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Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: findings from a study of 37,843 people in UK Biobank

Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: findings from a study of 37,843 people in UK Biobank
Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: findings from a study of 37,843 people in UK Biobank
Objective: we aimed to create an imaging biomarker for knee shape using knee dual-energy x-ray absorptiometry (DXA) scans and investigate its potential association with subsequent total knee replacement (TKR), independently of radiographic features of knee osteoarthritis and established risk factors.

Methods: using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight.

Results: the analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, reflecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC ​= ​0.87 vs 0.73).

Conclusions: using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.
Knee shape, Osteoarthritis, Osteophytes, Statistical shape modelling
2665-9131
Beynon, Rhona A.
3f5146c8-a3ea-4e9c-a983-0595b269b485
Saunders, Fiona R.
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Ebsim, Raja
fa3d2f2c-9d77-4b95-b0ff-c34b57142381
Frysz, Monika
bda9e219-ca28-43e4-babd-81f2d91ca3e4
Faber, Benjamin
85a38e7f-74a4-4ba7-a985-a1cff3392ed0
Gregory, Jennifer S.
6995d8fa-b32b-4f7c-aa15-8146acb4fd67
Lindner, Claudia
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Sarmanova, Aliya
511fb98c-63d3-4db6-822c-c1071c3cab1d
Aspden, Richard M.
71d1c790-5d9f-40b4-9130-bf7781b0e0dd
Harvey, Nicholas C.
ce487fb4-d360-4aac-9d17-9466d6cba145
Cootes, Timothy
f82f878a-ab1d-426c-9510-afa3f6de7aef
Tobias, Jonathan H.
514342d7-3491-4a7b-bbeb-b00dcf244daa
Beynon, Rhona A.
3f5146c8-a3ea-4e9c-a983-0595b269b485
Saunders, Fiona R.
a51cc79d-0928-4ab6-a479-3972c974670b
Ebsim, Raja
fa3d2f2c-9d77-4b95-b0ff-c34b57142381
Frysz, Monika
bda9e219-ca28-43e4-babd-81f2d91ca3e4
Faber, Benjamin
85a38e7f-74a4-4ba7-a985-a1cff3392ed0
Gregory, Jennifer S.
6995d8fa-b32b-4f7c-aa15-8146acb4fd67
Lindner, Claudia
9a4bb424-5446-40eb-9e44-adc5280e5846
Sarmanova, Aliya
511fb98c-63d3-4db6-822c-c1071c3cab1d
Aspden, Richard M.
71d1c790-5d9f-40b4-9130-bf7781b0e0dd
Harvey, Nicholas C.
ce487fb4-d360-4aac-9d17-9466d6cba145
Cootes, Timothy
f82f878a-ab1d-426c-9510-afa3f6de7aef
Tobias, Jonathan H.
514342d7-3491-4a7b-bbeb-b00dcf244daa

Beynon, Rhona A., Saunders, Fiona R., Ebsim, Raja, Frysz, Monika, Faber, Benjamin, Gregory, Jennifer S., Lindner, Claudia, Sarmanova, Aliya, Aspden, Richard M., Harvey, Nicholas C., Cootes, Timothy and Tobias, Jonathan H. (2024) Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: findings from a study of 37,843 people in UK Biobank. Osteoarthritis and Cartilage Open, 6 (2), [100468]. (doi:10.1016/j.ocarto.2024.100468).

Record type: Article

Abstract

Objective: we aimed to create an imaging biomarker for knee shape using knee dual-energy x-ray absorptiometry (DXA) scans and investigate its potential association with subsequent total knee replacement (TKR), independently of radiographic features of knee osteoarthritis and established risk factors.

Methods: using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight.

Results: the analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, reflecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC ​= ​0.87 vs 0.73).

Conclusions: using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.

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Accepted/In Press date: 3 April 2024
e-pub ahead of print date: 9 April 2024
Published date: June 2024
Additional Information: For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: © 2024 The Authors
Keywords: Knee shape, Osteoarthritis, Osteophytes, Statistical shape modelling

Identifiers

Local EPrints ID: 489211
URI: http://eprints.soton.ac.uk/id/eprint/489211
ISSN: 2665-9131
PURE UUID: d117eadc-3576-4d3d-90d8-94d6882041fb
ORCID for Nicholas C. Harvey: ORCID iD orcid.org/0000-0002-8194-2512

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Date deposited: 17 Apr 2024 16:56
Last modified: 21 May 2024 01:38

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Contributors

Author: Rhona A. Beynon
Author: Fiona R. Saunders
Author: Raja Ebsim
Author: Monika Frysz
Author: Benjamin Faber
Author: Jennifer S. Gregory
Author: Claudia Lindner
Author: Aliya Sarmanova
Author: Richard M. Aspden
Author: Timothy Cootes
Author: Jonathan H. Tobias

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