EPILOGUE
Comparison of techniques to estimate total body skeletal muscle mass in people of different age groups

D. N. Proctor1, P. C. O'Brien2, E. J. Atkinson2, and K. S. Nair3

1 Anesthesia and 3 Endocrine Research Units and 2 Biostatistics Department, Mayo Clinic and Foundation, Rochester, Minnesota 55905; and University of Vermont, Burlington, Vermont 05405


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

An estimate of total body muscle mass with dual-energy X-ray absorptiometry (DXA; appendicular muscle mass divided by 0.75) was compared with 24-h urinary creatinine excretion in 59 healthy men and women [20-30 yr (younger), 45-59 yr (middle age), and 60-79 yr (older)] who stayed in a clinical research center for 5 days. Total body water (2H2O dilution), fat (underwater weighing), bone mineral (DXA), and total body protein mass (based on a 4-compartment model) were also measured. Muscle mass estimates by DXA and creatinine were highly correlated (r = 0.80). However, stepwise multiple regression indicated that a significant amount of additional between-subject variability in DXA-based muscle mass estimates could be explained by total body water. Creatinine excretion, knee extensor strength, and total body protein mass all decreased with age, suggesting a decline in muscle cell mass with aging. However, DXA-based muscle mass and measures of nonfat body mass (i.e., lean body mass by 2H2O and fat-free body mass by underwater weighing) did not change with age. These results indicate that DXA and urinary creatinine excretion give different results regarding the decline in total body muscle mass with aging. The factor(s) responsible for the apparent underestimate of age-related sarcopenia by DXA remain to be fully defined, but changes in body water may be an important contributor.

body composition; creatinine excretion; dual-energy X-ray absorptiometry


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

SKELETAL MUSCLE accounts for <50% of body weight but is the largest component of the metabolically active cell mass of the body (8, 22). Quantification of muscle mass is important for cross-sectional studies when normalizing various physiological parameters such as muscle force production, metabolic rate, oxygen uptake, blood flow, and protein turnover. Investigations into the causes of skeletal muscle loss with advancing age (sarcopenia) have stimulated a renewed interest in quantifying this important component of body composition (2, 4, 14). Theoretically, urinary creatinine excretion is one of the most specific indexes of total body skeletal muscle mass because creatine, the precursor of creatinine, originates almost exclusively from skeletal muscle (13). However, creatinine excretion varies from day to day and the subject has to be on a meat-free diet for ~5 days and at least 3 days of urine collection are needed to calculate daily creatinine excretion (13, 14). These factors make it difficult to use urinary creatinine excretion as a measure of skeletal muscle mass. Fat-free body mass (FFM) has commonly been used as a surrogate measure of muscle mass but does not always accurately reflect specific changes in muscle mass or differences in muscle mass between individuals. Consequently, there is a need to critically evaluate and compare the available methods for quantifying skeletal muscle mass in both younger and older humans.

The recent application of dual-energy X-ray absorptiometry (DXA) for assessment of regional soft tissue masses has created an attractive alternative to more expensive (e.g., magnetic resonance imaging) or radioactive [e.g., computerized tomography (CT)] methods of muscle mass estimation (10, 15, 16, 31). Because muscle mass of the limbs accounts for an estimated 75-80% of total body muscle mass (14, 15), appendicular muscle mass by DXA (i.e., bone-free lean tissue) has been endorsed as a simple means of quantifying total body muscle mass. Although correlations between DXA-based estimates of muscle mass and other indexes of muscle mass (e.g., total body K+, multi-scan CT, and in vivo neutron activation) have been promising (15, 31), the accuracy of DXA for predicting muscle mass in people of different age groups and in some pathological conditions has not been established (14, 16, 28). For example, there is a tendency for DXA to produce higher values of muscle mass compared with other methods (i.e., total body K+ and multi-scan CT; Refs. 20, 31). In addition, DXA may be less sensitive to changes in muscle mass, per se, because this method does not differentiate between water and bone-free lean tissue (21, 28). Such errors could be more significant in elderly people due to extracellular fluid accumulation. Collectively, these limitations support the need to assess the utility of DXA-based estimates of muscle mass, particularly in elderly people.

In the present study, we compared the DXA and urinary creatinine excretion methods of assessing total body muscle mass. The major purpose was to determine if these methods detect similar differences in skeletal muscle mass as a function of age, and if not, to identify the factor(s) responsible for such a discrepancy. To examine these questions, we performed a battery of body composition measurements in younger (20-30 yr), middle-aged (45-59 yr), and older (60-79 yr) men and women while they stayed in a clinical research center for 5 days. These measurements were repeated after a 3-mo period in the middle-aged and older subjects, thereby providing an opportunity to compare the test-retest reproducibility among these techniques.


    SUBJECTS AND METHODS
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Subjects and study design. Subjects were 59 healthy male and female volunteers divided into younger (20-30 yr), middle-aged (45-59 yr), and older (60-79 yr) age categories. Volunteers were carefully screened to exclude women on estrogen replacement, subjects taking beta -adrenergic blockers or androgens, and subjects who performed vigorous exercise more than two times per week. All subjects provided informed written consent before the study in accordance with the University of Vermont Institutional Review Board and Mayo Clinic and Foundation guidelines.

The middle-aged and older subjects were participants in a study examining the effects of 3 mo of resistance training on muscle protein metabolism (1). Their data at baseline (pretraining) were used in combination with those of the 12 younger subjects for the present study. Twenty-two of the subjects in the resistance-training study served as nonexercise control subjects and had body composition measurements twice during the 3-mo study period. This enabled us to compare the reproducibility (i.e., long-term precision) among the various body composition techniques. The mean body composition characteristics for each of the subject groups at baseline are given in Table 1. Isometric knee extensor strength levels (Ledo isokinetic dynamometer) are also given in Table 1. All studies were conducted in the General Clinical Research Center (GCRC) at the University of Vermont.

                              
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Table 1.   Group averages for body composition variables and muscle strength

Urinary creatinine excretion. Muscle mass estimates based on urinary creatinine excretion were obtained with the method described by Forbes (8) and Heymsfield et al. (13). Subjects followed a meat-free, weight-maintaining diet (%calories from protein-fat-carbohydrate = 15:35:50) for 2 days as outpatients and for 4 additional days in the GCRC. Twenty- four-hour urine samples were collected on days 4, 5, and 6 to determine average urinary creatinine excretion. Creatinine concentration was measured with a colorimetric method based on the rate-Jaffe reaction (12). Total body muscle mass was computed based on a ratio of 20 kg muscle mass/g creatinine (13).

DXA. Subjects were scanned at a medium speed (25 min) with a DXA whole body scanner (Lunar DPX-L, Madison, WI). Lunar software (version 3.6y) was used to calculate total bone mineral content, total body fat, and regional fat-free soft tissue. Appendicular muscle mass was estimated as kilograms of fat-free soft tissue in the upper plus lower extremities as described by Heymsfield et al. (15). Total body muscle mass was then calculated as appendicular muscle mass divided by 0.75. This assumes that appendicular skeletal muscle accounts for 75% of the total skeletal muscle in the body (15). The DXA scanner was calibrated monthly with a series of beef blocks of known composition (17).

Total body water. Total body water was estimated with deuterium oxide (2H2O) dilution as previously described (11). Isotopic enrichment of 2H2O was measured in an isotope ratio mass spectrometer after zinc reduction. The correction factor used to correct for hydrogen exchange was 1.044 for women and 1.052 for men (11). Estimates of lean body mass by total body water were obtained assuming a hydration constant of 73.2% (8, 24).

Underwater weighing. The criterion estimate of total body fat was obtained by underwater weighing with simultaneous measurement of residual lung volume by helium dilution (30). Fat mass was estimated as body mass minus FFM.

Estimation of total body protein. Total body protein was estimated with a four-component model of body composition as described by Siconolfi et al. (29). Briefly, total body protein mass was derived from the following equation: protein (kg) = FFM (underwater weighing) - total body water mass - total body mineral mass (where total body water mass = body water in liters / density of water at body temperature; and total body mineral mass = bone mineral content / 0.88 / 0.824; Ref. 19).

Statistical analysis. Pearson correlations were calculated to assess the relation between pairs of variables in the entire sample of men and women. The relation between the DXA and creatinine excretion estimates of total body muscle mass was evaluated with forward stepwise multiple regression (SAS, PROC REG). For this analysis, DXA-estimated muscle mass was regressed against age, sex, and selected body composition variables with creatinine excretion included in all models. We also tested for two-way interactions and examined residual plots (observed vs. predicted values) of the final model. The reproducibility of each body composition measurement was assessed by regressing the posttest values against the pretest values for the middle-aged and older subjects and testing for a zero intercept and unit (equal to 1) slope. The 0.05 level of significance was used for all analyses. Statistical computations were performed with SAS version 6.01 software (SAS Institute, Cary, NC).


    RESULTS
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Reproducibility of body composition techniques. Body weight and body composition measurements in the middle-aged and older subjects were generally quite stable across the 3-mo control period. However, the test-retest reproducibility of urinary creatinine excretion was considerably less than for the other body composition methods (Table 2). Seven of the twenty-two subjects had differences in creatinine-based muscle mass that exceeded 5 kg. However, there was no statistically significant evidence that the relationship between the first and second values differed in a systematic way; i.e., the regression lines were not significantly different from the line of identity. In addition, the largest test-retest differences were evenly balanced across age and between genders. The variation noted for total body protein estimates probably reflects the combined impact of smaller errors in its determinants (i.e., body water, fat, and mineral).

                              
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Table 2.   Reproducibility (3-mo interval) of body composition measurement techniques in middle-aged and older subjects

Comparisons between DXA and creatinine excretion estimates of muscle mass. There were strong correlations (all r > 0.80, P < 0.05) between DXA and creatinine excretion-based estimates of total muscle mass in each of the three age groups (Fig. 1). In the younger group, the regression line expressing the relationship between DXA and creatinine excretion estimates of muscle mass did not differ from the line of identity (slope not different from 1 and intercept not different from 0). However, significant departures from the line of identity were observed in the middle-aged and older groups. The estimation of total muscle mass by DXA tended to be higher than creatinine excretion-based measurements, especially in the older group.


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Fig. 1.   Relationship between total body muscle mass by creatinine excretion vs. dual-energy X-ray absorptiometry (DXA) in younger (A), middle-aged (B), and older (C) men () and women (open circle ). Dashed lines represent least squares linear regression.

Table 3 shows the pairwise correlations. There was a strong inverse relationship between creatinine excretion-based muscle mass and chronological age in both men and women (Table 3 and Fig. 2). This was also observed for total body protein. However, no age-associated decline in DXA-estimated muscle mass was evident.

                              
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Table 3.   Pearson correlation coefficients of selected variables in all subjects



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Fig. 2.   Skeletal muscle mass (A, creatinine excretion; B, DXA), total body protein (C), and body water (D) as a function of age in men and women.

Table 4 shows the final regression model describing the relationship between DXA and urinary creatinine excretion. Variables added to the model included chronological age, sex, height, weight, body fat, body density, body water, and bone mineral content (total body protein was not included in the regression model because it is computed from, and thus correlated with, several independent variables in the model, i.e., body water, fat, and mineral). Together, the variables given in Table 4 explained 94% of the variance in DXA- estimated total body muscle mass. No two-way interactions between these main effects entered the model. Chronological age did not enter the multiple-regression model. However, the effects of aging could have been exerted through one or more biological variables (i.e., body water) that were included in the final model.

                              
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Table 4.   Stepwise regression analysis of DXA- estimated muscle mass against creatinine excretion, body water, sex, bone mineral content, and height


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

The major new finding of this study is that DXA and urinary creatinine excretion did not detect similar differences in total body skeletal muscle mass as a function of age. Much of the disparity between these two methods was accounted for by total body water. Other factors accounting for the disparity included gender, height, and bone mineral content. Theoretically, 24-h creatinine excretion represents specifically metabolically active muscle cell mass. The current study suggests that DXA is not as sensitive as urinary creatinine to age-related changes in total body muscle mass.

The relationship between DXA and creatinine excretion estimates of muscle mass. Total muscle mass estimated by DXA was significantly correlated (r = 0.80) with creatinine excretion in the present group of subjects. High correlations have been reported previously between muscle mass estimated by DXA and other indexes of skeletal muscle such as total body potassium and multiple-slice CT (9, 15, 31). However, a high correlation between two measurement methods does not necessarily signify a high level of agreement or predictive accuracy. The high correlations observed between DXA-based estimates of total muscle mass and other muscle or lean tissue mass measurement methods could simply reflect the fact that most (75%) of the skeletal muscle in the body is located in the limbs. To more appropriately evaluate the predictive accuracy of the DXA method, we performed a stepwise multiple-regression analysis (Table 4). We found that a substantial amount of additional variability (~30%) in the DXA-creatinine relationship could be explained by including additional variables, most notably total body water. This suggests that DXA muscle mass, by itself, is not a strong predictor of muscle mass as estimated by creatinine excretion.

The strong association we observed between total body water and DXA muscle mass is consistent with the results obtained by other investigators (15, 17, 23). For example, Jensen et al. (17) reported that DXA FFM alone accounted for most of the interindividual variability in total body water. This strong association reflects a potential disadvantage of the DXA methodology for estimating specific changes in muscle; i.e., DXA assumes a constant hydration of the skeletal muscle compartment. This is important because increases in muscle hydration due to edema or intramuscular fat deposition (e.g., obesity and aging) and possibly fibrous tissue (or collagen) will be detected as an increase in lean tissue by DXA even in the absence of changes in muscle cell mass. This does not imply that the DXA technology is inherently inaccurate, because phantom studies indicate that DXA predicts fat and FFM in meat blocks of known composition with a high degree of accuracy (17). Rather, the results obtained thus far suggest that DXA and other indexes of skeletal muscle (i.e., body potassium and creatinine excretion) predict related, but different, muscle compartments.

Gender and indicators of skeletal size (bone mineral content and height) also accounted for a significant amount of the discrepancy between DXA and creatinine excretion estimates of muscle mass. Height is a major determinant of absolute muscle mass and lean body mass (7) and would therefore be expected to influence absolute differences between two muscle mass measurement methods. Such an influence could also be expressed through gender, because women are generally shorter than men. However, both gender and height appeared in the final model, suggesting that they contribute independently in explaining differences between DXA and creatinine excretion. The fact that there were no gender interactions suggests that these factors account for intersubject variability in DXA- estimated muscle mass in the same way for men as for women.

We originally speculated that chronological age would enter our multiple-regression model as either a main effect or in combination with creatinine excretion (i.e., two-way interaction), but it did not. However, the apparent effect(s) of aging on the discrepancy between DXA and creatinine excretion estimates of muscle mass (Fig. 1) might be exerted through one or more biological variables that did enter the final regression model. In this context, age-associated increase in body water might have been a competing factor with chronological age.

Is urinary creatinine excretion a valid index of age-associated muscle loss? We observed a significant age-associated reduction in urinary creatinine excretion in both men and women, consistent with results obtained in previous investigations (6, 32). The absolute validity of the creatinine excretion method for estimating muscle mass in older (or younger) humans has not been established, particularly because there is currently no widely accepted gold standard for muscle mass measurement in humans. However, the creatinine excretion method probably provides a better estimate of age-related muscle loss than DXA for several reasons. First, autopsy measurements by Lexell et al. (18) and CT-magnetic resonance imaging scans (2, 14, 27, 32) clearly show a reduction in limb muscle cross-sectional area and volume during middle and old age. Second, total body muscle mass estimates by 24-h urinary creatinine excretion are closely correlated with limb (32) and total body muscle mass (14) measured by multiple-slice CT scans. Third, we observed age-associated declines in leg strength and total body protein, which are influenced, in large part, by the total body muscle mass (23, 25, 27, 32). More importantly, the decline in protein mass and muscle mass measured by urinary creatinine excretion is consistent with the reported decrease in the synthesis rates of muscle proteins (25). Although the age-associated decline in muscle strength could occur due to a variety of factors, muscle wasting remains by far the most important cause of muscle weakness (25, 27, 32). The decline in muscle strength observed in our older subjects is in agreement with a decline in muscle mass based on urinary creatinine excretion. Collectively, these findings support the conclusion that urinary creatinine excretion provides a more realistic estimate of age-related muscle loss than the conventional DXA-based estimate (appendicular muscle mass divided by 0.75), which showed no significant change with aging in the present study.

The ratio of muscle mass to daily urinary creatinine excretion has been reported to range from ~18.6 to 20.0 kg/g of creatinine in healthy people (13). We used the upper end of this range (20 kg/g) because our subjects did not eat meat during the study and because some limited recent data suggest that the average ratio may be slightly higher than previously assumed (i.e., 21.8 ± 1.3 kg/g; Ref. 14). Although it is possible that aging muscle may generate less creatinine (32), there is no consensus about which specific constant(s) should be used and at what specific ages they should be applied. Even if we calculate muscle mass in our older subjects (60-79 yr) with a ratio of 21.8 kg/g, there still remains a significant age-associated decline in muscle mass by the creatinine excretion method. This supports our assertion that creatinine excretion provides a better index of age-related muscle loss than DXA.

The reproducibility of creatinine excretion measurements in the middle-aged and older subjects we studied was markedly less than for the other body composition methods (Table 2). This occurred even though all studies were performed with subjects as inpatients in the GCRC under close supervision with complete urine collections and a meat-free diet. This lack of precision has been reported by others (13, 32).

Despite its lower reproducibility relative to other methods, creatinine excretion was the only independent body composition method to demonstrate a reduction in fat-free tissue mass across age groups. This suggests that urinary creatinine excretion has sufficient sensitivity to detect age-associated changes in metabolically active muscle mass. It is likely that anatomic estimates of muscle mass, including DXA, may not be equivalent to estimates based on creatinine excretion. For example, most anatomical measures of skeletal muscle include collagen and other extracellular matrix proteins that are reported to increase in older age (14, 20, 26, 27). These muscle components would not be reflected in the creatinine excretion method. The parallel age-associated declines seen in creatinine excretion and total body protein support the data of Cohn et al. (5) that muscle mass declines more rapidly with advancing age than does nonmuscle lean tissue mass (i.e., most of the protein loss with aging reflects a loss of muscle cell mass).

Potential limitations of DXA for estimating age-related muscle loss. The tendency for the DXA method to detect smaller age-associated differences in total body muscle mass vs. other measurement methods was also reported by Mazariegos et al. (20). The higher DXA-based muscle mass estimates in older people could result from an age-associated increase in body water content, which in turn is detected as lean tissue by DXA. For example, aging and other conditions of muscle wasting are thought to result in a greater loss of muscle cell mass than surrounding extracellular fluid and connective tissue (14). Muscle loss results in extra space for retention of water. This, in combination with age-associated declines in kidney function, reduced venous return to the heart and reduced lymph flow might cause muscle tissue in older humans to be relatively more hydrated than in younger humans.

Direct information on potential age differences in skeletal muscle hydration is limited, and the impact on DXA-based measurements is unclear. Forsberg et al. (9) did not find greater hydration in fat-extracted muscle biopsy samples of older muscle, but they did detect a slight age-associated increase in extracellular water. Measurements of water content in muscle biopsy samples are, however, subject to error because of the assumptions involved and the rapid evaporation of water after biopsy. In addition, the biopsy samples of Forsberg et al. were fat extracted. Because increases in intramuscular fat stores are known to occur with aging (3), there might also be higher water content in older muscle due to increases in adipose-associated water (i.e., 15-20% of adipose tissue mass; Ref. 17). DXA-based estimates of limb muscle mass would also include water associated with subcutaneous fat, which also increases with aging (15, 25, 27). Collectively, these age-associated factors would tend to increase the ratio of limb water to skeletal muscle mass. This might explain how DXA appears to overestimate "muscle mass" to a greater extent in older people; i.e., there is relatively more fluid in the limbs, which in turn is detected as "lean" tissue by DXA. The age differences we observed in the total body water-to-FFM ratio (Table 1) lend support to this hypothesis.

Errors in prediction of total body muscle mass by DXA could also result from variation in the actual appendicular-to-total body muscle mass ratio. Heymsfield et al. (15) have indicated that some gluteal and other muscles are probably included in the DXA appendicular skeletal muscle estimate that are not anatomically considered appendicular muscle. Also, it has been suggested that in elderly people, the appendicular muscle mass may be closer to 79-80% of total muscle mass, rather than the assumed value of 75%. However, the same ratio of 80% has recently been suggested for younger subject groups (14). Consequently, it is unlikely that the discrepancy between DXA and creatinine excretion in estimating age-associated muscle loss is attributed to age differences in the appendicular-to-total muscle ratio.

Being an indirect measurement of lean mass (fat-free tissue), DXA lacks specificity unlike creatinine, which originates from metabolically active muscle tissue. However, the DXA-based estimate of appendicular muscle mass (15) is very convenient and highly reproducible and is likely to remain a valuable tool in body composition research involving homogenous groups of subjects with a limited degree of water retention, obesity, or muscle wasting.

In conclusion, the current study raises concern about the use of DXA as a method for estimating total body muscle mass in healthy men and women of different age groups. A particular concern is the accuracy of DXA-based estimates of muscle mass in subjects with altered body water. In this regard, DXA did not detect age-associated differences in muscle mass as indicated by urinary creatinine excretion and as previously reported in anatomical studies. These results strongly suggest that DXA does not differentiate body water from metabolically active muscle cell mass and is a less reliable measure of muscle mass when populations with variable body water are compared. Additional factors such as the collagen content of muscle tissue may also contribute to the discrepancies between methods.


    ACKNOWLEDGEMENTS

We gratefully acknowledge the support of GCRC staff and Jody Utton for help with the performance of the study and David Ebenstein for technical support. We also thank Lianne Habinek for assistance with the DXA data analysis.


    FOOTNOTES

The study was supported by National Institutes of Health Grants R01-AG-09531, RR-00585, and MO1-RR-109.

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

Address for reprint requests: K. S. Nair, Joseph 5-194, Mayo Clinic and Foundation, 200 1st St. SW, Rochester, MN 55905 (E-mail: nair.Sree{at}mayo.edu).

Received 14 September 1998; accepted in final form 27 April 1999.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

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Am J Physiol Endocrinol Metab 277(3):E489-E495
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