A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Abstract

Understanding how bone adapts to mechanical stimuli is fundamental for optimising treatments against musculoskeletal diseases in preclinical studies, but the contribution of physiological loading to bone adaptation in mouse tibia has not been quantified so far. In this study, a novel mechanistic model to predict bone adaptation based on physiological loading was developed and its outputs were compared with longitudinal scans of the mouse tibia. Bone remodelling was driven by the mechanical stimuli estimated from micro-FEA models constructed from micro-CT scans of C57BL/6 female mice (N = 5) from weeks 14 and 20 of age, to predict bone changes in week 16 or 22. Parametric analysis was conducted to evaluate the sensitivity of the models to subject-specific or averaged parameters, parameters from week 14 or week 20, and to strain energy density (SED) or maximum principal strain (ε maxprinc ). The results at week 20 showed no significant difference in bone densitometric properties between experimental and predicted images across the tibia for both stimuli, and 59% and 47% of the predicted voxels matched with the experimental sites in apposition and resorption, respectively. The model was able to reproduce regions of bone apposition in both periosteal and endosteal surfaces (70% and 40% for SED and ε maxprinc , respectively), but it under-predicted the experimental sites of resorption by over 85%. This study shows for the first time the potential of a subject-specific mechanoregulation algorithm to predict bone changes in a mouse model under physiological loading. Nevertheless, the weak predictions of resorption suggest that a combined stimulus or biological stimuli should be accounted for in the model.

Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models

“…Nevertheless, excellent correlation was found between the structural properties predicted with hexahedral or tetrahedral models, highlighting that the geometry was consistently modelled with the two mesh types. However, such differences in local strains may affect the prediction of bone remodelling based on local mechanoregulation (Cheong et al 2020a , b ) and should be considered when designing multi-scale computational models. The material properties assignment mainly affected the correlation between experimental and predicted structural properties, as models with the same material properties resulted in similar slopes of the regression lines (Table 3 , Supplementary material 5).…”

Regional Nanoindentation Properties in Different Locations on the Mouse Tibia From C57BL/6 and Balb/C Female Mice

“…This difference may be due to the asymmetry in bone remodeling activities as observed with in vivo microCT imaging of the mouse tibia (Roberts et al, 2019). However, it needs to be further explored if this difference can be associated with the loading condition due to the effect of the curvature of the bone, as under physiological compressive loads a peak of compressive strains is located on the medial surface of the tibia below the tibio-fibular junction (Oliviero et al, 2018;Cheong et al, 2020). Nevertheless, this difference was not observed in Balb/C mice, which have a similar geometry of the mouse tibia but lower curvature.…”

Mechano-Regulation of Trabecular Bone Adaptation Is Controlled by the Local in vivo Environment and Logarithmically Dependent on Loading Frequency

“…Herein, simulating the distribution of strain energy density (SED)—defined as the increase in energy associated with the tissue deformation per unit volume (i.e., a measure of direct cell strain) —within the caudal vertebrae revealed that bone formation was more likely to occur at sites of high SED, whereas bone resorption was more likely to occur at sites of low SED ( Schulte et al, 2013 ; Lambers et al, 2015 ). While SED is widely used as a mathematical term to describe the mechanical signal influencing bone (re)modeling ( Huiskes et al, 2000 ; Schulte et al, 2013 ; Birkhold et al, 2017 ; Cheong et al, 2020 ), other mechanical signals, such as interstitial fluid flow through the lacuna-canalicular network (LCN), are also known to play a major role in determining the local mechanical environment surrounding osteocytes, the main mechanosensors in bone ( Fritton and Weinbaum, 2008 ; Weinbaum et al, 2011 ; Klein-Nulend et al, 2013 ). In this respect, it has been suggested that measures of fluid flow, such as the gradient in SED, would allow improved predictions of adaptive bone (re)modeling events ( Webster et al, 2015 ; Tiwari et al, 2018 ).…”