DXA predictions of human femoral mechanical properties depend on the load configuration

Dall'Ara, E.; Luisier, B.; Schmidt, R.; Pretterklieber, M.; Kainberger, F.; Zysset, Philippe; Pahr, D. (2013). DXA predictions of human femoral mechanical properties depend on the load configuration. Medical engineering & physics, 35(11), pp. 1564-1572. Elsevier 10.1016/j.medengphy.2013.04.008

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The aim of this study was to evaluate the ability of dual energy X-rays absorptiometry (DXA) areal bone mineral density (aBMD) measured in different regions of the proximal part of the human femur for predicting the mechanical properties of matched proximal femora tested in two different loading configurations.

36 pairs of fresh frozen femora were DXA scanned and tested until failure in two loading configurations: a fall on the side or a one-legged standing. The ability of the DXA output from four different regions of the proximal femur in predicting the femoral mechanical properties was measured and compared for the two loading scenarios.

The femoral neck DXA BMD was best correlated to the femoral ultimate force for both configurations and predicted significantly better femoral failure load (R2=0.80 vs. R2=0.66, P<0.05) when simulating a side than when simulating a standing configuration. Conversely, the work to failure was predicted similarly for both loading configurations (R2=0.54 vs. R2=0.53, P>0.05).

Therefore, neck BMD should be considered as one of the key factors for discriminating femoral fracture risk in vivo. Moreover, the better predictive ability of neck BMD for femoral strength if tested in a fall compared to a one-legged stance configuration suggests that DXA's clinical relevance may not be as high for spontaneous femoral fractures than for fractures associated to a fall.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]
UniBE Contributor:	Zysset, Philippe
Subjects:	500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health 600 Technology > 620 Engineering
ISSN:	1350-4533
Publisher:	Elsevier
Language:	English
Submitter:	Philippe Zysset
Date Deposited:	12 May 2014 09:19
Last Modified:	05 Dec 2022 14:30
Publisher DOI:	10.1016/j.medengphy.2013.04.008
URI:	https://boris.unibe.ch/id/eprint/45329