Fig 1.
Summary of materials and methods.
(A) HR-pQCT-based finite element models were developed to compute (left and right) hip stiffness and strength under loading conditions representative of one-legged stance and of a sideways fall. (B) MicroCT and SAM images from a cross-section of the left tibia midshaft (19.5 ± 3.8 cm away from the knee) of the same donors are used to characterize density and architecture of cortical bone. Microstructural measurements are obtained from a region of the bone that can be reached in vivo by diagnostic ultrasound (red arrow).
Fig 2.
SAM and microCT image processing.
(A) SAM cross section with endosteal boundary marked in green. (B) Anteromedial detail of A, with ROIUS highlighted: this region can be reached in vivo by ultrasound waves. A total number of 11.932 cortical bone pores were analyzed from the ROIUS of all samples. Cortical bone pores with diameter (Po.Dm) > 100 μm are colored in magenta. (C) Pore size distribution within the ROIUS of B: the tail (Po.Dm > 100 μm) of the histogram represents 53% of the total cortical bone porosity. (D) 20-mm longitudinal microCT section centered through the ROIUS.
Table 1.
Bone properties of the tibia midshaft measured with microCT and SAM.
Table 2.
Results from DXA and FE simulations.
Table 3.
Hip DXA, macroscopic geometry and vBMD of the tibia midshaft, architecture and composition of tibial cortical bone.
Fig 3.
Cortical bone microstructure of the anteromedial tibia in association with Ct.Po.
Ct.Po is independent from the density of canals (A). Its increase is largely explained by an increase of the density of large pores (B) or of the mean pore diameter (C).
Fig 4.
Associations with proximal femur mechanical competence.
Linear regression between DXA aBMD at the femur neck (A) as well as whole tibia cortical thickness (B), intracortical porosity (C) and relative porosity due to large pores (diameter > 100 μm) in the anteromedial tibia (D) with the FE-based femoral strength under standing and sideways falling loads.
Table 4.
Multivariate regression models of proximal femur stiffness and strength.