised the final analytic cohort (Supplemental Digital Content Figure 1, available at: http://links.lww.com/MS9/B242).
Figure 1.
Restricted cubic spline curves depicting the nonlinear association between cardiovascular health (LE8 score) and overactive bladder (OAB) outcomes. Multivariable-adjusted odds ratios (ORs) (solid lines) and 95% confidence intervals (shaded areas) for (A) OAB, (B) OAB score, (C) urgency urinary incontinence (UUI) score, and (D) nocturia score are derived from logistic and linear regression models, adjusted for Model 3. All associations were significantly nonlinear (P-nonlinear < 0.001). Inflection points, indicating a plateau in the protective effect, were identified at LE8 scores of 73.13 for OAB and 72.50 for OAB score.
Definition of OAB
Data on OAB symptoms, including urge urinary incontinence and nocturia, were obtained from the NHANES Kidney Conditions-Urology questionnaire. The severity of urge incontinence was assessed using the following two questions: (1) “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 couldn’t get to the toilet fast enough?” (2) “How frequently does this occur?” Nocturia severity was categorized based on a subsequent standalone question: “During the past 30 days, how many times per night did you most typically get up to urinate, from the time you went to bed at night until the time you got up in the morning?”
OAB symptoms were quantified using a well-validated questionnaire – the Overactive Bladder Symptom Score (OABSS), originally developed by Blaivas et al[15]. Detailed scoring criteria are provided in Supplemental Digital Content Table 1, available at: http://links.lww.com/MS9/B243. For each participant in NHANES, the total OABSS was calculated as the sum of the nocturia score and the urge urinary incontinence (UUI) score. A total score of ≥ 3 was used to define individuals with OAB disorder.
Table 1.
Characteristics by CVH (cardiovascular health) level.
| Characteristic | Overall, N = 6042 (100%)a | 1: Low, N = 877 (13%)a | 2: Moderate, N = 3810 (63%)a | 3: High, N = 1355 (25%)a | P-valueb |
|---|---|---|---|---|---|
| Age, years | 46 (32, 59) | 54 (44, 63) | 48 (33, 60) | 38 (27, 52) | < 0.001 |
| Age group | < 0.001 | ||||
| ≥ 60 | 9 353 388 (24%) | 1 790 096 (36%) | 6 334 631 (25%) | 1 228 662 (13%) | |
| 20–60 | 30 278 181 (76%) | 3 209 827 (64%) | 18 532 252 (75%) | 8 536 102 (87%) | |
| Sex | < 0.001 | ||||
| Female | 20 404 350 (51%) | 2 942 403 (59%) | 12 166 474 (49%) | 5 295 473 (54%) | |
| Male | 19 227 219 (49%) | 2 057 520 (41%) | 12 700 408 (51%) | 4 469 291 (46%) | |
| Race and ethnicity | < 0.001 | ||||
| Non-Hispanic White | 27 339 443 (69%) | 3 410 209 (68%) | 17 089 077 (69%) | 6 840 157 (70%) | |
| Other Hispanic | 5 099 576 (13%) | 511 768 (10%) | 2 965 103 (12%) | 1 622 705 (17%) | |
| Non-Hispanic Black | 4 070 561 (10%) | 752 043 (15%) | 2 755 171 (11%) | 563 347 (5.8%) | |
| Mexican American | 3 121 989 (7.9%) | 325 904 (6.5%) | 2 057 531 (8.3%) | 738 554 (7.6%) | |
| Marital status | < 0.001 | ||||
| Married/living with partner | 24 480 064 (62%) | 3 041 362 (61%) | 15 351 888 (62%) | 6 086 814 (62%) | |
| Never married | 8 173 375 (21%) | 706 537 (14%) | 4 726 290 (19%) | 2 740 548 (28%) | |
| Widowed/divorced/separated | 6 978 130 (18%) | 1 252 024 (25%) | 4 788 705 (19%) | 937 401 (9.6%) | |
| Education | < 0.001 | ||||
| College or above | 26 813 783 (68%) | 2 403 404 (48%) | 16 195 786 (65%) | 8 214 593 (84%) | |
| High school | 7 903 091 (20%) | 1 512 105 (30%) | 5 442 493 (22%) | 948 492 (9.7%) | |
| < high school | 4 914 696 (12%) | 1 084 414 (22%) | 3 228 604 (13%) | 601 678 (6.2%) | |
| PIR (family income-to-poverty ratio) | < 0.001 | ||||
| ≥ 3.5 | 17 799 420 (45%) | 1 465 156 (29%) | 10 897 017 (44%) | 5 437 246 (56%) | |
| 1.3–3.5 | 12 966 489 (33%) | 1 967 225 (39%) | 8 408 620 (34%) | 2 590 645 (27%) | |
| ≤ 1.3 | 8 865 660 (22%) | 1 567 542 (31%) | 5 561 246 (22%) | 1 736 872 (18%) | |
| Physical activity, minutes per week | 420 (0, 1050) | 0 (0, 0) | 420 (0, 1260) | 700 (420, 1260) | < 0.001 |
| Smoking status | < 0.001 | ||||
| Current smoker | 7 481 894 (19%) | 2 192 028 (44%) | 5 099 091 (21%) | 190 774 (2.0%) | |
| Former smoker quit < 1 y | 787 672 (2.0%) | 135 334 (2.7%) | 576 465 (2.3%) | 75 874 (0.8%) | |
| Former smoker quit 1–< 5 y | 1 466 087 (3.7%) | 201 555 (4.0%) | 1 037 955 (4.2%) | 226 577 (2.3%) | |
| Former smoker quit ≥ 5 y | 7 187 028 (18%) | 1 079 076 (22%) | 4 726 986 (19%) | 1 380 966 (14%) | |
| Never smoker | 22 708 888 (57%) | 1 391 929 (28%) | 13 426 386 (54%) | 7 890 572 (81%) | |
| Drinking status | 0.2 | ||||
| 10 + drinks/month | 7 566 861 (19%) | 697 731 (14%) | 4 845 349 (19%) | 2 023 782 (21%) | |
| 1–10 drinks/month | 24 271 277 (61%) | 3 372 002 (67%) | 14 970 607 (60%) | 5 928 669 (61%) | |
| Non-drinker | 7 777 707 (20%) | 930 191 (19%) | 5 035 204 (20%) | 1 812 312 (19%) | |
| Sleep duration, hours per day | 7.00 (6.00, 8.00) | 6.00 (5.00, 8.00) | 7.00 (6.00, 8.00) | 7.00 (7.00, 8.00) | < 0.001 |
| Diabetes mellitus, n(%) | 3 329 429 (8.4%) | 1 403 757 (28%) | 1 879 073 (7.6%) | 46 599 (0.5%) | < 0.001 |
| Hypertension, n(%) | 10 576 549 (27%) | 2 852 833 (57%) | 6 991 161 (28%) | 732 555 (7.5%) | < 0.001 |
| Body mass index, kg/m2 | < 0.001 | ||||
| < 25.0 | 12 159 461 (31%) | 327 968 (6.6%) | 5 889 102 (24%) | 5 942 391 (61%) | |
| 25.0–29.9 | 13 561 282 (34%) | 1 110 735 (22%) | 9 216 717 (37%) | 3 233 830 (33%) | |
| 30.0–34.9 | 7 796 585 (20%) | 1 414 720 (28%) | 5 908 925 (24%) | 472 939 (4.8%) | |
| 35.0–39.9 | 3 431 289 (8.7%) | 988 158 (20%) | 2 376 013 (9.6%) | 67 118 (0.7%) | |
| ≥ 40 | 2 682 952 (6.8%) | 1 158 343 (23%) | 1 476 126 (5.9%) | 48 484 (0.5%) | |
| Systolic blood pressure, mmHg | 119 (110, 129) | 131 (120, 142) | 120 (112, 131) | 112 (105, 117) | < 0.001 |
| Diastolic blood pressure, mmHg | 71 (65, 78) | 75 (66, 83) | 72 (65, 79) | 68 (62, 73) | < 0.001 |
| Total cholesterol, mg/dL | 192 (165, 219) | 209 (182, 240) | 195 (169, 222) | 176 (157, 200) | < 0.001 |
| HDL cholesterol, mg/dL | 51 (42, 62) | 46 (38, 54) | 50 (41, 61) | 56 (47, 67) | < 0.001 |
| Non-HDL cholesterol, mg/dL | 137 (113, 166) | 162 (135, 195) | 141 (117, 168) | 118 (100, 140) | < 0.001 |
| HbA1c, % | 5.40 (5.20, 5.70) | 5.80 (5.50, 6.30) | 5.40 (5.20, 5.70) | 5.20 (5.00, 5.40) | < 0.001 |
| LnSII (systemic immunity-inflammation)c | 6.13 (5.82, 6.46) | 6.25 (5.95, 6.62) | 6.15 (5.83, 6.47) | 6.03 (5.75, 6.34) | < 0.001 |
| BUN (blood urea nitrogen), mg/dL | 12.0 (10.0, 15.0) | 12.0 (9.0, 16.0) | 12.0 (10.0, 15.0) | 12.0 (10.0, 15.0) | 0.9 |
| eGFR, ml/min/1.73m2 | 111 (96, 129) | 107 (90, 124) | 111 (95, 128) | 115 (101, 132) | < 0.001 |
| Plasma osmolality, mOsm/L | 278.0 (275.0, 281.0) | 279.0 (275.0, 282.0) | 278.0 (275.0, 281.0) | 278.0 (275.0, 280.0) | 0.002 |
| Plasma sodium, mmol/L | 139.00 (138.00, 141.00) | 139.00 (138.00, 141.00) | 139.00 (138.00, 141.00) | 139.00 (138.00, 141.00) | 0.075 |
| Plasma potassium, mmol/L | 3.90 (3.80, 4.20) | 4.00 (3.80, 4.20) | 4.00 (3.80, 4.10) | 3.90 (3.70, 4.10) | 0.022 |
| Plasma calcium, mmol/L | 9.40 (9.20, 9.60) | 9.40 (9.20, 9.60) | 9.40 (9.20, 9.60) | 9.40 (9.20, 9.60) | 0.091 |
| OAB (overactive bladder) | 5 406 687 (14%) | 1 424 266 (28%) | 3 266 713 (13%) | 715 709 (7.3%) | < 0.001 |
| LE8 (Life’s Essential 8) score | 69 (58, 79) | 44 (38, 48) | 67 (60, 73) | 85 (82, 89) | < 0.001 |
Median (Q1, Q3); n (%).
Design-based KruskalWallis test; Pearson’s χ²: Rao & Scott adjustment.
SII was calculated using the following formula: (platelet count × neutrophil count)/lymphocyte count; LnSSI natural logarithm of SII.
Quantification of CVH
Detailed guidance on the application of the LE8 scoring algorithm for each metric within the NHANES adult dataset is provided in Supplemental Digital Content Table 2, available at: http://links.lww.com/MS9/B243. The LE8 score includes eight metrics, covering four health behaviors and four health factors[16]. The research employed an ordinal point system, ranging from 0 to 100 points, to evaluate each health metric. A lower score denoted poorer health, while a higher score indicated better health, in line with the recommendations of Lloyd-Jones et al[5]. Each participant’s total LE8 score was calculated by summing all eight component scores and dividing by eight. In the analysis, the score was treated as both a continuous and a categorical variable. Cutoff points of 50 and 79 were used to categorize participants into low, moderate, and high CVH groups. These cutoff points were based on American Heart Association guidelines[5].
Table 2.
Association between Life’s Essential 8 score and OAB, OAB score, UUI score, and nocturia score.
| Model | Cardiovascular health level | ||||
|---|---|---|---|---|---|
| Low CVH (0–49) | Moderate CVH (50–79) | High CVH (80–100) | P for trend | Per 10-point increase | |
| OR for OAB | |||||
| Model 1 | Ref | 0.38(0.28,0.52) | 0.20(0.13,0.29) | < 0.001 | 0.69(0.63,0.75) |
| P-values | < 0.001 | < 0.001 | < 0.001 | ||
| Model 2 | Ref | 0.56(0.41,0.77) | 0.46(0.31,0.70) | < 0.001 | 0.82(0.76,0.89) |
| P-values | 0.001 | < 0.001 | < 0.001 | ||
| Model 3 | Ref | 0.65(0.47,0.91) | 0.59(0.38,0.91) | < 0.001 | 0.87(0.80,0.95) |
| P-values | 0.017 | 0.024 | 0.005 | ||
| OR for UUI score ≥ 1 | |||||
| Model 1 | Ref | 0.50(0.42,0.59) | 0.24(0.19,0.30) | < 0.001 | 0.73(0.70,0.76) |
| P-values | < 0.001 | < 0.001 | < 0.001 | ||
| Model 2 | Ref | 0.68(0.57,0.82) | 0.49(0.38,0.63) | < 0.001 | 0.85(0.81,0.89) |
| P-values | < 0.001 | < 0.001 | < 0.001 | ||
| Model 3 | Ref | 0.71(0.59,0.85) | 0.52(0.40,0.67) | < 0.001 | 0.86(0.81,0.90) |
| P-values | < 0.001 | < 0.001 | < 0.001 | ||
| OR for nocturia score ≥ 1 | |||||
| Model 1 | Ref | 0.60(0.50,0.71) | 0.36(0.30,0.44) | < 0.001 | 0.79(0.76,0.82) |
| P-values | < 0.001 | < 0.001 | < 0.001 | ||
| Model 2 | Ref | 0.87(0.72,1.04) | 0.77(0.62,0.95) | < 0.001 | 0.93(0.89,0.97) |
| P-values | 0.13 | 0.015 | 0.001 | ||
| Model 3 | Ref | 0.93(0.77,1.12) | 0.87(0.69,1.09) | < 0.001 | 0.96(0.92,1.01) |
| P-values | 0.4 | 0.2 | 0.086 | ||
Model 1 was an unadjusted model.
Model 2 was adjusted for age (as a continuous variable), sex (male or female), race and ethnicity (non-Hispanic Black, non-Hispanic White, Mexican American, other Hispanic), marital status (married/living with partner, never married, widowed/divorced/separated), PIR (categorized as low income < 1.30, middle income 1.30–3.49, and high income ≥ 3.50), and educational level (less than high school, high school, college or above).
Model 3 included all covariates in Model 2 plus additional adjustments for alcohol consumption group, LnSII, BUN, eGFR, UA, plasma osmolality, calcium, potassium, and sodium.
The abbreviations are spelled out in Table 1.
Covariates
The covariates included in this analysis covered a range of demographic variables, such as age, gender (male and female), race and ethnicity (non-Hispanic Black, non-Hispanic White, Mexican American, other Hispanic, and other race – including multi-racial), marital status (married/living with partner, never married, widowed/divorced/separated), poverty-to-income ratio (PIR) (< 1.30, 1.30–3.49, and ≥ 3.50), and educational attainment (< high school, high school, college or above). Drinking status was categorized into 10 + drinks/month, 1–10 drinks/month, and non-drinker. Laboratory measurements included platelet count, neutrophil count, lymphocyte count, serum creatinine, total calcium, serum potassium, serum sodium, serum phosphorus, uric acid (UA), total cholesterol, and triglyceride levels. Plasma osmolality was calculated using the formula described by Worthley et al[17]: Plasma osmolality (mOsm/kg) = [(1.86 × Na) + (GLUC/18) + (BUN/2.8) + 9]. All covariates were obtained from the NHANES database and were measured or calculated following standardized protocols to ensure the accuracy and consistency of the data. The systemic immune-inflammation index (SII) was computed using the formula: SII = (platelet count × neutrophil count)/lymphocyte count. For analysis, the natural logarithm of SII (LnSII) was used. The estimated glomerular filtration rate (eGFR) was calculated according to the CKD-EPI creatinine equation[18].
Statistical analyses
To generate nationally representative estimates, all analyses incorporated the NHANES dietary day one sample weights (2005–2020) and accounted for the complex survey design, including stratification and clustering[19]. The use of sample weights is essential to account for the unequal probability of selection, non-response, and oversampling of certain subgroups, thereby ensuring that the results are generalizable to the non-institutionalized civilian U.S. population. Due to pandemic-related disruptions in 2019–2020, pre-pandemic data (March 2017–March 2020) were combined with 2005–2016 cycles. Survey weights were adjusted to reflect the total 15.2-year period: weights from 2005 to 2016 were multiplied by 2/15.2, and those from 2017 to 2020 by 3.2/15.2[20,21].
Categorical variables are summarized as weighted counts (proportions) and compared using Rao-Scott corrected chi-square tests. Continuous variables are reported as weighted medians (IQR) or means ± SE, as appropriate. Group comparisons employed weighted t-tests or ANOVA. Standard errors were estimated via Taylor linearization.
Associations between OAB-related outcomes (OAB, OAB score, UUI score, nocturia score) and LE8 score – analyzed both categorically (high/medium/low CVH) and continuously (per 10-point increase) – were evaluated using weighted multivariable logistic regression. Three models were constructed:
Model 1: Unadjusted.
Model 2: Adjusted for age, sex, race, marital status, PIR, and education.
Model 3: Additionally adjusted for alcohol use, LnSII, blood urea nitrogen (BUN), eGFR, UA, osmolality, calcium, potassium, and sodium.
Non-linear relationships were examined using restricted cubic splines (knots at the 5th, 35th, 65th, and 95th percentiles) within weighted logistic regression models for binary outcomes (e.g., OAB) or linear regression models for continuous outcomes (e.g., OAB score, UUI score, nocturia score). Model selection was guided by AIC via a backward stepwise procedure. The likelihood ratio test assessed non-linearity (P < 0.05). Threshold effects were evaluated via segmented logistic regression with bootstrap-derived breakpoints, using quasi-binomial models to handle overdispersion.
Prespecified subgroup analyses were conducted by age, sex, race, marital status, PIR, education, hypertension, and diabetes. Interaction terms were included to test effect modification. Sensitivity analyses included unweighted logistic regression and repeated RCS analyses after sequentially excluding each LE8 component.
Mediation analyses assessed potential mediating roles of LnSII and plasma osmolality in the LE8–OAB association, using the product-of-coefficients bootstrap approach (1000 replicates) to estimate average causal mediation effect (ACME), average direct effects (ADE), total effect, and proportion mediated. Sensitivity to unmeasured confounding was evaluated by varying the residual error correlation (ρ) between −0.6 and 0.6.
The relative contribution of each LE8 component to OAB outcomes was assessed using generalized linear models below outcome-specific LE8 score thresholds. Component scores were standardized, and contributions were determined based on the absolute values of standardized beta coefficients derived from models adjusted for demographic, clinical, and biochemical covariates. To evaluate and compare the relative importance of each component, we calculated percentage contributions as follows: (1) standardizing the regression coefficients (β); (2) taking the absolute values of the standardized β; (3) summing the absolute β values across all components; and (4) computing each component’s percentage contribution as (|β_component|/∑|β|) × 100.
All analyses were performed in R v4.2.2 (R Foundation) with two-tailed α = 0.05.
Results
Population characteristics
The analytical sample comprised 6042 participants (13% of the initial NHANES cohort), with a median age of 46 years (IQR: 32–59) and a balanced sex distribution (51% female). As shown in Supplemental Digital Content Table 3, available at: http://links.lww.com/MS9/B243, the included and excluded groups were generally comparable in sex, marital status, and income level. However, included participants were older, more likely to be non-Hispanic White, and had higher educational attainment.
Table 3.
Segmented logistic regression analysis for OAB, OAB score, UUI score, and nocturia score.
| Variable | β(SE) | OR (95% CI) | P-value |
|---|---|---|---|
| Break-point for OAB: 73.13, SE = 4.88 | |||
| Segment 1(≤ 73.13) | −0.021(0.005) | 0.979(0.972,0.987) | < 0.001 |
| Segment 2(> 73.13) | 0.010(0.012) | 1.010(0.987,1.035) | NA |
| Break-point for OAB score: 72.50, SE = 2.72 | |||
| Segment 1(≤ 72.50) | −0.014(0.002) | / | < 0.001 |
| Segment 2(> 72.50) | 0.019(0.004) | / | NA |
| Break-point for UUI score: 67.73, SE = 3.59 | |||
| Segment 1(≤ 67.73) | −0.007(0.001) | / | < 0.001 |
| Segment 2(> 67.73) | −0.001(0.001) | / | NA |
| Break-point for UUI score: 73.12, SE = 3.41 | |||
| Segment 1(≤ 73.12) | −0.007(0.002) | / | < 0.001 |
| Segment 2(> 73.12) | 0.005(0.003) | / | NA |
Variable selection was conducted via backward stepwise elimination using the Akaike Information Criterion (AIC). The initial full model incorporated the following covariates: sex, age, race, marital status, education level, PIR, alcohol consumption group, LnSII, BUN, eGFR, UA, plasma osmolality, calcium, potassium, and sodium.
The abbreviations are spelled out in Table 1.
When stratified by CVH level based on LE8 scores, pronounced gradients were observed across groups (Table 1). Participants in the high CVH group (LE8: 80–100) were younger (median: 38 years), had higher educational attainment and income, and were more likely to be never smokers and physically active compared to those with low CVH (LE8: 0–49). In contrast, the low CVH group exhibited a higher prevalence of diabetes (28%) and hypertension (57%), along with less favorable cardiometabolic profiles.
The distribution of LE8 component scores is detailed in Supplemental Digital Content Table 4, available at: http://links.lww.com/MS9/B243. Significant differences were observed across CVH levels for all metrics (all P < 0.001), with the high CVH group demonstrating superior scores in physical activity, nicotine exposure, sleep health, BMI, blood pressure, lipids, glucose, and diet. Similarly, the prevalence and severity of OAB varied significantly by CVH level (Supplemental Digital Content Table 5, available at: http://links.lww.com/MS9/B243). Higher OAB scores and greater severity of urgency incontinence and nocturia were more prevalent in the low CVH group (all P < 0.001).
Associations of LE8 with OAB
A strong inverse association was observed between the LE8 score and both the presence and severity of OAB. In the fully adjusted model (Model 3), participants with moderate CVH and those with high CVH had significantly lower odds of OAB compared to those with low CVH [odds ratio (OR) 0.65, 95% confidence interval (CI): 0.47–0.91, and OR 0.59, 95% CI: 0.38–0.91, respectively; P-trend < 0.001] (Table 2). Each 10-point increase in the LE8 score was associated with a 13% reduction in the odds of OAB (OR 0.87, 95% CI: 0.80–0.95, P = 0.005). Similar graded inverse associations were observed for UUI and nocturia. Higher LE8 scores were consistently associated with lower odds of UUI (e.g., Model 3: OR 0.86 per 10-point increase, 95% CI: 0.81–0.90, P < 0.001). For nocturia, the association was attenuated after full adjustment but remained significant in the trend analysis.
Notably, RCS analyses revealed significant nonlinear relationships between LE8 score and all OAB-related outcomes (all P-nonlinear < 0.001) (Fig. 1). Segmented regression identified inflection points at LE8 scores of 73.13 for OAB and 72.50 for OAB score, indicating a threshold beyond which the protective association plateaued (Table 3). Similarly, the UUI score exhibited inflection points at 67.73 and 73.12, suggesting differential dose–response patterns across symptom domains.
Subgroup and sensitivity analyses
Subgroup analyses revealed that the inverse association between the LE8 score and OAB was consistent across most demographic and clinical strata, although effect sizes varied (all P-interaction < 0.05 for age, sex, marital status, and diabetes mellitus). The protective association was more pronounced in younger participants (20–60 years), males, never-married individuals, and those without diabetes. For instance, the multivariable-adjusted OR for OAB in the high CVH group was 0.23 (95% CI: 0.11–0.49) in males and 0.31 (95% CI: 0.20–0.48) in females (P-interaction = 0.011). Similarly, the association was stronger in non-diabetic participants (OR 0.23, 95% CI: 0.15–0.36) than in those with diabetes (P-interaction = 0.004). No significant effect modification was observed by race, education, income, drinking status, or hypertension status (all P-interaction > 0.05) (Supplemental Digital Content Table 7, available at: http://links.lww.com/MS9/B243). Restricted cubic spline models confirmed consistent nonlinear relationships across age and sex subgroups (Supplemental Digital Content Figure 2, available at: http://links.lww.com/MS9/B242).
Figure 2.
Mediation analysis of the association between LE8 score and overactive bladder (OAB) by systemic immunity-inflammation index (SII) and plasma osmolality. The analysis demonstrates the average direct effect (ADE) of LE8 on OAB and the average causal mediation effect (ACME) through (A) natural log-transformed SII (LnSII) and (B) plasma osmolality. Models are adjusted for all covariates used in the primary analysis. The proportion of the total effect mediated was 4.8% for LnSII and 12.8% for plasma osmolality. Both the direct effects and mediation effects were statistically significant (P < 0.001).
Sensitivity analyses further supported the robustness of the main findings. Unweighted logistic regression yielded similar effect estimates (Supplemental Digital Content Table 6, available at: http://links.lww.com/MS9/B243). Moreover, iterative exclusion of individual LE8 components did not substantially alter the nonlinear association between the LE8 score and OAB or its related symptoms, indicating that the overall CVH construct, rather than any single metric, drives the observed association (Supplemental Digital Content Figure 3, available at: http://links.lww.com/MS9/B242).
Figure 3.
Relative contribution of individual Life’s Essential 8 (LE8) components to urological outcomes. Contributions are expressed as the percentage of the total explained association based on normalized absolute beta coefficients from multivariable models for (A) OAB (logistic regression), (B) OAB score, (C) UUI score, and (D) nocturia score (linear regression). Analyses were performed below the identified outcome-specific LE8 score thresholds. BMI consistently emerged as the strongest contributor across all outcomes.
Mediation analysis
Causal mediation analysis identified both LnSII and plasma osmolality as significant mediators of the association between the LE8 score and OAB. The ACME for LnSII accounted for 4.8% of the total effect. Similarly, plasma osmolality also mediated a significant proportion of this association, with a proportion mediated of 12.8%. The ADE of the LE8 score on OAB remained significant in both models (P < 0.001), consistent with partial mediation (Fig. 2).
Sensitivity analyses demonstrated that the mediation effects remained robust against potential unmeasured confounding across a broad range of residual error correlations (ρ), including values as high as |0.3|. These findings reinforce the reliability of the identified mediation pathways (Supplemental Digital Content Figure 4, available at: http://links.lww.com/MS9/B242).
Analysis of component contributions
In multivariable analyses below outcome-specific LE8 thresholds, several components were significantly associated with urological outcomes. For OAB, sleep health (β = –0.177, OR = 0.838, P < 0.001), BMI (β = –0.312, OR = 0.732, P < 0.001), and blood glucose (β = –0.146, OR = 0.865, P = 0.002) were associated with reduced risk. For the OAB score, significant associations were observed for physical activity (β = –0.051, P = 0.011), sleep health (β = –0.102, P < 0.001), BMI (β = –0.170, P < 0.001), blood lipids (β = 0.047, P = 0.033), and blood glucose (β = –0.066, P = 0.012). For the UUI score, only BMI demonstrated a significant negative association (β = –0.074, 95% CI: –0.102 to –0.047, P < 0.001). For the nocturia score, significant negative associations were identified for sleep health (β = –0.082, P < 0.001), BMI (β = –0.097, P < 0.001), blood pressure (β = –0.037, P = 0.049), and blood glucose (β = –0.054, P = 0.007).
The relative contributions of each LE8 component, derived from normalized absolute beta coefficients in adjusted models, indicated that BMI was the largest contributor across all outcomes: OAB (32.5%), OAB score (33.7%), UUI (43.0%), and nocturia (27.8%). Sleep health also consistently contributed substantially, particularly for OAB (18.4%) and nocturia (23.5%). These findings highlight the predominant role of metabolic and sleep-related factors in urological health (Fig. 3, Supplemental Digital Content Table 8, available at: http://links.lww.com/MS9/B243 for detailed statistical results).
Discussion
This large, cross-sectional study demonstrates a robust, graded inverse association between CVH, as assessed by the LE8 score, and the prevalence and severity of OAB and its related symptoms, including UUI and nocturia. Several key findings emerge from our analysis. First, the association is nonlinear, exhibiting a threshold effect beyond which further improvements in CVH yield diminishing protective returns. Second, this relationship is consistent across most demographic subgroups but is modified by factors such as age, sex, and diabetes status. Third, systemic inflammation (as reflected by LnSII) and plasma osmolality were identified as significant mediators, accounting for a portion of the association. Finally, among the LE8 components, BMI and sleep health emerged as the most substantial contributors to urological health outcomes. These findings underscore the integral role of comprehensive cardiovascular/metabolic health in the pathogenesis of OAB and suggest that promoting overall CVH may be a valuable strategy for its prevention and management.
The strong dose–response inverse relationship between the LE8 score and OAB aligns with a growing body of evidence linking cardiometabolic risk factors to lower urinary tract dysfunction[22]. Our findings extend prior research by utilizing a contemporary, holistic metric of CVH that encompasses both health behaviors and biological factors. The observed nonlinearity, with inflection points around LE8 scores of 67–73, is a novel and clinically significant insight. It suggests that there is a level of CVH attainment beyond which the marginal benefit for urological health plateaus. This implies that for patients with LE8 scores below this threshold (e.g., < 73), intensive, multi-faceted lifestyle interventions aimed at improving CVH could yield substantial benefits for OAB symptoms. Conversely, for those already above the threshold, the focus might shift from further broad CVH improvement to managing specific, persistent risk factors like elevated BMI or poor sleep, which our component analysis identified as dominant contributors.
The subgroup analyses revealed important heterogeneity in the strength of the association. The more pronounced protective effect observed in younger adults (< 60 years) highlights the critical importance of primordial and primary prevention. Early intervention to maintain ideal CVH may confer greater protective benefits against the development of OAB later in life[22]. The stronger association in males and non-diabetic individuals warrants further investigation. It may suggest that in high-risk groups (e.g., those with established diabetes), the pathophysiology of OAB is driven by more advanced and potentially irreversible end-organ damage, making it less responsive to variations in overall CVH[23]. Alternatively, the finding could reflect differences in disease etiology between sexes and glycemic states[24].
Our mediation analysis provides preliminary mechanistic insights into the link between CVH and OAB. The significant mediating roles of systemic inflammation and plasma osmolality propose two plausible pathways. Chronic low-grade inflammation, a hallmark of poor cardiometabolic health, may contribute to bladder dysfunction through microvascular damage, neural sensitization, and detrusor muscle fibrosis[8,25]. Similarly, higher plasma osmolality, often a consequence of poor diet (e.g., high sodium intake)[26] and subclinical metabolic dysregulation[27], may influence urinary frequency and nocturia through alterations in renal concentrating ability and thirst mechanisms. Moreover, these pathways may be interrelated, as inflammation can influence fluid-regulating hormones and renal function, while hyperosmolality may trigger inflammatory responses[28,29]. The fact that the direct effect of LE8 remained highly significant after accounting for these mediators indicates that other unmeasured pathways, such as autonomic nervous system dysfunction, endothelial function, or hormonal factors[9,30], are also likely involved.
The component-wise analysis offers granularity for targeted interventions. The consistent dominance of BMI as the largest contributor across all OAB outcomes reinforces the well-established link between obesity and urological dysfunction[31]. Emerging evidence reveals a specific molecular mechanism: adipose tissue dysfunction in obesity leads to dysregulation of adipokines such as adiponectin. Adiponectin, the most abundant adipokine with insulin-sensitizing and anti-inflammatory properties, is inversely associated with obesity. Experimental studies have demonstrated that adiponectin-knockout mice exhibit increased voiding frequency, reduced bladder smooth muscle contractility, and absence of purinergic contraction – key pathways for normal bladder emptying[32,33]. This suggests that adiponectin signaling directly regulates bladder smooth muscle function, and its disruption in obesity may represent a critical pathway linking elevated BMI to OAB pathogenesis.
The substantial contribution of sleep health, particularly for OAB and nocturia, is a notable finding that reinforces the bidirectional relationship between sleep and overall health[34]. Normal micturition follows a circadian rhythm: during nighttime, increased arousal threshold, elevated secretion of melatonin and vasopressin (antidiuretic hormone), and reduced bladder afferent sensory input collectively minimize urine production and maintain sufficient bladder capacity[35]. Disrupted sleep health – whether due to sleep disorders, irregular lifestyle, or circadian misalignment – impairs this finely orchestrated system. Specifically, poor sleep leads to reduced nocturnal vasopressin secretion and elevated natriuretic peptides, contributing to nocturnal polyuria[36]. This bidirectional relationship – where nocturia fragments sleep and poor sleep exacerbates nocturia – creates a self-perpetuating cycle that amplifies symptom severity[37]. Interestingly, diet, a cornerstone of CVH, did not emerge as a top contributor in the component-specific models below the threshold. This may suggest that its influence is more indirectly mediated through its effects on other components like BMI, blood glucose, and lipids, which were significantly associated[38].
Limitations and strengths
The interpretation of our findings should be considered within the context of several limitations. First, the cross-sectional nature of our data limits inferences regarding causality and temporality. Although mediation analyses were conducted to explore potential mechanisms, residual confounding due to unmeasured variables – such as detailed neurological history, pelvic floor morphology, or genetic predispositions – cannot be ruled out. Second, the diagnosis of OAB was based on symptom scores derived from the NHANES questionnaire rather than a formal clinical diagnosis, which may introduce misclassification bias. However, the OABSS used in this study is a well-validated tool, as detailed in the Methods section, which helps mitigate this concern. Third, despite the large overall sample size, the analytic cohort constituted a selected subset of the original NHANES population, which may affect the generalizability of our results. Finally, the extensive subgroup analyses performed, while informative, increase the risk of type I error due to multiple testing. To mitigate this concern, we have interpreted these exploratory findings with caution, prioritizing the consistency and clinical relevance of effect estimates over mere statistical significance.
Notable strengths include the use of a large, nationally representative dataset with detailed phenotyping, the application of a modern CVH scoring system, comprehensive adjustment for confounders, rigorous sensitivity analyses, and the exploration of nonlinear relationships and mechanistic pathways.
Conclusion
In conclusion, this study provides compelling evidence that ideal CVH, as defined by a high LE8 score, is strongly associated with a lower risk and severity of OAB. The relationship is nonlinear, exhibits threshold effects at approximately 73 points, and is partially mediated by inflammation and plasma osmolality, with BMI and sleep health emerging as the predominant contributing components. From a public health and clinical perspective, our results advocate for the integration of OAB prevention into the framework of primary cardiovascular disease prevention. Specifically, for individuals with LE8 scores below 73, targeted interventions to improve CVH may confer dual benefits for both cardiometabolic and urological outcomes. Prioritizing improvements in BMI and sleep health represents a potentially cost-effective strategy for preventing or ameliorating OAB symptoms, as these factors are modifiable through lifestyle interventions and have well-established cardiovascular benefits. Future longitudinal and interventional studies are needed to confirm causality and evaluate the effectiveness of such integrated prevention approaches.
Acknowledgements
The authors acknowledge the United States Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) for providing the NHANES 2005–2020 data.
Footnotes
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.lww.com/annals-of-medicine-and-surgery.
Contributor Information
Min Yan, Email: 372263748@qq.com.
Xiaowei Jiang, Email: Jiangxw@csu.edu.cn.
Ethical approval
Ethical approval for the NHANES study was obtained from the NCHS Research Ethics Review Board. The current analysis used de-identified public data and was exempt from further ethical review.
Consent
As this study utilized a publicly available, de-identified dataset, informed consent was waived.
Sources of funding
This work was supported by the Hunan Provincial Natural Science Foundation [Grant No. 2022JJ40794].
Author contributions
M.Y.: Data curation, formal analysis, writing – original draft. Xiaowei Jiang: Conceptualization, methodology, supervision, writing – review and editing.
Conflicts of interest disclosure
The authors declare that there are no conflicts of interest.
Research registration unique identifying number (UIN)
Not applicable. This study is a retrospective cross-sectional analysis of publicly available, de-identified data from the NHANES database and did not involve a prospective intervention or clinical trial registration.
Guarantor
Xiaowei Jiang.
Provenance and peer review
Not commissioned; externally peer-reviewed.
Data availability statement
The data supporting this study are publicly available from the NHANES repository at https://www.cdc.gov/nchs/nhanes/.
Assistance with the study
None.
Presentation
None.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting this study are publicly available from the NHANES repository at https://www.cdc.gov/nchs/nhanes/.