Skip to main content
SpringerLink
Log in

Conventional MRI-based subchondral trabecular biomarkers as predictors of knee osteoarthritis progression: data from the Osteoarthritis Initiative

  • Musculoskeletal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the reliability and validity of measuring subchondral trabecular biomarkers in “conventional” intermediate-weighted (IW) MRI sequences and to assess the predictive value of biomarker changes for predicting near-term symptomatic and structural progressions in knee osteoarthritis (OA).

Methods

For this study, a framework for measuring trabecular biomarkers in the proximal medial tibia in the “conventional” IW MRI sequence was developed. The reliability of measuring these biomarkers (trabecular thickness [cTbTh], spacing [cTbSp], connectivity density [cConnD], and bone-to-total volume ratio [cBV/TV]) was evaluated in the Bone Ancillary Study (within the Osteoarthritis Initiative [OAI]). The validity of these measurements was assessed by comparing to “apparent” biomarkers (from high-resolution steady-state MRI sequence) and peri-articular bone marrow density (BMD, from dual-energy X-ray absorptiometry). The association of these biomarker changes from baseline to 24 months (using the Reliable Change Index) with knee OA progression was studied in the FNIH OA Biomarkers Consortium (within the OAI). Pain and radiographic progression were evaluated by comparing baseline WOMAC pain score and radiographic joint space width with the 24-to-48-month scores/measurements. Associations between biomarker changes and these outcomes were studied using logistic regression adjusted for the relevant covariates.

Results

With acceptable reliability, the cTbTh and cBV/TV, but not cTbSp or cConnD, were modestly associated with the “apparent” biomarkers and peri-articular BMD (β: 1.10 [95% CI: 0.45–1.75], p value: 0.001 and β: 3.69 [95% CI: 2.56–4.83], p value: < 0.001, respectively). Knees with increased cTbTh had higher (OR: 1.44 [95% CI: 1.03–2.02], p value: 0.035) and knees with decreased cTbTh (OR: 0.69 [95% CI: 0.49–0.95], p value: 0.026) or decreased cBV/TV (OR: 0.67 [95% CI: 0.48–0.93], p value: 0.018) had lower odds of experiencing OA pain progression over the follow-ups.

Conclusions

Measurement of certain “conventional” MRI-based subchondral trabecular biomarkers has high reliability and modest validity. Though modest, there are significant associations between these biomarker changes and knee OA pain progression up to 48-month follow-up.

Key Points

• Despite the lower spatial resolution than what is required to accurately study the subchondral trabecular microstructures, the “conventional” IW MRI sequences may retain adequate information that allows quantification of trabecular microstructure biomarkers.

• Subchondral trabecular biomarkers obtained from “conventional” IW MRI sequences (i.e., cTbTh, cTbSp, and cBV/TV) are reliable and valid measures of trabecular microstructure changes compared to those from “apparent” trabecular biomarkers (from the FISP MRI sequence) and peri-articular BMD (from DXA).

• Increased trabecular thickness and bone-to-total ratio (cTbTh and cBV/TV, obtained from “conventional” IW MRI sequences) from baseline to 24-month visits may be associated with higher odds of knee OA pain progression over 48 months of follow-up.

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
Fig. 3

Similar content being viewed by others

Data availability

The de-identified clinical and demographic information of subjects is publically available at the OAI project data repository at https://oai.nih.gov. The codes for the image segmentation and analysis framework, the dataset of subchondral trabecular biomarker measurements, and the R codes used in this work are available from the corresponding author upon reasonable requests.

Abbreviations

μCT:

Micro-computed tomography

BMI:

Body mass index

BV/TV:

Bone-to-total volume ratio

CI:

Confidence interval

ConnD:

Connectivity density

CT:

Computed tomography

DXA:

Dual-energy X-ray absorptiometry

FISP:

3D fast imaging with steady-state precession

FNIH:

Foundation for the National Institutes of Health

HR-QCT:

High-resolution quantitative computed tomography

ICC:

Intraclass correlation coefficient

IW:

Intermediate-weighted

JSW:

Joint space width

KLG:

Kellgren and Lawrence grade

MRI:

Magnetic resonance imaging

OA:

Osteoarthritis

OAI:

Osteoarthritis Initiative

OR:

Odds ratio

RCI:

Reliable Change Index

ROIs:

Regions of interest

TbSp:

Trabecular spacing

TbTh:

Trabecular thickness

WOMAC:

Western Ontario and McMaster Universities Osteoarthritis Index

References

  1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborator (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the global burden of disease study 2017. Lancet 392:1789–1858

  2. Runhaar J, Zhang Y (2018) Can we prevent OA? Epidemiology and public health insights and implications. Rheumatology (Oxford) 57:iv3–iv9

    Article  Google Scholar 

  3. Kraus VB, Collins JE, Charles HC et al (2018) 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 70:80–87

    Article  CAS  Google Scholar 

  4. Zhu S, Zhu J, Zhen G et al (2019) Subchondral bone osteoclasts induce sensory innervation and osteoarthritis pain. J Clin Invest 129:1076–1093

    Article  Google Scholar 

  5. Lo GH, Schneider E, Driban JB et al (2018) Periarticular bone predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative. Semin Arthritis Rheum 48:155–161

    Article  Google Scholar 

  6. MacKay JW, Kapoor G, Driban JB et al (2018) Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study. Eur Radiol 28:4687–4695

    Article  Google Scholar 

  7. Lowitz T, Museyko O, Bousson V et al (2017) Advanced Knee Structure Analysis (AKSA): a comparison of bone mineral density and trabecular texture measurements using computed tomography and high-resolution peripheral quantitative computed tomography of human knee cadavers. Arthritis Res Ther 19:1

    Article  Google Scholar 

  8. Adams JE (2013) Advances in bone imaging for osteoporosis. Nat Rev Endocrinol 9:28–42

    Article  CAS  Google Scholar 

  9. Burghardt AJ, Link TM, Majumdar S (2011) High-resolution computed tomography for clinical imaging of bone microarchitecture. Clin Orthop Relat Res 469:2179–2193

    Article  Google Scholar 

  10. Graeff C, Campbell GM, Peña J et al (2015) Administration of romosozumab improves vertebral trabecular and cortical bone as assessed with quantitative computed tomography and finite element analysis. Bone 81:364–369

    Article  CAS  Google Scholar 

  11. Bolbos RI, Zuo J, Banerjee S et al (2008) Relationship between trabecular bone structure and articular cartilage morphology and relaxation times in early OA of the knee joint using parallel MRI at 3 T. Osteoarthritis Cartilage 16:1150–1159

    Article  CAS  Google Scholar 

  12. Hafezi-Nejad N, Guermazi A, Roemer FW et al (2017) Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements: data from the FNIH OA Biomarkers Consortium. Eur Radiol 27:464–473

    Article  Google Scholar 

  13. Kraus VB, Collins JE, Hargrove D et al (2017) Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium. Ann Rheum Dis 76:186–195

    Article  CAS  Google Scholar 

  14. Kraus VB, Hargrove DE, Hunter DJ, Renner JB, Jordan JM (2017) Establishment of reference intervals for osteoarthritis-related soluble biomarkers: the FNIH/OARSI OA Biomarkers Consortium. Ann Rheum Dis 76:179–185

    Article  CAS  Google Scholar 

  15. Hunter D, Nevitt M, Lynch J et al (2016) 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 75:1607–1614

    Article  Google Scholar 

  16. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–165

  17. Doube M, Kłosowski MM, Arganda-Carreras I et al (2010) BoneJ: free and extensible bone image analysis in ImageJ. Bone 47:1076–1079

    Article  Google Scholar 

  18. Haj-Mirzaian A, Guermazi A, Hafezi-Nejad N et al (2018) 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 28:4134–4145

    Article  Google Scholar 

  19. Wise EA (2004) Methods for analyzing psychotherapy outcomes: a review of clinical significance, reliable change, and recommendations for future directions. J Pers Assess 82:50–59

    Article  Google Scholar 

  20. Chiba K, Uetani M, Kido Y et al (2012) Osteoporotic changes of subchondral trabecular bone in osteoarthritis of the knee: a 3-T MRI study. Osteoporos Int 23:589–597

    Article  CAS  Google Scholar 

  21. Bedson J, Croft PR (2008) The discordance between clinical and radiographic knee osteoarthritis: a systematic search and summary of the literature. BMC Musculoskelet Disord 9:116

    Article  Google Scholar 

  22. Finan PH, Buenaver LF, Bounds SC et al (2013) Discordance between pain and radiographic severity in knee osteoarthritis: findings from quantitative sensory testing of central sensitization. Arthritis Rheum 65:363–372

    Article  Google Scholar 

  23. Neogi T, Felson D, Niu J et al (2009) Association between radiographic features of knee osteoarthritis and pain: results from two cohort studies. BMJ 339:b2844

    Article  Google Scholar 

  24. Birch CE, Mensch KS, Desarno MJ, Beynnon BD, Tourville TW (2018) Subchondral trabecular bone integrity changes following ACL injury and reconstruction: a cohort study with a nested, matched case-control analysis. Osteoarthritis Cartilage 26:762–769

    Article  CAS  Google Scholar 

  25. Lo GH, Niu J, McLennan CE et al (2008) Meniscal damage associated with increased local subchondral bone mineral density: a Framingham study. Osteoarthritis Cartilage 16:261–267

    Article  CAS  Google Scholar 

  26. Sharma AR, Jagga S, Lee SS, Nam JS (2013) Interplay between cartilage and subchondral bone contributing to pathogenesis of osteoarthritis. Int J Mol Sci 14:19805–19830

    Article  Google Scholar 

  27. Burr DB (2005) Increased biological activity of subchondral mineralized tissues underlies the progressive deterioration of articular cartilage in osteoarthritis. J Rheumatol 32:1156–1158 discussion 1158-1159

    PubMed  Google Scholar 

  28. Aso K, Shahtaheri SM, Hill R, Wilson D, McWilliams DF, Walsh DA (2019) Associations of symptomatic knee osteoarthritis with histopathologic features in subchondral bone. Arthritis Rheumatol 71:916–924

    Article  CAS  Google Scholar 

  29. Marques J, Genant HK, Lillholm M, Dam EB (2013) Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI. Magn Reson Med 70:568–575

    Article  Google Scholar 

  30. Newitt DC, van Rietbergen B, Majumdar S (2002) Processing and analysis of in vivo high-resolution MR images of trabecular bone for longitudinal studies: reproducibility of structural measures and micro-finite element analysis derived mechanical properties. Osteoporos Int 13:278–287

    Article  CAS  Google Scholar 

  31. Hildebrand T, Rüegsegger P (1997) A new method for the model-independent assessment of thickness in three-dimensional images. J Microsc 185:67–75

    Article  Google Scholar 

  32. Thomsen JS, Laib A, Koller B, Prohaska S, Mosekilde L, Gowin W (2005) Stereological measures of trabecular bone structure: comparison of 3D micro computed tomography with 2D histological sections in human proximal tibial bone biopsies. J Microsc 218:171–179

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The OAI was a collaborative project between the public and private sectors. This project included the five contracts N01-AR-2-2258, N01-AR-2-2259, N01-AR-2-2260, N01-AR-2-2261, and N01-AR-2-2262 and was conducted by the OAI project investigators. The OAI was financially supported by the National Institutes of Health (NIH). Private funding partners were Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline, and Pfizer, Inc.

In preparing this manuscript, OAI project publicly available datasets were used, and the results of this work do not necessarily reflect the opinions of the OAI project investigators, the NIH, or the private funding partners.

Funding

The authors state that this work has not received any funding.

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, Baltimore, MD, 21287, USA

    Farhad Pishgar & Shadpour Demehri

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

    Ali Guermazi & Frank W. Roemer

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

    Frank W. Roemer

  4. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    Thomas M. Link

Authors
  1. Farhad Pishgar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Guermazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank W. Roemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas M. Link
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shadpour Demehri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farhad Pishgar.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Shadpour Demehri MD (Johns Hopkins University).

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Subjects have given informed consent before participating in the OAI project.

Ethical approval

The medical ethics review boards of the University of California, San Francisco (Approval Number: 10-00532) and the four OAI project clinical centers recognized the project as Health Insurance Portability and Accountability Act (HIPAA)–compliant.

Methodology

• Retrospective

• Case-control study

Additional information

Publisher’s note

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

Supporting information

ESM 1

(DOCX 604 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pishgar, F., Guermazi, A., Roemer, F.W. et al. Conventional MRI-based subchondral trabecular biomarkers as predictors of knee osteoarthritis progression: data from the Osteoarthritis Initiative. Eur Radiol 31, 3564–3573 (2021). https://doi.org/10.1007/s00330-020-07512-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07512-2

Keywords

Search

Navigation