Skeletal fractures impose significant medical and economic burden (6). Epidemiologic studies report that more than 1.5 million osteoporotic fractures occur each year in the United States, reaching US $18 billion in health care costs. Risk factors for osteoporotic fractures include female sex, genetics, and hormonal factors among others (38). However, modifiable lifestyle factors, particularly physical activity and nutrition, have the potential to optimize bone health and may reduce fracture risk (9). Studies show that individuals who are more physically active have higher bone strength and bone mineral density (BMD) than those who are not as active and, as a result, have a reduced risk of developing skeletal fractures (21). Therefore, it is important to identify the osteogenic (bone-forming) potential of various physical activities that could help reduce societal burden of fractures.
Animal models suggest that mechanical loading or weight bearing activity, such as running, has a positive effect on bone health (19,34). However, studies exploring the effect of running on bone parameters in humans are less convincing. Although a few studies reported that runners have higher areal BMD (aBMD) than healthy controls (5,20,36), others report no difference (10,12), and several studies report a lower aBMD in runners compared with controls (16,17,32,37). Studies to date have used dual-energy x-ray absorptiometry (DXA) to assess aBMD as the primary outcome. Although DXA provides useful clinical information about fracture risk on a population level, aBMD may misrepresent changes in bone geometry that likely occur with mechanical loading (25,32,34). Animal studies suggest that bone adapts to changes in mechanical loading by adding bone primarily to the periosteal (outer) surface, thus increasing the bending strength of bone (32). Increased periosteal apposition with no change in the mineral would be measured as a lower aBMD by DXA, despite an increase in bending strength (Fig. 1). Traditional DXA outcomes do not provide important information about bone geometry and are confounded by bone and body size (29), a potential confounding factor in studies of runners. Peripheral quantitative computed tomography (PQCT) provides a three-dimensional assessment of cortical and trabecular bone density, bone geometry, and estimates of bone strength, which may more accurately portray bone adaptation to changes in mechanical load.
At each foot strike, running introduces loads to the bone from the weight of the body contacting the ground and from a pull of local contracting muscles. The peak ground reaction force during running is two to four times the body weight (26), which has been reported as a loading that is high enough to stimulate bone adaptation (30). It has become a common understanding that forces exerted on bone from contracting muscles are a key contributor to the bone deformation during loading. This resultant deformation leads to the initiation of signals for bone adaptation. In addition, muscle size (muscle cross-sectional area (MCSA), total lean body mass) is highly correlated with muscle force (1,21); therefore, these variables can be used to adjust bone strength to describe the muscle-bone relationship.
The purpose of this study, therefore, was to assess bone geometry, volumetric density, estimates of bone strength, and MCSA between collegiate runners and sedentary controls using PQCT. We hypothesize that male and female runners will have higher bone strength owing to greater total bone area (ToA) compared with sedentary controls.
Male (n = 21, aged 22 yr) and female (n = 28, aged 22 yr) runners were mainly recruited from intercollegiate cross-country teams (∼70% of participants) in and around the Minneapolis-Saint Paul area. Participants were also recruited from postcollegiate running clubs and road races (29% females, 33% males). To be eligible for study participation, runners had to be 18-35 yr of age, have trained and competed in middle- or long-distance running (800-m marathon) during the past 3 yr, and be currently running a minimum of 35 miles·wk−1 on average. Nonactive healthy male controls (n = 17, aged 21 yr) were recruited via flyers placed throughout the University of Minnesota campus. Nonactive was defined as participating in less than 3 h·wk−1 of regular exercise. Healthy female controls (n = 32, aged 25 yr) were drawn from baseline data of the Women In Steady Exercise Research study, an exercise intervention study of college-aged women. All women were excluded from the study if they had ever been pregnant or had used oral contraceptives in the past 2 yr. Both male and female participants were excluded if they were a smoker, had sustained a recent bone fracture (<1 yr), had current or history of respiratory disease, diabetes, metabolic bone disorders, rheumatoid arthritis, thyroid or parathyroid disease malignancy, cardiovascular, renal, or inflammatory bowel disease, or were taking any medications known to affect bone metabolism. Details of the study and testing procedures were explained to each participant and a written informed consent was obtained before data collection. The Institutional Review Board of the University of Minnesota approved this study.
The Eating Attitudes Test (EAT-26) was administered as a standardized measure of symptoms and characteristics of subtle and severe eating disorders (28). Responses were scored according to three subscales (i.e., Dieting, Bulimia, and Food Preoccupation and Oral Control), and subscale scores were computed by summing all items assigned to that particular scale. Individuals scoring a 20 or higher are considered to be "at risk" for a clinical eating disorder and would be excluded from study participation owing to potential adverse effects disordered eating may have on bone health (24). No participants were excluded on the basis of the EAT-26, as all scored <20.
Participants also completed a health history questionnaire to assess their current and past activity levels and medical history. Women reported the number of menses in the previous 12 months and were classified as eumenorrheic (10 or more cycles per year), oligomenorrheic (4-9 menstrual cycles per year), or amenorrheic (0-3 menstrual cycles per year) (7).
A wall-mounted stadiometer (Model 242; Seca, Hanover, MD) was used to measure height to the nearest 0.1 cm. An electronic scale (Model 840; Seca) was used to measure body weight to the nearest 0.1 kg. Tibia length was measured with an anthropometric tape measure to the nearest millimeter from the tibia plateau to the medial malleolus. The forearm was measured in a similar way, from the olecranon to the styloid process of the ulna. Three measurements were taken at each respective site, and the mean of the two closest measurements for each variable was used for the analysis. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.
PQCT (Norland/Stratec XCT-3000; Orthometrix, Inc., White Plains, NY) scans were acquired using a 2.3-mm slice at the distal (4%), midshaft (50%), and proximal (66%) sites of the left tibia. To place the anatomic reference line, a 30-mm planar scout view was obtained over the joint line. The scanner automatically identified the locations for the distal, midshaft, and proximal measurements by the percent (4%, 50%, and 66%) of tibia length. A scan speed of 25 mm·s−1 and a voxel size of 0.4 mm were used. Contour mode 3 (200 mg·cm−3), Peel mode 5 (automatic), and Cort mode 3 (200 mg·cm−3) were used to analyze the 4% site. This distal and highly trabecular site was assessed for bone geometry (ToA, mm2), total bone volumetric density (ToD, mg·mm−3), and an estimate of bone compressive strength (bone strength index (BSI), mg·mm−4 = ToA × ToD2 / 100,000) (22,24). At the 50% and 66% sites, Contour mode 1 (710 mg·cm−3), Peel mode 2 (540 mg·cm−3), and Cort mode 1 (710 and 480 for polar strength strain index (SSIp), mg·cm−3) were used. The variables assessed at the midshaft and proximal sites include bone geometry (ToA, cortical area (CoA, mm2), and cortical thickness (CoTh, mm)), cortical volumetric bone density (CoD, mg·mm−3), and estimates of bone bending strength, section modulus (Zp, mm3), and SSIp (mm3). MCSA was also assessed at the 50% and 66% sites of the tibia.
Similar to the tibia scan, measurements were also obtained at 4% and 50% sites of the participants' nondominant radius. Measurement protocol followed a similar method of tibia data collection. Bone outcomes for the distal (4%) and midshaft (50%) radius were the same for the distal and midshaft tibia. MCSA was determined at the 50% radius site. The total time required for all five scans was less than 30 min. One trained operator (A.T.) performed the measurements and analyzed all scans. Precision with repositioning was determined in our laboratory in adults (women = 11, men = 4, aged 28.5 ± 6.5 yr) as a coefficient of variation (%) and varied from 0.28 (ToD) to 1.20 (trabecular area (TrbA)) at the distal tibia and from 0.31 (CoD) to 0.41 (ToA) at the shaft. An anthropometric phantom provided by the manufacturer was scanned daily for quality assurance.
Independent-sample t-tests were used to compare the anthropometric, training history, and eating attitudes variables between runners and controls and are presented as means and SD. ANCOVA was used to compare bone outcomes between runners and controls and were adjusted for age, body weight, and tibia length. As the relationship between covariates and bone outcomes differed within each sex, separate models were run for males and females. Bone parameters are reported as means and 95% confidence intervals (CI). Differences are considered significant if P < 0.05 for all outcomes. SPSS 15.0 for Windows was used for all analyses.
Descriptive characteristics are shown in Table 1. Female runners were 2.7 yr younger than controls (22.2 vs 24.9 yr, P < 0.05) and had significantly lower BMI (−2.7 kg·m−2, P < 0.001). Female control participants all had greater than 10 cycles per year, whereas half of the runners had less than 10 cycles per year. Although runners reached menarche ∼1 yr later on average than controls, differences between groups were not significant (P = 0.10), and all participants reached menarche within the normal range (12-13 yr) (15).
There were no significant differences in age, height, or tibia length between male runners and controls. Male runners had significantly lower body weight (−14.1 kg, P < 0.001) and had lower BMI (−4.4 kg·m−2, P < 0.001) compared with controls. Although participating in less than 3 h of activity per week (on the basis of inclusion criteria), male controls did run 3.5 miles on average, whereas runners averaged more than 50 miles·wk−1.
Tibia bone outcomes are shown in Table 2. Compared to controls, female runners had significantly higher values of estimated bone strength at all tibia sites. At the distal tibia, estimated bone compressive strength (BSI) was significantly higher (+19%, P < 0.05) in female runners because ToA (+11%, P < 0.005) was greater, despite no difference in ToD at this site. At the midshaft and proximal tibia (50% and 66% sites), bone strength (SSIp and Zp) was also higher in female runners (+17%-19%, P < 0.001; Fig. 2), owing to greater bone size (ToA, +14%, P < 0.001; and CoA, +16%, P < 0.001) and CoTh (+8%-14%, P < 0.05). Despite significantly lower volumetric density (CoD, −2%, P < 0.05) in the female runners, the structural differences resulted in greater bone strength. After adjustment for leg MCSA, estimates of bone strength remained significantly greater in female runners compared with controls (data not shown).
There were no differences in estimates of bone strength or volumetric density at any tibia site in the male runners compared with controls (Table 2). However, male runners had significantly greater cortical area, CoA (+8%-11%, P < 0.05) and CoTh (+8%-14%, P < 0.05) at the 50% and 66% sites. These favorable differences in tibia bone structure did not translate to greater bone strength either before or after adjusting for MCSA.
After adjusting for age, weight, and ulna length (Table 3), Zp (+13%, P < 0.05) but not SSIp at the 50% site was significantly greater in female runners compared with female controls owing to significantly greater ToA (+9%, P < 0.05; Fig. 2). There were no differences in cortical area or volumetric density at the midshaft. At the 4% site, compressive bone strength was not significantly different between female groups; however, ToA was greater in female runners compared with female controls (+11%, P < 0.05). After substituting weight for MCSA in the model, the strength differences at the 50% site disappeared, and the difference in ToA at the 4% site remained significant.
There were no differences in any bone outcome between male runners and controls either before or after adjusting for MCSA.
Several findings from this study contribute to the understanding of the effects of running on skeletal health. The main findings include the following: 1) greater bone strength in female runners than female controls; 2) difference in bone geometry, but not strength in male runners compared with male controls; and 3) maintenance of significant findings after adjusting for MCSA. This study also highlights the importance of assessing bone structure in addition to bone density when exploring the role of physical activity on bone health.
Bone strength in runners.
In accordance with the mechanostat and other related theories of functional adaptation (11,31), our data suggest that running is associated with a beneficial adaptation of bone structure and bone strength. Female runners had substantially greater (17%-19%) estimates of tibia bone strength than controls at both the distal and midshaft sites and moderately greater (11%) bone strength at the radius midshaft. Interestingly, differences in bone strength outcomes were not observed between male runners and controls. However, male runners had favorable differences in cortical bone geometry compared with controls, suggesting that there are positive bone structural adaptations associated with running in males.
These findings contribute to the existing literature by characterizing the bone strength and geometric differences that underpin bone density outcomes in runners. Previous studies of the effects of running on bone have primarily used DXA and reported differences in aBMD as a surrogate for bone strength. Studies using DXA-based methodology in runners have conflicting findings (3,8,36). Some studies using DXA have reported higher aBMD (6%-12% on average) in male and female runners compared with controls, primarily at loaded sites such as the proximal femur (3,5,36). In contrast, several other studies reported lower aBMD values in runners compared with minimally active controls (4,16,17). For example, a study of healthy physically active men showed a negative correlation with weekly distance run and bone mineral content (BMC) of the trochanteric region of the hip (r = −0.34) (16). Overall, four studies reported 6%-12% lower aBMD in runners compared with minimally active controls, even at loaded sites, such as the femoral neck (4,17,32,37).
A plausible explanation for the discrepancies in the results of the these studies is the inability of standard DXA outcomes of aBMD to capture bone structural differences and small changes in the amount of bone material that translate to significant differences in bone strength. For instance, Robling et al. (33) loaded female rat ulnas, which resulted in only a 5% increase in BMC and aBMD, whereas actual bone breaking strength increased by 94%. In other words, small changes in bone mass or density translate to large changes in bone strength when bone is placed strategically on the outer surface of bone. These changes may not be captured by traditional DXA outcomes. Consistent with our findings, previous studies in humans that used some measure of bone structure using magnetic resonance imaging or QCT demonstrated skeletal benefits associated with running in young adults (13,20), although one study reported no significant difference in bone strength between adolescent runners and controls (14,23).
In contrast to human studies, animal studies demonstrated a clear benefit of run training which increased bone mass and/or improved bone mechanical properties (18,39). For example, Joo et al. showed that running increased bone strength (because of increased trabecular number and thickness) in male rats. Cortical bone strength increased because of femoral periosteal apposition (increased bone cross-sectional area) with no change in material properties (19). Other studies reported similar results (27) and showed that bone bending strength increased with endurance training with no change in bone material properties (2,18). Our findings in female runners are consistent with animal studies in that female runners had greater bone strength despite a lower cortical volumetric density (representative of material properties-but not a direct measure). Bone strength in female runners was greater because of geometric adaptations of greater ToA and CoA.
Structural bases for bone strength differences.
As noted above, differences in bone strength for female runners at both the tibia and radius were due to greater bone area rather than increased bone volumetric density. Interestingly, vBMD tended to be lower in female runners compared with controls. There are several possible explanations for lower bone density and greater bone area in runners. One such explanation for lower bone density (a factor of mineralization and porosity) is that mechanical loading leads to the production of microdamage, subsequent turnover of damaged bone tissue, and therefore, a transient increase in porosity (25). This theory is supported by a study of axial loading of turkey ulnae, which led to a substantial increase in the number of intracortical pores (34). Continued running on a more porous bone could in turn provide a mechanical stimulus for increased bone diameter, possibly explaining the observations of greater area in runners compared with controls.
Another possible explanation for the lower density values in runners or lack of difference between runner and control groups may be attributed to the role of estrogen. Estrogen has been shown to suppress bone turnover (2). Because a primary purpose of the present study was to explore bone strength differences between runners and sedentary controls in a loaded bone, the tibia, estrogen was not actually measured, although the finding of menstrual cycle irregularities in the running group was likely associated with lower estrogen exposure. The control group had regular cycles and possibly more regular estrogen exposure than the running group. Consequently, a potential lack of estrogen in runners may have lead to higher turnover and, therefore, lower density. Nevertheless, the cross-sectional nature of the present study does not allow determination of causal relationships, and explanations for the greater bone area and lower bone density in female runners compared with controls remain to be investigated.
Whereas CoA and CoTh were greater in male runners compared with controls, there was no significant difference in the ToA. Furthermore, these structural changes did not translate to differences in bone strength in males. The lack of difference in bone strength parameters between male runners and controls may be attributed to the higher activity levels seen in the male control group compared with the female control group. An accepted definition of inactive (37) was used for controls and defined male control participants as inactive if they participated in less than 3 h·wk−1 of activity. Male controls tended to be at the high end of that range and had 3 h of (unspecified) physical activity per week on average. In comparison, female controls were included in the study if they had been sedentary (less than two, 30-min sessions per week) for the previous 3 months leading up to the study. Given that bone needs very few loading cycles that are above the modeling threshold for a positive bone adaptation (33), possibly the activity level of the male controls was sufficient for bone adaptation. Our findings suggest that, although male runners have different bone geometry than controls, the geometric differences are no more beneficial than normal activity for bone strength. However, our data were not adequate to fully describe the type and intensity of activity male controls completed. Future studies are needed to establish a "minimal effective dose" of exercise required for optimization of bone strength.
Maintenance of significant findings after adjustments for MCSA.
Muscle force is responsible for a large portion of the load on bone, and recent work suggests the importance of adjusting for muscle force, rather than body weight (26). We therefore used MCSA as a surrogate for muscle force (35) to further explore the role of muscle in bone adaptation in runners. After adjustment for MCSA, significant differences in indices of bone strength remained for females at the tibia, but findings at the radius were no longer significant. These findings suggest that, at least at the tibia in female runners, external ground reaction forces associated with running also play an important role in determining bone strength. It is also possible that unlike the general population, MCSA is a poor surrogate for muscle force in runners. Further studies are needed that can better measure muscle force associated with bone strength.
Our study is limited by its cross-sectional design and potential confounding factors that we were unable to assess. As with any cross-sectional study, our data only suggest relationships between running and changes in bone geometry rather than cause-effect. Possibly, the runners had genetically determined taller stature and lower weight or bone mass and thus are more likely to participate in collegiate running. We were not able to assess traditional bone outcomes of aBMD or BMC at the hip or spine by DXA for this study and, therefore, cannot make direct comparisons to previous studies using DXA technology. However, our primary research question was about differences in bone geometry, volumetric density, and strength estimates, which are not assessed by standard DXA software. In addition, we were not able to assess hormones or nutrition directly, which should be explored in future studies as possible confounding factors in the bone response to loading. A final limitation is that female and male runners had lower body weight on average compared with the control groups. We therefore adjusted for body weight in our analysis, and PQCT is a three-dimensional technique that is not influenced by body size as DXA.
Estimates of bone strength were greater in female runners when compared with female controls, indicating that female runners may be at an advantage in bone geometry and strength. These strength differences are attributable to greater bone area rather than greater bone volumetric density. Strength differences were not observed between male runners and controls; however, male runners had some advantageous bone structural characteristics. Bone strength and structural differences between runners and controls remained after adjusting for MCSA, suggesting that external reaction forces associated with running may be an important osteogenic stimulus. Overall, these data suggest that running is associated with a positive adaptation of bone structure and/or bone strength, particularly, in women. However, future randomized controlled trials are needed to confirm these findings.
The authors thank the participants for their time and essential contribution to this study and Micaela Kruckenberg for her help with data management. This study was supported in part by a grant from the National Institutes of Health (NIAMS-K23 AR49040-01A1, M. Petit). The results of the present study do not constitute endorsement by ACSM.
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Keywords:©2009The American College of Sports Medicine
BONE STRENGTH; BONE MINERAL DENSITY; MECHANOSTAT; PERIPHERAL QUANTITATIVE COMPUTED TOMOGRAPHY (PQCT); EXERCISE