Interrelationships of serum testosterone and free testosterone index with FFM and strength in aging men

Tracey Ann Roy1, Marc R. Blackman2, S. Mitchell Harman1,3, Jordan D. Tobin1, Matthew Schrager1, and E. Jeffery Metter1

1 Laboratory of Clinical Investigation, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore 21224; 2 Laboratory of Clinical Investigation, Intramural Research Program, National Center for Complementary and Alternative Medicine, National Institutes of Health, Bethesda, Maryland 20892; and 3 Kronos Longevity Research Institute, Phoenix, Arizona 85018


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Muscle mass and strength losses during aging may be associated with declining levels of serum testosterone (T) in men. Few studies have shown a direct relationship between T and muscle mass and strength. Subjects were 262 men, aged 24-90 yr, from the Baltimore Longitudinal Study of Aging, who had T and sex hormone-binding globulin sex hormone-binding globulin (SHBG) measurements, from which the free T index (FTI) was calculated (T/SHBG) from serum samples collected longitudinally since 1963, total body fat mass and arm and leg fat-free mass (FFM) by dual-energy X-ray absorptiometry and arm and leg strength by dynanomometry. Mixed-effects models estimated T and FTI at the time of mass and strength measurements. Age, total body fat, arm and leg FFM, T, and FTI were significantly associated with concentric and eccentric strength. FTI, not T, was modestly, but directly, related to arm and leg strength after fat, arm and leg FFM, height, and age were accounted for and indirectly through body mass. FTI is a better predictor of arm and leg strength than T in aging men.

aging, skeletal muscle mass


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

AGING IS ASSOCIATED WITH DECREASES in muscle mass and strength (sarcopenia), the metabolic, physiological, and functional consequences of which are thought to contribute to disability and frailty in the elderly (9-12, 42). Sarcopenia has also been associated with reduced bone density and an increased incidence of falls and fractures in older individuals (8, 24, 31, 32, 37, 47). In some studies, the loss of muscle strength has been observed to begin as early as the fourth decade of life (2, 11, 20, 21, 23, 24, 48). Although age-related declines in strength and muscle mass are highly correlated with each other, measuring "muscle quality" as strength per unit of muscle mass may be an independent indicator of muscle performance useful in understanding age-related changes in skeletal muscle (11, 20, 29, 33).

Declining serum levels of various hormones, including androgenic steroids, occur with aging. Age-related declines in testosterone (T) and free T in men have been observed both cross-sectionally (14, 22, 36) and longitudinally (16, 30, 50) by many, but not all (17, 39) investigators. Androgenic steroids, particularly T, have been shown to exert a trophic effect on muscle mass and strength (4-6, 25, 35, 38, 41, 43, 45, 46). Studies of T administration to both healthy and hormone-deficient men have demonstrated increases in muscle mass and/or strength (4-6, 35, 45, 46)

Whether age-associated changes in serum T or free T can explain, completely or in part, the age-associated changes in muscle mass and strength has not been established. A few studies have found a modest relationship between these measures in small groups of healthy older subjects (2, 15). In the present study, we examined the relationships among serum levels of total and calculated free T [free testosterone index (FTI)], height, total body fat mass, arm and leg fat-free mass (FFM), and appendicular muscle strength in a population of men who spanned the entire adult age range. We used longitudinally collected serum T levels to predict serum levels at the time of strength testing. The goal of this study was to examine whether serum T and FTI are related to arm and leg muscle strength independently of the influence of age on strength across the adult life span.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Subjects. Subjects are male participants in the Baltimore Longitudinal Study of Aging (BLSA) (34). Subjects are generally well educated, largely middle class, and Caucasian. Subjects are examined at regular intervals, typically 2 yr. They participate in a number of research projects at each visit. Serum T and sex hormone-binding globulin (SHBG) were measured longitudinally in 901 men over a 33-yr period (mean follow-up 11.8 ± 8.32 yr) as part of a prostate study.

A subset of 262 men (aged 24-90 yr) has been evaluated for muscle mass, measured as FFM, using dual-energy X-ray absorptiometry (DEXA) beginning in 1992 and have had serum T measured at least once over a 33-yr period. These 262 male subjects formed the core of the current analysis. Of the 262, 239 men underwent both upper and lower limb strength testing and FFM measurement during the same visit. For the 23 men without concurrent tests, a strength test was used if it occurred within 3 yr of the DEXA and was considered to be done at the time of DEXA. To demonstrate that using data over a 3-yr time period offers a reasonable estimation for muscle strength, we compared strength testing from paired visits in 75 men who had the two testings over a period of 3 yr or less (mean time difference 1.92 ± 0.76 yr). The test-retest correlations for the eight strength measures used in this study ranged from 0.61 to 0.83, whereas only the mean difference for tricep eccentric strength was significantly different from zero at P < 0.05 by paired t-test. Thus the use of strength data from other visits within a 3-yr period offers a reasonable estimation for the muscle strength measures at the time of DEXA.

The differences in the numbers of men having T measurements and FFM and strength determinations resulted from the following: 1) many of the men having had T measurements done before 1992, which was when DEXA and current muscle strength measurements were initiated in the BLSA; 2) restrictions in DEXA scan availability at the time of visit; and 3) exclusions from scanning and/or strength testing based on health issues. The BLSA men studied were generally in good health but were nonetheless evaluated for musculoskeletal and cardiovascular problems that might be exacerbated by the strength testing procedure. Health reasons for exclusion from strength testing were as follows: 1) severe cardiovascular disease, 2) severe musculoskeletal disease, 3) active neck and back pain, 4) previous bone mineral density of the hip or lumbar spine below normal for their age, and 5) recent major surgery (<= 6 mo). Only a small percentage (<1%) of subjects participated in any type of regular strength training, and there were no significant differences in such participation by age group (21). Height and body weight were measured to the nearest 0.5 cm and 0.1 kg, respectively, by use of a standing Detecto medical beam scale. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m2). Those men weighing >114 kg (250 lb) were excluded from the DEXA muscle mass measurement, per manufacturer's recommendations (Lunar, Madison, WI).

Hormone determinations. Blood samples in the men had been collected longitudinally since 1963 and stored at -70°C. As part of another study, 3,621 samples from the serum bank were analyzed to examine the longitudinal changes in serum T in 901 men (16). Sampling strategies were used to maximize the information on serum T, whereas as little serum as possible was utilized. Samples were taken from each subject's most current four visits and visits closest to 10, 15, 20, 25, and 30 yr before the most recent visit. Blood samples were drawn between 6 AM and 8 AM after an overnight fast. All serum T and SHBG measurements were performed at Hazleton Laboratories (now Covance Laboratories, McLean, VA). The FTI was calculated by dividing serum T by SHBG. The FTI reasonably approximates bioavailable T with the use of equilibrium dialysis and has been shown to be suitable for clinical use (44).

Details of the hormonal assay have been previously published (16). Testosterone levels were determined in duplicate using 125I and double-antibody RIA kits obtained from Diagnostic Systems Laboratories (Webster, TX). Minimum detectable T levels averaged 0.42 nmol/l, with intra- and interassay coefficients of variation (CV) of 4.8 and 5.7%, respectively, at concentrations of 7.74 and 7.29 nmol/l and 3.3 and 6.4% at concentrations of 44.7 and 42.9 nmol/l. SHBG concentrations were measured using RIA kits purchased from Radim (Liege, Belgium), which employ 125I-labeled SHBG and polyethylene glycol-complexed second antibody. The sensitivity of the SHBG assay was ~10 nmol/l. The CV at 5 nmol/l was 22% and at 25 nmol/l was 5%, with intra- and interassay CVs of 3.4 and 10.8% at concentrations of 22 and 19 nmol/l and 1.8 and 7.7% at concentrations of 117 and 118 nmol/l, respectively. Preliminary analysis of data from the samples stored between 1961 and 1995 revealed a significant increase in T level with length of storage, independent of age. On investigation, we were able to demonstrate that the increase was due to a date-related assay artifact (16). A mixed-effects model was utilized to adjust T for the date effect.

In 129 of the 262 men who underwent DEXA and strength measurements, T and SHBG were not measured during the same visit as the muscle strength and FFM measurements. Harrell et al. (18) and others (1) argue that estimating missing data is a better strategy with less bias than removing the subject from the analysis. A comparison of the 133 men who had T data at the time of strength and muscle mass measurements with the 129 men who did not have concurrent T data did not differ by t-test (P > 0.05) on BMI, arm or leg muscle strength, arm or leg muscle mass, or age.

A mixed-effects model (7) based on the serum measurements from the 901 men was utilized using MLWIN (13) or SPLUS (Insight, Seattle, WA) software to estimate the T and FTI levels at the age when the muscle testing and mass measurements were performed in all 262 men. The model considered age, year, and BMI. The form of the equation was the same as that used by Harman et al. (16) and used the adjusted T and FTI derived in that work
T or FTI = (b<SUB>0</SUB> + b<SUB>0i</SUB>) + (b<SUB>1</SUB> + b<SUB>1i</SUB>)·age + b<SUB>2</SUB>·age<SUP>2</SUP> 

+ b<SUB>3</SUB>·age<SUP>2</SUP> + b<SUB>4</SUB>·date+ b<SUB>5</SUB>·date<SUP>2</SUP> + b<SUB>6</SUB>·BMI + e
where b0i and b1i are random effects that reflect individual variation from the mean effect for the intercept and age within subjects, and e refers to the residual not explained by the equation. The resulting T and FTI are the best linear unbiased predictors, and such predictors are good estimates of the actual level, with a lower error than observed measures (40). To test the model's accuracy, the predicted T and FTI were compared with the T value derived from Harman et al. (16) in the 133 subjects in whom T and FTI measurements were made during the same visit at which strength measures were taken. The correlation between the predicted and the measured T was 0.99 ± 0.002 for the whole model (fixed and random effects) and 0.78 ± 0.03 for the fixed effects, and the correlation for FTI and predicted FTI was 0.99 ± 0.001 for the full model and 0.75 ± 0.04 for the fixed effects, with the standard errors estimated by the bootstrap method. Bland-Altman analysis (3) for agreement found no evidence for a difference between the two measures for either T or FTI. The predicted T (mean 14.5 ± 3.0) and FTI (mean 0.22 ± 0.10) were subsequently used in analyses. Furthermore, the mean T and FTI by age decade for the estimated values in the entire sample were the same as the original values in the 133 men (Table 1, predicted T vs. T, and predicted FTI vs. FTI).

                              
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Table 1.   Sample characteristics by age group

FFM. Muscle mass as estimated by FFM was measured by DEXA on a Lunar model DPX-L (Lunar Radiation, Madison, WI) by use of a previously validated (42) three-compartment model (i.e., bone mineral mass, fat mass, and FFM) to assess body composition. Total body scans were analyzed using Lunar's software (version 3.65u) for body composition. This software allows for the separation of the total body scan data into regional measurements. Assessment of appendicular FFM by use of DEXA and dual-photon absorptiometry has been validated (12, 19). The scanner was calibrated daily according to the manufacturer's recommendations. The reliability of the DEXA scanner in our laboratory was ~0.1% difference between repeated total body studies (21).

Muscle strength. Knee flexors and extensors and elbow flexors and extensors were tested on the Kin-Com model 125E isokinetic dynamometer (Chattecx, Chattanooga, TN). The procedure has been described in detail previously (21, 23). Subjects were tested in the seated position with the back supported against a backrest, and they were strapped at the waist, thigh, and chest. The dominant arm and leg were tested. Dominance was determined by personal preference. Maximal isokinetic strength was measured concentrically and eccentrically in leg muscle groups at 0.52 and 0.79 rad/s, respectively, in the arm muscles. The joint arc was limited to 1.22 rad. We had subjects perform three maximal efforts with >= 2 min of rest between tests. The best effort was considered maximal. Both concentric and eccentric actions were included, because each contributes to ambulatory stability. Eccentric strength is more likely to lead to muscle damage with an adverse effect in the elderly, and age differences in eccentric strength may be less than with isokinetic and isometric actions (21). The mean CV of our Kin-Com machine was 5% and has been reported previously (21, 23).

Data analysis. Descriptive statistics and normality plots were run on the data by use of SPSS 10 (SPSS, Chicago, IL). Relationships among the variables were examined using regression analysis. All regressions were examined to determine whether the data were best described by linear or second-degree polynomial models.

Structural equation modeling was used to further examine the interrelationships among age, height, T, FTI, fat mass, arm and leg FFM, and strength. This method allows for testing specific relational models by comparing the covariance matrix resulting from the model and the covariance matrix of the data or a competing model. Structural equation modeling was performed using Amos 4.0 (Small Waters, Chicago, IL). Amos (analysis of moment structures) allows for the simultaneous analysis of a system of linear equations (1). We used this method to test the specific model, as described in RESULTS, to examine the relationships among the measured variables. Amos 4.0 uses full maximum likelihood estimates, which allows for inclusion of subjects in the event of missing data. The results include overall model fit statistics, standard errors, and parameter estimates. The models tested were accepted when the chi 2 test was nonsignificant (P >=  0.05), implying that the model explained the underlying covariance matrix.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The description of the 262 men by age decade is shown in Table 1. Older men were shorter and exhibited reduced FFM, strength, and T and FTI levels (P < 0.001 for age differences for all variables). The mean predicted T and FTI for all 262 men were similar to the measured T and FTI in the 133 men with measurements at the time of strength testing. The relationships between age and strength are shown by age-associated linear declines (Table 2). A second-degree polynomial term did not significantly improve the estimate for the measurements. Concentric strength in the arm and leg was more closely predicted by age than was eccentric strength, consistent with previous reports from the BLSA (21, 23). The difference in strength with age was proportionately the same in both arms and legs (P = 0.09, Fig. 1A). Likewise, the linear relationships between age and arm and leg FFM were proportionately similar, with age explaining ~22% of the variance for both extremities (P = 0.09, Fig. 1B). The T values of the oldest men were ~40-50% lower than those of the young men, and age explained ~31% of the variance (Fig. 2A). For FTI, the oldest men were 60% lower than the young men and age explained 43% of the variance (Fig. 2B).

                              
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Table 2.   Regression equations describing age-associated changes in arm and leg strength



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Fig. 1.   Scatterplot with linear regression of the age-associated changes in arm and leg strength (A) and fat-free mass (FFM; B). Strength and FFM are plotted on a log scale to demonstrate the proportionality of differences with age.



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Fig. 2.   Scatterplot with linear regression of age-associated changes in serum testosterone (A), total testosterone (B), and free testosterone index (FTI).

The bivariate correlations between T and FTI and the other measurements (i.e., regional FFM, fat mass, weight, BMI, height, and strength) are summarized in Table 3. T and FTI were positively correlated with height (P <=  0.01 and 0.001) and with both the FFM and the strength of the arms and legs (P <=  0.001). As an illustration of these relationships, scatterplots of quadriceps concentric strength by serum T and FTI are shown in Fig. 3, A and B. The correlations of the muscle strength measures with T range from 0.24 to 0.39 and with FTI range from 0.37 to 0.52. The correlations were similar to those observed for T and FTI actually measured at the time of strength testing in 133 men, as shown in Table 3.

                              
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Table 3.   Correlation (r value) between testosterone, FTI, FFM and strength



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Fig. 3.   Scatterplot with linear regression of quadriceps concentric muscle strength on serum testosterone levels. A: total testosterone; B: FTI.

Multiple regression analysis revealed that, after adjustment for the effects of age, height, fat mass, and extremity FFM, serum T and FTI were not independently related to arm or leg concentric or eccentric strength (Tables 4 and 5). Height was significantly related to arm muscle strength measures but not to leg muscle strength measures. Using only the T and FTI data actually measured at the time of strength measurements in 133 men, we found a similar lack of an independent effect for T on strength.

                              
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Table 4.   Multiple regression equations describing the relationships of various arm and leg strength measures to age, FFM, height, fat mass, and testosterone


                              
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Table 5.   Multiple regression equations describing the relationships of various arm and leg strength measures to age, FFM, height, fat mass, and FTI

Structural equation modeling was used to better understand the nature of T and FTI relationships with arm and leg FFM and with strength, since the correlations (Table 3) were moderately strong, whereas no independent effects were noted by multiple regression (Tables 4 and 5). The multiple regression considered the effect of T or FTI on strength while the other variables were held constant; it does not consider the relationships between T or FTI and the other variables. Structural equation analysis allows for an examination of the direct (independent) and indirect influences of either T or FTI on strength while considering the relationships with and among age, total body fat mass, arm and leg FFM, and height with the directionality of the relationships as proposed in the model (Fig. 4). The model presents standardized coefficients for each path or relationship in the analysis. The single-headed arrows represent the proposed direction of the path relationship, and the numbers represent the coefficients. Bidirectional arrows represent associations without defined causal relationships. Our models included T or FTI, total body fat mass, arm and leg FFM, height, and strength, which are shown in rectangles. Circles represent residual variance associated with the individual variables not accounted for by the model. Arm and leg strength are represented as latent variables, which were estimated by concentric and eccentric muscle strength measurements and diagrammed as an ellipse. A latent variable is not directly measured but rather is a factor respresenting common characteristics of the measured variables. In this case, the common relationships between the concentric and eccentric strength measures are labeled the latent variables called "arm strength" and "leg strength" and are represented by the arrows going from "strength" to the concentric and eccentric strength measures. However, the strength measurements reflect several different properties in addition to just force generation; concentric/eccentric being one, another being flexor/extensor. In developing the models, we found that, in the arm, the flexor/extensor relationships were important (as shown by the double-headed arrow that arcs between the two flexor and two extensor measurements) with a relationship that is not accounted for by arm and leg strength. In the leg, neither the flexor/extensor nor the concentric/eccentric relationship was completely accounted for by leg strength. The advantage of using this approach is that the models allow for addressing the common features between the measurements, which can be lost by examination of each alone. In setting up the models, we found that two FTI measures were extreme outliers (>3 standard deviations from the mean) which were set to missing for the analyses. The initial structural models (not shown) included 1) the effect of age on arm and leg FFM, total body fat mass, height, FTI (or T), and strength; 2) the effect of arm and leg FFM, fat mass, and height on arm and leg strength; 3) nonmodeled relationships between height, body fat, and arm and leg FFM; and 4) the effect of FTI (or T) on arm and leg strength, body fat, height, and arm and leg FFM. Paths were removed (i.e., set to zero) if the coefficient had P > 0.1. 


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Fig. 4.   Structural equation model examining the final model showing relationships between arm and leg strength on serum FTI (chi 2 = 64.8, df = 57, P = 0.15). The model assumes that age is an exogenous variable that influences the other variables, FTI influences FFM and strength (s), FFM influences strength, and height (h) is related to regional FFM, fat mass, and influences strength. Strength is represented by latent variables (ellipses), which express the common relationships between the concentric (c) and eccentric (e) strength measurements in either the arm (a) or leg (l). Each variable has a series of arrows that reflect the direction of the relationship between the variables. Circles represent the residuals (r) associated with the variable to which they belong. Path coefficients are standardized and represent the change that occurs in the variable at the head of an arrow for each standard deviation change in the variable at the tail of the arrow. fti, FTI; ffm, FFM; w, weight; quad or q, quadriceps; ham or h, hamstring.

The model for FTI is shown in Fig. 4. FTI exerts a marginal (P < 0.1) independent effect on arm and leg muscle strength (i.e., the coefficient between FTI and arm strength is 0.11 and leg strength 0.12) when the other variables are considered. FTI did not have an impact on FFM, but we had left the relationship with leg FFM in the model (P = 0.12), as FTI had a direct effect on fat mass. Fat mass had no direct effect on arm or leg strength but was related to arm and leg FFM through unmodeled factors. Both arm and leg FFM impacted on arm and leg strength. From the model, FTI influence on arm strength consists of a direct relationship of 0.11; i.e., for every standard deviation increase in FTI, arm strength increases by 0.11 standard deviation. In addition, FTI has an indirect effect through fat mass (FTI to fat mass to arm FFM to arm strength, the effect being 0.26 · 0.23 · 0.23 = 0.014). In addition, the correlation between FTI and arm strength is adjusted by the relationship between age and FTI, age and arm FFM, age, and height, and the direct effect of age on arm strength. Comparing the arm and leg demonstrates that age had a greater negative direct effect on leg strength (i.e., age to leg strength = -0.39) than on arm strength (i.e., age to arm strength = -0.23).

Structural equation modeling was also used to understand the relationship of serum T with arm and leg FFM and strength. However, serum T did not exert an independent effect on arm or leg strength when the other variables were considered (data not shown). Likewise, a model was examined replacing fat mass with weight; this model did not adequately explain the data.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

In generally healthy men, age, arm and leg regional FFM, serum T, and FTI are significantly associated with concentric and eccentric strength in the arm and leg (Table 3). When age, arm and leg regional FFM, fat mass, and serum T or FTI are considered together, differences in strength are directly related to age and FFM but not to serum levels of T or FTI (Tables 4 and 5). The multiple regression models assume that these variables are independent (i.e., correlations are zero), which clearly is not the case.

The structural equation models provide some understanding of the impact of FTI and serum T on age-associated strength loss in the extremities by modeling the relationships between variables. Many models can fit the data. The chosen model is based on current understanding of the effects of T on strength and body composition. FTI, but not T, is marginally directly related to arm and leg muscle strength after the effects of fat mass, arm and leg FFM, height, and age are accounted for. If FTI has a direct effect on strength independent of muscle mass, the effect is likely to alter muscle quality, i.e., the amount of force generated per unit of muscle. The literature suggests that there is a positive relationship between T and muscle strength, as T administration in hypogonadal elderly (5, 46) and young (4, 12) athletic men can increase muscle mass and strength. However, the homeostatic effects of endogenous physiological levels of total T may not exert the same impact or, more likely, does so via free T. We may be unable to detect the true impact, because we are looking at circulating levels rather than the availability or action of T on muscle cell androgen receptors.

Previous studies also have shown modest relationships among age, T, and muscle strength despite differences in sample size, age, and strength measures. Baumgartner et al. (2) found, in 121 men aged 65-97 yr, that grip strength and appendicular muscle mass (sum of arm and leg FFM) were significantly correlated to age, physical activity, serum insulin-like growth factor (IGF)-I, and total and free T. Muscle mass accounted for 36%, and age for 7%, of the total variance in grip strength. Neither T nor the other variables were independent predictors. The variance in muscle mass was explained by the following variables: free T (36%), knee height (11%), physical activity (5%), cardiovascular disease (3%), and IGF-I (2%). Age did not explain any of the variance in muscle mass once the other variables were entered into the regression equation. Using a broader age sample of men, we found very similar relationships for serum T, particularly FTI, with strength. Both studies demonstrated that a great deal of the variability in both muscle mass and strength could not be explained by measures of total or free T.

Hakkinen and Pakarinen (15) found no significant differences in serum T concentrations in 20 men aged 44-73 yr, although FTI was lower in the older vs. the younger men (older: 0.49 ± 0.11; younger: 0.71 ± 0.16, P<= 0.05). They observed no significant correlation between serum total or free T and muscle strength, in contrast to the findings of Baumgartner et al. (2) and those of the present study. Their small sample size and narrower age grouping may have led to the apparent differences. Hakkinen and Pakarinen did, however, detect a significant relationship between T levels and muscle cross-sectional area and muscle strength measures in women (15).

The present findings are consistent with experimental studies showing modest to moderate increases in muscle mass and strength in nonelderly hypogonadal men after T replacement therapy. Brodsky et al. (6) demonstrated a 15% increase in FFM in five hypogonadal men after 6 mo of T treatment. Similarly, Bhasin et al. (5) and Wang et al. (46) found significant increases in muscle mass and muscle strength associated with T administration in hypogonadal men.

Several studies have examined the effect of T on FFM and muscle strength in young eugonadal men. Bhasin et al. (4) reported an increase in total body FFM, cross-sectional area of the arm and leg muscle, and muscle strength after the administration of a supraphysiological dose of T to eugonadal men 19-40 yr of age. They also demonstrated that the increases in FFM, muscle size, and muscle strength in these men after combined T and strength training were greater than after T alone. Using a different experimental design, Mauras et al. (26) studied six healthy young men (mean age = 23.2 yr) before and after gonadal steroid suppression with a gonadotropin-releasing hormone (GnRH) analog. The GnRH treatment caused transient androgen deficiency and resulted in decreases in total body FFM and leg strength. Young et al. (49) examined the effects of contraceptive doses of T enanthate in 13 nonathletic 20- and 30-yr-old men and 8 healthy controls of comparable age. After 6 mo, the treated men increased FFM by 10%, but only one of six muscle strength measurements increased significantly (19.2%). The control group did not demonstrate any significant changes in body composition or muscle strength.

Studies have also examined the effects of T replacement in elderly, "andropausal" men with low normal to low T levels. In one small study of healthy men (mean age 67 yr) with T levels of <480 ng/dl, T administration increased leg muscle strength and muscle protein synthesis (43). In contrast, in a recent, larger study, 3 yr of T replacement via transdermal patches failed to improve muscle strength in men >65 yr of age with serum T levels of <475 ng/dl despite significant increases in lean body mass (38). Overall, these studies demonstrate only small to moderate changes in strength despite greater increases in muscle mass in older men given T supplementation.

We observed the impact of height on muscle strength to be related to its influence on muscle mass. Taller individuals tend to have more muscle mass, which impacts on strength. This effect has importance in interpreting sex differences in strength, as women tend to be shorter and have less strength relative to men. Although women were not studied here, our previous work is consistent with the concept that height contributes importantly to the sex differences in muscle strength measures (21, 23, 28).

Certain limitations of this study require comment. The analyses presented herein are based on longitudinal serum T measurements collected as part of a separate BLSA study, whereas muscle mass and strength measurements were collected over a relatively short period of time and analyzed cross-sectionally. T levels were directly available in only about one-half of the men at the time of muscle mass and strength testing. Using a mixed-effects model, we found a strong, direct correlation between predicted levels of total T and androgen values derived from men in whom measurements were performed at the time of muscle mass and strength assessments. The predictions may be less accurate when the measurements are projected beyond the period of T measurements, although these projections are based on the age-related impact on T as observed in these men. The likely effect would be shrinkage of the residuals toward the average overall model, but the estimates are modified on the basis of the subjects' individual measurements.

In conclusion, in healthy men, serum T levels are correlated with age-associated differences in FFM and muscle strength, but the associations are not direct and appear to result mainly from the effects of age on FFM, strength, and serum T. In contrast, there is a direct, independent, albeit small, relationship of FTI with upper and lower body strength. The molecular mechanisms underlying the interrelationships of circulating and skeletal muscle androgens and their contribution to age-related sarcopenia in men await elucidation.


    ACKNOWLEDGEMENTS

We thank the participants and staff of the Baltimore Longitudinal Study of Aging who have made this work possible.


    FOOTNOTES

The research was done as part of the Intramural Research Program of the National Instititute on Aging.

Address for reprint requests and other correspondence: E. J. Metter, Gerontology Research Center, 5600 Nathan Shock Dr., Baltimore, MD 21224 (E-mail: metterj{at}grc.nia.nih.gov).

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. Section 1734 solely to indicate this fact.

April 9, 2002;10.1152/ajpendo.00334.2001

Received 23 July 2001; accepted in final form 21 March 2002.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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