Skip to main content
SpringerLink
Log in

Conventional MRI-derived subchondral trabecular biomarkers and their association with knee cartilage volume loss as early as 1 year: a longitudinal analysis from Osteoarthritis Initiative

  • Scientific Article
  • Published:
Skeletal Radiology Aims and scope Submit manuscript

Abstract

Objective

To study associations between MRI-derived subchondral trabecular biomarkers obtained from conventional MRI sequences and knee cartilage loss over 12 and 24 months, using the FNIH osteoarthritis (OA) biomarkers consortium.

Materials and methods

Data of the 600 subjects in the FNIH OA biomarkers consortium (a nested case–control study within Osteoarthritis Initiative [OAI]) were extracted from the online database. Baseline knee MRI (intermediate-weighted (IW) sequences) were evaluated to determine conventional MRI-derived trabecular thickness (cTbTh) and bone-to-total ratio (cBV/TV). The measurements for medial and lateral volumes of cartilages using baseline, 12-, and 24-month knee MRI were extracted from the OAI database, and cartilage volume loss over 12 and 24 months of follow-up were determined using Relative Change Index. The association between conventional MRI-based subchondral trabecular biomarkers and cartilage volume loss were studied using logistic regression models, adjusted for relevant confounders including age, sex, body mass index (BMI), vitamin D use, Kellgren Lawrence grade (KLG), and tibiofemoral alignment.

Results

Higher medial cTbTh and cBV/TV at baseline were associated with increased odds of medial tibial cartilage volume loss over 12 months (ORs: 1.01 [1.00–1.02] and 1.24 [1.10–1.39] per 1-SD change) and 24 months (ORs: 1.01 [1.00–1.02] and 1.22 [1.08–1.37], per 1-SD change). No significant association was observed between medial subchondral trabecular biomarkers and lateral tibial or femoral (medial or lateral) cartilage volume loss over the first and second follow-up years.

Conclusions

Conventional MRI-derived subchondral trabecular biomarkers (higher medial cTbTh and cBV/TV) may be associated with increased medial tibial cartilage volume loss as early as 1 year.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Vina ER, Kwoh CK. Epidemiology of osteoarthritis: literature update. Curr Opin Rheumatol. 2018;30(2):160–7.

    Article  PubMed Central  Google Scholar 

  2. Hochberg MC, Guermazi A, Guehring H, Aydemir A, Wax S, Fleuranceau-Morel P, et al. Effect of intra-articular sprifermin vs placebo on femorotibial joint cartilage thickness in patients with osteoarthritis: the FORWARD randomized clinical trial. JAMA. 2019;322(14):1360–70.

    Article  CAS  PubMed Central  Google Scholar 

  3. Zhang W, McWilliams DF, Ingham SL, Doherty SA, Muthuri S, Muir KR, et al. Nottingham knee osteoarthritis risk prediction models. Ann Rheum Dis. 2011;70(9):1599–604.

    Article  Google Scholar 

  4. Roos EM, Arden NK. Strategies for the prevention of knee osteoarthritis. Nat Rev Rheumatol. 2016;12(2):92–101.

    Article  CAS  Google Scholar 

  5. Pishgar F, Guermazi A, Roemer FW, Link TM, Demehri S. Conventional MRI-based subchondral trabecular biomarkers as predictors of knee osteoarthritis progression: data from the Osteoarthritis Initiative. Eur Radiol. 2020:1–10.

  6. Felson DT, Chaisson CE, Hill CL, Totterman SM, Gale ME, Skinner KM, et al. The association of bone marrow lesions with pain in knee osteoarthritis. Ann Intern Med. 2001;134(7):541–9.

    Article  CAS  Google Scholar 

  7. Wluka AE, Wang Y, Davies-Tuck M, English DR, Giles GG, Cicuttini FM. Bone marrow lesions predict progression of cartilage defects and loss of cartilage volume in healthy middle-aged adults without knee pain over 2 yrs. Rheumatology (Oxford). 2008;47(9):1392–6.

    Article  CAS  Google Scholar 

  8. MacKay JW, Kapoor G, Driban JB, Lo GH, McAlindon TE, Toms AP, et al. Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study. Eur Radiol. 2018;28(11):4687–95.

    Article  PubMed Central  Google Scholar 

  9. Chiba K, Uetani M, Kido Y, Ito M, Okazaki N, Taguchi K, et al. Osteoporotic changes of subchondral trabecular bone in osteoarthritis of the knee: a 3-T MRI study. Osteoporos Int. 2012;23(2):589–97.

    Article  CAS  Google Scholar 

  10. MacKay JW, Murray PJ, Kasmai B, Johnson G, Donell ST, Toms AP. Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry. Osteoarthritis Cartilage. 2017;25(5):700–7.

    Article  CAS  Google Scholar 

  11. Marques J, Genant HK, Lillholm M, Dam EB. Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI. Magn Reson Med. 2013;70(2):568–75.

    Article  Google Scholar 

  12. Goldring MB, Goldring SR. Articular cartilage and subchondral bone in the pathogenesis of osteoarthritis. Ann N Y Acad Sci. 2010;1192:230–7.

    Article  CAS  Google Scholar 

  13. Neogi T, Nevitt M, Niu J, Sharma L, Roemer F, Guermazi A, et al. Subchondral bone attrition may be a reflection of compartment-specific mechanical load: the MOST Study. Ann Rheum Dis. 2010;69(5):841–4.

    Article  Google Scholar 

  14. Castañeda S, Roman-Blas JA, Largo R, Herrero-Beaumont G. Subchondral bone as a key target for osteoarthritis treatment. Biochem Pharmacol. 2012;83(3):315–23.

    Article  Google Scholar 

  15. Hafezi-Nejad N, Guermazi A, Roemer FW, Hunter DJ, Dam EB, Zikria B, et al. Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements: data from the FNIH OA biomarkers consortium. Eur Radiol. 2017;27(2):464–73.

    Article  Google Scholar 

  16. Haj-Mirzaian A, Guermazi A, Pishgar F, Roemer FW, Sereni C, Hakky M, et al. Patellofemoral morphology measurements and their associations with tibiofemoral osteoarthritis-related structural damage: exploratory analysis on the osteoarthritis initiative. Eur Radiol. 2020;30(1):128–40.

    Article  Google Scholar 

  17. Hunter D, Nevitt M, Lynch J, Kraus VB, Katz JN, Collins JE, et al. Longitudinal validation of periarticular bone area and 3D shape as biomarkers for knee OA progression? Data from the FNIH OA Biomarkers Consortium. Ann Rheum Dis. 2016;75(9):1607–14.

    Article  Google Scholar 

  18. Culvenor AG, Engen CN, Øiestad BE, Engebretsen L, Risberg MA. Defining the presence of radiographic knee osteoarthritis: a comparison between the Kellgren and Lawrence system and OARSI atlas criteria. Knee Surg Sports Traumatol Arthrosc. 2015;23(12):3532–9.

    Article  Google Scholar 

  19. Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging. 2004;13(1):146–66.

    Article  Google Scholar 

  20. Doube M, Kłosowski MM, Arganda-Carreras I, Cordelières FP, Dougherty RP, Jackson JS, et al. BoneJ: Free and extensible bone image analysis in ImageJ. Bone. 2010;47(6):1076–9.

    Article  PubMed Central  Google Scholar 

  21. Dam EB, Lillholm M, Marques J, Nielsen M. Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative. J Med Imaging (Bellingham). 2015;2(2):024001.

    Article  Google Scholar 

  22. Haj-Mirzaian A, Guermazi A, Hafezi-Nejad N, Sereni C, Hakky M, Hunter DJ, et al. Superolateral Hoffa’s fat pad (SHFP) oedema and patellar cartilage volume loss: quantitative analysis using longitudinal data from the Foundation for the National Institute of Health (FNIH) Osteoarthritis Biomarkers Consortium. Eur Radiol. 2018;28(10):4134–45.

    Article  Google Scholar 

  23. Wise EA. Methods for analyzing psychotherapy outcomes: a review of clinical significance, reliable change, and recommendations for future directions. J Pers Assess. 2004;82(1):50–9.

    Article  Google Scholar 

  24. Kraus VB, Collins JE, Charles HC, Pieper CF, Whitley L, Losina E, et al. Predictive validity of radiographic trabecular bone texture in knee osteoarthritis: the Osteoarthritis Research Society International/Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheumatol (Hoboken, NJ). 2018;70(1):80–7.

    Article  CAS  Google Scholar 

  25. Lo GH, Schneider E, Driban JB, Price LL, Hunter DJ, Eaton CB, et al. Periarticular bone predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative. Semin Arthritis Rheum. 2018;48(2):155–61.

    Article  PubMed Central  Google Scholar 

  26. Tanamas SK, Wluka AE, Pelletier JP, Pelletier JM, Abram F, Berry PA, et al. Bone marrow lesions in people with knee osteoarthritis predict progression of disease and joint replacement: a longitudinal study. Rheumatology (Oxford). 2010;49(12):2413–9.

    Article  Google Scholar 

  27. Liu C, Liu C, Ren X, Si L, Shen H, Wang Q, et al. Quantitative evaluation of subchondral bone microarchitecture in knee osteoarthritis using 3T MRI. BMC Musculoskelet Disord. 2017;18(1):496.

    Article  PubMed Central  Google Scholar 

  28. Liu C, Liu C, Si L, Shen H, Wang Q, Yao W. Relationship between subchondral bone microstructure and articular cartilage in the osteoarthritic knee using 3T MRI. J Magn Reson Imaging: JMRI. 2018.

  29. Crema MD, Cibere J, Sayre EC, Roemer FW, Wong H, Thorne A, et al. The relationship between subchondral sclerosis detected with MRI and cartilage loss in a cohort of subjects with knee pain: the knee osteoarthritis progression (KOAP) study. Osteoarthritis Cartilage. 2014;22(4):540–6.

    Article  CAS  Google Scholar 

  30. Chappard C, Peyrin F, Bonnassie A, Lemineur G, Brunet-Imbault B, Lespessailles E, et al. Subchondral bone micro-architectural alterations in osteoarthritis: a synchrotron micro-computed tomography study. Osteoarthritis Cartilage. 2006;14(3):215–23.

    Article  CAS  Google Scholar 

  31. Yuan XL, Meng HY, Wang YC, Peng J, Guo QY, Wang AY, et al. Bone-cartilage interface crosstalk in osteoarthritis: potential pathways and future therapeutic strategies. Osteoarthritis Cartilage. 2014;22(8):1077–89.

    Article  CAS  Google Scholar 

  32. Findlay DM, Atkins GJ. Osteoblast-chondrocyte interactions in osteoarthritis. Curr Osteoporos Rep. 2014;12(1):127–34.

    Article  PubMed Central  Google Scholar 

  33. Wang Y, Wluka AE, Cicuttini FM. The determinants of change in tibial plateau bone area in osteoarthritic knees: a cohort study. Arthritis Res Ther. 2005;7(3):R687.

    Article  PubMed Central  Google Scholar 

  34. Beuf O, Ghosh S, Newitt DC, Link TM, Steinbach L, Ries M, et al. Magnetic resonance imaging of normal and osteoarthritic trabecular bone structure in the human knee. Arthritis Rheum. 2002;46(2):385–93.

    Article  Google Scholar 

  35. Ding C, Cicuttini F, Jones G. Tibial subchondral bone size and knee cartilage defects: relevance to knee osteoarthritis. Osteoarthritis Cartilage. 2007;15(5):479–86.

    Article  CAS  Google Scholar 

  36. Dore D, Martens A, Quinn S, Ding C, Winzenberg T, Zhai G, et al. Bone marrow lesions predict site-specific cartilage defect development and volume loss: a prospective study in older adults. Arthritis Res Ther. 2010;12(6):R222.

    Article  PubMed Central  Google Scholar 

  37. Hunter DJ, Gerstenfeld L, Bishop G, Davis AD, Mason ZD, Einhorn TA, et al. Bone marrow lesions from osteoarthritis knees are characterized by sclerotic bone that is less well mineralized. Arthritis Res Ther. 2009;11(1):R11.

    Article  PubMed Central  Google Scholar 

  38. Roemer FW, Jarraya M, Niu J, Duryea J, Lynch JA, Guermazi A. Knee joint subchondral bone structure alterations in active athletes: a cross-sectional case-control study. Osteoarthritis Cartilage. 2015;23(12):2184–90.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The OAI was a public-private partnership comprised of several contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health (NIH), a branch of the Department of Health and Human Services, and conducted by the Osteoarthritis Initiative (OAI) Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public-use dataset and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners.

Moreover, several grants and direct or in-kind contributions provide the publicly available data from the FNIH OA Biomarkers Consortium, including AbbVie, Amgen, Arthritis Foundation, Artialis; Bioiberica, BioVendor, DePuy, Flexion Therapeutics, GSK, IBEX, IDS, Merck Serono, Quidel, Rottapharm | Madaus, Sanofi, Stryker, the Pivotal OAI MRI Analyses (POMA) study, NIH HHSN2682010000 21C, and the Osteoarthritis Research Society International.

The summary of data related to this study has been presented as a congress abstract (available at https://doi.org/10.1016/j.joca.2021.02.126).

Author information

Authors and Affiliations

  1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N Caroline St, JHOC 4240, Baltimore, MD, 21287, USA

    Farhad Pishgar & Shadpour Demehri

  2. Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Science Institute, Tehran University of Medical Sciences, Tehran, Iran

    Amir Ashraf-ganjouei

  3. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Mahsa Dolatshahi

  4. Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA

    Ali Guermazi & Frank W. Roemer

  5. Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Bashir Zikria, Xu Cao & Mei Wan

  6. Department of Radiology, University of Erlangen-Nuremberg, Erlangen, Germany

    Frank W. Roemer

  7. Machine Learning Section, Department of Computer Science, University of Copenhagen, Kobenhavns, Denmark

    Erik Dam

Authors
  1. Farhad Pishgar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Ashraf-ganjouei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahsa Dolatshahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Guermazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bashir Zikria
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mei Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Frank W. Roemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Erik Dam
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shadpour Demehri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farhad Pishgar.

Ethics declarations

Ethics 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.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pishgar, F., Ashraf-ganjouei, A., Dolatshahi, M. et al. Conventional MRI-derived subchondral trabecular biomarkers and their association with knee cartilage volume loss as early as 1 year: a longitudinal analysis from Osteoarthritis Initiative. Skeletal Radiol 51, 1959–1966 (2022). https://doi.org/10.1007/s00256-022-04042-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00256-022-04042-4

Keywords

Search

Navigation