Department of Biological Sciences, Ohio University, Athens, OH 45701, United States of America; Ohio Musculoskeletal and Neurological Institute, Ohio University, Athens, OH 45701, United States of America.
Department of Biological Sciences, Ohio University, Athens, OH 45701, United States of America.
Bone. 2019 Mar;120:336-346. doi: 10.1016/j.bone.2018.11.018. Epub 2018 Nov 26.
High error rates in the prediction of fragility fractures by bone mineral density have motivated searches for better clinical indicators of bone strength, and the high incidence of non-hip, non-spine fractures has raised interest in cortical bone. The aim of this study was to assess the accuracy of Cortical Bone Mechanics Technology™. CBMT is a new non-invasive 3-point bending technique for measuring the mechanical properties of cortical bone in the ulnas of living humans.
35 cadaveric human arms were obtained from small women and large men ranging widely in age (17 < Age < 99 years) and body size (14 < BMI < 40 kg/m). Noninvasive CBMT measurements of the flexural rigidity of the ulna bones within these arms (EI) were compared to measurements of EI by Quasistatic Mechanical Testing in the ulnas excised from those arms (EI). Ulna bending strength was also measured by QMT as the peak moment before fracture (M). The open source BoneJ plugin to ImageJ image processing software was used to calculate cortical porosity (CP) in micro-computed tomography images of a 2 mm length of the mid-shaft of each fractured ulna, and the interosseous diameter (IOD) of each ulna was also measured in those images.
EI measurements (13 < EI < 97 Nm) explained 99% of the variance in QMT measurements of ulna bending strength (11 < M < 90 Nm), but EI was biased high by 30% (p < 0.0001) relative to EI (11 < EI < 69 Nm). After correcting this bias, EI and EI measurements lay along the identity line (y = 1.00x, R = 0.99, SEE = 3.1 Nm). Predictions of M by EI were less accurate than predictions by EI (both R = 0.99; SEE = 5.9 Nm vs SEE = 4.5 Nm, F = 2.92, p = 0.001), but EI predictions were substantially more accurate than those by IOD (R = 0.79; SEE = 10.6 Nm, F = 3.30, p < 0.001) and CP (R = 0.35; SEE = 18.9 Nm, F = 10.45, p < 10). Predictions by EI were also more accurate than predictions by arm donor height (R = 0.63; SEE = 14.3 Nm, F = 5.87, p < 10), body weight (R = 0.77; SEE = 11.1 Nm, F = 3.54, p < 0.001) and BMI (R = 0.64; SEE = 14.1 Nm, F = 2.39, p < 0.01). In forward stepwise multiple regression beginning with EI, only age explained any additional variance in ulna bending strength (ΔR = 0.3%, F = 8.03, p = 0.008).
Noninvasive CBMT measurements of ulna EI explain 99% of individual differences in QMT measurements of ulna bending strength in cadaveric human arms.
骨密度预测脆性骨折的准确率不高，这促使人们寻找更好的骨强度临床指标，而髋部和脊柱外骨折的高发率则增加了人们对皮质骨的兴趣。本研究旨在评估 Cortical Bone Mechanics Technology™（CBMT）的准确性。CBMT 是一种新的非侵入性三点弯曲技术，用于测量活体人类桡骨的皮质骨机械性能。
从年龄（17<年龄<99 岁）和体型（14<BMI<40 kg/m）范围广泛的小女性和大男性的 35 具尸体手臂中获得。这些手臂中桡骨的非侵入性 CBMT 弯曲刚度（EI）测量值与从这些手臂中取出的桡骨进行的准静态机械测试的 EI 测量值进行了比较（EI）。也通过 QMT 测量桡骨弯曲强度作为骨折前的峰值力矩（M）。使用 ImageJ 图像处理软件的 BoneJ 插件计算每个断裂桡骨中段 2mm 长度的微计算机断层扫描图像的皮质孔隙率（CP），并在这些图像中测量每个桡骨的骨间直径（IOD）。
EI 测量值（13<EI<97 Nm）解释了 QMT 测量的桡骨弯曲强度（11<M<90 Nm）变异的 99%，但 EI 相对于 EI（11<EI<69 Nm）偏高 30%（p<0.0001）。在纠正了这种偏差后，EI 和 EI 测量值沿着身份线（y=1.00x，R=0.99，SEE=3.1 Nm）。EI 对 M 的预测不如 EI 准确（R 均为 0.99；SEE=5.9 Nm 与 SEE=4.5 Nm，F=2.92，p=0.001），但 EI 预测明显比 IOD（R=0.79；SEE=10.6 Nm，F=3.30，p<0.001）和 CP（R=0.35；SEE=18.9 Nm，F=10.45，p<10）准确。EI 的预测也比手臂捐赠者身高（R=0.63；SEE=14.3 Nm，F=5.87，p<10）、体重（R=0.77；SEE=11.1 Nm，F=3.54，p<0.001）和 BMI（R=0.64；SEE=14.1 Nm，F=2.39，p<0.01）的预测更准确。从 EI 开始的逐步多元回归中，只有年龄解释了桡骨弯曲强度的任何额外变异（ΔR=0.3%，F=8.03，p=0.008）。
在尸体人类手臂中，桡骨 EI 的非侵入性 CBMT 测量值解释了 QMT 测量桡骨弯曲强度个体差异的 99%。
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