Skip to main content
SpringerLink
Log in

New Applications for DXA

  • Chapter
  • First Online:
Bone Densitometry in Clinical Practice

Part of the book series: Current Clinical Practice ((CCP))

  • 1469 Accesses

Abstract

Densitometry has primarily been a quantitative measurement technique. The first skeletal images from a densitometer, as seen in Fig. 2-7 in an earlier chapter, were only vaguely reminiscent of the actual bone. The poor image quality had little effect on the ability to quantify the bone density, which was the primary purpose of the various techniques. Imaging, as a potential application of densitometry, has been anticipated for over a decade. Continued improvements in DXA technology combined with modern computer capabilities have resulted in dramatic improvements in imaging and acquisition speed. Truly remarkable images of the spine such as the RVA™ image from a Hologic Discovery1 seen in Fig. 13-1 are possible today. When combined with the very low radiation exposure in comparison to conventional X-ray studies, the use of lateral spine DXA imaging for the diagnosis of vertebral fractures and assessment of aortic calcification has become a clinical reality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    RVA™ is a trademarked reference for radiographic vertebral assessment from Hologic. Specifications for the Hologic Discovery can be found in Chapter 15.

  2. 2.

    A prevalent fracture is a fracture that is already present at the time the patient is seen. An incident fracture is a fracture that develops at some point in time after the initial evaluation.

  3. 3.

    DVA™ is a trademarked reference to Dual-energy Vertebral Assessment as performed on GE Lunar fan-array DXA devices. Specifications for the GE Lunar Prodigy may be found in Chapter 15.

  4. 4.

    Parallax refers to an apparent displacement of an object due to a change in the observer’s position. In the case of spine radiography, it refers to the angle at which the X-ray beam passes through the vertebral bodies. If the X-ray beam is not parallel to the vertebral endplates, the shape of the vertebrae may be distorted, making morphometric measurements inaccurate.

  5. 5.

    See Chapter 3for a discussion of precision.

  6. 6.

    See Appendix VIII for a listing and discussion of relevant CPT codes.

  7. 7.

    See Chapter 9 and Appendix IV for a discussion of the WHO criteria for the diagnosis of osteoporosis based on the measurement of bone density.

  8. 8.

    See Chapter 2 for a discussion of the potential effect of aortic calcification on BMD measured at the PA lumbar spine.

  9. 9.

    The Rotterdam study is a prospective population-based cohort study of 7983 women and men age ≥55 years in which the incidence and causes of chronic disabling diseases were investigated.

  10. 10.

    The Framingham Heart Study is a prospective epidemiologic cohort study begun in 1948 to identify potential risk factors for coronary heart disease.

  11. 11.

    Coronary heart disease refers to angina, unstable angina, myocardial infarction, and coronary disease death.

  12. 12.

    See Chapter 3 for a discussion of receiver operator characteristic curves.

  13. 13.

    Diagnostic tests with an AU-ROC of 0.70–0.90 when compared to a “gold standard” test are considered to have acceptable predictive accuracy. An AU-ROC > 0.90 is superb.

  14. 14.

    EPIDOS is a multicenter prospective study of 7575 women living at home aged 75–95 years in France. The data in this study are from 320 women from the Lyon and Montpellier centers.

  15. 15.

    The Dubbo Osteoporosis Epidemiology Study is a case-control study involving 1902 men and women who were recruited between 1989 and 1993.

  16. 16.

    The CMSI is a measure of the distribution of the mineral within the cross-section. It is not a measure of bone strength but directly affects the section modulus, which is a measure of strength in bending.

  17. 17.

    The CSA is a measure of the bone mineral content in the cross section. It is directly proportional to the ability of the bone to withstand axial compression.

  18. 18.

    The section modulus is a measure of the strength of the bone in bending. Note that the section modulus is indicated by the abbreviation “Z,” which is unrelated to the z-score used in densitometry.

  19. 19.

    The BR is the ratio of the diameter of the cross section to its cortical thickness. High BRs suggest the possibility of local failure or buckling due to an excessively thinned cortex.

  20. 20.

    This was originally called the Fall Index.

  21. 21.

    This technique is also known as hydrostatic weighing or hydrodensitometry.

  22. 22.

    See Chapter 11 for a discussion of the percent coefficient of variation and the number of subjects and studies needed for a valid precision study.

  23. 23.

    Chemical fat is actually a component of adipose tissue. Fat can be found in other tissues as well. Similarly, adipose tissue also contains protein, minerals, and water. The terms fat and adipose tissue are unfortunately often used interchangeably, even though they are clearly not synonymous.

  24. 24.

    See Chapter 5 for a discussion of the effective dose equivalent (HE).

  25. 25.

    The Tanner and Whitehouse method is often indicated by the abbreviation TW2 to indicate the method proposed in 1983 rather than an earlier method proposed in 1975.

  26. 26.

    This is the same James Mourilyan Tanner as in the Tanner and Whitehouse bone age method.

  27. 27.

    A centile scale reflects values from 0 to 100. The terms centiles and percentiles are often used interchangeably. The location of the value plotted on the scale indicates the percentage of individuals in the population in question, who have a similar or poorer value. For example, if the centile scale reflects a value of 40, 40% of individuals in that population have the same or poorer value for the quantity in question. Conversely, 60% will have a higher value. If the value on the centile scale is 50, an appropriate interpretation is that half of the population will have a better value and half will have a poorer value. A centile value of 5 or less is generally a cause for concern although some circumstances may dictate concern at higher centile values.

  28. 28.

    See Chapter 9 for a discussion of the World Health Organization criteria for the diagnosis of osteoporosis based on the measurement of bone density.

  29. 29.

    Menarche is defined as the age at which the first menstrual period occurs.

  30. 30.

    See Chapter 3 for a discussion of the normal distribution.

References

  1. Ross PD, Davis JW, Epstein RS, Wasnich RD. Pre-existing fractures and bone mass predict vertebral fracture incidence in women. Ann Intern Med 1991;114:919–923.

    PubMed  CAS  Google Scholar 

  2. Nevitt MC, Ross PD, Palermo L, Muslinger T, Genant HK, Thompson DE. Association of prevalent vertebral fractures, bone density, and alendronate treatment with incident vertebral fractures: effect of number and spinal location of fractures. Bone 1999;25:613–619.

    Article  PubMed  CAS  Google Scholar 

  3. Ross PD, Genant HK, Davis JW, Miller PD, Wasnich RD. Predicting vertebral fracture incidence from prevalent fractures and bone density among non-black, osteoporotic women. Osteoporos Int 1993;3:120–126.

    Article  PubMed  CAS  Google Scholar 

  4. Black DM, Arden NK, Palermo L, Pearson J, Cummings SR. Prevalent vertebral deformities predict hip fractures and new vertebral deformities but not wrist fractures. J Bone Min Res 1999;14:821–828.

    Article  CAS  Google Scholar 

  5. Melton LJ, Atkinson EJ, Cooper C, O’Fallon WM, Riggs BL. Vertebral fractures predict subsequent fractures. Osteoporos Int 1999;10:214–221.

    Article  PubMed  Google Scholar 

  6. Burger H, van Daele PLA, Algra D, et al. Vertebral deformities as predictors of non-vertebral fractures. BMJ 1994;309:991–992.

    Article  PubMed  CAS  Google Scholar 

  7. Klotzbuecher CM, Ross PD, Landsman PB, Abbott TA, Berger M. Patients with prior fractures have an increased risk of future fractures: a summary of the literature and statistical synthesis. J Bone Miner Res 2000;15:721–739.

    Article  PubMed  CAS  Google Scholar 

  8. Cooper C, Atkinson EJ, O’Fallon WM, Melton LJ. Incidence of clinically diagnosed vertebral fractures: a population-based study in Rochester, Minnesota, 1985–1989. J Bone Miner Res 1992;7:221–227.

    Article  PubMed  CAS  Google Scholar 

  9. Ensrud KE, Nevitt MC, Palermo L, et al. What proportion of incident morphometric vertebral fractures are clinically diagnosed and vice versa? Abstract. J Bone Miner Res 1999;14:S138.

    Google Scholar 

  10. Morris CA, Carrino JA, Lang P, Solomon DH. Incidental vertebral fractures on chest radiographs. Recognition, documentation, and treatment. J Gen Intern Med 2006;21(4):352–356.

    Article  PubMed  Google Scholar 

  11. Majundar SR, Kim N, Colman I, et al. Incidental vertebral fractures discovered with chest radiography in the emergency department: prevalence, recognition, and osteoporosis management in a cohort of elderly patients. Arch Intern Med 2005;165(8):905–909.

    Article  Google Scholar 

  12. Gehlbach SH, Bigelow C, Heimisdottir M, May S, Walker M, Kirkwood JR. Recognition of vertebral fracture in a clinical setting. Osteoporos Int 2000;11(7):577–582.

    Article  PubMed  CAS  Google Scholar 

  13. Kim N, Rowe BH, Raymond G, et al. Underreporting of vertebral fractures on routine chest radiography. Am J Roentgenol 2004;182(2):297–300.

    CAS  Google Scholar 

  14. Genant HK, Wu CY, Van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 1993;8:1137–1148.

    Article  PubMed  CAS  Google Scholar 

  15. Fletcher H. Anterior vertebral wedging-frequency and significance. AJR 1947;57:232–238.

    CAS  Google Scholar 

  16. Barent E, Nordin BEC. The radiological diagnosis of osteoporosis: a new approach. Clin Radiol 1960;11:166–174.

    Article  Google Scholar 

  17. Black DM, Cummings SR, Stone K, Hudes E, Palermo L, Steiger P. A new approach to defining normal vertebral dimensions. J Bone Miner Res 1991;6:883–892.

    Article  PubMed  CAS  Google Scholar 

  18. Melton LJ, Kan SH, Frye MA, Wahner HW, O’Fallon WM, Riggs BL. Epidemiology of vertebral fractures in women. Am J Epidemiol 1989;129:1000–1011.

    PubMed  Google Scholar 

  19. Smith-Bindman R, Cummings SR, Steiger P, Genant HK. A comparison of morphometric definitions of vertebral fracture. J Bone Miner Res 1991;6:25–34.

    Article  PubMed  CAS  Google Scholar 

  20. Ross PD, Davis JW, Epstein RS, Wasnich RD. Ability of vertebral dimensions from a single radiograph to identify fractures. Calcif Tissue Int 1992;51:95–99.

    Article  PubMed  CAS  Google Scholar 

  21. Eastell R, Cedel SL, Wahner HW, Riggs BL, Melton LJ. Classification of vertebral fractures. J Bone Miner Res 1991;2:207–214.

    Google Scholar 

  22. McCloskey E, Spector TD, Eyres KS, et al. The assessment of vertebral deformity: a method for use in population studies and clinical trials. Osteoporos Int 1993;3:138–147.

    Article  PubMed  CAS  Google Scholar 

  23. McCloskey EV, Kanis JA. The assessment of vertebral deformity. In: Genant HK, Jergas M, Van Kuijk C, eds. Vertebral fracture in osteoporosis. University of California, San Francisco: Radiology Research and Education Foundation, 1995:215–233.

    Google Scholar 

  24. Sauer P, Leidig G, Minne HW, et al. Spine deformity index versus other objective procedures of vertebral identification in patients with osteoporosis: a comparative study. J Bone Miner Res 1991;6:227–238.

    Article  PubMed  CAS  Google Scholar 

  25. Black DM, Palermo L, Nevitt MC, Genant HK, Christensen L, Cummings SR. Defining incident vertebral deformity: a prospective comparison of several approaches. J Bone Miner Res 1999;14:90–101.

    Article  PubMed  CAS  Google Scholar 

  26. Lunt M, Ismail AA, Felsenberg D, et al. Defining incident vertebral deformities in population studies: a comparison of morphometric criteria. Osteoporos Int 2002;13:809–815.

    Article  PubMed  CAS  Google Scholar 

  27. National Osteoporosis Foundation Working Group on Vertebral Fractures. Assessing vertebral fractures. J Bone Miner Res 1995;10:518–523.

    Google Scholar 

  28. Leidig-Bruckneer G, Genant HK, Minne HW, et al. Comparison of a semiquantitative and quantitative method for assessing vertebral fractures in osteoporosis. Osteoporos Int 1994;3:154–161.

    Article  Google Scholar 

  29. Genant HK, Jergas M, Palermo L, et al. Comparison of semiquantitative visual and quantitative morphometric assessment of prevalent and incident vertebral fractures in osteoporosis: The Study of Osteoporotic Fractures Research Group. J Bone Miner Res 1996;11:984–996.

    Article  PubMed  CAS  Google Scholar 

  30. Grados F, Roux C, de Vernejoul MC, Utard G, Sebert JL, Fardellonne P. Comparison of four morphometric definitions and a semiquantitative consensus reading for assessing prevalent vertebral fractures. Osteoporos Int 2001;12:716–722.

    Article  PubMed  CAS  Google Scholar 

  31. Li J, Wu CY, Jergas H, Genant HK. Diagnosing prevalent vertebral fractures: a comparison between quantitative morphometry and a standardized visual (semiquantitative) approach. In: Genant HK, Jergas M, Van Kuijk C, eds. Vertebral fracture in osteoporosis. University of California, San Francisco: Radiology Research and Education Foundation, 1995:271–279.

    Google Scholar 

  32. Wu CY, Li J, Jergas M, Genant HK. Diagnosing incident vertebral fractures: a comparison between quantitative morphometry and a standardized visual (semiquantitative) approach. In: Genant HK, Jergas M, Van Kuijk C, eds. Vertebral fracture in osteoporosis. University of California, San Francisco: Radiology Research and Education Foundation, 1995:281–291.

    Google Scholar 

  33. Rea JA, Li J, Blake GM, Steiger P, Genant HK, Fogelman I. Visual assessment of vertebral deformity by X-ray absorptiometry: a highly predictive method to exclude vertebral deformity. Osteoporos Int 2000;11:660–668.

    Article  PubMed  CAS  Google Scholar 

  34. Schousboe JT, DeBold CR, Bowles C, Glickstein S, Rubino RK. Prevalence of vertebral compression fracture deformity by X-ray absorptiometry of lateral thoracic and lumbar spines in a population referred for bone densitometry. J Clin Densitom 2002;5:239–246.

    Article  PubMed  Google Scholar 

  35. Davis JW, Grove JS, Wasnich RD, Ross PD. Spatial relationships between prevalent and incident spine fractures. Bone 1999;24:261–264.

    Article  PubMed  CAS  Google Scholar 

  36. Faulkner KG, Barden HS, Weynand L, Burke P. Frequency of spine fractures assessed with LVA in normal, osteopenic, and osteoporotic postmenopausal women. J Bone Miner Res 2002;17:S110.

    Google Scholar 

  37. Ferrar L, Jiang G, Eastell R. Longitudinal evaluation of morphometric X-ray absorptiometry for the identification of vertebral deformities. Osteoporos Int 2001;12:661–671.

    Article  PubMed  CAS  Google Scholar 

  38. Rea JA, Chen MB, Li J, et al. Vertebral morphometry: a comparison of long-term precision of morphometric X-ray absorptiometry and morphometric radiography in normal and osteoporotic subjects. Osteoporos Int 2001;12:158–166.

    Article  PubMed  CAS  Google Scholar 

  39. National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. 1–30. 2008. Washington, D.C., National Osteoporosis Foundation.

    Google Scholar 

  40. Schousboe JT, Vokes T, Broy SB, et al. Vertebral fracture assessment: the 2007 ISCD official positions. J Clin Densitom 2008;11(1):92–108.

    Article  PubMed  Google Scholar 

  41. Tankó LB, Christiansen C, Cox DA, Geiger MJ, McNabb MA, Cummings SR. Relationship between osteoporosis and cardiovascular disease in postmenopausal women. J Bone Miner Res 2005;20(11):1912–1920.

    Article  PubMed  Google Scholar 

  42. von der Recke P, Hansen MA, Hassager C. The association between low bone mass at the menopause and cardiovascular mortality. Am J Med 1999;106:273–278.

    Article  PubMed  Google Scholar 

  43. Samelson EJ, Kiel DP, Broe KE, et al. Metacarpal cortical area and risk of coronary heart disease. The Framingham Study. Am J Epidemiol 2004;159:589–595.

    Article  PubMed  Google Scholar 

  44. Browner WS, Seeley DG, Vogt TM, Cummings SR. Non-trauma mortality in elderly women with low bone mineral density. Lancet 1991;338:355–358.

    Article  PubMed  CAS  Google Scholar 

  45. Trivedi DP, Khaw KT. Bone mineral density at the hip predicts mortality in elderly men. Osteoporos Int 2001;12:259–265.

    Article  PubMed  CAS  Google Scholar 

  46. Kiel DP, Kauppila LI, Cupples LA, Hannan MT, O’Donnell CJ, Wilson PW. Bone loss and the progression of abdominal aortic calcification over a 25-year period: the Framingham Heart Study. Calcif Tissue Int 2001;68(5):271–276.

    Article  PubMed  CAS  Google Scholar 

  47. Bagger YZ, Tankó LB, Alexandersen P, Qin G, Christiansen C. Radiographic measure of aorta calcification is a site-specific predictor of bone loss and fracture risk at the hip. J Intern Med 2006;259:598–605.

    Article  PubMed  CAS  Google Scholar 

  48. Naves M, Rodríguez-García M, Díaz-López JB, Gómez-Alonso C, Annata-Andía JB. Progression of vascular calcifications is associated with greater bone loss and increased bone fractures. Osteoporos Int 2008;19(8):1161–1166.

    Article  PubMed  CAS  Google Scholar 

  49. Schulz E, Kiumars A, Liu X, Sayre J, Gilsanz V. Aortic calcification and the risk of osteoporosis and fractures. J Clin Endocrinol Metab 2004;89(9):4246–4253.

    Article  PubMed  CAS  Google Scholar 

  50. Wilson PW, Kauppila LI, O’Donnell CJ, et al. Abdominal aortic calcific deposits are an important predictor of vascular morbidity and mortality. Circulation 2001;103(11):1529–1534.

    Article  PubMed  CAS  Google Scholar 

  51. van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study. Circulation 2004;109(9):1089–1094.

    Article  PubMed  Google Scholar 

  52. Hollander M, Hak AE, Koudstaal PJ, et al. Comparison between measures of atherosclerosis and risk of stroke: the Rotterdam Study. Stroke 2003;34(10):2367–2372.

    Article  PubMed  CAS  Google Scholar 

  53. Walsh CR, Cupples LA, Levy D, et al. Abdominal aortic calcific deposits are associated with increased risk for congestive heart failure: the Framingham Heart Study. Am Heart J 2002;144(4):733–739.

    PubMed  Google Scholar 

  54. Kauppila LI, Polak JF, Cupples LA, Hannan MT, Kiel DP, Wilson PW. New indices to classify location, severity, and progression of calcific lesions in the abdominal aorta: a 25-year follow-up study. Atherosclerosis 1997;132(2):245–250.

    Article  PubMed  CAS  Google Scholar 

  55. Schousboe JT, Wilson KE, Kiel DP. Detection of abdominal aortic calcification with lateral spine imaging using DXA. J Clin Densitom 2006;9(3):302–308.

    Article  PubMed  Google Scholar 

  56. Schousboe JT, Wilson KE, Hangartner TN. Detection of aortic calcification during vertebral fracture assessment (VFA) compared to digital radiography. PLoS ONE 2007;2(1):e715.

    Article  PubMed  Google Scholar 

  57. Schousboe JT, Taylor BC, Kiel DP, Ensrud KE, Wilson KE, McCloskey EV. Abdominal aortic calcification detected on lateral spine images from a bone densitometer predicts incident myocardial infarction or stroke in older women. J Bone Miner Res 2008;23(3):409–416.

    Article  PubMed  Google Scholar 

  58. Faulkner KG, Cummings SR, Black D, Palermo L, Glüer CC, Genant HK. Simple measurement of femoral geometry predicts hip fracture: the study of osteoporotic fractures. J Bone Miner Res 1993;8:1211–1217.

    Article  PubMed  CAS  Google Scholar 

  59. Faulkner KG, Mcclung M, Cummings SR. Automated evaluation of hip axis length for predicting hip fracture. J Bone Miner Res 1994;9:1065–1070.

    Article  PubMed  CAS  Google Scholar 

  60. Duboeuf F, Hans D, Schott AM, et al. Different morphometric and densitometric parameters predict cervical and trochanteric hip fracture: the EPIDOS study. J Bone Miner Res 1997;12:1895–1902.

    Article  PubMed  CAS  Google Scholar 

  61. Center JR, Nguyen TV, Pocock NA, et al. Femoral neck axis length, height loss, and risk of hip fracture in males and females. Osteoporos Int 1998;8:75–81.

    Article  PubMed  CAS  Google Scholar 

  62. Bergot C, Bousson V, Meunier A, Laval-Jeantet M, Laredo JD. Hip fracture risk and proximal femur geometry from DXA scans. Osteoporos Int 2002;13:542–550.

    Article  PubMed  CAS  Google Scholar 

  63. Cummings SR, Cauley JA, Palermo L, et al. Racial differences in hip axis lengths might explain racial differences in rates of hip fracture. Osteoporos Int 1994;4:226–229.

    Article  PubMed  CAS  Google Scholar 

  64. Nakamura T, Turner CH, Yoshikawa T, et al. Do variations in hip geometry explain differences in hip fracture risk between Japanese and white Americans? J Bone Miner Res 1994;9:1071–1076.

    Article  PubMed  CAS  Google Scholar 

  65. Bonnick SL, Lewis LA. The precision of PA spine, dual-femur and single-femur bone density studies on the GE Lunar Prodigy, a DXA fan-array device. J Clin Densitom 2002;5:S48.

    Google Scholar 

  66. Gomez Alonso C, Diaz Curiel M, Hawkins Carranza F, Perez Cano R, Diez Perez A. Femoral bone mineral density, neck shaft angle, and mean femoral neck width as predictors of hip fractures in men and women. Osteoporos Int 2000;11:714–720.

    Article  Google Scholar 

  67. Gnudi S, Ripamonti C, Gualtieri G, Malavolta N. Geometry of proximal femur in the prediction of hip fracture in osteoporotic women. Br J Radiol 1999;72:729–733.

    PubMed  CAS  Google Scholar 

  68. Karlsson KM, Sernbo I, Obrant KJ, Redlund-Johnell I, Johnell O. Femoral neck geometry and radiographic signs of osteoporosis as predictors of hip fracture. Bone 1996;18:327–330.

    Article  PubMed  CAS  Google Scholar 

  69. Partanen J, Jämsä T, Jalovaara P. Influence of the upper femur and pelvic geometry on the risk and type of hp fractures. J Bone Miner Res 2001;16:1540–1546.

    Article  PubMed  CAS  Google Scholar 

  70. Peacock M, Turner CH, Liu G, et al. Better discrimination of hip fracture using bone density geometry and architecture. Osteoporos Int 1995;5:167–173.

    Article  PubMed  CAS  Google Scholar 

  71. Yoshikawa T, Turner CH, Peacock M, et al. Geometric structure of the femoral neck measured using dual-energy X-ray absorptiometry. J Bone Miner Res 1994;9:1053–1064.

    Article  PubMed  CAS  Google Scholar 

  72. Martin RB, Burr DB. Non-invasive measurement of long bone cross-sectional moment of inertia by photon absorptiometry. J Biomech 1984;17:195–201.

    Article  PubMed  CAS  Google Scholar 

  73. Beck TJ, Ruff CB, Warden KE, et al. Predicting femoral neck strength from bone mineral data. A structural approach. Invest Radiol 1990;25:6–18.

    Article  PubMed  CAS  Google Scholar 

  74. Beck T. Measuring the structural strength of bones with dual-energy X-ray absorptiometry: principles, technical limitations, and future possibilities. Osteoporos Int 2003;14(S5):S81–S88.

    Article  PubMed  Google Scholar 

  75. Beck TJ. Extending DXA beyond bone mineral density: understanding hip structure analysis. Curr Osteoporos Rep 2007;5:49–55.

    Article  PubMed  Google Scholar 

  76. Faulkner KG, Wacker WK, Barden HS, et al. Femur strength index predicts hip fracture independent of bone density and hip axis length. Osteoporos Int 2006;17:593–599.

    Article  PubMed  CAS  Google Scholar 

  77. Beck TJ, Ruff CB, Scott WW, Jr, et al. Sex differences in geometry of the femoral neck with aging: a structural analysis of bone mineral data. Calcif Tissue Int 1992;50:24–29.

    Article  PubMed  CAS  Google Scholar 

  78. Beck TJ, Ruff CB, Bissessur K. Age-related changes in female femoral neck geometry: implications for bone strength. Calcif Tissue Int 1993;53(Sl)1:S41–S46.

    Article  PubMed  Google Scholar 

  79. Beck TJ, Looker AC, Ruff CB, et al. Structural trends in the aging femoral neck and proximal shaft: analysis of the Third National Health and Nutrition Examination Survey dual-energy X-ray absorptiometry data. J Bone Miner Res 2000;15:2297–2304.

    Article  PubMed  CAS  Google Scholar 

  80. Beck TJ, Stone KL, Oreskovic TL, et al. Effects of current and discontinued estrogen replacement therapy on hip structural geometry: the study of osteoporotic fractures. J Bone Miner Res 2001;16:2103–2110.

    Article  PubMed  CAS  Google Scholar 

  81. Greenspan SL, Beck TJ, Resnick NM, et al. Effect of hormone replacement, alendronate, or combination therapy on hip structural geometry: a 3-year, double-blind, placebo-controlled clinical trial. J Bone Miner Res 2005;20:1525–1532.

    Article  PubMed  CAS  Google Scholar 

  82. Uusi-Rasi K, Beck TJ, Semanick LM, et al. Structural effects of raloxifene on the proximal femur: results from the multiple outcomes of raloxifene evaluation trial. Osteoporos Int 2006;17:575–586.

    Article  PubMed  CAS  Google Scholar 

  83. Uusi-Rasi K, Semanick LM, Zanchetta JR, et al. Effects of teriparatide [rhPTH (1–34)] treatment on structural geometry of the proximal femur in elderly osteoporotic women. Bone 2005;36:948–958.

    Article  PubMed  CAS  Google Scholar 

  84. Szulc P, Duboeuf F, Schott AM, et al. Structural determinants of hip fracture in elderly women: re-analysis of the data from the EPIDOS study. Osteoporos Int 2006;17:231–236.

    Article  PubMed  CAS  Google Scholar 

  85. Ahlborg HG, Nguyen ND, Nguyen TV, et al. Contribution of hip strength indices to hip fracture risk in elderly men and women. J Bone Miner Res 2005;20:1820–1827.

    Article  PubMed  Google Scholar 

  86. Melton LJ, III, Beck TJ, Amin S, et al. Contributions of bone density and structure to fracture risk assessment in men and women. Osteoporos Int 2005;16:460–467.

    Article  PubMed  Google Scholar 

  87. Crabtree NJ, Kroger H, Martin A, et al. Improving risk assessment: hip geometry, bone mineral distribution, and bone strength in hip fracture cases and controls. The EPOS Study. Osteoporos Int 2002;13:48–54.

    Article  PubMed  CAS  Google Scholar 

  88. Khoo BC, Beck TJ, Qiao QH, et al. In vivo short-term precision of hip structure analysis variables in comparison with bone mineral density using paired dual-energy X-ray absorptiometry scans from multi-center clinical trials. Bone 2005;37:112–121.

    Article  PubMed  Google Scholar 

  89. Brown JK, Qiao Q, Weigert J, et al. Improved precision of hip structure analysis using optimized projection images from segmented 3D CT scans of the hip. J Bone Miner Res 2005;20(S1):S337.

    Google Scholar 

  90. Garrow JS, Webster J. Quetelet’s index (W/H2) as a measure of fatness. Int J Obes 1985;9:147–153.

    PubMed  CAS  Google Scholar 

  91. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO expert committee. 1–452. 1995. Geneva, World Health Organization. WHO Technical Report Series 854.

    Google Scholar 

  92. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation on obesity. 1–158. 1998. Geneva, World Health Organization.

    Google Scholar 

  93. Wang J, Heymsfield SB, Aulet M, Thornton JC, Pierson RN. Body fat from body density: underwater weighing vs. dual-photon absorptiometry. Am J Physiol. 1989;256:E829–E834.

    PubMed  CAS  Google Scholar 

  94. Heymsfield SB, Wang J, Lichtman S, et al. Body composition in elderly subjects: a critical appraisal of clinical methodology. Am J Clin Nutr 1989;50:1167–1175.

    PubMed  CAS  Google Scholar 

  95. Siri WE. Body composition from fluid spaces and density: analysis of methods. University of California Radiation Laboratory Report 3349, 1956.

    Google Scholar 

  96. Durnin JVGA, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 1974;32:77–97.

    Article  PubMed  CAS  Google Scholar 

  97. Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr 1978;40:497–504.

    Article  PubMed  CAS  Google Scholar 

  98. Jackson AS, Pollock ML, Ward A. Generalized equations for predicting body density of women. Med Sci Sports Exerc 1980;12:175–182.

    PubMed  CAS  Google Scholar 

  99. McCrory MA, Gomez TD, Bernauer EM, Molé PA. Evaluation of a new air displacement plethysmograph for measuring human body composition. Med Sci Sports Exerc 1995;27:1686–1691.

    PubMed  CAS  Google Scholar 

  100. Fields DA, Goran MI, McCrory MA. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr 2002;75:453–467.

    PubMed  CAS  Google Scholar 

  101. McLean KP, Skinner JS. Validity of Futrex-5000 for body composition determination. Med Sci Sports Exerc 1992;24:253–258.

    PubMed  CAS  Google Scholar 

  102. Brodie DA, Eston RG. Body fat estimations by electrical impedance and infrared interactance. Int J Sports Med 1992;13:319–325.

    Article  PubMed  CAS  Google Scholar 

  103. Heyward VH, Cook KL, Hicks VL, et al. Predictive accuracy of three field methods for estimating relative body fatness of nonobese and obese women. Int J Sports Nutr 1992;2:75–86.

    CAS  Google Scholar 

  104. Horber FF, Thomi F, Casez JP, Fonteille J, Jaeger P. Impact of hydration status on body composition as measured by dual-energy X-ray absorptiometry in normal volunteers and patients on haemodialysis. Br J Radiol 1992;65:895–900.

    Article  PubMed  CAS  Google Scholar 

  105. Pietrobelli A, Wang Z, Formica C, Heymsfield SB. Dual-energy X-ray absorptiometry: fat estimation errors due to variation in soft tissue hydration. Am J Physiol 1998;274:E808–E816.

    PubMed  CAS  Google Scholar 

  106. Dalsky GP, Kraemer W, Zetterlund AE, et al. A comparison of methods to assess body composition. Abstract. American College of Sports Medicine, Salt Lake City, UT, May 22–25, 1990.

    Google Scholar 

  107. Kiebzak GM, Leamy LJ, Person LM, Nord RH, Zhang ZY. Measurement precision of body composition variables using the Lunar DPX-L densitometer. J Clin Densitom 2000;3:35–41.

    Article  PubMed  CAS  Google Scholar 

  108. Black E, Petersen L, Kreutzer M, et al. Fat mass measured by DXA varies with scan velocity. Obes Res 2002;10:69–77.

    Article  PubMed  Google Scholar 

  109. Soriano JM, Ioannidou E, Wang J, et al. Pencil-beam vs fan-beam dual-energy X-ray absorptiometry comparisons across four systems: body composition and bone mineral. J Clin Densitom 2004;7:281–289.

    Article  PubMed  Google Scholar 

  110. Tothill P, Avenell A, Love J, Reid DM. Comparisons between Hologic, Lunar, and Norland dual-energy X-ray absorptiometers and other techniques used for whole-body soft tissue measurements. Eur J Clin Nutr 1994;48:781–794.

    PubMed  CAS  Google Scholar 

  111. Ellis KJ, Shypailo RJ. Bone mineral and body composition measurements: cross-calibration of pencil-beam and fan-beam dual-energy X-ray absorptiometers. J Bone Miner Res 1998;13:1613–1618.

    Article  PubMed  CAS  Google Scholar 

  112. Abrahamsen B, Gram J, Hansen TB, Beck-Nielsen H. Cross-calibration of QDR 2000 and QDR 1000 dual-energy X-ray densitometers for bone mineral and soft-tissue measurements. Bone 1995;16:385–390.

    Article  PubMed  CAS  Google Scholar 

  113. Grundy SM. Metabolic syndrome scientific statement by the American Heart Association and the National Heart, Lung, and Blood Institute. Arterioscler Thromb Vasc Biol 2005;25(11):2243–2244.

    Article  PubMed  CAS  Google Scholar 

  114. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet 2005;366(9491):1059–1062.

    Article  PubMed  Google Scholar 

  115. Zhu S, Wang Z, Heshka S, Heo M, Faith MS, Heymsfield SB. Waist circumference and obesity-associated risk factors among whites in the third National Health and Nutrition Examination Survey: clinical action thresholds. Am J Clin Nutr 2002;76(4):743–749.

    PubMed  CAS  Google Scholar 

  116. Pouliot MC, Despres JP, Lemieux S, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73:460–468.

    Article  PubMed  CAS  Google Scholar 

  117. Zamboni M, Turcato E, Armellini F, et al. Sagittal abdominal diameter as a practical predictor of visceral fat. Int J Obes Relat Metab Disord 1998;22(7):655–660.

    Article  PubMed  CAS  Google Scholar 

  118. Clasey JL, Bouchard C, Teates CD, et al. The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 1999;7(3):256–264.

    Article  PubMed  CAS  Google Scholar 

  119. Rankinen T, Kim SY, Perusse L, Despres JP, Bouchard C. The prediction of abdominal visceral fat level from body composition and anthropometry: ROC analysis. Int J Obes Relat Metab Disord 1999;23(8):801–809.

    Article  PubMed  CAS  Google Scholar 

  120. Onat A, Avci GS, Barlan MM, Uyarel H, Uzunlar B, Sansoy V. Measures of abdominal obesity assessed for visceral adiposity and relation to coronary risk. Int J Obes Relat Metab Disord 2004;28(8):1018–1025.

    Article  PubMed  CAS  Google Scholar 

  121. Larsson B, Seidell J, Svardsudd K, et al. Obesity, adipose tissue distribution, and health in men – the study of men born in 1913. Appetite 1989;13(1):37–44.

    Article  PubMed  CAS  Google Scholar 

  122. Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 1990;10(4):497–511.

    Article  PubMed  CAS  Google Scholar 

  123. Stevens J, Keil JE, Rust PF, Tyroler HA, Davis CE, Gazes PC. Body mass index and body girths as predictors of mortality in black and white women. Arch Intern Med 1992;152(6):1257–1262.

    Article  PubMed  CAS  Google Scholar 

  124. Stevens J, Keil JE, Rust PF, et al. Body mass index and body girths as predictors of mortality in black and white men. Am J Epidemiol 1992;135(10):1137–1146.

    PubMed  CAS  Google Scholar 

  125. Folsom AR, Kaye SA, Sellers TA, et al. Body fat distribution and 5-year risk of death in older women. JAMA 1993;269(4):483–487.

    Article  PubMed  CAS  Google Scholar 

  126. Reeder BA, Senthilselvan A, Despres JP, et al. The association of cardiovascular disease risk factors with abdominal obesity in Canada. Canadian Heart Health Surveys Research Group: CMAJ 1997;157(Suppl 1):S39–S45.

    PubMed  Google Scholar 

  127. Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol 2006;35(1):83–92.

    Article  PubMed  CAS  Google Scholar 

  128. Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH. Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab 2000;278(5):E941–E948.

    PubMed  CAS  Google Scholar 

  129. Monzon JR, Basile R, Heneghan S, Udupi V, Green A. Lipolysis in adipocytes isolated from deep and superficial subcutaneous adipose tissue. Obes Res 2002;10(4):266–269.

    Article  PubMed  Google Scholar 

  130. Shen W, Wang Z, Punyanita M, et al. Adipose tissue quantification by imaging methods: a proposed classification. Obes Res 2003;11(1):5–16.

    Article  PubMed  Google Scholar 

  131. Park YW, Heymsfield SB, Gallagher D. Are dual-energy X-ray absorptiometry regional estimates associated with visceral adipose tissue mass? Int J Obes Relat Metab Disord 2002;26(7):978–983.

    Article  PubMed  Google Scholar 

  132. Snijder MB, Visser M, Dekker JM, et al. The prediction of visceral fat by dual-energy X-ray absorptiometry in the elderly: a comparison with computed tomography and anthropometry. Int J Obes Relat Metab Disord 2002;26(7):984–993.

    Article  PubMed  CAS  Google Scholar 

  133. Glickman SG, Marn CS, Supiano MA, Dengel DR. Validity and reliability of dual-energy X-ray absorptiometry for the assessment of abdominal adiposity. J Appl Physiol 2004;97(2):509–514.

    Article  PubMed  Google Scholar 

  134. Albanese CV, Diessel E, Genant HK. Clinical applications of body composition measurements using DXA. J Clin Densitom 2003;6:75–85.

    Article  PubMed  Google Scholar 

  135. Paradisi G, Smith L, Burtner C, et al. Dual-energy X-ray absorptiometry assessment of fat mass distribution and its association with the insulin resistance syndrome. Diabetes Care 1999;22(8):1310–1317.

    Article  PubMed  CAS  Google Scholar 

  136. Bachrach LK. Bare-bones fact—children are not small adults. N Engl J Med 2004;351:924–926.

    Article  PubMed  CAS  Google Scholar 

  137. Gafni RI, Baron J. Overdiagnosis of osteoporosis in children due to misinterpretation of dual-energy X-ray absorptiometry (DEXA). J Pediatr 2004;144:253–257.

    Article  PubMed  Google Scholar 

  138. Leonard MB, Feldman HI, Zemel BS, et al. Evaluation of low-density spine software for the assessment of bone mineral density in children. J Bone Miner Res 1998;13:1687–1690.

    Article  PubMed  CAS  Google Scholar 

  139. Njeh CF, Samat SB, Nightingale A, McNeil EA, Boivin CM. Radiation dose and in vitro precision in paediatric bone mineral density measurement using dual X-ray absorptiometry. Br J Radiol 1997;70(835):719–727.

    PubMed  CAS  Google Scholar 

  140. Thomas SR, Kalkwarf HJ, Buckley DD, Heubi JE. Effective dose of dual-energy X-ray absorptiometry scans in children as a function of age. J Clin Densitom 2005;8(4):415–422.

    Article  PubMed  Google Scholar 

  141. Blake GM, Naeem M, Boutros M. Comparison of effective dose to children and adults from dual X-ray absorptiometry examinations. Bone 2006;38(6):935–942.

    Article  PubMed  Google Scholar 

  142. International Commission on Radiological Protection. 1990 recommendations of the International Commission on Radiological Protection [ICRP Publication 60]. Ann ICRP 1991;21:1–201.

    Google Scholar 

  143. Abrahams PH, Marks SC, Hutchings RT. McMinn’s Color Atlas of Human Anatomy 5th ed. Edinburgh: Elsevier Science Limited, 2003.

    Google Scholar 

  144. Greulich WW, Pyle SI, Waterhouse AM. A radiographic standard of reference for the growing hand and wrist. Chicago: Case Western Reserve University, 1971.

    Google Scholar 

  145. Tanner JM, Whitehouse RH, Cameron N, et al. Assessment of skeletal maturity and prediction of adult height, 2nd ed. London: Academic Press, 1983.

    Google Scholar 

  146. Milner GR, Levick RK, Kay R. Assessment of bone age: a comparison of the Greulich and Pyle and the Tanner and Whitehouse methods. Clin Radiol 1986;37:119–121.

    Article  PubMed  CAS  Google Scholar 

  147. King DG, Steventon DM, O’Sullivan MP, et al. Reproducibility of bone ages when performed by radiology registrars: an audit of Tanner and Whitehouse II versus Greulich and Pyle methods. Br J Radiol 1994;67:848–851.

    Article  PubMed  CAS  Google Scholar 

  148. Tanner JM. Growth of adolescents. Oxford: Blackwell Scientific Publishing, 1962.

    Google Scholar 

  149. Carter DR, Bouxsein ML, Marcus R. New approaches for interpreting projected bone densitometry data. J Bone Miner Res 1992;7:137–145.

    Article  PubMed  CAS  Google Scholar 

  150. Jergas M, Breitenseher M, Gluer CC, Yu W, Genant HK. Estimates of volumetric bone density from projectional measurements improve the discriminatory capability of dual X-ray absorptiometry. J Bone Miner Res 1995;10:1101–1110.

    Article  PubMed  CAS  Google Scholar 

  151. Mølgaard C, Thomsen BL, Prentice A, Cole TJ, Michaelsen KF. Whole body bone mineral content in healthy children and adolescents. Arch Dis Child 1997;76:9–15.

    Article  PubMed  Google Scholar 

  152. CDC, National Center for Health Statistics. Clinical growth charts. http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/cliical_charts.htm Accessed 01/05/2005.

  153. Nguyen TV, Maynard LM, Towne B, et al. Sex differences in bone mass acquisition during growth: the Fels Longitudinal Study. J Clin Densitom 2001;4:147–157.

    Article  PubMed  CAS  Google Scholar 

  154. Lin Y-C, Lyle RM, Weaver CM, et al. Peak spine and femoral neck bone mass in young women. Bone 2003;32:546–553.

    Article  PubMed  Google Scholar 

  155. Zanchetta JR, Plotkin H, Alvarez Filgueira ML. Bone mass in children: normative values for the 2–20-year old population. Bone 1995;16:393S–399S.

    PubMed  CAS  Google Scholar 

  156. Teegarden D, Proulx WR, Martin BR, et al. Peak bone mass in young women. J Bone Miner Res 1995;10:711–715.

    Article  PubMed  CAS  Google Scholar 

  157. Sabatier J-P, Guaydier-Souquières G, Laroche A, et al. Bone mineral acquisition during adolescence and early adulthood: a study in 574 healthy females 10–24 years of age. Osteoporos Int 1996;6:141–148.

    Article  PubMed  CAS  Google Scholar 

  158. Theintz G, Buchs B, Rizzoli R, et al. Longitudinal monitoring of bone mass accumulation in healthy adolescents: evidence for a marked reduction after 16 years of age at the levels of lumbar spine and femoral neck in female subjects. J Clin Endocrinol Metab 1992;75:1060–1065.

    Article  PubMed  CAS  Google Scholar 

  159. Kalkwarf HJ, Zemel BS, Gilsamz V, et al. The bone mineral density in childhood study: bone mineral content and density according to age, sex, and race. J Clin Endocrinolol Metab 2007;92:2087–2099.

    Article  CAS  Google Scholar 

  160. Leonard MB, Propert KJ, Zemel BS, Stallings VA, Feldman HI. Discrepancies in pediatric bone mineral density reference data: potential for misdiagnosis of osteopenia. J Pediatr 1999;135:182–188.

    Article  PubMed  CAS  Google Scholar 

  161. Bachrach LK, Hastie T, Wang MC, Narasimhan B, Marcus R. Bone mineral acquisition in healthy Asian, Hispanic, black, and Caucasian youth: a longitudinal study. J Clin Endocrinol Metab 1999;84:4702–4712.

    Article  PubMed  CAS  Google Scholar 

  162. Cole TJ, Green PJ. Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med 1992;11:1305–1319.

    Article  PubMed  CAS  Google Scholar 

  163. Lieb ES, Lewiecki M, Binkley N, Hamdy RC. Official positions of the International Society for Clinical Densitometry. J Clin Densitom 2004;7(1):1–5.

    Article  Google Scholar 

  164. Khan AA, Bachrach L, Brown JP, et al. Standards and guidelines for performing central dual-energy X-ray absorptiometry in premenopausal women, men, and children: a report from the Canadian Panel of the International Society for Clinical Densitometry. J Clin Densitom 2004;7:51–63.

    Article  PubMed  Google Scholar 

  165. Baim S, Leonard MB, Bianchi M, et al. Official positions of the International Society for Clinical Densitometry and executive summary of the 2007 ISCD pediatric position development conference. J Clin Densitom 2008;11(1):6–21.

    Article  PubMed  Google Scholar 

  166. Krassas GE. Idiopathic juvenile osteoporosis. Ann NY Acad Sci 2000;900:409–412.

    Article  PubMed  CAS  Google Scholar 

  167. Saggese G, Baroncelli GI, Bertelloni S. Osteoporosis in children and adolescents: diagnosis, risk factors, and prevention. J Pediatr Endocrinol Metab 2001;14:833–859.

    Article  PubMed  CAS  Google Scholar 

  168. Shaw NJ, Boivin CM, Crabtree NJ. Intravenous pamidronate in juvenile osteoporosis. Arch Dis Child 2000;83:143–145.

    Article  PubMed  CAS  Google Scholar 

  169. Glorieux FH. Bisphosphonate therapy for severe osteogenesis imperfecta. J Pediatr Endocrinol Metab 2000;13:989–992.

    PubMed  Google Scholar 

  170. van der Sluis IM, de Muinck Keizer-Schrama SMPF. Osteoporosis in childhood: bone density of children in health and disease. J Pediatr Endocrinol Metab 2001;14:817–832.

    PubMed  Google Scholar 

  171. Sawyer AJ, Bachrach LK, Fung EB. Bone densitometry in growing patients. Totowa, NJ: Humana Press, 2007.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Clinical Research Center of North Texas, 2921, Country Club Road, #101, Denton, TX, USA

    Sydney Lou Bonnick MD, FACP

Authors
  1. Sydney Lou Bonnick MD, FACP
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sydney Lou Bonnick MD, FACP .

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Humana Press, a part of Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Bonnick, S.L. (2010). New Applications for DXA. In: Bone Densitometry in Clinical Practice. Current Clinical Practice. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60327-499-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-60327-499-9_13

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60327-498-2

  • Online ISBN: 978-1-60327-499-9

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation