Are DXA/aBMD and QCT/FEA Stiffness and Strength Estimates Sensitive to Sex and Age?

Rezaei, Asghar; Giambini, Hugo; Rossman, Timothy; Carlson, Kent; Yaszemski, Michael J.; Lu, Lichun; Dragomir-Daescu, Dan

doi:10.1007/s10439-017-1914-5

Cited by 16 publications

(16 citation statements)

References 32 publications

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“…(2) as the femoral strength model, the variable sex accounted for a significant difference of > 1000 N in femoral strength between women and men. The importance of age for proximal femur strength, even when including aBMD and sex, was also shown in previous studies [13,17]. …”

Section: Discussionsupporting

confidence: 77%

Factors associated with proximal femur fracture determined in a large cadaveric cohort

Dragomir-Daescu

Rossman

Rezaei

et al. 2018

Bone

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Many researchers have used cadaveric fracture tests to determine the relationship between proximal femur (hip) fracture strength and a multitude of possible explanatory variables, typically considered one or two at a time. These variables include subject-specific proximal femur variables such as femoral neck areal bone mineral density (aBMD), sex, age, and geometry, as well as physiological hip fracture event variables such as fall speed and angle of impact. However, to our knowledge, no study has included all of these variables simultaneously in the same experimental dataset. To address this gap, the present study simultaneously included all of these subject-specific and fracture event variables in multivariate models to understand their contributions to femoral strength and fracture type. The primary aim of this study was to determine not only whether each of these variables contributed to the prediction of femoral strength, but also to determine the relative importance of each variable in strength prediction. A secondary aim was to similarly characterize the importance of these variables for the prediction of fracture type. To accomplish these aims, we characterized 197 proximal femurs (covering a wide range of subject-specific variables) with DXA and CT scans, and then tested the femurs to fracture in a sideways fall on the hip configuration. Each femur was tested using one of three fall speed conditions and one of four angles of impact (bone orientations). During each test, we acquired measurements of relevant force and displacement data. We then reduced the test data to determine femoral strength, and we used post-fracture CT scans to classify the fracture type (e.g., trochanteric, cervical). Using these results, the explanatory variables were analyzed with mixed statistical models to explain variations in hip fracture strength and fracture type, respectively. Five explanatory variables were statistically significant in explaining the variability in femoral strength: aBMD, sex, age, fall speed, and neck-shaft angle (P ≤ 0.0135). These five variables, including significant interactions, explained 80% of the variability in hip fracture strength. Additionally, when only aBMD, sex, and age (P < 0.0001) were considered in the model, again including significant interactions, these three variables alone explained 79% of the variability in hip fracture strength. So while fall speed (P = 0.0135) and neck-shaft angle (P = 0.0041) were statistically significant, the inclusion of these variables did not appreciably improve the prediction of hip fracture strength compared to the model that considered only aBMD, sex and age. For the variables we included in this study, in the ranges we considered, our findings indicate that the clinically-available information of patient age, sex and aBMD are sufficient for femoral strength assessment. These findings also suggest that there is little value in the extra effort required to characterize the effect of femoral geometry on strength, or to account for the probabilistic nature o...

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Section: Discussionsupporting

confidence: 77%

Factors associated with proximal femur fracture determined in a large cadaveric cohort

Dragomir-Daescu

Rossman

Rezaei

et al. 2018

Bone

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“…12 The force at the greater trochanter and the displacement at the femoral head were measured during fracture testing, and the slope of the linear portion of the force-displacement curve (from 20% to 80% of the measured yield force) was taken as the femoral stiffness. 29 Figure 1 shows the QCT scanning setup (Fig. 1A), the mechanical testing setup (Fig.…”

Section: Experimental Approachmentioning

confidence: 99%

“…The range of Hounsfield units covering the tissue domain was thus divided into 301 bins, approximating a continuous distribution of grey scale values. 29 Ash density (ρ ash ) for each element was calculated based on the HU values and the manufacturer's phantom calibration specifications (Mindways Inc., Austin TX). This resulted in a relationship of the form: 5 ρ ash = m × HU + n, (1) where m and n were determined for each QCT scan using the known density of the rods in the calibration phantom.…”

Section: Qct/fea Approachmentioning

confidence: 99%

“…Although sex seems to be significant in addition to DXA/aBMD, previous studies found that the differences in geometry between women and men did not explain the sex dimorphism in femoral strength. 28,29 Quantitative computed tomography-based finite element analysis (QCT/FEA) is a tool that uses a more accurate representation of the 3D geometry and mineral distribution of the proximal femur to calculate femur properties. Many studies have shown QCT/FEA to be a better predictor of femoral properties than DXA/aBMD.…”

Section: Introductionmentioning

confidence: 99%

“…Several prior studies have focused on developing suitable mathematical equations between ash density and bone elastic modulus. 15,27 In a previous QCT/FEA study, 29 we used an ash density-elastic modulus relationship from the literature. 24 When comparing measured stiffness and strength values with corresponding QCT/FEA predictions, we found that age used as an independent variable was statistically significant, but that sex was insignificant.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Accuracy of Proximal Femur Elastic Modulus Equations

et al. 2019

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Quantitative computed tomography-based finite element analysis (QCT/FEA) is a promising tool to predict femoral properties. One of the modeling parameters required as input for QCT/FEA is the elastic modulus, which varies with the location-dependent bone mineral density (ash density). The aim of this study was to develop optimized equations for the femoral elastic modulus. An inverse QCT/FEA method was employed, using an optimization process to minimize the error between the predicted femoral stiffness values and experimental values. We determined optimal coefficients of an elastic modulus equation that was a function of ash density only, and also optimal coefficients for several other equations that included along with ash density combinations of the variables sex and age. All of the optimized models were found to be more accurate than models from the literature. It was found that the addition of the variables sex and age to ash density made very minor improvements in stiffness predictions compared to the model with ash density alone. Even though the addition of age did not remarkably improve the statistical metrics, the effect of age was reflected in the elastic modulus equations as a decline of about 9% over a 60year interval.

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Distal radius bone microarchitecture: what are the differences between age 25 and old age?

Pan

Yang

et al. 2020

Arch Osteoporos

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This study reported that the transitional zones in older adults were enlarged at the expense of the compact-appearing cortex with a greater in porosity in all cortical sub-compartments. The magnitude of differences in areal and volumetric bone mineral density (aBMD, vBMD) between older and younger groups were similar.Introduction Aging is strongly associated with bone loss, but little is known about magnitudes of differences in bone microarchitectures, aBMD and vBMD from peak bone mass (PBM) to senescence. We aimed to describe differences in aBMD, vBMD and bone microarchitecture parameters at the distal radius between older and young adults. MethodsWe compared 201 participants, aged 62-89 years (female 47%) and 196 participants, aged 24-28 years (female 38%). Bone microarchitecture parameters at distal radius were measured using high-resolution peripheral computed tomography (HRpQCT). aBMD was measured using dual energy X-ray absorptiometry (DXA). Unpaired t-tests and chi square tests were used to compare differences in means and proportions as appropriate. ResultsOlder adults had thinner compact-appearing cortices with larger (cross-sectional area: outer 30.96mm 2 vs. 28.38mm 2 , inner 36.34mm 2 vs. 32.93mm 2 ) and thicker (outer 0.57mm vs. 0.54mm, inner 0.71mm vs. 0.65mm) transitional zones compared to young adults (all p<0.05). Cortical porosity was modestly higher in older adults than in young adults (54% vs. 49%, p<0.001). The magnitude of the difference in hip aBMD between older and young adults was slightly lower than of total radial vBMD (-0.51 SD vs. -0.78 SD). ConclusionCompared with young adults at the time of PBM, the transitional zones in older adults were enlarged at the expense of the compact-appearing cortex with a greater porosity in all cortical sub-compartments.The similar SD differences in aBMD and vBMD between older and younger groups suggest that the differences in bone area are not leading to major artefactual change in aBMD.

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Are DXA/aBMD and QCT/FEA Stiffness and Strength Estimates Sensitive to Sex and Age?

Cited by 16 publications

References 32 publications

Factors associated with proximal femur fracture determined in a large cadaveric cohort

Factors associated with proximal femur fracture determined in a large cadaveric cohort

Optimizing Accuracy of Proximal Femur Elastic Modulus Equations

Distal radius bone microarchitecture: what are the differences between age 25 and old age?

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