Abstract
Objective
The cardiometabolic index (CMI) is a marker currently used to evaluate metabolism. Although the association between overactive bladder (OAB) and obesity has been evaluated in previous studies, the role of the CMI in adult OAB remains unclear. This study aimed to explore the association between the CMI and OAB in a representative nationwide population.
Methods
This cross-sectional study used the 2011–2018 data from the National Health and Nutrition Examination Survey database. The CMI was calculated by multiplying the ratio of triglycerides to high-density lipoprotein cholesterol and that of waist circumference to height. We used the Overactive Bladder Symptom Score to define OAB. Participants with an Overactive Bladder Symptom Score of ≥3 were considered as having OAB. Smoothed curve fitting and multivariate logistic regression analyses were used to examine the association between the CMI and OAB in adults. Consistency of these results was examined across various population subgroups.
Results
The OAB group included 1549 (21%) individuals. Statistically significant differences (p<0.05) were observed between the OAB group and the non-OAB group for all variables. After correcting for potential confounders, we discovered a strong positive association between the CMI and the risk of OAB. For every one-unit increase in CMI, the likelihood of OAB increased by 54%. Subgroup analysis of the association between the CMI and the odds of OAB showed that there were no significant interaction effects within most subgroups. However, the association may be altered by the presence of certain diseases, such as cardiovascular diseases (p=0.022) and diabetes (p=0.023).
Conclusion
A higher CMI correlated with a higher risk of OAB development. However, the causal relationship requires further verification.
Keywords: Overactive bladder, Cardiometabolic index, Lipid metabolism, National Health and Nutrition Examination Survey, Population-based study
1. Introduction
Overactive bladder (OAB) is characterized by urinary symptoms—urgency, frequency, and increased nocturia—with or without urge urinary incontinence (UUI) [1]. According to the International Continence Society, these symptoms are usually caused by overactivity of the detrusor muscle; however, these may also be caused by other manifestations of bladder dysfunction [2]. OAB is a globally prevalent disorder, causing a huge economic burden. Studies have shown that the economic burden of OAB increases annually with the aging population and has a serious impact on the patients' mental health and quality of sleep and life [3,4]. In recent years, the prevalence of OAB has increased in response to social pressures and changes in lifestyle habits [5]. The overall prevalence of OAB has rapidly increased in the United States. According to statistics, its prevalence has increased to 38.5% in the 2021–2022 period. Its prevalence is higher in females than in males and increases with age [[6], [7], [8]]. Although some theories explain the pathophysiology of OAB, to date, there is no universally accepted definitive doctrine, and neurological disorders, chronic diseases, physical activity, and diet have been shown to be risk factors for OAB. Lifestyle changes, medications, and invasive therapies have been used to alleviate the symptoms, but there is still no complete cure for OAB [9].
Obesity is reportedly a significant risk factor for OAB [10]. Body mass index (BMI) is the most common indicator for assessing obesity; however, there are some drawbacks to BMI in measuring obesity, as it does not reflect changes in body fat and muscle mass with age [11]. The cardiometabolic index (CMI), a comprehensive assessment tool based on anthropometric and lipid metrics, is commonly used to predict the likelihood of metabolic diseases. It effectively combines the ratio of triglycerides (TGs) to high-density lipoprotein cholesterol (HDL-C) (TG/HDL-C ratio) with an obesity marker, the waist-to-height ratio [12]. Of these, waist circumference (WC) is more closely correlated with the accumulation of abdominal fat, and the waist-to-height ratio is more reflective of the degree of centripetal obesity than BMI [13]. CMI has been used to assess atherosclerosis, hyperuricemia, metabolism-related fatty liver disease, and other diseases [[14], [15], [16]] and is correlated with chronic diseases, including diabetes mellitus, left ventricular dilatation, and ischemic stroke [13,17,18]. Epidemiological research has revealed that obesity, especially abdominal obesity, plays an important role in OAB [19]. Our study introduces a new valuable metric, the CMI, to explore this progression. Some researchers have examined the association between OAB and obesity as evaluated by BMI [20]. However, to the best of our knowledge, the role of the CMI in adult OAB remains unclear. Using data from the National Health and Nutrition Examination Survey (NHANES), we aimed to explore the correlation between the CMI and the likelihood of OAB in adults based on a nationwide representative sample.
2. Patients and methods
2.1. Data sources
The datasets from the NHANES 2011–2018 were the original information resources for the present analysis. The NHANES database provides a comprehensive range of laboratory test results, disease information, human examination results, and population characteristics. The reliability of the data in this database stems from the fact that it was collected based on a nationwide representative sample in the United States via a comprehensive investigation, the use of the most advanced and reliable techniques of information collection, and organizational and statistical methods for data analysis. These data were subsequently subjected to additional analyses and used to study the risk factors for a variety of diseases. A sophisticated multistage probabilistic approach was used in this study to ensure the representativeness and accuracy of the samples used. The inclusion of human participants in NHANES was approved by the National Center for Health Statistics Ethics Review Board, and the participants provided written informed consent at enrollment (https://www.cdc.gov/nchs/nhanes/about/erb.html?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/irba98.htm). Ethical approval and consent were not required for the present study as the study was based on publicly available de-identified data.
The data that support the findings of this study are openly available in NHANES at https://www.cdc.gov/nchs/nhanes/. The data were used to analyze the prevalence of disease among the US population. Data were extracted from the public data files and analyzed. The use of NHANES data in this study complies with all applicable laws and regulations, and the data were accessed and analyzed in accordance with the NHANES Data Release and Access Policy.
2.2. Research population
A total of 39 156 participants were selected from NHANES 2011–2018 database. The dataset was selected based on the information on variables related to urinary incontinence to calculate the CMI. The exclusion criteria were as follows: age <20 years, lack of sociodemographic data, confounders, incomplete information on urinary incontinence and increased nocturia, and incomplete data required for CMI calculations. A total of 7473 participants with comprehensive information were included in this cross-sectional investigation after excluding those who met the aforementioned criteria (Fig. 1).
Figure 1.
The flowchart depicting participant selection in the study. NHANES, National Health and Nutrition Examination Survey; OAB, overactive bladder; PIR, poverty-to-income ratio; BMI, body mass index; CVD, cardiovascular disease; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; AST, aspartate transaminase; ALP, alkaline phosphatase; LDH, lactate dehydrogenase; WC, waist circumference.
2.3. Definition of exposed variables
CMI was regarded in the current investigation as an exposure variable, calculated as follows: CMI=(TG [mmol/L]/HDL-C [mmol/L])×(WC [cm]/height [cm]) [21]. HDL-C and TG levels were assessed using a Cobas 6000 device (Roche Diagnostics, Basel, Switzerland) [22]. Specialized medical technicians carefully measured the participants' WC and height at a Mobile Examination Center. We treated the CMI as both a categorical and a continuous variable for correlation studies to further investigate the association between OAB and the CMI.
2.4. Definition of the outcome variable
The likelihood of OAB was considered the outcome variable. The OAB disorder was diagnosed based on the criteria of the International Continence Society, characterized by urinary urgency (usually accompanied by increased nocturia), with or without UUI, in the absence of a urological infection or other serious pathologies. We assessed OAB using the Urologic Questionnaire for Kidney Disease from the NHANES database. The degree of UUI was defined as the frequency of urination before going to the toilet. The number of nocturnal urination episodes was used to assess the severity of nocturia. The new Overactive Bladder Symptom Score quantifies the degree of severity of OAB syndrome in each participant, and those with an Overactive Bladder Symptom Score of ≥3 points were considered to have a confirmed diagnosis of OAB [23].
2.5. Covariates
Factors that may affect the association between OAB and the CMI were regarded as covariates. Covariates considered in this study included sociodemographic factors, including age (≤40 years, 41–60 years, or >60 years), educational level (high school graduate or General Educational Development or equivalent, college graduate or above, or below 12th grade), ethnicity (Mexican American, other Hispanic, or non-Hispanic), family poverty-to-income ratio (PIR) (≤1.0, 1.1–4.0, or >4.0), and marital condition (living alone or married and living with partner). Health-related factors, such as diabetes, alcohol consumption, smoking status, physical activity, hypertension, hyperlipidemia, and cardiovascular diseases (CVDs) (all described as yes/no) were also included. PIR is a measurement of socioeconomic position, defined as the ratio of income to the United States Census Bureau's poverty level for a family. Individuals are classified as current smokers, never smokers, and former smokers based on questions such as “Do you personally use cigarettes or tobacco?” and “Have you smoked more than 100 cigarettes in your lifetime?”. Based on “drinking at least 12 times a year or over a lifetime”, the definition of a drinker includes current, never, and former drinkers. Based on the questionnaire information, self-reported diseases, including diabetes, were considered. Diagnoses of CVDs, hypertension, and hyperlipidemia were based on a self-reported history of specific diseases. CVDs included coronary heart disease, stroke, congestive heart failure, angina, and myocardial infarction. Laboratory data included liver enzymes, lipoprotein, lipid, lactate dehydrogenase, total bilirubin, creatinine, and uric acid levels.
2.6. Statistical analyses
Continuous and categorical variables were used to characterize the participants at baseline. Continuous variables were described in terms of means with standard deviations, and categorical variables were described by numbers and percentages. Weighted t-tests and chi-square tests were used to compare the baseline characteristics of the participants. A weighted logistic regression model was used to assess the corrected odds ratio (OR) and 95% confidence interval (CI) to investigate the association between the CMI and OAB. Three models were used with varying degrees of adjustment for the covariates (Model 1 was unadjusted for covariates; Model 2 was adjusted for educational level, sex, ethnicity, marital status, PIR, and age; and Model 3 was adjusted for marital status, educational level, PIR, physical activity, age, alcohol consumption, ethnicity, sex, smoking status, hypertension, CVDs, hyperlipidemia, diabetes, and laboratory test indicators). In all three models, the CMI was used as a continuous variable. In addition, the dose-response association between the CMI and likelihood of OAB was evaluated using the restricted cubic spline method. Multiple logistic regression models included continuous and categorical variables. The CMI was divided into quartiles, and linear trends were determined by considering the median value of every subgroup as the continuous variable. In addition, subgroup analyses were conducted using general information, disease condition, alcohol consumption, smoking status, and physical activity. Interaction analyses were performed to examine different associations between the subgroups. All statistical analyses were conducted using R 4.3.3 (The R Foundation, Vienna, Austria) and SPSS 26.0 (IBM, Armonk, NY, USA). Statistical significance was set at a p-value <0.05.
3. Results
3.1. Baseline characteristics of the participants
A total of 7473 participants were finally included in our study. A flowchart of the exclusion and inclusion criteria is shown in Fig. 1. The baseline characteristics of the participants are presented in Table 1. Statistically significant differences were observed in the OAB population for all the variables (all p<0.001).
Table 1.
Baseline characteristics of participants.
| Characteristic | Participant |
p-Valueb | ||
|---|---|---|---|---|
| Totala (n=7473) | With OABa (n=1549) | Without OABa (n=5924) | ||
| Sex | <0.001 | |||
| Male | 3680 | 619 (37) | 3061 (52) | |
| Female | 3793 | 930 (63) | 2863 (48) | |
| Age, year | <0.001 | |||
| ≤40 | 2478 | 202 (16) | 2276 (39) | |
| 41–60 | 2489 | 464 (35) | 2025 (37) | |
| >60 | 2506 | 883 (49) | 1623 (24) | |
| Ethnicity | <0.001 | |||
| Mexican American | 975 | 205 (8.0) | 770 (8.0) | |
| Other Hispanic | 5355 | 1198 (86) | 4157 (84) | |
| Non-Hispanic | 1143 | 146 (6.0) | 997 (8.4) | |
| Educational level | <0.001 | |||
| Below 12th grade | 1500 | 439 (21) | 1061 (12) | |
| High school graduate or GED or equivalent | 1662 | 386 (25) | 1276 (22) | |
| College graduate or above | 4311 | 724 (54) | 3587 (66) | |
| Marital status | <0.001 | |||
| Married and living with a partner | 4470 | 840 (60) | 3630 (65) | |
| Living alone | 3003 | 709 (40) | 2294 (35) | |
| PIR | <0.001 | |||
| ≤1.0 | 1593 | 434 (22) | 1159 (13) | |
| 1.1–4.0 | 3959 | 842 (52) | 3117 (49) | |
| >4.0 | 1921 | 273 (27) | 1648 (38) | |
| Smoking | <0.001 | |||
| Current | 1448 | 324 (21) | 1124 (18) | |
| Former | 1829 | 445 (27) | 1384 (26) | |
| Never | 4196 | 780 (52) | 3416 (56) | |
| Drinking | <0.001 | |||
| Current | 5392 | 1010 (70) | 4382 (80) | |
| Former | 1117 | 299 (17) | 818 (11) | |
| Never | 964 | 240 (13) | 724 (8.8) | |
| Diabetes | <0.001 | |||
| Yes | 1020 | 402 (21) | 618 (8.2) | |
| No | 6253 | 1093 (76) | 5160 (90) | |
| Prediabetes | 200 | 54 (3.5) | 146 (2.2) | |
| Hypertension | <0.001 | |||
| Yes | 2751 | 891 (52) | 1860 (30) | |
| No | 4722 | 658 (49) | 4064 (70) | |
| Hyperlipidemia | <0.001 | |||
| Yes | 2639 | 763 (49) | 1876 (32) | |
| No | 4834 | 786 (51) | 4048 (68) | |
| CVD | <0.001 | |||
| Yes | 813 | 341 (19) | 472 (7.1) | |
| No | 6660 | 1208 (81) | 5452 (93) | |
| Physical activity | <0.001 | |||
| Yes | 1748 | 158 (11) | 1590 (29) | |
| No | 5725 | 1391 (89) | 4334 (71) | |
| Height, cm | 7473 | 164.86±9.60 (165.60±9.42) | 167.95±9.85 (169.52±9.83) | <0.001 |
| BMI, kg/m2 | 7473 | 31.42±7.71 (31.65±7.94) | 28.68±6.72 (28.80±6.68) | <0.001 |
| Waistline, cm | 7473 | 105.40±16.95 (106.15±17.67) | 98.28±16.46 (99.06±16.46) | <0.001 |
| TG, mmol/L | 7473 | 1.30±0.71 (1.32±0.70) | 1.26±0.74 (1.27±0.76) | <0.001 |
| HDL, mmol/L | 7473 | 1.41±0.43 (1.42±0.43) | 1.40±0.41 (1.41±0.42) | <0.001 |
| CMI | 7473 | 0.70±0.63 (0.71±0.61) | 0.63±0.56 (0.64±0.58) | <0.001 |
OAB, overactive bladder; GED, General Educational Development; PIR, poverty-to-income ratio; BMI, body mass index; CVD, cardiovascular disease; HDL, high-density lipoprotein; TG, triglyceride; CMI, cardiometabolic index.
Values are presented as n, n (weighted percentage, %), and mean±standard deviation (weighted mean±standard deviation).
Continuous variables were analyzed via one-way analysis of variance; categorical variables were analyzed using chi-square test or Fisher's exact test, and a p-value <0.05 was considered statistically significant.
3.2. Association between the CMI and OAB
Model 1 showed that the likelihood of OAB was increased by 21% (OR 1.21, 95% CI 1.10–1.32, p<0.001) for every unit increase in the CMI. Model 2 showed a 24% increase in the likelihood of OAB per unit increase in the CMI (OR 1.24, 95% CI 1.12–1.38, p<0.001). In Model 3, the likelihood of OAB was significantly increased by 54% with every unit increase in the CMI (OR 1.54, 95% CI 1.17–2.02, p=0.002) (Table 2).
Table 2.
Weighted regression models and trend tests elucidating the association between the CMI and the likelihood of OAB.
| CMI | OAB (Model 1) |
OAB (Model 2) |
OAB (Model 3) |
|||
|---|---|---|---|---|---|---|
| OR (95% CI) | p-Value | OR (95% CI) | p-Value | OR (95% CI) | p-Value | |
| Continuous | 1.21 (1.10–1.32) | <0.001 | 1.24 (1.12–1.38) | <0.001 | 1.54 (1.17–2.02) | 0.002 |
| Quartile | ||||||
| 1 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | |||
| 2 | 1.56 (1.32–1.85) | <0.001 | 1.40 (1.17–1.68) | <0.001 | 1.52 (1.22–1.88) | <0.001 |
| 3 | 1.62 (1.37–1.91) | <0.001 | 1.38 (1.15–1.66) | <0.001 | 1.63 (1.25–2.13) | <0.001 |
| 4 | 1.70 (1.44–2.00) | <0.001 | 1.64 (1.36–1.97) | <0.001 | 2.24 (1.53–3.26) | <0.001 |
| p-Value for trend | <0.001 | <0.001 | 0.002 | |||
CMI, cardiometabolic index; OAB, overactive bladder; PIR, poverty-to-income ratio; OR, odds ratio; CI, confidence interval.
Note: Model 1 was adjusted for none; Model 2 was adjusted for age, sex, ethnicity, educational level, marital status, and PIR; and Model 3 was adjusted for age, ethnicity, sex, educational level, marital status, PIR, physical activity, smoking status, alcohol consumption, hypertension, cardiovascular diseases, hyperlipidemia, diabetes, and laboratory test indicators.
As shown in Table 2, the CMI was categorized into quartiles and compared with the first quartile as the reference. In Model 1, participants in the highest quartile 4 of the CMI had 1.70 times the odds of OAB compared with those in the lowest quartile 1 (OR 1.70, 95% CI 1.44–2.00). In Model 2, the odds for the quartile 4 group were 64% higher than for the quartile 1 group (OR 1.64, 95% CI 1.36–1.97). In Model 3, participants in quartile 4 had 2.24 times the odds of OAB compared with those in quartile 1 (OR 2.24, 95% CI 1.53–3.26). In all three models, a remarkable dose-response trend was observed with an increase in the CMI (p for overall <0.001) (Fig. 2).
Figure 2.
The restricted cubic spline analysis of the dose-response relationship between the CMI and OAB. (A) Model 1 (adjusted for none); (B) Model 2 (adjusted for age, sex, ethnicity, educational level, marital status, and PIR); (C) Model 3 (adjusted for age, ethnicity, sex, educational level, marital status, PIR, physical activity, smoking status, alcohol consumption, hypertension, cardiovascular diseases, hyperlipidemia, diabetes, and laboratory test indicators). The solid red line indicates the OR, and the red shaded area indicates the 95% CI. PIR, poverty-to-income ratio; OR, odds ratio; CI, confidence interval; CMI, cardiometabolic index.
3.3. Subgroup analyses
The association between the CMI and OAB for each stratification indicator, analyzed using weighted logistic regression, is shown in Table 3. For every unit increase in the CMI, female participants had 1.90 times the odds of OAB (OR 1.90, 95% CI 1.67–2.17). However, there was no association between the CMI and odds of OAB in males. For every unit increase in the CMI, participants aged 41–60 years had 1.74 times the odds of OAB (OR 1.74, 95% CI 1.33–2.27) and participants aged ≤40 years had 1.71 times the odds of OAB (OR 1.71, 95% CI 1.22–2.40). However, there was no association between the CMI and odds of OAB in participants aged >60 years. In terms of ethnicity, Mexican American participants had 2.01 times the odds of developing OAB per unit increase in CMI (OR 2.01, 95% CI 1.27–3.16). Similarly, other Hispanic participants had 1.52 times the odds (OR 1.52, 95% CI 1.28–1.80) and non-Hispanic participants had 2.54 times the odds (OR 2.54, 95% CI 1.59–4.07) of developing OAB per unit increase in CMI. The results of the stratified analysis showed that no interaction effect was observed between CMI and the odds of OAB across different levels of education, and similarly, no interaction effect was detected across different PIR levels. The stratified analysis of marital status showed that for every unit increase in CMI, the odds for those living alone were 2.18 times higher (OR 2.18, 95% CI 1.76–2.70), and the odds for those married and living with a partner were 1.33 times higher (OR 1.33, 95% CI 1.07–1.65). Regarding smoking and alcohol consumption, for every unit increase in the CMI, the odds were 2.10 times higher for participants with a history of smoking (OR 2.10, 95% CI 1.49–2.95) and 1.77 times higher for current alcohol consumers (OR 1.77, 95% CI 1.45–2.15), and no interaction effect was observed (p=0.3 for both interactions; Table 3). However, a positive association between the CMI and OAB was demonstrated in participants without CVD (OR 1.72, 95% CI 1.46–2.03) or diabetes (OR 1.57, 95% CI 1.32–1.86). However, there was no interaction effect between the CMI and the odds of OAB in subgroups stratified by hypertension or hyperlipidemia.
Table 3.
Subgroup analysis of the association between the CMI and the likelihood of OAB.
| Subgroup | OR (95% CI) | p-Value | p-Value for interaction |
|---|---|---|---|
| Sex | <0.001 | ||
| Male | 1.07 (0.89–1.27) | 0.5 | |
| Female | 1.90 (1.67–2.17) | <0.001 | |
| Age, year | 0.048 | ||
| ≤40 | 1.71 (1.22–2.40) | 0.002 | |
| 41–60 | 1.74 (1.33–2.27) | <0.001 | |
| >60 | 1.16 (0.92–1.47) | 0.2 | |
| Ethnicity | 0.083 | ||
| Mexican American | 2.01 (1.27–3.16) | 0.003 | |
| Other Hispanic | 1.52 (1.28–1.80) | <0.001 | |
| Non-Hispanic | 2.54 (1.59–4.07) | <0.001 | |
| Educational level | 0.8 | ||
| Below 12th grade | 1.46 (1.05–2.05) | 0.026 | |
| High school graduate or GED or equivalent | 1.39 (1.02–1.91) | 0.039 | |
| College graduate or above | 1.55 (1.26–1.91) | <0.001 | |
| Marital status | 0.001 | ||
| Married and living with a partner | 1.33 (1.07–1.65) | 0.009 | |
| Living alone | 2.18 (1.76–2.70) | <0.001 | |
| PIR | 0.4 | ||
| ≤1.0 | 1.79 (1.33–2.40) | <0.001 | |
| 1.1–4.0 | 1.58 (1.29–1.95) | <0.001 | |
| >4.0 | 1.31 (0.92–1.86) | 0.14 | |
| Hypertension | 0.073 | ||
| Yes | 1.21 (0.96–1.52) | 0.099 | |
| No | 1.61 (1.30–1.99) | <0.001 | |
| Hyperlipidemia | 0.4 | ||
| Yes | 1.36 (1.06–1.73) | 0.014 | |
| No | 1.55 (1.27–1.89) | <0.001 | |
| Diabetes | 0.023 | ||
| Yes | 1.14 (0.74–1.76) | 0.6 | |
| No | 1.57 (1.32–1.86) | <0.001 | |
| Prediabetes | 0.53 (0.23–1.23) | 0.14 | |
| CVD | 0.022 | ||
| No | 1.72 (1.46–2.03) | <0.001 | |
| Yes | 1.01 (0.66–1.55) | 1 | |
| Smoking | 0.3 | ||
| Current | 1.54 (1.06–2.25) | 0.025 | |
| Former | 2.10 (1.49–2.95) | <0.001 | |
| Never | 1.56 (1.29–1.88) | <0.001 | |
| Drinking | 0.3 | ||
| Current | 1.77 (1.45–2.15) | <0.001 | |
| Former | 1.64 (1.17–2.29) | 0.004 | |
| Never | 1.27 (0.91–1.78) | 0.16 |
CMI, cardiometabolic index; OAB, overactive bladder; GED, General Educational Development; PIR, poverty-to-income ratio; CVD, cardiovascular disease; OR, odds ratio; CI, confidence interval.
4. Discussion
Our investigation primarily examined the association between the CMI and OAB using the NHANES database. This study included 7473 individuals. The weighted multivariable logistic regression analysis revealed a positive and statistically significant association between the CMI and OAB after controlling for all covariates. In addition, the restricted cubic spline curves revealed a nonlinear positive association between the CMI and OAB. The odds of OAB increased progressively with increasing CMI quartiles. There was no interaction effect between the CMI and the odds of OAB in subgroups stratified by hypertension or hyperlipidemia. Notably, subgroup analyses showed that the association between the CMI and OAB might be altered because of CVD and diabetes.
A growing body of research suggests that OAB is a common condition in adults [6]. OAB has a significant influence on the quality of life and status of patients [24], as well as a remarkable influence on the psychological well-being of individuals [24,25]. However, the pathophysiological mechanisms underlying OAB remain unclear. A significant number of investigations have established that females with a high BMI are at risk for UUI [26]. A 4-year longitudinal study based on a Chinese population showed that the rate of urinary incontinence remission was lower in the female population with a higher BMI [6]. WC, an important indicator, is more likely to correlate with lower urinary tract symptoms in females with larger WC [27]. BMI does not include WC as an indicator in its calculation and does not allow for differentiation between muscle mass and fat distribution [11]. We introduced the CMI, which combines the TG/HDL-C ratio and waist-to-height ratio to more accurately reflect the level of abdominal obesity and lipid metabolism in the body.
Several potential mechanisms may explain the positive association between the CMI and OAB. First, an increase in the CMI may reflect visceral fat accumulation and the body's metabolic levels, particularly in the abdomen. Being overweight and obesity increase pressure in the abdomen, leading to extra pressure on the bladder and affecting normal urinary storage function. Second, obesity can indirectly affect bladder function by affecting estrogen and testosterone levels, which in turn affect the regulation of bladder function and urethral closure pressure [28]. Obesity is also associated with chronic inflammation and a state of oxidative stress in the body, which may impair bladder function and urethral tissues as well as affect nerve conduction function and the central nervous system controlling the bladder and urethra, increasing the likelihood of bladder overactivity [29]. In addition, obese patients have decreased sleep quality and are less likely to engage in physical activities, both of which may reduce the exercise of the pelvic floor muscles, leading to weakness and decreased function [29]. Finally, obesity is also associated with hypertension, diabetes, and abnormal cholesterol levels, which may affect microvascular and nervous system functions, and thus contribute to OAB. It has also been demonstrated that the body roundness index is strongly associated with the likelihood of OAB, and that the body roundness index, as a novel anthropometric method, is superior to traditional anthropometric methods in screening for OAB [30]. Weight-adjusted waist index, a new obesity index that combines the advantages of WC and abdominal fat accumulation, showed a positive association with the prevalence of OAB [31]. Previous studies have revealed an association between inflammation and OAB progression [32,33]. Wei et al. [34] found that the systemic immunity-inflammation index, a marker of inflammation, may be an important factor in the development and progression of OAB.
According to recent studies, the prevalence of obesity in the global population continues to increase, affecting physical and mental health in various ways. The global prevalence of OAB is increasing yearly, and obesity is closely associated with OAB. Our investigation provides a novel demonstration of the remarkable relationship between the CMI and the likelihood of OAB, which has significant implications for the future diagnosis and treatment of OAB.
The present study has some strengths. This is the first cross-sectional study to investigate the association between the CMI and the likelihood of OAB based on the NHANES database. Simultaneously, our study is representative because it contains basic data from a large number of the United States respondents. However, it is worth noting that this study has some limitations. First, some of the disease information in the NHANES database was obtained through self-reporting by the respondents, and self-reporting deviations could lead to imprecise information, which may affect the accuracy of the results. Second, this investigation could not establish a direct causal association between the risk of OAB and the CMI but only inferred an association because of the cross-sectional nature of the study.
5. Conclusion
This study found a remarkable positive association between the risk of OAB and the CMI in the United States. A higher CMI correlated with a higher likelihood of OAB. This finding suggests that abnormal visceral fat metabolism may be an influencing factor in OAB. The clinical importance of our research is that assessing the CMI may help identify patients with a higher likelihood of OAB. The incorporation of the CMI into routine clinical assessment may facilitate the early detection of OAB and guide personalized care strategies.
Author contributions
Study concept and design: Bin Chen, Yanghao Tai, Yingming Kong, Jiaao Li.
Data acquisition: Bin Chen, Yanghao Tai, Yingming Kong, Jiaao Li.
Data analysis: Bin Chen, Yanghao Tai, Yingming Kong.
Drafting of the manuscript: Bin Chen, Yanghao Tai, Yingming Kong.
Critical revision of the manuscript: Xuening Wang.
Conflicts of interest
The authors declare no conflict of interest.
Footnotes
Peer review under responsibility of Tongji University
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