1 Department of Community Health and Epidemiology, Queens University, Kingston, Ontario, Canada.
2 Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.
3 Division of Epidemiology and Preventive Medicine, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM.
4 School of Physical and Health Education, Queens University, Kingston, Ontario, Canada.
Received for publication May 6, 2003; accepted for publication September 3, 2003.
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ABSTRACT |
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activities of daily living; aging; disability evaluation; men; muscle, skeletal; risk; women
Abbreviations: Abbreviations: CI, confidence interval; Lneg, likelihood ratio for negative result; Lpos, likelihood ratio for positive result; NHANES III, Third National Health and Nutrition Examination Survey; SMI, skeletal muscle index.
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INTRODUCTION |
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To date, three epidemiologic studies have shown a relation among sarcopenia, functional impairment, and physical disability (810). Without exception, these studies used an arbitrary cutpoint to determine those subjects with or without sarcopenia. Baumgartner et al. (8) and Melton et al. (9) defined sarcopenia in older adults as a height-adjusted appendicular muscle mass of 2 or more standard deviations below the mean of young adults. Janssen et al. (10) used a similar approach but used muscle mass relative to body weight. Although these three studies demonstrated that sarcopenia is associated with physical disability in older persons, they did not systematically examine the relation between muscle mass and physical disability. Thus, the specific skeletal muscle cutpoint below which physical disability increases is unknown.
Until recently, the size or mass of skeletal muscle could be determined only in small-scale laboratory studies. However, equations for predicting whole-body muscle mass using bioelectrical impedance analysis (11) and anthropometry (12) have recently been developed. With the exception of the anthropometric technique in obese subjects, these methods provide simple, inexpensive, and reliable estimates of whole-body muscle mass in adults (11, 12) that are appropriate for use in large-scale epidemiologic and laboratory-based studies.
The increasing older population, the availability of simple tools for measuring muscle mass (11, 12), and the developing interest of the scientific and medical communities in determining the impact of sarcopenia on morbidity dictate the need to establish the cutpoint at which sarcopenia becomes a significant health problem. Therefore, the main objective of our study was to determine skeletal muscle cutpoints for identifying elevated physical disability risk in older adults.
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MATERIALS AND METHODS |
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Study population
NHANES III was conducted by the National Center for Health Statistics to estimate the prevalence of major diseases, nutritional disorders, and risk factors for these diseases. NHANES III was a nationally representative, two-phase, 6-year cross-sectional survey conducted from 1988 through 1994. The complex sampling plan used a stratified, multistage, probability cluster design. The total sample included 33,199 subjects. Full details of the study design, recruitment, and procedures are available elsewhere (16). The full evaluation included a home interview and a physical examination in a mobile center.
Of the total sample, 4,502 subjects were aged 60 years or more and of non-Hispanic White, non-Hispanic Black, and Mexican-American ethnicity. Bioelectrical impedance analysis measures, height and weight (which were needed to compute muscle mass), and physical disability measures were acquired. Other races, in whom the bioelectrical impedance analysis-muscle method has not been validated, were excluded from the data analysis. Informed consent was obtained from all participants, and the protocol was approved by the National Center for Health Statistics.
Physical disability
Physical disability was defined as having difficulty performing activities of daily living using the following two questions: 1) "Because of any impairment or health problem, do you need the help of other persons with personal care needs, such as eating, bathing, dressing, or getting around the home?" and 2) "Because of any impairment or health problem, do you need the help of other persons in handling routine needs, such as everyday household chores, doing necessary business, shopping, or getting around for other purposes?" (16). Subjects were classified as physically disabled if they answered "yes" to one or both of these questions and nondisabled if they answered "no" to both questions. These physical disability questions were selected from the classic works of Rosow and Breslau (13), Lawton and Brody (14), and Katz et al. (15). Physical disability should not to be confused with functional impairment, which is defined as having limitations in mobility performance (e.g, walking 0.25 mile (0.402 km), climbing 10 stairs, lifting/carrying 10 pounds (4.54 kg), standing from a chair). In the framework of the Nagi model of the disablement process, functional impairment precedes physical disability (17, 18).
Body composition
Body weight and height were measured to the nearest 0.1 kg and 0.1 cm using standardized equipment and procedures (19). Bioelectrical impedance analysis resistance (ohms, ) was obtained using a Valhalla 1990B Bio-Resistance Body Composition Analyzer (Valhalla Medical, San Diego, California) with an operating frequency of 50 kHz at 800 µA. Whole-body bioelectrical impedance analysis measurements were taken between the right wrist and ankle with the subject in a supine position (20) after the subjects completed a minimum 6-hour fast.
Skeletal muscle mass measurements
Muscle mass was calculated using the bioelectrical impedance analysis equation of Janssen et al. (11): skeletal muscle mass (kg) = [(height2/bioelectrical impedance analysis resistance x 0.401) + (gender x 3.825) + (age x 0.071)] + 5.102, where height is measured in centimeters; bioelectrical impedance analysis resistance is measured in ohms; for gender, men = 1 and women = 0; and age is measured in years. This bioelectrical impedance analysis equation was developed and cross-validated against magnetic resonance imaging measures of whole-body muscle mass in a sample of 269 men and women who varied widely in age (1886 years) and adiposity (body mass index, 1648 kg/m2). In that study, the correlation between bioelectrical impedance analysis-predicted and magnetic resonance imaging-measured muscle mass was 0.93 with a standard error of the estimate of 9 percent (11). Absolute muscle mass (kg) was normalized for height (muscle mass (kg)/height (m)2) and termed the skeletal muscle index (SMI).
Covariates for multivariate odds ratio analysis
Age and race
Age was included in the multivariate analysis as a continuous variable. Race was coded as 0 for non-Hispanic Whites, 1 for non-Hispanic Blacks, and 2 for Hispanics.
Health behaviors
Alcohol consumption was graded as being none (0 drinks/month), moderate (115 drinks/month), or heavy (>15 drinks/month). Subjects were considered current smokers if they smoked cigarettes, cigars, or pipe tobacco at the time of the interview; previous smokers if they smoked 100 cigarettes, 20 cigars, or 20 pipes of tobacco in their lifetime; and nonsmokers if they smoked less than these amounts.
Comorbidity
The chronic illnesses included in the present study were coronary heart disease (myocardial infarction, congestive heart failure), stroke, cancer, lung disease (chronic bronchitis, emphysema), diabetes mellitus other than gestational diabetes, and arthritis (rheumatoid and osteoarthritis). These conditions were considered present for those who had ever been told by a physician that they had the condition.
Body fat
Body fat is related to physical disability independently of lean body mass or muscle mass (21, 22). Fat mass is also correlated to muscle mass (23, 24). Thus, to determine the independent effect of SMI on physical disability, it was important to control for fat mass in our analyses. Lean body mass was calculated using the gender-specific bioelectrical impedance analysis formulas of Sun et al. (25), which were developed for use in epidemiologic studies. Fat mass was subsequently determined by subtracting lean body mass from body weight. Fat mass was normalized for height (kg/m2) and included in the multivariate analysis as a continuous variable.
Statistical analysis
The Intercooled Stata 7 program (Stata Corporation, College Station, Texas) was used to properly weight the sample and to take into account the complex sampling strategy of the NHANES III design. The purpose of weighting the sample was to produce statistical estimates that would have been obtained if the entire US population had been sampled.
Receiver operating characteristics analysis was used to develop skeletal muscle cutpoints associated with physical disability. For each gender, the relative frequencies of subjects with and without physical disability were determined at SMI intervals of 0.25 kg/m2. These relative frequencies represent sensitivity (true positives) and specificity (true negatives) values. In the next step, the likelihood ratios for positive [sensitivity/(1 specificity)] and negative [(1 sensitivity)/specificity] results were calculated at the 0.25-kg/m2 intervals. The goal of this analysis is to find a cutpoint that maximizes the likelihood ratio for positive results (Lpos) while minimizing the likelihood ratio for negative results (Lneg) (26). When no single cutpoint has both a high Lpos and a low Lneg value, as was the case for our analysis, two cutpoints can be selectedone with a relatively high Lpos and one with a relatively low Lneg (26). We selected the two cutpoints by looking for large changes in the Lpos and Lneg values when moving from one SMI interval to the next and by visually examining the relation between SMI and physical disability (figure 1). The selection of two cutpoints allowed us to classify our subjects into one of three categories: 1) high risk = subjects with SMI values below the Lpos cutpoint, 2) moderately increased risk = subjects with SMI values between the Lpos and Lneg cutpoints, and 3) low risk = subjects with SMI values above the Lneg cutpoint. For simplicity we have referred to the Lpos and Lneg cutpoints as the high-risk and moderately increased risk cutpoints, respectively, in the Results and Discussion sections.
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RESULTS |
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DISCUSSION |
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Three previous studies have shown a relation among sarcopenia, functional impairment, and physical disability (810). Baumgartner et al. (8) reported that sarcopenia is independently associated with physical disability in 808 older men and women. Melton et al. (9) reported that sarcopenia is associated with having difficulty walking in 345 older men. Using the NHANES III data set, Janssen et al. (10) reported that the likelihood of functional impairment and physical disability is approximately twofold greater in older men and threefold greater in older women with severe sarcopenia by comparison with older men and women with a normal muscle mass, respectively. Without exception, these studies used an arbitrary cutpoint to determine subjects with sarcopenia. Specifically, Baumgartner et al. (8) and Melton et al. (9) defined sarcopenia as height-adjusted appendicular (arm + leg) muscle mass of 2 or more standard deviations below the mean of young adults. Janssen et al. (10) used a similar approach but used whole-body muscle mass relative to body weight.
The cutoff values derived in the present study are for whole-body muscle mass, which can be estimated using a variety of widely available techniques including magnetic resonance imaging (1, 27, 28), total-body potassium counting (29), bioelectrical impedance analysis (11), and anthropometry (12, 30). Dual-energy x-ray absorptiometry-measured appendicular muscle can also predict whole-body muscle mass [whole-body muscle = (1.17 x appendicular muscle) 1.01] and explains 96 percent of the between-subject variation in whole-body muscle (31). It is important to note that the skeletal muscle cutpoints determined in this study are similar to the arbitrary cutpoints determined in previous studies (8, 9, 32). For example, using the 2 standard deviations below the young adult mean, Baumgartner et al. (8) used cutoff values of 8.1 kg/m2 in men and 5.9 kg/m2 in women to define sarcopenia (note: these values were converted from appendicular to whole-body muscle using a published algorithm (31)). These values are similar to the high-risk sarcopenia cutpoints calculated in the present study of 8.50 kg/m2 in men and 5.75 kg/m2 in women. However, the sarcopenia cutpoints calculated in the present study predict physical disability to a better degree than do the 2 standard deviation values. Using the NHANES III data, we determined that the odds ratios for physical disability in older men and women with SMI values 2 standard deviations or more below the young adult mean (<8.48 kg/m2 in men, <6.06 kg/m2 in women) were 1.61 and 1.66, respectively, by comparison with older men and women having SMI values above the 2 standard deviation cutpoint (data not shown). These odds ratios are considerably smaller than those for the older adults whose SMI values fell within the high-risk categories determined from receiver operating characteristics analysis in the current study.
It is important to note that our cutpoints were for physical disability (difficulty performing activities of daily living). It is possible that the SMI cutpoints would have been different had we assessed the relation between SMI and functional impairment (limitations in mobility such as walking). It is also possible that other characteristics such as age and chronic disease status could substantially alter the association between SMI and physical disability. Future studies are needed to determine whether or not different sarcopenia cutpoints are required for different population subgroups.
We were unable to identify a single cutpoint that was associated with a high likelihood of correctly identifying subjects both with and without physical disability. For both men and women, two cutpoints were identified, one with a large likelihood ratio for positive results (Lpos) and one with a low likelihood ratio for negative results (Lneg). This allowed classification of subjects with a high likelihood of physical disability (values below Lpos), subjects with a moderately increased likelihood of physical disability (values between Lpos and Lneg), and subjects with a low likelihood of physical disability (values above Lneg). For example, in men SMI values of 8.50 kg/m2 were associated with a relatively high frequency of true positives, and SMI values of
10.75 kg/m2 were associated with a relatively low frequency of true negatives. Subsequent analysis revealed that men with SMI values of
8.50 kg/m2 (high risk) were about seven times as likely to have physical disability by comparison with men having SMI values of
10.76 kg/m2 (low risk), whereas men with SMI values between 8.51 and 10.75 kg/m2 (moderately increased risk) were only 3.5 times as likely to have physical disability by comparison with men having SMI values of
10.76 kg/m2.
In the women, we observed a "J"-shaped relation between SMI and physical disability (figure 1). The incidence of physical disability was increased in women with both low and very high SMI values. The increased physical disability risk in women with very high SMI values may have in part reflected the increased fat mass and obesity in these subjects. Fat mass is an independent predictor of physical disability (21, 22), and fat mass was considerably higher (39.2 kg vs. 28.1 kg) in women with very high SMI values (9.00 kg/m2) than in women with moderately high SMI values (6.758.99 kg/m2). The women with very high SMI values were also quite obese, while the women with moderately high SMI values were only moderately overweight (body mass index of 37.5 vs. 28.4 kg/m2). Previous studies have shown that there is not increased risk or likelihood for functional limitations in older women until a high level of obesity (body mass index of
35.0 kg/m2) is reached (33, 34). In addition to fat mass and obesity level, other undetermined factors may have played a role in elevating the likelihood of physical disability in the women with very high SMI values.
In this study, the SMI cutpoints for predicting physical disability were considerably higher in men than in women. The reason for this gender difference is unclear. Sarcopenia, as determined from the SMI cutpoints, was also a stronger predictor of physical disability in men than in women. For example, the adjusted odds ratios for disability for the men and women in the high-risk SMI categories were 4.71 and 3.31, respectively. This observation is consistent with that of Visser et al. (35), who report that mid-thigh muscle size is more strongly associated with lower extremity performance in older men than women in the Health, Aging, and Body Composition Study. These authors also report that fat mass is a better predictor of lower extremity performance in older women than men (35), which is also consistent with our findings as very high SMI values, which were associated with a high fat mass, were associated with increased physical disability in women but not men (figure 1). The implication of these observations is that interventions aimed at improving function and decreasing physical disability through changes in body composition may need to have a different emphasis in older men and women.
Based on the findings reported here, we conclude that approximately 10 percent of the older American population is considerably more likely to have physical disability and that approximately 35 percent of the older American population is somewhat more likely to have physical disability in relation to a low SMI. These numbers confirm that low muscle mass has an impact on the health and well-being of a considerable number of older Americans. Given the health-care costs associated with physical disability (36, 37), these findings also suggest that sarcopenia imposes a significant economic burden on the US health-care system.
The NHANES III subjects were a representative sample of the noninstitutionalized US population. Therefore, our results can be applied to most Americans aged 60 years or above. However, because NHANES III was conducted among the noninstitutionalized population and because the NHANES III participants who were physically unable to make it to the mobile examination center were not included in our analysis (bioelectrical impedance analysis measures were not obtained in these subjects), the prevalences of sarcopenia and physical disability in the entire elderly population is likely higher than what is reported here. Further, it is possible that the relation between muscle mass and physical disability in institutionalized subjects is different from that reported here.
Our study has other limitations that warrant recognition. First, the cross-sectional nature of this study precludes definitive causal inferences about the relation between sarcopenia and physical disability. Although no longitudinal studies report that sarcopenia causes physical disability, muscular strength, which is in large measure determined by muscle mass in older adults (38), is predictive of physical disability in longitudinal studies (7, 39). Second, many of the variables examined in NHANES III, including the physical disability measures, were based on self-report, and the reliability of self-reported physical function in older persons is only about 85 percent (40). Finally, we used bioelectrical impedance analysis to estimate muscle mass. Previous studies have noted inaccuracies when using bioelectrical impedance analysis to assess lean body mass in the elderly, which may in part be caused by changes in the hydration of lean mass and the cylindrical shape of the appendicular muscles (41, 42). However, the skeletal muscle bioelectrical impedance analysis equation used in the current study was developed in a heterogeneous sample (11) that varied widely in age (1886 years) and muscle mass. Further, in that sample (11), age explained only an additional 12 percent of the variance in muscle mass that was not already explained by body mass index measures. Another limitation of the bioelectrical impedance analysis method is that the standard error of the estimate for predicting muscle mass in both genders is 9 percent (11). Thus, because imprecision biases the results toward the null hypothesis, we likely underestimated the true odds ratios for sarcopenia. Further, the magnitude of the bioelectrical impedance analysis measurement error suggests that it may not be precise enough for the clinical setting. The feasibility of using bioelectrical impedance analysis or other techniques to estimate SMI in the clinical setting needs to be addressed in future studies.
In conclusion, our study presents disability-related sarcopenia cutpoints for older men and women. Using these cutpoints, we demonstrated that SMI is a strong independent predictor of physical disability. Future applications of these cutpoints include the comparison of morbidity and mortality risk in older persons with normal muscle mass with those having sarcopenia, the determination and comparison of sarcopenia prevalences at the population level, and the estimation of health-care costs attributable to sarcopenia. These and other applications should lead to an improved understanding of the public health impact of sarcopenia.
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ACKNOWLEDGMENTS |
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Any opinions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the US Department of Agriculture.
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NOTES |
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REFERENCES |
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