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Supporting data for "Multiscale Predictions of Vertebral Strength: Tissue, Microarchitecture, and Body "

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posted on 2022-04-06, 08:36 authored by Yongqiang Jin, Jason Pui Yin CheungJason Pui Yin Cheung, Kam Yim SzeKam Yim Sze, William Weijia LuWilliam Weijia Lu

Osteoporosis is a global health crisis with the fatal consequence of fragility fractures. Almost all bones in the human skeleton could occur fragility fractures, among which vertebral fractures are the most common but the hardest to define. In the past half-century, researchers in the field have put a large amount of effort to understand vertebral strength for predicting vertebral fracture risk. Generally, vertebral strength depends on three scales of mechanical characteristics. They are tissue properties (µm), trabecular microarchitecture (mm), and vertebral body (cm). Even though the previous studies have answered many significant problems, there are still some consequential questions in all three levels.


Firstly, no study has asked if the mechanical properties of bone tissue could vary during deformation. Secondly, no parameter could quantify the contribution of microarchitecture to mechanical properties of cancellous bone. The third question is at the vertebral level in clinical practice. The clinical screening of patients at high vertebral fracture risk is dependent on the lumbar bone mineral density (BMD) regardless of the type of vertebral fracture. But there are various forms of BMD and three different types of vertebral fractures. Nobody has asked if the predictor of fracture risk is dependent on the type of fracture. Fourth, nonlinear finite element models that consider the fracture type are rare.


The current project aims to solve the above problems. Data related to the first three problems have already been published. The current dataset mainly contains the data related to the fourth problem. It contains bone mineral measures, mechanical testing results, and finite element modelling results that relate to vertebral strength.




Funding

Theme-based Research Scheme T13-402/17-N

RGC HKJLFS/M-702/18

SK Yee Medical Foundation Grant (Ref# 2171223)

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