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Bone has the ability to adapt to external loading conditions. Especially the beneficial effect of short-term cyclic loading has been investigated in a number of in vivo animal studies. The aim of this study was to assess the long-term effect (>10 weeks) of cyclic mechanical loading on the bone microstructure, bone stiffness, and bone remodeling rates. Mice were subjected to cyclic mechanical loading at the sixth caudal vertebra with 8N or 0N (control) three times per week for a total period of 14 weeks. Structural bone parameters were determined from in vivo micro-computed tomography (micro-CT) scans performed at week 0, 4, 6, 8, 10, 12, and 14. Mechanical parameters were derived from micro-finite element analysis. Dynamic bone morphometry was calculated using registration of serial micro-CT scans. Bone volume fraction and trabecular thickness increased significantly more for the loaded group than for the control group (p = 0.006 and p = 0.002 respectively). The trabecular bone microstructure adapted to the load of 8N in approximately ten weeks, indicated by the trabecular bone volume fraction, which increased from 16.7% at 0 weeks to 21.6% at week 10 and only showed little change afterwards (bone volume fraction of 21.5% at 14 weeks). Similarly bone stiffness - (at the start of the experiment 649N/mm) - reached 846N/mm at 10 weeks in the loaded group and was maintained to the end of the experiment (850N/mm). At 4 weeks the bone formation rate was 32% greater and the bone resorption rate 22% less for 8N compared to 0N. This difference was significantly reduced as the bone adapted to 8N, with 8N remodeling rates returning to the values of the 0N group at approximately 10 weeks. Together these data suggest that once bone has adapted to a new loading state, the remodeling rates reduce gradually while maintaining bone volume fraction and stiffness.

The mechanical integrity of the bone-implant system is maintained by the process of bone remodeling. Specifically, the interplay between bone resorption and bone formation is of paramount importance to fully understand the net changes in bone structure occurring in the peri-implant bone, which are eventually responsible for the mechanical stability of the bone-implant system. Using time-lapsed in vivo micro-computed tomography combined with new composite material implants, we were able to characterize the spatio-temporal changes of bone architecture and bone remodeling following implantation in living mice. After insertion, implant stability was attained by a quick and substantial thickening of the cortical shell which counteracted the observed loss of trabecular bone, probably due to the disruption of the trabecular network. Within the trabecular compartment, the rate of bone formation close to the implant was transiently higher than far from the implant mainly due to an increased mineral apposition rate which indicated a higher osteoblastic activity. Conversely, in cortical bone, the higher rate of bone formation close to the implant compared to far away was mostly related to the recruitment of new osteoblasts as indicated by a prevailing mineralizing surface. The behavior of bone resorption also showed dissimilarities between trabecular and cortical bone. In the former, the rate of bone resorption was higher in the peri-implant region and remained elevated during the entire monitoring period. In the latter, bone resorption rate had a bigger value away from the implant and decreased with time. Our approach may help to tune the development of smart implants that can attain a better long-term stability by a local and targeted manipulation of the remodeling process within the cortical and the trabecular compartments and, particularly, in bone of poor health.

Trabecular bone is a highly dynamic tissue due to bone remodeling, mineralization and demineralization. The mineral content and its spatial heterogeneity are main contributors to bone quality. Using time-lapsed in vivo micro-computed tomography (micro-CT), it is now possible to resolve in three dimensions where bone gets resorbed and deposited over several weeks. In addition, the gray values in the micro-CT images contain quantitative information about the local tissue mineral density (TMD). The aim of this study was to measure how TMD increases with time after new bone formation and how this mineralization kinetics is influenced by mechanical stimulation. Our analysis of changes in TMD was based on an already reported experiment on 15-week-old female mice (C57BL/6), where in one group the sixth caudal vertebra was mechanically loaded with 8 N, while in the control group no loading was applied. Comparison of two consecutive images allows the categorization of bone into newly formed, resorbed, and quiescent bone for different time points. Gray values of bone in these categories were compared layer-wise to minimize the effects of beam hardening artifacts. Quiescent bone in the control group was found to mineralize with a rate of 8 ± 1 mgHA/cm3 per week, which is about half as fast as observed for newly formed bone. Mechanical loading increased the rate of mineral incorporation by 63% in quiescent bone. The week before bone resorption, demineralization could be observed with a drop of TMD by 36 ± 4 mgHA/cm3 in the control and 34 ± 3 mgHA/cm3 in the loaded group. In conclusion, this study shows how time-lapsed in vivo micro-CT can be used to assess changes in TMD of bone with high spatial and temporal resolution. This will allow a quantification of how bone diseases and pharmaceutical interventions influence not only microarchitecture of trabecular bone, but also its material quality.

Animal models are widely used to gain insight into the role of genetics on bone structure and function. One of the main strategies to map the genes regulating specific traits is called quantitative trait loci (QTL) analysis, which generally requires a very large number of animals (often more than 1000) to reach statistical significance. QTL analysis for mechanical traits has been mainly based on experimental mechanical testing, which, in view of the large number of animals, is time consuming. Hence, the goal of the present work was to introduce an automated method for large-scale high-throughput quantification of the mechanical properties of murine femora. Specifically, our aims were, first, to develop and validate an automated method to quantify murine femoral bone stiffness. Second, to test its high-throughput capabilities on murine femora from a large genetic study, more specifically, femora from two growth hormone (GH) deficient inbred strains of mice (B6- lit/lit and C3.B6- lit/lit ) and their first (F1) and second (F2) filial offsprings. Automated routines were developed to convert micro-computed tomography (micro-CT) images of femora into micro-finite element (micro-FE) models. The method was experimentally validated on femora from C57BL/6J and C3H/HeJ mice: for both inbred strains the micro-FE models closely matched the experimentally measured bone stiffness when using a single tissue modulus of 13.06 GPa. The mechanical analysis of the entire dataset (n = 1990) took approximately 44 CPU hours on a supercomputer. In conclusion, our approach, in combination with QTL analysis could help to locate genes directly involved in controlling bone mechanical competence.

Computational models are an invaluable tool to test different mechanobiological theories and, if validated properly, for predicting changes in individuals over time. Concise validation of in silico models, however, has been a bottleneck in the past due to a lack of appropriate reference data. Here, we present a strain-adaptive in silico algorithm which is validated by means of experimental in vivo loading data as well as by an in vivo ovariectomy experiment in the mouse. The maximum prediction error following four weeks of loading resulted in 2.4% in bone volume fraction (BV/TV) and 8.4% in other bone structural parameters. Bone formation and resorption rate did not differ significantly between experiment and simulation. The spatial distribution of formation and resorption sites matched in 55.4% of the surface voxels. Bone loss was simulated with a maximum prediction error of 12.1% in BV/TV and other bone morphometric indices, including a saturation level after a few weeks. Dynamic rates were more difficult to be accurately predicted, showing evidence for significant differences between simulation and experiment (p