Calcified Tissue International

, Volume 102, Issue 6, pp 644–650 | Cite as

Cortical Fractal Analysis and Collagen Crosslinks Content in Femoral Neck After Osteoporotic Fracture in Postmenopausal Women: Comparison with Osteoarthritis

  • Gustavo Davi Rabelo
  • Jean-Paul Roux
  • Nathalie Portero-Muzy
  • Evelyne Gineyts
  • Roland Chapurlat
  • Pascale Chavassieux
Original Research
  • 134 Downloads

Abstract

The femoral neck (FN) has been previously characterized by thinner cortices in osteoporotic fracture (HF) when compared to hip osteoarthritis (HOA). The purposes of this study were to complete the previous investigations on FNs from HF and HOA by analyzing the complexity of the cortical structure and to approach the intrinsic properties of cortical bone by assessing the collagen crosslink contents. FN samples were obtained during arthroplasty in 35 postmenopausal women (HF; n = 17; mean age 79 ± 2 years; HOA; n = 18; mean age 66 ± 2 years). The cortical fractal dimension (Ct.FD) and lacunarity (Ct.Lac) derived from high-resolution peripheral quantitative tomography (isotropic voxel size: 82 μm) images of FN by using Ctan software and Fraclac running in ImageJ were analyzed. The collagen crosslinks content [pyridinoline, deoxypyridinoline, pentosidine (PEN)] were assessed in cortical bone. Ct.FD was significantly lower (p < 0.0001) in HF than HOA reflecting a decreased complexity and was correlated to the age and BMD. In two sub-groups, BMD- and age-matched, respectively, Ct.FD remained significantly lower in HF than HOA (p < 0.001). Ct.Lac was not different between HF and HOA. PEN content was two times higher in HF than HOA (p < 0.0001) independently of age. In conclusion, FN with HF was characterized by a less complex cortical texture and higher PEN content than HOA. In addition to the decreased bone mass and BMD previously reported, these modifications contribute to the lower bone quality in HF than HOA in postmenopausal women.

Keywords

Cortical bone Osteoporosis Femoral neck Lacunarity Fractal dimension 

Notes

Acknowledgements

The author Gustavo Davi Rabelo thanks the “Ciência sem Fronteiras - Conselho Nacional de Desenvolvimento Científico e Tecnológico/Brasil” (Processo número 245336/2012-5) for the Post-doc scholarship.

Author Contribution

GDR and PC designed the study and were evolved in all phases of the study and in writing the manuscript. JPR, NPM, and EG contributed to the experimental work and drafting the results. GDR, JPR, and PC were responsible for statistical analysis of the data. RC was responsible for the discussion of the results and supervision. All authors revised the paper critically for intellectual content and approved the final version. All authors agree to be accountable for the work and to ensure that any questions relating to the accuracy and integrity of the paper are investigated and properly resolved.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

References

  1. 1.
    Bouxsein ML (2001) Biomechanics of age-related fractures. In: Marcus R, Feldman D, Kelsey J (eds) Osteoporosis, vol 1, 2nd edn. Academic Press, San Diego, pp 509–531CrossRefGoogle Scholar
  2. 2.
    Lotz JC, Cheal EJ, Hayes WC (1995) Stress distributions within the proximal femur during gait and falls: Implications for osteoporotic fracture. Osteoporos Int 5:252–261.  https://doi.org/10.1007/BF01774015 CrossRefPubMedGoogle Scholar
  3. 3.
    Seeman E, Delmas PD (2006) Bone quality—the material and structural basis of bone strength and fragility. N Engl J Med 354:2250–2261.  https://doi.org/10.1056/NEJMra053077 CrossRefPubMedGoogle Scholar
  4. 4.
    Chavassieux P, Seeman E, Delmas PD (2007) Insights into material and structural basis of bone fragility from diseases associated with fractures: how determinants of the biomechanical properties of bone are compromised by disease. Endocr Rev 28:151–164.  https://doi.org/10.1210/er.2006-0029 CrossRefPubMedGoogle Scholar
  5. 5.
    World Health Organisation (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group. Tech Rep Ser 843:1–129Google Scholar
  6. 6.
    Blain H, Chavassieux P, Portero-Muzy N et al (2008) Cortical and trabecular bone distribution in the femoral neck in osteoporosis and osteoarthritis. Bone 43:862–868.  https://doi.org/10.1016/j.bone.2008.07.236 CrossRefPubMedGoogle Scholar
  7. 7.
    Boutroy S, Vilayphiou N, Roux JP et al (2011) Comparison of 2D and 3D bone microarchitecture evaluation at the femoral neck, among postmenopausal women with hip fracture or hip osteoarthritis. Bone 49:1055–1061.  https://doi.org/10.1016/j.bone.2011.07.037 CrossRefPubMedGoogle Scholar
  8. 8.
    Bala Y, Lefèvre E, Roux JP et al (2016) Pore network microarchitecture influences human cortical bone elasticity during growth and aging. J Mech Behav Biomed Mater 63:164–173.  https://doi.org/10.1016/j.jmbbm.2016.05.018 CrossRefPubMedGoogle Scholar
  9. 9.
    Tjong W, Nirody J, Burghardt AJ et al (2014) Structural analysis of cortical porosity applied to HR-pQCT data. Med Phys 41:13701.  https://doi.org/10.1118/1.4851575 CrossRefGoogle Scholar
  10. 10.
    Majumdar S, Lin J, Link T et al (1999) Fractal analysis of radiographs: assessment of trabecular bone structure and prediction of elastic modulus and strength. Med Phys 26:1330–1340.  https://doi.org/10.1118/1.598628 CrossRefPubMedGoogle Scholar
  11. 11.
    Gudea A, Stefan A (2013) Histomorphometric, fractal and lacunarity comparative analysis of sheep (Ovis aries), goat (Capra hircus) and roe deer (Capreollus capreollus) compact bone samples. Folia Morphol (Warsz) 72:239–248.  https://doi.org/10.5603/FM.2013.0039 CrossRefGoogle Scholar
  12. 12.
    De Melo RHC, Conci A (2013) How Succolarity could be used as another fractal measure in image analysis. Telecommun Syst 52:1643–1655.  https://doi.org/10.1007/s11235-011-9657-3 CrossRefGoogle Scholar
  13. 13.
    Dong P (2000) Lacunarity for spatial heterogeneity measurement in GIS. Geogr Inf Sci 6:20–26.  https://doi.org/10.1080/10824000009480530 Google Scholar
  14. 14.
    Sanchez-Molina D, Velazquez-Ameijide J, Quintana V et al (2013) Fractal dimension and mechanical properties of human cortical bone. Med Eng Phys 35:576–582.  https://doi.org/10.1016/j.medengphy.2012.06.024 CrossRefPubMedGoogle Scholar
  15. 15.
    Bauer JS, Kohlmann S, Eckstein F et al (2006) Structural analysis of trabecular bone of the proximal femur using multislice computed tomography: a comparison with dual X-ray absorptiometry for predicting biomechanical strength in vitro. Calcif Tissue Int 78:78–89.  https://doi.org/10.1007/s00223-005-0070-3 CrossRefPubMedGoogle Scholar
  16. 16.
    Viguet-Carrin S, Garnero P, Delmas PD (2006) The role of collagen in bone strength. Osteoporos Int 17(3):319–336.  https://doi.org/10.1007/s00198-005-2035-9 CrossRefPubMedGoogle Scholar
  17. 17.
    Saito M, Marumo K (2015) Effects of collagen crosslinking on bone material properties in health and disease. Calcif Tissue Int 97(3):242–261.  https://doi.org/10.1007/s00223-015-9985-5 CrossRefPubMedGoogle Scholar
  18. 18.
    Karim L, Vashishth D (2012) Heterogeneous glycation of cancellous bone and its association with bone quality and fragility. PLoS ONE 7:e35047.  https://doi.org/10.1371/journal.pone.0035047 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Viguet-Carrin S, Follet H, Gineyts E et al (2010) Association between collagen cross-links and trabecular microarchitecture properties of human vertebral bone. Bone 46:342–347.  https://doi.org/10.1016/j.bone.2009.10.001 CrossRefPubMedGoogle Scholar
  20. 20.
    De Laet C, Reeve J (2001) Epidemiology of osteoporotic fractures in Europe. In: Marcus R, Feldman D, Kelsey J (eds) Osteoporosis, vol 1, 2nd edn. Academic Press, San Diego, pp 585–597Google Scholar
  21. 21.
    Greendale GA, Barrett-Connor E (2001) Outcomes of osteoporotic fractures. In: Marcus R, Feldman D, Kelsey J (eds) Osteoporosis, vol 1, 2nd edn. Academic Press, San Diego, CA, pp 819–829CrossRefGoogle Scholar
  22. 22.
    Smith TG, Lange GD, Marks WB (1996) Fractal methods and results in cellular morphology—dimensions, lacunarity and multifractals. J Neurosci Methods 69:123–136.  https://doi.org/10.1016/S0165-0270(96)00080-5 CrossRefPubMedGoogle Scholar
  23. 23.
    Melo RHC, Vieira ED, Conci A (2006) Characterizing the lacunarity of objects and image sets and its use as a technique for the analysis of textural patterns. Adv Concepts Intell Vis Syst Proc 4179:208–219.  https://doi.org/10.1007/11864349 CrossRefGoogle Scholar
  24. 24.
    Karperien AL, Jelinek HF (2015) Fractal, multifractal, and lacunarity analysis of microglia in tissue engineering. Front Bioeng Biotechnol 3:51.  https://doi.org/10.3389/fbioe.2015.00051 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Viguet-Carrin S, Gineyts E, Bertholon C, Delmas PD (2009) Simple and sensitive method for quantification of fluorescent enzymatic mature and senescent crosslinks of collagen in bone hydrolysate using single-column high performance liquid chromatography. J Chromatogr B Analyt Technol Biomed Life Sci 877:1–7.  https://doi.org/10.1016/j.jchromb.2008.10.043 CrossRefPubMedGoogle Scholar
  26. 26.
    Link TM, Majumdar S, Lin JC et al (1998) Assessment of trabecular structure using high resolution CT images and texture analysis. J Comput Assist Tomogr 22:15–24CrossRefPubMedGoogle Scholar
  27. 27.
    Topoliński T, Mazurkiewicz A, Jung S et al (2012) Microarchitecture parameters describe bone structure and its strength better than BMD. ScientificWorldJournal 2012:502781.  https://doi.org/10.1100/2012/502781 PubMedPubMedCentralGoogle Scholar
  28. 28.
    Weinstein RS, Majumdar S (1994) Fractal geometry and vertebral compression fractures. J Bone Miner Res 9:1797–1802.  https://doi.org/10.1002/jbmr.5650091117 CrossRefPubMedGoogle Scholar
  29. 29.
    Zebaze R, Seeman E (2015) Cortical bone: a challenging geography. J Bone Miner Res 30:24–29.  https://doi.org/10.1002/jbmr.2419 CrossRefPubMedGoogle Scholar
  30. 30.
    Bernhard A, Milovanovic P, Zimmermann EA et al (2013) Micro-morphological properties of osteons reveal changes in cortical bone stability during aging, osteoporosis, and bisphosphonate treatment in women. Osteoporos Int 24:2671–2680.  https://doi.org/10.1007/s00198-013-2374-x CrossRefPubMedGoogle Scholar
  31. 31.
    Saito M, Fujii K, Soshi S, Tanaka T (2006) Reductions in degree of mineralization and enzymatic collagen cross-links and increases in glycation-induced pentosidine in the femoral neck cortex in cases of femoral neck fracture. Osteoporos Int 17:986–995.  https://doi.org/10.1007/s00198-006-0087-0 CrossRefPubMedGoogle Scholar
  32. 32.
    Vashishth D, Gibson GJ, Khoury JI et al (2001) Influence of nonenzymatic glycation on biomechanical properties of cortical bone. Bone 28:195–201.  https://doi.org/10.1016/S8756-3282(00)00434-8 CrossRefPubMedGoogle Scholar
  33. 33.
    Hernandez CJ, Tang SY, Baumbach BM et al (2005) Trabecular microfracture and the influence of pyridinium and non-enzymatic glycation-mediated collagen cross-links. Bone 37:825–832.  https://doi.org/10.1016/j.bone.2005.07.019 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Sroga GE, Wu PC, Vashishth D (2015) Insulin-like growth factor 1, glycation and bone fragility: Implications for fracture resistance of bone. PLoS ONE 10:e0117240.  https://doi.org/10.1371/journal.pone.0117046 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Issever AS, Link TM, Kentenich M et al (2010) Assessment of trabecular bone structure using MDCT: comparison of 64- and 320-slice CT using HR-pQCT as the reference standard. Eur Radiol 20:458–468.  https://doi.org/10.1007/s00330-009-1571-7 CrossRefPubMedGoogle Scholar
  36. 36.
    Issever AS, Link TM, Kentenich M et al (2009) Trabecular bone structure analysis in the osteoporotic spine using a clinical in vivo setup for 64-slice MDCT imaging: comparison to microCT imaging and microFE modeling. J Bone Miner Res 24:1628–1637.  https://doi.org/10.1359/JBMR.090311 CrossRefPubMedGoogle Scholar
  37. 37.
    Berteau JP, Gineyts E, Pithioux M et al (2015) Ratio between mature and immature enzymatic cross-links correlates with post-yield cortical bone behavior: an insight into greenstick fractures of the child fibula. Bone 79:190–195.  https://doi.org/10.1016/j.bone.2015.05.045 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.INSERM UMR 1033, Université de LyonLyonFrance
  2. 2.INSERM UMR 1033, UFR de Médecine Lyon-EstLyon Cedex 08France

Personalised recommendations