For each participant, weight was determined to the nearest 0.05 kg, height was determined to the nearest 0.5 cm, and the results were used to calculate BMI (kg·m−2).
Serum Hormones and Bone Turnover Markers
To control for diurnal variation in serum hormones and bone turnover markers, blood was drawn (15 mL) in the early morning (06:00-09:00) after an overnight fast via an antecubital vein by a trained phlebotomist. Participants were instructed to refrain from exercise during the 24 hours before the blood collection. Blood was dispensed into serum separator tubes and allowed to coagulate at room temperature or on ice, according to assay protocols. The coagulated blood was centrifuged at 2000g for 15 minutes, and the serum was removed and frozen at −80° C. All hormone and bone turnover marker assessments were done in duplicate, and all assays were performed in a single run to eliminate interassay variability. The concentrations of total testosterone, sex hormone binding globulin (SHBG), dehydroepiandrosterone, cortisol, and fT3 in serum were determined using commercially available chemiluminescent immunoassays (Immulite 1000, Diagnostic Products Corporation, Los Angeles, Calif; intraassay coefficients of variation [CVs] < 5%). The free androgen index was calculated as (testosterone/SHBG) × 100. The concentration of total insulin-like growth factor-I was measured after insulin-like growth factor-I binding proteins were removed by acid precipitation using a commercially available enzyme-linked immunosorbent assay (ELISA; Diagnostic Systems Laboratories, Inc., Webster, Tex; intraassay CV = 2.7%). Serum estradiol (Bio-Quant, San Diego, Calif; intraassay CV = 10.5%) and 25(OH) vitamin D (Immunodiagnostic Systems, Ltd., Fountain Hills, Ariz; intraassay CV = 6.0%) also were measured using ELISA.
Serum markers of bone formation and resorption can be used as indirect measures of bone remodeling. Markers of bone formation include bone-alkaline phosphatase (bone-AP) and osteocalcin (OC), which are secreted by osteoblasts during bone formation. The C terminal telopeptide of type I collagen (CTX) is released when bone collagen is broken down during bone resorption. Serum OC, bone-AP, and CTX were measured by ELISA (Nordic Bioscience Diagnostics, Denmark; intraassay CVs < 10%). The anti-OC antibody recognizes only intact OC; therefore, because the antibody does not bind OC fragments, which are released during bone resorption, OC measured using this ELISA results only from de novo synthesis. Cross-reactivity of the antihuman bone-AP antibody with liver AP is 3-8%, and with intestinal bone-AP it is 0.4%.
Bone Mineral Content and Density
Dual X-ray absorptiometry (DXA; Hologic Delphi A, Waltham, Mass) was used to measure bone mineral content (BMC) and areal BMD at the lumbar spine, total hip, femoral neck, arm, leg, and whole body. Areal BMD (g·cm−2) was calculated from bone area (cm2) and BMC (g). All DXA scans were performed and analyzed by one investigator (P.S.H.). The CVs for BMC and BMD were < 1%. The World Health Organization definitions were used to categorize participants as having normal BMD (> −1.0 SD), osteopenia (≤ −1.0 SD, > −2.5 SD), or osteoporosis (≤ −2.5 SD) based on T-scores of the spine and hip (20).
Current physical activity was quantified using a 7-day written training log of activity type, duration, intensity, and frequency. The Compendium of Physical Activities was used to estimate daily energy expended during purposeful exercise (1). Nutrient intake was assessed using 7-day written diet records. Food diaries, not including multivitamin supplements, were analyzed for energy and macro- and micronutrient content (Food Processor 8.0, ESHA, Salem, Ore).
Subjects completed a medical history questionnaire and the Historical Leisure Activity Questionnaire (HLAQ) (23). The HLAQ was developed to measure historical leisure-time physical activity across the lifespan and to relate prior activity to bone density in postmenopausal women. The original interviewer-administrated version of the HLAQ has been modified for self-administration with good reliability: intraclass correlation coefficients of approximately 0.86 for lifetime vigorous-intensity activities (9). The HLAQ has been used to examine the relationship between lifetime weight-bearing activity and current bone density in adult men (11,35) and women (42). In the present study, the HLAQ was used to assess participation in leisure-time physical activity during 3 periods of the lifespan: adolescence (13-18 years), young adulthood (19-29 years), and adulthood (30-59 years). To enhance recall of past physical activity, participants were provided standardized verbal prompting by study personnel (P.S.H.). Study personnel reviewed each subject's responses on the medical history and HLAQ to verify completeness and accuracy of the written history.
Bone Loading History
Questionnaires that assess the effect of physical activity history on BMD must include information regarding activity type, frequency, duration, loading on bone, and the developmental period during which the physical activity occurred (2). Thus, bone loading impact scores were calculated for adolescence, young adulthood, and adulthood (> 30 years), using the responses provided in the HLAQ and biomechanical ground-reaction force (GRF) for each activity, as described by Groothausen et al. (16). Based on the GRF, all reported activities were classified into 4 categories (0-3): 0 (GRF < 1 × body weight, e.g, cycling, swimming), 1 (GRF between 1 and 2 × body weight, e.g, rowing, aquarobics), 2 (GRF between 2 and 4 × body weight, e.g, jogging), and 3 (GRF > 4 × body weight, e.g, basketball, soccer, volleyball).
A bone loading exposure score then was calculated for each developmental period as the product of the frequency, duration, and classification score (0-3) for each activity. A lifetime cumulative bone loading exposure score was calculated as the sum of the load exposure scores for adolescence, young adulthood, and adulthood. This method of quantifying bone loading is similar to those described by Dolan et al. (12) and Daly and Bass (11) in that the GRF, frequency, and duration of each activity determine the score. Bone load history quantified in this manner was positively associated with BMD in adult women (11,12) and cortical BMC in adult men (11). An annualized bone loading exposure score also was calculated for each developmental period and for lifetime cumulative exposure by dividing the load exposure score for each period by the number of years in that period. The purpose of the annualized score was to allow comparison of bone loading exposure during adolescence, young adulthood, and adulthood. A bone loading score for the prior 12 months also was calculated.
Data were examined for normality of distribution. Outcome variables (i.e., BMD and bone turnover markers) were normally distributed. Bone loading scores were not normally distributed and, therefore, were transformed by taking the square root; the mean ± SEM of untransformed data are presented in the results. Group differences in descriptive variables (demographic, anthropometric, nutrient intake, physical activity, and bone loading variables) were tested using analysis of variance (ANOVA). Because serum hormone (3) and bone turnover marker concentrations change with aging, age was included as a covariate in the ANCOVA when assessing group differences in these variables. The least significant difference post hoc test was used for pairwise comparisons of mean and least squared mean values when the overall model was significant. Data are presented as mean or least squared mean ± SEM. p Values ≤ 0.05 were considered statistically significant.
One-way ANOVA was used to test our first hypothesis that the RT and RUN athletes would have greater BMD than the CYCLE athletes. The second hypothesis, that LBM and bone loading have independent effects on BMD, was tested using 2 different analyses. In the first analysis, ANCOVA was used to examine differences among groups with LBM and/or fat mass in the generalized linear model. Using LBM and fat mass as covariates allowed us to examine the unique effects of these 2 compartments, which may have very different effects on bone mass (41). It should be noted that body weight was highly correlated with LBM and fat mass; therefore, to avoid collinearity, it was not included as a covariate. Thus, differences in bone loading were captured by examining sport-related differences independent of body composition. In the second analysis, bivariate relationships between LBM and BMD were evaluated for each sport. Our third hypothesis, that serum markers of bone formation would be elevated in association with increased bone loading, also was evaluated in 2 separate analyses. First, the effects of bone loading on bone formation markers were compared among athletes participating in activities that have different loading characteristics using ANCOVA. Second, linear regression was used to examine relationships between bone turnover markers and current bone loading.
The RT athletes had significantly greater whole-body and regional BMD than the runners and cyclists at all sites, except for the lumbar spine, at which the runners did not differ from the RT athletes (Table 5). Moreover, the RT athletes had significantly greater age-adjusted hip and spine T-scores than the RUN and CYCLE groups (hip T-score: RT = 1.36 ± 0.26,a RUN = 0.30 ± 0.27,b CYCLE = −0.16 ± 0.19b; spine T-score: RT = 1.1 ± 0.3,a RUN = 0.19 ± 0.35,b CYCLE = −0.82 ± 0.24b; p < 0.05). Group differences in whole-body and regional BMD were unchanged after adjusting for cumulative load exposure (data not shown). However, after adjusting for LBM, only BMD of the lumbar spine differed between groups (p = 0.004, Table 5). The RUN group had significantly higher spine BMD and tended to have higher whole-body BMD (p = 0.10) than the CYCLE group, whereas spine BMD in the RT group was not significantly different from spine BMD in the RUN and CYCLE groups.
Lean body mass accounted for a large proportion of the variance in the linear regression models of whole-body and regional BMD. Adjusted R 2 were as follows: whole body, 66%; spine, 58%; arm, 52%; leg, 61%; hip, 54%; and femoral neck, 60%. Similar adjusted R 2 values were obtained using body weight as the dependent variable, and fat mass predicted approximately 30% of the variance in whole-body and regional BMD. In addition, fat mass was no longer significant when LBM was included in models of BMD (data not shown). Bivariate relationships between LBM and BMD were evaluated for each sport. For the RT group, LBM was positively associated with BMD at all sites, and LBM was correlated with whole-body, leg, and spine BMD in the CYCLE group. Subjects' LBM values were not significantly correlated with BMD at any site in the RUN group (Figure 1).
There were no group differences in markers of formation (bone-AP: RT = 9.0 ± 1.7, RUN = 7.2 ± 1.9, CYCLE = 7.0 ± 1.3 U·L−1; OC: RT = 16.0 ± 3.0, RUN = 20.4 ± 3.5, CYCLE = 14.3 ± 2.3 μg·L−1) or resorption (CTX: RT = 0.72 ± 0.11, RUN = 0.84 ± 0.13, CYCLE = 0.68 ± 0.09 μg·L−1), even when controlling for age. Correcting for whole-body BMC did not alter this finding (data not shown). Measurements of bone-AP were positively correlated with BMD at all measured sites; however, the correlations were no longer significant after controlling for LBM (data not shown). Current bone loading was positively correlated with serum OC (r = 0.480, p = 0.002).
The results of the present study are consistent with those of earlier studies documenting increased BMD in resistance trained men athletes (17,49) and decreased BMD in cyclists compared with runners (39). Moreover, spine and hip BMD of the RT athletes were increased relative to the reference population of young adult men, as evidenced by T-scores > 1, and BMD was reduced in cyclists compared with the reference population. Similar to the findings of Nevill et al. (34), we found that the sport differences in BMD were diminished after adjusting for LBM (Table 5).
Body weight accounts for a large proportion of the variance in BMD in adults (15,40); however, body weight, LBM, and fat mass are highly correlated, making it difficult to separate the effects of each component on BMD. Because muscle contractions exert greater forces on bone than the forces resulting from mass loading (8), it is expected that muscle strength would be positively associated with BMD, independent of body or muscle mass. Fat mass is thought to enhance BMD by the synthesis and secretion of hormones (41), such as estrogen (47) and leptin (21). In addition, the effects of LBM and fat mass are site-specific, with fat mass having a greater impact on weight-bearing bones, whereas LBM positively affects both weight-bearing and non-weight-bearing bones (27,34,50).
There is emerging evidence that the relationship between muscle strength and/or LBM and BMD is dependent on physical activity level and type (15,26). The relationship between muscle mass and regional BMD is stronger in resistance trained individuals compared with sedentary populations (6,10,49). In contrast, the association between LBM and BMD is weaker in athletes who participate in high-impact sports such as hockey (36,38), gymnastics (48), and soccer (46). Consistent with these previous observations, in the present study, LBM was positively associated with BMD only in the RT athletes and cyclists and not in the runners. Milgrom et al. (30), using in vivo measurement of strain, demonstrated that the tibial strain rates produced by running exceed those resulting from cycling and weight lifting (leg press) approximately 5- to 6-fold. Thus, it seems that high-impact activity, such as running, overrides any benefit of added LBM on BMD.
As described previously, site-specific differences in the relationships between LBM, fat mass, and BMD demonstrate the importance of muscle contraction to the preservation of bone mass. Resistance training of the major muscle groups of the upper and lower body exerts muscle contraction forces on both the arm and leg bones. Consistent with upper- and lower-body loading, in the present study, LBM was positively associated with BMD at each site measured in the RT athletes. In particular, the positive relationship between LBM and BMD of the arm-a non-weight-bearing bone-suggests that muscle contraction makes a significant contribution to the LBM-associated increases in BMD. Cycling, in contrast to whole-body resistance training, results in the repetitive exertion of muscle contraction forces on only the leg bones. As a result, in the CYCLE group, LBM was positively associated with leg, but not arm, BMD. Thus, different relationships between LBM and site-specific BMD between resistance trained athletes and cyclists support the importance of muscle contraction to maintenance of BMD.
Although the cross-sectional design of this study cannot demonstrate causality, comparison of athletic populations overcomes the difficulties of subject retention and compliance associated with longitudinal exercise-based intervention studies. We used LBM as an indicator of muscular strength because the 2 are significantly correlated in adult men (24,28). It is important to note that greater muscle strength also serves to reduce impact forces via eccentric muscle contractions that protect bone from gravitational force during movement (5). In addition, because resistance training-induced gains in muscle strength do not invariably produce increases in BMD (14,45), Wang et al. (50) have suggested that muscle strength may be an indicator of the long-term use of bone and that the positive associations between muscle strength and bone may describe the relationship between bone use and bone strength. Another perspective on the positive relationship between muscle and bone has been offered by Rubin et al. (43), who have hypothesized that constant, low-magnitude, high-frequency loading of bone by muscle during nonvigorous daily activity is a key determinant of bone mass.
We observed a positive association between serum OC, a marker of bone formation, and current bone loading scores. Bone loading scores were calculated using GRF, frequency, and duration for each activity. Therefore, the results of the present study suggest that high-impact activity is associated with elevated osteoblast differentiation, as assessed by serum OC concentrations (25). This result is consistent with what is known about how bone cells sense and respond to mechanical loading. It seems that intracortical fluid flow and the resulting fluid shear stress that result from mechanical loading are the proximal signals that stimulate osteocyte and osteoblast activity (51). Activities that produce greater strain rates result in enhanced intracortical fluid flow, thereby inducing a larger cellular response (19).
From a practical perspective, the results of the present study support the exercise prescription of weight-bearing endurance exercise, activities that involve jumping, and resistance exercise that targets all major muscle groups for preservation of bone mass (22,33). On the basis of the present study's results, we hypothesize that resistance training that increases LBM will benefit individuals whose primary mode of exercise does not involve impact activities. Thus, individuals whose primary mode of exercise is not weight bearing, such as cycling, swimming, or rowing, should be encouraged to add bone-strengthening activities, including resistance training or running, to their training regimens.
This study was funded by the Department of Nutritional Sciences at the University of Missouri-Columbia, the F21C Summer Research Intern Program. The authors gratefully acknowledge the editorial comments provided by Joanne Loethen.
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Keywords:© 2009 National Strength and Conditioning Association
bone density; bone turnover; bone loading; osteopenia; sports