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Clinical Evaluation of Bone Strength and Fracture Risk

  • Biomechanics (M Silva and K Jepsen, Section Editors)
  • Published:
Current Osteoporosis Reports Aims and scope Submit manuscript

A Response to this article was published on 29 June 2017

Abstract

Purpose of Review

This paper seeks to evaluate and compare recent advances in the clinical assessment of the changes in bone mechanical properties that take place as a result of osteoporosis and other metabolic bone diseases and their treatments.

Recent Findings

In addition to the standard of DXA-based areal bone mineral density (aBMD), a variety of methods, including imaging-based structural measurements, finite element analysis (FEA)-based techniques, and alternate methods including ultrasound, bone biopsy, reference point indentation, and statistical shape and density modeling, have been developed which allow for reliable prediction of bone strength and fracture risk. These methods have also shown promise in the evaluation of treatment-induced changes in bone mechanical properties.

Summary

Continued technological advances allowing for increasingly high-resolution imaging with low radiation dose, together with the expanding adoption of DXA-based predictions of bone structure and mechanics, as well as the increasing awareness of the importance of bone material properties in determining whole-bone mechanics, lead us to anticipate substantial future advances in this field.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Miller PD. Clinical use of bone mass measurements in adults for the assessment and management of osteoporosis. In: Favus MJ, editor. Primer on the metabolic bone disease and disorders of mineral metabolism. 6th ed. Washington DC: American Society for Bone and Mineral Research; 2006. p. 150–61.

    Google Scholar 

  2. Stone KL, Seeley DG, Lui LY, Cauley JA, Ensrud K, Browner WS, et al. BMD at multiple sites and risk of fracture of multiple types: long-term results from the study of osteoporotic fractures. J Bone Miner Res. 2003;18:1947–54.

    Article  PubMed  Google Scholar 

  3. Schuit SC, van der Klift M, Weel AE, de Laet CE, Burger H, Seeman E, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone. 2004;34:195–202.

    Article  CAS  PubMed  Google Scholar 

  4. Pothuaud L, Carceller P, Hans D. Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: applications in the study of human trabecular bone microarchitecture. Bone. 2008;42:775–87.

    Article  PubMed  Google Scholar 

  5. Hans D, Barthe N, Boutroy S, Pothuaud L, Winzenrieth R, Krieg MA. Correlations between trabecular bone score, measured using anteroposterior dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J Clin Densitom. 2011;14:302–12.

    Article  PubMed  Google Scholar 

  6. Roux JP, Wegrzyn J, Boutroy S, Bouxsein ML, Hans D, Chapurlat R. The predictive value of trabecular bone score (TBS) on whole lumbar vertebrae mechanics: an ex vivo study. Osteoporos Int. 2013;24:2455–60.

    Article  CAS  PubMed  Google Scholar 

  7. Winzenrieth R, Michelet F, Hans D. Three-dimensional (3D) microarchitecture correlations with 2D projection image gray-level variations assessed by trabecular bone score using high-resolution computed tomographic acquisitions: effects of resolution and noise. J Clin Densitom. 2013;16:287–96.

    Article  PubMed  Google Scholar 

  8. Maquer G, Lu Y, Dall’Ara E, Chevalier Y, Krause M, Yang L, et al. The initial slope of the variogram, foundation of the trabecular bone score, is not or is poorly associated with vertebral strength. J Bone Miner Res. 2016;31:341–6.

    Article  PubMed  Google Scholar 

  9. Harvey NC, Gluer CC, Binkley N, McCloskey EV, Brandi ML, Cooper C, et al. Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice. Bone. 2015;78:216–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Silva BC, Leslie WD, Resch H, Lamy O, Lesnyak O, Binkley N, et al. Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res. 2014;29:518–30.

    Article  PubMed  Google Scholar 

  11. • McCloskey EV, Oden A, Harvey NC, Leslie WD, Hans D, Johansson H, et al. A meta-analysis of trabecular bone score in fracture risk prediction and its relationship to FRAX. J Bone Miner Res. 2016;31:940–8. This study conducted a meta-analysis of 17,809 men and women in 14 prospective population-based cohorts to examine the association between baseline TBS, FRAX risk variables, and major osteoporotic fractures during follow-up (mean 6.7 years). Their results demonstrated that TBS was a significant predictor for fracture risk and provide information independent of FRAX.

    Article  PubMed  Google Scholar 

  12. Kanis JA, Oden A, Johnell O, Johansson H, De Laet C, Brown J, et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int. 2007;18:1033–46.

    Article  CAS  PubMed  Google Scholar 

  13. Kolta S, Paratte S, Amphoux T, Persohn S, Campana S, Skalli W, et al. Bone texture analysis of human femurs using a new device (BMA) improves failure load prediction. Osteoporos Int. 2012;23:1311–6.

    Article  CAS  PubMed  Google Scholar 

  14. Le Corroller T, Halgrin J, Pithioux M, Guenoun D, Chabrand P, Champsaur P. Combination of texture analysis and bone mineral density improves the prediction of fracture load in human femurs. Osteoporos Int. 2012;23:163–9.

    Article  CAS  PubMed  Google Scholar 

  15. Lespessailles E, Gadois C, Lemineur G, Do-Huu JP, Benhamou L. Bone texture analysis on direct digital radiographic images: precision study and relationship with bone mineral density at the os calcis. Calcif Tissue Int. 2007;80:97–102.

    Article  CAS  PubMed  Google Scholar 

  16. Vokes TJ, Giger ML, Chinander MR, Karrison TG, Favus MJ, Dixon LB. Radiographic texture analysis of densitometer-generated calcaneus images differentiates postmenopausal women with and without fractures. Osteoporos Int. 2006;17:1472–82.

    Article  CAS  PubMed  Google Scholar 

  17. Naylor KE, McCloskey EV, Eastell R, Yang L. Use of DXA-based finite element analysis of the proximal femur in a longitudinal study of hip fracture. J Bone Miner Res. 2013;28:1014–21.

    Article  PubMed  Google Scholar 

  18. Yang L, Palermo L, Black DM, Eastell R. Prediction of incident hip fracture with the estimated femoral strength by finite element analysis of DXA scans in the study of osteoporotic fractures. J Bone Miner Res. 2014;29:2594–600.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Testi D, Viceconti M, Cappello A, Gnudi S. Prediction of hip fracture can be significantly improved by a single biomedical indicator. Ann Biomed Eng. 2002;30:801–7.

    Article  PubMed  Google Scholar 

  20. Yang L, Peel N, Clowes JA, McCloskey EV, Eastell R. Use of DXA-based structural engineering models of the proximal femur to discriminate hip fracture. J Bone Miner Res. 2009;24:33–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Roberts BJ, Thrall E, Muller JA, Bouxsein ML. Comparison of hip fracture risk prediction by femoral aBMD to experimentally measured factor of risk. Bone. 2010;46:742–6.

    Article  PubMed  Google Scholar 

  22. Vaananen SP, Jurvelin JS, Isaksson H. Estimation of 3D shape, internal density and mechanics of proximal femur by combining bone mineral density images with shape and density templates. Biomech Model Mechanobiol. 2012;11:791–800.

    Article  PubMed  Google Scholar 

  23. Vaananen SP, Grassi L, Flivik G, Jurvelin JS, Isaksson H. Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image. Med Image Anal. 2015;24:125–34.

    Article  PubMed  Google Scholar 

  24. Langton CM, Pisharody S, Keyak JH. Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur. Med Eng Phys. 2009;31:668–72.

    Article  CAS  PubMed  Google Scholar 

  25. Les CM, Keyak JH, Stover SM, Taylor KT, Kaneps AJ. Estimation of material properties in the equine metacarpus with use of quantitative computed tomography. J Orthop Res. 1994;12:822–33.

    Article  CAS  PubMed  Google Scholar 

  26. Kopperdahl DL, Morgan EF, Keaveny TM. Quantitative computed tomography estimates of the mechanical properties of human vertebral trabecular bone. J Orthop Res. 2002;20:801–5.

    Article  PubMed  Google Scholar 

  27. Bessho M, Ohnishi I, Matsuyama J, Matsumoto T, Imai K, Nakamura K. Prediction of strength and strain of the proximal femur by a CT-based finite element method. J Biomech. 2007;40:1745–53.

    Article  PubMed  Google Scholar 

  28. Koivumaki JE, Thevenot J, Pulkkinen P, Kuhn V, Link TM, Eckstein F, et al. Cortical bone finite element models in the estimation of experimentally measured failure loads in the proximal femur. Bone. 2012;51:737–40.

    Article  PubMed  Google Scholar 

  29. Schileo E, Balistreri L, Grassi L, Cristofolini L, Taddei F. To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations? J Biomech. 2014;47:3531–8.

    Article  PubMed  Google Scholar 

  30. Nishiyama KK, Gilchrist S, Guy P, Cripton P, Boyd SK. Proximal femur bone strength estimated by a computationally fast finite element analysis in a sideways fall configuration. J Biomech. 2013;46:1231–6.

    Article  PubMed  Google Scholar 

  31. Crawford RP, Cann CE, Keaveny TM. Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography. Bone. 2003;33:744–50.

    Article  PubMed  Google Scholar 

  32. Dall’Ara E, Pahr D, Varga P, Kainberger F, Zysset P. QCT-based finite element models predict human vertebral strength in vitro significantly better than simulated DEXA. Osteoporos Int. 2012;23:563–72.

    Article  PubMed  Google Scholar 

  33. Imai K. Analysis of vertebral bone strength, fracture pattern, and fracture location: a validation study using a computed tomography-based nonlinear finite element analysis. Aging Dis. 2015;6:180–7.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Liebschner MA, Kopperdahl DL, Rosenberg WS, Keaveny TM. Finite element modeling of the human thoracolumbar spine. Spine (Phila Pa 1976). 2003;28:559–65.

    Google Scholar 

  35. • Keyak JH, Sigurdsson S, Karlsdottir GS, Oskarsdottir D, Sigmarsdottir A, Kornak J, et al. Effect of finite element model loading condition on fracture risk assessment in men and women: the AGES-Reykjavik study. Bone. 2013;57:18–29. In this age- and sex-matched case-control study, baseline (pre-fracture) QCT-based FEA was performed for measuring hip strength. The reductions in strength associated with fracture in men were more than twice those in women. Bone strength was no longer a significant predictor for fracture risk after correcting for aBMD in women, whereas in men, there remained a significant association between bone strength and fracture risk, even after correcting for aBMD, indicating that gender differences may play a role in prediction of hip fracture.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Melton 3rd LJ, Riggs BL, Keaveny TM, Achenbach SJ, Hoffmann PF, Camp JJ, et al. Structural determinants of vertebral fracture risk. J Bone Miner Res. 2007;22:1885–92.

    Article  PubMed  Google Scholar 

  37. Orwoll ES, Marshall LM, Nielson CM, Cummings SR, Lapidus J, Cauley JA, et al. Finite element analysis of the proximal femur and hip fracture risk in older men. J Bone Miner Res. 2009;24:475–83.

    Article  PubMed  Google Scholar 

  38. Wang X, Sanyal A, Cawthon PM, Palermo L, Jekir M, Christensen J, et al. Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J Bone Miner Res. 2012;27:808–16.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Amin S, Kopperdhal DL, Melton 3rd LJ, Achenbach SJ, Therneau TM, Riggs BL, et al. Association of hip strength estimates by finite-element analysis with fractures in women and men. J Bone Miner Res. 2011;26:1593–600.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Nishiyama KK, Ito M, Harada A, Boyd SK. Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporos Int. 2014;25:619–26.

    Article  CAS  PubMed  Google Scholar 

  41. Boutroy S, Bouxsein ML, Munoz F, Delmas PD. In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab. 2005;90:6508–15.

    Article  CAS  PubMed  Google Scholar 

  42. Liu XS, Zhang XH, Sekhon KK, Adams MF, McMahon DJ, Bilezikian JP, et al. High-resolution peripheral quantitative computed tomography can assess microstructural and mechanical properties of human distal tibial bone. J Bone Miner Res. 2010;25:746–56.

    CAS  PubMed  Google Scholar 

  43. MacNeil JA, Boyd SK. Accuracy of high-resolution peripheral quantitative computed tomography for measurement of bone quality. Med Eng Phys. 2007;29:1096–105.

    Article  PubMed  Google Scholar 

  44. Cheung AM, Adachi JD, Hanley DA, Kendler DL, Davison KS, Josse R, et al. High-resolution peripheral quantitative computed tomography for the assessment of bone strength and structure: a review by the Canadian Bone Strength Working Group. Curr Osteoporos Rep. 2013;11:136–46.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Nishiyama KK, Shane E. Clinical imaging of bone microarchitecture with HR-pQCT. Curr Osteoporos Rep. 2013;11:147–55.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Pistoia W, van Rietbergen B, Lochmuller EM, Lill CA, Eckstein F, Ruegsegger P. Estimation of distal radius failure load with micro-finite element analysis models based on three-dimensional peripheral quantitative computed tomography images. Bone. 2002;30:842–8.

    Article  CAS  PubMed  Google Scholar 

  47. MacNeil JA, Boyd SK. Bone strength at the distal radius can be estimated from high-resolution peripheral quantitative computed tomography and the finite element method. Bone. 2008;42:1203–13.

    Article  PubMed  Google Scholar 

  48. Varga P, Pahr DH, Baumbach S, Zysset PK. HR-pQCT based FE analysis of the most distal radius section provides an improved prediction of Colles’ fracture load in vitro. Bone. 2010;47:982–8.

    Article  PubMed  Google Scholar 

  49. Zhou B, Wang J, Yu YE, Zhang Z, Nawathe S, Nishiyama KK, et al. High-resolution peripheral quantitative computed tomography (HR-pQCT) can assess microstructural and biomechanical properties of both human distal radius and tibia: ex vivo computational and experimental validations. Bone. 2016;86:58–67.

    Article  PubMed  Google Scholar 

  50. Boutroy S, Van Rietbergen B, Sornay-Rendu E, Munoz F, Bouxsein ML, Delmas PD. 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;23:392–9.

    Article  PubMed  Google Scholar 

  51. Melton 3rd LJ, Riggs BL, van Lenthe GH, Achenbach SJ, Muller R, Bouxsein ML, et al. Contribution of in vivo structural measurements and load/strength ratios to the determination of forearm fracture risk in postmenopausal women. J Bone Miner Res. 2007;22:1442–8.

    Article  PubMed  Google Scholar 

  52. Stein EM, Liu XS, Nickolas TL, Cohen A, Thomas V, McMahon DJ, et al. Abnormal microarchitecture and stiffness in postmenopausal women with ankle fractures. J Clin Endocrinol Metab. 2011;96:2041–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Liu XS, Cohen A, Shane E, Yin PT, Stein EM, Rogers H, et al. Bone density, geometry, microstructure, and stiffness: relationships between peripheral and central skeletal sites assessed by DXA, HR-pQCT, and cQCT in premenopausal women. J Bone Miner Res. 2010;25:2229–38.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Amstrup AK, Jakobsen NF, Moser E, Sikjaer T, Mosekilde L, Rejnmark L. Association between bone indices assessed by DXA, HR-pQCT and QCT scans in post-menopausal women. J Bone Miner Metab. 2015;34:638–45.

    Article  PubMed  Google Scholar 

  55. Chevalley T, Bonjour JP, van Rietbergen B, Ferrari S, Rizzoli R. Fracture history of healthy premenopausal women is associated with a reduction of cortical microstructural components at the distal radius. Bone. 2013;55:377–83.

    Article  CAS  PubMed  Google Scholar 

  56. Stein EM, Liu XS, Nickolas TL, Cohen A, Thomas V, McMahon DJ, et al. Abnormal microarchitecture and reduced stiffness at the radius and tibia in postmenopausal women with fractures. J Bone Miner Res. 2010;25:2572–81.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Vilayphiou N, Boutroy S, Sornay-Rendu E, Van Rietbergen B, Munoz F, Delmas PD, et al. Finite element analysis performed on radius and tibia HR-pQCT images and fragility fractures at all sites in postmenopausal women. Bone. 2010;46:1030–7.

    Article  PubMed  Google Scholar 

  58. Wang J, Stein EM, Zhou B, Nishiyama KK, Yu YE, Shane E, et al. Deterioration of trabecular plate-rod and cortical microarchitecture and reduced bone stiffness at distal radius and tibia in postmenopausal women with vertebral fractures. Bone. 2016;88:39–46.

    Article  PubMed  Google Scholar 

  59. • Stein EM, Liu XS, Nickolas TL, Cohen A, McMahon DJ, Zhou B, et al. Microarchitectural abnormalities are more severe in postmenopausal women with vertebral compared to nonvertebral fractures. J Clin Endocrinol Metab. 2012;97:E1918–26. This study found that women with vertebral fractures have a more severe reduction in HR-pQCT measures of trabecular and cortical microarchitectural and whole bone stiffness than do those with nonvertebral fractures, particularly at the tibia.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Rizzoli R, Chapurlat RD, Laroche JM, Krieg MA, Thomas T, Frieling I, et al. Effects of strontium ranelate and alendronate on bone microstructure in women with osteoporosis. Results of a 2-year study. Osteoporos Int. 2012;23:305–15.

    Article  CAS  PubMed  Google Scholar 

  61. Schafer AL, Burghardt AJ, Sellmeyer DE, Palermo L, Shoback DM, Majumdar S, et al. Postmenopausal women treated with combination parathyroid hormone (1–84) and ibandronate demonstrate different microstructural changes at the radius vs. tibia: the PTH and Ibandronate Combination Study (PICS). Osteoporos Int. 2013;24:2591–601.

    Article  CAS  PubMed  Google Scholar 

  62. Burghardt AJ, Kazakia GJ, Sode M, de Papp AE, Link TM, Majumdar S. A longitudinal HR-pQCT study of alendronate treatment in postmenopausal women with low bone density: relations among density, cortical and trabecular microarchitecture, biomechanics, and bone turnover. J Bone Miner Res. 2010;25:2558–71.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Liu XS, Wang J, Zhou B, Stein E, Shi X, Adams M, et al. Fast trabecular bone strength predictions of HR-pQCT and individual trabeculae segmentation-based plate and rod finite element model discriminate postmenopausal vertebral fractures. J Bone Miner Res. 2013;28:1666–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wehrli FW, Gomberg BR, Saha PK, Song HK, Hwang SN, Snyder PJ. Digital topological analysis of in vivo magnetic resonance microimages of trabecular bone reveals structural implications of osteoporosis. J Bone Miner Res. 2001;16:1520–31.

    Article  CAS  PubMed  Google Scholar 

  65. Liu XS, Zhang XH, Rajapakse CS, Wald MJ, Magland J, Sekhon KK, et al. Accuracy of high-resolution in vivo micro magnetic resonance imaging for measurements of microstructural and mechanical properties of human distal tibial bone. J Bone Miner Res. 2010;25:2039–50.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Krug R, Carballido-Gamio J, Burghardt AJ, Kazakia G, Hyun BH, Jobke B, et al. Assessment of trabecular bone structure comparing magnetic resonance imaging at 3 Tesla with high-resolution peripheral quantitative computed tomography ex vivo and in vivo. Osteoporos Int. 2008;19:653–61.

    Article  CAS  PubMed  Google Scholar 

  67. Phan CM, Matsuura M, Bauer JS, Dunn TC, Newitt D, Lochmueller EM, et al. Trabecular bone structure of the calcaneus: comparison of MR imaging at 3.0 and 1.5 T with micro-CT as the standard of reference. Radiology. 2006;239:488–96.

    Article  PubMed  Google Scholar 

  68. Sell CA, Masi JN, Burghardt A, Newitt D, Link TM, Majumdar S. Quantification of trabecular bone structure using magnetic resonance imaging at 3 Tesla—calibration studies using microcomputed tomography as a standard of reference. Calcif Tissue Int. 2005;76:355–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Hudelmaier M, Kollstedt A, Lochmuller EM, Kuhn V, Eckstein F, Link TM. Gender differences in trabecular bone architecture of the distal radius assessed with magnetic resonance imaging and implications for mechanical competence. Osteoporos Int. 2005;16:1124–33.

    Article  PubMed  Google Scholar 

  70. Lammentausta E, Hakulinen MA, Jurvelin JS, Nieminen MT. Prediction of mechanical properties of trabecular bone using quantitative MRI. Phys Med Biol. 2006;51:6187–98.

    Article  CAS  PubMed  Google Scholar 

  71. Link TM, Vieth V, Langenberg R, Meier N, Lotter A, Newitt D, et al. Structure analysis of high resolution magnetic resonance imaging of the proximal femur: in vitro correlation with biomechanical strength and BMD. Calcif Tissue Int. 2003;72:156–65.

    Article  CAS  PubMed  Google Scholar 

  72. Lam SC, Wald MJ, Rajapakse CS, Liu Y, Saha PK, Wehrli FW. Performance of the MRI-based virtual bone biopsy in the distal radius: serial reproducibility and reliability of structural and mechanical parameters in women representative of osteoporosis study populations. Bone. 2011;49:895–903.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Newitt DC, Majumdar S, van Rietbergen B, von Ingersleben G, Harris ST, Genant HK, et al. In vivo assessment of architecture and micro-finite element analysis derived indices of mechanical properties of trabecular bone in the radius. Osteoporos Int. 2002;13:6–17.

    Article  CAS  PubMed  Google Scholar 

  74. Chang G, Rajapakse CS, Babb JS, Honig SP, Recht MP, Regatte RR. In vivo estimation of bone stiffness at the distal femur and proximal tibia using ultra-high-field 7-Tesla magnetic resonance imaging and micro-finite element analysis. J Bone Miner Metab. 2012;30:243–51.

    Article  PubMed  Google Scholar 

  75. Chang G, Honig S, Liu Y, Chen C, Chu KK, Rajapakse CS, et al. 7 Tesla MRI of bone microarchitecture discriminates between women without and with fragility fractures who do not differ by bone mineral density. J Bone Miner Metab. 2015;33:285–93.

    Article  PubMed  Google Scholar 

  76. Krug R, Banerjee S, Han ET, Newitt DC, Link TM, Majumdar S. Feasibility of in vivo structural analysis of high-resolution magnetic resonance images of the proximal femur. Osteoporos Int. 2005;16:1307–14.

    Article  PubMed  Google Scholar 

  77. Rajapakse CS, Magland J, Zhang XH, Liu XS, Wehrli SL, Guo XE, et al. Implications of noise and resolution on mechanical properties of trabecular bone estimated by image-based finite-element analysis. J Orthop Res. 2009;27:1263–71.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Rajapakse CS, Magland JF, Wald MJ, Liu XS, Zhang XH, Guo XE, et al. Computational biomechanics of the distal tibia from high-resolution MR and micro-CT images. Bone. 2010;47:556–63.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Chang G, Honig S, Brown R, Deniz CM, Egol KA, Babb JS, et al. Finite element analysis applied to 3-T MR imaging of proximal femur microarchitecture: lower bone strength in patients with fragility fractures compared with control subjects. Radiology. 2014;272:464–74.

    Article  PubMed  PubMed Central  Google Scholar 

  80. van Rietbergen B, Majumdar S, Newitt D, MacDonald B. High-resolution MRI and micro-FE for the evaluation of changes in bone mechanical properties during longitudinal clinical trials: application to calcaneal bone in postmenopausal women after one year of idoxifene treatment. Clin Biomech (Bristol, Avon). 2002;17:81–8.

    Article  Google Scholar 

  81. Wehrli FW, Rajapakse CS, Magland JF, Snyder PJ. Mechanical implications of estrogen supplementation in early postmenopausal women. J Bone Miner Res. 2010;25:1406–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Zhang XH, Liu XS, Vasilic B, Wehrli FW, Benito M, Rajapakse CS, et al. In vivo microMRI-based finite element and morphological analyses of tibial trabecular bone in eugonadal and hypogonadal men before and after testosterone treatment. J Bone Miner Res. 2008;23:1426–34.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Zhang N, Magland JF, Rajapakse CS, Bhagat YA, Wehrli FW. Potential of in vivo MRI-based nonlinear finite-element analysis for the assessment of trabecular bone post-yield properties. Med Phys. 2013;40:052303.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Roux C, Dougados M. Quantitative ultrasound in postmenopausal osteoporosis. Curr Opin Rheumatol. 2000;12:336–45.

    Article  CAS  PubMed  Google Scholar 

  85. Hakulinen MA, Day JS, Toyras J, Timonen M, Kroger H, Weinans H, et al. Prediction of density and mechanical properties of human trabecular bone in vitro by using ultrasound transmission and backscattering measurements at 0.2–6.7 MHz frequency range. Phys Med Biol. 2005;50:1629–42.

    Article  PubMed  Google Scholar 

  86. Nicholson PH, Alkalay R. Quantitative ultrasound predicts bone mineral density and failure load in human lumbar vertebrae. Clin Biomech (Bristol, Avon). 2007;22:623–9.

    Article  CAS  Google Scholar 

  87. Riekkinen O, Hakulinen MA, Toyras J, Jurvelin JS. Spatial variation of acoustic properties is related with mechanical properties of trabecular bone. Phys Med Biol. 2007;52:6961–8.

    Article  CAS  PubMed  Google Scholar 

  88. Lochmuller EM, Muller R, Kuhn V, Lill CA, Eckstein F. Can novel clinical densitometric techniques replace or improve DXA in predicting bone strength in osteoporosis at the hip and other skeletal sites? J Bone Miner Res. 2003;18:906–12.

    Article  PubMed  Google Scholar 

  89. Chaffai S, Peyrin F, Nuzzo S, Porcher R, Berger G, Laugier P. Ultrasonic characterization of human cancellous bone using transmission and backscatter measurements: relationships to density and microstructure. Bone. 2002;30:229–37.

    Article  CAS  PubMed  Google Scholar 

  90. Padilla F, Jenson F, Bousson V, Peyrin F, Laugier P. Relationships of trabecular bone structure with quantitative ultrasound parameters: in vitro study on human proximal femur using transmission and backscatter measurements. Bone. 2008;42:1193–202.

    Article  CAS  PubMed  Google Scholar 

  91. Riekkinen O, Hakulinen MA, Lammi MJ, Jurvelin JS, Kallioniemi A, Toyras J. Acoustic properties of trabecular bone—relationships to tissue composition. Ultrasound Med Biol. 2007;33:1438–44.

    Article  CAS  PubMed  Google Scholar 

  92. Cortet B, Boutry N, Dubois P, Legroux-Gerot I, Cotten A, Marchandise X. Does quantitative ultrasound of bone reflect more bone mineral density than bone microarchitecture? Calcif Tissue Int. 2004;74:60–7.

    Article  CAS  PubMed  Google Scholar 

  93. Karjalainen JP, Riekkinen O, Toyras J, Hakulinen M, Kroger H, Rikkonen T, et al. Multi-site bone ultrasound measurements in elderly women with and without previous hip fractures. Osteoporos Int. 2012;23:1287–95.

    Article  CAS  PubMed  Google Scholar 

  94. Roux C, Roberjot V, Porcher R, Kolta S, Dougados M, Laugier P. Ultrasonic backscatter and transmission parameters at the os calcis in postmenopausal osteoporosis. J Bone Miner Res. 2001;16:1353–62.

    Article  CAS  PubMed  Google Scholar 

  95. Hans D, Durosier C, Kanis JA, Johansson H, Schott-Pethelaz AM, Krieg MA. Assessment of the 10-year probability of osteoporotic hip fracture combining clinical risk factors and heel bone ultrasound: the EPISEM prospective cohort of 12,958 elderly women. J Bone Miner Res. 2008;23:1045–51.

    Article  PubMed  Google Scholar 

  96. Moayyeri A, Adams JE, Adler RA, Krieg MA, Hans D, Compston J, et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis. Osteoporos Int. 2012;23:143–53.

    Article  CAS  PubMed  Google Scholar 

  97. Gonnelli S, Cepollaro C, Montagnani A, Martini S, Gennari L, Mangeri M, et al. Heel ultrasonography in monitoring alendronate therapy: a four-year longitudinal study. Osteoporos Int. 2002;13:415–21.

    Article  CAS  PubMed  Google Scholar 

  98. To WW, Wong MW. Bone mineral density changes in pregnancies with gestational hypertension: a longitudinal study using quantitative ultrasound measurements. Arch Gynecol Obstet. 2011;284:39–44.

    Article  PubMed  Google Scholar 

  99. To WW, Wong MW. Bone mineral density changes during pregnancy in actively exercising women as measured by quantitative ultrasound. Arch Gynecol Obstet. 2012;286:357–63.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Allen MR, McNerny EM, Organ JM, Wallace JM. True gold or pyrite: a review of reference point indentation for assessing bone mechanical properties in vivo. J Bone Miner Res. 2015;30:1539–50.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Katsamenis OL, Jenkins T, Thurner PJ. Toughness and damage susceptibility in human cortical bone is proportional to mechanical inhomogeneity at the osteonal-level. Bone. 2015;76:158–68.

    Article  PubMed  Google Scholar 

  102. Diez-Perez A, Guerri R, Nogues X, Caceres E, Pena MJ, Mellibovsky L, et al. Microindentation for in vivo measurement of bone tissue mechanical properties in humans. J Bone Miner Res. 2010;25:1877–85.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Granke M, Makowski AJ, Uppuganti S, Does MD, Nyman JS. Identifying novel clinical surrogates to assess human bone fracture toughness. J Bone Miner Res. 2015;30:1290–300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Abraham AC, Agarwalla A, Yadavalli A, Liu JY, Tang SY. Microstructural and compositional contributions towards the mechanical behavior of aging human bone measured by cyclic and impact reference point indentation. Bone. 2016;87:37–43.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Krege JB, Aref MW, McNerny E, Wallace JM, Organ JM, Allen MR. Reference point indentation is insufficient for detecting alterations in traditional mechanical properties of bone under common experimental conditions. Bone. 2016;87:97–101.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Granke M, Coulmier A, Uppuganti S, Gaddy JA, Does MD, Nyman JS. Insights into reference point indentation involving human cortical bone: sensitivity to tissue anisotropy and mechanical behavior. J Mech Behav Biomed Mater. 2014;37:174–85.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Abraham AC, Agarwalla A, Yadavalli A, McAndrew C, Liu JY, Tang SY. Multiscale predictors of femoral neck in situ strength in aging women: contributions of BMD, cortical porosity, reference point indentation, and nonenzymatic glycation. J Bone Miner Res. 2015;30:2207–14.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Karim L, Van Vliet M, Bouxsein ML. Comparison of cyclic and impact-based reference point indentation measurements in human cadaveric tibia. Bone. 2015. doi:10.1016/j.bone.2015.03.021.

    PubMed Central  Google Scholar 

  109. Guerri-Fernandez RC, Nogues X, Quesada Gomez JM, Torres Del Pliego E, Puig L, Garcia-Giralt N, et al. Microindentation for in vivo measurement of bone tissue material properties in atypical femoral fracture patients and controls. J Bone Miner Res. 2013;28:162–8.

    Article  CAS  PubMed  Google Scholar 

  110. Malgo F, Hamdy NA, Papapoulos SE, Appelman-Dijkstra NM. Bone material strength as measured by microindentation in vivo is decreased in patients with fragility fractures independently of bone mineral density. J Clin Endocrinol Metab. 2015;100:2039–45.

    Article  CAS  PubMed  Google Scholar 

  111. Rudang R, Zoulakis M, Sundh D, Brisby H, Diez-Perez A, Johansson L, et al. Bone material strength is associated with areal BMD but not with prevalent fractures in older women. Osteoporos Int. 2016;27:1585–92.

    Article  CAS  PubMed  Google Scholar 

  112. • Farr JN, Drake MT, Amin S, Melton 3rd LJ, McCready LK, Khosla S. In vivo assessment of bone quality in postmenopausal women with type 2 diabetes. J Bone Miner Res. 2014;29:787–95. In this age-matched case-control study, reference point indentation was used to compare bone material strength in postmenopausal women with type II diabetes (T2D) for >10 years with non-diabetic controls. T2D patients had significantly lower BMSi than controls even after adjustment of BMI and other risk factors, while no difference in aBMD and bone microarchitecture was found between T2D patients and controls.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Mellibovsky L, Prieto-Alhambra D, Mellibovsky F, Guerri-Fernandez R, Nogues X, Randall C, et al. Bone tissue properties measurement by reference point indentation in glucocorticoid-induced osteoporosis. J Bone Miner Res. 2015;30:1651–6.

    Article  CAS  PubMed  Google Scholar 

  114. Bala Y, Depalle B, Farlay D, Douillard T, Meille S, Follet H, et al. Bone micromechanical properties are compromised during long-term alendronate therapy independently of mineralization. J Bone Miner Res. 2012;27:825–34.

    Article  CAS  PubMed  Google Scholar 

  115. Boivin GY, Chavassieux PM, Santora AC, Yates J, Meunier PJ. Alendronate increases bone strength by increasing the mean degree of mineralization of bone tissue in osteoporotic women. Bone. 2000;27:687–94.

    Article  CAS  PubMed  Google Scholar 

  116. Borah B, Dufresne TE, Ritman EL, Jorgensen SM, Liu S, Chmielewski PA, et al. Long-term risedronate treatment normalizes mineralization and continues to preserve trabecular architecture: sequential triple biopsy studies with micro-computed tomography. Bone. 2006;39:345–52.

    Article  CAS  PubMed  Google Scholar 

  117. Boskey AL, Donnelly E, Boskey E, Spevak L, Ma Y, Zhang W, et al. Examining the relationships between bone tissue composition, compositional heterogeneity, and fragility fracture: a matched case-controlled FTIRI study. J Bone Miner Res. 2016;31:1070–81.

    Article  CAS  PubMed  Google Scholar 

  118. Paschalis EP, Glass EV, Donley DW, Eriksen EF. Bone mineral and collagen quality in iliac crest biopsies of patients given teriparatide: new results from the fracture prevention trial. J Clin Endocrinol Metab. 2005;90:4644–9.

    Article  CAS  PubMed  Google Scholar 

  119. Thomsen JS, Ebbesen EN, Mosekilde L. Relationships between static histomorphometry and bone strength measurements in human iliac crest bone biopsies. Bone. 1998;22:153–63.

    Article  CAS  PubMed  Google Scholar 

  120. Wang X, Sudhaker Rao D, Ajdelsztajn L, Ciarelli TE, Lavernia EJ, Fyhrie DP. Human iliac crest cancellous bone elastic modulus and hardness differ with bone formation rate per bone surface but not by existence of prevalent vertebral fracture. J Biomed Mater Res B Appl Biomater. 2008;85:68–77.

    Article  PubMed  CAS  Google Scholar 

  121. Nicolella DP, Bredbenner TL. Development of a parametric finite element model of the proximal femur using statistical shape and density modelling. Comput Methods Biomech Biomed Engin. 2012;15:101–10.

    Article  PubMed  Google Scholar 

  122. Castro-Mateos I, Pozo JM, Cootes TF, Wilkinson JM, Eastell R, Frangi AF. Statistical shape and appearance models in osteoporosis. Curr Osteoporos Rep. 2014;12:163–73.

    Article  PubMed  Google Scholar 

  123. Bredbenner TL, Mason RL, Havill LM, Orwoll ES, Nicolella DP. Osteoporotic fractures in Men S. Fracture risk predictions based on statistical shape and density modeling of the proximal femur. J Bone Miner Res. 2014;29:2090–100.

    Article  PubMed  PubMed Central  Google Scholar 

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  1. McKay Orthopaedic Research Laboratory, Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, 426C Stemmler Hall, 36th Street and Hamilton Walk, Philadelphia, PA, 19104, USA

    Chantal M. J. de Bakker, Wei-Ju Tseng, Yihan Li, Hongbo Zhao & X. Sherry Liu

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Chantal de Bakker, Wei-Ju Tseng, Yihan Li, Hongbo Zhao, and X. Sherry Liu declare no conflict of interest.

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de Bakker, C.M.J., Tseng, WJ., Li, Y. et al. Clinical Evaluation of Bone Strength and Fracture Risk. Curr Osteoporos Rep 15, 32–42 (2017). https://doi.org/10.1007/s11914-017-0346-3

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