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. Author manuscript; available in PMC: 2013 Dec 1.
Published in final edited form as: Gend Med. 2012 Nov 2;9(6):445–456. doi: 10.1016/j.genm.2012.10.004

Gender Differences in Anthropometric Predictors of Physical Performance in Older Adults

Maren S Fragala 1,2, MH Clark 2, Stephen J Walsh 3, Alison Kleppinger 1, James O Judge 4, George A Kuchel 1, Anne M Kenny 1
PMCID: PMC3536819  NIHMSID: NIHMS420696  PMID: 23123187

Abstract

Background

Both high body fat and low muscle mass have been associated with physical disability in older adults. However, men and women differ markedly in body composition, where men generally have more absolute and relative lean muscle mass and less fat mass than women. It is not known how these anthropometric differences differentially impact physical ability in men and women.

Objectives

This study examines differences in anthropometric predictors of physical performance in older women and men.

Methods

Participants were 470 older women and men aged 72.9±7.9y. Body composition was measured using DXA. Maximum leg strength and power were measured using a leg press. Muscle quality (MQ) was calculated as relative strength (leg press strength per kg of leg muscle mass). Gait speed and chair rise were used to assess mobility performance and functional strength.

Results

BMI, age and muscle quality emerged as predictors (p<0.05) of functional strength and mobility in men and women somewhat differently. After accounting for age and sample, leg muscle quality was related to chair rise time and gait speed in men but not women. BMI was related to gait speed in both men and women, but BMI was related to chair rise time in only women and not men.

Discussion

Results implicate the prioritized importance of healthy weight and muscle maintenance in older women and men, respectively for maintained physical functioning with aging.

Keywords: Physical Function, Muscle Strength, Muscle Mass, Aging, Body Composition

INTRODUCTION

Age-related declines in lean muscle mass have often been attributed to declining physical performance capacity, disability, mobility impairments, and frailty in older adults. However, contrary to this notion low skeletal muscle mass has been shown to be a surprisingly poor predictor of physical disability.1 Instead, relative body fat has been associated with disability in older adults.1 This disability may result from an insufficient ability to carry one’s weight if lean muscle mass is low relative to body size, fat mass, and height.2 However, this notion is complicated by gender differences in body composition measures.

Adult men and women differ markedly in body composition, where men generally have more absolute and relative lean muscle mass and less fat mass than women. 3 Men, specifically have more appendicular muscle than women, 4,5 particularly in the upper body. 5 However, with aging men lose lean muscle mass at a faster rate than women.3,6 In fact, annual rates of appendicular skeletal muscle mass loss in men have been shown to be twice that in women. 7 This loss is primarily due to a decrease in lower body skeletal muscle mass in both men and women. 5,7

Clinical definitions of the age-related decline in muscle mass, quality, and function, termed sarcopenia, have not yet been established.8 Thus, the prevalence rates of sarcopenia vary by definition.9–11,12 When sarcopenia is defined as low fat free mass, prevalence has been shown to be 4% in men and 3% in women aged 70–75, increasing to 16% in men and 13% in women aged 85 and older.9 However, when sarcopenia, is defined as appendicular skeletal muscle mass scaled to height, the prevalence rates are higher, with a larger apparent gender difference, 22.6% in women and 26.8% in men aged 64–93 y and in 31.0% women and 52.9% in men over age 80 y.10 Nevertheless, sarcopenia increases risk of physical disability in both older men and women.12 Moreover, the etiology of sarcopenia appears to develop differently in men and women 12,13 where sarcopenia is more prevalent in women at younger old ages (59–69y) and men at older old ages (over age 80y).13 Additionally, in men sarcopenia may mask an increase in body fat. 7

Although being overweight is commonly associated with functional limitations and disability in both older men and women, 14,15,16,17,18 gender differences in anthropometric characteristics impacting physical performance are apparent. For example, muscle area has previously been shown to be the strongest component of lower extremity performance in older men, while in women, total body fat was most strongly associated.19 Moreover, for men being classified as overweight (BMI 25.0–34.9) has been associated with a decreased risk of functional limitations compared to normal weight (BMI 18.5–24.9).14 In comparison to women who have an increased risk of functional limitations with a BMI over 35, men also do not see the functional limitation until BMI is over 40.14 Hence, anthropometric predictors of physical performance differ in older men and women, where it is hypothesized that lean mass becomes a critical limiting factor in least fit/most frail older women over time, while fat mass assumes this role in their male counterparts.20 However, this hypothesis has not been tested in comparable samples of older men and women. To date, we do not know the relative benefit of muscle quality or burden of body load (BMI) on physical functioning ability between older men and women.

Given gender differences in body composition, sarcopenia, muscle mass and strength, we hypothesize that there may be contrasting anthropometric influences on physical function in older men and women. Thus, the purpose of this investigation was to determine whether older women and men differ in baseline anthropometric predictors of physical performance. Clarifying how older men and women differ in body composition predictors of functional performance will help in preventive and treatment interventions to maintain functional muscle mass, mobility and performance in older adults. Moreover, clarifying this potential gender difference is important to determining whether clinically relevant definitions of sarcopenia need to be gender specific.

METHODS

Older adults residing in the community or assisted living facilities in Hartford County, CT were recruited and screened on the basis of age, bone mineral density, frailty, and hormonal status for potential participation in one of four interventional studies21–25 to assess the effects of exercise, dehydroepiandrosterone, testosterone, or estrogen on bone and physical function in women and men. Volunteers were recruited by newspaper advertisement, letter of invitation from existing databases, and community presentations to senior centers and residential retirement communities. Data for this study are pooled baseline assessments from the aforementioned studies prior to the initiation of any interventions. Baseline data were measured in 470 older adults (281 women and 193 men) aged 72.9±7.9 yrs (Table 1). Since some participants did not complete all tests, regression models included those participants with complete data. All study participants provided written informed consent. The Institutional Review Board at the University of Connecticut Health Center approved all study procedures.

Table 1.

Anthropometric characteristics of study participants

Women Men
Age Group 57–70 y 71–77 y 78+ y 57–70 y 71–77 y 78+ y
Age (y) 65.2± 3.0 (n=146) 73.6 ± 1.9 (n=84) 81.3 ± 3.4 (n=47) 66.1± 3.8 (n=31) 74.4 ± 2.1 (n=83) 82.8 ± 4.1 (n=79)
BMI (kg/m2) 25.5 ± 4.7* 26.0 ± 5.1 26.0 ± 4.7 28.3 ± 5.0* 26.7 ± 3.9 26.3 ± 3.3
Height (cm) 160.9 ± 6.1* 159.3 ± 6.5* 157.6 ± 6.3* 175.0 ± 5.9* 172.8 ± 7.9* 171.3 ± 6.7*
Weight (Kg) 66.1 ± 11.9* 65.9 ± 12.6* 64.7 ± 13.3* 86.8 ± 16.6* 80.1 ± 14.2* 77.1 ± 11.2*
Fat % 37.1 ± 7.2* 38.6 ± 6.6* 37.1 ± 6.1* 29.2 ± 8.3* 27.2 ± 6.3* 27.7 ± 7.0*
ASM (Kg) 15.8 ± 2.2* 15.3 ± 2.7* 15.1 ± 2.5* 24.5 ± 4.0* 23.2 ± 3.3* 21.8 ± 2.8*
Muscle Quality 4.3 ± 0.9* 3.9 ± 0.8* 4.0 ± 0.9 4.8 ± 1.4* 4.4 ± 1.1* 4.0 ± 1.1
BMD (g/cm2) 1.111 ± 0.074* 1.087 ± 0.100* 1.042 ± 0.097* 1.173 ± 0.094* 1.198 ± 0.098* 1.158 ± 0.103*
Lean mass (Kg) 37.9 ± 4.3* 37.6 ± 5.2* 37.8 ± 5.3* 56.9 ± 8.0* 54.1 ± 6.1* 52.1 ± 5.6*
Fat mass (Kg) 23.8 ± 8.9 25.4 ± 9.2* 24.5 ± 8.5 25.9 ± 10.7 22.2 ± 8.4* 21.7 ± 7.9
Trunk mass (Kg) 11.7 ± 4.0* 12.4 ± 3.9 11.9 ± 4.1 13.9 ± 5.7* 12.7 ± 4.6 12.1 ± 4.3
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*

p < 0.05 based on analysis of variance comparing gender, stratified by age

ASM = Appendicular skeletal mass; BMD = Bone mineral density

Body Composition

Total and regional skeletal muscle and fat mass were determined using a whole-body dual x-ray absorptiometry (DXA) using a DPX-IQ scanner (GE Medical Systems Lunar, Madison, WI). Total lean body mass (LBM (kg)), total fat mass (kg), and total-body bone density (BMD) (g/cm2) were measured in the total body scan. Appendicular skeletal muscle mass (ASM) was determined by combining the lean tissue mass of the arm and leg regions, excluding all other regions from analysis. Leg skeletal muscle mass was used in the determination of muscle quality (MQ) of the lower body.

Physical Performance Descriptive Measures

Leg Press Strength & Power

Lower body voluntary isotonic strength and power26,27 were evaluated on the Keiser K400 pneumatic seated leg-press machine (Kaiser Corp., Fresno, CA) equipped with a force plate. Strength was determined by testing the maximum amount of weight each participant could lift for one repetition (1-RM). Participants sat in a semi-recumbent position on the leg-press machine. The starting position was set at a knee flexion angle of ~90°, hip flexion of ~45° and ankle dorsiflexion of ~ 5°. After a brief familiarization, participants warmed-up with three unilateral warm-up trials of five repetitions for each leg at 20% of body weight in Newtons, alternately. Resistance was then increased for the 1-RM attempts, based on the participants’ perceived difficulty (rated 1–4 for easy to very hard), until he/she could not complete a lift (failure). Participants rested at least 1 minute between attempts, alternating each leg between trials. Standardized verbal encouragement was given to each participant before and during each attempt. The participant’s 1RM was determined as the greatest resistance (N) that the participant was able to overcome (maximal extension) under this protocol. A trial was considered complete after each leg was unable to complete a leg press. MQ was calculated as relative strength (leg press strength per kg of leg muscle mass). Lower body power (W) was measured as the peak work rate (product of the instantaneous resistance (N) x instantaneous velocity). The leg press attempt that developed the maximal power was usually at between 50 and 70% of the 1-RM.

Single-leg stance

Balance was evaluated with the single-leg stance test. 28 This test evaluated the ability of volunteers to balance in a standing position on one leg with eyes open. Participants wore flat-heel, rubber-soled shoes. Participants shifted their weight to the dominant foot and lifted and held the non-dominant lower extremity at 0° of extension at the hip and 80° to 90° of flexion at the knee. The time that participants balanced on the single-leg was recorded in seconds. The test was terminated after 30 seconds, since it was considered a successful outcome if participants were able to maintain balance for this duration. Time elapsed was measured with a stopwatch.

Outcome Measures

Gait Speed

Normal walking gait (mobility) was evaluated with the 8-foot walk test. 29 An 8-foot level course was marked by tape in an indoor corridor. Participants were instructed to walk at their ‘normal pace’ and were allowed to use any usual walking aids. Spotters walked along the side of the volunteer’s during the test to ensure safety. Time began when the volunteer initiated foot movement and stopped when one foot (completely) crossed the end line (8-foot mark). Time was measured with a stopwatch to the nearest one hundredth of a second. Two trials were completed. The best time of the two attempts was recorded.

Chair Rise

Functional lower body strength was evaluated with the chair rise test. 29 This test required participants to stand from a seated position in a 17-inch armless chair five times, consecutively. Participants were instructed to do so as rapidly as possible with the arms folded across the chest. If participants could not perform a single chair rise without the use of their arms the test was concluded. Otherwise, time to perform five consecutive chair rises was measured with a stopwatch in seconds.

Statistical Analysis

Anthropometric and performance measures are reported as means and standard deviations stratified by gender and age. Group differences were evaluated using analysis of variance stratified by age. For all analyses, the level of significance was p≤0.05. Total body fat, trunk fat, and appendicular fat mass were eliminated from the model, since all were highly correlated to BMI (r > 0.8) and did not provide any unique contributions when predicting physical performance. BMI was selected to represent a ‘load’ hypothesized to hinder mobility. MQ was selected to represent ‘functional tissue’ hypothesized to facilitate mobility. Simple slope regression models included sampling group as a fixed factor to account for potential variations between samples. Since samples were nested within gender (two samples included only women and two samples included only men), adjustments for samples were made only within each level of gender. Although a more common practice is to treat samples as a random factor in a hierarchical design, having only two samples within each level of gender would under power our analyses. Therefore, we included the samples as a fixed factor in each simple slope regression model.

Preliminary Analyses

To reduce threats to statistical conclusion validity30, we tested several statistical assumptions for ordinary least squares regression and made some adjustments to our statistical models when these assumption were violated. For those violated assumptions that could not be corrected, we discuss the potential implications for validity in the limitations section of this paper.

The residual errors for a regression should be normally distributed

Results from the Shapiro-Wilk test indicated that the residual errors were not normally distributed when either chair rise time (SW (448) = .919, p < .001) or gait (SW (451) = .819, p < .001) was the dependent variable. To reduce the skewness and kurtosis of the residual errors, we used a square root transformation on each dependent variable. Although the transformation reduced both skewness (g1raw = 1.416, g1trans = 0.588) and kurtosis (g2raw = 4.915, g2trans = 1.603) of the residual errors when all of the significant predictor variables were regressed on chair rise time, they were still not normally distributed (SW (448) = .975, p < .001). Similar results were obtained by transforming gait speed. Skewness (g1raw = 2.637, g1trans = 1.334) and kurtosis (g2raw = 14.356, g2trans = 5.281) of the residual errors were reduced, but not normally distributed (SW = .907, p < .001). Although transforming the dependent variables did not completely correct for this violation, hopefully it will reduce the impact that it had on our results.

Continuous predictor variables should be linearly related to the dependent variables

Curve fit estimates in SPSS were used to determine if age, BMI or muscle quality had curvilinear relationships with either dependent variable. We found that both age and BMI violated this statistical assumption. There was a significant quadratic trend between age and chair rise time, β = 1.611, t(445)=1.611, p = .025; and age and gait speed, β = 3.799, b = .003, t(448)=5.573, p < .001. There was also a quadratic relationship between BMI and gait speed, β = −.430, b = −.334, t(448) = −2.603, p = .010. Therefore, we included quadratic trends in each of our regression models.

There should be no outliers

Examining stem and leaf plots in SPSS, we found that there were outliers for all of the continuous predictor variables: 1 in age, 14 in BMI, and 11 in muscle quality. We tried transforming these variables to reduce the number of outliers, but this had little impact; therefore we used the raw scores for the predictor variables. Although dropping cases with outliers was another possibility, we chose to retain these cases to improve external validity. The impact of these outliers was considered when interpreting results that would likely be affected by them.

Inferential Analyses

Two sequential multiple regressions were used to test whether gender moderated the relationship between the anthropometric variables (age, BMI, and muscle quality) and physical performance, as measured by (a) chair rise time and (b) gait speed. Quadratic trends for age, BMI and muscle quality were also included in the models to account for the non-linear trends found in the preliminary analyses. To reduce multicollinearity, a stepwise procedure in SPSS version 19 was used to test each block of the non-linear and interaction terms. Statistics for all models are shown in Tables 3 and 4.

Table 3.

Statistics for Chair Rise Time

Model Anthropometric Variable β df t p
All Participants
 Main Effects Gender .150 443 3.251 .001
Age .327 443 7.045 < .001
BMI .219 443 5.139 < .001
Muscle Quality −.093 443 −2.130 .034
 Non-linear Age2 1.249 442 1.838 .067
BMI2 .789 442 2.895 .004
Muscle Quality2 .155 442 0.639 .523
 Interactions Gender*Age .066 441 0.141 .888
Gender*BMI −.416 441 −1.524 .128
Gender*MQ −.301 441 −1.670 .096
Gender*BMI2 −.182 441 −1.304 .193
Men Sample .355 166 4.887 < .001
Age .220 163 2.964 .003
BMI .054 163 0.762 .447
Muscle Quality −.172 163 −2.368 .019
Age2 1.455 162 1.344 .181
BMI2 .885 162 1.462 .146
Women Sample .465 278 8.766 < .001
Age .140 275 1.985 .048
BMI .212 275 3.638 < .001
Muscle Quality −.028 275 −0.494 .621
Age2 −.163 276 −0.170 .865
BMI2 .387 276 1.183 .238
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Table 4.

Statistics for Gait Speed

Model Anthropometric Variable β df t p
All Participants
 Main Effects Gender .128 446 2.954 .003
Age .368 446 8.466 < .001
BMI .305 446 7.623 < .001
Muscle Quality −.175 446 −4.290 < .001
 Non-linear Age2 3.551 444 5.733 < .001
BMI2 .628 444 2.528 .012
Muscle Quality2 .143 444 0.650 .516
 Interactions Gender*Age2 −.250 442 −0.992 .322
Gender*BMI2 .329 442 2.593 .010
Gender*MQ −.336 442 −2.028 .043
Men Sample .336 168 4.619 < .001
Age .297 165 4.373 < .001
BMI .287 165 4.394 < .001
Muscle Quality −.225 165 −3.383 .001
Age2 2.811 163 2.913 .004
BMI2 1.412 163 2.618 .010
Women Sample .534 279 10.546 < .001
Age .252 276 3.850 < .001
Muscle Quality −.051 276 −.985 .325
Age2 2.245 275 2.554 .011
BMI2 .182 275 0.600 .549
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RESULTS

Anthropometric characteristics stratified by age group and gender are listed in Table 1. The mean age for each stratified age group were (57 – 70 y) women 65.2y ± 3.0 and men 66.1y ± 3.8, (57 – 70 y), (71 – 77y) women 73.6 ± 1.9 and men 74.4y ± 2.1, (78y+) women 81.3y ± 3.4 and men 82.8y ± 4.1. As expected, men were taller and heavier than women (p<0.05). Also, men had greater lean mass, bone mineral density (BMD), appendicular skeletal mass (ASM), and less relative body fat (p<0.05). Interestingly, men had higher BMI than women and more trunk mass in the young-old age group (57–70 yr) only (p<0.05). Neither BMI nor trunk mass differed between men and women in the older age groups (71–77 y or 78+ y). Muscle quality was higher in men in the younger age groups (57–70 y and 71–77 y) compared to women (p<0.05). There were no differences in muscle quality in the oldest age group. Fat mass was only higher in women in the 71–77 y old age group (p<0.05), but not the 57–70 y or 71–77 y age group.

Performance characteristics stratified by age group and gender are listed in Table 2. As expected, men were stronger in the leg press and were able to generate significantly higher leg power than women in all age groups (p<0.05). Interestingly, women performed better in the timed single-leg stance balance test, chair rise, and gait speed than men in the younger age groups.

Table 2.

Performance Characteristics

Women Men
Age 57–70 y (n=146) 71–77 y (n=84) 78+ y (n=47) 57–70 y (n=31) 71–77 y (n=83) 78+ y (n=79)
Single leg stance (s) 22.6 ± 10.7* 13.1 ± 10.1 6.9 ± 6.9 15.2 ± 11.7* 11.5 ± 10.5 6.7 ± 6.6
8-ft walk (s) 2.1 ± 0.4* 2.4 ± 0.5 2.8 ± 0.8 2.6 ± 0.9* 2.6 ± 0.9 3.1 ± 1.2
Chair rise time (s) 10.4 ± 3.9* 11.6 ± 3.7* 14.8 ± 6.3 13.0 ± 5.2* 13.6 ± 6.0* 15.8 ± 6.3
Leg press strength (N) 507.4 ± 139.1* 435.2 ± 100.8* 434.1 ± 132.9* 834.7 ± 274.7* 719.8 ± 209.7* 632.4 ± 202.8*
Leg press power (w) 236.9 ± 65.5* 177.1 ± 60.7* 145.1 ± 61.2* 349.0 ± 158.0* 321.7 ± 140.1* 226.6 ± 100.6*
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*

p < 0.05 based on analysis of variance comparing gender, stratified by age

Chair Rise Time

Age and muscle quality did not have non-linear relationships with rise time; and after accounting for samples, neither did BMI. Since there was a non-linear relationship between BMI and rise time (Figure 1) before accounting for samples, gender was tested as a moderator between both the quadratic and linear terms for BMI and rise time. Before accounting for different samples, gender did not moderate any of the relationships between any of the predictor variables and chair rise time. However, after accounting for samples, it moderated the relationships between BMI and rise time and muscle quality and rise time. For both men and women, older age was associated with greater chair rise times – that is, slower chair rise (Table 3). After accounting for age and sample, BMI was not related to rise time among men, but leg muscle quality was. For men, as leg muscle quality decreased the time it took them to rise out of a chair increased. Among women, there was no relationship between leg muscle quality and rise time, but BMI was positively related to rise time. As BMI increased in women, so did the time it took them to rise from a chair.

Figure 1.

Figure 1

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Relationship between chair rise time and BMI for all participants.

Gait Speed

Although muscle quality was linearly related to gait speed, both age and BMI had quadratic relationships with gait speed. As in the previous analysis, gender was tested as a moderator between the quadratic terms for these two predictor variables and gait speed. Gender did not moderate the relationship between age and gait speed. However, it did moderate the relationships between BMI and gait speed and leg muscle quality and gait speed.

For both men and women, as participants got older it took them longer to walk eight feet – that is a greater gait time and slower gait speed. However, Figure 2 shows that gait time exponentially increases after 75 years of age. After accounting for age and sample, both BMI and leg muscle quality were related to gait speed among men. For all men, as BMI increased so did the time it took them to walk eight feet. However, as shown in Figure 3, this relationship is stronger for men whose BMI scores exceed 30. Leg muscle quality was negatively related to gait speed; the time it took men to walk eight feet increased as muscle quality decreased.

Figure 2.

Figure 2

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Relationship between gait speed (time to walk eight feet) and age for women and men.

Figure 3.

Figure 3

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Relationship between gait speed (time to walk eight feet) and body mass index for women and men.

Among women, there was no relationship between leg muscle quality and gait speed. However, BMI was positively and linearly related to gait speed for females. Unlike the quadratic relationship that was found for men, women consistently took longer to walk eight feet as their BMI scores increased.

DISCUSSION

This study examined differences in anthropometric predictors of functional performance between older men and women. Primary results indicate that higher MQ and lower BMI were both associated with better performance, even after accounting for age and sampling group. However, this impact differed between older men and women and depended on the performance measure. MQ appeared to have a stronger influence on functional performance in men for both tasks, while that of BMI was greater in women. Although BMI was related to gait speed for both men and women; increasing BMI scores in women consistently decreased their gait speed, but BMI did not influence men’s gait speed until their BMI scores exceeded 30. These findings support prior findings of differences in characteristics of sarcopenia between men and women and provide further insight on the anthropometric and functional impact of the age associated changes in body composition.

Gender interactions observed in our findings are likely due to physiological differences in muscle function and structure. Absolute lower body strength on the leg press was significantly lower in women compared to men. The magnitude of this discrepancy is similar to prior data.31 Lower strength in women can likely be explained by differences in muscle mass between older men and women, 31 since in the present study men had significantly higher muscle mass than women. The reduction of these gender differences when expressing strength per kilogram of muscle mass in the present study is also consistent with previous data, 31 supporting the role of muscle mass in explaining gender differences in absolute lower extremity strength.

Interestingly, although men were both able to produce greater leg press strength and power and had favorable anthropomretric characteristics for performance (greater absolute and relative lean muscle mass) than women in the present study, this did not translate to better functional performance. These findings would indicate that factors other than muscle mass, body composition, strength and power production are likely important to mobility, functional strength, and balance performance in older adults. These additional factors could include muscle quality, neural activation, motor recruitment patterns, or motivation, factors important in physical performance, but not measured in the present study. However, these contributions were likely small, since neural activation and motor unit recruitment were captured in the strength and power measures. Moreover, all participants received similar familiarization, instruction, and encouragement to reduce the potential impact of motivation. Hence, true physiologic gender differences in muscle, fat, and weight distributions between older men and women impact functional performance.

An alternative explanation may be that despite having favorable body composition and strength, men in the present study were generally more frail or fragile than the women. Reasons for the potential gender discrepancy due to level of fragility are unknown, but it has previously been proposed that lean mass becomes a critical limiting factor in least fit/most frail older women over time, while fat mass assumes this role in their male counterparts. 20,32 Our data supports this concept and quantifies the relative burden of the load imposed by higher BMI on physical functioning in older women. Prior research has similarly reported associations between higher fat mass, BMI, and percent body fat and different measures physical functioning (e.g. strength, 6-min walk, self-reported physical abilities), particularly in older women. 33,34 Prior data from our lab revealed that appendicular skeletal muscle mass relative to body mass is a better mobility predictor, than absolute sarcopenia.35 Similarly, the importance of leg muscle mass for functional performance (timed walking test and a timed chair stand test) specifically in older men has previously been reported. 36 However, interestingly, in women in the present study, muscle quality, a relative measure accounting for muscle mass, was not an important predictor of physical performance in women. We do, however, report muscle quality as being important to function in men.

In the present study, muscle quality emerged as an important predictor of both chair rise time and gait speed, but only in men. Muscle quality, as measured in this study, indicates the relative muscle strength to regional muscle mass of the legs. Since, our data indicate that muscle quality impacts mobility in older men to a larger extent than older women, differences in muscle physiologic characteristics between older men and women may exist. Previous reports indicate that muscle quality is associated with lower extremity performance19 and is lower in less fit older adults.32

Participants in the present study included a broad range of functional abilities, ranging from fully functional community dwelling21,24 individuals to individuals demonstrating some aspects of frailty.22,23 It may be that there is a threshold of muscle mass, body weight and strength required to perform such functional tasks. These thresholds are likely relative to the demands of the functional tasks. Hence it may be that BMI and MQ have a greater impact on performance only at threshold levels. We found stronger associations between BMI and gait speed in men whose BMI exceeded 30. However, the few participants in our sample with a high BMI restrict the generalizability of this trend. Similarly, a prior study by Friedmann and colleagues found that disability risk increases in men who have a BMI over 40 and at a BMI over 35 in women.14 Moreover, another study found that lower extremity muscle mass is an important determinant of physical performance among older adults with functional limitations. 37 Hence, further studies are needed to evaluate muscle mass, quality, and body mass thresholds that may be important for performance.

Although our analyses attempted to reduce threats to statistical conclusion validity by evaluating statistical assumptions for ordinary least squares regressions and making adjustments to our statistical models when assumptions were violated, our results should be interpreted with consideration of statistical assumptions that could not be corrected. First, although variable transformations were made to our dependent variables to improve the distribution of the residual errors from the predictor variables, the residual errors were still significantly skewed and kurtotic even after the optimal transformations. This could result in model misspecification either as Type I or Type II errors. Therefore, some of the relationships between variables in the older adult population may not have been detected in our sample or some of the non-linear trends that we did find (or failed to find) in our sample may not accurately represent the population. Second, the predictor variables had outliers that we retained in our analyses. The outliers may have been responsible for the significant relationships that we found. Therefore, the detected relationship between BMI and physical performance may be explained by the additional (and more extreme) outliers of BMI scores among women compared to men. Including outliers may allow us to generalize our results to older adults with exceptionally high or low BMI or MQ scores, but our results may not accurately reflect the relationships between these anthropometric characteristics and physical performance for the majority of older adults.

CONCLUSIONS

In summary, findings from the present study suggest that gender specific criteria may be needed in defining clinically relevant definitions of sarcopenia that translate to functional performance. Moreover, it is possible that BMI is a more important determinant of functional ability in these samples of women. However, in men, the muscle quality is more important to functional performance. These findings point to the need to maintain a healthy body composition and muscle functioning with aging. Particularly, these findings indicate the importance of maintaining a healthy body weight in older women, while maintaining high quality skeletal muscle is particularly important for older men. Both goals can be achieved with carefully designed physical activity and exercise targeted at weight maintenance and muscle quality improvements with aging and hence improving functional performance. Additionally, future research should examine the proper exercise intervention strategies to maintain favorable anthropometric characteristics related to performance with aging for men and women.

Acknowledgments

This work was supported by grants from the Brookdale Foundation, American Federation for Aging Research Beeson Award, FNIH Biomarkers Consortium, National Aeronautic and Space Administration grant NNG04GK63G, NIH grant 5P60-AG13631, and R01 AG18887. This abstract was submitted and presented at the 2010 meeting of the American Geriatrics Society, May 12–15, 2010 in Orlando, Florida.

Footnotes

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