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Physical Performance in Relation to Body Composition and Bone Mineral Density in Healthy, Overweight, and Obese Postmenopausal Women

Shin, Hyehyung PhD, MPH; Liu, Pei-Yang PhD, RD; Panton, Lynn B. PhD; Ilich, Jasminka Z. PhD, RD

Author Information

1Research Institute of Insurance & Finance, Samsung Life Insurance, Seoul, South Korea.

2School of Nutrition and Dietetics, University of Akron, Akron, Ohio.

3Nutrition, Food and Exercise Sciences, Florida State University, Tallahassee.

Address correspondence to: Jasminka Z. Ilich, PhD, RD, Nutrition, Food and Exercise Sciences, Florida State University, 120 Convocation Way, 480 Sandels Bldg, Tallahassee, FL 32306 ([email protected]).

This study was supported by grant (PI, J.Z.I.): USDA/CSREES/NRI #2004-05287.

The authors declare no conflicts of interest.

Journal of Geriatric Physical Therapy: January/March 2014 - Volume 37 - Issue 1 - p 7-16
doi: 10.1519/JPT.0b013e31828af203
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Abstract

INTRODUCTION

The process of aging is characterized by gradual declines in physical and cognitive functions. Older individuals experience a decline in muscle mass and strength, which ultimately leads to functional disability and diminished physical performance.1 Diminished physical performance can be detrimental among the older adults, causing falls and subsequent fractures, loss of independence, and increased morbidity and mortality rates.2,3 Thus, it is important to maintain functional ability from the early onset of aging. Some studies examining the relationship between body composition and functional ability among older adults show that both lower fat mass and higher muscle mass are associated with greater physical performance, while other studies do not support these relationships.1,4–8 In addition, handgrip strength, an indicator of physical performance and overall strength, was positively associated with bone mineral density (BMD) at various skeletal sites,9,10 suggesting that there may be a positive association between BMD and physical performance in older adults. However, it is unclear whether the decline in functional ability can be explained by changes in body composition, fat and muscle mass, and/or bone mass occurring with aging. This makes studying the changes in body composition and their relationship with physical performance a challenging issue, particularly in middle-aged and older women.

Women after menopause have an increased propensity for gaining weight with the fat tissue infiltrating the muscle, as well as being deposited in and redistributed to the central (abdominal) region.11,12 Simultaneously, there is a loss of muscle leading to sarcopenia and a loss of bone leading to osteopenia or osteoporosis; both frequently evolving into sarcopenic obesity and osteopenic obesity, 2 conditions now receiving renewed attention.13 These changes in body composition in postmenopausal women have been associated with decreased functional abilities, which could lead to multiple morbid conditions.14–16 While gain in weight might lead to decreased functionality, the latter one might be a subsequent, although not the only, cause of bone loss, giving rise to perpetual relationships. Similarly, increased adiposity and its infiltration in muscle may decrease the muscle strength and functionality.

Several dietary factors, including vitamin D, calcium, energy, and protein intake, may play roles in body composition changes,17–20 but they are often neglected when physical performance is evaluated. This is particularly relevant for the calcium and vitamin D, which have been proposed to affect physical performance.21,22 One of the major roles of circulating calcium ion is its regulation of muscle contraction and relaxation; therefore, normal serum calcium is crucial for any movement or functionality. As vitamin D receptors are present in virtually all tissues, including both smooth and skeletal muscle,20,23 the role of vitamin D has been investigated to understand its influence on muscle movement, strength, and physical performance. However, there is still no consensus that vitamin D supplementation might improve these conditions, particularly in individuals whose vitamin D status is adequate.24,25 Regarding protein, its intake typically decreases in older adults, contributing to muscle mass loss and lower basal metabolic rate, leading subsequently to increased weight, even with the same energy intake.

Similarly, various habitual or recreational activities should be taken into account when evaluating the relationship between physical performance and body composition. Several studies have shown that smoking is one of the predictors of poor physical performance in older adults26,27 and that it is detrimental for skeletal muscle function and for increased bone loss.28–30 Although participants of this study were not heavy smokers, the smoking status was nevertheless considered as a covariate.

In general, inconsistent findings regarding the relationship between functional ability and body composition/bone mass might be due to several factors: (1) the relationship between muscle, fat and/or bone mass, and functional ability may be the outcome of an interaction between loss of bone and muscle mass31–33; (2) other lifestyle factors, including diet and physical activity, are not taken into account34; and (3) the use of different measures for the assessment of both functional ability and body composition/bone mass.35

The purpose of this study was to investigate the relationship between physical performance and body composition (bone, fat, and lean mass) of early postmenopausal overweight/obese white women within a design in which most of the limitations of previous studies were reduced or eliminated. We hypothesized that greater muscle and bone mass and lower amount of fat tissue will be associated with better physical performance in this population.

METHODS

Study Participants

Early-postmenopausal white women (2-10 years after menopause), with body mass index (BMI) between 26 and 40 kg/m2, were recruited through newspaper advertisements, as part of the weight-loss clinical trial, as described previously.36 In short, the inclusion criteria required generally healthy participants without any chronic conditions such as hypertension, thyroid problems, severe osteoporosis, or diabetes. The participants could not smoke more than 1 pack per day, be on weight-loss medication, participate in other weight-loss programs, or be on hormone replacement therapy 3 months before enrolling in the study. Data from 97 participants from the baseline assessments were used in this study. Each participant signed the informed consent, and the protocol was approved by the Florida State University institutional review board.

Anthropometric and Body Composition Measurements

Body weight was measured in kilograms to the nearest 10th in normal indoor clothing without shoes, on an electronic platform balance (Seca, Hanover, Maryland). Standing stature was recorded to the nearest 10th of a centimeter without shoes on a wall stadiometer. Weight and height were used to calculate BMI (kg/m2). The BMD and body composition of soft (lean and fat) tissue were measured by dual-energy x-ray absorptiometry (DXA) using a Lunar iDXA instrument (GE Medical Systems, Madison, Wisconsin) with software Encore 2006 (version 9.1). Participants were measured for BMD (g/cm2) of total body, anterior-posterior spine (L1-L4), femur (neck and total), and forearm (radius at ultra distal and 33% distance from styloid process and total forearm). For body composition analysis, the total fat (%), total lean mass (kg), arm fat (%), arm lean mass (kg), leg fat (%), leg lean mass (kg), android fat (%), and gynoid fat (%) were derived from the total body scan. The quality analysis for the densitometer was conducted on a daily basis using a standard aluminum spine block (phantom) provided by the manufacturer. Measurements of the phantom were within the manufacturer's precision, with coefficient of variation less than 0.5%.

Dietary and Activity Assessment

Each participant completed a 3-day dietary record (2 weekdays and 1 weekend day) as described previously.36 All multivitamin/mineral or other supplements were also recorded to calculate total intake of each. The dietary records were analyzed by Food Processor, version 10.1.0 (ESHA Research, Salem, Oregon) and total calcium intake was used as a confounder in the statistical analyses. For physical activity assessment, each participant completed the Allied Dunbar National Fitness Survey for older adults,37 which has been described previously.38 Briefly, activities assessed included heavy housework, gardening, do-it-yourself activities (e.g., wall-papering, wall-painting), walking, and recreational and sport activities. Data collected included frequency and duration of each activity and were reported as hours per week on the basis of the previous 4 weeks. Total activity score was created by summing hours per week engaged in all of these activities. Components of both dietary and activity assessments were used as confounders in the statistical analyses. A questionnaire to assess the smoking status of participants was administered as well, and smoking habits were included as a covariate.

Physical Performance Tests

The measures of physical performance tests used in this study were previously validated.39–41Handgrip strength was measured by a Lafayette Instrument hand dynamometer model #78010 (Lafayette, Indiana). The participant held the hand dynamometer with the hand of one of the arms stretched out to the side of the body at a 45° angle and squeezed the handgrip as hard as possible while exhaling. Handgrip strength was then measured on the opposite hand. A total of 3 measurements were taken for each arm. The highest strength was selected for each hand and the measurements were added together. In the 8-foot Timed Get-Up-and-Go Test, the participant starts seated in a standard-height armless chair. On the command “go,” the participant stands, walks around a cone placed 8 feet away, walks back to the chair, and returns to the seated position, while being timed. The test was repeated 2 times; the shortest time was recorded. The timed chair sit-to-stand test records the number of times a participant can rise to a complete stand and return to the seated position without the use of arms for support. A standard-height armless chair was used for the sit-to-stand test. The duration for the test was a total of 30 seconds. While the test was being performed, the participant was asked to cross her arms and put hands on the shoulders to restrain her from using her arms while standing up from the chair. For the one-leg-stance time test, the participant stands on one leg with the opposite leg raised while time is recorded in seconds, stopping when the participant touches any supporting surface. As shown in other studies, one-leg-stance time test has good reliability for assessment of both cognitive and physical functions. It also gives insight about mobility and balance, crucial for individuals' ability to complete activities of daily living.42,43 The maximum time counted was 30 seconds per trial. The test was repeated 2 times and the longest trial time was recorded. The 8-meter normal and brisk walks are timed measures of both the participant's walking speed and the number of steps taken within an 8-m distance for both normal and brisk walks to determine gait speed and step length. Each test was repeated 2 times, and the shortest time and the number of steps were recorded. All measurements were performed with participants in indoor clothing and in comfortable shoes.

Serum 25-Hydroxy Vitamin D Concentration

An overnight fasting blood sample was obtained by venous puncture. Serum was separated from red blood cells and stored at −80°C until analysis. For the assessment of vitamin D status, serum 25-hydroxy vitamin D (25OHD) concentration was determined by a competitive enzyme-linked immunosorbent assay (ELISA; Alpco Diagnostics, Salem, New Hampshire). The serum 25OHD was used as a confounder in the statistical analyses, as a more reliable measure of vitamin D status than one obtained from dietary records.

Statistical Analyses

All data were analyzed using the Statistical Analysis Software (version 9.2; SAS Institute Inc, Cary, North Carolina). The characteristics of participants were reported as means and standard deviations for anthropometrics, body composition, physical performance measures, and covariates (age, physical activity level, total calcium intake, serum 25OHD concentration, and smoking status). The one-leg-stance time, for both left and right legs, was recoded as dichotomous variable because of the violation of normality assumption as a continuous variable; the participants were divided into 2 groups: those who could perform 30 seconds of one-leg-stance-time (“1”) or those who could not (“0”). To test the association between body composition (fat mass [%], lean mass [kg], and BMD [g/cm2] for each skeletal site and total body) and each item of physical performance measures, the Pearson product moment correlation coefficients (controlled for age) between weight, BMI, and body composition/BMD and physical performance measures were calculated to obtain some primary associations. A multiple regression method with each of the physical performance measures as a dependent variable and body composition as independent variables was used to find the best predictors. Each model was controlled for age, weight (for BMD only), height, physical activity level, total calcium intake (dietary + supplemental from multivitamin supplements), serum 25OHD as an indicator of vitamin D status, and smoking status. Multiple logistic regression models were applied to find the best predictor for the ability to perform One-Leg-Stance Time Test for each leg after controlling for the confounders mentioned previously.

RESULTS

A total of 97 early postmenopausal women included in the study were 56.0 ± 4.3 years old with BMI of 30.3 ± 3.8 kg/m2 and had 44.8 (4.7%) body fat (mean (SD)). Table 1 presents the descriptive characteristics of the participants. The percentage of android and gynoid fat was 54.4 (6.1%) and 54.9 (4.9%), respectively. Total, spine, femur, and forearm BMD (g/cm2) were 1.130 (0.108), 1.139 (0.143), 0.975 (0.117), and 0.497 (0.054), respectively. Total energy, calcium, and vitamin D intake were 1685.6 (396.9) kcal/d, 870.2 (405.9) mg/d, and 330.9 (290.0) IU/d, respectively. Based on the recommended dietary allowance of 1200 mg/d for calcium and 600 IU/d for vitamin D,39 calcium and vitamin D intake was significantly less than the recommended dietary allowance in these participants (P < .01). The serum 25OHD concentration was 68.3 ± 28.7 nmol/L with 25.8% of participants (n + 25) having less than 50 nmol/L, placing them at risk for inadequacy.39 Participants engaged in 8.7 ± 7.7 hours of physical activities per week and about 5% (n + 5) of them were smokers, but smoked less than 1 pack per day. The handgrip strength from both hands was 48.8 ± (10.8) kg and 83.5% (n + 81) of participants were able to stand with at least one leg for 30 seconds. The mean time of Get-Up-and-Go Test and 8-m normal and brisk walking test were 5.5 (0.8), 5.6 (1.2), and 4.3 (1.0) seconds, respectively. The number of chair sit-to-stand for 30 seconds was 13.8 (2.9).

T1-2
Table 1:
Descriptive Characteristics of Participants, Mean (SD)

Relationship of Physical Performance Measures With Body Weight and BMI

Weight was negatively correlated with one-leg-stance time in both nondominant (r = −0.314, P < .01) and dominant sides (r = −0.238, P < .05) and the correlation was stronger with the nondominant leg. Handgrip strength and the number of chair sit-to-stand measures were significantly correlated with weight (r = 0.207 and r = −0.326, P < .05). Higher BMI was correlated with longer time to complete 8-m walking at both normal and brisk pace (r = 0.363 and r = 0.282, P < .01).

Relationship of Physical Performance Measures With Body Fat (%)

In general, higher percentage of body fat was associated with poorer physical performance measures. Total body fat was significantly correlated with one-leg-stance time in nondominant leg (r = −0.240, P < .05), Timed Get-Up-and-Go Test (r = 0.228, P < .01), 8-m walking time in both normal and brisk pace (r = 0.356 and 0.308, respectively, P < .01), and timed chair sit-to-stand (r = −0.287, P < .01) (Figure 1).

F1-2
Figure 1:
Scatterplots with Timed Get-Up-and-Go (TGUG) Test (r = 0.228, P < .01), timed chair sit-to-stand (STS) test (r = −0.287, P < .01), and total body fat (%).

In the multiple regression analyses controlling for covariates, total body fat was positively related to 8-m walking time at both normal and brisk paces (r2 = 0.23 and 0.15, respectively, P < .05), indicating that heavier individuals needed more time to walk a designated distance (Table 2). The percentage of fat in the arms and android regions was also positively associated with walking time (r2 = 0.12-0.22, P < .05). In addition, the number of steps during the 8-m brisk walking was positively related with the percentage of fat in the gynoid regions (r2 = 0.30, P < .05), indicating that heavier individuals needed to make shorter and more frequent steps to cover the designated distance. The number of timed chair sit-to-stand was negatively associated with percent fat in the android regions (r2 = 0.22, P < .04) while the relationship with gynoid fat was not significant.

T2-2
Table 2:
Multiple Regression Models With Physical Performance Measures as Dependent Variables (n + 97)a

Higher percentage of fat in legs contributed to poorer physical performance related to lower extremities, such as Timed Get-Up-and-Go Test, 8-m walking time at normal and brisk paces, and the number of timed chair sit-to-stand (r2 = 0.13-0.23, P < .05; Table 2). Similarly, higher percentage of fat in legs predicted slower walking speed at both normal and brisk walking speed and the lower number of chair sit-to-stand attempts. With the one-leg-stance time test for both dominant and nondominant legs, the results of the logistic regression analyses showed that an increase of 1% of fat in legs would decrease the chance of being able to stand with dominant and nondominant leg for 30 seconds by 20.1% (odds ratio [OR] + 0.799, P < .01) and 12.1% (OR + 0.879, P < .05), respectively (Table 3). This analysis also showed that an increase of 1% of fat in the total body and gynoid regions would decrease the chance of being able to stand with each leg for 30 seconds by 21.4% and 21.7% (OR + 0.783-0.786; P < .05), respectively (Table 3).

T3-2
Table 3:
Logistic Regressions With One-Leg-Stance as a Dependent Variable (n + 97)a

Relationship of Physical Performance Measures With Lean Mass and BMD

Lean mass and physical performance had a positive relationship with handgrip strength (the handgrip strength is the only item that is used to directly measure muscle strength). The Pearson correlation showed that there was a positive relationship between handgrip strength, as a determinant of muscle strength, and total lean mass (r = 0.345, P < .01; Figure 2). Positive correlations of lean mass in arms (r = 0.254, P < .05) and radius 33% BMD (r = 0.207, P < .05) also were found, indicating that these relationship are site-specific (Figure 3). However, these relationships were not significant in multiple regression analyses when controlling for the noted covariates.

F2-2
Figure 2:
Scatterplot with handgrip strength and total lean mass (kilogram); r = 0.345, P < .01.
F3-2
Figure 3:
Scatterplot with handgrip strength and forearm (radius 33%) bone mineral density (BMD); r = 0.207, P < .05.

The timed chair sit-to-stand, another physical performance measure for the assessment of body strength, showed a positive relationship with lean mass. Multiple regression analysis, when controlling for covariates, indicated that total lean mass could predict the number of timed chair sit-to-stand (r2 = 0.19, P < .05), but not any other physical performance measure. Similarly to the upper body, site-specific relationships between lean mass in the legs and physical performance in lower extremities were observed. Higher amount of lean mass in legs predicted faster walk speed at normal pace (r2 = 0.14, P < .05) and higher number of timed chair sit to stands (r = 0.219, P < .01).

DISCUSSION

The objective of this study was to examine the relationship between bone and body composition (lean and fat mass) with various physical performance measures in overweight/obese early postmenopausal women, while controlling for influential confounders. This study reveals that fat mass is a major negative determinant of physical performance among overweight/obese postmenopausal women and that lean mass is a positive predictor of several physical performance measures. In addition, handgrip strength, as an indicator of muscle strength, had a significant positive correlation with BMD and lean mass, especially in the forearm region. Other relationships were site-specific showing that body composition of the lower extremities was related to the physical performance of the lower extremities, such as walking and one-leg-stand time.

In a recently published review article on this topic, it was reported that previous studies have been inconsistent in identifying which components of body composition (fat or muscle) were correlated with physical performance and most of them were performed in adults much older than participants in this study.1 Although the changes in body fat and its distribution, as well as the loss of bone and lean mass, are critical in postmenopausal women, they have not been examined with regard to the physical performance outcomes.40 These changes, combined with poorer physical performance, may increase the risk of falls and create other functional limitations, all leading to increased disabilities.41,44 The finding that fat mass had a negative relationship with various physical performance measures is consistent with that of the Lebrun et al45 study, which showed that fat mass was a major negative determinant of physical function in postmenopausal women in their sixties, although not all of these women were overweight and obese and they were older.

Although many studies have used BMI as a surrogate for body fat and have found stronger correlations between high BMI (as a proxy for body fat) and poorer physical performance, this study used iDXA to analyze body composition, controlling for all of the important confounders including age, weight (for BMD only), calcium intake, serum 25OHD, physical activity, and smoking status; therefore, the results are more comprehensive and robust. In addition, the most recent evaluation of the National Health and Nutrition Examination Survey (NHANES) data in adults older than 55 years examining body composition, muscle strength, and muscle quality, indicated that fat mass (negative) and muscle strength (positive) were independent factors in predicting physical function, further concurring with the results of this study.46

In our study, the abdominal fat, or more precisely, the estimated android fat by iDXA, was significantly negatively correlated with most of the physical performance measures, especially those for lower extremities, which is critical for maintaining mobility. Keeping in mind an increased awareness of the abdominal or central obesity as one of the risk factors for morbidity, mortality, and functional limitations, several cross-sectional studies have found an inverse association between abdominal fat and functional limitations. A study with a population-based cohort of 2325 community-dwelling older adults showed that abdominal obesity, measured by waist circumference, in older adults predicted self-reported mobility as well as agility.47 In another study with 9416 men and women older than 53 years, in some 9 years of follow-up, the abdominal obesity, assessed by waist circumference and waist-to-hip ratio, showed ability to predict the functional limitations, activities of daily living, and instrumental activities of daily living in late adulthood.48 Depending on the gender and race, one standard deviation increment in waist circumference or waist-to-hip ratio increased the likelihood of severe functional limitations and activities of daily living and instrumental activities of daily living impairments by 1.41 to 2.66 times.

Not surprisingly, the relationship between body composition and physical performance is often site-specific, as has been shown in other studies as well.10,49 Body composition of the upper extremities was significantly related to physical performance measures corresponding to upper extremities, and the same relationship was observed with lower extremities. Interestingly, lean mass was not such a strong explanatory factor for physical performance measures as expected, except for handgrip strength, which is an indicator of overall muscle strength. Gender may be a key variable explaining these results as was found in other studies, suggesting that women, not men, show a stronger negative relationship between fat mass and physical performance, compared with that of lean mass and physical performance.10,47 Since this study included only female participants, fat mass was the major negative explanatory factor for physical performance, which is consistent with the gender difference findings noted in other studies. Although the relationships in our study showed relatively weak statistical significance, it needs to be emphasized that the study had a rather homogenous population and was conducted with a great rigor and the major confounders were accounted for, which was not the case in some of the previous studies. Therefore, despite the low statistical significance, we believe that the biological/clinical significance is relevant and particularly important for application in overweight/obese, early postmenopausal women.

Bone mineral density of various skeletal sites was not significantly correlated with physical performance measures, except with handgrip strength. This might be due to the fact that the participants in this study were generally healthy women (without osteoporosis), with little variability in BMD among themselves. Most studies investigating the relationship between BMD and physical performance were based on the evidences that a lower BMD was a high-risk variable for falls and higher BMD had a positive effect on physical performance lowering the risk of falling. In this study, however, only the participants with BMD within the normal range and without any clinical symptoms of osteoporosis or osteopenia were included leading to little variance in BMD. Our hypothesis about the relationship between BMD and physical performance was not supported, except for the handgrip strength, probably because of the small between-subject variability and inability to detect the incremental changes and obtain significant relationship with physical performance measures.

While handgrip strength is one of several measures for strength and is widely used as a good predictor for mortality and one's overall health, most studies that have examined the relationship between handgrip strength and BMD/body composition concluded that the relationship was significantly positive and often site-specific with forearm BMD or appendicular muscle/fat mass. Our study concurred with these studies in that handgrip strength showed a positive correlation with total and appendicular lean mass when controlling for age. It also indicated a significant correlation with BMD in the forearm region of the midradius (at 33%), similarly as other studies in postmenopausal women have shown,50,51 although the significance disappeared after controlling for other confounding covariates in multiple regression models. Other studies have noted that the handgrip strength is a robust predictor of many health outcomes including mortality, disability morbidity, and longevity.52 Gale and colleagues53 examined grip strength among the older adults in the United Kingdom and concluded that it could be a long-term predictor of mortality in men, but not women, and independent from lean mass or the percentage of body fat. This study supports the fact that handgrip strength is a good predictor of muscle and lean tissue, which could be indirectly related to other risk factors in these relatively healthy postmenopausal women.

Both calcium intake and vitamin D status in our participants were taken into account in the statistical analyses. On average, the participants had both low calcium and vitamin D intake, but adequate vitamin D status. Serum calcium was measured as well (results not presented) and was normal, not surprisingly because of the homeostatic control of circulating calcium in healthy individuals. Regarding vitamin D intake, it also is not unusual for dietary vitamin D to be unrelated to serum 25OHD, because of many limitations in assessing vitamin D intake, as well as to its conversion in the body under the sunlight,54 explaining the discrepancy between low intake and on average adequate 25OHD status (68.1 ± 28.3 nmol/L). The Institute of Medicine recommendations for adequate status are 50 nmol/L or greater.39 Vitamin D–deficient patients have been found to have decreased proximal hip muscle strength that affects gait stability and can predispose them to falls.55 Likewise, vitamin D was associated with a reduction in falls in individuals who had low baseline levels (<50 nmol/L) by improving muscle strength and lower-extremity function. A meta-analysis performed by O'Donnell et al56 found a 34% decreased risk for falling in the group treated with vitamin D compared with placebo. Despite these strong influences of vitamin D status on some of the physical performance measures, we believe that controlling for its status enabled us to avoid reflection of its effects on some of the functionality measures.

Limitations of the Study

Several limitations of this study should be addressed. Assessing body composition by DXA is a reliable and widely used method, but it has limitation regarding the muscle. Dual energy x-ray absorptiometry gives the analysis of the lean tissue, which for the most part, but not all, represents the muscle. Lean tissue also includes body water and other organs (in addition to bone tissue that was analyzed separately). Therefore, assessing lean tissue by DXA and using it as a proxy for muscle could have brought some bias and inconsistencies in the results. In addition, recent findings support the evidence of the association between muscle-fat infiltration, especially in the lower extremities, and its relationship with mobility affecting physical performance. Fat infiltration in muscle could not be detected by DXA and therefore is an unknown variable in this study. Since only overweight/obese, postmenopausal white women were included in this study, the results cannot be generalized to other populations, age or ethnic groups of women or men, although they make it stronger for that particular population. However, considering the overwhelming prevalence of overweight/obesity in the United States, along with the consequences of obesity on the population health including increased risk of physical disabilities,57 it is crucial to develop strategies to reduce obesity-related public health concerns and address specifically postmenopausal, overweight, and obese women.

CONCLUSION

The relationship between physical performance measures and body composition/BMD indicated that body fat was a significant negative predictor, while muscle mass was a significant positive predictor of various physical performance measures among overweight/obese early postmenopausal women. The handgrip strength showed positive relationship with both muscle mass and BMD of the forearm region. The site-specific relationship was also noted in both upper and lower extremities showing that fat and muscle mass in legs was variably related to the walking and one-leg-stance time. Overall, a higher percentage of body fat was related to poorer physical performance whereas the higher lean tissue, reflecting muscle mass, was related to better physical performance. When relationships were examined separately for upper and lower body sections, the results with muscle mass and body fat were site specific, meaning that body composition of lower extremities (lean mass and body fat in legs) was associated with physical performance measures of lower extremities (walking and the timed chair sit-to-stand) and the same held for the upper extremities. Therefore, it would be reasonable to recommend that reducing fat mass while minimizing the loss of muscle mass in lower extremities would help maintaining mobility, which is important for achieving active lifestyle and freedom as getting older. In particular, overweight/obese postmenopausal women might need to focus on site-specific interventions to improve physical performance.

Further investigation of muscle-fat infiltration or any type of quantitative measures of muscle quality may be beneficial for a better understanding of the role of body composition in possible preventing the decline in physical performance among middle-age and older adults, especially those who are overweight or obese. The scientific community is in agreement that there is a pressing need for studies to investigate the relationship between body composition and physical performance, especially among overweight/obese individuals.14,15

ACKNOWLEDGMENT

We are deeply appreciative and thankful to all participants of this study for their dedication and time.

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Keywords:

chair sit-to-stand test; handgrip strength; obesity; one-leg-stance time; postmenopause; 8-foot Timed Get-Up-and-Go Test; walking speed

© 2014 Academy of Geriatric Physical Therapy, APTA