Impact of Long-Term Swimming Exercise on Rat Femur Bone Quality

Considering the conflicting evidence regarding the potential long-term detrimental effect of swimming during growth on femur quality and fracture risk, our aim was to investigate the effect of eight months of swimming on femur quality. Twenty male eight-week-old Wistar rats were assigned into a swimming (SW; n = 10; 2 h/day, 5 days/week) or active control group (CG; n = 10, housed with running wheel) for eight months. Plasma osteocalcin and C-terminal telopeptide of type I collagen concentrations (ELISA) were assessed at baseline, four, and eight months of protocol. Femur structure (micro-computed tomography), biomechanical properties (three-point bending), and cellular density (histology) were determined after the protocol. SW displayed a lower uncoupling index, suggesting higher bone resorption, lower empty lacunae density, cortical and trabecular femur mass, femur length and cortical thickness, and higher cortical porosity than CG (p < 0.05). Although both biomarkers' concentrations decreased in both groups throughout the experiment (p < 0.001), there were no significant differences between groups (p > 0.05). No differences were also found regarding biomechanical properties, bone marrow adiposity, and osteocyte and osteoclast densities (p > 0.05). Long-term swimming was associated with unbalanced bone turnover and compromised femur growth, lower femur mass, and deteriorated cortical bone microarchitecture. However, femur trabecular microarchitecture and biomechanical properties were not affected by swimming.

RANK ligand converts the NCoR/HDAC3 co-repressor to a PGC1β- and RNA-dependent co-activator of osteoclast gene expression

The nuclear receptor co-repressor (NCoR) complex mediates transcriptional repression dependent on histone deacetylation by histone deacetylase 3 (HDAC3) as a component of the complex. Unexpectedly, we found that signaling by the receptor activator of nuclear factor κB (RANK) converts the NCoR/HDAC3 co-repressor complex to a co-activator of AP-1 and NF-κB target genes that are required for mouse osteoclast differentiation. Accordingly, the dominant function of NCoR/HDAC3 complexes in response to RANK signaling is to activate, rather than repress, gene expression. Mechanistically, RANK signaling promotes RNA-dependent interaction of the transcriptional co-activator PGC1β with the NCoR/HDAC3 complex, resulting in the activation of PGC1β and inhibition of HDAC3 activity for acetylated histone H3. Non-coding RNAs Dancr and Rnu12, which are associated with altered human bone homeostasis, promote NCoR/HDAC3 complex assembly and are necessary for RANKL-induced osteoclast differentiation in vitro. These findings may be prototypic for signal-dependent functions of NCoR in other biological contexts.

Mechanical loading prediction through accelerometry data during walking and running

Currently, there is no way to assess mechanical loading variables such as peak ground reaction forces (pGRF) and peak loading rate (pLR) in clinical settings. The purpose of this study was to develop accelerometry-based equations to predict both pGRF and pLR during walking and running. One hundred and thirty one subjects (79 females; 76.9 ± 19.6kg) walked and ran at different speeds (2-14km·h^-1) on a force plate-instrumented treadmill while wearing accelerometers at their ankle, lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Our pGRF prediction equation was compared with a reference equation previously published. Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration rate for pLR prediction. All pGRF equation coefficients of determination were above 0.96, and a good agreement between actual and predicted pGRF was observed, with a mean absolute percent error (MAPE) below 7.3%. Accuracy indices from our equations were better than previously developed equations. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF. Walking and running pGRF can be predicted with high accuracy by accelerometry-based equations, representing an easy way to determine mechanical loading in free-living conditions. The pLR prediction equations yielded a somewhat lower prediction accuracy compared with the pGRF equations.