Abstract
OBJECTIVES
To operationalize a new definition for bladder health, we examined the distribution of lower urinary tract symptoms (LUTS) and impact, along with associated factors, among women in the Coronary Artery Risk Development in Young Adults (CARDIA) study.
METHODS
We performed cluster analyses using validated LUTS symptom burden and impact scales collected between 2005–2006 and 2010–2011. We performed multinomial logistic regression analyses to evaluate cardiovascular factors (metabolic syndrome, cardiovascular health behaviors, and inflammation) between clusters after adjusting for covariates (demographic, obstetric/gynecologic, comorbidities).
RESULTS
Among CARDIA women (median age 51, range 42–59) with complete LUTS data (n = 1302), we identified and compared 4 cluster groups: women who reported no or very mild symptoms and no impact on well-being (bladder health, 44%, n = 569), versus women with LUTS and negative impact on well-being ranging from mild (31%, n = 407), moderate (20%, n = 259), to severe (5%, n = 67). With each 1-point lower BMI (kg/m2), odds of membership in mild (OR 0.97, CI 0.95–0.99), moderate (OR 0.95, CI 0.93–0.98), and severe (OR 0.90, CI 0.88–0.94) LUTS cluster groups versus the bladder health group were lower. Compared to women with metabolic syndrome, women without metabolic syndrome had lower odds of membership in mild (OR 0.67, CI 0.45–0.99), moderate (OR 0.51, CI 0.33–0.79), and severe (OR 0.48, CI 0.24–0.94) LUTS cluster groups versus the bladder health group.
CONCLUSION
Two out of 5 midlife women met our definition of bladder health. Bladder health and cardiovascular health among women may share common factors, including lower BMI and the absence of metabolic syndrome.
BACKGROUND
Lower urinary tract symptoms (LUTS) include storage symptoms, such as incontinence, urgency, and frequency; and emptying symptoms, such as weak stream, hesitancy, and interrupted stream.1 LUTS are common in women (40%−70%) and have a negative impact on quality of life.2,3 The Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium defines bladder health as: “A complete state of physical, mental, and social well-being related to bladder function [that] permits daily activities [and] allows optimal well-being.”4 From existing community-based cohort data, this definition of bladder health was applied by integrating data on LUTS and their interference with activities.5
Very few community- and population-based studies have extensive LUTS data along with data on the impact of LUTS on well-being that would enable the evaluation of the distribution of bladder health according to the PLUS Consortium definition.6,7 A prior publication from Hall et al. sought to quantify the spectrum of LUTS among women aged 30–79 years of age who completed the Boston Area Community Health (BACH) Survey, drawing upon its collection of information on LUTS. Using cluster analytic methodology, this study found 4 distinct clusters varying from the absence of LUTS (24% asymptomatic) to mild LUTS (41%), moderate LUTS (18.2%), and severe/very severe LUTS (10.4%).7 However, the authors did not account for the impact of LUTS on daily activities and well-being in the clusters. In a recent re-analysis of the BACH data conducted by the PLUS Consortium, a more holistic definition of bladder health was applied by integrating data on LUTS and their interference with activities. As a result, lower numbers of women were classified as having bladder health (17.5%), defined as the absence of LUTS and without bother, whereas the remaining 82.5% reported some frequency of LUTS and interference: 15.1% rarely; 21.7% a few times; 22.6% fairly often/usually; and 22.9% almost always.5
Using this definition, which does not define bladder health as the absence of LUTS, researchers can better describe bladder health estimates and guide future LUTS prevention research. To provide novel data among women on the distribution of bladder health using the PLUS definition and evaluate potential shared factors related to cardiovascular health, we operationalized bladder health with data from the population-based Coronary Artery Risk Development in Adults (CARDIA) study. In addition, we examined whether cardiovascular factors collected through CARDIA distinguish women with bladder health from women with LUTS.
METHODOLOGY
Study Population
CARDIA is a longitudinal cohort study that recruited from the populations of four U.S. cities (Birmingham, AL; Minneapolis, MN; Chicago, IL; and Oakland, CA). CARDIA initially was designed to study the evolution of cardiovascular risk and disease. At baseline (1985–1986), 5,115 black and white women and men aged 18–30 years participated in interviews and examinations. Details on study design have been previously reported.8 Written informed consent was obtained at each exam, and the IRB at each center approved the study protocols. In-person follow-up examinations were conducted 2, 5, 7, 10, 15, 20, 25 and 30 years after baseline with response rates of 91%, 86%, 81%, 79%, 74%, 72%, 72% and 71% of the surviving cohort, respectively. Details of quality control activities that were conducted at CARDIA field centers, the coordinating center, laboratories, and reading centers are available at http://www.cardia.dopm.uab.edu.
Analytic Sample
CARDIA collected data related to benign genitourinary conditions that included LUTS between the year 25 (2010–2011) and year 30 examinations (2015–2016). Data were collected via questionnaire mailings in 2014 (n = 1,981).
Bladder Health and LUTS Definitions
We used validated questionnaires to define four constructs: LUTS severity, LUTS impact, UI severity, and UI impact. The presence and severity of LUTS were measured with the American Urologic Association Symptom Index (AUASI) score. Summed scores ranged from 0–35, with higher scores indicating more severe symptoms.9–11 We defined categories for LUTS severity based on prior publications: no symptoms (score 0); mild symptoms (score 1–7); moderate symptoms (score 8–19); and severe symptoms (score 20–35). LUTS impact on quality of life (QOL) was measured with one question from the AUASI with 7 response options that we collapsed into 4 categories: “pleased or delighted” (0 or 1); “mostly satisfied” (2); “mixed satisfied/dissatisfied” (3); and “mostly dissatisfied,” “unhappy,” or ”terrible” (4, 5, or 6). UI severity was measured with 2 questions that ascertained frequency and amount of urine leakage. The total severity score ranged from 0 to 12 with severity categories used for our analyes.12 UI impact on well-being was measured using the Incontinence Impact Questionnaire with scaled scores ranging from 0–100 and UI impact categories defined as: no impact (score 0); mild impact (score 1–25); moderate impact (score 26–50); and great or very great impact (score 51–100).13 Missing data rates on questionnaires ranged from 4.8% for LUTS severity, 3.7 % for LUTS impact, 2.1% for UI severity, and 2.2% for UI impact (data not shown in tables).
Cardiovascular Factors of Interest
Our main factors of interest are related to cardiovascular factors, including metabolic syndrome, cardiovascular health behaviors (physical activity, tobacco use status at 25 years, fast food and sugared beverage intake, and a composite health behavior score), and high-sensitivity C-Reactive Protein (hs-CRP) as a marker of inflammation. For the definition of metabolic syndrome, women had to meet 3 out of 5 criteria: central, visceral, abdominal obesity, with a waist size more than 35 inches (for women); fasting blood glucose levels of 100 mg/dL or above; blood pressure of 130/85 mm/Hg or above; blood triglycerides of 150 mg/DL or above; and high-density lipoprotein levels of 50 mg/DL or less (for women).14 The cardiovascular health behavior index combined known factors related to cardiovascular health, including physical activity score, tobacco use status at year 25, and consumption of specific sugar sweetened beverages and fast food meals. It ranged from 0–6, with higher scores indicating more ideal behaviors.15 We used a nonspecific biomarker of systemic inflammation, hs-CRP, which has been linked with both LUTS severity and hypertension.16,17 At year 25, hs-CRP was measured using fasting plasma samples with a Roche latex-particle enhanced immunoturbidimetric assay kit and read on the Roche Modular P Chemistry analyzer. The assay range for hs-CRP was 0.175 to 1.100 mg/mL.17
Covariates
At each examination, participants completed questionnaires. Covariates included sociodemographic variables (age, race, and educational attainment), obstetric and gynecologic history (parity, history of oral contraceptive use, hysterectomy, oophorectomy, and menopausal status), and medical history (hypertension, diabetes, and asthma). From the physical examination, participants had weight (kg), height (m), and waist circumference (cm) measured. Body mass index (kg/m2) was used as a continuous measure and also defined as obesity ≥30 kg/m2.
Cluster Analysis
We used a cluster analysis approach that combined LUTS severity, LUTS impact, UI severity, and UI impact, similar to the methods by Hall et al. in BACH.7 We first range-standardized the four factors (from 0 to 1) and used the standardized factors for cluster analysis. The spider plot shown in Figure 1 was constructed based on the standardized factors. To identify an appropriate number of clusters, we considered both experts’ input and the result from the elbow method under the k-means clustering algorithm (“fviz_nbclust”, R). Then, we applied hierarchical clustering, in particular, agglomerative clustering, to the data using Ward’s method to calculate the distance between clusters and identified the partition of the observations into four groups (“hclust”, R). The last step involved validating the cluster solutions internally, and we validated that observations between clusters were well separated and those within a cluster were compact.
Figure 1.
Spider plot for bladder health and LUTS severity/impact cluster analysis, n = 1302.
After defining distinct bladder health and LUTS clusters, we used descriptive statistics to compare covariates for sociodemo-graphic, obstetrical/gynecologic, medical comorbidities, metabolic syndrome, cardiovascular health behavioral factors, and an inflammatory biomarker for inflammation across the cluster groups. In addition, we built models to identify the relationship between covariates and LUTS clusters. Because the proportional odds assumption was not valid across all the models, we chose multinomial logistic regression for our model analysis. The three clusters of mild, moderate, and severe symptoms were compared to the bladder health cluster individually for covariates in specific blocks, controlling for age, tobacco use, and BMI.
RESULTS
Out of the 1,981 surviving CARDIA women who completed the year 25 examination, LUTS questionnaires were subsequently completed by 1,465 women (74.0%). More white women (54%) compared to black women (46%) completed the LUTS questionnaires, P < .001. A total of 1,302 women had complete data on all four LUTS questionnaires used in the analysis.
With the four-factor cluster approach (Table 1 and Fig. 1), we identified four clusters of women who reported no or very mild symptoms of LUTS and UI and no impact (defined here as bladder health) versus mild, moderate, or severe symptoms and impact. Among women with complete data (n = 1302), 44% (n = 569) were classified into the bladder health cluster compared to those classified into mild (31%, n = 407), moderate (20%, n = 259), and severe (5%, n = 67) symptoms/impact clusters. Table 1 shows that the mean values for each variable (UI severity, UI impact, LUTS severity, LUTS impact) increased across the four 4 cluster groups. Severity means were very low and impact means were close to zero within the bladder health group. Only 4.4% of women reported no LUTS and 39.9% no UI (supplemental table).
Table 1.
Four clusters of bladder health and symptoms for key constructs by symptom severity and burden (N = 1302)
| Cluster N (%) |
Bladder Health 569 (43.7) |
Mild Symptoms 407 (31.3) |
Moderate Symptoms 259 (19.9) |
Severe Symptoms 67 (5.1) |
|
|---|---|---|---|---|---|
| Symptoms | |||||
| UI Severity, range 0–12 | Standardized Mean (SD)* | 0.04 (0.07) | 0.15 (0.13) | 0.31 (0.18) | 0.65 (0.24) |
| Original Mean (SD) | 0.50 (0.83) | 1.85 (1.57) | 3.74 (2.12) | 7.81 (2.89) | |
| LUTS Severity, range 0–35 | Standardized Mean (SD)* | 0.11 (0.08) | 0.22 (0.11) | 0.37 (0.19) | 0.55 (0.21) |
| Original Mean (SD) | 3.52 (2.67) | 6.93 (3.61) | 11.69 (5.91) | 17.57 (6.58) | |
| Impact | |||||
| UI Impact, range 0–100 | Standardized Mean (SD)* | 0.003 (0.03) | 0.02 (0.07) | 0.08 (0.10) | 0.37 (0.27) |
| Original Mean (SD) | 0.26 (2.79) | 2.29 (6.66) | 7.76 (9.71) | 37.39 (26.90) | |
| LUTS QOL, Likert Scale 0–7 | Standardized Mean (SD)* | 0.08 (0.08) | 0.35 (0.10) | 0.59 (0.15) | 0.77 (0.18) |
| Original Mean (SD) | 0.45 (0.50) | 2.09 (0.61) | 3.54 (0.89) | 4.64 (1.06) |
Range-standardization used for the validated scales, range from 0 to 1.
Table 2 shows that cluster groups did not differ according to mean age (P = .50) or race (P = .19); however, educational attainment differed across the groups (P = .01), with more educated women being classified into the bladder health cluster. Cluster groups had differences according to obstetric and gynecologic history for parity rates (P = .05) and use of oral contraceptives (P = .03). Key differences existed for medical history variables among the cluster groups for obesity prevalence (P < .001), mean BMI (P < .001), hypertension (P = .02), and asthma (P < .001), as well measured waist circumference (P < .001) and metabolic syndrome prevalence (P < .001). Cardiovascular health behavior scores differed (P = .002) across the cluster groups; women in the bladder health and mild symptom clusters (score 3.8±1.5 for both) and moderate symptom cluster (score 3.7±1.4) had better health than women in the severe cluster group (3.0±1.5). No differences between groups were seen in the individual components of the cardiovascular behavior score. Mean CRP levels differed across the cluster groups (P < .001).
Table 2.
CARDIA Y25 associated factors in relation to bladder health/LUTS clusters
| Total Sample (n = 1302) | Bladder Heath (n = 569) | Mild Symptoms & Impact (n = 407) | Moderate Symptoms & Impact (n = 259) | Severe Symptoms & Impact (n = 67) | P-value† | |
|---|---|---|---|---|---|---|
| Age (y) at Y 25, Mean ± SD | 50.3 ± 3.6 | 50.2 ± 3.6 | 50.5 ± 3.6 | 50.1 ± 3.6 | 49.9 ± 4.1 | .50 |
| Race, n (%) | ||||||
| Black | 597 (45.9) | 272 (47.8) | 172 (42.3) | 117 (45.2) | 36 (53.7) | .19 |
| White | 705 (54.1) | 297 (52.2) | 235 (57.7) | 142 (54.8) | 31 (46.3) | |
| Educational Attainment, n (%) | ||||||
| High school or less | 219 (18.8) | 86 (17.2) | 63 (16.9) | 55 (23.6) | 15 (26.3) | .01 |
| Some college | 324 (27.9) | 150 (30.0) | 93 (25.0) | 59 (25.3) | 22 (38.6) | |
| College graduates | 619 (53.3) | 264 (52.8) | 216 (58.1) | 119 (51.1) | 20 (35.1) | |
| *Parity, Mean ± SD | 1.5 ± 1.3 | 1.4 ± 1.3 | 1.6 ± 1.3 | 1.6 ± 1.4 | 1.8 ± 1.3 | .05 |
| Oral Contraceptive Use (ever), n (%) | 948 (81.6) | 416 (83.2) | 309 (82.8) | 184 (79.3) | 39 (68.4) | .03 |
| Hysterectomy, n (%) | 213 (18.3) | 83 (16.6) | 67 (18.0) | 52 (22.4) | 11 (19.3) | .30 |
| Gone through menopause, n (%) | 491 (42.4) | 206 (41.4) | 162 (43.5) | 88 (38.1) | 35 (61.4) | .06 |
| *Obesity, BMI ≥30 mg/kg2 | 510 (43.9) | 193 (38.5) | 166 (44.7) | 113 (48.5) | 38 (66.7) | <.001 |
| *BMI, Mean ± SD | 30.2 ± 7.9 | 28.9 ± 7.3 | 30.1 ± 7.4 | 31.5 ± 8.3 | 36.2 ± 10.3 | <.001 |
| Waist Circumference, Mean ± SD | 89.9 ± 16.1 | 87.1 ± 15.2 | 89.9 ± 15.5 | 93.0 ± 16.0 | 102.5 ± 20.0 | <.001 |
| Hypertension, n (%) | 382 (32.8) | 148 (29.5) | 118 (31.6) | 91 (39.1) | 25 (43.9) | .02 |
| Diabetes, n (%) | 136 (11.7) | 53 (10.6) | 40 (10.8) | 31 (13.3) | 12 (21.1) | .10 |
| Asthma, current, n (%) | 219 (18.8) | 86 (17.1) | 62 (16.6) | 47 (20.3) | 24 (42.1) | <.001 |
| Metabolic Syndrome, n (%) | 172 (13.2) | 56 (9.8) | 57 (14.0) | 46 (17.8) | 13 (19.4) | .005 |
| Physical activity (exercise units) | 305.5 ± 252.3 | 319.5 ± 252.9 | 302.2 ± 256.3 | 288.4 ± 236.8 | 274.5 ± 279.6 | .13 |
| *Tobacco use (lifetime pack-years), Mean ± SD | 4.6 ± 8.7 | 4.0 ± 8.1 | 4.6 ± 8.4 | 5.1 ± 9.4 | 7.9 ± 11.8 | .02 |
| *Tobacco use status at year 25 y, n (%) | .14 | |||||
| Never smokers or quit >12 mo | 947 (82.8) | 411 (83.4) | 311 (84.3) | 187 (82.0) | 38 (70.4) | |
| Former smokers, quit ≤12 mo | 31 (2.7) | 17 (3.4) | 8 (2.2) | 4 (1.8) | 2 (3.7) | |
| Current smokers | 166 (14.5) | 65 (13.2) | 50 (13.6) | 37 (16.2) | 14 (25.9) | |
| Fast Food and Sugared Beverage Intake | .07 | |||||
| Poor | 433 (37.6) | 179 (36.4) | 131 (35.4) | 91 (39.2) | 32 (56.1) | |
| Intermediate | 475 (41.3) | 209 (42.5) | 156 (42.2) | 90 (38.8) | 20 (35.1) | |
| Ideal | 243 (21.1) | 104 (21.1) | 83 (22.4) | 51 (22.0) | 5 (8.8) | |
| Health Behavior Score (smoking, physical activity, and diet, higher is better), Mean ± SD | 3.7 ± 1.5 | 3.8 ± 1.5 | 3.8 ± 1.5 | 3.7 ± 1.4 | 3.0 ± 1.5 | .002 |
| C-Reactive Protein (higher level, more inflammation) | 3.5 ± 5.8 | 3.1 ± 6.3 | 3.6 ± 4.7 | 3.8 ± 5.6 | 6.2 ± 7.8 | <.001 |
Derived variables from CARDIA longitudinal data
Chi-square testing used to derive P-values for categorical variables. Kruskall-Wallis testing used to derive P-values for numerical variables.
Table 3 shows multinomial models in which covariates were examined for association with cluster group membership after controlling for socidemographic variables. With each 1-point lower BMI (kg/m2), odds of membership in mild (OR 0.97, CI 0.95–0.99), moderate (OR 0.95, CI 0.93–0.98), and severe (OR 0.90, CI 0.88–0.94) LUTS/UI cluster groups versus the bladder health cluster group were lower. For our cardiovascular factors of interest, Model 3 shows that compared to women with metabolic syndrome, women without metabolic syndrome had lower odds of membership in mild (OR 0.67, 95% CI 0.45, 0.99), moderate (OR 0.51, 95% CI 0.33–0.79), and severe (OR 0.48, 95% CI 0.24–0.94) LUTS/UI cluster groups versus the bladder health group. Model 4 shows that having better cardiovascular health index scores (OR 0.71, 95% CI 0.57, 0.88) was associated with lower odds, respectively, of membership in the severe versus the bladder health cluster group. Model 5 shows no associations with CRP levels with the LUTS cluster groups. In exploratory analysis, we did not find any significant interactions between race and each of the variables in the five models.
Table 3.
Multinomial regression of LUTS clusters versus bladder health on potential risk and protective factors*
| Bladder Heath (n = 569) | Mild LUTS & Impact (n = 407) OR (95% CI) |
Moderate LUTS & Impact (n = 259) OR (95% CI) |
Severe LUTS & Impact (n = 67) OR (95% CI) |
|
|---|---|---|---|---|
| Model 1: Obstetric And Gynecologic Factors | ||||
| No Oral Contraceptive Use | Reference | 1.00 (0.69, 1.45) | 1.25 (0.83, 1.90) | 1.83 (0.95, 3.52) |
| No Prior Hysterectomy | Reference | 0.84 (0.57, 1.24) | 0.63 (0.41, 0.96) | 1.06 (0.48, 2.34) |
| Not Yet Gone Through Menopause | Reference | 0.93 (0.65, 1.33) | 1.17 (0.76, 1.79) | 0.33 (0.15, 0.73) |
| Lower Parity | Reference | 0.90 (0.81, 1.00) | 0.88 (0.77, 1.00) | 0.80 (0.65, 0.99) |
| Model 2: Medical Comorbidities | ||||
| Lower BMI | Reference | 0.97 (0.95, 0.99) | 0.95 (0.93, 0.97) | 0.91 (0.88, 0.94) |
| No Hypertension | Reference | 0.91 (0.65, 1.27) | 0.69 (0.47, 1.00) | 0.92 (0.47, 1.79) |
| No Diabetes | Reference | 1.19 (0.75, 1.91) | 1.18 (0.70, 1.98) | 0.77 (0.35, 1.68) |
| No Asthma | Reference | 1.09 (0.75, 1.58) | 0.91 (0.60, 1.37) | 0.39 (0.21, 0.72) |
| Model 3: Metabolic Factors | ||||
| No Metabolic Syndrome | Reference | 0.67 (0.45, 0.99) | 0.51 (0.33, 0.79) | 0.48 (0.24, 0.94) |
| Model 4: CV Health Index | ||||
| Health Behavior Score (Smoking, Physical Activity, And Diet, Higher Is Better), 1-Point Increase In Score | Reference | 0.96 (0.86, 1.06) | 0.94 (0.83, 1.06) | 0.71 (0.57, 0.88) |
| Model 5: Inflammation | ||||
| Hsc-Reactive Protein (Higher Level, More Inflammation), 1-Point Increase In Score | Reference | 1.01 (0.98, 1.03) | 1.00 (0.97, 1.03) | 1.00 (0.95, 1.05) |
When more than one factor is listed within a model, these factors were entered simultaneously into the regression analysis. All models controlled for sociodemographic factors. Models 1, and 5 also controlled for tobacco use status at year 25 and BMI; whereas Model 2 was only controlled for tobacco use status at year 25. Missing observations for the response or explanatory variables were as follows for each model: Model 1, missing 152 observations; Model 2, missing 147 observations; Model 3, missing 140 observations; Model 4, missing 174 observations; Model 5, missing 146 observations.
DISCUSSION
Two out of 5 middle-aged women met our definition of bladder health in this cluster analysis of CARDIA cohort data. Middle-aged women with lower BMI and those not meeting criteria for metabolic syndrome had lower odds of having mild, moderate, or severe LUTS/UI in comparison to bladder health. Prevention of cardiovascular risk factors, such as obesity, insulin resistance, dyslipidemia, and hypertension, may be important for maintaining bladder health among middle-aged women. While similar findings exist for LUTS in men,18–22 this is one of only a few studies to report these associations for LUTS among women.7,23
Compared to a similar cluster group analysis of BACH data that used the PLUS Research Consortium definition, we found a higher prevalence of women with bladder health in CARDIA (44%) versus BACH (33% when combining women without any symptoms and those reported as “rarely”).5 The differences observed in bladder health prevalence from community-based cohorts may include diversity in race/ethnicity, geographic differences, and differences in types of LUTS and impact scales. In subgroup analysis among women aged 45–64 years of age from BACH study, the authors found lower rates of bladder health (29% when including women without any symptoms and “rarely” having symptoms) compared to women in CARDIA (age range of 42–59) from this study. The BACH study included LUTS related to bladder pain and urinary tract infections not included in this analysis. BACH used a LUTS impact scale originally designed for male LUTS;24 whereas, CARDIA used questions on UI impact specifically designed for women and on LUTS question on quality of life impact.11,13 Both studies assessed individual LUTS with the AUASI that was originally designed for male LUTS and subsequently validated for use in women.11 Also, only 4% of women in CARDIA reported the complete absence of LUTS compared to 14% in the BACH study among women with similar age ranges.5 Given the high prevalence of LUTS and UI in women, we focused our cluster definition of bladder health more on the absence of symptom impact rather than a complete lack of symptoms, partially achieving the criteria contained in the PLUS Consortium definition.4
Our findings suggest commonalities exist between cardiovascular health and bladder health. Ideal cardiovascular health is defined by the presence of ideal health behaviors (nonsmoking, body mass index <25 kg/m2, physical activity at goal levels, and pursuit of a diet consistent with current guideline recommendations) and ideal health factors (untreated total cholesterol <200 mg/dL, untreated blood pressure <120/<80 mm Hg, and fasting blood glucose <100 mg/dL).25 There appear to be graded associations between both BMI and metabolic syndrome and membership in the mild, moderate, and severe LUTS groups versus bladder health among women. Three potential mechanisms may explain these relationships. First, obesity, especially increased central obesity, may increase pressure in the pelvis and interfere with bladder function directly by increasing pressure on the bladder or indirectly by decreasing blood flow.26 A second mechanism involves alterations in peripheral nerves via diabetes or insulin resistance and peripheral neuropathy that interferes with bladder sensation and autonomic control.27 A third mechanism is pelvic ischemia via atherosclerosis. Accumulating evidence from observational and small clinical studies suggests that ischemia may be an independent factor in the development of LUTS.28 More data are needed to support proposed mechanisms, especially the effects of atherosclerosis in the pelvic region in women. A recent systematic review concluded that the literature on metabolic syndrome and LUTS in women is limited and has poor quality (3 studies with small sample sizes).23 However, the authors concluded that good evidence exists to support a relationship between cardiovascular risk factors and LUTS/UI in women.
Despite having a community-based study with well-characterized cardiovascular risk factors and extensive LUTS and UI data, limitations exist to our study that deserve mention. Given the sampling design of CARDIA, this study was limited to women aged 42–59 years and may not be generalizable to all racial and ethnic groups. The validated LUTS questionnaire, the AUASI, only had one question that addressed the impact of LUTS on well-being to use in this analysis. An additional limitation is that we did not analyze data on individual LUTS. We instead chose to analyze women in clusters based on validated symptom severity scales to enhance the clinical relevance of our findings, similar to other recent analyses.29 Our findings did not take into account adaptive behaviors to accommodate bladder symptoms, such as fluid restriction, urge suppression, voiding without urgency (eg, voiding prior to leaving the house to avoid symptoms), or the use of absorbent products to manage leakage, which may all affect symptom impact on well-being and QOL. Lastly, we did not account for prior or current treatment for LUTS in this analysis.
Including the impact of LUTS on well-being is an important component of bladder health. When using the PLUS Research Consortium bladder health definition, only 2 in 5 middle-aged women may endorse bladder health, and only if very mild symptoms with no impact are included in the definition of bladder health. Associations between cardiovascular health behaviors and factors and bladder health deserve further exploration in other studies, especially longitudinal studies. Such studies may inform questions about possible mechanisms linking shared risk and protective factors to cardiovascular and bladder health outcomes, as well as optimal time points for prevention and intervention strategies.
Supplementary Material
Funding Support:
DK08499702. The Coronary Artery Risk Development in Young Adults Study (CARDIA) is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201800005I & HHSN268201800007I), Northwestern University (HHSN268201800003I), University of Minnesota (HHSN268201800006I), andKaiser Foundation Research Institute (HHSN268201800004I). This manuscript has been reviewed by CARDIA for scientific content.
Footnotes
SUPPLEMENTARY MATERIALS
Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.urology.2021.05.032.
References
- 1.Lowder JL, Bavendam TG, Berry A, et al. Terminology for bladder health research in women and girls: prevention of lower urinary tract symptoms transdisciplinary consortium definitions. Neurourol Urodyn. 2019;38:1339–1352. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Coyne KS, Sexton CC, Thompson CL, et al. The prevalence of lower urinary tract symptoms (LUTS) in the USA, the UK and Sweden: results from the epidemiology of LUTS (EpiLUTS) study. BJU Int. 2009;104:352–360. [DOI] [PubMed] [Google Scholar]
- 3.Kupelian V, Wei JT, O’Leary MP, et al. Prevalence of lower urinary tract symptoms and effect on quality of life in a racially and ethnically diverse random sample: the Boston Area Community Health (BACH) Survey. Arch Intern Med. 2006;166:2381–2387. [DOI] [PubMed] [Google Scholar]
- 4.Lukacz ES, Bavendam TG, Berry A, et al. A novel research definition of bladder health in women and girls: implications for research and public health promotion. J Womens Health (Larchmt). 2018;27:974–981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Sutcliffe S, Bavendam T, Cain C, et al. The spectrum of bladder health: the relationship between lower urinary tract symptoms and interference with activities. J Womens Health (Larchmt). 2019;28: 827–841. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Irwin DE, Milsom I, Hunskaar S, et al. Population-based survey of urinary incontinence, overactive bladder, and other lower urinary tract symptoms in five countries: results of the EPIC study. Eur Urol. 2006;50:1306. [DOI] [PubMed] [Google Scholar]
- 7.Hall SA, Link CL, Tennstedt SL, et al. Urological symptom clusters and health-related quality-of-life: results from the Boston Area community health survey. BJU Int. 2009;103:1502–1508. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41:1105–1116. [DOI] [PubMed] [Google Scholar]
- 9.Barry MJ, Link CL, McNaughton-Collins MF, McKinlay JB, Boston Area Community Health I. Overlap of different urological symptom complexes in a racially and ethnically diverse, community-based population of men and women. BJU Int. 2008;101:45–51. [DOI] [PubMed] [Google Scholar]
- 10.Barry MJ, Fowler FJ, O’Leary MP, et al. The American Urological Association symptom index for benign prostatic hyperplasia. The measurement committee of the american urological association. J Urol. 1992;148:1549–1557. discussion: 1564. [DOI] [PubMed] [Google Scholar]
- 11.Scarpero HM, Fiske J, Xue X, Nitti VW. American urological association symptom index for lower urinary tract symptoms in women: correlation with degree of bother and impact on quality of life. Urology. 2003;61:1118. [DOI] [PubMed] [Google Scholar]
- 12.Sandvik H, Seim A, Vanvik A, Hunskaar S. A severity index for epidemiological surveys of female urinary incontinence: comparison with 48-hour pad-weighing tests. Neurourol Urodyn. 2000;19:137–145. [DOI] [PubMed] [Google Scholar]
- 13.Uebersax JS, Wyman JF, Shumaker SA, McClish DK, Fantl JA. Short forms to assess life quality and symptom distress for urinary incontinence in women: the Incontinence impact questionnaire and the urogenital distress inventory. continence program for women research group. Neurourol Urodyn. 1995;14:131–139. [DOI] [PubMed] [Google Scholar]
- 14.Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National heart, lung, and blood institute scientific statement. Circulation. 2005;112:2735–2752. [DOI] [PubMed] [Google Scholar]
- 15.Whitaker KM, Jacobs DR Jr., Kershaw KN, et al. Racial disparities in cardiovascular health behaviors: the Coronary Artery Risk Development in Young Adults study. Am J Prev Med. 2018;55:63–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kupelian V, Rosen RC, Roehrborn CG, Tyagi P, Chancellor MB, McKinlay JB. Association of overactive bladder and C-reactive protein levels. Results from the Boston Area Community Health (BACH) Survey. BJU Int. 2012;110:401–407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ebong IA, Schreiner P, Lewis CE, Appiah D, Ghelani A, Wellons M. The association between high-sensitivity C-reactive protein and hypertension in women of the CARDIA study. Menopause. 2016;23:662–668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rohrmann S, Smit E, Giovannucci E, Platz EA. Association between markers of the metabolic syndrome and lower urinary tract symptoms in the third national health and nutrition examination survey (NHANES III). Int J Obes. 2005;29:310–316. [DOI] [PubMed] [Google Scholar]
- 19.Pashootan P, Ploussard G, Cocaul A, de Gouvello A, Desgrandchamps F. Association between metabolic syndrome and severity of lower urinary tract symptoms (LUTS): an observational study in a 4666 European men cohort. BJU Int. 2015;116:124–130. [DOI] [PubMed] [Google Scholar]
- 20.Kupelian V, McVary KT, Kaplan SA, et al. Association of lower urinary tract symptoms and the metabolic syndrome: results from the Boston Area Community Health Survey. J Urol. 2009;182:616–624. discussion: 624–615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gacci M, Corona G, Sebastianelli A, et al. Male lower urinary tract symptoms and cardiovascular events: a systematic review and meta-analysis. Eur Urol. 2016;70:788–796. [DOI] [PubMed] [Google Scholar]
- 22.Wehrberger C, Temml C, Gutjahr G, et al. Is there an association between lower urinary tract symptoms and cardiovascular risk in men? A cross sectional and longitudinal analysis. Urology. 2011;78:1063–1067. [DOI] [PubMed] [Google Scholar]
- 23.Bunn F, Kirby M, Pinkney E, et al. Is there a link between overactive bladder and the metabolic syndrome in women? A systematic review of observational studies. Int J Clin Pract. 2015;69:199–217. [DOI] [PubMed] [Google Scholar]
- 24.Epstein RS, Deverka PA, Chute CG, et al. Validation of a new quality of life questionnaire for benign prostatic hyperplasia. J Clin Epidemiol. 1992;45:1431–1445. [DOI] [PubMed] [Google Scholar]
- 25.Lloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121: 586–613. [DOI] [PubMed] [Google Scholar]
- 26.Swenson CW, Kolenic GE, Trowbridge ER, et al. Obesity and stress urinary incontinence in women: compromised continence mechanism or excess bladder pressure during cough? Int Urogynecol J. 2017;28:1377–1385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sakakibara R, Takahashi O, Nishimura H, et al. The relationship between bladder, periarterial and somatic neuropathy in diabetes. Intern Med. 2018;57:2165–2168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Ladanchuk T, Kwak S, Bates L, et al. Vascular measures of atherosclerosis in detrusor overactivity and controls. Neurourol Urodyn. 2018;37:2827–2832. [DOI] [PubMed] [Google Scholar]
- 29.Andreev VP, Liu G, Yang CC, et al. Symptom based clustering of women in the LURN observational cohort study. J Urol. 2018;200:1323–1331. [DOI] [PMC free article] [PubMed] [Google Scholar]
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