Abstract
The in vivo mouse tibial loading model is used to evaluate the effectiveness of mechanical loading treatment against skeletal diseases. Although studies have correlated bone adaptation with the induced mechanical stimulus, predictions of bone remodeling remained poor, and the interaction between external and physiological loading in engendering bone changes have not been determined. The aim of this study was to determine the effect of passive mechanical loading on the strain distribution in the mouse tibia and its predictions of bone adaptation. Longitudinal micro-computed tomography (micro-CT) imaging was performed over 2 weeks of cyclic loading from weeks 18 to 22 of age, to quantify the shape change, remodeling, and changes in densitometric properties. Micro-CT based finite element analysis coupled with an optimization algorithm for bone remodeling was used to predict bone adaptation under physiological loads, nominal 12N axial load and combined nominal 12N axial load superimposed to the physiological load. The results showed that despite large differences in the strain energy density magnitudes and distributions across the tibial length, the overall accuracy of the model and the spatial match were similar for all evaluated loading conditions. Predictions of densitometric properties were most similar to the experimental data for combined loading, followed closely by physiological loading conditions, despite no significant difference between these two predicted groups. However, all predicted densitometric properties were significantly different for the 12N and the combined loading conditions. The results suggest that computational modeling of bone’s adaptive response to passive mechanical loading should include the contribution of daily physiological load.
Highlights
Bone is a dynamic tissue that adapts its mass and geometry to mechanical and biological factors
A multi-scale mechanoadaptation model that assumes that local bone adaptation is regulated by the load at the organ level, and determines the parameters of bone remodeling by minimizing the difference in the shape change between the predicted and experimental dataset was used
This is the first time different loading conditions based on nominal passive mechanical loading, physiological loading or a combination of them is implemented
Summary
Bone is a dynamic tissue that adapts its mass and geometry to mechanical and biological factors. Bone adaptation is key for bone homeostasis (Javaheri et al, 2020) Age and diseases such as osteoporosis disrupt this balance by causing a net bone loss and deterioration in mechanical properties (Birkhold et al, 2014; Razi et al, 2015b; Meakin et al, 2017; Roberts et al, 2019). In vivo imaging and dynamic 4D (time and space) assessment of bone adaptation enable the detailed evaluation of the lasting benefits of mechanical loading on healthy (Javaheri et al, 2020) and ovariectomized (Roberts et al, 2020) mouse tibia during treatment and after its withdrawal. An understanding of how mechanical loading modifies baseline bone adaptation in response to normal physiological loading will benefit the optimization of treatment strategies to arrest bone loss, improve fracture healing and enhance rehabilitation (Cheong et al, 2020a; Main et al, 2020)
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