Journal of Physical Activity and Health, 2016, 13, 296 -302 http://dx.doi.org/10.1123/jpah.2015-0078 © 2016 Human Kinetics, Inc.
ORIGINAL RESEARCH
Physical Activity Energy Expenditure and Sarcopenia in Black South African Urban Women Herculina S. Kruger, Lize Havemann-Nel, Chrisna Ravyse, Sarah J. Moss, and Michael Tieland Background: Black women are believed to be genetically less predisposed to age-related sarcopenia. The objective of this study was to investigate lifestyle factors associated with sarcopenia in black South African (SA) urban women. Methods: In a cross-sectional study, 247 women (mean age 57 y) were randomly selected. Anthropometric and sociodemographic variables, dietary intakes, and physical activity were measured. Activity was also measured by combined accelerometery/heart rate monitoring (ActiHeart), and HIV status was tested. Dual energy x-ray absorptiometry was used to measure appendicular skeletal mass (ASM). Sarcopenia was defined according to a recently derived SA cutpoint of ASM index (ASM/height squared) < 4.94 kg/m2. Results: In total, 8.9% of the women were sarcopenic, decreasing to 8.1% after exclusion of participants who were HIV positive. In multiple regressions with ASM index, grip strength, and gait speed, respectively, as dependent variables, only activity energy expenditure (β = .27) was significantly associated with ASM index. Age (β = –.50) and activity energy expenditure (β = .17) were significantly associated with gait speed. Age (β = –.11) and lean mass (β = .21) were significantly associated with handgrip strength. Conclusions: Sarcopenia was prevalent among these SA women and was associated with low physical activity energy expenditure. Keywords: aging, body composition
Age-related changes in body composition, known as sarcopenia, is defined as a syndrome of progressive and generalized loss of skeletal muscle mass and strength associated with adverse outcomes, such as disability and death.1,2 The causes of sarcopenia may include decreased anabolic stimulation, denervation, inflammation, insulin resistance, physical inactivity, and undernutrition.2,3 Although sarcopenia affects primarily the elderly, it may develop in middle-aged people due to physical inactivity, malnutrition, and chronic inflammation.1 Recently several groups proposed definitions and cutpoints for sarcopenia. Initially the cutpoints were based on low muscle mass but additional measures of strength or physical performance are now recommended.2,4 According to the widely adopted definition of the European Working Group on Sarcopenia in Older People (EWGSOP) sarcopenia is an appendicular skeletal mass (ASM) index (ASM divided by height squared) of < 5.45 kg/m2 in women, plus a criterion of low muscle strength or low physical performance.4 This cutpoint is based on an ASM index more than 2 SD below the mean of a young healthy white reference group1,5 and low gait speed.6 The appropriateness of this cutpoint for black women has not been confirmed, but a cutpoint of an ASM index < 4.94 kg/m2 was recently derived from a young South African black reference group.1,7 Age-related changes in body composition vary by age and sex, but also according to race and ethnicity.8 Although black Americans were included in some studies from the United States,9,10 to our knowledge no studies on the determinants of sarcopenia in blacks have been published. A study in a multiethnic population confirmed that black women had greater ASM compared with Kruger ([email protected]), Havemann-Nel, and Ravyse are with the Centre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa. Moss is with the Physical Activity Sport and Recreation Research Focus Area, North-West University, Potchefstroom, South Africa. Tieland is with the Division of Human Nutrition, Wageningen University, Wageningen, Netherlands. 296
white women.11 The question arises whether black women are genetically less predisposed to sarcopenia and which lifestyle factors contribute to their greater muscle mass. Limited ethnological and genetic data indicate that South African black people can be divided into 2 major groups: the Nguni (Xhosa, Zulu, Swazi, and Ndebele) and the Sotho (Tswana, Pedi, and Sotho) groups, mainly descendants from a southern branch of east Africans. Intermixing between groups resulted in genetic variation, especially in urban areas. The South African black population may be genetically different from black Americans,12 and, therefore, more research in this understudied population is necessary. A large proportion of the South African black population is represented by low income groups with monotonous diets low in animal protein foods.7,13 Low socioeconomic status during early life stages with inadequate protein intake may contribute to early undernutrition, resulting in smaller muscle mass in adulthood.14 The aim of this study was to investigate lifestyle factors associated with sarcopenia in a group of older urban black women in South Africa.
Methods Participants and Sampling Procedures In this cross-sectional study, we analyzed data collected October 2012–June 2013 from 247 urban black women of the South African leg of the Prospective Urban and Rural Epidemiological (PURE) study.15 Participants were selected randomly from households in the Tlokwe municipality. Inclusion criteria were black women, apparent physical and psychological health, and age older than 45 years in 2012. Those with serious diseases or disabilities or taking anabolic steroids or protein supplements were excluded, although apparently healthy persons who were HIV positive living independently and not acutely ill could participate. Due to the stigma attached to HIV infection, potential study participants are seldom willing to disclose their status. A power calculation for multiple regression analysis
Activity Energy Expenditure and Sarcopenia 297
based on an expected R2 of 0.2, a maximum of 6 predictors in the final model, and a confidence level of 0.95 indicated a minimum sample size of 200 participants.16 All women gave informed consent. The study protocol was approved by the Institutional Review Board of the North-West University, and the experimental protocol complied with the ethics rules for human experimentation in the Declaration of Helsinki.
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Procedures Anthropometric measurements were performed by trained anthropometrists using standard methods and calibrated instruments.17 Weight was measured to the nearest 0.1 kg on an electronic scale (Seca, Birmingham, UK), and stature was measured to the nearest 0.1 cm using a stadiometer (Seca). Fat and fat-free soft tissue masses were measured for the whole body, arms, and legs by a registered radiographer using the default settings of dual energy x-ray absorptiometry (Hologic Discovery W, APEX system software version 2.3.1). Appendicular skeletal muscle mass was derived as the sum of the fat-free soft tissue mass (excluding bone) of the arms and the legs.18 After counseling, registered nurses performed the rapid HIV test using the First Response HIV card test (PMC Medical, India). If positive, the test result was confirmed with follow-up tests. Counselors provided individual counseling on the HIV results, and women who tested positive for HIV were referred for a confirmation CD4 test and treatment. Trained fieldworkers collected health and sociodemographic information, including medication use and smoking, using structured questionnaires. Dietary intakes were measured by a validated quantitative food frequency questionnaire19 coded and analyzed for energy and nutrient content using local software.20 Physical activity was measured by a validated intervieweradministered questionnaire.21 The questionnaire derives a physical activity score (range 0–20), and participants were categorized as inactive (0–2.249), moderately active (2.25–2.806), and highest activity level (2.807–20) based on an earlier study.22 Each participant wore an accelerometer with combined heart rate monitor (ActiHeart, Camtech, UK) for 7 days. Resting heart rate was taken for individual calibration to obtain an estimated age-specific maximum heart rate. Total energy expenditure and physical activity energy expenditure (PAEE) were determined in 60-second epochs using the heart rate and activity counts. The participants followed their normal daily routine. Fieldworkers made home visits for the next week to ensure that the ActiHeart was secure and to record possible problems with wearing the device. After 7 days the data were downloaded. Intensity and time of activity were summarized with intensity < 1.5 metabolic equivalents (METs) defined as sedentary, 1.5 to 3 METs as light activity, and > 3 METs as moderate-to-vigorous physical activity. Total energy expenditure and PAEE (kJ) were calculated. The reliability and validity of this device to measure physical activity has been confirmed in sub-Saharan Africans.23 Grip strength was measured for the dominant hand using a handgrip dynamometer (Jamar; Sammons Preston Inc, Bollingbrook, IL),24 recording the maximum of 3 repeated measurements. Gait speed for each participant in the 6-meter walk test was calculated.25
Statistical Analyses Descriptive statistics, Mann-Whitney U tests for groups, χ2 tests to compare categorical variables, Pearson correlation, and backward stepwise multiple regression were performed. Dietary energy and animal protein intakes, PAEE, selected sociodemographic variables,
tobacco use, and HIV infection were entered as independent variables with ASM index as the dependent variable in the regression models. Separate multivariate regression models were also used for the same variables, together with self-reported osteoarthritis, lean body mass, and percentage body fat as independent variables with walking speed and handgrip strength, respectively, as dependent variables. Walking speed and handgrip strength are regarded as criteria of low muscle strength or low physical performance and components of the sarcopenia syndrome.4 Statistical analyses were performed using SPSS, version 22 (SPSS Inc, Chicago, IL).
Results The women were from low- to middle-income groups in the Tlokwe municipal area in the North West province of South Africa. Most participants were from the Tswana ethnic group (96.3%) and lived in brick houses (84.3%) with electricity (89.5%), piped water (97.6%), and flush toilets (90.7%). Most had a television (91.1%), refrigerator (82.2%), and electric stove (69.6%), but only 18.1% of households owned a car. About two-thirds (66.8%) had an average household income of 500 to 3000 South African Rand per month (50–300 US dollars [USD]), mainly from government grants. Twenty-five of the women (10.1%) were premenopausal, defined as still menstruating. Only 2 women used hormonal replacement therapy, and 11.3% reported suffering from osteoarthritis, but 60% of the women were taking antihypertensive drugs. Almost one-half of the women were current or past tobacco users (cigarette smoking and chewing tobacco). Descriptive statistics are shown in Table 1. According to the EWGSOP definition, 12.6% of the women were sarcopenic,4 whereas 8.9% were sarcopenic if the South African cutpoint was applied.7 Of the total group, 27 women (10.9%) were HIV positive and most used antiretroviral therapy. When the HIV-infected women were excluded, 10.9% were sarcopenic according to the EWGSOP definition and 8.1% according to the South African definition. There was a large variation in energy and protein intakes, but mean intakes were generally higher than the recommended dietary allowances for women aged > 50 years.26 The physical activity scores ranged between 0.9 and 8 out of a maximum of 20. Selfreported physical activity scores were not significantly different between sarcopenic women compared with nonsarcopenic women. Although 17 women (6.8%) reported participating in sport, their participation ranged from irregular light exercises (n = 14) to organized regular exercise (n = 3). Occupation-related activities contributed most to the total physical activity score of most, whereas only 15 women spent time walking while commuting. ActiHeart data were recorded for an average 6.97 days, with a minimum of 4 days. Data for 245 women were available for activity analysis. These results indicated that the women spent on average 52.8% of their time (12.7 h) in sedentary activity (< 1.5 METs), 34% in light activity (8.2 h, 1.5–3 METs), and 13.2% in moderateto-vigorous activity (3.2 h, > 3 METs). Only 0.9% of total time (13 minutes) was spent in activities representing > 5 METs. In total, 73 women (29.8%) accumulated 10 minutes or more on average daily in activities with intensity of > 5 METs. The women who were HIV positive were younger than their counterparts who were HIV negative (50.1 y vs 59.1 y, P < .001) and had significantly smaller adiposity-related measures than women who were HIV negative, but no significant differences in ASM index (6.34 kg/m2 vs 6.82 kg/m2, P = .09), gait speed (1.42 m/s vs 1.35 m/s, P = .30), or grip strength (P = .60) were found. Table 1 shows differences in variables between sarcopenic and
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Table 1 Age, Sociodemographic, Body Composition, Dietary Intakes, and Physical Parameters of the Women Total group (N = 247)a
Sarcopenicb (n = 22)
Nonsarcopenic (n = 225)
P valuec
57.0 ± 10.2
57.9 ± 7.6
59.0 ± 9.7
.62
66 (26.6)
6 (27.3)
60 (26.7)
.78
Primary school (grade 1–7)
89 (35.9)
13 (59.1)
76 (33.8)
High school (grade 8–12)
86 (34.7)
2 (9.0)
84 (34.4)
Age (y) Educational status No formal schooling
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Tertiary qualification (certificate/diploma/degree)
7 (2.8)
1 (4.5)
6 (2.7)
Car ownership
45 (18.1)
1 (4.5)
44(19.6)
.07
HIV-infected
27 (10.9)
7 (22.6)
20 (9.8)
.43
Using tobacco products (present and past)
123 (49.6)
19 (59.1)
104 (44.0)
.12
Physical activity scored
2.11 ± 0.90
2.02 ± 0.66
2.12 ± 0.92
.65
11,823 ± 4540
7258 ± 2415
12,277 ± 1059
.001
Physical activity energy expenditure (kJ)e
4893 ± 3763
1744 ± 1092
5179 ± 3740
< .001
Gait speed (m/s)
1.36 ± 0.33
1.24 ± 0.32
1.37 ± 0.32
.08
Handgrip strength (kg)
20.4 ± 6.7
15.8 ± 7.0
20.9 ± 6.5
.001
Weight (kg)
70.0 ± 18.9
42.4 ± 5.8
72.8 ± 17.5
< .001
Height (cm)
156.2 ± 6.7
155.5 ± 7.0
156.3 ± 6.7
.55
Total energy expenditure (kJ)e
28.8 ± 7.6
17.4 ± 2.2
29.8 ± 6.8
< .001
Body fat (%)
39.6 ± 8.7
30.4 ± 5.5
40.7 ± 6.8
< .001
Lean body mass (kg)
38.9 ± 7.5
28.8 ± 3.3
40.4 ± 6.8
< .001
Appendicular skeletal mass/height squared
6.77 ± 1.4
4.80 ± 0.4
7.00 ± 1.2
< .001
Dietary energy intake (kJ)
10,604 ± 3718
11,248 ± 3508
10,537 ± 3759
.30
Animal protein intake (g)
42.3 ± 26.1
47.5 ± 33.0
41.8 ± 25.1
.36
Body mass index
(kg/m2)
Note. Data are given as mean ± SD or n (%). aNumber
of participants varies for the different measurements due to some missing data.
b Sarcopenic cP
according to cutpoint for black South African women: appendicular skeletal muscle mass index < 4.94 kg/m2.
value for the difference between sarcopenic and nonsarcopenic women, Mann-Whitney U test for continuous variables, c2 test for categorical variables.
d Physical e Energy
activity measured by modified Baecke questionnaire, maximum score = 20.
expenditure measured by accelerometry/heart rate monitoring (ActiHeart).
nonsarcopenic women according to the South African cutpoint.7 Sarcopenic participants had significantly lower ASM index (P < .001) and handgrip strength (P = .001) and tended to have lower gait speed (P = .08) than nonsarcopenic women. Other significant differences between the 2 groups included lower values of all anthropometric variables, except height in the sarcopenic women, with no significant differences in prevalence of HIV infection and car ownership. Two out of 22 sarcopenic women (9.1%) were younger than 50 years, whereas 14.4% of nonsarcopenic women were older than 70 years. Table 2 shows correlations of ASM index, gait speed, and grip strength with the independent variables. Physical activity scores, gait speed, handgrip strength, and PAEE showed significant negative correlations with age (P = .02 to < .001). Physical activity scores showed no correlation with PAEE or gait speed, but PAEE correlated positively with gait speed (r = 0.21, P = .002), indicating that the objective physical activity measurement may be a more sensitive indicator of habitual physical activity than self-reported physical activity. We subsequently adjusted for age, but the direction and significance of the correlations in Table 2 did not change.
The results of the regression analyses are shown in Tables 3 and 4. In the total group, age was not significantly associated with ASM index. Only PAEE (by ActiHeart) was significantly associated with ASM index in the final model and explained 11.2% of the variance in ASM index, together with tobacco use, HIV status, car ownership, and educational status (nonsignificant associations). When participants who were HIV positive were excluded, PAEE remained positively associated with ASM index, whereas age and smoking were negatively but not significantly associated with ASM index (results not shown). Lean mass was positively associated with handgrip strength, whereas age was negatively associated with both grip strength and gait speed. PAEE was positively associated with gait speed and together with age and self-reported osteoarthritis explained 25.2% of the variation in gait speed.
Discussion This is the first study to our knowledge to determine lifestyle factors associated with sarcopenia in black women in Africa. Physical activity measured by an objective method was positively associated
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Activity Energy Expenditure and Sarcopenia 299
Table 2 Correlations (r) Between Age, Muscle Strength, Dietary Intakes, and Physical Activity With Body Composition Measured by Dual Energy X-Ray Absorptiometry Age
Fat percentage
Lean mass
ASM index
—
r = 0.05; P = .40
r = –0.18; P = .006
r = –0.09; P = .15
Gait speed
r = –0.52; P < .001
r = –0.03; P = .66
r = 0.14; P = .03
r = 0.10; P = .14
Grip strength
r = –0.23 ;P = .004
r = 0.22; P < .001
r = 0.36; P < .001
r = 0.31; P < .001
Physical activity energy expenditure
r = –0.16; P = .02
r = 0.30; P < .001
r = 0.32; P < .001
r = 0.49; P < .001
Physical activity score
r = –0.22; P < .001
r = –0.09; P = .17
r = 0.08; P = .19
r = 0.03; P = .57
Dietary energy intake
r = –0.12; P = .02
r = 0.01; P = .96
r = 0.02; P = .74
r = 0.01; P = .83
Animal protein intake
r = –0.03; P = .55
r= –0.06; P = .37
r = 0.02; P = .77
r = 0.04; P = .58
Age
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Abbreviations: ASM, appendicular skeletal mass.
Table 3 Multiple Regression of Sociodemographic, Dietary, and Physical Activity Parameters with Grip Strength, Gait Speed, and Appendicular Skeletal Mass Index as the Dependent Variables (n = 247), Full Model ASM Index
Grip Strength
Gait Speed
Standardized
Model 1 (full model)
Standardized
Independent Variables
𝛃 coefficient
P value
Constant
7.49
Age (y)
–0.05
Physical activity energy expenditure (kJ)
Standardized
𝛃 coefficient
P value
𝛃 coefficient
P value
.001
12.9
.49
–0.13
.04
2.3
< .001
.09
–0.45
< .001
0.27
.001
0.07
.45
0.15
.04
Tobacco use (0 = no/1 = yes)
–0.10
HIV status (0 = negative/1 = positive)
–0.11
.14
0.09
.22
–0.06
.38
.14
–0.005
.94
–0.05
.42
Energy intake (kJ)
–0.03
.81
–0.10
.29
–0.11
.23
Animal protein intake (g)
0.00
.97
0.07
.45
0.08
.59
Educational status (1–4)
0.08
.23
0.01
.55
0.03
.59
Car ownership (1 = yes/2 = no)
–0.11
.10
–0.02
.78
–0.02
.96
Self-reported osteoarthritis (1 = yes/2 = no)
0.07
.29
0.08
.23
0.06
.37
Lean body mass (kg)
—
—
0.22
.01
0.01
.86
%Body fat (%)
—
—
0.15
.08
–0.07
Adjusted
R2
= 0.103, P = .001
with ASM index. This finding indicates that physical activity may be an important protective factor against sarcopenia among these women. This is also the first study to report ASM index and physical activity, both measured using objective methods in black women in Africa. According to the questionnaire data, the women were generally inactive and only 6.8% reported sport participation. The Actiheart measurements showed that a small proportion of the day on average was spent in moderate-to-vigorous activity or vigorous activity. Less than one-third of women (29.8%) had an activity level close to the World Health Organization guideline for older adults of at least 75 minutes of vigorous-intensity physical activity per week.27 Earlier studies in the North West province of South Africa reported low physical activity among adult women and few opportunities for leisure time and sport participation,22 although higher physical activity was recorded using pedometry among rural South African women in the Limpopo province.28 The proportion of time spent in sedentary and moderate-to-vigorous activity in the current
Adjusted
R2
= 0.19, P = .001
Adjusted
.39 R2
= 0.228, P < .001
study was similar to those reported for rural Kenyan women, where ActiHeart measurements were also performed.29 Physical inactivity contributes to sarcopenia in a vicious cycle, causing the elderly to become weaker and less able to participate in daily activities. Physical inactivity reflects disuse atrophy, with advanced sarcopenia and associated impaired physical performance as an endpoint.30–33 Sarcopenia in our study participants was associated with significantly lower handgrip strength and a trend of lower gait speed than in nonsarcopenic women. Age-dependent functional changes such as muscle denervation or reduced blood flow also have an effect on these functional outcomes.30 Strength may not be linearly related to muscle mass, but may deteriorate more rapidly with aging than measurable changes in muscle mass.31 In the current study, 8.9% of women were sarcopenic according to a country- and ethnicity-specific cutpoint.7 Only a small number of large representative studies describing the prevalence of sarcopenia in populations are available, making comparison across studies
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Table 4 Multiple Regression of Sociodemographic, Dietary, and Physical Activity Parameters with Grip Strength, Gait Speed, and Appendicular Skeletal Mass Index as the Dependent Variables (n = 247), Final Model ASM Index Final model with highest adjusted
R2
Independent variables
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Age (y)
Gait Speed Standardized
Standardized 𝛃 coefficient
Grip Strength Standardized
P value
𝛃 coefficient
P value
𝛃 coefficient
P value
—
—
–0.50
< .001
–0.11
.04
Physical activity energy expenditure (kJ)
0.27
< .001
0.17
.01
—
—
Tobacco use (0 = no/1 = yes)
–0.10
.22
—
—
0.09
.22
HIV status (0 = negative/1 = positive)
–0.16
.16
—
—
—
—
Car ownership (1 = yes/2 = no)
–0.10
.08
—
—
—
—
Educational status (1–5)
0.08
.25
—
—
—
—
Lean mass (kg)
—
—
—
—
0.21
.01
Body fat (%)
—
—
—
—
0.16
.06
—
—
–0.06
.27
—
—
Self-reported osteoarthritis (1 = yes/2 = no)
Adjusted R2 = 0.112,
Adjusted R2 = 0.252,
Adjusted R2 = 0.258,
P < .001, R2 change = 0.009
P < .001, R2 change = 0.06
P < .001, R2 change = 0.03
impossible. Furthermore, age range and definition of sarcopenia vary among studies.33–35 However, our results indicate that African populations are also vulnerable to sarcopenia. Other factors considered to be associated with low ASM index in our study included age, HIV status, diet, smoking, osteoarthritis, and socioeconomic variables. The estimated overall HIV prevalence in South Africa is approximately 10%.36 Almost 11% of the women in our study were HIV positive, but HIV infection was not significantly associated with ASM index in this group. This may be due to the fact that the participants who were HIV positive received treatment, were not acutely ill, and were able to live independently. There was a significant negative correlation between age and lean body mass, but age was not significantly associated with ASM index in regression models, suggesting that dual energy x-ray absorptiometry measurement of muscle mass may not be sufficiently sensitive to detect age-related muscle composition changes, such as selective atrophy of type II fibers.31 Interestingly, 9.1% of the sarcopenic women were younger than 50 years old. The prevalence of HIV infection was higher in the younger study participants, which could have played a role, but it was also evident that an important proportion of the women older than 70 years could remain free from sarcopenia. Apparently the variation in physical activity had a stronger effect on ASM index than age, because PAEE was significantly associated with ASM index, as well as gait speed in all regression models. Older women with higher physical activity levels had a greater ASM index than younger inactive women. This is an indication of a protective effect of higher levels of physical activity on muscle mass of the legs and arms, even at an advanced age.31 The findings of a significant negative correlation between age and gait speed and grip strength, respectively, confirm the results of a study of black and white women in the United States, aged 34 to 58 years, reporting that loss of muscle mass and compromised physical functioning started in the middle-age group.31 The primary functional consequence of sarcopenia is the loss of muscle strength, which may eventually lead to mobility dysfunction with adverse effects on quality of life.30 Although lean mass correlated positively with gait speed and grip strength, no correlation between percentage body fat and gait speed was found in our study. This partly confirms the findings
in a study of independently living older women with a positive association between muscle strength and lean body mass, but is in contrast to their finding of higher fat mass being associated with a lower physical performance.32 The impact of body fat on physical performance could still increase over years and may result in a significant impact after the age of 75 years, as reported previously.33 A study in the United States suggests that obese elderly women may be protected from sarcopenia regardless of their physical activity level. This could be partly due to the trophic effects of increased weight bearing on muscle.31 Mean animal protein intakes of sarcopenic and nonsarcopenic women were not significantly different. It is possible that the dietary assessment method of self-report was not sensitive enough to discriminate between low and higher protein intakes.19 Regression models showed that car ownership tended to be positively related to ASM index (P = .08–.10). In another South African study, car ownership was associated with low physical activity.28 In our study, car ownership appears to be an indicator of socioeconomic status and not necessarily related to physical activity level. It is thus possible that car ownership in this study reflects differences in socioeconomic status with effects on quality of diet and medical care over time, which could have had beneficial effects on the preservation of muscle mass. Limitations of this study include the cross-sectional design. Sarcopenia develops over time and predictors of low ASM index should ideally be investigated in prospective studies to establish causal roles for the multiple factors associated with the loss of muscle mass and strength with age. The contribution of diet to low ASM index could probably not be assessed with precision in this population where dietary intake assessment is problematic.19 Although the sample size may be adequate according to power calculations, the results may still not be generalizable to the South African black population due to ethnic and socioeconomic differences between groups.
Conclusions This study shows that black women are not protected against sarcopenia and that environmental factors are associated with age-related
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loss of muscle mass and function. Most of the variability in ASM index in this group was explained by differences in PAEE, whereas age explained a larger proportion of the variability in physical performance.
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Acknowledgments The authors acknowledge the contribution of Dr I.M. Kruger and Professor A. Kruger to the overall study coordination of the PURE North-West South Africa study and participant recruitment. They also thank the PURENWP-SA research team, field workers and office staff in the Africa Unit for Trans-disciplinary Health Research (AUTHeR), North-West University, Potchefstroom, South Africa. The authors acknowledge the contribution of PURE International: Dr S. Yusuf and the PURE project office staff at the Population Health Research Institute (PHRI), Hamilton Health Sciences and McMaster University, Ontario, Canada. This work was supported by a grant (nr 01.67801.2.RM77005) from the South African Medical Research Council.
References 1. Baumgartner RN, Stauber PM, McHugh D, et al. Cross-sectional age differences in body composition in persons 60+ years of age. J Gerontol A Biol Sci Med Sci. 1995;50A:M307–M316. PubMed doi:10.1093/gerona/50A.6.M307 2. Fielding RA, Vellas B, Evans W, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International Working Group on Sarcopenia. J Am Med Dir Assoc. 2011;12:249–256. PubMed doi:10.1016/j. jamda.2011.01.003 3. Baumgartner RN, Waters DL, Gallagher D, et al. Predictors of skeletal muscle mass in elderly men and women. Mech Ageing Dev. 1999;107:123–136. PubMed doi:10.1016/S0047-6374(98)00130-4 4. Cruz-Jentoft AJ, Baeyens JP, Bauer J, et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing. 2010;39:412–423. PubMed doi:10.1093/ageing/afq034 5. Baumgartner RN, Koehler K, Gallagher D, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998;147:755–763. PubMed doi:10.1093/oxfordjournals.aje.a009520 6. Cesari M, Kritchevsky SB, Newman AB, et al. Added value of physical performance measures in predicting adverse health-related events: results from the health, aging and body composition study. J Am Geriatr Soc. 2009;57:251–259. PubMed doi:10.1111/j.15325415.2008.02126.x 7. Kruger HS, Micklesfield L, Wright HH, Havemann-Nel L, Goedecke JH. Ethnic-specific cut-points for sarcopenia: evidence from black South African women. Eur J Clin Nutr. 2015;69:. PubMed doi:10.1038/ejcn.2014.279 8. Han SS, Kim KW, Kim KI, et al. Lean mass index: a better predictor of mortality than body mass index in elderly Asians. J Am Geriatr Soc. 2010;58:312–317. PubMed doi:10.1111/j.1532-5415.2009. 02672.x 9. Baumgartner RN, Wayne SJ, Waters DL, et al. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res. 2004;12:1995–2004. PubMed doi:10.1038/oby. 2004.250 10. Davison KK, Ford ES, Cogswell ME, Dietz WH. Percentages of body fat and body mass index are associated with mobility limitations in people aged 70 and older from NHANES III. J Am Geriatr Soc. 2002;50:1802–1809. PubMed doi:10.1046/j.1532-5415.2002. 50508.x 11. Ortiz O, Russell M, Daley TL, et al. Differences in skeletal muscle and bone mineral mass between black and white females and their relevance to estimates of body composition. Am J Clin Nutr. 1992;55:8–13. PubMed 12. Lane AB, Soodyall H, Arndt S, et al. Genetic substructure in South African bantu-speakers: evidence from autosomal DNA and
Y-chromosome studies. Am J Phys Anthropol. 2002;119:175–185. PubMed doi:10.1002/ajpa.10097 13. MacIntyre UE, Kruger HS, Venter CS, Vorster HH. Dietary intakes of an African population in different stages of transition in the North West Province, South Africa: the THUSA study. Nutr Res. 2002;22(3):239–256. doi:10.1016/S0271-5317(01)00392-X 14. Sayer AA, Cooper C. Fetal programming of body composition and musculoskeletal development. Early Hum Dev. 2005;81:735–744. PubMed doi:10.1016/j.earlhumdev.2005.07.003 15. Teo K, Chow CK, Vaz M, Rangarajan S, Yusuf S, Group PI-W. The Prospective Urban Rural Epidemiology (PURE) study: examining the impact of societal influences on chronic noncommunicable diseases in low-, middle-, and high-income countries. Am Heart J. 2009;158(1):1–7e1. 16. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41:1149–1160. PubMed doi:10.3758/ BRM.41.4.1149 17. International Society for the Advancement of Kinanthropometry. International Standards for Anthropometric Assessment. Potchefstroom, South Africa: The International Society for the Advancement of Kinanthropometry; 2006. 18. Heymsfield SB, Smith R, Aulet M, et al. Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr. 1990;52:214–218. PubMed 19. MacIntyre UE, Venter CS, Vorster HH. A culture-sensitive quantitative food frequency questionnaire used in an African population: 2. relative validation by 7-day weighed records and biomarkers. Public Health Nutr. 2001;4:63–71. PubMed 20. Wolmarans P, Danster N, Dalton A, et al. Condensed Food Composition Tables for South Africa. Parow Valley. Cape Town, South Africa: Medical Research Council; 2010. 21. Kruger HS, Venter CS, Steyn HS. A standardised physical activity questionnaire for a population in transition. Afr J Phy Health Educ Recr Dance. 2000;6:54–64. 22. Kruger HS, Venter CS, Vorster HH, Margetts BM. Physical inactivity is the major determinant of obesity in black women in the North West Province, South Africa: the THUSA study. Nutrition. 2002;18:422– 427. PubMed doi:10.1016/S0899-9007(01)00751-1 23. Assah FK, Ekelund U, Brage S, et al. Accuracy and validity of a combined heart rate and motion sensor for the measurement of freeliving physical activity energy expenditure in adults in Cameroon. Int J Epidemiol. 2011;40:112–120. PubMed doi:10.1093/ije/dyq098 24. Taekema DG, Gussekloo J, Maier AB, et al. Handgrip strength as a predictor of functional, psychological and social health. A prospective population-based study among the oldest old. Age Ageing. 2010;39:331–337. PubMed doi:10.1093/ageing/afq022 25. Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci. 2000;55:M221– M231. PubMed doi:10.1093/gerona/55.4.M221 26. Institute of Medicine (IOM). Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty acids, Cholesterol, Protein, and Amino Acids. Washington, DC: The National Academy Press; 2005. 27. World Health Organization (WHO). Global Recommendations on Physical Activity for Health. Geneva, Switzerland: WHO Press, World Health Organization; 2010. 28. Cook I, Alberts M, Lambert EV. Relationship between adiposity and pedometer-assessed ambulatory activity in adult, rural African women. Int J Obes (Lond). 2008;32:1327–1330. PubMed doi:10.1038/ ijo.2008.26 29. Christensen DL, Faurholt-Jepsen D, Boit MK, et al. Cardiorespiratory fitness and physical activity in Luo, Kamba and Maasai of rural Kenya. Am J Hum Biol. 2012;24:723–729. PubMed doi:10.1002/ajhb.22303 30. Evans W. Functional and metabolic consequences of sarcopenia. J Nutr. 1997;127:998S–1003S. PubMed 31. Sowers MR, Crutchfield M, Richards K, et al. Sarcopenia are related to physical functioning and leg strength in middle-aged women. J
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34. Batsis JA, Barre LK, Mackenzie TA, Pratt SI, Lopez-Jimenez F, Bartels SJ. Variation in the prevalence of sarcopenia and sarcopenic obesity in older adults associated with different research definitions: dual-energy x-ray absorptiometry data from the National Health and Nutrition Examination Survey 1999-2004. J Am Geriatr Soc. 2013;61(6):974–980. PubMed doi:10.1111/jgs.12260 35. Cederholm TE, Bauer JM, Boirie Y, et al. Toward a definition of sarcopenia. Clin Geriatr Med. 2011;27:341–353. PubMed doi:10.1016/j. cger.2011.04.001 36. Statistics South Africa. Mid-year population estimates. http://www. statssa.gov.za/publications/p0302/p03022013.pdf 2013.
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Gerontol A Biol Sci Med Sci. 2005;60:486–490. PubMed doi:10.1093/ gerona/60.4.486 32. Lebrun CEI, Van der Schouw YT, De Jong FH, et al. Fat mass rather than muscle strength is the major determinant of physical function and disability in postmenopausal women younger than 75 years of age. Menopause. 2006;13:474–481. PubMed doi:10.1097/01. gme.0000222331.23478.ec 33. Rolland Y, Lauwers-Cances V, Cristini C, et al. Difficulties with physical function associated with obesity, sarcopenia, and sarcopenic-obesity in community-dwelling elderly women: the EPIDOS (EPIDemiologie de l’OSteoporose) Study. Am J Clin Nutr. 2009;89:1895–1900. PubMed doi:10.3945/ajcn.2008.26950
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ORIGINAL RESEARCH
Physical Activity Energy Expenditure and Sarcopenia in Black South African Urban Women Herculina S. Kruger, Lize Havemann-Nel, Chrisna Ravyse, Sarah J. Moss, and Michael Tieland Background: Black women are believed to be genetically less predisposed to age-related sarcopenia. The objective of this study was to investigate lifestyle factors associated with sarcopenia in black South African (SA) urban women. Methods: In a cross-sectional study, 247 women (mean age 57 y) were randomly selected. Anthropometric and sociodemographic variables, dietary intakes, and physical activity were measured. Activity was also measured by combined accelerometery/heart rate monitoring (ActiHeart), and HIV status was tested. Dual energy x-ray absorptiometry was used to measure appendicular skeletal mass (ASM). Sarcopenia was defined according to a recently derived SA cutpoint of ASM index (ASM/height squared) < 4.94 kg/m2. Results: In total, 8.9% of the women were sarcopenic, decreasing to 8.1% after exclusion of participants who were HIV positive. In multiple regressions with ASM index, grip strength, and gait speed, respectively, as dependent variables, only activity energy expenditure (β = .27) was significantly associated with ASM index. Age (β = –.50) and activity energy expenditure (β = .17) were significantly associated with gait speed. Age (β = –.11) and lean mass (β = .21) were significantly associated with handgrip strength. Conclusions: Sarcopenia was prevalent among these SA women and was associated with low physical activity energy expenditure. Keywords: aging, body composition
Age-related changes in body composition, known as sarcopenia, is defined as a syndrome of progressive and generalized loss of skeletal muscle mass and strength associated with adverse outcomes, such as disability and death.1,2 The causes of sarcopenia may include decreased anabolic stimulation, denervation, inflammation, insulin resistance, physical inactivity, and undernutrition.2,3 Although sarcopenia affects primarily the elderly, it may develop in middle-aged people due to physical inactivity, malnutrition, and chronic inflammation.1 Recently several groups proposed definitions and cutpoints for sarcopenia. Initially the cutpoints were based on low muscle mass but additional measures of strength or physical performance are now recommended.2,4 According to the widely adopted definition of the European Working Group on Sarcopenia in Older People (EWGSOP) sarcopenia is an appendicular skeletal mass (ASM) index (ASM divided by height squared) of < 5.45 kg/m2 in women, plus a criterion of low muscle strength or low physical performance.4 This cutpoint is based on an ASM index more than 2 SD below the mean of a young healthy white reference group1,5 and low gait speed.6 The appropriateness of this cutpoint for black women has not been confirmed, but a cutpoint of an ASM index < 4.94 kg/m2 was recently derived from a young South African black reference group.1,7 Age-related changes in body composition vary by age and sex, but also according to race and ethnicity.8 Although black Americans were included in some studies from the United States,9,10 to our knowledge no studies on the determinants of sarcopenia in blacks have been published. A study in a multiethnic population confirmed that black women had greater ASM compared with Kruger ([email protected]), Havemann-Nel, and Ravyse are with the Centre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa. Moss is with the Physical Activity Sport and Recreation Research Focus Area, North-West University, Potchefstroom, South Africa. Tieland is with the Division of Human Nutrition, Wageningen University, Wageningen, Netherlands. 296
white women.11 The question arises whether black women are genetically less predisposed to sarcopenia and which lifestyle factors contribute to their greater muscle mass. Limited ethnological and genetic data indicate that South African black people can be divided into 2 major groups: the Nguni (Xhosa, Zulu, Swazi, and Ndebele) and the Sotho (Tswana, Pedi, and Sotho) groups, mainly descendants from a southern branch of east Africans. Intermixing between groups resulted in genetic variation, especially in urban areas. The South African black population may be genetically different from black Americans,12 and, therefore, more research in this understudied population is necessary. A large proportion of the South African black population is represented by low income groups with monotonous diets low in animal protein foods.7,13 Low socioeconomic status during early life stages with inadequate protein intake may contribute to early undernutrition, resulting in smaller muscle mass in adulthood.14 The aim of this study was to investigate lifestyle factors associated with sarcopenia in a group of older urban black women in South Africa.
Methods Participants and Sampling Procedures In this cross-sectional study, we analyzed data collected October 2012–June 2013 from 247 urban black women of the South African leg of the Prospective Urban and Rural Epidemiological (PURE) study.15 Participants were selected randomly from households in the Tlokwe municipality. Inclusion criteria were black women, apparent physical and psychological health, and age older than 45 years in 2012. Those with serious diseases or disabilities or taking anabolic steroids or protein supplements were excluded, although apparently healthy persons who were HIV positive living independently and not acutely ill could participate. Due to the stigma attached to HIV infection, potential study participants are seldom willing to disclose their status. A power calculation for multiple regression analysis
Activity Energy Expenditure and Sarcopenia 297
based on an expected R2 of 0.2, a maximum of 6 predictors in the final model, and a confidence level of 0.95 indicated a minimum sample size of 200 participants.16 All women gave informed consent. The study protocol was approved by the Institutional Review Board of the North-West University, and the experimental protocol complied with the ethics rules for human experimentation in the Declaration of Helsinki.
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Procedures Anthropometric measurements were performed by trained anthropometrists using standard methods and calibrated instruments.17 Weight was measured to the nearest 0.1 kg on an electronic scale (Seca, Birmingham, UK), and stature was measured to the nearest 0.1 cm using a stadiometer (Seca). Fat and fat-free soft tissue masses were measured for the whole body, arms, and legs by a registered radiographer using the default settings of dual energy x-ray absorptiometry (Hologic Discovery W, APEX system software version 2.3.1). Appendicular skeletal muscle mass was derived as the sum of the fat-free soft tissue mass (excluding bone) of the arms and the legs.18 After counseling, registered nurses performed the rapid HIV test using the First Response HIV card test (PMC Medical, India). If positive, the test result was confirmed with follow-up tests. Counselors provided individual counseling on the HIV results, and women who tested positive for HIV were referred for a confirmation CD4 test and treatment. Trained fieldworkers collected health and sociodemographic information, including medication use and smoking, using structured questionnaires. Dietary intakes were measured by a validated quantitative food frequency questionnaire19 coded and analyzed for energy and nutrient content using local software.20 Physical activity was measured by a validated intervieweradministered questionnaire.21 The questionnaire derives a physical activity score (range 0–20), and participants were categorized as inactive (0–2.249), moderately active (2.25–2.806), and highest activity level (2.807–20) based on an earlier study.22 Each participant wore an accelerometer with combined heart rate monitor (ActiHeart, Camtech, UK) for 7 days. Resting heart rate was taken for individual calibration to obtain an estimated age-specific maximum heart rate. Total energy expenditure and physical activity energy expenditure (PAEE) were determined in 60-second epochs using the heart rate and activity counts. The participants followed their normal daily routine. Fieldworkers made home visits for the next week to ensure that the ActiHeart was secure and to record possible problems with wearing the device. After 7 days the data were downloaded. Intensity and time of activity were summarized with intensity < 1.5 metabolic equivalents (METs) defined as sedentary, 1.5 to 3 METs as light activity, and > 3 METs as moderate-to-vigorous physical activity. Total energy expenditure and PAEE (kJ) were calculated. The reliability and validity of this device to measure physical activity has been confirmed in sub-Saharan Africans.23 Grip strength was measured for the dominant hand using a handgrip dynamometer (Jamar; Sammons Preston Inc, Bollingbrook, IL),24 recording the maximum of 3 repeated measurements. Gait speed for each participant in the 6-meter walk test was calculated.25
Statistical Analyses Descriptive statistics, Mann-Whitney U tests for groups, χ2 tests to compare categorical variables, Pearson correlation, and backward stepwise multiple regression were performed. Dietary energy and animal protein intakes, PAEE, selected sociodemographic variables,
tobacco use, and HIV infection were entered as independent variables with ASM index as the dependent variable in the regression models. Separate multivariate regression models were also used for the same variables, together with self-reported osteoarthritis, lean body mass, and percentage body fat as independent variables with walking speed and handgrip strength, respectively, as dependent variables. Walking speed and handgrip strength are regarded as criteria of low muscle strength or low physical performance and components of the sarcopenia syndrome.4 Statistical analyses were performed using SPSS, version 22 (SPSS Inc, Chicago, IL).
Results The women were from low- to middle-income groups in the Tlokwe municipal area in the North West province of South Africa. Most participants were from the Tswana ethnic group (96.3%) and lived in brick houses (84.3%) with electricity (89.5%), piped water (97.6%), and flush toilets (90.7%). Most had a television (91.1%), refrigerator (82.2%), and electric stove (69.6%), but only 18.1% of households owned a car. About two-thirds (66.8%) had an average household income of 500 to 3000 South African Rand per month (50–300 US dollars [USD]), mainly from government grants. Twenty-five of the women (10.1%) were premenopausal, defined as still menstruating. Only 2 women used hormonal replacement therapy, and 11.3% reported suffering from osteoarthritis, but 60% of the women were taking antihypertensive drugs. Almost one-half of the women were current or past tobacco users (cigarette smoking and chewing tobacco). Descriptive statistics are shown in Table 1. According to the EWGSOP definition, 12.6% of the women were sarcopenic,4 whereas 8.9% were sarcopenic if the South African cutpoint was applied.7 Of the total group, 27 women (10.9%) were HIV positive and most used antiretroviral therapy. When the HIV-infected women were excluded, 10.9% were sarcopenic according to the EWGSOP definition and 8.1% according to the South African definition. There was a large variation in energy and protein intakes, but mean intakes were generally higher than the recommended dietary allowances for women aged > 50 years.26 The physical activity scores ranged between 0.9 and 8 out of a maximum of 20. Selfreported physical activity scores were not significantly different between sarcopenic women compared with nonsarcopenic women. Although 17 women (6.8%) reported participating in sport, their participation ranged from irregular light exercises (n = 14) to organized regular exercise (n = 3). Occupation-related activities contributed most to the total physical activity score of most, whereas only 15 women spent time walking while commuting. ActiHeart data were recorded for an average 6.97 days, with a minimum of 4 days. Data for 245 women were available for activity analysis. These results indicated that the women spent on average 52.8% of their time (12.7 h) in sedentary activity (< 1.5 METs), 34% in light activity (8.2 h, 1.5–3 METs), and 13.2% in moderateto-vigorous activity (3.2 h, > 3 METs). Only 0.9% of total time (13 minutes) was spent in activities representing > 5 METs. In total, 73 women (29.8%) accumulated 10 minutes or more on average daily in activities with intensity of > 5 METs. The women who were HIV positive were younger than their counterparts who were HIV negative (50.1 y vs 59.1 y, P < .001) and had significantly smaller adiposity-related measures than women who were HIV negative, but no significant differences in ASM index (6.34 kg/m2 vs 6.82 kg/m2, P = .09), gait speed (1.42 m/s vs 1.35 m/s, P = .30), or grip strength (P = .60) were found. Table 1 shows differences in variables between sarcopenic and
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298 Kruger et al
Table 1 Age, Sociodemographic, Body Composition, Dietary Intakes, and Physical Parameters of the Women Total group (N = 247)a
Sarcopenicb (n = 22)
Nonsarcopenic (n = 225)
P valuec
57.0 ± 10.2
57.9 ± 7.6
59.0 ± 9.7
.62
66 (26.6)
6 (27.3)
60 (26.7)
.78
Primary school (grade 1–7)
89 (35.9)
13 (59.1)
76 (33.8)
High school (grade 8–12)
86 (34.7)
2 (9.0)
84 (34.4)
Age (y) Educational status No formal schooling
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Tertiary qualification (certificate/diploma/degree)
7 (2.8)
1 (4.5)
6 (2.7)
Car ownership
45 (18.1)
1 (4.5)
44(19.6)
.07
HIV-infected
27 (10.9)
7 (22.6)
20 (9.8)
.43
Using tobacco products (present and past)
123 (49.6)
19 (59.1)
104 (44.0)
.12
Physical activity scored
2.11 ± 0.90
2.02 ± 0.66
2.12 ± 0.92
.65
11,823 ± 4540
7258 ± 2415
12,277 ± 1059
.001
Physical activity energy expenditure (kJ)e
4893 ± 3763
1744 ± 1092
5179 ± 3740
< .001
Gait speed (m/s)
1.36 ± 0.33
1.24 ± 0.32
1.37 ± 0.32
.08
Handgrip strength (kg)
20.4 ± 6.7
15.8 ± 7.0
20.9 ± 6.5
.001
Weight (kg)
70.0 ± 18.9
42.4 ± 5.8
72.8 ± 17.5
< .001
Height (cm)
156.2 ± 6.7
155.5 ± 7.0
156.3 ± 6.7
.55
Total energy expenditure (kJ)e
28.8 ± 7.6
17.4 ± 2.2
29.8 ± 6.8
< .001
Body fat (%)
39.6 ± 8.7
30.4 ± 5.5
40.7 ± 6.8
< .001
Lean body mass (kg)
38.9 ± 7.5
28.8 ± 3.3
40.4 ± 6.8
< .001
Appendicular skeletal mass/height squared
6.77 ± 1.4
4.80 ± 0.4
7.00 ± 1.2
< .001
Dietary energy intake (kJ)
10,604 ± 3718
11,248 ± 3508
10,537 ± 3759
.30
Animal protein intake (g)
42.3 ± 26.1
47.5 ± 33.0
41.8 ± 25.1
.36
Body mass index
(kg/m2)
Note. Data are given as mean ± SD or n (%). aNumber
of participants varies for the different measurements due to some missing data.
b Sarcopenic cP
according to cutpoint for black South African women: appendicular skeletal muscle mass index < 4.94 kg/m2.
value for the difference between sarcopenic and nonsarcopenic women, Mann-Whitney U test for continuous variables, c2 test for categorical variables.
d Physical e Energy
activity measured by modified Baecke questionnaire, maximum score = 20.
expenditure measured by accelerometry/heart rate monitoring (ActiHeart).
nonsarcopenic women according to the South African cutpoint.7 Sarcopenic participants had significantly lower ASM index (P < .001) and handgrip strength (P = .001) and tended to have lower gait speed (P = .08) than nonsarcopenic women. Other significant differences between the 2 groups included lower values of all anthropometric variables, except height in the sarcopenic women, with no significant differences in prevalence of HIV infection and car ownership. Two out of 22 sarcopenic women (9.1%) were younger than 50 years, whereas 14.4% of nonsarcopenic women were older than 70 years. Table 2 shows correlations of ASM index, gait speed, and grip strength with the independent variables. Physical activity scores, gait speed, handgrip strength, and PAEE showed significant negative correlations with age (P = .02 to < .001). Physical activity scores showed no correlation with PAEE or gait speed, but PAEE correlated positively with gait speed (r = 0.21, P = .002), indicating that the objective physical activity measurement may be a more sensitive indicator of habitual physical activity than self-reported physical activity. We subsequently adjusted for age, but the direction and significance of the correlations in Table 2 did not change.
The results of the regression analyses are shown in Tables 3 and 4. In the total group, age was not significantly associated with ASM index. Only PAEE (by ActiHeart) was significantly associated with ASM index in the final model and explained 11.2% of the variance in ASM index, together with tobacco use, HIV status, car ownership, and educational status (nonsignificant associations). When participants who were HIV positive were excluded, PAEE remained positively associated with ASM index, whereas age and smoking were negatively but not significantly associated with ASM index (results not shown). Lean mass was positively associated with handgrip strength, whereas age was negatively associated with both grip strength and gait speed. PAEE was positively associated with gait speed and together with age and self-reported osteoarthritis explained 25.2% of the variation in gait speed.
Discussion This is the first study to our knowledge to determine lifestyle factors associated with sarcopenia in black women in Africa. Physical activity measured by an objective method was positively associated
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Activity Energy Expenditure and Sarcopenia 299
Table 2 Correlations (r) Between Age, Muscle Strength, Dietary Intakes, and Physical Activity With Body Composition Measured by Dual Energy X-Ray Absorptiometry Age
Fat percentage
Lean mass
ASM index
—
r = 0.05; P = .40
r = –0.18; P = .006
r = –0.09; P = .15
Gait speed
r = –0.52; P < .001
r = –0.03; P = .66
r = 0.14; P = .03
r = 0.10; P = .14
Grip strength
r = –0.23 ;P = .004
r = 0.22; P < .001
r = 0.36; P < .001
r = 0.31; P < .001
Physical activity energy expenditure
r = –0.16; P = .02
r = 0.30; P < .001
r = 0.32; P < .001
r = 0.49; P < .001
Physical activity score
r = –0.22; P < .001
r = –0.09; P = .17
r = 0.08; P = .19
r = 0.03; P = .57
Dietary energy intake
r = –0.12; P = .02
r = 0.01; P = .96
r = 0.02; P = .74
r = 0.01; P = .83
Animal protein intake
r = –0.03; P = .55
r= –0.06; P = .37
r = 0.02; P = .77
r = 0.04; P = .58
Age
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Abbreviations: ASM, appendicular skeletal mass.
Table 3 Multiple Regression of Sociodemographic, Dietary, and Physical Activity Parameters with Grip Strength, Gait Speed, and Appendicular Skeletal Mass Index as the Dependent Variables (n = 247), Full Model ASM Index
Grip Strength
Gait Speed
Standardized
Model 1 (full model)
Standardized
Independent Variables
𝛃 coefficient
P value
Constant
7.49
Age (y)
–0.05
Physical activity energy expenditure (kJ)
Standardized
𝛃 coefficient
P value
𝛃 coefficient
P value
.001
12.9
.49
–0.13
.04
2.3
< .001
.09
–0.45
< .001
0.27
.001
0.07
.45
0.15
.04
Tobacco use (0 = no/1 = yes)
–0.10
HIV status (0 = negative/1 = positive)
–0.11
.14
0.09
.22
–0.06
.38
.14
–0.005
.94
–0.05
.42
Energy intake (kJ)
–0.03
.81
–0.10
.29
–0.11
.23
Animal protein intake (g)
0.00
.97
0.07
.45
0.08
.59
Educational status (1–4)
0.08
.23
0.01
.55
0.03
.59
Car ownership (1 = yes/2 = no)
–0.11
.10
–0.02
.78
–0.02
.96
Self-reported osteoarthritis (1 = yes/2 = no)
0.07
.29
0.08
.23
0.06
.37
Lean body mass (kg)
—
—
0.22
.01
0.01
.86
%Body fat (%)
—
—
0.15
.08
–0.07
Adjusted
R2
= 0.103, P = .001
with ASM index. This finding indicates that physical activity may be an important protective factor against sarcopenia among these women. This is also the first study to report ASM index and physical activity, both measured using objective methods in black women in Africa. According to the questionnaire data, the women were generally inactive and only 6.8% reported sport participation. The Actiheart measurements showed that a small proportion of the day on average was spent in moderate-to-vigorous activity or vigorous activity. Less than one-third of women (29.8%) had an activity level close to the World Health Organization guideline for older adults of at least 75 minutes of vigorous-intensity physical activity per week.27 Earlier studies in the North West province of South Africa reported low physical activity among adult women and few opportunities for leisure time and sport participation,22 although higher physical activity was recorded using pedometry among rural South African women in the Limpopo province.28 The proportion of time spent in sedentary and moderate-to-vigorous activity in the current
Adjusted
R2
= 0.19, P = .001
Adjusted
.39 R2
= 0.228, P < .001
study was similar to those reported for rural Kenyan women, where ActiHeart measurements were also performed.29 Physical inactivity contributes to sarcopenia in a vicious cycle, causing the elderly to become weaker and less able to participate in daily activities. Physical inactivity reflects disuse atrophy, with advanced sarcopenia and associated impaired physical performance as an endpoint.30–33 Sarcopenia in our study participants was associated with significantly lower handgrip strength and a trend of lower gait speed than in nonsarcopenic women. Age-dependent functional changes such as muscle denervation or reduced blood flow also have an effect on these functional outcomes.30 Strength may not be linearly related to muscle mass, but may deteriorate more rapidly with aging than measurable changes in muscle mass.31 In the current study, 8.9% of women were sarcopenic according to a country- and ethnicity-specific cutpoint.7 Only a small number of large representative studies describing the prevalence of sarcopenia in populations are available, making comparison across studies
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Table 4 Multiple Regression of Sociodemographic, Dietary, and Physical Activity Parameters with Grip Strength, Gait Speed, and Appendicular Skeletal Mass Index as the Dependent Variables (n = 247), Final Model ASM Index Final model with highest adjusted
R2
Independent variables
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Age (y)
Gait Speed Standardized
Standardized 𝛃 coefficient
Grip Strength Standardized
P value
𝛃 coefficient
P value
𝛃 coefficient
P value
—
—
–0.50
< .001
–0.11
.04
Physical activity energy expenditure (kJ)
0.27
< .001
0.17
.01
—
—
Tobacco use (0 = no/1 = yes)
–0.10
.22
—
—
0.09
.22
HIV status (0 = negative/1 = positive)
–0.16
.16
—
—
—
—
Car ownership (1 = yes/2 = no)
–0.10
.08
—
—
—
—
Educational status (1–5)
0.08
.25
—
—
—
—
Lean mass (kg)
—
—
—
—
0.21
.01
Body fat (%)
—
—
—
—
0.16
.06
—
—
–0.06
.27
—
—
Self-reported osteoarthritis (1 = yes/2 = no)
Adjusted R2 = 0.112,
Adjusted R2 = 0.252,
Adjusted R2 = 0.258,
P < .001, R2 change = 0.009
P < .001, R2 change = 0.06
P < .001, R2 change = 0.03
impossible. Furthermore, age range and definition of sarcopenia vary among studies.33–35 However, our results indicate that African populations are also vulnerable to sarcopenia. Other factors considered to be associated with low ASM index in our study included age, HIV status, diet, smoking, osteoarthritis, and socioeconomic variables. The estimated overall HIV prevalence in South Africa is approximately 10%.36 Almost 11% of the women in our study were HIV positive, but HIV infection was not significantly associated with ASM index in this group. This may be due to the fact that the participants who were HIV positive received treatment, were not acutely ill, and were able to live independently. There was a significant negative correlation between age and lean body mass, but age was not significantly associated with ASM index in regression models, suggesting that dual energy x-ray absorptiometry measurement of muscle mass may not be sufficiently sensitive to detect age-related muscle composition changes, such as selective atrophy of type II fibers.31 Interestingly, 9.1% of the sarcopenic women were younger than 50 years old. The prevalence of HIV infection was higher in the younger study participants, which could have played a role, but it was also evident that an important proportion of the women older than 70 years could remain free from sarcopenia. Apparently the variation in physical activity had a stronger effect on ASM index than age, because PAEE was significantly associated with ASM index, as well as gait speed in all regression models. Older women with higher physical activity levels had a greater ASM index than younger inactive women. This is an indication of a protective effect of higher levels of physical activity on muscle mass of the legs and arms, even at an advanced age.31 The findings of a significant negative correlation between age and gait speed and grip strength, respectively, confirm the results of a study of black and white women in the United States, aged 34 to 58 years, reporting that loss of muscle mass and compromised physical functioning started in the middle-age group.31 The primary functional consequence of sarcopenia is the loss of muscle strength, which may eventually lead to mobility dysfunction with adverse effects on quality of life.30 Although lean mass correlated positively with gait speed and grip strength, no correlation between percentage body fat and gait speed was found in our study. This partly confirms the findings
in a study of independently living older women with a positive association between muscle strength and lean body mass, but is in contrast to their finding of higher fat mass being associated with a lower physical performance.32 The impact of body fat on physical performance could still increase over years and may result in a significant impact after the age of 75 years, as reported previously.33 A study in the United States suggests that obese elderly women may be protected from sarcopenia regardless of their physical activity level. This could be partly due to the trophic effects of increased weight bearing on muscle.31 Mean animal protein intakes of sarcopenic and nonsarcopenic women were not significantly different. It is possible that the dietary assessment method of self-report was not sensitive enough to discriminate between low and higher protein intakes.19 Regression models showed that car ownership tended to be positively related to ASM index (P = .08–.10). In another South African study, car ownership was associated with low physical activity.28 In our study, car ownership appears to be an indicator of socioeconomic status and not necessarily related to physical activity level. It is thus possible that car ownership in this study reflects differences in socioeconomic status with effects on quality of diet and medical care over time, which could have had beneficial effects on the preservation of muscle mass. Limitations of this study include the cross-sectional design. Sarcopenia develops over time and predictors of low ASM index should ideally be investigated in prospective studies to establish causal roles for the multiple factors associated with the loss of muscle mass and strength with age. The contribution of diet to low ASM index could probably not be assessed with precision in this population where dietary intake assessment is problematic.19 Although the sample size may be adequate according to power calculations, the results may still not be generalizable to the South African black population due to ethnic and socioeconomic differences between groups.
Conclusions This study shows that black women are not protected against sarcopenia and that environmental factors are associated with age-related
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Activity Energy Expenditure and Sarcopenia 301
loss of muscle mass and function. Most of the variability in ASM index in this group was explained by differences in PAEE, whereas age explained a larger proportion of the variability in physical performance.
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Acknowledgments The authors acknowledge the contribution of Dr I.M. Kruger and Professor A. Kruger to the overall study coordination of the PURE North-West South Africa study and participant recruitment. They also thank the PURENWP-SA research team, field workers and office staff in the Africa Unit for Trans-disciplinary Health Research (AUTHeR), North-West University, Potchefstroom, South Africa. The authors acknowledge the contribution of PURE International: Dr S. Yusuf and the PURE project office staff at the Population Health Research Institute (PHRI), Hamilton Health Sciences and McMaster University, Ontario, Canada. This work was supported by a grant (nr 01.67801.2.RM77005) from the South African Medical Research Council.
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