BMI, waist circumferences and urinary incontinence in older women compared with older men: findings from three prospective longitudinal cohort studies

BMI, waist circumferences and urinary incontinence in older women compared with older men: findings from three prospective longitudinal cohort studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article BMI, waist circumferences and urinary incontinence in older women compared with older men: findings from three prospective longitudinal cohort studies Xiyin Chen, Shaoxiang Jiang, Yao Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2441866/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Oct, 2023 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Background Obesity and urinary incontinence (UI) among older people, particularly older men, are yet to be fully explored. Utilising multiple nationwide prospective longitudinal cohorts representative of the US, UK, and European samples, we examined the association of body mass index (BMI) and waist circumference (WC) with UI among both older women and men. Methods We derived the data from the Health and Retirement Study (HRS, 2010-2018), the English Longitudinal Study of Aging (ELSA, 2011-2019), and the Survey of Health, Ageing and Retirement in Europe (SHARE, 2004-2010) that surveyed UI. Participants were asked if they had experienced urine leakage within the past 12 months (HRS and ELSA) or within the past six months (SHARE). The measure of obesity was based on BMI and WC. We employed a random-effect logistic model to associate BMI and WC with UI, adjusting for covariates including age, race, education, residence area, marital status, number of children, smoking, drinking, hypertension, diabetes, cancer, stroke, functional ability, and cognitive impairment. We visualised the associations by using restricted cubic spline curves. Findings A total of 200,717 participants with 718,822 observations (207,805 in HRS; 98,158 in ELSA; 412,859 in SHARE) were included in the baseline analysis. The 12-months prevalence of UI among female and male participants were 15.6% and 6.6% in the HRS, 10.6% and 4.4% in the ELSA. The 6-months prevalence of UI were 2.8% and 1.4% in the SHARE’s female and male participants. Compared to those without UI, both female and male participants with UI demonstrated a higher BMI and WC. Among females, the fully adjusted models showed linear associations between BMI, WC, and UI ( P s<0.001) in three cohorts. However, we observed U-shaped associations of BMI, WC with UI among males. The lowest likelihood of having UI was found among male participants with a BMI between 24 and 35 kg/m 2 . Interpretation Findings from our study revealed that the associations of obesity indices with UI varied among older men compared to older women. As a result, weight loss interventions could be applied to older women rather than older men as a means of treating UI. Interventions aimed at preventing UI among older adults must take sex into account. Health sciences/Diseases Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Research In Context Evidence before this study We searched PubMed for studies related to obesity and urinary incontinence (UI) in the older population without any time restriction. We used the following keywords "Body Mass Index", "Waist Circumference", "Obesity", "Urinary Incontinence", "Aged", "Female", and "Male". In PubMed, 302 articles were collected using all of these keywords, in which 47 articles studied the association of obesity indices and UI. However, only seven studies considered sex differences regarding the relationship between UI and obesity indices. Specifically, there are four cross-sectional studies and three longitudinal studies analysed the UI-obesity relationships by sexes. There has been only one study, based on the Boston Area Community Health (BACH) Survey, that reported that central obesity increases the odds of weekly urinary leakage for women, but not for men. However, there has been little prospective longitudinal research conducted to investigate the sex differences in associations between body mass index (BMI), waist circumference (WC) and UI, limiting the effectiveness of interventions for UI in older women and men. Added value of this study By utilising multiple nationwide prospective longitudinal cohorts representative of the US, UK, and European samples (HRS, ELSA, and SHARE), we provide a unique insight and comprehensive assessment of the association of BMI and WC with UI among older women compared to men. As far as we know, this is among the first longitudinal study with large sample size to investigate the association of BMI and WC with UI among older adults by taking sex into consideration. According to our study, after controlling for a variety of confounders, BMI is linearly associated with higher prevalence of UI among older women, while it has a U-shaped association among older men. Implications of all the available evidence Previous studies suggested weight reduction for UI interventions among older people. However, our study revealed that the association of obesity indices with UI varied among older men compared to older women. We observed linear associations between BMI, WC, and UI in women, while we observed U-shaped associations among men. The lowest likelihood of having UI was found among male participants with a BMI between 24 and 35 kg/m 2 . Weight loss intervention could therefore only be applied to older women rather than to older men as a way to treat UI. Developing interventions to address UI among older adults should take sex into account. Introduction A rapidly ageing population, coupled with age-related illnesses, is a pressing concern around the globe. Urinary incontinence (UI) is a common geriatric syndrome that has been associated with adverse health outcomes and poor quality of life. It is estimated that 17 to 55% of older women and 11 to 34% of older males had UI, which places a substantial financial and care burden on society and families. 1 The direct costs of UI in 1995 were estimated at $ 26.3 billion for those older than 65 years in the USA. 2 Furthermore, the annual cost-of-illness estimate for urgency urinary incontinence (UUI) in Canada, Germany, Italy, Spain, Sweden, and the United Kingdom was €7 billion in a 2005 multinational study. 3 UI is also associated with a reduced quality of life (QoL) and self-esteem for those who have a stressful, incapacitating condition and a social isolation element, with sufferers reporting embarrassment, distress, and depression 4 . The caregivers of older adults with UI are also more likely to feel a strain both physically and mentally than those without UI. 4 Thus, identifying and managing of modifiable risk factors of UI are of paramount importance. In spite of the fact that obesity is well documented as a modifiable risk factor for the development of UI, most epidemiological, biological, and clinical research focuses on older women rather than older men. 5 – 18 Meanwhile, despite some studies focused on both sexes, 19 – 26 few longitudinal research targeting the older population investigated the sex heterogeneity in patterns of associations between UI and obesity. 21 , 22 Moreover, there are varied obesity indices including Body mass index (BMI) and waist circumference (WC). BMI estimates general obesity established by the World Health Organization (WHO), while WC better describes central obesity, although it cannot discriminate visceral fat from subcutaneous fat. However, the relationship between obesity indices, i.e., BMI and WC, and UI in older men and women has not been thoroughly examined in prior longitudinal studies. Furthermore, although the EpiLUTS study comprised research subjects from three countries, 21 samples in most prior findings were based on samples from one country or region, which led to poor generalization of the study's findings. Extant evidence has reported an “obesity paradox” in which BMI is negatively associated with mortality, which indicates that being normal weight and overweight could be more beneficial to health than being underweight. 27 This paradoxical association of obesity with mortality has been inconsistent due to a discrepancy between BMI and central obesity. 28 However, whether this paradox applies to older women and men in terms of UI was largely unknow. Therefore, study with a representative sample of older men and women from a global perspective should be conducted to reexamine the associations between obesity indices, including BMI and WC, and UI. By utilising multiple nationwide prospective longitudinal cohorts representative of the US, UK, and European samples, we contribute to the literature by analysing the association between BMI, WC, and UI among older women compared to men. This study aimed to illustrate the sex difference in impact of obesity on UI and provide indication of evidence for future intervention. Methods Study design and participants Data were accessed from three international cohorts of aging: Health and Retirement Study (HRS), English Longitudinal Study of Ageing (ELSA), Survey of Health, Ageing and Retirement in Europe (SHARE), which were used to provide the representative sample and comparable measures on BMI, WC, UI, and other covariates, covering 21 countries including both developed and developing ones on two continents. Given the availability of UI measurements and similar time ranges, we used data from the following time period in this analysis: 2010–2018 for HRS, 2010–2018 for ELSA, and 2004–2010 for SHARE. Participants who were younger than 50 years old were excluded. Following the exclusion criteria, 121,450 SHARE participants with 360,800 observations, 19,791 ELSA participants with 98,158 observations, and 42,132 HRS participants with 207,805 observations were analyzed. Data collection Data on urinary incontinence were collected through questionnaires. In HRS and ELSA, UI was assessed by asking: “During the last 12 months, have you lost any amount of urine beyond your control?” In SHARE, the dependent variable was constructed based on the question: “For the past six months at least, have you been bothered by incontinence or involuntary loss of urine?” The responses ranged from 0 = ‘‘No’’, which meant the respondent hadn’t been bothered by any urinary incontinence in the time frame corresponding to the question, to 1 = ‘‘Yes’’, which was considered as the respondent had been bothered by any urinary incontinence in the time frame corresponding to the question. The measures of obesity indices were based on BMI and WC. The BMI (kg/m 2 ) was derived by dividing weight by the square of height. The measurement of WC was reported in centimeters. The obesity indices (BMI and WC) were then equally divided into six quantiles when analyzing the association of obesity with UI prevalence. In addition, covariates for sociodemographic status (age, race, residence area, marital status, number of children), lifestyles (smoking, drinking), the history of the disease (hypertension, diabetes, cancer, stroke) as well as educational attainments, functional ability, and cognitive impairment were added. The race was divided into white or not. Residence area was attributed to whether living in rural or urban areas. Marital status was imputed to married/partnered or not. We used the harmonized education attainments classification, namely lower than the secondary/upper secondary and vocational training/tertiary. Physical active participants were defined as those without difficulty using the toilet. Cognitive impairment was defined as a score of 8 or below on a 35-point scale for HRS 29 , 30 , while a score between 0 and 11 on a 27-point scale for the ELSA 31 . In SHARE, a summary cognitive function score of averaged z-scores of the verbal fluency, immediate and delayed recall, orientation, and numeracy for individuals was constructed using the mean and standard deviation of the first wave, where respondents were labeled as having cognitive impairment if their score fell within the lowest decile 32 . Statistical analysis Separate descriptive characteristics of the observations in the HRS, ELSA, and SHARE were provided, in which the mean (SD) or the median (IQR) was used for continuous variables, and number (percentage) was used for categorical variables. The observations of BMI, WC, and other covariates were then displayed by whether with UI in both females and males for HRS, ELSA, and SHARE, respectively. Additionally, the statistically significant difference between those with UI and without UI in BMI, WC, and other covariates was reported to identify the driving factors of UI by sex among older adults in the HRS, ELSA, and SHARE. All statistical analyses were conducted by females and males, seperately. Chi-squared tests were used to evaluate differences among those with UI and without UI for categorical variables, whereas t-tests were used for continuous variables. We employed random-effect logistic models to control the individual-specific characteristics that might change in different waves and to construct odds ratios (ORs) and 95% confidence intervals (CIs) to analyze the relationships between obesity indices and the prevalence of UI. The dependent variable is the prevalence of UI. The sociodemographic status, lifestyle factors, history of diseases, educational attainments, functional ability, and cognitive impairment were incorporated as the co-variates. Three models were conducted in the analyses by sex. Model 1 was a crude model. In Model 2, we controlled for age, race (except for the SHARE), educational attainments, residence area (except for the ELSA), marital status, and the number of children. Model 3 was further adjusted for current smoking, ever-smoked, alcohol consumption, physical activity, hypertension, diabetes, stroke, cancer, and cognitive impairments, which was regarded as the fully adjusted model. Statistical significance was considered to be P < 0.05. Accordingly, the restricted cubic spline (RCS) curves were plotted to visualize the associations of UI with BMI by sex in HRS, ELSA, and SHARE, as well as the associations of UI with WC by sex in HRS rather than in ELSA and SHARE due to only one wave for WC statistics in ELSA and data unavailability of WC in SHARE. Additionally, subgroup analyses were performed for each age group (50–59/60–69/70–79/80 over) within each gender (Female/Male). The RCS curves were shown for relationships between UI and BMI for each age group in HRS and SHARE, as well as the associations between UI and WC for each age group in HRS. The subgroup analyses in ELSA, however, were not conducted because there was no significantly discernible gender difference in the associations. All statistical analyses were performed by Stata 17.0. Results The descriptive characteristics of observations by sex in the HRS, ELSA, and SHARE are presented in Table 1 . The 666,763 participant observations (HRS 207,805; ELSA 98,158; SHARE 360,800) were collected for the baseline survey. The median ages included in this analysis for HRS, ELSA, and SHARE studies were 66, 67, and 64 for females, and 65, 67, and 64 for males, respectively. The white was 72% in HRS and 95.4% in ELSA for females, as well as 73.6% in HRS and 95.4% in ELSA for males. More importantly, the females and males with UI were responsible for 15.6% & 6.6% in the HRS study, 10.6% & 4.4% in the ELSA study, and 2.8% & 1.4% in the SHARE study, respectively. The average BMI in the surveys of HRS, ELSA, and SHARE was 28.6 (± 6.5), 28.20 (± 5.6), and 26.5 (± 4.8) for females, and 28.5 (± 5.3), 28.2 (± 4.4), and 27.0 (± 4.0) for males, respectively. Meanwhile, the average WC for females and males were 99.3 (± 14.5) and 105.2 (± 12.4) in HRS, and 92.0 (± 13.6) and 102.2 (± 11.9) in ELSA. The BMI, WC, and covariates of observations by sex in analyses were displayed in Table 2 . Across three cohort studies, there are 29,187 female observations with UI and 10,241 male observations with UI being incorporated. Notably, males and females with and without UI exhibited significant differences in baseline characteristics. Specifically, females with UI showed significantly higher median age, the proportion of white, married and partnered, and physically inactive, the prevalence of chronic diseases and cognitive impairments (except HRS study), and higher BMI and WC, compared to females without UI. Comparably, most baseline characteristics for males with and without UI indicated similar trends with females but with less statistical significance in the race, residence area, marital status, the number of children, variables for lifestyle, and obesity indices. The association between BMI, WC and the prevalence of UI was outlined in Table 3 . The fully adjusted model for females shows a linear trend of monotonically increasing prevalence of UI as BMI and WC rise. Specifically, the first quantile had the lowest prevalence of UI and the sixth quantile had the significantly highest prevalence of UI compared to participants in the second quantile of BMI. Similarly, compared to participants in the second quantile of WC, those in the first quantile had the lowest prevalence of UI, whereas the significant highest prevalence of UI was observed in the sixth quantile. However, a U-shaped trajectory in the UI prevalence can be found with rising BMI and WC in the fully adjusted model for males, even though the linear associations between obesity indices and UI were less pronounced than in women. In comparison to those in the second quantile of BMI, the lowest prevalence of UI was found in the fourth quantile for HRS, the third quantile for ELSA and SHARE study, and those in the sixth quantile of BMI had the significant highest prevalence of UI. In terms of WC, we discovered that those in the fourth quantile of WC for both HRS and ELSA experienced the lowest prevalence of UI. However, those statistics were not statistically significant. Additionally, although the statistic in the ELSA study did not reach statistical significance, the statistic for the HRS in the sixth quantile of WC had the significant highest UI prevalence, compared to those in the second quantile. Additionally, we draw the restricted cubic splines (RCS) curve to visualize the association between UI and BMI by sex in HRS, ELSA, and SHARE, as shown in Fig. 1 . Besides, the association between UI and WC by sex in the HRS study was also plotted in Fig. 2 . The shapes of RCS curves showed heterogeneity across gender in HRS and SHARE studies. Specifically, among three longitudinal cohort studies, the RCS curves for females concerning the association of UI with both BMI and WC were almost monotonically increasing, and the slopes of these curves were relatively larger at higher BMI and higher WC. However, for males in HRS and SHARE, it was obvious that a “U-” shaped curve was traced with increasing OR of the prevalence of UI on the Y axis and increasing BMI or increasing WC on the X axis, even though this pattern was not apparent for the figure in ELSA study. To assess the gender and age heterogeneity of UI on BMI and WC, RCS curves were also drawn in the Supplementary information to visualize the UI-BMI association and UI-WC association in different age groups within each gender in HRS and SHARE. As can be seen, we found that both the UI-BMI association and the UI-WC association were more pronounced in the 60–69 age group for males in HRS and SHARE. Discussion By utilising three longitudinal cohorts representative of the US, UK, and European samples, this study offered unique insight and extensive evidence on the relationship between obesity and UI in older men and women. Specifically, the higher prevalence of UI in older women is linearly correlated with higher BMI and WC, while there was a U-shaped association between BMI and UI in older men. Our study indicated a revisiting of association between obesity and UI among older men compared to women. Prior epidemiological studies reported that obesity is a modifiable risk factor for UI development in older women or men 5 – 8 , 10 , 11 , 14 , 15 , 17 , 18 , and some clinical trials provided evidence that weight reduction can reduce the incidence of UI 9 , 12 , 13 , 16 . However, only seven studies—three longitudinal and four cross-sectional—focused on men and women seperately regarding the relationship between UI and obesity indices 19,21−26 . In particular, the Subak et al.’s cohort study based on the longitudinal assessment of bariatric surgery 2 revealed that obese participants who lose weight via surgical interventions experience reduced UI 24 , which was in conflict with the male analysis of our study since this study focused on severely obese people with BMI (kg/m^2) varied from 41.6 to 52.8, whereas our study subjects concentrated on the general elderly population. In addition, the cohort study by Tsui et al. from a British birth cohort study demonstrated that increased BMI at age 60–64 years was an independent risk factor for urgent urinary incontinence (UUI) in men and women 26 . Another article by Tennstedt et al. examining the population-based adults aged 30–79 years based on the Boston Area Community Health (BACH) Survey investigated that WC was a risk factor for leakage in women rather than men, with the odds of weekly leakage increasing by 15 percent with each 10-cm increase 25 . In general, our findings concurred with these investigations. However, the participants in these two research specifically targeted the elderly in Britain and Boston, respectively, rather than the elderly worldwide. Second, only UUI, rather than general UI, was used as the outcome assessment in the Tsui et al. study. Furthermore, we could only be enlightened if obesity was related to UUI or UI rather than the movement of UI as WC grew and the tendency of UUI with growing BMI. Also, Tsui et al.’s study did not take central obesity into consideration. Moreover, even though there were four cross-sectional studies also considering both genders in regard to the UI-obesity associations 19,21−23 , one of them from the French 3C study that concentrated on the elderly subjects aged 65–101 suggested that the relationship tended to be linear for UI and obesity in females, whereas the pattern that a higher risk of UI for both underweight and obese subjects was found in males 23 . This study was in line with our findings but concentrated primarily on French and did not take into account the obesity indices for central adiposity. In terms of mechanisms, several biological studies have pointed out that obesity is associated with low-grade systemic inflammation and the release of pro-inflammatory cytokines, generating reactive oxygen species and oxidative stress, which alters collagen metabolism. At the same time, intra-abdominal pressure increases with obesity that bears down on pelvic tissues, causing chronic strain, stretching, and weakening of the muscles, nerves, and other structures of the pelvic floor, which thus leads to negatively affecting pelvic organ function. These physical and biochemical stresses predispose obese geriatric patients to develop UI 8 . Comparably, being underweight also resulted in a higher prevalence of UI for males, according to our study. This could be as a result of the several mechanisms through which being underweight may cause disability, especially a lack of physical activity and a higher risk of falls, increasing the chance of UI incidence 33 – 35 . The disability would, in turn, lead to underweight due to partial loss of life skills, thus less accessibility to get nutrition through cooking and etc. Nonetheless, the association between being underweight and UI is still debatable and appears to be weaker than the relationship with obesity. The discovery of permanent rather than reversible alterations to the prostate caused by weight loss once men approach old age 11 , as well as postmenopausal hormonal changes in women, may be viable explanations in light of the mechanism underlying the gender difference. The type of incontinence, however, was not taken into account in this study's outcome measures, which was likely the cause of the less substantial linear association in men compared to women. This study delivers distinctive and substantial policy implications. On the one hand, insufficient attention has been paid to the relationship between UI and obesity indices in men. For instance, earlier research has focused less on the patterns between UI and obesity in males than in women. Male participants were not even asked in the Korean Longitudinal Study on Aging (KLoSA) questionnaire if they had ever experienced UI within a certain time period. Although men are less prevalent than women to report UI, it is nevertheless essential to revisit the UI-obesity association in men to better corroborate our findings and thus further improve our present UI prevention and treatment practices. On the other hand, the majority of developed countries examine UI and fecal incontinence separately due to crucial diversity in pathogenic mechanisms, risk factors, and treatment approaches, which HRS, ELSA, KLoSA, and SHARE could support. Contrarily, some nationwide cohort studies of aging, including the China Health and Retirement Longitudinal Study (CHARLS), did not differentiate these two disorders clearly and instead created a single question to target them explicitly. Therefore, it was imperative to distinguish UI and fecal incontinence when developing survey questionnaires to advance pertinent intervention research. There are several strengths. Firstly, our study's excellent generalizability can be attributed to the utilization of three sizable, diverse, well-characterized, cross-cultural longitudinal studies that included older adults from 22 developed and developing countries on two continents. Second, the validity of our study was strengthened by the use of nationally representative samples for participant recruitment, standardization of the three surveys for database comparisons, and a more extended follow-up period of almost ten years. Third, random-effect logistic models regulate individual-level traits that could alter over longitudinal data waves and thus reducing the likelihood of misestimation. Finally, this study provides a basis for demonstrating the obesity paradox in the relationship between obesity and urinary incontinence in men, showing that those older men with both lower and higher BMI and WC tend to report higher UI prevalence compared to those with BMI or WC in the normal range, although this relationship is not particularly significant. However, our study has limitations. Firstly, due to data availability for UI measurements, the present study has an early but narrower time period for SHARE compared to HRS and ELSA, which could cause an overestimation or underestimation of the underlying relationships between obesity indices and outcomes. Secondly, this study's UI, BMI, and WC measurements rely on self-report rather than clinician diagnosis, which might be a limitation as responses could be influenced by recall and social desirability biases. Additionally, even though we considered numerous potential confounders that had a significant impact on our findings, there may still be confounding variables that are unaccounted for due to limited information, such as ethnicity in SHARE and residential areas in ELSA. A previous study provided insights that indicators of central obesity, including WC and waist-to-hip ratio, appear to be more sensitive than BMI (a proxy for body fat) in explaining the association between obesity and urine leakage 22 , 25 . However, our study did not fully consider the indicators of central obesity due to a lack of comparable data. Finally, the type of incontinence was not specified in the longitudinal cohort, which poses an unavoidable obstacle to further research on its mechanisms. In summary, the associations between obesity indices and UI is different in older women compared to older men. Therefore, weight loss as a treatment for UI can only be applied to older women but not necessarily to men. With this in mind, developing interventions to address UI among older adults should take sex into account. Declarations Data availability Original survey datasets from HRS, ELSA, and SHARE are freely available to all bona fide researchers. The data that support the findings of this study are available from the GATEWAY TO GLOBAL AGING DATA ( https://g2aging.org/ ). Competing interests The authors declare that they have no competing interests. Funding This study received no external funding. Acknowledgement We thank the GATEWAY TO GLOBAL AGING DATA for providing the harmonised data. We thank the Dr. Rui Yang and Mingzhi Yu from Peking University for gaving us technical support in data analyses and interpretation. We also thank the support from the Healthy Aging Group of the China Cohort Consortium (see http://chinacohort.bjmu.edu.cn/). Contributors Yao Yao designed the study. Data collection and analysis was led by Xiyin Chen. 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Mayo Clin Proc 2010; 85 (2): 112-4. Donini LM, Pinto A, Giusti AM, Lenzi A, Poggiogalle E. Obesity or BMI Paradox? Beneath the Tip of the Iceberg. Front Nutr 2020; 7 : 53. Suthers K, Kim JK, Crimmins E. Life expectancy with cognitive impairment in the older population of the United States. J Gerontol B Psychol Sci Soc Sci 2003; 58 (3): S179-86. Herzog AR, Wallace RB. Measures of cognitive functioning in the AHEAD Study. J Gerontol B Psychol Sci Soc Sci 1997; 52 Spec No : 37-48. Williams BD, Pendleton N, Chandola T. Cognitively stimulating activities and risk of probable dementia or cognitive impairment in the English Longitudinal Study of Ageing. SSM Popul Health 2020; 12 : 100656. Leist AK, Glymour MM, Mackenbach JP, van Lenthe FJ, Avendano M. Time away from work predicts later cognitive function: differences by activity during leave. Ann Epidemiol 2013; 23 (8): 455-62. Davison KK, Ford ES, Cogswell ME, Dietz WH. Percentage of body fat and body mass index are associated with mobility limitations in people aged 70 and older from NHANES III. J Am Geriatr Soc 2002; 50 (11): 1802-9. Galanos AN, Pieper CF, Cornoni-Huntley JC, Bales CW, Fillenbaum GG. Nutrition and function: is there a relationship between body mass index and the functional capabilities of community-dwelling elderly? J Am Geriatr Soc 1994; 42 (4): 368-73. Manandhar MC. Functional ability and nutritional status of free-living elderly people. Proc Nutr Soc 1995; 54 (3): 677-91. Tables Table 1. Descriptive statistics by sex in HRS, ELSA, and SHARE Characteristics HRS(N = 207,805) ELSA(N = 98,158) SHARE(N = 360,800) Female(N = 115,849) Male(N = 91,956) Female(N = 53,325) Male(N = 44,833) Female(N = 198,788) Male(N = 162,012) Age (years) 66(58,76) 65(58,75) 67 (60, 75) 67 (61, 74) 64 (57, 73) 64 (57, 72) White (%) 83,372(72.0%) 67,635(73.6%) 50,857 (95.4%) 42,776 (95.4%) - - Educational attainments (%) Less than secondary 17,590 (15.2%) 15,178 (16.5%) 19,469 (36.5%) 13,181 (29.4%) 91,628 (46.1%) 62,338 (38.5%) Upper secondary and vocational training 36,638 (31.6%) 44,463 (48.4%) 21,542 (40.4%) 20,322 (45.3%) 69,998 (35.2%) 62,676 (38.7%) Tertiary 16,088 (13.9%) 14,082 (15.3%) 6,613 (12.4%) 8,015 (17.9%) 37,162 (18.7%) 36,998 (22.8%) Residence area (%) Rural 28,101 (24.3%) 23,160 (25.2%) - - 45,775 (23.0%) 39,663 (24.5%) Urban 78,431 (67.7%) 61,214 (66.6%) - - 102,982 (51.8%) 83,292 (51.4%) Married and partnered (%) 61,393 (53.0%) 27,527 (29.9%) 20,607 (38.6%) 11,283 (25.2%) 58,966 (29.7%) 27,708 (17.1%) Number of Children (n) 2(1,4) 2(1,4) 2(1,3) 2(1,3) 2(1,3) 2(1,3) Urinary incontinence (%) 18,070 (15.6%) 6,065(6.6%) 5,629(10.6%) 1,973 (4.4%) 5,488 (2.8%) 2,203 (1.4%) Current smoking (%) 7,148(6.2%) 6,175(6.7%) 2,817 (5.3%) 2,387 (5.3%) 10,286 (5.2%) 12,422 (7.7%) Ever smoked (%) 27,041 (23.3%) 25,978 (28.3%) 14,419 (27.0%) 14,141 (31.5%) 22,454 (11.3%) 34,081 (21.0%) Alcohol consumption (%) 27,408 (23.7%) 25,528 (27.8%) 18,412 (34.5%) 15,956 (35.6%) 37,284 (18.8%) 36,532 (22.5%) Physically active (%) 50,811 (43.9%) 38,153 (41.5%) 24,689 (46.3%) 20,305 (45.3%) 65,096 (32.7%) 53,100 (32.8%) History of diseases Hypertension (%) 33,872 (29.2%) 24,563 (26.7%) 10,802 (20.3%) 9,490 (21.2%) 27,516 (13.8%) 20,544 (12.7%) Diabetes (%) 13,547 (11.7%) 10,716 (11.7%) 2,607 (4.9%) 2,988 (6.7%) 7,873 (4.0%) 7,163 (4.4%) Cancer (%) 8,127(7.0%) 6,253(6.8%) 3,203 (6.0%) 2,272 (5.1%) 4,662 (2.3%) 3,300 (2.0%) Stroke (%) 4,981(4.3%) 3,850(4.2%) 1,162 (2.2%) 1,159 (2.6%) 3,017 (1.5%) 3,032 (1.9%) Cognitive impairment (%) 5,110(4.4%) 4,677(5.1%) 1,736 (3.3%) 1,264 (2.8%) 7,738 (3.9%) 5,532 (3.4%) BMI (kg/m^2) 28.6±6.5 28.5±5.3 28.2±5.6 28.2±4.4 26.5±4.8 27.0±4.0 Waist circumferences (cm) 99.3±14.5 105.2±12.4 92.0±13.6 102.2±11.9 - - Data are presented as mean ± SD or median (IQR), n (%) HRS, the Health and Retirement Study; ELSA, the English Longitudinal Study of Ageing; SHARE, Survey of Health, Ageing and Retirement in Europe BMI, body mass index Table 2 and 3 are available in the Supplementary Files section. Additional Declarations There is NO Competing Interest. Supplementary Files 03Appendix.docx Tables.docx Cite Share Download PDF Status: Published Journal Publication published 11 Oct, 2023 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2441866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":167501064,"identity":"4aad55a8-d9ba-4d47-8065-b81b2880d3db","order_by":0,"name":"Xiyin Chen","email":"","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Xiyin","middleName":"","lastName":"Chen","suffix":""},{"id":167501065,"identity":"60fc1de1-49ac-4dc2-8a08-32660b62d90a","order_by":1,"name":"Shaoxiang Jiang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Shaoxiang","middleName":"","lastName":"Jiang","suffix":""},{"id":167501066,"identity":"7c60c4c6-f975-496f-beda-b3d55f4ea6ef","order_by":2,"name":"Yao Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBADOQOGAyCamXgtxqRrSdwAoYnQYnD88AFmnhqb9O2Mp9MkGCqsExvYzx7Ar+VMWgIzz7G03J0NZ7dJMJxJT2zgyUvAr+VAjgEzD9vh3A0HgFoY2w4nNkjwGODXcv4NUMu/w+kGYC3/iNFyA2gLb9vhBIiWBiK0SN54lnBwbl+aIdAvmy0SjqUbt/Hk4NfCdz754IM332zkzSXObrzxocZatp/9DH4tCgcYGA7xgFgSQFYCkGbDqx4I5BsYGBh/gFj8DYTUjoJRMApGwUgFAGktSp6LSIjUAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Yao","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2023-01-04 08:25:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2441866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2441866/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-023-00367-w","type":"published","date":"2023-10-11T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":31708148,"identity":"c33edd93-b704-47a0-8f3f-d2a38008aca8","added_by":"auto","created_at":"2023-01-17 20:37:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49224,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between UI and BMI by sex in HRS, ELSA and SHARE\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2441866/v1/c11854533b8625867cf047c2.jpg"},{"id":31708147,"identity":"c306b906-2165-4909-930c-0bb2e70fc62c","added_by":"auto","created_at":"2023-01-17 20:37:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34990,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between UI and WC by sex in HRS, 2010-2018\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2441866/v1/fd101bbc69d20ce4017f36f9.jpg"},{"id":44488032,"identity":"b6f32a72-0779-41fc-9192-d32875386fbd","added_by":"auto","created_at":"2023-10-12 07:10:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":446104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2441866/v1/78cd4629-2892-4a06-acf5-0adf3067b3a7.pdf"},{"id":31708150,"identity":"f3b3be55-d88a-45ad-b2e4-6897793fb5c0","added_by":"auto","created_at":"2023-01-17 20:37:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1011151,"visible":true,"origin":"","legend":"","description":"","filename":"03Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-2441866/v1/097bb9d2028cdcdc43140cc9.docx"},{"id":31709005,"identity":"798ad340-46d2-4a37-a792-b658e3b7d643","added_by":"auto","created_at":"2023-01-17 20:45:37","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":40347,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-2441866/v1/638ce4297b3a87fc1b9dec6c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"BMI, waist circumferences and urinary incontinence in older women compared with older men: findings from three prospective longitudinal cohort studies","fulltext":[{"header":"Research In Context","content":"\u003cp\u003e\u003cstrong\u003eEvidence before this study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe searched PubMed for studies related to obesity and urinary incontinence (UI) in the older population without any time restriction. We used the following keywords \"Body Mass Index\", \"Waist Circumference\", \"Obesity\", \"Urinary Incontinence\", \"Aged\", \"Female\", and \"Male\". In PubMed, 302 articles were collected using all of these keywords, in which 47 articles studied the association of obesity indices and UI. However, only seven studies considered sex differences regarding the relationship between UI and obesity indices. Specifically, there are four cross-sectional studies and three longitudinal studies analysed the UI-obesity relationships by sexes.\u0026nbsp; There has been only one study, based on the Boston Area Community Health (BACH) Survey, that reported that central obesity increases the odds of weekly urinary leakage for women, but not for men. However, there has been little prospective longitudinal research conducted to investigate the sex differences in associations between body mass index (BMI), waist circumference (WC) and UI, limiting the effectiveness of interventions for UI in older women and men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdded value of this study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy utilising multiple nationwide prospective longitudinal cohorts representative of the US, UK, and European samples (HRS, ELSA, and SHARE), we provide a unique insight and comprehensive assessment of the association of BMI and WC with UI among older women compared to men. As far as we know, this is among the first longitudinal study with large sample size to investigate the association of BMI and WC with UI among older adults by taking sex into consideration. According to our study, after controlling for a variety of confounders, BMI is linearly associated with higher prevalence of UI among older women, while it has a U-shaped association among older men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications of all the available evidence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies suggested weight reduction for UI interventions among older people. However, our study revealed that the association of obesity indices with UI varied among older men compared to older women. We observed linear associations between BMI, WC, and UI in women, while we observed U-shaped associations among men. The lowest likelihood of having UI was found among male participants with a BMI between 24 and 35 kg/m\u003csup\u003e2\u003c/sup\u003e. Weight loss intervention could therefore only be applied to older women rather than to older men as a way to treat UI. Developing interventions to address UI among older adults should take sex into account.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eA rapidly ageing population, coupled with age-related illnesses, is a pressing concern around the globe. Urinary incontinence (UI) is a common geriatric syndrome that has been associated with adverse health outcomes and poor quality of life. It is estimated that 17 to 55% of older women and 11 to 34% of older males had UI, which places a substantial financial and care burden on society and families.\u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/sup\u003e The direct costs of UI in 1995 were estimated at \u003cspan\u003e$\u003c/span\u003e26.3\u0026nbsp;billion for those older than 65 years in the USA.\u003csup\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003c/sup\u003e Furthermore, the annual cost-of-illness estimate for urgency urinary incontinence (UUI) in Canada, Germany, Italy, Spain, Sweden, and the United Kingdom was \u0026euro;7\u0026nbsp;billion in a 2005 multinational study.\u003csup\u003e \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e \u003c/sup\u003e UI is also associated with a reduced quality of life (QoL) and self-esteem for those who have a stressful, incapacitating condition and a social isolation element, with sufferers reporting embarrassment, distress, and depression\u003csup\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e. The caregivers of older adults with UI are also more likely to feel a strain both physically and mentally than those without UI.\u003csup\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e Thus, identifying and managing of modifiable risk factors of UI are of paramount importance.\u003c/p\u003e \u003cp\u003eIn spite of the fact that obesity is well documented as a modifiable risk factor for the development of UI, most epidemiological, biological, and clinical research focuses on older women rather than older men.\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Meanwhile, despite some studies focused on both sexes,\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e few longitudinal research targeting the older population investigated the sex heterogeneity in patterns of associations between UI and obesity.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Moreover, there are varied obesity indices including Body mass index (BMI) and waist circumference (WC). BMI estimates general obesity established by the World Health Organization (WHO), while WC better describes central obesity, although it cannot discriminate visceral fat from subcutaneous fat. However, the relationship between obesity indices, i.e., BMI and WC, and UI in older men and women has not been thoroughly examined in prior longitudinal studies. Furthermore, although the EpiLUTS study comprised research subjects from three countries,\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e samples in most prior findings were based on samples from one country or region, which led to poor generalization of the study's findings.\u003c/p\u003e \u003cp\u003eExtant evidence has reported an \u0026ldquo;obesity paradox\u0026rdquo; in which BMI is negatively associated with mortality, which indicates that being normal weight and overweight could be more beneficial to health than being underweight.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e This paradoxical association of obesity with mortality has been inconsistent due to a discrepancy between BMI and central obesity.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e However, whether this paradox applies to older women and men in terms of UI was largely unknow. Therefore, study with a representative sample of older men and women from a global perspective should be conducted to reexamine the associations between obesity indices, including BMI and WC, and UI.\u003c/p\u003e \u003cp\u003eBy utilising multiple nationwide prospective longitudinal cohorts representative of the US, UK, and European samples, we contribute to the literature by analysing the association between BMI, WC, and UI among older women compared to men. This study aimed to illustrate the sex difference in impact of obesity on UI and provide indication of evidence for future intervention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eData were accessed from three international cohorts of aging: Health and Retirement Study (HRS), English Longitudinal Study of Ageing (ELSA), Survey of Health, Ageing and Retirement in Europe (SHARE), which were used to provide the representative sample and comparable measures on BMI, WC, UI, and other covariates, covering 21 countries including both developed and developing ones on two continents. Given the availability of UI measurements and similar time ranges, we used data from the following time period in this analysis: 2010\u0026ndash;2018 for HRS, 2010\u0026ndash;2018 for ELSA, and 2004\u0026ndash;2010 for SHARE. Participants who were younger than 50 years old were excluded. Following the exclusion criteria, 121,450 SHARE participants with 360,800 observations, 19,791 ELSA participants with 98,158 observations, and 42,132 HRS participants with 207,805 observations were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData on urinary incontinence were collected through questionnaires. In HRS and ELSA, UI was assessed by asking: \u0026ldquo;During the last 12 months, have you lost any amount of urine beyond your control?\u0026rdquo; In SHARE, the dependent variable was constructed based on the question: \u0026ldquo;For the past six months at least, have you been bothered by incontinence or involuntary loss of urine?\u0026rdquo; The responses ranged from 0 = \u0026lsquo;\u0026lsquo;No\u0026rsquo;\u0026rsquo;, which meant the respondent hadn\u0026rsquo;t been bothered by any urinary incontinence in the time frame corresponding to the question, to 1 = \u0026lsquo;\u0026lsquo;Yes\u0026rsquo;\u0026rsquo;, which was considered as the respondent had been bothered by any urinary incontinence in the time frame corresponding to the question. The measures of obesity indices were based on BMI and WC. The BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) was derived by dividing weight by the square of height. The measurement of WC was reported in centimeters. The obesity indices (BMI and WC) were then equally divided into six quantiles when analyzing the association of obesity with UI prevalence.\u003c/p\u003e \u003cp\u003eIn addition, covariates for sociodemographic status (age, race, residence area, marital status, number of children), lifestyles (smoking, drinking), the history of the disease (hypertension, diabetes, cancer, stroke) as well as educational attainments, functional ability, and cognitive impairment were added. The race was divided into white or not. Residence area was attributed to whether living in rural or urban areas. Marital status was imputed to married/partnered or not. We used the harmonized education attainments classification, namely lower than the secondary/upper secondary and vocational training/tertiary. Physical active participants were defined as those without difficulty using the toilet. Cognitive impairment was defined as a score of 8 or below on a 35-point scale for HRS\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, while a score between 0 and 11 on a 27-point scale for the ELSA\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In SHARE, a summary cognitive function score of averaged z-scores of the verbal fluency, immediate and delayed recall, orientation, and numeracy for individuals was constructed using the mean and standard deviation of the first wave, where respondents were labeled as having cognitive impairment if their score fell within the lowest decile\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSeparate descriptive characteristics of the observations in the HRS, ELSA, and SHARE were provided, in which the mean (SD) or the median (IQR) was used for continuous variables, and number (percentage) was used for categorical variables. The observations of BMI, WC, and other covariates were then displayed by whether with UI in both females and males for HRS, ELSA, and SHARE, respectively. Additionally, the statistically significant difference between those with UI and without UI in BMI, WC, and other covariates was reported to identify the driving factors of UI by sex among older adults in the HRS, ELSA, and SHARE.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted by females and males, seperately. Chi-squared tests were used to evaluate differences among those with UI and without UI for categorical variables, whereas t-tests were used for continuous variables. We employed random-effect logistic models to control the individual-specific characteristics that might change in different waves and to construct odds ratios (ORs) and 95% confidence intervals (CIs) to analyze the relationships between obesity indices and the prevalence of UI. The dependent variable is the prevalence of UI. The sociodemographic status, lifestyle factors, history of diseases, educational attainments, functional ability, and cognitive impairment were incorporated as the co-variates. Three models were conducted in the analyses by sex. Model 1 was a crude model. In Model 2, we controlled for age, race (except for the SHARE), educational attainments, residence area (except for the ELSA), marital status, and the number of children. Model 3 was further adjusted for current smoking, ever-smoked, alcohol consumption, physical activity, hypertension, diabetes, stroke, cancer, and cognitive impairments, which was regarded as the fully adjusted model. Statistical significance was considered to be P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Accordingly, the restricted cubic spline (RCS) curves were plotted to visualize the associations of UI with BMI by sex in HRS, ELSA, and SHARE, as well as the associations of UI with WC by sex in HRS rather than in ELSA and SHARE due to only one wave for WC statistics in ELSA and data unavailability of WC in SHARE.\u003c/p\u003e \u003cp\u003eAdditionally, subgroup analyses were performed for each age group (50\u0026ndash;59/60\u0026ndash;69/70\u0026ndash;79/80 over) within each gender (Female/Male). The RCS curves were shown for relationships between UI and BMI for each age group in HRS and SHARE, as well as the associations between UI and WC for each age group in HRS. The subgroup analyses in ELSA, however, were not conducted because there was no significantly discernible gender difference in the associations. All statistical analyses were performed by Stata 17.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe descriptive characteristics of observations by sex in the HRS, ELSA, and SHARE are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The 666,763 participant observations (HRS 207,805; ELSA 98,158; SHARE 360,800) were collected for the baseline survey. The median ages included in this analysis for HRS, ELSA, and SHARE studies were 66, 67, and 64 for females, and 65, 67, and 64 for males, respectively. The white was 72% in HRS and 95.4% in ELSA for females, as well as 73.6% in HRS and 95.4% in ELSA for males. More importantly, the females and males with UI were responsible for 15.6% \u0026amp; 6.6% in the HRS study, 10.6% \u0026amp; 4.4% in the ELSA study, and 2.8% \u0026amp; 1.4% in the SHARE study, respectively. The average BMI in the surveys of HRS, ELSA, and SHARE was 28.6 (\u0026plusmn;\u0026thinsp;6.5), 28.20 (\u0026plusmn;\u0026thinsp;5.6), and 26.5 (\u0026plusmn;\u0026thinsp;4.8) for females, and 28.5 (\u0026plusmn;\u0026thinsp;5.3), 28.2 (\u0026plusmn;\u0026thinsp;4.4), and 27.0 (\u0026plusmn;\u0026thinsp;4.0) for males, respectively. Meanwhile, the average WC for females and males were 99.3 (\u0026plusmn;\u0026thinsp;14.5) and 105.2 (\u0026plusmn;\u0026thinsp;12.4) in HRS, and 92.0 (\u0026plusmn;\u0026thinsp;13.6) and 102.2 (\u0026plusmn;\u0026thinsp;11.9) in ELSA.\u003c/p\u003e\n\u003cp\u003eThe BMI, WC, and covariates of observations by sex in analyses were displayed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Across three cohort studies, there are 29,187 female observations with UI and 10,241 male observations with UI being incorporated. Notably, males and females with and without UI exhibited significant differences in baseline characteristics. Specifically, females with UI showed significantly higher median age, the proportion of white, married and partnered, and physically inactive, the prevalence of chronic diseases and cognitive impairments (except HRS study), and higher BMI and WC, compared to females without UI. Comparably, most baseline characteristics for males with and without UI indicated similar trends with females but with less statistical significance in the race, residence area, marital status, the number of children, variables for lifestyle, and obesity indices.\u003c/p\u003e\n\u003cp\u003eThe association between BMI, WC and the prevalence of UI was outlined in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The fully adjusted model for females shows a linear trend of monotonically increasing prevalence of UI as BMI and WC rise. Specifically, the first quantile had the lowest prevalence of UI and the sixth quantile had the significantly highest prevalence of UI compared to participants in the second quantile of BMI. Similarly, compared to participants in the second quantile of WC, those in the first quantile had the lowest prevalence of UI, whereas the significant highest prevalence of UI was observed in the sixth quantile.\u003c/p\u003e\n\u003cp\u003eHowever, a U-shaped trajectory in the UI prevalence can be found with rising BMI and WC in the fully adjusted model for males, even though the linear associations between obesity indices and UI were less pronounced than in women. In comparison to those in the second quantile of BMI, the lowest prevalence of UI was found in the fourth quantile for HRS, the third quantile for ELSA and SHARE study, and those in the sixth quantile of BMI had the significant highest prevalence of UI. In terms of WC, we discovered that those in the fourth quantile of WC for both HRS and ELSA experienced the lowest prevalence of UI. However, those statistics were not statistically significant. Additionally, although the statistic in the ELSA study did not reach statistical significance, the statistic for the HRS in the sixth quantile of WC had the significant highest UI prevalence, compared to those in the second quantile.\u003c/p\u003e\n\u003cp\u003eAdditionally, we draw the restricted cubic splines (RCS) curve to visualize the association between UI and BMI by sex in HRS, ELSA, and SHARE, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Besides, the association between UI and WC by sex in the HRS study was also plotted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The shapes of RCS curves showed heterogeneity across gender in HRS and SHARE studies. Specifically, among three longitudinal cohort studies, the RCS curves for females concerning the association of UI with both BMI and WC were almost monotonically increasing, and the slopes of these curves were relatively larger at higher BMI and higher WC. However, for males in HRS and SHARE, it was obvious that a \u0026ldquo;U-\u0026rdquo; shaped curve was traced with increasing OR of the prevalence of UI on the Y axis and increasing BMI or increasing WC on the X axis, even though this pattern was not apparent for the figure in ELSA study.\u003c/p\u003e\n\u003cp\u003eTo assess the gender and age heterogeneity of UI on BMI and WC, RCS curves were also drawn in the Supplementary information to visualize the UI-BMI association and UI-WC association in different age groups within each gender in HRS and SHARE. As can be seen, we found that both the UI-BMI association and the UI-WC association were more pronounced in the 60\u0026ndash;69 age group for males in HRS and SHARE.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy utilising three longitudinal cohorts representative of the US, UK, and European samples, this study offered unique insight and extensive evidence on the relationship between obesity and UI in older men and women. Specifically, the higher prevalence of UI in older women is linearly correlated with higher BMI and WC, while there was a U-shaped association between BMI and UI in older men. Our study indicated a revisiting of association between obesity and UI among older men compared to women.\u003c/p\u003e \u003cp\u003ePrior epidemiological studies reported that obesity is a modifiable risk factor for UI development in older women or men\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and some clinical trials provided evidence that weight reduction can reduce the incidence of UI\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, only seven studies\u0026mdash;three longitudinal and four cross-sectional\u0026mdash;focused on men and women seperately regarding the relationship between UI and obesity indices\u003csup\u003e19,21\u0026minus;26\u003c/sup\u003e. In particular, the Subak et al.\u0026rsquo;s cohort study based on the longitudinal assessment of bariatric surgery 2 revealed that obese participants who lose weight via surgical interventions experience reduced UI\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, which was in conflict with the male analysis of our study since this study focused on severely obese people with BMI (kg/m^2) varied from 41.6 to 52.8, whereas our study subjects concentrated on the general elderly population.\u003c/p\u003e \u003cp\u003eIn addition, the cohort study by Tsui et al. from a British birth cohort study demonstrated that increased BMI at age 60\u0026ndash;64 years was an independent risk factor for urgent urinary incontinence (UUI) in men and women\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Another article by Tennstedt et al. examining the population-based adults aged 30\u0026ndash;79 years based on the Boston Area Community Health (BACH) Survey investigated that WC was a risk factor for leakage in women rather than men, with the odds of weekly leakage increasing by 15 percent with each 10-cm increase\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In general, our findings concurred with these investigations. However, the participants in these two research specifically targeted the elderly in Britain and Boston, respectively, rather than the elderly worldwide. Second, only UUI, rather than general UI, was used as the outcome assessment in the Tsui et al. study. Furthermore, we could only be enlightened if obesity was related to UUI or UI rather than the movement of UI as WC grew and the tendency of UUI with growing BMI. Also, Tsui et al.\u0026rsquo;s study did not take central obesity into consideration.\u003c/p\u003e \u003cp\u003eMoreover, even though there were four cross-sectional studies also considering both genders in regard to the UI-obesity associations\u003csup\u003e19,21\u0026minus;23\u003c/sup\u003e, one of them from the French 3C study that concentrated on the elderly subjects aged 65\u0026ndash;101 suggested that the relationship tended to be linear for UI and obesity in females, whereas the pattern that a higher risk of UI for both underweight and obese subjects was found in males\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This study was in line with our findings but concentrated primarily on French and did not take into account the obesity indices for central adiposity.\u003c/p\u003e \u003cp\u003eIn terms of mechanisms, several biological studies have pointed out that obesity is associated with low-grade systemic inflammation and the release of pro-inflammatory cytokines, generating reactive oxygen species and oxidative stress, which alters collagen metabolism. At the same time, intra-abdominal pressure increases with obesity that bears down on pelvic tissues, causing chronic strain, stretching, and weakening of the muscles, nerves, and other structures of the pelvic floor, which thus leads to negatively affecting pelvic organ function. These physical and biochemical stresses predispose obese geriatric patients to develop UI\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Comparably, being underweight also resulted in a higher prevalence of UI for males, according to our study. This could be as a result of the several mechanisms through which being underweight may cause disability, especially a lack of physical activity and a higher risk of falls, increasing the chance of UI incidence\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The disability would, in turn, lead to underweight due to partial loss of life skills, thus less accessibility to get nutrition through cooking and etc. Nonetheless, the association between being underweight and UI is still debatable and appears to be weaker than the relationship with obesity. The discovery of permanent rather than reversible alterations to the prostate caused by weight loss once men approach old age\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, as well as postmenopausal hormonal changes in women, may be viable explanations in light of the mechanism underlying the gender difference. The type of incontinence, however, was not taken into account in this study's outcome measures, which was likely the cause of the less substantial linear association in men compared to women.\u003c/p\u003e \u003cp\u003eThis study delivers distinctive and substantial policy implications. On the one hand, insufficient attention has been paid to the relationship between UI and obesity indices in men. For instance, earlier research has focused less on the patterns between UI and obesity in males than in women. Male participants were not even asked in the Korean Longitudinal Study on Aging (KLoSA) questionnaire if they had ever experienced UI within a certain time period. Although men are less prevalent than women to report UI, it is nevertheless essential to revisit the UI-obesity association in men to better corroborate our findings and thus further improve our present UI prevention and treatment practices. On the other hand, the majority of developed countries examine UI and fecal incontinence separately due to crucial diversity in pathogenic mechanisms, risk factors, and treatment approaches, which HRS, ELSA, KLoSA, and SHARE could support. Contrarily, some nationwide cohort studies of aging, including the China Health and Retirement Longitudinal Study (CHARLS), did not differentiate these two disorders clearly and instead created a single question to target them explicitly. Therefore, it was imperative to distinguish UI and fecal incontinence when developing survey questionnaires to advance pertinent intervention research.\u003c/p\u003e \u003cp\u003eThere are several strengths. Firstly, our study's excellent generalizability can be attributed to the utilization of three sizable, diverse, well-characterized, cross-cultural longitudinal studies that included older adults from 22 developed and developing countries on two continents. Second, the validity of our study was strengthened by the use of nationally representative samples for participant recruitment, standardization of the three surveys for database comparisons, and a more extended follow-up period of almost ten years. Third, random-effect logistic models regulate individual-level traits that could alter over longitudinal data waves and thus reducing the likelihood of misestimation. Finally, this study provides a basis for demonstrating the obesity paradox in the relationship between obesity and urinary incontinence in men, showing that those older men with both lower and higher BMI and WC tend to report higher UI prevalence compared to those with BMI or WC in the normal range, although this relationship is not particularly significant.\u003c/p\u003e \u003cp\u003eHowever, our study has limitations. Firstly, due to data availability for UI measurements, the present study has an early but narrower time period for SHARE compared to HRS and ELSA, which could cause an overestimation or underestimation of the underlying relationships between obesity indices and outcomes. Secondly, this study's UI, BMI, and WC measurements rely on self-report rather than clinician diagnosis, which might be a limitation as responses could be influenced by recall and social desirability biases. Additionally, even though we considered numerous potential confounders that had a significant impact on our findings, there may still be confounding variables that are unaccounted for due to limited information, such as ethnicity in SHARE and residential areas in ELSA. A previous study provided insights that indicators of central obesity, including WC and waist-to-hip ratio, appear to be more sensitive than BMI (a proxy for body fat) in explaining the association between obesity and urine leakage\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, our study did not fully consider the indicators of central obesity due to a lack of comparable data. Finally, the type of incontinence was not specified in the longitudinal cohort, which poses an unavoidable obstacle to further research on its mechanisms.\u003c/p\u003e \u003cp\u003eIn summary, the associations between obesity indices and UI is different in older women compared to older men. Therefore, weight loss as a treatment for UI can only be applied to older women but not necessarily to men. With this in mind, developing interventions to address UI among older adults should take sex into account.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal survey datasets from HRS, ELSA, and SHARE are freely available to all bona fide researchers. The data that support the findings of this study are available from the GATEWAY TO GLOBAL AGING DATA (\u003cu\u003ehttps://g2aging.org/\u003c/u\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the GATEWAY TO GLOBAL AGING DATA for providing the harmonised data. We thank the Dr. Rui Yang and Mingzhi Yu from Peking University for gaving us technical support in data analyses and interpretation. We also thank the support from the Healthy Aging Group of the China Cohort Consortium (see http://chinacohort.bjmu.edu.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYao Yao designed the study. Data collection and analysis was led by Xiyin Chen. The manuscript was written by Xiyin Chen and verified by Shaoxiang Jiang. All authors have edited and reviewed the manuscript. All authors had full access to the data and accept the responsibility to submit the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThom D. Variation in estimates of urinary incontinence prevalence in the community: effects of differences in definition, population characteristics, and study type. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e 1998; \u003cstrong\u003e46\u003c/strong\u003e(4): 473-80.\u003c/li\u003e\n\u003cli\u003eWagner TH, Hu TW. 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Vascular risk factors for male and female urgency urinary incontinence at age 68 years from a British birth cohort study. \u003cem\u003eBJU Int\u003c/em\u003e 2018; \u003cstrong\u003e122\u003c/strong\u003e(1): 118-25.\u003c/li\u003e\n\u003cli\u003eAdes PA, Savage PD. The obesity paradox: perception vs knowledge. \u003cem\u003eMayo Clin Proc\u003c/em\u003e 2010; \u003cstrong\u003e85\u003c/strong\u003e(2): 112-4.\u003c/li\u003e\n\u003cli\u003eDonini LM, Pinto A, Giusti AM, Lenzi A, Poggiogalle E. Obesity or BMI Paradox? Beneath the Tip of the Iceberg. \u003cem\u003eFront Nutr\u003c/em\u003e 2020; \u003cstrong\u003e7\u003c/strong\u003e: 53.\u003c/li\u003e\n\u003cli\u003eSuthers K, Kim JK, Crimmins E. Life expectancy with cognitive impairment in the older population of the United States. \u003cem\u003eJ Gerontol B Psychol Sci Soc Sci\u003c/em\u003e 2003; \u003cstrong\u003e58\u003c/strong\u003e(3): S179-86.\u003c/li\u003e\n\u003cli\u003eHerzog AR, Wallace RB. Measures of cognitive functioning in the AHEAD Study. \u003cem\u003eJ Gerontol B Psychol Sci Soc Sci\u003c/em\u003e 1997; \u003cstrong\u003e52 Spec No\u003c/strong\u003e: 37-48.\u003c/li\u003e\n\u003cli\u003eWilliams BD, Pendleton N, Chandola T. Cognitively stimulating activities and risk of probable dementia or cognitive impairment in the English Longitudinal Study of Ageing. \u003cem\u003eSSM Popul Health\u003c/em\u003e 2020; \u003cstrong\u003e12\u003c/strong\u003e: 100656.\u003c/li\u003e\n\u003cli\u003eLeist AK, Glymour MM, Mackenbach JP, van Lenthe FJ, Avendano M. Time away from work predicts later cognitive function: differences by activity during leave. \u003cem\u003eAnn Epidemiol\u003c/em\u003e 2013; \u003cstrong\u003e23\u003c/strong\u003e(8): 455-62.\u003c/li\u003e\n\u003cli\u003eDavison KK, Ford ES, Cogswell ME, Dietz WH. Percentage of body fat and body mass index are associated with mobility limitations in people aged 70 and older from NHANES III. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e 2002; \u003cstrong\u003e50\u003c/strong\u003e(11): 1802-9.\u003c/li\u003e\n\u003cli\u003eGalanos AN, Pieper CF, Cornoni-Huntley JC, Bales CW, Fillenbaum GG. Nutrition and function: is there a relationship between body mass index and the functional capabilities of community-dwelling elderly? \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e 1994; \u003cstrong\u003e42\u003c/strong\u003e(4): 368-73.\u003c/li\u003e\n\u003cli\u003eManandhar MC. Functional ability and nutritional status of free-living elderly people. \u003cem\u003eProc Nutr Soc\u003c/em\u003e 1995; \u003cstrong\u003e54\u003c/strong\u003e(3): 677-91.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Descriptive statistics by sex in HRS, ELSA, and SHARE\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"1317\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.829157175398635%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.056947608200456%\"\u003e\n \u003cp\u003eHRS(N = 207,805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"24.98101746393318%\"\u003e\n \u003cp\u003eELSA(N = 98,158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.13287775246773%\"\u003e\n \u003cp\u003eSHARE(N = 360,800)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003eFemale(N = 115,849)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003eMale(N = 91,956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003eFemale(N = 53,325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003eMale(N = 44,833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003eFemale(N = 198,788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003eMale(N = 162,012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e66(58,76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e65(58,75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e67 (60, 75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e67 (61, 74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e64 (57, 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e64 (57, 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e83,372(72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e67,635(73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e50,857 (95.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e42,776 (95.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational attainments (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Less than secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e17,590 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e15,178 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e19,469 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e13,181 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e91,628 (46.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e62,338 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Upper secondary and vocational training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e36,638 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e44,463 (48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e21,542 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e20,322 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e69,998 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e62,676 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Tertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e16,088 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e14,082 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e6,613 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e8,015 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e37,162 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e36,998 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence area (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e28,101 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e23,160 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e45,775 (23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e39,663 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e78,431 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e61,214 (66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e102,982 (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e83,292 (51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarried and partnered (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e61,393 (53.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e27,527 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e20,607 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e11,283 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e58,966 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e27,708 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Children (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e2(1,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e2(1,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrinary incontinence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e18,070 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e6,065(6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e5,629(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e1,973 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e5,488 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e2,203 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent smoking (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e7,148(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e6,175(6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2,817 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e2,387 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e10,286 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e12,422 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver smoked (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e27,041 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e25,978 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e14,419 (27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e14,141 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e22,454 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e34,081 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e27,408 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e25,528 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e18,412 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e15,956 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e37,284 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e36,532 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysically active (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e50,811 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e38,153 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e24,689 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e20,305 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e65,096 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e53,100 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Hypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e33,872 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e24,563 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e10,802 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e9,490 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e27,516 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e20,544 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Diabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e13,547 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e10,716 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e2,607 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e2,988 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e7,873 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e7,163 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Cancer (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e8,127(7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e6,253(6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e3,203 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e2,272 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e4,662 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e3,300 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u0026nbsp;Stroke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e4,981(4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e3,850(4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e1,162 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e1,159 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e3,017 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e3,032 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive impairment (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e5,110(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e4,677(5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e1,736 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e1,264 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e7,738 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e5,532 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m^2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e28.6\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e28.5\u0026plusmn;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e28.2\u0026plusmn;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e28.2\u0026plusmn;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e26.5\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e27.0\u0026plusmn;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.848024316109424%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumferences (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.677811550151976%\"\u003e\n \u003cp\u003e99.3\u0026plusmn;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003e105.2\u0026plusmn;12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.221884498480243%\"\u003e\n \u003cp\u003e92.0\u0026plusmn;13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e102.2\u0026plusmn;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.297872340425531%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.854103343465045%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"38.525835866261396%\"\u003e\n \u003cp\u003eData are presented as mean \u0026plusmn; SD or median (IQR),\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"88.1638846737481%\"\u003e\n \u003cp\u003eHRS, the Health and Retirement Study;\u0026nbsp;ELSA, the English Longitudinal Study of Ageing; SHARE, Survey of Health, Ageing and Retirement in Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"24.848024316109424%\"\u003e\n \u003cp\u003eBMI, body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/br\u003e\n\u003cp\u003eTable 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2441866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2441866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eObesity and urinary incontinence (UI) among older people, particularly older men, are yet to be fully explored. Utilising multiple nationwide prospective longitudinal cohorts representative of the US, UK, and European samples, we examined the association of body mass index (BMI) and waist circumference (WC) with UI among both older women and men.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe derived the data from the Health and Retirement Study (HRS, 2010-2018), the English Longitudinal Study of Aging (ELSA, 2011-2019), and the Survey of Health, Ageing and Retirement in Europe (SHARE, 2004-2010) that surveyed UI. Participants were asked if they had experienced urine leakage within the past 12 months (HRS and ELSA) or within the past six months (SHARE). The measure of obesity was based on BMI and WC. We employed a random-effect logistic model to associate BMI and WC with UI, adjusting for covariates including age, race, education, residence area, marital status, number of children, smoking, drinking, hypertension, diabetes, cancer, stroke, functional ability, and cognitive impairment. We visualised the associations by using restricted cubic spline curves.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 200,717 participants with 718,822 observations (207,805 in HRS; 98,158 in ELSA; 412,859 in SHARE) were included in the baseline analysis. The 12-months prevalence of UI among female and male participants were 15.6% and 6.6% in the HRS, 10.6% and 4.4% in the ELSA. The 6-months prevalence of UI were 2.8% and 1.4% in the SHARE’s female and male participants. Compared to those without UI, both female and male participants with UI demonstrated a higher BMI and WC. Among females, the fully adjusted models showed linear associations between BMI, WC, and UI (\u003cem\u003eP\u003c/em\u003es\u0026lt;0.001) in three cohorts. However, we observed U-shaped associations of BMI, WC with UI among males. The lowest likelihood of having UI was found among male participants with a BMI between 24 and 35 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFindings from our study revealed that the associations of obesity indices with UI varied among older men compared to older women. As a result, weight loss interventions could be applied to older women rather than older men as a means of treating UI. Interventions aimed at preventing UI among older adults must take sex into account.\u003c/p\u003e","manuscriptTitle":"BMI, waist circumferences and urinary incontinence in older women compared with older men: findings from three prospective longitudinal cohort studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-17 20:37:32","doi":"10.21203/rs.3.rs-2441866/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b2ab638-a978-4897-b232-3c95097c7968","owner":[],"postedDate":"January 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":18388615,"name":"Health sciences/Diseases"},{"id":18388616,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2023-10-12T07:10:25+00:00","versionOfRecord":{"articleIdentity":"rs-2441866","link":"https://doi.org/10.1038/s43856-023-00367-w","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2023-10-11 04:00:00","publishedOnDateReadable":"October 11th, 2023"},"versionCreatedAt":"2023-01-17 20:37:32","video":"","vorDoi":"10.1038/s43856-023-00367-w","vorDoiUrl":"https://doi.org/10.1038/s43856-023-00367-w","workflowStages":[]},"version":"v1","identity":"rs-2441866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2441866","identity":"rs-2441866","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}