Effects of in vivo fatigue-induced subchondral bone microdamage on the mechanical response of cartilage-bone under a single impact compression

Journal Pre-proofs Effects of in vivo fatigue-induced subchondral bone microdamage on the mechanical response of cartilage-bone under a single impact compression Fatemeh Malekipour, Peta L. Hitchens, R. Chris Whitton, Peter Vee-Sin Lee PII: DOI: Reference:

S0021-9290(19)30857-7 https://doi.org/10.1016/j.jbiomech.2019.109594 BM 109594

To appear in:

Journal of Biomechanics

Received Date: Revised Date: Accepted Date:

8 August 2019 3 December 2019 21 December 2019

Please cite this article as: F. Malekipour, P.L. Hitchens, R. Chris Whitton, P.V-S. Lee, Effects of in vivo fatigueinduced subchondral bone microdamage on the mechanical response of cartilage-bone under a single impact compression, Journal of Biomechanics (2019), doi: https://doi.org/10.1016/j.jbiomech.2019.109594

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© 2019 Published by Elsevier Ltd.

Effects of in vivo fatigue-induced subchondral bone microdamage on the mechanical response of cartilage-bone under a single impact compression

Fatemeh Malekipoura, Peta L. Hitchensb, R. Chris Whittonb, Peter Vee-Sin Leea a

Department of Biomedical Engineering, University of Melbourne, Parkville, VIC 3010,

Australia b

Equine Centre, Faculty of Veterinary and Agricultural Sciences, University of Melbourne,

Werribee, VIC 3030, Australia

Corresponding address: Prof. Peter V.S. Lee Department of Biomedical Engineering University of Melbourne Victoria 3010, Australia Email: [email protected]

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1.

ABSTRACT

Subchondral bone (SCB) microdamage is prevalent in the joints of human athletes and animals subjected to high rate and magnitude cyclic loading of the articular surface. Quantifying the effect of such focal in vivo fatigue-induced microdamage on the mechanical response of the tissue is critical for the understanding of joint surface injury and the development of osteoarthritis. Thus, we aimed to quantify the mechanical properties of cartilage-bone from equine third metacarpal (MC3) condyles, which is a common area of accumulated microdamage due to repetitive impact loading. We chose a non-destructive technique, i.e. highresolution microcomputed tomography (µCT) imaging, to identify various degrees of in vivo microdamage in SCB prior to mechanical testing; because µCT imaging can only identify a proportion of accumulated microdamage, we aimed to identify racing and training history variables that provide additional information on the prior loading history of the samples. We then performed unconfined high-rate compression of approximately 2% strain at 45%/s strain rate to simulate a cycle of gallop and used real-time strain measurements using digital image correlation (DIC) techniques to find the stiffness and shock absorbing ability (relative energy loss) of the cartilage-bone unit, and those associated with cartilage and SCB. Results indicated that stiffness of cartilage-bone and those associated with the SCB decreased with increasing grade of damage. Whole specimen stiffness also increased, and relative energy loss decreased with higher TMD, whereas bone volume fraction of the SCB was only associated negatively with the stiffness of the bone. Overall, the degree of subchondral bone damage observed with µCT was the main predictor of stiffness and relative energy loss of the articular surface of the third metacarpal bone of Thoroughbred racehorses under impact loading. Keywords: cartilage-bone, subchondral bone, impact compression, stiffness, shockabsorbing, equine, fatigue-induced microdamage, micro-computed tomography

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2.

INTRODUCTION

Subchondral bone (SCB) microdamage is prevalent in the joints of human athletes and animals subjected to high rate and magnitude cyclic loading of the articular surface (Barr et al., 2009; Devas, 1958; Matcuk et al., 2016; Riggs et al., 1999; Turley et al., 2014). Under these conditions microdamage may accumulate in the calcified cartilage and the SCB as a function of the applied load and the number of cycles of load (Burr and Schaffler, 1997; Fazzalari et al., 2002; Lacourt et al., 2012; Martig et al., 2013; Muratovic et al., 2018; Norrdin and Stover, 2006; Radin et al., 1970; Whitton et al., 2018). In the young, non-athletic joint, microdamage may heal through targeted bone remodelling triggered by the microdamage itself (Burr et al., 1985). However, in athletic joints, under high magnitude cyclic loading, bone remodelling is inhibited, allowing accumulation of microdamage with the potential for catastrophic failure or subchondral bone injury and subsequent osteoarthritis (Holmes et al., 2014; Kawcak and McIlwraith, 2001; Muir et al., 2006; Norrdin and Stover, 2006; Seref-Ferlengez et al., 2015). The mechanical properties of bone are influenced by its architecture, volume and mineralisation, which are also important predictors of resistance to fatigue (Burr et al., 1997; Fatihhi et al., 2015). However, bone volume and mineralisation appear to have less influence on the mechanical behaviour of subchondral bone at sites that undergo high magnitude cyclical loading (Malekipour et al., 2018; Rubio-Martínez et al., 2008). The presence of fatigue damage at these sites may explain this observation if it affects the subchondral bone mechanical behaviour. Therefore, quantifying the effect of focal fatigue-induced microdamage on the mechanical response of the tissue is critical for the understanding of joint surface injury and the development of osteoarthritis (Burr et al., 1991; Burr and Gallant, 2012; Kawcak et al., 2001; Stewart and Kawcak, 2018). Few studies have investigated the effect of in vivo fatigue-induced microdamage on the mechanical properties of bone (Rubio-Martínez et al., 2008; Zioupos and Currey, 1998). Accumulated microdamage and cartilage defects due to repetitive impact loading are commonly observed in equine carpal and metacarpophalangeal subchondral bone (Lacourt et al., 2012; Muir et al., 2008; Turley et al., 2014). Microcracks form in the calcified cartilage and propagate to the subchondral bone where they can accumulate in large numbers (Muir et al., 2008; Turley et al., 2014; Whitton et al., 2018). Cartilage thickening has been also reported in association with subchondral bone injury (Turley et al., 2014). This combination of cartilage 3

and bone changes associated with subchondral bone fatigue injury makes it essential to consider the cartilage-bone unit when investigating the effect of microdamage on the joint mechanical performance (Burgin and Aspden, 2007; Malekipour et al., 2013). In the present study, we aimed to quantify the mechanical properties of the cartilage-bone unit, and those associated with cartilage and subchondral bone from equine third metacarpal bone (MC3) condyles with various degrees of in vivo fatigue-induced microdamage. We chose a non-destructive technique, i.e. high-resolution µCT imaging, to identify any pre-existing microcracks and microfractures prior to mechanical testing. We hypothesized that cartilagebone specimens with SCB microdamage (observed on µCT images) would have greater shockabsorbing ability and a lower compressive stiffness compared to cartilage-bone with no identified microdamage. Additionally, because microCT imaging can only identify a proportion of accumulated fatigue-induced microdamage, we aimed to identify racing and training history variables that provide additional information on the prior loading history of the samples. 3.

METHODS a.

Specimen preparation

Cartilage-bone plugs (n=25; 6.5 mm diameter x 10 mm length) were extracted from the distopalmar aspect of the metacarpal condyles of Thoroughbred racehorses (n=17) which died or were euthanatized on racetracks in Victoria, Australia. The distopalmar aspect of the equine metacarpophalangeal joint is subjected to a high rate and magnitude of compression when galloping during training and racing (Harrison et al., 2014). Specimens were extracted 3-5mm palmar to the transverse ridge, from the middle third (Martig et al., 2014) of both medial (n=15) and lateral (n=11) condyles to include various degrees of microdamage (Pinchbeck et al., 2013; Trope et al., 2011). Cores were extracted using a diamond drill bit (Starlite Industries Inc., USA) while specimens were irrigated with saline. During harvesting, the axis of the drill was oriented perpendicular to the cartilage surface. Cores were further cut to a final length of 10 mm using a precision diamond saw (IsoMet, Buehler, USA). Subsequently, a 1 mm thick (at its widest point) vertical section was removed from the cartilage-bone to create a flat surface perpendicular to the articular surface for further real-time imaging of specimens during mechanical testing (Fig. 1). The final width of the stained plane was 4.91±0.35 mm averaged

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for all specimens. This plane was stained with fine graphite particles which were generated by rubbing pencil lead on sand paper. b. Racing and training history data Racing histories were obtained from an official race records repository (Racing Australia; http://www.racingaustralia.horse/). Age at death and age at first start were calculated using the date of birth, date of death, and date of first start, respectively. Years racing was calculated using the date of first and last race starts. Time between the last two and five starts was calculated from the date of the last start. Highest rating was the highest handicap rating achieved during each horse’s career. Career places included first to third. Training histories were obtained from the trainer questionnaire required by the official racing authority at time of death. The data is provided in Appendix A. c.

MicroCT scanning and microdamage identification

Prior to mechanical testing, cartilage-bone plugs were imaged using a µCT scanner (µCT50; Scanco Medical, Switzerland) at an isotropic resolution of 4 µm (70 kVp, 200 µA, and 1050 ms) to identify microcracks and microfractures, and to assess bone volume fraction (BVF) and bone tissue mineral density (TMD). A phantom was used to convert CT grayscale intensity values (Hounsfield units, HU) into equivalent bone mineral density (TMD, mg HA/ccm). Damage was identified by visual examination of all µCT slices in the sagittal and transverse plane, prior to mechanical testing assessing for localised linear lucencies or linear areas of increased mineralisation (Boyde, 2003; Whitton et al., 2018). Three damage groups were identified based on the severity of pre-existing microdamage in µCT images (Simpleware, Synopsis, US): NDmg: no damage was identified in any of the µCT slices, MDmg: minor linear microcracks with crack openings less than 100 µm in the sagittal cross-sections, usually confined with surrounding bone tissue, and SDmg: severe microfractures with crack openings greater than 200 µm with their lengths extended across the widths of the specimens along with projection of cartilage into the calcified cartilage, i.e. cartilage thickening (Fig. 2). Following microdamage identification, images were down-sampled to 24 µm to measure BVF and TMD, cartilage thickness, and bone thickness (Simpleware, Synopsis, US). The reduction in the image resolution did not change the measurements by more than 0.39 % yet allowed for a faster analysis of the data.

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d. Mechanical testing Cartilage-bone specimens were tested in under a single non-destructive unconfined compression perpendicular to the cartilage surface (Malekipour et al., 2018). The proximal end of each specimen was fixed onto a base plate with a thin layer of cyanoacrylate glue. The time to peak strain was specified at 0.05 Sec to simulate the high-rate loading experienced by the articular surface in a galloping horse (Swanstrom et al., 2005). Displacements of 0.214 ± 0.033 mm were applied to the cartilage surface using a half-sine signal at 5 Hz via a stainless-steel compression plate attached to a hydraulic mechanical testing machine (Instron, UK). Overall strain and stress were calculated by dividing the overall displacement and axial force by the overall thickness and cross-sectional area of each specimen, respectively. e.

Cartilage and bone deformation (digital image correlation technique)

To separate the contribution of cartilage and bone from the overall displacement of cartilagebone unit, the flat stained plane was imaged during mechanical testing to estimate the vertical displacement (V) of the cartilage-bone interface, and thus the deformation of cartilage and bone in each specimen. The cartilage-bone surface was imaged using a MP-E 65 mm macro photo lens (Canon U.S.A. Inc., Melville, NY) with a capability of magnifications ranging from 1× to 5× using manual focusing, attached a digital high-speed-camera (PHOTRON, FASTCAM, 1024PCI, Japan) at 2000 frames per second. As a result, a sequence of 200 images were collected from the time the compression plate touched the cartilage surface until the end of decompression and analysed using digital image correlation (VIC-2D software, Correlated Solutions Inc., Columbia, USA). Images included the entire cartilage-bone section and a rigid grid which was attached to the compression plate. The rigid grid was glued to the compression plate such that it would not touch the specimen during loading but was visible in all images. This grid was used for calibration and for synchronising the Instron data with the images by matching the time to peak of Instron- and image-measured displacements. Displacements of every 5 pixels on the cartilage-bone was calculated with a search subset size of 35 pixels which resulted in an accuracy of 0.83 % for displacement measurements. The accuracy was calculated by comparing the reading of the Instron and the measurement derived from the VIC2D for the vertical displacement of the rigid grid. Due to the large deformation of the cartilage, vertical displacement of grid points was not available on the bone at the interface or immediately beneath. After analysis of all specimens, a 0.3 mm distance proximal from the interface was 6

determined as the distance where displacements could be reliably (confidence interval < 0.01 pixels) calculated without being influenced by the cartilage lateral deformation which led to blurring of cartilage in the imaged plane at maximum compression. The average vertical displacement (V) of the all points at this location (0.3 mm below interface) was then averaged as interface displacement (𝑉𝑖𝑛𝑡𝑒𝑟𝑓𝑎𝑐𝑒). Cartilage and subchondral bone axial strains (ɛ) were specified as: ɛ𝑐𝑎𝑟𝑡𝑖𝑙𝑎𝑔𝑒 =

𝑉𝑖𝑛𝑡𝑒𝑟𝑓𝑎𝑐𝑒 ― 𝑉𝑐𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑝𝑙𝑎𝑡𝑒 𝑇ℎ𝑘𝑐𝑎𝑟𝑡𝑖𝑙𝑎𝑔𝑒

, ɛ𝑆𝐶𝐵 =

𝑉𝑖𝑛𝑡𝑒𝑟𝑓𝑎𝑐𝑒 𝑇ℎ𝑘𝑆𝐶𝐵

A constant compressive stress was assumed across the thickness of each specimen. Stressstrain curves were plotted for cartilage and bone, separately. The linear portion (R2>0.98) of loading curves of overall and bone-related stress-strain curves was specified as overall and bone stiffness (𝐾𝑆𝐶𝐵). The “shock-absorbing ability” or relative energy loss (𝐸𝑙𝑜𝑠𝑠) of overall cartilage-bone, and those related to cartilage and bone was defined as the area enclosed by the loading-unloading curve (hysteresis) of each specimen divided by the area underneath the loading curve. To be comparative between specimens, cartilage stiffness (𝐾𝐴𝐶) was specified as the slope of a line tangential to the stress-strain curve at three stress levels: peak, 20MPa (if available) and 7MPa (the smallest peak stress among all specimens). f.

Statistical analysis

Box plots for categorical predictor variables and scatter plots for continuous predictor variables were generated to assess relationships with the continuous outcome variables stiffness and energy loss. For ordinal predictors (e.g. damage group, age, race distance) fitted in the linear regression models, we created dummy variables for each level and compared to the base reference (Suits, 1957). Continuous variables were assessed for normality using the ShapiroWilk test. We investigated associations in separate models for each continuous outcome (i.e. cartilage stiffness, SCB stiffness, cartilage-bone stiffness, and cartilage Eloss, SCB Eloss, and cartilage-bone Eloss). For each model, predictor variables (morphological properties, racing and training histories) were fitted in univariable linear regression models, adjusting for clustering at the horse-level to account for medial and lateral samples from the same horse. Candidate variables were considered for inclusion in the multivariable linear regression models (one multivariable model for each of the six outcomes) if they were significant at the P<0.20 level in univariable analysis. Variables were dropped using a backwards stepwise approach and retained in the multivariable linear regression models if P<0.05. Regression coefficients and their 95% confidence intervals (95% CI) as well as goodness-of-fit (r2) are presented. 7

Regression diagnostics included goodness-of-fit (r2), assessing normality of residuals (residuals lie within -2 to 2), leverage (greater than (2k+2)/n where k is the number of predictors and n is the number of observations), and collinearity (variance inflation factor; VIF<10) normality of residuals, leverage, and collinearity (variance inflation factor). Due to the small sample size, the final models were assessed for overfitting using 5-fold cross-validation (Daniels, 2012). The final multivariable models were fitted with and without outliers that did not adhere to the diagnostic requirements and those models without outliers did not change their effect direction or significance (Tables A1-A3). Fitting of uni and multivariable linear regression models was conducted using Stata/SE (v.15.1, StataCorp., College Station, TX). 4.

Results

In n=15 specimens no sign of microdamage was observed on µCT images (NDmg, Fig. 2A), whereas in n=7 specimens microdamage in the form of irregular linear lucencies in the bone beneath the cartilage were observed (MDmg, Fig. 2B). N=3 specimens exhibited cartilage collapsed into the subchondral bone and distinct microfractures with opening widths ranging from 0.1-0.3 mm (SDmg, Fig. 2C). SDmg specimens had a greater cartilage thickness, and a lower TMD compared to the remaining specimens (Table 1). Mean ± SD of the applied strain was 2.23 ± 0.35 (%) for all specimens which generated overall axial stresses of 35.24 ± 15.19 MPa and respective strains of 0.83 ± 0.38 % and 19.2 ± 5.16 % in the SCB and cartilage. Stiffness of the cartilage was lowest in the SDmg specimens (Fig. 3, Table 2, 3). The severely damaged cartilage-bone had 82.6% and 74.3% lower stiffness and 67.7% and 32.1% greater energy dissipation compared to undamaged and mildly damaged specimens, respectively (Table 2). Univariable associations between mechanical properties of cartilage-bone, cartilageonly and SCB-only and other specimen properties, i.e. horse characteristics, and racing and training histories are presented in Appendix A. The multivariable models explained 50-80% of the variability of stiffness and energy loss, with damage group explaining the majority of the variance in these models (Tables 3 and 4). In the multivariable models (Table 3, 4), stiffness of the cartilage-bone unit and SCB were greatest in specimens with less damage and greater TMD even when accounting for cartilage thickness. Similarly, relative energy loss of the cartilage-bone unit and SCB was greater in specimens with severe damage than those with no observable damage and in association with lower TMD (Fig. 3, 4). When the stiffness of only the subchondral bone was examined it was greater in specimens with less damage and greater 8

TMD but also lower bone volume fraction (Table 3). Cartilage stiffness at 20 MPa was highly correlated with cartilage stiffness at 7MPa (rho 0.924, P<0.001) and at peak stress (rho 0.917, P<0.001), and similarly cartilage stiffness at 7MPa with peak stress (rho 0.840, P<0.001). 5.

Discussion

The degree of subchondral bone damage observed with microCT was the main predictor of stiffness and relative energy loss of the articular surface of the third metacarpal bone of Thoroughbred racehorses under impact loading as we hypothesised. The stiffness of cartilagebone specimens decreased with increasing grade of subchondral bone damage. Whole specimen stiffness also increased, and energy loss decreased with higher TMD, whereas bone volume fraction of the subchondral bone was only associated negatively with the stiffness of the bone. The microdamage we observed in the samples from the metacarpophalangeal joint of racehorses was similar to previously observed microcracks and microfractures in the calcified cartilage and the SCB in association with osteoarthritis, ageing, and repetitive overloading of the joint (Boyce et al., 1998; Burr and Schaffler, 1997; Diab Deepak, 2007; Lacourt et al., 2012; Landrigan et al., 2011; Martig et al., 2018; Mori et al., 1993; Muratovic et al., 2018; Norrdin and Stover, 2006; Sokoloff, 1993; Whitton et al., 2018). Ex vivo fatigue-induced microdamage has also been associated with reduced cortical and trabecular bone stiffness (Burr et al., 1998; Hernandez et al., 2014). In addition, equine metacarpal subchondral bone stiffness reduces gradually toward the end of its fatigue life in vitro (Martig et al., 2013). Our finding, however, differed from a previous study of equine SCB (with no articular cartilage in situ), which did not find any difference in the stiffness of SCB from joints with mild and severe SCB injury (Rubio-Martínez et al. 2008a). In addition to a lower compression rate, classification of SCB injury in that study was performed at a lower resolution and across the whole distal metacarpus so the actual damage present in the tested explants may not have reflected the overall grading. Because of its ability to respond to injury, damage to the subchondral bone in vivo rarely occurs without associated changes in bone structure and composition. With increasing extent of subchondral bone microdamage in the distal third metacarpal bone, bone volume fraction is 9

higher and TMD is lower (Whitton et al., 2018). Therefore, the association between TMD and articular surface stiffness could be due to a direct effect of TMD and/or a direct effect of microdamage (Table 3). The association in the current study was independent of observed subchondral bone damage, however we cannot rule out an effect of microdamage that was below the resolution of the microCT. Our finding that bone stiffness was greater in specimens with lower bone volume fraction (Table 3), while counterintuitive, is also consistent with the assumption that we did not detect all of the microdamage in our specimens as higher bone volume fraction is associated with subchondral bone damage (Whitton et al., 2018). Further investigation is required to confirm this assumption. We included data on each horse’s racing history in our analysis as a proxy for previous loading of the subchondral bone because we were not able to detect all microdamage with microCT and it is likely that bone material properties are affected by fatigue prior to any detectable microdamage (Burr et al., 1998). However, no racing history variables improved the prediction of specimen mechanical properties suggesting that TMD and BV/TV are more sensitive indicators of developing bone material fatigue. Additionally, the lower TMD of severely damaged specimens suggest a higher bone turnover in these specimens. When segmenting the bone in damaged specimens, care was taken not to segment the crack openings as bone. However, in the presence of diffuse microdamage and fine cracks, the partial volume effect in µCT images might have contributed to the lower grayscale (and TMD) of the damaged area. Cartilage thickening and the collapse of AC into the SCB has been reported in the equine carpal and fetlock joints associated with advanced OA and subchondral bone microdamage (Bani Hassan et al., 2016; Lacourt et al., 2012; Norrdin and Stover, 2006). Consistent with our observed reduced stiffness in the cartilage of the SDmg specimens, fibrillation and a reduced proteoglycan content has been reported in equine carpal cartilage subjected to strenuous exercise overlying damaged SCB (Burgin and Aspden, 2008; Finlay and Repo, 1978; Lacourt et al., 2012; Murray et al., 1999). However, we did not observe any change in stiffness and thickness in MDmg specimens, even though reductions in proteoglycan content in association with mild subchondral bone damage of equine carpus have been reported (Murray et al., 2000, 1999). Turley et al. (2011) also observed a greater cartilage stiffness, although measured at much lower loading rates, at sites on the third metacarpal bone articular surface that are rarely injured compared to the site used in the current study that is more commonly injured. It is

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possible that the change in cartilage PG content associated with mild cartilage damage is too low to affect its mechanical stiffness.

Our results are consistent with softening of the joint surface in the presence of cartilage-bone damage. Areas of the joint surface with moderate subchondral bone damage have approximately 70% of the stiffness of undamaged regions, whereas severely damaged areas have approximately 20% of the stiffness of undamaged regions. These reductions in the stiffness of bone/cartilage near the joint surface will affect the amount of load and strain that can be transferred to the underlying bone as well as adjacent areas of the articular surface. Decreased stiffness in focal areas of articular cartilage and subchondral bone have been studied using finite element models of the human knee that show decreased stress in the area designated as having reduced bone stiffness and increased stress in the surrounding articular cartilage and subchondral bone (Shirazi and Shirazi-Adl, 2009; Zevenbergen et al., 2018). Because bone is highly responsive to its loading environment, the local changes in loading associated with subchondral bone injury will affect local bone turnover and stimulate focal bone adaptation. For example, the normal exercise-induced inhibition of remodelling is offset at sites of fatigue fracture in horses in race training (Whitton et al., 2013). The findings of the current study provide a mechanism by which this may occur. 6.

Limitations

Care needs to be taken when using the cartilage stiffness data. Cartilage cross-sectional area increases during compression which could have led to an overestimated axial stress and stiffness. This assumption, however, did not affect our SCB properties results and comparison between specimens. Our findings on the mechanical properties associated with cartilage and SCB are based on the deformation in a single plane. In specimens where cartilage collapsed into the SCB or cartilage thickness was non-uniform, a three-dimensional measurement of deformation may be required. Future finite element models of such defects can investigate the local properties of bone in three-dimensions. Even though the cartilage was tested in unconfined compression in the present study, a small aspect ratio of less than 0.2 (thickness/diameter) in our specimens minimized barrelling effects at the cartilage edges and, therefore, reasonably mimicked in situ cartilage compression (Repo and Finlay, 1977). In the present study we were limited by the contrast and resolution of the µCT. Current techniques of 11

enhancing the µCT contrast of microdamage do not allow for staining prior to mechanical loading (Landrigan et al., 2011). Finally, as well as the number of specimens with severe damage being limited, the overall sample size was small, thereby restricting the number of variables that could be fitted in our multivariable models. Additionally, the small sample size precluded fitting of interaction terms which may have also been useful in assessing differences in bone material properties across damage groups. In summary, we measured the properties of the osteochondral unit under impact loading as a whole and the associated individual properties of cartilage and bone and found that the degree of subchondral bone injury had the greatest effect on stiffness of and energy loss in the osteochondral unit and the bone. The technique used allowed us to measure the mechanical performance of the subchondral bone while its adjacent articular cartilage was still in-place. In vivo, load is transferred to the underlying bone through the cartilage-bone complex. Our results provide new data on the effect of fatigue-induced microdamage on the mechanical performance of cartilage-bone from metacarpal condyles of racehorses, which can help better understand the mechanism of stress fracture development and osteoarthritis of the joint. Additionally, these results are essential as input values to finite element and mathematical models of the whole joint which will help improve the understanding of injury to the SCB. Acknowledgment This study was funded by Racing Victoria Limited and the Victorian Racing Industry Fund of the Victorian State Government and The University of Melbourne. Conflict of interest statement None of the authors above has any financial or personal relationship with other people or organizations that could inappropriately influence this work, including employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications/registrations, and grants or other funding.

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Relationship between cartilage and subchondral bone lesions in repetitive impact trauma-induced equine osteoarthritis. Osteoarthritis Cartilage 20, 572–83. Landrigan, M.D., Li, J., Turnbull, T.L., Burr, D.B., Niebur, G.L., Roeder, R.K., 2011. Contrast-enhanced micro-computed tomography of fatigue microdamage accumulation in human cortical bone. Bone 48, 443–50. Malekipour, F., Whitton, C., Oetomo, D., Lee, P.V.S., 2013. Shock absorbing ability of articular cartilage and subchondral bone under impact compression. J. Mech. Behav. Biomed. Mater. 26, 127–35. Malekipour, F., Whitton, C.R., Lee, P.V.S., 2018. Stiffness and energy dissipation across the superficial and deeper third metacarpal subchondral bone in Thoroughbred racehorses under high-rate compression. J. Mech. Behav. Biomed. Mater. 85, 51–56. Martig, S., Chen, W., Lee, P.V.S., Whitton, R.C., 2014. Bone fatigue and its implications for injuries in racehorses. Equine Vet. J. 46, 408–415. Martig, S., Hitchens, P.L., Stevenson, M.A., Whitton1, R.C., 2018. Subchondral bone morphology in the metacarpus of racehorses in training changes with distance from the articular surface but not with age. J. Anat. 232, 919–930. Martig, S., Lee, P.V.S., Anderson, G. a, Whitton, R.C., 2013. Compressive fatigue life of subchondral bone of the metacarpal condyle in thoroughbred racehorses. Bone 57, 392– 8. Matcuk, G.R., Mahanty, S.R., Skalski, M.R., Patel, D.B., White, E.A., Gottsegen, C.J., 2016. Stress fractures: pathophysiology, clinical presentation, imaging features, and treatment options. Emerg. Radiol. 1–11. Mori, S., Harruff, R., Burr, D.B., 1993. Microcracks in articular calcified cartilage of human femoral heads. Arch. Pathol. Lab. Med. 117, 196–198. Muir, P., McCarthy, J., Radtke, C.L., Markel, M.D., Santschi, E.M., Scollay, M.C., Kalscheur, V.L., 2006. Role of endochondral ossification of articular cartilage and functional adaptation of the subchondral plate in the development of fatigue

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microcracking of joints. Bone 38, 342–9. Muir, P., Peterson, A.L., Sample, S.J., Scollay, M.C., Markel, M.D., Kalscheur, V.L., 2008. Exercise-induced metacarpophalangeal joint adaptation in the Thoroughbred racehorse. J. Anat. 213, 706–717. Muratovic, D., Findlay, D.M., Cicuttini, F.M., Wluka, A.E., Lee, Y.R., Kuliwaba, J.S., 2018. Bone matrix microdamage and vascular changes characterize bone marrow lesions in the subchondral bone of knee osteoarthritis. Bone 108, 193–201. Murray, R.C., Janicke, H.C., Henson, F.M.D., Goodship, A., 2000. Equine carpal articular cartilage fibronectin distribution associated with training, joint location and cartilage deterioration. Equine Vet. J. 32, 47–51. Murray, R.C., Zhu, C.F., Goodship, A.E., Lakhani, K.H., Agrawal, C.M., Athanasiou, K.A., 1999. Exercise affects the mechanical properties and histological appearance of equine articular cartilage. J. Orthop. Res. 17, 725–731. Norrdin, R.W., Stover, S.M., 2006. Subchondral bone failure in overload arthrosis: a scanning electron microscopic study in horses. J. Musculoskelet. Neuronal Interact. 6, 251–7. Pinchbeck, G.L., Clegg, P.D., Boyde, A., Riggs, C.M., 2013. Pathological and clinical features associated with palmar/plantar osteochondral disease of the metacarpo/metatarsophalangeal joint in Thoroughbred racehorses. Equine Vet. J. 45, 587–592. Radin, E.L., Paul, I.L., Tolkoff, M.J., 1970. Subchondral bone changes in patients with early degenerative joint disease. Arthritis Rheum. 13, 400–405. Riggs, C.M., Whitehouse, G.H., Boyde, a, 1999. Pathology of the distal condyles of the third metacarpal and third metatarsal bones of the horse. Equine Vet. J. 31, 140–8. Rubio-Martínez, L.M., Cruz, A.M., Gordon, K., Hurtig, M.B., 2008. Mechanical properties of subchondral bone in the distal aspect of third metacarpal bones from Thoroughbred racehorses. Am. J. Vet. Res. 69, 1423–1433.

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Seref-Ferlengez, Z., Kennedy, O.D., Schaffler, M.B., 2015. Bone microdamage, remodeling and bone fragility: how much damage is too much damage? Bonekey Rep. 4, 644. Shirazi, R., Shirazi-Adl, a, 2009. Computational biomechanics of articular cartilage of human knee joint: effect of osteochondral defects. J. Biomech. 42, 2458–65. Sokoloff, L., 1993. Microcracks in the calcified layer of articular cartilage. Arch. Pathol. Lab. Med. 117, 191–195. Stewart, H.L., Kawcak, C.E., 2018. The Importance of Subchondral Bone in the Pathophysiology of Osteoarthritis. Front. Vet. Sci. 5, 1–9. Swanstrom, M.D., Zarucco, L., Hubbard, M., Stover, S.M., Hawkins, D. a., 2005. Musculoskeletal Modeling and Dynamic Simulation of the Thoroughbred Equine Forelimb During Stance Phase of the Gallop. J. Biomech. Eng. 127, 318. Trope, G.D., Anderson, G. a., Whitton, R.C., 2011. Patterns of scintigraphic uptake in the fetlock joint of Thoroughbred racehorses and the effect of increased radiopharmaceutical uptake in the distal metacarpal/tarsal condyle on performance. Equine Vet. J. 43, 509–515. Turley, S.M., Thambyah, A., Riggs, C.M., Firth, E.C., Broom, N.D., 2014. Microstructural changes in cartilage and bone related to repetitive overloading in an equine athlete model. J. Anat. 224, 647–658. Whitton, R.C., Ayodele, B.A., Hitchens, P.L., Mackie, E.J., 2018. Subchondral bone microdamage accumulation in distal metacarpus of Thoroughbred racehorses. Equine Vet. J. 50, 766–773. Whitton, R.C., Mirams, M., Mackie, E.J., Anderson, G. a, Seeman, E., 2013. Exerciseinduced inhibition of remodelling is focally offset with fatigue fracture in racehorses. Osteoporos. Int. 24, 2043–8. Zevenbergen, L., Smith, C.R., Rossom, S. Van, Thelen, D.G., Famaey, N., Sloten, J. Vander, Jonkers, I., 2018. Cartilage defect location and stiffness predispose the tibiofemoral joint to aberrant loading conditions during stance phase of gait 1–22.

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Zioupos, P., Currey, J.D., 1998. Changes in the stiffness, strength, and toughness of human cortical bone with age. Bone 22, 57–66.

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19

20

Figure Captions

Figure 1: (A) a schematic of the top-view (transverse cross-section) of the cartilagebone indicating the removed 1 mm vertical cross-section, (B) three-dimensional reconstructed µCT image of a typical cartilage-bone and the flat plane which was imaged real-time for strain measurements using DIC (digital image correlation) technique. Vertical displacement of compression plate and interface were averaged over the indicated lines to find the displacement and axial strain of cartilage and bone.

Figure 2: µCT images of a sagittal cross-section of a typical cartilage-bone from distopalmar aspect of the MC3 condyle of Thoroughbred racehorses from each damage group: (A) NDmg: no damage, (B) MDmg: minor linear microcracks and microfractures parallel to the articular surface, and (C) SDmg: severe microfractures and projection of cartilage into the calcified cartilage (cartilage thickening). Figure 3: Box plot of stiffness (K) and relative energy dissipation (Eloss) for three damage groups: NDmg: no damage, MDmg: minor linear microcracks and microfractures parallel to the articular surface, SDmg: severe microfractures and projection of cartilage into the

21

calcified cartilage (cartilage thickening). * indicates a significant difference between (P<0.05) based on the univariable model.

Figure 4: Scatter plot of SCB stiffness against SCB tissue mineral density (TMD) for three damage groups, and the predicted stiffness based on the multivariable model (solid line, coef. 95% CI).

22

Tables: Table 1: Mean ± standard deviation of morphology and density of cartilage-bone averaged for each damage group. Damage Cartilage thk TMD Sp. No. BV/TV group (mm) (mg/cm3 HA) NDmg

15

0.49 ± 0.09 a

0.91 ± 0.04a

895.25 ± 22.60a

MDmg

7

0.57 ± 0.10 a

0.93 ± 0.04a

906.54 ± 20.32a

SDmg

3

0.86 ± 0.20 b

0.91 ± 0.01a

845.75 ± 10.62b

Within each column, values with different superscript letters are significantly (P< 0.05) different. The detailed results for n=25 specimens are provided in Appendix Table A4. a,b,c

Table 2: Mean ± standard deviation of mechanical properties of cartilage-bone unit, and those associated with cartilage and subchondral bone averaged for each damage group. Stiffness (MPa) Relative energy loss (%) Damage Sp. Overall Cartilage Overall Cartilage group No. SCB SCB (20 MPa) (20 MPa) a £,a £,a a £,a NDmg 15 2377.3 ± 475.7 322.4 ± 87.4 5319.4 ± 1060 35.6 ± 9.7 29.9 ± 10.9 35.6 ± 9.7 £,a MDmg

7

1611.8 ± 241.0 b 310.9 ± 37.0 ʄ,a 3687.6 ± 923.3 ʄ,b

45.2 ± 7.2 b

SDmg

3

413.9 ± 191.0 c

59.7 ± 3.7 c

85.1 ± 40.8 b

1066.1 ± 354.6 c

33.8 ± 12.8 ζ,a 46.5 ± 13.8 ζ,a 53.7 ± 7.0 b

63.6 ± 7.1 b

£ N=3

specimens excluded because of asymmetry in the imaged plane and thus inaccurate DIC measured deformations, specimen excluded because of asymmetry in the imaged plane and inaccurate DIC results, ζ n=2 specimens failed during the test, no Eloss data was available due to blurring of images, hence excluded. a,b,c Within each column, values with different superscript letters are significantly (P < 0.05) different. ʄ n=1

Table 3: Multivariable model for the relationship between overall stiffness of cartilage-bone (GPa) (n=25), and those associated with cartilage (n=22) and subchondral bone (n=22) and damage groups, TMD and BV/TV. Specimen properties NDmg

Overall cartilage-bone Coef. (95% CI) p-value Ref

Cartilage (20 MPa) Coef. (95% CI) p-value Ref

SCB Coef. (95% CI) Ref

p-value

MDmg

-0.9 (-1.07, -0.63) <0.001

-0.01 (-0.07,0.05)

0.689

-1.7 (-2.14, -1.23)

<0.001

SDmg

-1.6 (-1.98, -1.18) <0.001

-0.24 (-0.30, -0.17)

<0.001

-3.34 (-4.14, -2.53)

<0.001

0.02 (0.00, 0.03)

0.007

TMD (mgHA/cm3)

0.01 (0, 0.01)

0.029

--

--

BV/TV

--

--

--

--

R2

0.789

-13.71 (-18.91, -8.51) <0.001

0.590

0.847

Table 4: Multivariable model for the relationship between overall relative energy loss (Eloss, %) of cartilagebone, and those associated with cartilage and subchondral bone and damage groups, TMD and BV/TV. Specimen Overall cartilage-bone Cartilage SCB properties Coef. (95% CI) p-value Coef. (95% CI) p-value Coef. (95% CI) p-value NDmg Ref Ref Ref MDmg

13.16 (6.66, 19.67) <0.001

7.8 (-0.4, 16)

0.062

10.89 (-0.96, 22.75)

0.072

SDmg

14.61 (5.96, 23.25) 0.001

13.0 (1.61, 24.39)

0.025

27.92 (19.01, 36.84)

<0.001

-0.21 (-.33, -0.10) <0.001

-0.22 (-0.33, -0.12) <0.001

--

--

TMD (mgHA/cm3)

23

R2

0.713

0.499

24

0.498

Appendix A Table 1A. Associations between bone-cartilage stiffness and energy loss with other specimen properties, horse characteristics, and racing and training histories (n=25). Variable Stiffness (MPa) Energy Loss Specimen properties Coef. (95% CI) pR2 Coef. (95% CI) p-value value Limb Left Ref Ref Right -599.59 (-1062.27, -136.92) 0.011 0.128 8.99 (0.50, 17.49) 0.038 Condyle Lateral Ref Ref Medial -146.00 (-643.45, 351.45) 0.565 0.009 3.25 (-4.77, 11.27) 0.427 Damage group 1 2 3 Cartilage thickness (mm) Bone thickness (mm) BV/TV TMD (mg HA/cm3) Fracture No fracture Fracture Horse characteristics Age 3-4 years 5-6 years 7-8 years Age at first start (years) Years racing Sex Entire Gelding Female Racing and training history Training or resting Resting Training Races last 30 days Distance of last race (m) Race distance Sprint / Mile Intermediate / Long Extended Rest period last (≥8weeks) 2 starts No rest period Rest period Time between last 2 race starts (weeks) Time between last 5 race starts (weeks)

Ref -765.49 (-1052.82, -478.15) -1963.38 (-2252.08, -1674.68) -3301.99 (-4503.76, -2100.24)

<0.001 <0.001 <0.001

371.48 (-183.97, 926.93) -3204.97 (-9825.24, 3415.29) 15.40 (3.09, 27.71)

R2

0.129 0.021

0.747 0.467

Ref 10.75 (3.71, 17.79) 25.19 (18.91, 31.48) 21.96 (-1.49, 45.40)

0.003 <0.001 0.066

0.570 0.093

0.190 0.343 0.014

0.062 0.027 0.299

-3.31 (-10.62, 4.01) 92.90 (-6.10, 191.91) -0.26 (-0.37, -0.14)

0.376 0.066 <0.001

0.022 0.103 0.371

Ref -173.98 (-779.58, 431.63)

0.573

0.012

Ref 3.31 (-8.33, 14.96)

0.577

0.020

Ref -1023.34 (-1396.03, -650.65) 302.26 (-86.15, 690.66) 626.32 (169.64, 1083.01) 52.84 (-62.87, 168.56)

<0.001 0.127 0.007 0.371

0.372 0.115 0.015

Ref 10.53 (3.96, 17.10) -12.78 (-18.51, -7.05) -9.13 (-20.32, 2.05) -2.71 (-4.38, -1.04)

0.002 <0.001 0.109 0.001

0.604 0.110 0.171

Ref 217.65 (-706.05, 1141.34) 227.15 (-673.33, 1127.62)

0.644 0.621

0.011

Ref 2.79 (-14.21, 19.79) 6.86 (-11.11, 24.83)

0.748 0.455

0.020

Ref 28.42 (-336.15, 392.98) 234.39 (-80.43, 549.20) 0.27 (-0.01, 0.55)

0.879 0.144 0.062

0.000 0.072 0.075

Ref 2.73 (-6.69, 12.14) -4.12 (-8.36, 0.12) -0.00 (-0.01, -0.00)

0.570 0.057 0.045

0.006 0.100 0.092

Ref -563.14 (-1134.19, 7.90) 428.48 (-54.45, 911.41)

0.053 0.082

0.229

Ref 10.42 (0.20, 20.64) -7.50 (-15.53, 0.53)

0.046 0.067

0.337

Ref 416.11 (-182.80, 1015.03) 4.55 (-25.58, 34.65)

0.173 0.768

0.049 0.001

Ref -6.81 (-20.24, 6.63) -0.03 (-0.62, 0.56)

0.321 0.927

0.059 0.000

-19.80 (-91.02, 51.42)

0.586

0.008

-0.14 (-1.46, 1.18)

0.837

0.002

25

Table 2A. Associations between bone stiffness and energy loss with other specimen properties, horse characteristics, and racing and training histories (n=22). Variable Stiffness Energy Loss Specimen properties Coef. (95% CI) p-value R2 Coef. (95% CI) p-value Limb Left Ref Ref Right -1111.83 (-2557.75, 334.08) 0.132 0.083 10.90 (0.23, 21.58) 0.045 Condyle Lateral Ref Ref Medial -1187.08 (-2331.79, -42.38) 0.042 0.115 15.20 (6.86, 23.53) <0.001 Damage group 1 2 3 Cartilage thickness (mm) Bone thickness (mm) BV/TV TMD (mg HA/cm3) Fracture No fracture fractured Horse characteristics Age 3-4 years 5-6 years 7-8 years Age at first start (years) Years racing Sex Entire Gelding Female Racing and training history Training or resting Resting Training Races last 30 days Distance of last race (m) Race distance Sprint / Mile Intermediate / Long Extended Rest period (≥8weeks) last 2 starts No rest period Rest period Time between last 2 race starts (weeks) Time between last 5 race starts (weeks)

Ref -1631.82 (-2495.68, -767.95) -4253.28 (-4949.22, -3557.33) -6927.67 (-9220.08, -4635.25)

Ref 10.89 (-0.96, 22.75) 27.92 (19.01, 36.84) 29.41 (3.39, 55.42)

<0.001 <0.001 <0.001

0.724 0.441

0.213 0.031

0.044 0.127

0.033

0.252

-8.64 (-18.68, 1.40) 78.19 (-19.85, 176.23) -0.24 (-0.41, -0.06)

Ref -1242.48 (-2772.80, 287.84)

0.112

0.126

Ref -1850.37 (-3042.74, -658.00) 1211.02 (-229.63, 2651.68) 1521.53 (-132.48, 3175.54) 200.53 (-139.92, 540.97)

0.002 0.099 0.071 0.248

Ref -33.22 (-2908.4, 2841.95) 713.36 (-2148.60, 3575.33)

R2 0.125 0.297

0.072 <0.001 0.027

0.498 0.123

0.092 0.118

0.106 0.045

0.007

0.236

Ref 1.14 (-12.41, 14.68)

0.869

0.002

0.363 0.112 0.044

Ref 14.06 (5.10, 23.02) -13.79 (-23.77, -3.80) -12.38 (-24.62, -0.13) -2.46 (-4.760, -0.17)

0.002 0.007 0.048 0.035

0.527 0.144 0.100

0.982 0.625

0.015

Ref 4.98 (-15.40, 25.36) 3.70 (-15.10, 22.49)

0.632 0.700

0.019

Ref -471.66 (-1576.30, 632.98) 369.41 (-418.72, 1157.54) 0.50 (-0.29, 1.29)

0.403 0.358 0.214

0.009 0.039 0.052

Ref 1.06 (-8.99, 11.11) -6.03 (-11.42, -0.64) -0.01 (-0.01, -0.00)

0.836 0.028 0.014

0.001 0.161 0.178

Ref -2331.67 (-3704.24, -959.09) 1056.45 (-25.93, 2138.82)

0.001 0.056

0.475

Ref 15.54 (1.52, 29.55) -13.63 (-23.72, 3.55)

0.030 0.008

0.498

Ref 1395.93 (-171.80, 2963.66) 39.30 (-44.02, 122.63)

0.081 0.355

0.116 0.017

Ref -4.24 (-18.65, 10.16) 0.13 (-0.60, 0.86)

0.564 0.728

0.017 0.003

35.16 (-166.60, 236.91)

0.733

0.005

0.13 (-1.47, 1.73)

0.876

0.001

702.68 (-403.23, 1808.60) -15615.07 (-29829.81, 1400.32) 30.73 (2.42, 59.04)

26

Table 3A. Associations between cartilage stiffness (at 20 MPa) and energy loss with other specimen properties, horse characteristics, and racing and training histories (n=25). Similar relationships were found for cartilage stiffness at 7 MPa and max. compression. Therefore only results for 20 MPa has been presented here. Variable Stiffness Energy Loss Specimen properties Coef. (95% CI) p-value R2 Coef. (95% CI) p-value Limb Left Ref Ref Right -67.62 (-169.37, 34.13) 0.193 0.082 5.95 (-5.72, 17.62) 0.318 Condyle Lateral Ref Ref Medial -26.94 (-112.40, 58.51) 0.537 0.016 -0.11 (-10.63, 10.42) 0.984 Damage group 1 2 3 Cartilage thickness (mm) Bone thickness (mm) BV/TV TMD (mg HA/cm3) Fracture No fracture fractured Horse characteristics Age 3-4 years 5-6 years 7-8 years Age at first start (years) Years racing Sex Entire Gelding Female Racing and training history Training or resting Resting Training Races last 30 days Distance of last race (m) Race distance Sprint / Mile Intermediate / Long Extended Rest period last 2 starts (≥8weeks) No rest period Rest period Time between last 2 race starts (weeks) Time between last 5 race starts (weeks)

Ref -11.51 (-67.87, 44.85) -237.32 (-300.06, -174.58) -254.06 (-528.44, 20.32)

0.689 <0.001 0.070

43.75 (-56.53, 144.03) 61.48 (-696.10, 819.07) 2.50 (1.50, 3.50)

R2 0.042 0.000

0.583 0.158

Ref 3.92 (-6.50, 14.35) 23.80 (14.40, 33.20) 21.30 (-11.74, 54.33)

0.461 <0.001 0.206

0.379 0.073

0.392 0.874 <0.001

0.045 0.001 0.447

-0.58 (-8.92, 7.77) 49.10 (-92.21, 190.41) -0.28 (-0.40, -0.16)

0.892 0.496 <0.001

0.001 0.020 0.371

Ref 38.67 (-60.55, 137.89)

0.445

0.033

Ref 0.12 (-12.90, 13.13)

0.986

0.000

Ref -119.09 (-196.84, -41.34) 103.00 (45.34, 160.66) 67.36 (-42.48, 177.20) 21.84 (7.64, 36.04)

0.003 <0.001 0.229 0.003

0.541 0.058 0.139

Ref 8.84 (-4.05, 21.74) -12.63 (-20.35, -4.90) -5.62 (-17.57, 6.34) -2.54 (-4.50, -0.58)

0.179 0.001 0.357 0.011

0.389 0.033 0.120

Ref 71.32 (-93.40, 236.05) -24.28 (-185.31, 136.75)

0.396 0.768

0.107

Ref -2.02 (-21.55, 17.51) 11.14 (-10.51, 32.79)

0.839 0.313

0.084

Ref 6.61 (-110.27, 123.50) 54.71 (22.93, 86.48) 0.05 (0.01, 0.09)

0.912 0.001 0.019

0.001 0.225 0.125

Ref -1.03 (-12.26, 10.20) -3.76 (-8.33, 0.80) -0.00 (-0.01, 0.00)

0.857 0.106 0.193

0.001 0.070 0.041

Ref -40.69 (-176.02, 94.63) 72.42 (-8.39, 153.22)

0.556 0.079

0.137

Ref 6.17 (-12.74, 25.08) -4.35 (-14.91, 6.20)

0.523 0.419

0.072

Ref 15.03 (-93.62, 123.69) -2.95 (-7.18, 1.27)

0.786 0.171

0.004 0.025

Ref -5.52 (19.75, 8.716) 0.08 (-0.46, 0.63)

0.447 0.762

0.032 0.001

-6.15 (-16.84, 4.55)

0.260

0.046

-0.15 (-1.56, 1.27)

0.838

0.002

27

Table 4A: Morphological and measures of cartilage-bone properties for individual specimens (n=25). Sp. Dmg. No. Group

Cartilage Thk (mm)

Bone Age at Stiffness Eloss TMD Med/ Training/ Thk BV/TV R/L Age first Fracture 3 (MPa) (%) (mgHA/cm ) Lat resting (mm) start

1

1

0.45

9.24

1786

39

0.97

888.7

RF

M

3

2.1

1

0

2 3 4 5 6 7 8 9

1 1 1 1 1 1 2 1

0.35 0.54 0.38 0.50 0.52 0.47 0.50 0.59

9.34 9.20 9.70 9.47 9.79 9.68 8.78 9.83

2156 2024 1942 2568 2084 1619 1157 2621

50 44 45 36 32 50 56 35

0.94 0.90 0.87 0.97 0.93 0.98 0.97 0.92

892.3 860.5 860.5 888.7 902.8 904.6 883.4 879.9

RF LF LF LF LF LF RF LF

L M L M L L L L

4 3 3 3 7 4 5 7

2.8 2.5 2.5 2.8 3.7 2.4 2.4 3.5

NA NA 0 1 1 1 1 1

0 0 0 1 1 1 1 1

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

1 3 1 3 1 3 2 2 2 1 1 1 2 1 1 1

0.46 0.76 0.50 0.74 0.47 1.09 0.68 0.55 0.70 0.33 0.52 0.51 0.61 0.71 0.53 0.47

7.33 9.23 8.86 8.45 8.77 8.57 9.19 9.05 9.16 9.23 8.62 9.28 9.18 8.96 8.93 9.16

1909 609 2901 405 1901 227 1762 1480 1747 3526 2818 2732 1616 2107 2117 2368

40 61 39 63 48 56 34 46 38 30 24 23 45 23 30 27

0.94 0.92 0.90 0.90 0.91 0.92 0.95 0.96 0.85 0.95 0.84 0.89 0.92 0.92 0.85 0.87

872.8 844.6 909.8 856.9 908.0 835.8 918.6 925.7 876.3 904.5 922.1 927.4 929.2 936.2 885.1 897.5

LF LF LF LF RF RF RF RF LF LF LF LF LF LF LF LF

M M M M M M M L L M L M M L M L

3 4 4 6 6 6 7 7 7 7 8 8 8 8 7 7

2.9 2.7 2.7 2.7 2.9 2.9 2.5 2.5 2.9 2.9 3.5 3.5 2.2 2.2 2.7 2.7

1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1

1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 0

Table 5A: Regression diagnostics for the multivariable model of overall stiffness of cartilage-bone. Full model Removal of sample (id number) 11 13 15 19 NDmg Ref Ref Ref Ref Ref MDmg -0.85 -0.85 -0.85 -0.85 -0.76 SDmg -1.58 -1.68 -1.53 -1.53 -1.56 TMD (mgHA/cm3) 0.01 0.01 0.01 0.01 0.01 Constant -4.53 -4.56 -4.68 -4.38 -3.63 Residual of removed sample n/a 0.65 -0.30 -0.35 3.79 Leverage of removed sample n/a 0.33 0.35 0.34 0.08

Table 6A: Regression diagnostics for the multivariable model of stiffness of subchondral bone. Full model Removal of sample (id number) 19 24 NDmg Ref Ref Ref MDmg -1.68 -1.46 -1.74 28

SDmg TMD (mgHA/cm3) BV/TV Constant Residual of removed sample Leverage of removed sample

-3.34 0.02 -13.71 1.98 n/a n/a

-3.27 0.02 -15.96 5.66 2.93 0.13

-3.50 0.02 -16.80 5.77 -2.16 0.20

Table 7A: Regression diagnostics for the multivariable model of stiffness of cartilage. Full model Removal of sample (id number) 2 4 11 13 NDmg Ref Ref Ref Ref Ref MDmg -0.01 -0.02 -0.02 -0.01 -0.01 SDmg -0.24 -0.25 -0.25 -0.26 -0.23 Constant 0.32 0.33 0.33 0.32 0.32 Residual of removed n/a -2.19 -2.22 0.76 -0.23 sample Leverage of removed n/a 0.08 0.08 0.33 0.33 sample Table 8A: Regression diagnostics for the multivariable model of relative energy loss (Eloss, %) of cartilage-bone. Energy loss overall Full model Removal of sample (id number) 2 11 13 15 NDmg Ref Ref Ref Ref Ref MDmg 13.16 14.19 13.16 13.27 13.27 SDmg 14.61 15.94 14.23 11.34 17.31 TMD (mgHA/cm3) -0.21 -0.21 -0.21 -0.22 -0.22 Constant 225.92 220.37 225.79 234.42 234.56 Residual of removed n/a 2.75 0.14 1.03 -1.19 sample Leverage of removed n/a 0.07 0.33 0.35 0.34 sample

15 Ref -0.01 -0.22 0.32 -0.52 0.33

18 Ref 16.33 11.90 -0.27 274.92 -2.71 0.24

Table 9A: Regression diagnostics for the multivariable model of relative energy loss (Eloss, %) of subchondral bone. Energy loss bone Full model Removal of sample (id number) 6 11 13 15 NDmg Ref Ref Ref Ref Ref MDmg 10.89 9.45 10.89 10.89 10.89 SDmg 27.92 26.48 25.61 26.16 31.99 Constant 35.65 37.09 35.65 35.65 35.65 Residual of removed sample n/a -2.00 0.53 0.40 -0.95 Leverage of removed sample n/a 0.07 0.33 0.33 0.33 Table 10A: Regression diagnostics for the multivariable model of relative energy loss (Eloss, %) of cartilage. Full model Removal of sample (id number) 2 16 NDmg Ref Ref Ref MDmg 7.79 9.46 12.44 SDmg 12.99 15.01 13.95 TMD (mgHA/cm3) -0.22 -0.22 -0.20 Constant 229.16 222.57 211.62 29

18 Ref 16.38 27.92 35.65 -2.71 0.20

Residual of removed sample Leverage of removed sample

n/a n/a

2.73 0.07

30

-2.49 0.21

CRediT author statement Fatemeh Malekipour: Conceptualization, Methodology, Software, Investigation, Visualization, Writing- Original draft preparation. Peta Hitchens: Writing - Review & Editing, Formal Analyses. Chris Whitton: Conceptualization, Methodology, Writing Review & Editing, Supervision, Funding acquisition. Peter V.S.Lee: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Funding acquisition.

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