Abstract
Objective:
To investigate the association of body composition with symptoms of pelvic floor disorders ie, stress urinary incontinence, urgency urinary incontinence, fecal incontinence, and feeling of pelvic organ prolapse among middle-aged women.
Methods:
A longitudinal study with two measurement points 4 years apart was performed using a population sample of 376 Finnish women aged 47 to 55 years at the baseline. Total and regional body composition was assessed with dual x-ray absorptiometry (DXA) and multifrequency bioelectrical impedance analyzer (BIA). Body height, weight, and waist circumference were measured, and body mass index (BMI) was calculated. The symptoms of pelvic floor disorders were assessed using self-report questionnaire. Generalized estimating equations models were used to investigate associations. Models were adjusted with demographical, gynecologic, and physical activity variables.
Results:
The change in body composition was not associated with the change in the symptoms of pelvic floor disorders after 4-year follow-up. In cross-sectional analysis, the symptoms of stress urinary incontinence were found to be associated with total fat mass (OR 1.03, 95% CI: 1.01-1.06, P=0.017), trunk fat mass (OR 1.06, 95% CI: 1.02-1.11, P=0.009), android fat mass (OR 1.33, 95% CI: 1.05-1.70, P=0.020), visceral fat area (OR 1.01, 95% CI: 1.00-1.02, P=0.019), BMI (OR 1.07, 95% CI: 1.01-1.13, P=0.027), and waist circumference (OR 1.03, 95% CI: 1.01-1.05, P=0.008). No significant associations were found for other symptoms of pelvic floor disorders.
Conclusions:
Having a higher total or regional body fat mass, higher BMI, or larger waist circumference may increase the risk of stress urinary incontinence in middle-aged women.
Key Words: Body composition, Menopausal women, Middle-aged women, Pelvic floor disorders, Pelvic floor function
Pelvic floor disorders, an umbrella term for conditions such as incontinence and pelvic organ prolapse, are usually a result of a combination of different factors such as hormonal changes during menopause, physiological and functional tissue-level deteriorations that occur with aging, pregnancy, and childbirth as well as factors increasing intra-abdominal pressure.1,2 Lifestyle choices such as quality of nutrition, eating behavior, and physical activity also contribute to pelvic floor disorders.3,4,5 Body composition is partly a reflection of lifestyle choices, thus it can be considered as a modifiable factor revealing a woman’s risk for experiencing symptoms of pelvic floor disorders.
High body mass index (BMI) and waist circumference are well-established risk factors for pelvic floor disorders in women of all ages.6,7,8,9 Especially abdominal obesity may cause pelvic tissues to encounter chronic strain and stretching that eventually weakens the structures of the pelvic floor.10 When studying middle-aged women, it is worth noting that menopause transition accompanied by significant hormonal changes may contribute to changes in body composition.11 Menopause is known to accelerate adipose tissue accumulation, especially in the waist area.12
Previous studies have mainly utilized BMI or waist circumference,6,7,8,9,13 when evaluating the risk for pelvic floor disorders. However, BMI alone is an insufficient biomarker of abdominal adiposity,14 which, in turn, is a highly relevant measure when estimating the strain directed on the pelvic floor. Likewise, waist circumference only accounts for excess weight gain in the abdominal region, neglecting other areas of the body. In the current study, total and regional body composition was assessed with dual x-ray absorptiometry (DXA) and multifrequency bioelectrical impedance analyzer (BIA). These methods can provide precise fat distribution measurements. The objective was to investigate if different parameters of body composition are differentially associated with the symptoms of pelvic floor disorders, including stress urinary incontinence, urgency urinary incontinence, fecal incontinence, and feeling of pelvic organ prolapse among women during menopause. We hypothesized that unfavorable changes in body composition, especially accumulation of excess adipose tissue in the central body region, might predispose to pelvic floor disorders.
METHODS
Study design and participants
The data reported are from the observational Estrogenic Regulation of Muscle Apoptosis (ERMA) study15 and its follow-up study, the Estrogen, MicroRNAs, and the Risk of Metabolic Dysfunction (EsmiRs)16 (data set: https://doi.org/10.17011/jyx/dataset/83491). All participants were Finnish women aged 47-55 years at the baseline (Fig. 1). Exclusion criteria of ERMA study included conditions or use of medications affecting ovarian function, obesity (BMI>35 kg/m2, calculated from self-reported weight and height) and such chronic diseases, or medications lowering physical functioning and thereby prohibiting participation in the physiological measurements. For the current study, the 811 women who, during the ERMA study, provided consent to be contacted for potential future studies, form a baseline sample. Of them, 494 also participated in questionnaire surveys and 304 in physiological measurements of the follow-up study EsmiRs, conducted 4 years after baseline measurements. To be included in the current study, a participant needed to have filled out the pelvic floor questionnaire at baseline and follow-up and have body composition measurements taken at least at baseline. Therefore, the final maximal longitudinal sample size of the study is 376.
FIG. 1.
Flowchart for the recruitment process. ERMA study forms a baseline sample, and its 4-year follow-up study, EsmiRs, forms a follow-up sample for the current study. At baseline, 707 participants answered the main questionnaire that included PFD questions. Of them, 591 also participated in baseline body composition measurements. Of the current study, those participants who had missing PFD or body composition data at follow-up were excluded from the baseline sample. Therefore, the baseline analytical study sample included a maximum of 376 participants, of whom 368 body composition was measured by DXA and 374 by BIA. Similarly, at follow-up, 494 participants answered the main questionnaire that included PFD questions, and 118 of them were excluded because the PFD answers were missing at baseline; therefore, the follow-up analytical study sample for PDF included 376 participants. At follow-up, body composition was measured from 304 participants. Of them, DXA measurements were missing at baseline from 49 participants and BIA measurements from 49 participants; therefore, the follow-up analytical study sample for body composition included 243 participants measured with DXA and 249 participants measured with BIA. BIA, multifrequency bioelectrical impedance analyzer; DXA, dual x-ray absorptiometry; PFD, pelvic floor disorders.
The studies were conducted according to the Declaration of Helsinki and were approved by the Ethics Committee of the Central Finland Health Care District (ERMA 8U/2014 and EsmiRs 9U/2018) before the onset of data collection.
Pelvic floor disorders
The occurrence of pelvic floor disorders was assessed by structured questionnaires with dichotomized (yes/no) answering options. In both time points, the participants were asked if they had experienced the symptoms of stress urinary incontinence, urgency urinary incontinence, fecal incontinence, and the feeling of pelvic organ prolapse within a month preceding data collection. The questions were “Have you, during the past month, had urinary incontinence during physical effort or coughing?”, “Have you, during the past month, had urge or urgency-related urinary incontinence?”, “Have you, during the past month, had fecal incontinence?”, “Have you, during the past month, had a feeling that something would be bulging out of your vagina?” The questionnaire used is not validated as we chose to use short symptom-specific common questions that are easy to understand and answer when self-reporting.
Anthropometrics and body composition
Anthropometrics and body composition were measured between 7:00 and 10:00 am after overnight fasting. Body weight was measured with a beam scale and height by a stadiometer with the participant wearing only undergarments. BMI was calculated as body mass (kg) divided by height squared (m2). Waist circumference was measured mid-way between the superior iliac spine and the lower rib margin. Total, trunk, android, and gynoid fat mass, as well as visceral fat mass area, were assessed by both multifrequency bioelectrical impedance analyzer (InBody 720; Biospace, Seoul, Korea) and with dual x-ray absorptiometry (DXA, LUNAR; GE Healthcare).17
Demographical and gynecologic variables
Participants' baseline age was calculated from the date of birth to the date of answering to the prequestionnaire. Education was self-reported with a structured question with eight answer options from primary school to doctoral level, and participants were classified into three groups based on their answers: primary, secondary, and tertiary education. Participants were assigned to premenopausal, early and late perimenopausal, and postmenopausal groups based on the follicle-stimulating hormone (FSH) concentrations and self-reported menstrual bleeding diaries.18 They also reported data on gestations, parity, and whether they had undergone hysterectomy.
Physical activity variables
Physical activity was assessed using three different variables: current work-related physical activity, current leisure-time physical activity, and past physical activity. Work-related physical activity was self-reported, and participants were classified into the following groups based on the answers: mainly sedentary work (light), work that includes standing and walking (moderate), and heavy work that includes also lifting (heavy). Current physical activity was assessed with a structured questionnaire19 including four questions about the frequency, intensity and duration of leisure-time physical activity bouts as well as the average time spent in active commuting. Based on the answers, a metabolic equivalent of hours per day (MET-h/d) for intensity and volume of current physical activity was calculated. Past physical activity included the activity conducted at the age of 17-29 years. It was assessed by asking “What kind of regular physical activity have you done at different stages of your life?” and allowing participants to select one or more of the following four options: no physical activity, regular independent leisure-time physical activity, regular competitive sport and related training, and regular other supervised physical activity.20 The responses were combined into a single variable consisting of “no exercise,” “regular physical activity,” and “competitive sport.”21
Statistical analysis
Group differences between baseline participants and nonparticipants were evaluated with χ2 and one-way ANOVA tests. The associations of different body composition-related parameters with symptoms of pelvic floor disorders were analyzed using generalized estimating equations (GEE) logistic regression models with an unstructured correlation structure. The parameters studied included total, trunk, android, and gynoid fat mass, visceral fat mass area, BMI, and waist circumference. Each GEE model included follow-up time, one body composition-related parameter as an independent variable, and an interaction term (time x the used independent body composition parameter) to assess if a change in the parameter was associated with a change in pelvic floor disorder status. Models were adjusted for menopausal status, age, height, physical workload, current physical activity (MET-h/d), past physical activity (age 17-29), parity, and hysterectomy. Missing data were assumed to be missing completely at random and handled accordingly by the GEE method. Statistical analyses were performed using IBM SPSS Statistics 22.0 (SPSS Inc.). The level of significance was set at P≤0.05.
RESULTS
At baseline, the mean age of the participants was 51.2 years (SD 2.0 y). Demographics of nonparticipants and participants at baseline are shown in (Table 1). Nonparticipants are those who were invited to take part in the follow-up study but opted out or were otherwise excluded (Fig. 1). Compared with participants, nonparticipants’ BMI and waist circumference were, on average, larger. The groups did not differ significantly from each other in other measurements. Menopausal status, experienced symptoms of pelvic floor disorders, and body composition measures at baseline and follow-up are presented in (Table 2).
TABLE 1.
Descriptive data of nonparticipants and participants at baseline
| Nonparticipants | Baseline | P | |
|---|---|---|---|
| n=435 | n=376 | ||
| Education, n (%) | — | — | 0.376 |
| Primary | 8 (2.4) | 4 (1.1) | — |
| Secondary | 184 (55.6) | 214 (56.9) | — |
| Tertiary | 139 (42.0) | 158 (42.0) | — |
| Missing data, n | 104 | 0 | — |
| Physical workload, n (%) | — | — | 0.908 |
| Light | 162 (54.2) | 190 (54.9) | — |
| Moderate | 62 (20.7) | 67 (19.4) | — |
| Heavy | 75 (25.1) | 89 (25.7) | — |
| Missing data, n | 136 | 30 | — |
| Physical activity, (MET-h/d), mean (SD) | 4.5 (4.2) | 5.0 (4.1) | 0.143 |
| Missing data, n | 105 | 2 | — |
| BMI, kg/m2, mean (SD) | 25.6 (3.8) | 25.1 (3.6) | 0.036 |
| Missing data, n | 1 | 2 | — |
| Waist circumference, cm, mean (SD) | 84.9 (10.8) | 83.1 (9.9) | 0.043 |
| Missing data, n | 219 | 1 | — |
| Parity, mean (SD) | 2.0 (1.2) | 2.1 (1.3) | 0.309 |
| Missing data, n | 105 | 0 | — |
| Hysterectomy, n (%) | — | — | 0.335 |
| No | 393 (90.3) | 346 (92.3) | — |
| Yes | 42 (9.7) | 29 (7.7) | — |
| Missing data, n | 0 | 1 | — |
Bold values are statistically significant at P ≤ 0.05.
BMI, body mass index; MET-h/d, metabolic equivalent hours per day.
TABLE 2.
Menopausal status, pelvic floor disorders, and body composition measures at baseline and follow-up
| Baseline | Follow-up | |
|---|---|---|
| n=376 | n=376 | |
| Menopausal status, n (%) | ||
| Premenopausal | 108 (28.7) | 16 (4.3) |
| Early perimenopausal | 78 (20.7) | 25 (6.6) |
| Late perimenopausal | 84 (22.3) | 26 (6.9) |
| Postmenopausal | 106 (28.2) | 309 (82.2) |
| Pelvic floor disorders, n (%) | ||
| Stress urinary incontinence | 155 (41.3) | 138 (36.7) |
| missing data, n | 1 | 0 |
| Urgency urinary incontinence | 54 (14.4) | 63 (16.8) |
| missing data, n | 2 | 0 |
| Fecal incontinence | 13 (3.5) | 16 (4.3) |
| missing data, n | 3 | 0 |
| Feeling of pelvic organ prolapse | 18 (4.8) | 19 (5.1) |
| missing data, n | 1 | 0 |
| Laboratory-measured body composition parameters | ||
| n=376 | n=254 | |
| BMI, kg/m2, mean (SD) | 25.2 (3.7) | 25.9 (4.1) |
| missing data, n | 1 | 0 |
| Waist circumference, cm, mean (SD) | 83.1 (9.9) | 84.0 (10.4) |
| missing data, n | 1 | 0 |
| DXA, mean (SD) | n=368 | n=243 |
| Total fat mass, kg | 24.4 (8.4) | 26.2 (9.1) |
| Trunk fat mass, kg | 12.4 (5.0) | 13.6 (5.5) |
| Android fat mass, kg | 2.2 (0.9) | 2.4 (1.0) |
| Gynoid fat mass, kg | 4.8 (1.4) | 4.9 (1.5) |
| BIA, mean (SD) | n=374 | n=249 |
| Visceral fat mass area, cm2 | 107.7 (25.0) | 120.5 (25.4) |
BIA, multifrequency bioelectrical impedance analyzer; BMI, body mass index; DXA, dual x-ray absorptiometry.
The change in any of the body composition measures, BMI, or waist circumference were not associated with the change in the symptoms of pelvic floor disorders (Table 3 and Supplemental Digital Content 1, http://links.lww.com/MENO/B383). Instead, the current total fat mass was associated with the symptoms of stress urinary incontinence (OR 1.03, 95% CI: 1.01-1.06, P=0.017) (Table 3). The association with the symptoms of stress urinary incontinence was found also in trunk fat mass (OR 1.06, 95% CI: 1.02-1.11, P=0.009), in android fat mass (OR 1.33, 95% CI: 1.05-1.70, P=0.020), in visceral fat area (OR 1.01, 95% CI: 1.00-1.02, P=0.019), in BMI (OR 1.07, 95% CI: 1.01-1.13, P=0.027), and in waist circumference (OR 1.03, 95% CI: 1.01-1.05, P=0.008). Therefore, among the body composition measures, android fat mass seems to have the strongest effect on the pelvic floor: 1 kg higher android fat mass increases the risk for stress urinary incontinence by 33%. No significant associations were found for other symptoms of pelvic floor disorders (Supplemental Digital Content 1, http://links.lww.com/MENO/B383).
TABLE 3.
Generalized estimating equations models for body composition measures and symptoms of stress urinary incontinence
| Stress urinary incontinence | ||
|---|---|---|
| OR (95% CI) | P | |
| Total fat | 1.03 (1.01-1.06) | 0.017 |
| Time | 0.99 (0.50-1.98) | 0.987 |
| Total fat x time | 1.00 (0.97-1.03) | 0.938 |
| Trunk fat | 1.06 (1.02-1.11) | 0.009 |
| Time | 0.94 (0.53-1.67) | 0.844 |
| Trunk fat x time | 1.00 (0.96-1.04) | 0.962 |
| Android fat | 1.33 (1.05-1.70) | 0.020 |
| Time | 0.96 (0.55-1.68) | 0.885 |
| Android fat x time | 1.00 (0.79-1.27) | 0.985 |
| Gynoid fat | 1.16 (0.99-1.36) | 0.070 |
| Time | 1.14 (0.46-2.83) | 0.783 |
| Gynoid fat x time | 0.98 (0.81-1.18) | 0.792 |
| Visceral fat | 1.01 (1.00-1.02) | 0.019 |
| Time | 1.00 (0.38-2.64) | 0.995 |
| Visceral fat x time | 1.00 (0.99-1.01) | 0.980 |
| BMI | 1.07 (1.01-1.13) | 0.027 |
| Time | 0.80 (0.20-3.22) | 0.757 |
| BMI x time | 1.01 (0.95-1.07) | 0.751 |
| Waist circumference | 1.03 (1.01-1.05) | 0.008 |
| Time | 1.04 (0.18-6.08) | 0.968 |
| Waist circumference x time | 1.00 (0.98-1.02) | 0.998 |
BMI, body mass index; MET-h/d, metabolic equivalent hours per day; OR, odds ratio.
Adjusted with menopausal status, age, height, physical workload, past physical activity (age 17-29), current physical activity (MET-h/d), parity, and hysterectomy.
DISCUSSION
Different types of pelvic floor disorders are prevalent clinical problems, causing notable personal, social, and economic burdens. This study examined the association of different body composition parameters with the symptoms of pelvic floor disorders in middle-aged women. We found that current total, trunk and android fat mass, and visceral fat area as well as BMI and waist circumference were associated with the symptoms of stress urinary incontinence. However, the change in these body composition or anthropometric measures was not associated with the change in the occurrence of symptoms of pelvic floor disorders during the four-year follow-up. Understanding how the distribution of body fat and anthropometrics influence the onset of pelvic floor disorders can be important when promoting preventive and therapeutic actions.
Stress urinary incontinence is defined as a complaint of involuntary loss of urine on effort or physical exertion, or on sneezing or coughing.22 A myriad of risk factors for this condition have been suggested, including a few anatomic explanations, for example, an association with excess fat accumulation in the central body region. Higher intra-abdominal pressure caused by excess fat tissue may increase the intravesical pressure and urethal mobility as well as strain pelvic floor muscles, supportive structures, and nerves.23,24 In addition to increased pressure to the bladder and pelvic floor, central adiposity increases systemic inflammation and oxidative stress that are metabolic effects associated with lower urinary track symptoms, including urinary incontinence.25 Symptoms of urgency urinary incontinence, complaint of involuntary loss of urine associated with urgency,22 differ from the symptoms of stress urinary incontinence, which suggests different pathology. However, similar to stress urinary incontinence, risk for experiencing symptoms of urgency urinary incontinence increases with higher BMI.26 It has therefore been shown that both the mechanical and the metabolic stresses of overweight and excess adipose tissue predispose an individual to the development of urinary incontinence.
In the present study, an association of the current status of body composition and anthropometric measures with the current symptoms of stress urinary incontinence was found. It can be debated whether our statistically significant findings on the associations of the body fat parameters with pelvic floor disorders are clinically relevant; however, our findings are in line with the results of some previous studies: Ferreira et al27 analyzed the association between overweight/obesity and urinary incontinence in a cross-sectional case-control study including 62 women with mean age of 34 years. Segmental body composition was measured with BIA. They found that women with higher level of visceral fat had higher risk of presenting urinary incontinence, and the association was more evident in overweight and obese women. Moreno-Vecino et al28 studied the associations between body composition and urinary incontinence among 471 noninstitutionalized postmenopausal women in a cross-sectional setting: Women with urinary incontinence (28.5%) had higher body fat percentage measured with BIA, as well as higher BMI and waist circumference compared with asymptomatic women.
Various methods for assessing visceral fat have been used to study its association with urinary incontinence: Visceral Adiposity Index (VAI) is based on physical and metabolic parameters, such as waist circumference, BMI, triglycerides, and high-density lipoprotein (HDL) cholesterol.29 In a Turkish study,30 with 125 middle-aged incontinent women, an association between overweight/obesity, VAI, and stress urinary incontinence was found. VAI levels were significantly higher in women with urinary incontinence compared with asymptomatic women. Albeit different methods were used for assessing the importance of visceral fat in stress urinary incontinence, the study corroborates our findings using DXA and BIA.
Associations of BMI and waist circumference with incident urinary incontinence was examined among women aged 54-79 years in the Nurse’s Health Study.9 When BMI and waist circumference were included in models simultaneously, BMI was associated with urgency and mixed urinary incontinence but not stress urinary incontinence; however, waist circumference was associated only with stress urinary incontinence. In our population of menopausal women, BMI was associated with stress urinary incontinence even when adjusted with demographic, gynecologic, and physical activity variables. The same applied to waist circumference, pointing again to the significance of fat accumulation in the central area of the body. Anthropometrics or body composition parameters were not associated with urgency urinary incontinence.
The associations between segmental adipose tissue depots are understudied. Currently there is only one study that has investigated this with DXA: total body and segmental fat were quantified in a population-based study with 5,792 Korean women.31 Total fat mass, trunk fat mass, trunk fat/leg fat mass ratio, total body fat percentage, and trunk fat percentage were significantly higher in the urinary incontinence group (n=482). Of the DXA parameters, the trunk fat/leg fat ratio was the strongest in predicting the presence of urinary incontinence. In addition, women with urinary incontinence had significantly higher mean waist circumference and BMI than asymptomatic women. According to the study, obesity parameters obtained using DXA as well as anthropometric measures are closely related to urinary incontinence, a conclusion in line with our results with stress urinary incontinence.
To the best of our knowledge, there are no previous studies on the association of body composition measured with DXA or BIA with fecal incontinence, a condition characterized by the uncontrolled passage of solid or liquid stool.32 Again, only the association between BMI and fecal incontinence have been under inspection: previous cross-sectional analyses have observed an increased risk of fecal incontinence among women with higher BMI,8,33,34 whereas others have found no association between BMI and fecal incontinence.35,36 However, it is speculated that increased intra-abdominal pressure associated with increased BMI may lead to greater perineal descent.37 Perineal descent is found in individuals with fecal incontinence and pudendal nerve neuropathy, presumably caused by pressure and chronic stretch on the pelvic floor.37,38 In our study, we found that elevated total body or regional fat mass, or BMI or waist circumference did not associate with the risk of fecal incontinence.
Pelvic organ prolapse is defined as the descent of one or more of the anterior vaginal wall, posterior vaginal wall, the uterus, or the apex of the vagina.22 Miedel at al7 studied nonobstetric risk factors for symptomatic pelvic organ prolapse and found that BMI of 25 or higher and waist circumference of 88 cm or more were significantly associated with prolapse. They did not measure body composition with DXA or BIA, nonetheless, higher waist circumference may be an indicator of higher android fat mass. In addition to the strain of excess weight on the central body region, biomechanical stresses on the pelvic floor have also been suggested, since oxidative stress promoted by adipose tissue may be involved in the pathophysiology of pelvic organ prolapse by contributing to collagen metabolic disorder in pelvic fibroblasts.39 However, we did not find an association between body composition and the symptoms of pelvic organ prolapse. Our population-based sample included a rather small number of women experiencing pelvic organ prolapse (18 cases at baseline and 19 cases at follow-up) as well as fecal incontinence (13 cases at baseline and 16 cases at follow-up), which may indicate that our results of not finding significant associations cannot be considered conclusive.
Several strengths and limitations can be recognized in the present study. Overall, the extent of this study is notable since we studied four different symptoms of pelvic floor disorders and evaluated multiple body composition parameters over a 4-year follow-up. The study was conducted in a homogenous cohort of relatively healthy White women, which can simultaneously be considered as a strength or limitation because it reduces internal variability thereby increasing statistical power but limits the reproducible of our results in more heterogenous populations. Considering potential confounding factors, we were able to utilize previously gained knowledge on the same population and control for both past and current physical activity. Available assessments of menopausal status of the participants enabled us to reliably evaluate its association with symptoms of pelvic floor disorders instead of needing to rely on the age of the participants as a proxy of menopausal status.
The experienced symptoms of pelvic floor disorders were determined by a postal questionnaire at both time points. We are aware that the subject may be considered sensitive, which may result in under-reporting. However, use of self-administered questionnaires should reduce the possible under-reporting that has been observed when questions are obtained by interviewer administration.40 Incontinence and prolapse symptoms were assessed using a simplistic, short pelvic floor disorder questionnaire, rather than voiding diaries or detailed questions assessing the degree of symptoms. As participants did not undergo clinical evaluation to confirm the disorder, we chose to use a self-report questionnaire that is fast and easy to fill. It is a limitation that the questionnaire used is not validated. In addition, self-reporting may lead to measurement bias and affect negatively to the generalizability of the study.
Pelvic floor disorders have previously been associated with higher BMI and greater weight.6,7,8 However, our results cannot be generalized to individuals with severe obesity, since women with BMI>35 kg/m2 were excluded from the study. Although BMI and waist circumference are easy methods to evaluate the risk of pelvic floor disorders in research and clinical settings, they do not provide a precise explanation of total and regional body composition’s significance on the symptoms of pelvic floor disorders. In the present study, a longitudinal setting with DXA and BIA measurements was used to reveal the association of body composition with the experienced symptoms of pelvic floor disorders. On the other hand, we acknowledge that DXA and BIA are less accessible methods for larger scale epidemiological studies or in clinical settings, and simplified strategies for body composition analysis may be more practical. Therefore, BMI and waist circumference were included into analyses for comparison.
The results of this study suggest that, against our hypothesis, the change in body composition does not associate with the change in the symptoms of pelvic floor disorders during 4-year follow-up. It is plausible that the changes in body composition measures and the progression of the symptoms of pelvic floor disorders were not large enough for the associations to emerge in the analyses of the present study. In addition, more symptoms and changes in body composition might arise over time, suggesting that future studies would benefit from longer follow-up. Still, associations between changes in body composition and changes in stress urinary incontinence frequency have been found even in shorter time.41
Potential clinical value
Information on significance of body composition on symptoms of pelvic floor disorders is important when counseling adult female patients about modifiable risk factors. Unfavorable body composition, whether detected with DXA, BIA, BMI, and/or waist circumference, could indicate that weight loss is a therapeutic target for the treatment of stress urinary incontinence. Easily accessible anthropometric measures such as BMI and waist circumference seem to be valid and sufficient methods when evaluating the risk for stress urinary symptoms in clinical settings.
CONCLUSIONS
We aimed to study whether body composition or the accumulation of fat mass into certain areas of the body would be sensitive indicators for evaluating the risk for pelvic floor disorders. Measurements with dual x-ray absorptiometry (DXA) and multifrequency bioelectrical impedance analyzer (BIA) revealed that higher regional and total body fat mass may contribute to the higher risk of stress urinary incontinence in middle age. Assessment of body composition with BMI and waist circumference attested the result obtained by measuring regional fat mass, suggesting that these more simplified methods would be sufficient in clinical use. Associations between symptoms of urgency urinary incontinence, fecal incontinence, or pelvic organ prolapse and body adiposity measures were not confirmed.
Supplementary Material
Footnotes
Funding/support: The study was supported by the Research Council of Finland (grant numbers 275323, 309504, 314181, and 335249).
Financial disclosure/conflicts of interest: H.-K.J. is a member of the Finnish Menopause Society, and received a single payment from one lecture given to the Finnish Menopause Society. The other authors have nothing to disclose.
Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.menopause.org.
Contributor Information
Mari A. Kuutti, Email: mari.a.kuutti@jyu.fi.
Enni-Maria Hietavala, Email: enni.hietavala@jyu.fi.
Hanna-Kaarina Juppi, Email: hanna.juppi@tuni.fi.
Sarianna Sipilä, Email: sarianna.sipila@jyu.fi.
Pauliina Aukee, Email: pauliina.aukee@hyvaks.fi.
Eija K. Laakkonen, Email: eija.k.laakkonen@jyu.fi.
REFERENCES
- 1. DeLancey JOL, Kane Low L, Miller JM, Patel DA, Tumbarello JA. Graphic integration of causal factors of pelvic floor disorders: an integrated life span model. Am J Obstet Gynecol 2008;199:610.e1-610.e5. doi: 10.1016/j.ajog.2008.04.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Johnston SL. Pelvic floor dysfunction in midlife women. Climacteric 2019;22:270-276. doi: 10.1080/13697137.2019.1568402 [DOI] [PubMed] [Google Scholar]
- 3. Dallosso H, Matthews R, McGrother C, Donaldson M. Diet as a risk factor for the development of stress urinary incontinence: a longitudinal study in women. Eur J Clin Nutr 2004;58:920-926. doi: 10.1038/sj.ejcn.1601913 [DOI] [PubMed] [Google Scholar]
- 4. Kuutti MA, Hyvärinen M, Lankila H, Aukee P, Hietavala EM, Laakkonen EK. Association of eating behavior with symptoms of pelvic floor disorders in middle-aged women: an observational study. Womens Health 2024;20:17455057241305075. doi: 10.1177/17455057241305075 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Bø K, Nygaard IE. Is physical activity good or bad for the female pelvic floor? A narrative review. Sports Med 2020;50:471-484. doi: 10.1007/s40279-019-01243-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Hannestad YS, Rortveit G, Daltveit AK, Hunskaar S. Are smoking and other lifestyle factors associated with female urinary incontinence? The Norwegian EPINCONT Study. BJOG Int J Obstet Gynaecol 2003;110:247-254. doi: 10.1046/j.1471-0528.2003.02327.x [DOI] [PubMed] [Google Scholar]
- 7. Miedel A, Tegerstedt G, Mæhle-Schmidt M, Nyrén O, Hammarström M. Nonobstetric risk Factors sor Symptomatic pelvic organ prolapse. Obstet Gynecol 2009;113:1089-1097. doi: 10.1097/AOG.0b013e3181a11a85 [DOI] [PubMed] [Google Scholar]
- 8. Varma MG. Fecal incontinence in females older than aged 40 years: who is at risk? Dis Colon Rectum 2006;49:841. doi: 10.1007/s10350-006-0535-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Townsend MK, Curhan GC, Resnick NM, Grodstein F. BMI, waist circumference, and incident urinary incontinence in older women. Obesity 2008;16:881-886. doi: 10.1038/oby.2008.14 [DOI] [PubMed] [Google Scholar]
- 10. Hunskaar S. Epidemiology and natural history of urinary incontinence. Int Urogynecol J 2000;11:301-319; 10.1007/s001920070021. [DOI] [PubMed] [Google Scholar]
- 11. Greendale GA, Sternfeld B, Huang M, et al. Changes in body composition and weight during the menopause transition. JCI Insight 2019;4:e124865. doi: 10.1172/jci.insight.124865 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Ambikairajah A, Walsh E, Tabatabaei-Jafari H, Cherbuin N. Fat mass changes during menopause: a metaanalysis. Am J Obstet Gynecol 2019;221:393-409.e50. doi: 10.1016/j.ajog.2019.04.023 [DOI] [PubMed] [Google Scholar]
- 13. Krause MP, Albert SM, Elsangedy HM, Krinski K, Goss FL, daSilva SG. Urinary incontinence and waist circumference in older women. Age Ageing 2010;39:69-73. doi: 10.1093/ageing/afp208 [DOI] [PubMed] [Google Scholar]
- 14. Ross R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol 2020;16:177-189. doi: 10.1038/s41574-019-0310-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kovanen V, Aukee P, Kokko K, et al. Design and protocol of Estrogenic Regulation of Muscle Apoptosis (ERMA) study with 47 to 55-year-old women’s cohort: novel results show menopause-related differences in blood count. Menopause 2018;25:1020-1032. doi: 10.1097/GME.0000000000001117 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Hyvärinen M, Juppi HK, Taskinen S, et al. Metabolic health, menopause, and physical activity—a 4-year follow-up study. Int J Obes 2005 2022;46:544-554. doi: 10.1038/s41366-021-01022-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Van Der Ploeg GE, Withers RT, Laforgia J. Percent body fat via DEXA: comparison with a four-compartment model. J Appl Physiol 2003;94:499-506. doi: 10.1152/japplphysiol.00436.2002 [DOI] [PubMed] [Google Scholar]
- 18. Harlow SD, Gass M, Hall JE, et al. STRAW + 10 Collaborative Group. Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging. J Clin Endocrinol Metab 2012;97:1159-1168. doi: 10.1210/jc.2011-3362 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Kujala UM, Kaprio J, Sarna S, Koskenvuo M. Relationship of leisure-time physical activity and mortality: the Finnish twin cohort. JAMA 1998;279:440-444. doi: 10.1001/jama.279.6.440 [DOI] [PubMed] [Google Scholar]
- 20. Hirvensalo M, Lintunen T, Rantanen T. The continuity of physical activity—a retrospective and prospective study among older people. Scand J Med Sci Sports 2000;10:37-41. doi: 10.1034/j.1600-0838.2000.010001037.x [DOI] [PubMed] [Google Scholar]
- 21. Kuutti MA, Hyvärinen M, Kauppinen M, Sipilä S, Aukee P, Laakkonen EK. Early adulthood and current physical activity and their association with symptoms of pelvic floor disorders in middle-aged women: an observational study with retrospective physical activity assessment. BJOG Int J Obstet Gynaecol 2023;130:664-673. doi: 10.1111/1471-0528.17397 [DOI] [PubMed] [Google Scholar]
- 22. Haylen BT, de Ridder D, Freeman RM, et al. An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction. Int Urogynecol J 2010;21:5-26. doi: 10.1007/s00192-009-0976-9 [DOI] [PubMed] [Google Scholar]
- 23. Ramalingam K, Monga A. Obesity and pelvic floor dysfunction. Best Pract Res Clin Obstet Gynaecol 2015;29:541-547. doi: 10.1016/j.bpobgyn.2015.02.002 [DOI] [PubMed] [Google Scholar]
- 24. Cummings JM, Rodning CB. Urinary stress incontinence among obese women: review of pathophysiology therapy. Int Urogynecol J 2000;11:41-44. doi: 10.1007/s001920050008 [DOI] [PubMed] [Google Scholar]
- 25. Marcelissen T, Anding R, Averbeck M, Hanna-Mitchell A, Rahnama’i S, Cardozo L. Exploring the relation between obesity and urinary incontinence: Pathophysiology, clinical implications, and the effect of weight reduction, ICI-RS 2018. Neurourol Urodyn 2019;38(suppl 5):S18-S24. doi: 10.1002/nau.24072 [DOI] [PubMed] [Google Scholar]
- 26. Parazzini F, Chiaffarino F, Lavezzari M, Giambanco V, Group on behalf of VS, Page 932 A list of V members is provided on . Risk factors for stress, urge or mixed urinary incontinence in Italy. BJOG Int J Obstet Gynaecol 2003;110:927-933. doi: 10.1111/j.1471-0528.2003.02343.x [DOI] [PubMed] [Google Scholar]
- 27. Ferreira R, Sacramento J, Brasil C, et al. Relationship of body composition and urinary incontinence in women: a cross-sectional case-control study. Female Pelvic Med Reconstr Surg 2020;26:447-451. doi: 10.1097/SPV.0000000000000834 [DOI] [PubMed] [Google Scholar]
- 28. Moreno-Vecino B, Arija-Blázquez A, Pedrero-Chamizo R, et al. Associations between obesity, physical fitness, and urinary incontinence in non-institutionalized postmenopausal women: the elderly EXERNET multi-center study. Maturitas 2015;82:208-214. doi: 10.1016/j.maturitas.2015.07.008 [DOI] [PubMed] [Google Scholar]
- 29. Amato MC, Giordano C, Galia M, et al. Visceral adiposity index. Diabetes Care 2010;33:920-922. doi: 10.2337/dc09-1825 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Dursun M, Otunctemur A, Ozbek E, Sahin S, Besiroglu H, Koklu I. Stress urinary incontinence and visceral adipose index: a new risk parameter. Int Urol Nephrol 2014;46:2297-2300. doi: 10.1007/s11255-014-0832-9 [DOI] [PubMed] [Google Scholar]
- 31. Park YW, Lee JH. Female urinary incontinence and obesity assessed by anthropometry and dual-energy X-ray absorptiometry: analysis from the 2008‐09 Korean National Health and Nutrition Examination Survey. Low Urin Tract Symptoms 2019;11:O28-O33; 10.1111/luts.12207. [DOI] [PubMed] [Google Scholar]
- 32. Macmillan AK, Merrie AE, Marshall RJ, Parry BR. The prevalence of fecal incontinence in community-dwelling adults: a systematic review of the literature. Dis Colon Rectum 2004;47:1341-1349. doi: 10.1007/s10350-004-0593-0 [DOI] [PubMed] [Google Scholar]
- 33. Townsend MK, Matthews CA, Whitehead WE, Grodstein F. Risk factors for fecal incontinence in older women. Am J Gastroenterol 2013;108:113-119. doi: 10.1038/ajg.2012.364 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Rømmen K, Schei B, Rydning A, H Sultan A, Mørkved S. Prevalence of anal incontinence among Norwegian women: a cross-sectional study. BMJ Open 2012;2:e001257. doi: 10.1136/bmjopen-2012-001257 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Markland AD, Goode PS, Burgio KL, et al. Incidence and risk factors for fecal incontinence in Black and White older adults: a population-based study. J Am Geriatr Soc 2010;58:1341-1346. doi: 10.1111/j.1532-5415.2010.02908.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Staller K, Song M, Grodstein F, et al. Physical activity, BMI, and risk of fecal incontinence in the nurses’ health study. Clin Transl Gastroenterol 2018;9:200. doi: 10.1038/s41424-018-0068-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Doumouchtsis S, Loganathan J, Pergialiotis V. The role of obesity on urinary incontinence and anal incontinence in women: a review. BJOG Int J Obstet Gynaecol 2022;129:162-170. doi: 10.1111/1471-0528.16848 [DOI] [PubMed] [Google Scholar]
- 38. Baek HN, Hwang YH, Jung YH. Clinical significance of perineal descent in pelvic outlet obstruction diagnosed by using defecography. J Korean Soc Coloproctol 2010;26:395-401. doi: 10.3393/jksc.2010.26.6.395 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. LIU C, Yang Q, Fang G, et al. Collagen metabolic disorder induced by oxidative stress in human uterosacral ligament-derived fibroblasts: a possible pathophysiological mechanism in pelvic organ prolapse. Mol Med Rep 2016;13:2999-3008. doi: 10.3892/mmr.2016.4919 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Rhodes T, Girman CJ, Jacobsen SJ, et al. Does the mode of questionnaire administration affect the reporting of urinary symptoms? Urol 1995;46:341-345. doi: 10.1016/S0090-4295(99)80217-9 [DOI] [PubMed] [Google Scholar]
- 41. Suskind AM, Cawthon PM, Nakagawa S, et al. Urinary incontinence in older women: the role of body composition and muscle strength from the Health, Aging, and Body Composition Study. J Am Geriatr Soc 2017;65:42-50. doi: 10.1111/jgs.14545 [DOI] [PMC free article] [PubMed] [Google Scholar]