Enlighten Publications

In this section

Prediction of risk of fracture in the tibia due to altered bone mineral density distribution resulting from disuse: a finite element study

Gislason, M. K., Coupaud, S., Sasagawa, K., Tanabe, Y., Purcell, M., Allan, D. B. and Tanner, K. E. (2014) Prediction of risk of fracture in the tibia due to altered bone mineral density distribution resulting from disuse: a finite element study. Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine, 228(2), pp. 165-174. (doi: 10.1177/0954411914522438)

Full text not currently available from Enlighten.

Abstract

The disuse-related bone loss that results from immobilisation following injury shares characteristics with osteoporosis in post-menopausal women and the aged, with decreases in bone mineral density leading to weakening of the bone and increased risk of fracture. The aim of this study was to use the finite element method to: (i) calculate the mechanical response of the tibia under mechanical load and (ii) estimate of the risk of fracture; comparing between two groups, an able-bodied group and spinal cord injury patients group suffering from varying degrees of bone loss. The tibiae of eight male subjects with chronic spinal cord injury and those of four able-bodied age-matched controls were scanned using multi-slice peripheral quantitative computed tomography. Images were used to develop full three-dimensional models of the tibiae in Mimics (Materialise) and exported into Abaqus (Simulia) for calculation of stress distribution and fracture risk in response to specified loading conditions – compression, bending and torsion. The percentage of elements that exceeded a calculated value of the ultimate stress provided an estimate of the risk of fracture for each subject, which differed between spinal cord injury subjects and their controls. The differences in bone mineral density distribution along the tibia in different subjects resulted in different regions of the bone being at high risk of fracture under set loading conditions, illustrating the benefit of creating individual material distribution models. A predictive tool can be developed based on these models, to enable clinicians to estimate the amount of loading that can be safely allowed onto the skeletal frame of individual patients who suffer from extensive musculoskeletal degeneration (including spinal cord injury, multiple sclerosis and the ageing population). The ultimate aim is to reduce fracture occurrence in these vulnerable groups.

Item Type:	Articles
Keywords:	Spinal cord injury, disuse osteoporosis, fracture risk, paraplegia, finite element model
Status:	Published
Refereed:	Yes
Glasgow Author(s) Enlighten ID:	Tanner, Professor Kathleen and Sasagawa, Dr Keisuke and Coupaud, Dr Sylvie
Authors:	Gislason, M. K., Coupaud, S., Sasagawa, K., Tanabe, Y., Purcell, M., Allan, D. B., and Tanner, K. E.
Subjects:	R Medicine > RD Surgery T Technology > T Technology (General)
College/School:	College of Science and Engineering College of Science and Engineering > School of Engineering > Biomedical Engineering
Research Group:	Biomedical Enginering
Journal Name:	Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine
Publisher:	Professional Engineering Publishing
ISSN:	0954-4119
ISSN (Online):	2041-3033

University Staff: Request a correction | Enlighten Editors: Update this record

Altmetric

References

1. Frost H. Perspectives: a proposed general model of the ‘mechanostat’. Anat Rec 1996; 244(2): 139–147. 2. Rubin C, Turner AS, Mallinckrodt C, et al. Mechanical strain, induced noninvasively in the high-frequency domain, is anabolic to cancellous bone, but not cortical bone. Bone 2002; 30(3): 445–452. 3. Schriefer JL, Warder SJ, Saxon LK, et al. Cellular accommodation and the response of bone to mechanical loading. J Biomech 2005; 38(9): 1838–1845. 4. Turner CH. Toward a mathematical description of bone biology: the principle of cellular accommodation. Calcif Tissue Int 1999; 65(6): 466–471. 5. Bloomfield SA. Changes in musculoskeletal structure and function with prolonged bed rest. Med Sci Sports Exerc 1997; 29(2): 197–206. 6. De Bruin ED, Vanwanseele B, Dambacher MA, et al. Long-term changes in the tibia and radius bone mineral density following spinal cord injury. Spinal Cord 2005; 43(2): 96–101. 7. Ozgocmen S, Bulut S, Ilhan N, et al. Vitamin D deficiency and reduced bone mineral density in multiple sclerosis: effect of ambulatory status and functional capacity. J Bone Miner Metab 2005; 23(4): 309–313. 8. Smeltzer SC, Zimmerman V and Capriotti T. Osteoporosis risk and low bone mineral density in women with physical disabilities. Arch Phys Med Rehabil 2005; 86(3): 582–586. 9. Vico L, Collet P, Guignandon A, et al. Effects of longterm microgravity exposure on cancellous and cortical weight-bearing bones of cosmonauts. Lancet 2000; 355(9215): 1607–1611. 10. Zehnder Y, Markus Lu¨ thi M, Michel D, et al. Long-term changes in bone metabolism, bone mineral density, quantitative ultrasound parameters, and fracture incidence after spinal cord injury: a cross-sectional observational study in 100 paraplegic men. Osteoporos Int 2004; 15(1): 180–189. 11. Vestergaard P, Krogh K, Rejnmark L, et al. Fracture rates and risk factors for fractures in patients with spinal cord injury. Spinal Cord 1998; 36(11): 790–796. 12. Eser P, Frotzler A, Zehnder Y, et al. Relationship between the duration of paralysis and bone structure: a pQCT study of spinal cord injured individuals. Bone 2004; 34(5): 869–880. 13. Coupaud S, McLean AN and Allan DB. Role of peripheral quantitative computed tomography in identifying disuse osteoporosis in paraplegia. Skeletal Radiol 2009; 38(10): 989–995. 14. Eser P, Frotzler A, Zehnder Y, et al. Fracture threshold in the femur and tibia of people with spinal cord injury as determined by peripheral quantitative computed tomography. Arch Phys Med Rehabil 2005; 86(3): 498–504. 15. Morse LR, Battaglino RA, Stolzmann KL, et al. Osteoporotic fractures and hospitalization risk in chronic spinal cord injury. Osteoporos Int 2009; 20(3): 385–392. 16. Ragnarsson KT and Sell GH. Lower extremity fractures after spinal cord injury: a retrospective study. Arch Phys Med Rehabil 1981; 62(9): 418–423. 17. Lazo MG, Shirazi P, Sam M, et al. Osteoporosis and risk of fracture in men with spinal cord injury. Spinal Cord 2001; 39(4): 208–214. 18. Coupaud S, McLean AN, Lloyd S, et al. Predicting patient-specific rates of bone loss at fracture-prone sites after spinal cord injury. Disabil Rehabil 2012; 34(26): 2242–2250. 19. Dambacher MA, Neff M, Kissling R, et al. Highly precise peripheral quantitative computed tomography for the evaluation of bone density, loss of bone density and structures. Drugs Aging 1998; 12(Suppl. 1): 15–24. 20. Boutroy S, Van Rietbergen B, Sornay-Rendu E, et al. Finite element analysis based on in vivo HR-pQCT images of the distal radius is associated with wrist fracture in postmenopausal women. J Bone Miner Res 2008; 23(3): 392–399. 21. Ural A. Prediction of Colle’s fracture load in human radius using cohesive finite element modelling. J Biomech 2009; 42(1): 22–28. 22. Varga P, Baumbach S, Pahr D, et al. Validation of an anatomy specific finite element model of Colle’s fracture. J Biomech 2009; 42(11): 1726–1731. 23. Vilayphiou N, Boutroy S, Sornay-Rendu E, et al. Finite element analysis performed on radius and tibia HRpQCT images and fragility fractures at all sites in postmenopausal women. Bone 2010; 46(4): 1030–1037. 24. Keller TS. Predicting the compressive mechanical behaviour of bone. J Biomech 1994; 27(9): 1159–1168. 25. Keyak JH, Rossi SA, Jones KA, et al. Prediction of femoral fracture load using automated finite element modelling. J Biomech 1998; 31(2): 125–133. 26. Cristofolini L and Viceconti M. Mechanical validation of whole bone composite tibia models. J Biomech 2000; 33(3): 279–288. 27. Kutzner I, Heinlein B, Graichen F, et al. Loading of the knee joint during activities of daily living measured in vivo in five subjects. J Biomech 2010; 43(11): 2164–2173. 28. Wong C, Mikkelsen P, Hansen L, et al. Finite element analysis of tibial fractures. Dan Med Bull 2010; 57(5): A4148. 29. Gray HA, Taddei F, Zavatsky AB, et al. Experimental validation of a finite element model of a human cadaveric tibia. J Biomech Eng 2008; 130(3): 031016. 30. Iwaki H, Pinskerova V and Freeman MAR. Tibiofemoral movement 1: the shapes and relative movements of the femur and tibia in the unloaded cadaver knee. J Bone Joint Surg Br 2000; 82(8): 1189–1195. 31. Nalla RK, Kinney JH and Ritchie RO. Mechanistic fracture criteria for the failure of human cortical bone. Nat Mater 2003; 2(3): 164–168. 32. Bessho M, Ohnishi I, Matsuyama J, et al. Prediction of strength and strain of the proximal femur by a CT based finite element method. J Biomech 2007; 40(8): 1745–1753. 33. Liu D, Manske SL, Kontulainen SA, et al. Tibial geometry is associated with failure load ex vivo: a MRI, pQCT and DXA study. Osteoporos Int 2007; 18(7): 991–997.

Deposit and Record Details

ID Code:	90953
Depositing User:	Professor Kathleen Tanner
Datestamp:	10 Feb 2014 09:09
Last Modified:	23 Sep 2021 15:01
Date of first online publication:	February 2014