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Novel Assessment Tools for Osteoporosis Diagnosis and Treatment

  • Bone Quality in Osteoporosis (MD Grynpas and JS Nyman, Section Editors)
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Abstract

This review describes new technologies for the diagnosis and treatment, including fracture risk prediction, of postmenopausal osteoporosis. Four promising technologies and their potential for clinical translation and basic science studies are discussed. These include reference point indentation (RPI), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and magnetic resonance imaging (MRI). While each modality exploits different physical principles, the commonality is that none of them require use of ionizing radiation. To provide context for the new developments, brief summaries are provided for the current state of biomarker assays, fracture risk assessment (FRAX), and other fracture risk prediction algorithms and quantitative ultrasound (QUS) measurements.

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References

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

  1. Szulc P, Delmas PD. Biochemical markers of bone turnover: potential use in the investigation and management of postmenopausal osteoporosis. Osteoporos Int. 2008;19(12):1683–704. doi:10.1007/s00198-008-0660-9.

    Article  CAS  PubMed  Google Scholar 

  2. Garnero P. Bone markers in osteoporosis. Curr Osteoporos Rep. 2009;7(3):84–90. doi:10.1007/s11914-009-0014-3.

    Article  PubMed  Google Scholar 

  3. McCloskey EV. Official positions for FRAX clinical regarding biochemical markers from Joint Official Positions Development Conference of the International Society for Clinical Densitometry and International Osteoporosis Foundation on FRAX. J Clin Densitom. 2011;14(3):220–2. doi:10.1016/j.jocd.2011.05.008.

    Article  PubMed  Google Scholar 

  4. Michelsen J, Wallaschofski H, Friedrich N, Spielhagen C, Rettig R, Ittermann T, et al. Reference intervals for serum concentrations of three bone turnover markers for men and women. Bone. 2013;57(2):399–404. doi:10.1016/j.bone.2013.09.010.

    Article  CAS  PubMed  Google Scholar 

  5. Nishizawa Y, Ohta H, Miura M, Inaba M, Ichimura S, Shiraki M, et al. Guidelines for the use of bone metabolic markers in the diagnosis and treatment of osteoporosis (2012 edition). J Bone Miner Metab. 2013;31(1):1–15. doi:10.1007/s00774-012-0392-y.

    Article  CAS  PubMed  Google Scholar 

  6. Chopin F, Biver E, Funck-Brentano T, Bouvard B, Coiffier G, Garnero P, et al. Prognostic interest of bone turnover markers in the management of postmenopausal osteoporosis. Joint Bone Spine. 2012;79(1):26–31. doi:10.1016/j.jbspin.2011.05.004.

    Article  PubMed  Google Scholar 

  7. Vasikaran S, Cooper C, Eastell R, Griesmacher A, Morris HA, Trenti T, et al. International Osteoporosis Foundation and International Federation of Clinical Chemistry and Laboratory Medicine position on bone marker standards in osteoporosis. Clin Chem Lab Med. 2011;49(8):1271–4. doi:10.1515/cclm.2011.602. Position paper attempting to standardize bone biomarkers.

    Article  CAS  PubMed  Google Scholar 

  8. Bieglmayer C, Dimai HP, Gasser RW, Kudlacek S, Obermayer-Pietsch B, Woloszczuk W, et al. Biomarkers of bone turnover in diagnosis and therapy of osteoporosis. Wien Med Wochenschr. 2012;162(21–22):464–77. doi:10.1007/s10354-012-0133-9.

    Article  PubMed  Google Scholar 

  9. Brown JP, Albert C, Nassar BA, Adachi JD, Cole D, Davison KS, et al. Bone turnover markers in the management of postmenopausal osteoporosis. Clin Biochem. 2009;42(10–11):929–42. doi:10.1016/j.clinbiochem.2009.04.001.

    Article  CAS  PubMed  Google Scholar 

  10. Ihara M, Yoshikawa A, Wu Y, Takahashi H, Mawatari K, Shimura K, et al. Micro OS-ELISA: rapid noncompetitive detection of a small biomarker peptide by open-sandwich enzyme-linked immunosorbent assay (OS-ELISA) integrated into microfluidic device. Lab Chip. 2010;10(1):92–100. doi:10.1039/b915516c.

    Article  CAS  PubMed  Google Scholar 

  11. Numthuam S, Ginoza T, Zhu M, Suzuki H, Fukuda J. Gold-black micropillar electrodes for microfluidic ELISA of bone metabolic markers. Analyst. 2011;136(3):456–8. doi:10.1039/c0an00619j.

    Article  CAS  PubMed  Google Scholar 

  12. Baxter I, Rogers A, Eastell R, Peel N. Evaluation of urinary N-telopeptide of type I collagen measurements in the management of osteoporosis in clinical practice. Osteoporos Int. 2013;24(3):941–7. doi:10.1007/s00198-012-2097-4.

    Article  CAS  PubMed  Google Scholar 

  13. Durosier C, van Lierop A, Ferrari S, Chevalley T, Papapoulos S, Rizzoli R. Association of circulating sclerostin with bone mineral mass, microstructure, and turnover biochemical markers in healthy elderly men and women. J Clin Endocrinol Metab. 2013;98(9):3873–83. doi:10.1210/jc.2013-2113. First demonstration of differences in epitope responses of sclerostin biomarker kits.

    Article  CAS  PubMed  Google Scholar 

  14. Garnero P, Sornay-Rendu E, Munoz F, Borel O, Chapurlat RD. Association of serum sclerostin with bone mineral density, bone turnover, steroid and parathyroid hormones, and fracture risk in postmenopausal women: the OFELY study. Osteoporos Int. 2013;24(2):489–94. doi:10.1007/s00198-012-1978-x.

    Article  CAS  PubMed  Google Scholar 

  15. Ardawi M-SM, Rouzi AA, Al-Sibiani SA, Al-Senani NS, Qari MH, Mousa SA. High serum sclerostin predicts the occurrence of osteoporotic fractures in postmenopausal women: the center of excellence for osteoporosis research study. J Bone Miner Res. 2012;27(12):2592–602. doi:10.1002/jbmr.1718.

    Article  CAS  PubMed  Google Scholar 

  16. Collins GS, Michaelsson K. Fracture risk assessment: state of the art, methodologically unsound, or poorly reported? Curr Osteoporos Rep. 2012;10(3):199–207. doi:10.1007/s11914-012-0108-1. Critical review on the design, validation, and transparency of three leading risk prediction models.

    Article  PubMed  Google Scholar 

  17. Rubin KH, Friis-Holmberg T, Hermann AP, Abrahamsen B, Brixen K. Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review. J Bone Miner Res. 2013;28(8):1701–17. doi:10.1002/jbmr.1956.

    Article  PubMed  Google Scholar 

  18. Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: prospective open cohort study. Br Med J. 2012;344:e3427.

  19. Forstein DA, Bernardini C, Cole RE, Harris ST, Singer A. Before the breaking point: reducing the risk of osteoporotic fracture. J Am Osteopath Assoc. 2013;113(2 Suppl 1):S5–24. Paper based on roundtable discussions on fracture risk prediction and treatment of osteoporotic fractures.

    PubMed  Google Scholar 

  20. Baim S, Leslie W. Assessment of fracture risk. Curr Osteoporos Rep. 2012;10(1):28–41. doi:10.1007/s11914-011-0093-9. Overview of clinical practice guidelines for the diagnosis and management of osteoporosis. Discusses performance of risk assessment instruments in populations separate from their development cohorts.

    Article  PubMed  Google Scholar 

  21. Dennison EM, Compston JE, Flahive J, Siris ES, Gehlbach SH, Adachi JD, et al. Effect of co-morbidities on fracture risk: findings from the Global Longitudinal Study of Osteoporosis in Women (GLOW). Bone. 2012;50(6):1288–93. doi:10.1016/j.bone.2012.02.639.

    Article  PubMed  Google Scholar 

  22. Dobson R, Leddy SG, Gangadharan S, Giovannoni G. Assessing fracture risk in people with MS: a service development study comparing three fracture risk scoring systems. BMJ Open. 2013;3(3). doi:10.1136/bmjopen-2012-002508.

  23. Nayak S, Edwards DL, Saleh AA, Greenspan SL. Performance of risk assessment instruments for predicting osteoporotic fracture risk: a systematic review. Osteoporos Int. 2014;25(1):23–49. doi:10.1007/s00198-013-2504-5. Review of the performance of risk assessment instruments in populations separate from their development cohorts.

    Article  CAS  PubMed  Google Scholar 

  24. van Geel TACM, Eisman JA, Geusens PP, van den Bergh JPW, Center JR, Dinant G-J. The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice. Maturitas. 2014;77(2):174–9. doi:10.1016/j.maturitas.2013.10.021.

    Article  PubMed  Google Scholar 

  25. Geusens P, van Geel T, van den Bergh J. Can hip fracture prediction in women be estimated beyond bone mineral density measurement alone? Ther Adv Musculoskelet Dis. 2010;2(2):63–77. doi:10.1177/1759720x09359541.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Olszynski WP, Brown JP, Adachi JD, Hanley DA, Ioannidis G, Davison KS, et al. Multisite quantitative ultrasound for the prediction of fractures over 5 years of follow-up: the Canadian Multicentre Osteoporosis Study. J Bone Miner Res. 2013;28(9):2027–34. doi:10.1002/jbmr.1931.

    Article  PubMed  Google Scholar 

  27. 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(3):518–30. doi:10.1002/jbmr.2176. Review of trabecular bone score and its potential for inclusion in the FRAX algorithm.

    Article  PubMed  Google Scholar 

  28. Leslie WD, Aubry-Rozier B, Lamy O, Hans D. TBS (Trabecular Bone Score) and diabetes-related fracture risk. J Clin Endocrinol Metab. 2013;98(2):602–9. doi:10.1210/jc.2012-3118.

    Article  CAS  PubMed  Google Scholar 

  29. Schwartz AV, Vittinghoff E, Bauer DC, Hillier TA, Strotmeyer ES, Ensrud KE, et al. Association of BMD and FRAX score with risk of fracture in older adults with type 2 diabetes. JAMA. 2011;305(21):2184–92. doi:10.1001/jama.2011.715.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Giangregorio LM, Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, et al. FRAX underestimates fracture risk in patients with diabetes. J Bone Miner Res. 2012;27(2):301–8. doi:10.1002/jbmr.556.

    Article  PubMed  Google Scholar 

  31. Hans D, Krieg MA. Quantitative ultrasound for the detection and management of osteoporosis. Salud Publica Mex. 2009;51 Suppl 1:S25–37. doi:10.1590/S0036-36342009000700006.

    Article  PubMed  Google Scholar 

  32. Guglielmi G, Adams J, Link TM. Quantitative ultrasound in the assessment of skeletal status. Eur Radiol. 2009;19(8):1837–48. doi:10.1007/s00330-009-1354-1.

    Article  PubMed  Google Scholar 

  33. Floter M, Bittar CK, Zabeu JLA, Carneiro ACR. Review of comparative studies between bone densitometry and quantitative ultrasound of the calcaneus in osteoporosis. Acta Reumatol Port. 2011;36(4):327–35.

    PubMed  Google Scholar 

  34. Krieg MA, Barkmann R, Gonnelli S, Stewart A, Bauer DC, Barquero LDR, et al. Quantitative ultrasound in the management of osteoporosis: the 2007 ISCD Official Positions. J Clin Densitom. 2008;11(1):163–87. doi:10.1016/j.jocd.2007.12.011.

    Article  PubMed  Google Scholar 

  35. Hans D, Dargent-Molina P, Schott AM, Sebert JL, Cormier C, Kotzki PO, et al. Ultrasonographic heel measurements to predict hip fracture in elderly women: the EPIDOS prospective study. Lancet. 1996;348(9026):511–4. doi:10.1016/S0140-6736(95)11456-4.

    Article  CAS  PubMed  Google Scholar 

  36. Khaw KT, Reeve J, Luben R, Bingham S, Welch A, Wareham N, et al. Prediction of total and hip fracture risk in men and women by quantitative ultrasound of the calcaneus: EPIC-Norfolk prospective population study. Lancet. 2004;363(9404):197–202. doi:10.1016/S0140-6736(03)15325-1.

    Article  PubMed  Google Scholar 

  37. Chan MY, Nguyen ND, Center JR, Eisman JA, Nguyen TV. Quantitative ultrasound and fracture risk prediction in non-osteoporotic men and women as defined by WHO criteria. Osteoporos Int. 2013;24(3):1015–22. doi:10.1007/s00198-012-2001-2.

    Article  CAS  PubMed  Google Scholar 

  38. 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(1):143–53. doi:10.1007/s00198-011-1817-5.

    Article  CAS  PubMed  Google Scholar 

  39. Edelmann-Schafer B, Berthold LD, Stracke H, Luhrmann PM, Neuhauser-Berthold M. Identifying elderly women with osteoporosis by spinal dual X-ray absorptiometry, calcaneal quantitative ultrasound and spinal quantitative computed tomography: a comparative study. Ultrasound Med Biol. 2011;37(1):29–36. doi:10.1016/j.ultrasmedbio.2010.10.003.

    Article  PubMed  Google Scholar 

  40. Boonen S, Nijs J, Borghs H, Peeters H, Vanderschueren D, Luyten FP. Identifying postmenopausal women with osteoporosis by calcaneal ultrasound, metacarpal digital X-ray radiogrammetry and phalangeal radiographic absorptiometry: a comparative study. Osteoporos Int. 2005;16(1):93–100. doi:10.1007/s00198-004-1660-z.

    Article  PubMed  Google Scholar 

  41. Iwamoto J, Sato Y, Uzawa M, Takeda T, Matsumoto H. Three-year experience with alendronate treatment in postmenopausal osteoporotic Japanese women with or without renal dysfunction: a retrospective study. Drugs Aging. 2012;29(2):133–42. doi:10.2165/11598440-000000000-00000.

    Article  CAS  PubMed  Google Scholar 

  42. Gonnelli S, Martini G, Caffarelli C, Salvadori S, Cadirni A, Montagnani A, et al. Teriparatide’s effects on quantitative ultrasound parameters and bone density in women with established osteoporosis. Osteoporos Int. 2006;17(10):1524–31. doi:10.1007/s00198-006-0157-3.

    Article  CAS  PubMed  Google Scholar 

  43. Lakatos P, Balogh A, Czerwinski E, Dimai HP, Hans D, Holzer G, et al. New considerations on the management of osteoporosis in Central and Eastern Europe (CEE): summary of the “3rd Summit on Osteoporosis-CEE”, November 2009, Budapest, Hungary. Arch Osteoporos. 2011;6(1–2):1–12. doi:10.1007/s11657-010-0048-2.

    Article  PubMed  Google Scholar 

  44. 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(8):1877–85. doi:10.1002/jbmr.73.

  45. 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(1):162–8. doi:10.1002/jbmr.1731.

    Article  CAS  PubMed  Google Scholar 

  46. Randall C, Bridges D, Guerri R, Nogues X, Puig L, Torres E, et al. Applications of a new handheld reference point indentation instrument measuring bone material strength. J Med Device. 2013;7(4):410051–6. doi:10.1115/1.4024829.

    PubMed  Google Scholar 

  47. Aref M, Gallant MA, Organ JM, Wallace JM, Newman CL, Burr DB, et al. In vivo reference point indentation reveals positive effects of raloxifene on mechanical properties following 6 months of treatment in skeletally mature beagle dogs. Bone. 2013;56(2):449–53. doi:10.1016/j.bone.2013.07.009.

    Article  CAS  PubMed  Google Scholar 

  48. Rasoulian R, Raeisi Najafi A, Chittenden M, Jasiuk I. Reference point indentation study of age-related changes in porcine femoral cortical bone. J Biomech. 2013;46(10):1689–96. doi:10.1016/j.jbiomech.2013.04.003.

    Article  PubMed  Google Scholar 

  49. Hammond MA, Gallant MA, Burr DB, Wallace JM. Nanoscale changes in collagen are reflected in physical and mechanical properties of bone at the microscale in diabetic rats. Bone. 2014;60:26–32. doi:10.1016/j.bone.2013.11.015.

    Article  CAS  PubMed  Google Scholar 

  50. Gallant MA, Brown DM, Organ JM, Allen MR, Burr DB. Reference-point indentation correlates with bone toughness assessed using whole-bone traditional mechanical testing. Bone. 2013;53(1):301–5. doi:10.1016/j.bone.2012.12.015. Study of the relationship between RPI parameters and standard bone biomechanical parameters.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Paschalis E, Mendelsohn R, Boskey A. Infrared assessment of bone quality: a review. Clin Orthop Relat Res. 2011;469(8):2170–8. doi:10.1007/s11999-010-1751-4. Overview of FTIR microspectroscopy and its role in establishing bone quality.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Morris MD, Mandair GS. Raman Assessment of bone quality. Clin Orthop Relat Res. 2011;469(8):2160–9. doi:10.1007/s11999-010-1692-y. Overview of Raman spectroscopy and its prospects for noninvasive assessment of bone quality.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Matousek P, Stone N. Recent advances in the development of Raman spectroscopy for deep non-invasive medical diagnosis. J Biophotonics. 2013;6(1):7–19. doi:10.1002/jbio.201200141.

    Article  CAS  PubMed  Google Scholar 

  54. Gourion-Arsiquaud S, West P, Boskey A. Fourier Transform-infrared microspectroscopy and microscopic imaging. Methods Mol Biol 2008;455:293–303.

  55. Nyman JS, Makowski AJ, Patil CA, Masui TP, O’Quinn EC, Bi XH, et al. Measuring differences in compositional properties of bone tissue by confocal Raman spectroscopy. Calcif Tissue Int. 2011;89(2):111–22. doi:10.1007/s00223-011-9497-x. Useful methods paper outlining the utility of Raman spectroscopy in the analysis of bone tissue composition.

    Article  CAS  PubMed  Google Scholar 

  56. Paschalis EP, Verdelis K, Doty SB, Boskey AL, Mendelsohn R, Yamauchi M. Spectroscopic characterization of collagen cross-links in bone. J Bone Miner Res. 2001;16(10):1821–8. doi:10.1359/jbmr.2001.16.10.1821.

    Article  CAS  PubMed  Google Scholar 

  57. Gourion-Arsiquaud S, Allen MR, Burr DB, Vashishth D, Tang SY, Boskey AL. Bisphosphonate treatment modifies canine bone mineral and matrix properties and their heterogeneity. Bone. 2010;46(3):666–72. doi:10.1016/j.bone.2009.11.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Donnelly E, Meredith DS, Nguyen JT, Boskey AL. Bone tissue composition varies across anatomic sites in the proximal femur and the iliac crest. J Orthop Res. 2012;30(5):700–6. doi:10.1002/jor.21574.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Gourion-Arsiquaud S, Faibish D, Myers E, Spevak L, Compston J, Hodsman A, et al. Use of FTIR spectroscopic imaging to identify parameters associated with fragility fracture. J Bone Miner Res. 2009;24(9):1565–71. doi:10.1359/jbmr.090414.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Malluche HH, Porter DS, Mawad H, Monier-Faugere M-C, Pienkowski D. Low-energy fractures without low T-scores characteristic of osteoporosis: a possible bone matrix disorder. J Bone Joint Surg Am. 2013;95(19):e1391–6. doi:10.2106/jbjs.l.01281.

    Article  PubMed  Google Scholar 

  61. Boskey AL. Bone composition: relationship to bone fragility and antiosteoporotic drug effects. BoneKEy Rep. 2013;2. doi:10.1038/bonekey.2013.181. Comprehensive overview outlining the effects of anti-osteoporotic drugs on bone mineral and matrix composition.

  62. Gourion-Arsiquaud S, Lukashova L, Power J, Loveridge N, Reeve J, Boskey AL. Fourier transform infrared imaging of femoral neck bone: reduced heterogeneity of mineral-to-matrix and carbonate-to-phosphate and more variable crystallinity in treatment-naive fracture cases compared with fracture-free controls. J Bone Miner Res. 2013;28(1):150–61. doi:10.1002/jbmr.1724.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Follet H, Farlay D, Bala Y, Viguet-Carrin S, Gineyts E, Burt-Pichat B, et al. Determinants of microdamage in elderly human vertebral trabecular bone. PLoS One. 2013;8(2):e55232. doi:10.1371/journal.pone.0055232.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hofstetter B, Gamsjaeger S, Phipps RJ, Recker RR, Ebetino FH, Klaushofer K, et al. Effects of alendronate and risedronate on bone material properties in actively forming trabecular bone surfaces. J Bone Miner Res. 2012;27(5):995–1003. doi:10.1002/jbmr.1572.

    Article  CAS  PubMed  Google Scholar 

  65. Gamsjaeger S, Buchinger B, Zoehrer R, Phipps R, Klaushofer K, Paschalis EP. Effects of one year daily teriparatide treatment on trabecular bone material properties in postmenopausal osteoporotic women previously treated with alendronate or risedronate. Bone. 2011;49(6):1160–5. doi:10.1016/j.bone.2011.08.015.

    Article  CAS  PubMed  Google Scholar 

  66. Burket JC, Brooks DJ, MacLeay JM, Baker SP, Boskey AL, van der Meulen MCH. Variations in nanomechanical properties and tissue composition within trabeculae from an ovine model of osteoporosis and treatment. Bone. 2013;52(1):326–36. doi:10.1016/j.bone.2012.10.018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Gamsjaeger S, Hofstetter B, Zwettler E, Recker R, Gasser JA, Eriksen EF, et al. Effects of 3 years treatment with once-yearly zoledronic acid on the kinetics of bone matrix maturation in osteoporotic patients. Osteoporos Int. 2013;24(1):339–47. doi:10.1007/s00198-012-2202-8.

    Article  CAS  PubMed  Google Scholar 

  68. Tamminen IS, Yli-Kyyny T, Isaksson H, Turunen MJ, Tong XY, Jurvelin JS, et al. Incidence and bone biopsy findings of atypical femoral fractures. J Bone Miner Metab. 2013;31(5):585–94. doi:10.1007/s00774-013-0448-7.

    Article  PubMed  Google Scholar 

  69. Donnelly E, Meredith DS, Nguyen JT, Gladnick BP, Rebolledo BJ, Shaffer AD, et al. Reduced cortical bone compositional heterogeneity with bisphosphonate treatment in postmenopausal women with intertrochanteric and subtrochanteric fractures. J Bone Miner Res. 2012;27(3):672–8. doi:10.1002/jbmr.560.

    Article  CAS  PubMed  Google Scholar 

  70. 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(4):825–34. doi:10.1002/jbmr.1501.

    Article  CAS  PubMed  Google Scholar 

  71. Boskey AL, Spevak L, Weinstein RS. Spectroscopic markers of bone quality in alendronate-treated postmenopausal women. Osteoporos Int. 2009;20(5):793–800. doi:10.1007/s00198-008-0725-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wehrli FW. Structural and functional assessment of trabecular and cortical bone by micro magnetic resonance imaging. J Magn Reson Imaging. 2007;25(2):390–409. doi:10.1002/jmri.20807.

  73. Magland JF, Zhang N, Rajapakse CS, Wehrli FW. Computationally-optimized bone mechanical modeling from high-resolution structural images. PLoS One. 2012;7(4):e35525. doi:10.1371/journal.pone.0035525.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Benito M, Vasilic B, Wehrli FW, Bunker B, Wald M, Gomberg B, et al. Effect of testosterone replacement on bone architecture in hypogonadal men. J Bone Miner Res. 2005;20(10):1785–91. doi:10.1359/JBMR.050606.

    Article  CAS  PubMed  Google Scholar 

  75. Chesnut III CH, Majumdar S, Newitt DC, Shields A, Van Pelt J, Laschansky E, et al. Effects of salmon calcitonin on trabecular microarchitecture as determined by magnetic resonance imaging: results from the QUEST study. J Bone Miner Res. 2005;20(9):1548–61. doi:10.1359/JBMR.050411.

    Article  CAS  PubMed  Google Scholar 

  76. 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(9):1426–34. doi:10.1359/jbmr.080405.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Wehrli FW, Ladinsky GA, Jones C, Benito M, Magland J, Vasilic B, et al. In vivo magnetic resonance detects rapid remodeling changes in the topology of the trabecular bone network after menopause and the protective effect of estradiol. J Bone Miner Res. 2008;23(5):730–40. doi:10.1359/jbmr.080108.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Wehrli FW, Rajapakse CS, Magland JF, Snyder PJ. Mechanical implications of estrogen supplementation in early postmenopausal women. J Bone Miner Res. 2010;25(6):1406–14. doi:10.1002/jbmr.33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Folkesson J, Goldenstein J, Carballido-Gamio J, Kazakia G, Burghardt AJ, Rodriguez A, et al. Longitudinal evaluation of the effects of alendronate on MRI bone microarchitecture in postmenopausal osteopenic women. Bone. 2011;48(3):611–21. doi:10.1016/j.bone.2010.10.179.

    Article  CAS  PubMed  Google Scholar 

  80. Rajapakse CS, Leonard MB, Bhagat YA, Sun W, Magland JF, Wehrli FW. Micro-MR imaging-based computational biomechanics demonstrates reduction in cortical and trabecular bone strength after renal transplantation. Radiology. 2012;262(3):912–20. doi:10.1148/radiol.11111044. First report on short-term structural and mechanical manifestations of kidney transplanation.

    Article  PubMed  PubMed Central  Google Scholar 

  81. 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(2):488–96.

    Article  PubMed  Google Scholar 

  82. 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(3):556–63. doi:10.1016/j.bone.2010.05.039.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Lin W, Ladinsky GA, Wehrli F, Song HK. Image metric-based correction (autofocusing) of motion artifacts in high-resolution trabecular bone imaging. J Magn Reson Imaging. 2007;26:191–7. doi:10.1002/jmri.20958.

    Article  PubMed  Google Scholar 

  84. Blumenfeld J, Carballido-Gamio J, Krug R, Blezek DJ, Hancu I, Majumdar S. Automatic prospective registration of high-resolution trabecular bone images of the tibia. Ann Biomed Eng. 2007;35(11):1924–31. doi:10.1007/s10439-007-9365-z.

    Article  PubMed  Google Scholar 

  85. Magland JF, Jones CE, Leonard MB, Wehrli FW. Retrospective 3D registration of trabecular bone MR images for longitudinal studies. J Magn Reson Imaging. 2009;29(1):118–26. doi:10.1002/jmri.21551.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Seeman E, Delmas PD. Bone quality—the material and structural basis of bone strength and fragility. N Engl J Med. 2006;354(21):2250–61. doi:10.1056/NEJMra053077.

    Article  CAS  PubMed  Google Scholar 

  87. Gomberg BR, Saha PK, Wehrli FW. Method for cortical bone structural analysis from magnetic resonance images. Acad Radiol. 2005;12(10):1320–32. doi:10.1016/j.acra.2005.06.012.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Manske SL, Liu-Ambrose T, de Bakker PM, Liu D, Kontulainen S, Guy P, et al. Femoral neck cortical geometry measured with magnetic resonance imaging is associated with proximal femur strength. Osteoporos Int. 2006;17(10):1539–45. doi:10.1007/s00198-006-0162-6.

    Article  CAS  PubMed  Google Scholar 

  89. Techawiboonwong A, Song HK, Leonard MB, Wehrli FW. Cortical bone water: in vivo quantification with ultrashort echo-time MR imaging. Radiology. 2008;248(3):824–33. doi:10.1148/radiol.2482071995.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Horch RA, Gochberg DF, Nyman JS, Does MD. Non-invasive predictors of human cortical bone mechanical properties: T(2)-discriminated H NMR compared with high resolution X-ray. PLoS One. 2011;6(1):e16359. doi:10.1371/journal.pone.0016359.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Ong HH, Wright AC, Wehrli FW. Deuterium nuclear magnetic resonance unambiguously quantifies pore and collagen-bound water in cortical bone. J Bone Miner Res. 2012;27(12):2573–81. doi:10.1002/jbmr.1709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Du J, Hermida JC, Diaz E, Corbeil J, Znamirowski R, D’Lima DD, et al. Assessment of cortical bone with clinical and ultra-short echo time sequences. Magn Reson Med. 2013;70:697–704. doi:10.1002/mrm.24497.

    Article  Google Scholar 

  93. Manhard MK, Horch RA, Harkins KD, Gochberg DF, Nyman JS, Does MD. Validation of quantitative bound- and pore-water imaging in cortical bone. Magn Reson Med. 2013. doi:10.1002/mrm.24870.

    PubMed  Google Scholar 

  94. Fazeli PK, Horowitz MC, MacDougald OA, Scheller EL, Rodeheffer MS, Rosen CJ, et al. Marrow fat and bone—new perspectives. J Clin Endocrinol Metab. 2013;98(3):935–45. doi:10.1210/jc.2012-3634.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Wehrli FW, Hopkins JA, Hwang SN, Song HK, Snyder PJ, Haddad JG. Cross-sectional study of osteopenia by quantitative magnetic resonance and bone densitometry. Radiology. 2000;217:527–38. doi:10.1148/radiology.217.2.r00nv20527.

    Article  CAS  PubMed  Google Scholar 

  96. Yeung DK, Griffith JF, Antonio GE, Lee FK, Woo J, Leung PC. Osteoporosis is associated with increased marrow fat content and decreased marrow fat unsaturation: a proton MR spectroscopy study. J Magn Reson Imaging. 2005;22(2):279–85. doi:10.1002/jmri.20367.

    Article  PubMed  Google Scholar 

  97. Li X, Kuo D, Schafer AL, Porzig A, Link TM, Black D, et al. Quantification of vertebral bone marrow fat content using 3 Tesla MR spectroscopy: reproducibility, vertebral variation, and applications in osteoporosis. J Magn Reson Imaging. 2011;33(4):974–9. doi:10.1002/jmri.22489.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Patsch JM, Li X, Baum T, Yap SP, Karampinos DC, Schwartz AV, et al. Bone marrow fat composition as a novel imaging biomarker in postmenopausal women with prevalent fragility fractures. J Bone Miner Res. 2013;28(8):1721–8. doi:10.1002/jbmr.1950. Compelling demonstration of association between marrow fat and osteoporotic fractures.

    Article  PubMed  PubMed Central  Google Scholar 

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Conflict of Interest

B. Gong, G.S. Mandair, and F.W. Wehrli all declare that they have no conflicts of interest. M.D. Morris has received research support from the National Institutes of Health; consultant fees from Kaiser Optical Systems, Inc.; and is a member of Biomatrix Photonics, LLC.

Human and Animal Rights and Informed Consent

All studies by B. Gong, G.S. Mandair, and M.D. Morris involving animal and/or human subjects were performed after approval by the appropriate institutional review boards. When required, written informed consent was obtained from all participants.

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Correspondence to Michael D. Morris.

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B. Gong and G. S. Mandair contributed equally to this work.

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Gong, B., Mandair, G.S., Wehrli, F.W. et al. Novel Assessment Tools for Osteoporosis Diagnosis and Treatment. Curr Osteoporos Rep 12, 357–365 (2014). https://doi.org/10.1007/s11914-014-0215-2

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