Bone Imaging Presentation O-182 S185

500 m500 m

THE INFLUENCE OF THE CORTICAL CANAL NETWORK ON MURINE BONE MECHANICS

Philipp Schneider (1), Romain Voide (1), Leah Rae Donahue (2), Marco Stampanoni (3), and Ralph Müller (1)

1. Institute for Biomechanics, ETH Zurich, Switzerland; 2. The Jackson Laboratory,

Bar Harbor, USA; 3. Swiss Light Source, Paul Scherrer Institut, Switzerland

Introduction Recent data have shown that predicting bone strength can be greatly improved by including microarchitectural parameters in the analysis. Our

results [Schneider et al, 2007] showed that the ultrastructural canal network (Figure 1) is a major contributor to cortical tissue porosity, and therefore, can be linked to mechanical bone properties. Consequently, the goal of this study was to investigate the influence of the cortical canal network on murine bone

mechanics.

Figure 1: Osteocyte lacunae (prolate ellipsoids in yellow) and canal network (tubes in red) within murine femoral mid-diaphysis (semitransparent).

To study the canal network morphometry within murine cortical femoral bone, we used a mouse model, including two mouse strains C57BL/6J-Ghrhrlit/J (B6-lit/+) and C57BL/6J-Ghrhrlit/J (C3.B6-lit/+) representing low and high bone mass, respectively [Donahue et al, 2003]. Methods In total, 12 left femora each of the two mouse strains were dissected upon necropsy at the age of 4 months. The femoral mid-diaphyses of all mice were scanned using a synchrotron radiation (SR)-based micro-computed tomography (μCT) setup providing a nominal resolution of 3.5 μm, as described elsewhere [Schneider et al, 2007]. For quantitative analysis of the cortical bone on the macroscopic level, morphometric indices such as bone volume (BV), bone volume density (BV/TV) or cortical thickness (Ct.Th) were calculated. On the tissue level, the canal network was characterized by indices such as canal spacing (Ca.Sp), mean canal volume (<Ca.V>) or mean canal length (<Ca.Le>) [Schneider et al, 2007]. For mechanical testing, three-point bending tests were performed for all contralateral, right femora. Ultimate force (Fu), work to failure (U), and

stiffness (S) were derived from the load-displacement curves [Turner and Burr, 1993]. Multiple linear regression models were built to explain the variation in the mechanical parameters Fu, U, and S in terms of the morphometric indices. Results For both mouse strains and for all mechanical parameters, the prediction power of the multiple linear regression models based on macroscopic cortical indices only was increased significantly by including cannular morphometric measures. For example, the goodness of fit for Fu was increased from R2

adj = 0.51 to 0.89 and from R2adj = 0.90 to

0.95 for B6-lit/+ and C3.B6-lit/+, respectively, which represents respective relative increases in prediction power of 75% and 6% (Figure 2).

0

10

20

30

40

0 10 20 30 40

Measured Fu [N]

Pred

icte

d F

u [N

]

R2adj = 0.89

C3.B6-lit/+B6-lit/+

R2adj = 0.95

0

10

20

30

40

0 10 20 30 40

Measured Fu [N]

Pred

icte

d F

u [N

]

R2adj = 0.89

C3.B6-lit/+B6-lit/+

R2adj = 0.95

Figure 2: Prediction of ultimate force (Fu), including ultrastructural cannular contributions. Discussion We provided strong evidence for a significant influence of the canal network on murine bone mechanics and we believe that morphometric analysis of ultrastructural phenotypes will provide new insights in the assessment of bone quality. References Schneider et al, JBMR, 22:1557-1570, 2007. Donahue et al, JBMR, 18:S123, 2003. Turner and Burr, Bone, 14:595-608, 1993.

16th ESB Congress, Oral Presentations, Tuesday 8 July 2008 Journal of Biomechanics 41(S1)