From the Geriatric Epidemiology Section, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, National Institutes of Health, Bethesda, MD.
Received for publication December 19, 2001; accepted for publication April 2, 2002.
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ABSTRACT |
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aging; body composition; cachexia; disability evaluation; methods; obesity
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INTRODUCTION |
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From these changes have come important hypotheses regarding the contribution of each of these components of body composition to health. Loss of bone over a lifetime is recognized as a major factor contributing to risk of hip fracture in both men and women (4). The increase in body fat is associated with predisposition to the metabolic syndrome that includes higher insulin and fasting glucose, hypertension, dyslipidemias affecting high density lipoprotein cholesterol and triglycerides, and an increased risk of proinflammatory factors such as C-reactive protein (5). Less well-established is the hypothesis that the loss of lean mass with age, termed sarcopenia, may contribute to increased disability and frailty, particularly in older women, who have less lean mass than do men at all ages (6).
The evidence supporting these hypotheses has grown from applications of new technologies to assess body composition. Anthropometric measurements for weight, height, and fat distribution have been incorporated in many epidemiologic studies but cannot directly differentiate lean and fat. Bone-specific measures using dual energy x-ray absorptiometry and calcaneal ultrasound have predicted fracture and have shown the power of measuring body components directly (4, 7, 8). Data on body composition allow a more direct assessment of the contribution to health of fat and lean. Each measure of body composition (bioelectric impedance, dual energy x-ray absorptiometry, computerized tomography, and magnetic resonance imaging) has advantages and disadvantages (9). There is a paucity of longitudinal data relating measures of fat and lean to health outcomes.
It is well-established that the nitrogen component of body mass is critical to life (10). In diseases of wasting, loss of lean mass has been associated with poorer functional health outcomes, although it is unclear whether this is a threshold or continuous effect (11, 12). It is also unknown whether the findings from studies of diseases of wasting can be generalized to the slower loss of muscle with age. Cross-sectional studies of the association of lean mass with function in old age have provided mixed results. One study, in which sarcopenia was defined as a absolute level of lean mass, showed a significant relation with poorer functional status (13). Two other studies have, however, shown that fat mass, rather than lean mass, was associated with functional disability (14, 15). The new data by Sternfeld et al. (16) lend further support to the view that fat mass, rather than lean mass, is the important body composition determinant of poorer functional status in old age.
Sternfeld et al. used single-frequency bioelectric impedance to assess body composition. Bioelectric impedance is easy to obtain and has been collected as part of the National Health and Nutrition Examination Surveys (17). While bioelectric impedance allows a better measurement of body composition than weight, it is limited by assumptions of water distribution and requires population-specific equations for analysis (17). In the paper by Sternfeld et al. (16), a population-specific equation was derived to estimate fat mass and nonfat lean mass (both muscle and bone). One potential problem is the selection of the subgroup for the development of the equation in this study. The cohort was sampled equally at five intervals over the distribution of body weight for those above and those below the median age of the sample. This technique may have weighted the tails of the distribution so that the equation may not adequately represent the body composition distribution in the population.
The challenges associated with appropriate use of body composition measures extend to analysis as well. Taller people have bigger bodies with more muscle and more fat, as do heavier people, emphasizing the need to control for body size to interpret the results of body composition associations. Sternfeld et al. (16) also show that components of body composition are collinear. There is as yet no widely accepted method for analysis of these data. Sternfeld et al. chose to use a model from the nutritional epidemiology field, creating residualized values to remove the intercorrelation of variables. Even in the field of nutritional epidemiology, this is still a somewhat controversial method (18); it is further complicated here by the use of multiple factors to create the residuals, making the interpretation more difficult, especially where multiple residual variables are used in a multivariate model. The alternative presented in the paper, the use of a ratio of lean to fat, is easier to understand, but issues have been raised about the modeling of ratios in regression models (19). Whether the model with the ratios differs substantially from a model using simple terms of lean and fat is unclear. Another alternative from the nutrition literature is to consider a substitution model, used for the analysis of constitutive factors in the diet, such as well-done meats (20). However, this requires more-detailed information on body composition than is obtained from bioelectric impedance.
The analysis of the data is further complicated by the fact that the bioelectrical impedance data do not allow assessment of the relative contribution of appendicular lean, which is mostly bone and muscle, versus trunk lean, which includes organ protein. Grip strength might be best assessed relative to arm muscle mass. Among the functional outcomes in this paper, only grip strength shows a direct relation with lean mass. In the multivariate modeling, this is the most complex set of models, showing interactions with weight in women and with age in men. It is unclear that arm muscle declines with age in a manner similar to leg lean mass. In studies of long-term bed rest, muscle mass was lost primarily from the legs with relatively little change in the arms (21). Therefore, having models in which arm mass and strength could be directly compared might clarify the associations seen in this paper.
Reported function is a staple of geriatric assessment and predicts progression of disability and mortality. Function has enormous societal implications for health care costs and personal implications in terms of quality of life. It is considered by many to be a more important outcome in old age than risk of death (22). In this younger and higher-functioning population, there is little disability affecting activities of daily living essential for independence, so the authors focus on measures of function associated with its early decline. However, they group upper-extremity function tasks (lifting or carrying) with lower-extremity function (walking, stooping, crouching, or kneeling) and also group simple tasks (lifting and carrying) with those that are more complex and could be carried out either with or without aids (for instance, getting up from a stooping, crouching, or kneeling position). This decision tends to obscure the relation to body composition. For instance, did reported walking function have body composition relations similar to those of the 60-second walk? The decision to group functional tasks is also potentially problematic because specific diseases differentially affect function and body composition. As an example, stroke affecting only the upper-extremity muscle mass may still affect balance and therefore impair walking and stooping.
Performance measures have been suggested to address problems with self-report of function, and two such measures, grip strength and a 60-second walk, were used in this paper. However, performance measures have their own limitations. All performance measurements are volitional, and this may confound the results (23). For example, a depressed person is unlikely to give a maximal effort. All measures are affected by cognitive function, and it is difficult to obtain maximal effort if the subject has difficulty comprehending instructions. Further, it is difficult to choose performance measures that will provide a distribution for both active and frail participants (24). The measures chosen for this study, the 60-second walk and grip strength, are particularly good because they do not generally have floor or ceiling effects. This is not as true for measures such as a one-leg stand, which would be difficult for the very frail, or standing from an armless chair, which is easy for the very active.
As the authors report, the results of this study have the limitation of being from a relatively affluent, largely Caucasian, young-old population that is high functioning. There are relatively few smokers in the cohort, and those who participated were likely to be better educated than those who did not. The univariate data in this study show that higher fat mass and waist circumference are associated with poorer function, as is higher lean mass, which is surprising. However, when lean mass was controlled for fat mass size, the relation of lean with poorer function became clear, suggesting that for their size, heavier people had relatively low lean mass. This disproportionate low lean mass, termed "sarcopenic obesity" was present in this cohort. Few people in the cohort would be considered frail, and the contribution of lean mass to function may be greater among the frail, who have probably lost more mass secondary to aging and disuse. Therefore, in those who are relatively high functioning, the relation of body fat to function may be most important, but in the frail, the level of lean mass may be the determining factor. This would be consistent with studies in which there is a subgroup of frail persons and the relation between poor function and weight has been shown to be bimodal, including both very thin and very heavy persons (25). Whether this bimodal association reflects the true contribution of thinness to poor function or is an artifact of weight loss associated with diseases causing increased disability awaits other studies (26).
This study, using body composition data to examine functional associations, is an advance in the effort to understand the health effects of weight in old age. In the future, we anticipate more data with detailed body composition results providing the opportunity to examine these issues longitudinally. In addition, it is hoped that the advent of body composition measures will allow a reassessment of the complex issues related to specific diseases and risk of death as well.
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NOTES |
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REFERENCES |
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