This study identified imaging parameter(s) which best predict the mechanical properties of distal tibia. Seventeen human cadaver tibiae were assessed by PQCT at four, eight and ten percent site from distal and tested in compression at the twenty-five percent distal portion. Ultimate compressive loads were recorded with a mean of 8276 ± 2915 N. Spearson rank correlation and stepwise regression analysis revealed that CoA, total BMC, SSI and SSI4-TrA4-CoD4 combination had statistically significant correlations with the failure loads. Among all imaging parameters, SSI had the highest relevance due to its account for geometry, density and material distribution, important factors for structural properties.

Musculoskeletal diseases, especially hip fractures, have huge and growing impact on Canadian society. To develop techniques for identification of high risk population, we needed a link between clinical evaluations and laboratory measures of bone health. This study identified imaging parameter(s) which best predict the mechanical properties of distal tibia.

Seventeen human cadaver tibiae were considered in this study (mean age seventy-four, SD six years). PQCT was used to assess the four, eight and ten percent site. It measured the cross-sectional area, bone mineral content and bone mineral density of the cortical bone, trabecular bone and combined. Strength Strain Index (SSI) was calculated from these measurements. Each tibia was cut at twenty-five percent distal. Compressive force was applied uniaxially through a custom-made PMMA indentor onto the distal plateau along the longitudinal axis of the tibia at a rate of 10mm/s. Load and displacement data were recorded. Spearson rank correlation and stepwise regression analysis were used to identify individual and combination of imaging variables that were related to ultimate failure load.

Ultimate failure loads were recorded with a mean of 8276 ± 2915 N. Cortical area (R_0.72), total BMC (R_0.72) and SSI (R_0.86) had statistically significant correlations with the failure load. Stepwise regression revealed that the combination of SSI, TrA, CoD at 4% site explained the greatest amount of variance (R2 = 0.868) and SSI was the major contributor. SSI takes the polar moment of inertia (geometry), density and distribution of material into account. This explains its relevance towards predicting the ultimate failure load.

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