Does lower-body BMD develop at the expense of upper body BMD in female runners
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Purpose: Evidence suggests that exercise plays an important role in stimulating site-specific bone mineral density (BMD). However, what is less well understood is how these benefits dissipate throughout the body. Hence, the purpose of the present study was to compare the levels of, and the correlation between, BMD recorded at 10 sites in female endurance runners, and to investigate possible determinants responsible for any inter-site differences observed. Methods: Repeated measures ANOVA was used to compare the BMD between sites and factor analysis was used to describe the pattern of intersite correlations. Allometric ANCOVA was used to identify the primary determinants of bone mass and how these varied between sites. Results: The ANOVA and factor analysis identified systematic differences in BMD between sites, with the greatest BMD being observed in the lower-body sites, in particular the legs. An investigation into the possible mechanisms responsible for these differences revealed "distances run" (km(.)wk(-1)) as a positive, and "years of training" as a negative determinant of bone mass (P <0.001). However, the effect of a number of determinants varied between sites (P <0.05). Specifically, the ANCOVA identified that running further distances resulted in higher bone mass in the arms and legs. In contrast, training for additional years appeared to result in lower bone mass in the arms and lumber spine. Calcium intake was also found to be positively associated with bone mass in the legs but negatively associated at all other sites. Conclusions: A combination Of running exercise and calcium intake would appear to stimulate the bone mass of women endurance runners at lower-body sites but at the expense of bone mass at upper-body sites.
|Number of pages||7|
|Journal||Medicine and Science in Sports and Exercise|
|Publication status||Published - 1 Oct 2003|
- intersite correlations, ancova, factor analysis, bone mineral density, proportional allometric regression model