Simulation of Subject-Specific Progression of Knee Osteoarthritis and Comparison to Experimental Follow-up Data: Data from the Osteoarthritis Initiative

Achieving Precision Healthcare through Nanomedicine and Enhanced Model Systems

The ability to customize medical choices according to an individual's genetic makeup and biomarker patterns marks a significant advancement toward overall improved healthcare for both individuals and society at large. By transitioning from the conventional one-size-fits-all approach to tailored treatments that can account for predispositions of different patient populations, nanomedicines can be customized to target the specific molecular underpinnings of a patient's disease, thus mitigating the risk of collateral damage. However, for these systems to reach their full potential, our understanding of how nano-based therapeutics behave within the intricate human body is necessary. Effective drug administration to the targeted organ or pathological niche is dictated by properties such as nanocarrier (NC) size, shape, and targeting abilities, where understanding how NCs change their properties when they encounter biomolecules and phenomena such as shear stress in flow remains a major challenge. This Review specifically focuses on vessel-on-a-chip technology that can provide increased understanding of NC behavior in blood and summarizes the specialized environment of the joint to showcase advanced tissue models as approaches to address translational challenges. Compared to conventional cell studies or animal models, these advanced models can integrate patient material for full customization. Combining such models with nanomedicine can contribute to making personalized medicine achievable.

Sensitivity of simulated knee joint mechanics to selected human and bovine fibril-reinforced poroelastic material properties

Fibril-reinforced poroviscoelastic material models are considered state-of-the-art in modeling articular cartilage biomechanics. Yet, cartilage material parameters are often based on bovine tissue properties in computational knee joint models, although bovine properties are distinctly different from those of humans. Thus, we aimed to investigate how cartilage mechanical responses are affected in the knee joint model during walking when fibril-reinforced poroviscoelastic properties of cartilage are based on human data instead of bovine. We constructed a finite element knee joint model in which tibial and femoral cartilages were modeled as fibril-reinforced poroviscoelastic material using either human or bovine data. Joint loading was based on subject-specific gait data. The resulting mechanical responses of knee cartilage were compared between the knee joint models with human or bovine fibril-reinforced poroviscoelastic cartilage properties. Furthermore, we conducted a sensitivity analysis to determine which fibril-reinforced poroviscoelastic material parameters have the greatest impact on cartilage mechanical responses in the knee joint during walking. In general, bovine cartilage properties yielded greater maximum principal stresses and fluid pressures (both up to 30%) when compared to the human cartilage properties during the loading response in both femoral and tibial cartilage sites. Cartilage mechanical responses were very sensitive to the collagen fibril-related material parameter variations during walking while they were unresponsive to proteoglycan matrix or fluid flow-related material parameter variations. Taken together, human cartilage material properties should be accounted for when the goal is to compare absolute mechanical responses of knee joint cartilage as bovine material parameters lead to substantially different cartilage mechanical responses.

A novel knee joint model in FEBio with inhomogeneous fibril-reinforced biphasic cartilage simulating tissue mechanical responses during gait: data from the osteoarthritis initiative

We developed a novel knee joint model in FEBio to simulate walking. Knee cartilage was modeled using a fibril-reinforced biphasic (FRB) formulation with depth-wise collagen architecture and split-lines to account for cartilage structure. Under axial compression, the knee model with FRB cartilage yielded contact pressures, similar to reported experimental data. Furthermore, gait analysis with FRB cartilage simulated spatial and temporal trends in cartilage fluid pressures, stresses, and strains, comparable to those of a fibril-reinforced poroviscoelastic (FRPVE) material in Abaqus. This knee joint model in FEBio could be used for further studies of knee disorders using physiologically relevant loading.

A chemo-mechano-biological modeling framework for cartilage evolving in health, disease, injury, and treatment

BACKGROUND AND OBJECTIVE

Osteoarthritis (OA) is a pervasive and debilitating disease, wherein degeneration of cartilage features prominently. Despite extensive research, we do not yet understand the cause or progression of OA. Studies show biochemical, mechanical, and biological factors affect cartilage health. Mechanical loads influence synthesis of biochemical constituents which build and/or break down cartilage, and which in turn affect mechanical loads. OA-associated biochemical profiles activate cellular activity that disrupts homeostasis. To understand the complex interplay among mechanical stimuli, biochemical signaling, and cartilage function requires integrating vast research on experimental mechanics and mechanobiology-a task approachable only with computational models. At present, mechanical models of cartilage generally lack chemo-biological effects, and biochemical models lack coupled mechanics, let alone interactions over time.

METHODS

We establish a first-of-its kind virtual cartilage: a modeling framework that considers time-dependent, chemo-mechano-biologically induced turnover of key constituents resulting from biochemical, mechanical, and/or biological activity. We include the "minimally essential" yet complex chemical and mechanobiological mechanisms. Our 3-D framework integrates a constitutive model for the mechanics of cartilage with a novel model of homeostatic adaptation by chondrocytes to pathological mechanical stimuli, and a new application of anisotropic growth (loss) to simulate degradation clinically observed as cartilage thinning.

RESULTS

Using a single set of representative parameters, our simulations of immobilizing and overloading successfully captured loss of cartilage quantified experimentally. Simulations of immobilizing, overloading, and injuring cartilage predicted dose-dependent recovery of cartilage when treated with suramin, a proposed therapeutic for OA. The modeling framework prompted us to add growth factors to the suramin treatment, which predicted even better recovery.

CONCLUSIONS

Our flexible framework is a first step toward computational investigations of how cartilage and chondrocytes mechanically and biochemically evolve in degeneration of OA and respond to pharmacological therapies. Our framework will enable future studies to link physical activity and resulting mechanical stimuli to progression of OA and loss of cartilage function, facilitating new fundamental understanding of the complex progression of OA and elucidating new perspectives on causes, treatments, and possible preventions.

Influence of material heterogeneity on the mechanical response of articulated cartilages in a knee joint

Structurally, the articular cartilages are heterogeneous owing to nonuniform distribution and orientation of its constituents. The oversimplification of this soft tissue as a homogeneous material is generally considered in the simulation domain to estimate contact pressure along with other physical responses. Hence, there is a need for investigating knee cartilages for their actual response to external stimuli. In this article, impact of material and geometrical heterogeneity of the cartilage is resolved using well known material models. The findings are compared with conventional homogeneous models. The results indicate vital differences in contact pressure distribution and tissue deformation. Further, this study paves way for standardizing material models to extract maximum information possible for investigating knee mechanics with variable geometry and case specific parameters.

Rapid X-Ray-Based 3-D Finite Element Modeling of Medial Knee Joint Cartilage Biomechanics During Walking

Finite element (FE) modeling is becoming an increasingly popular method for analyzing knee joint mechanics and biomechanical mechanisms leading to osteoarthritis (OA). The most common and widely available imaging method for knee OA diagnostics is planar X-ray imaging, while more sophisticated imaging methods, e.g., magnetic resonance imaging (MRI) and computed tomography (CT), are seldom used. Hence, the capability to produce accurate biomechanical knee joint models directly from X-ray imaging would bring FE modeling closer to clinical use. Here, we extend our atlas-based framework by generating FE knee models from X-ray images (N = 28). Based on measured anatomical landmarks from X-ray and MRI, knee joint templates were selected from the atlas library. The cartilage stresses and strains of the X-ray-based model were then compared with the MRI-based model during the stance phase of the gait. The biomechanical responses were statistically not different between MRI- vs. X-ray-based models when the template obtained from X-ray imaging was the same as the MRI template. However, if this was not the case, the peak values of biomechanical responses were statistically different between X-ray and MRI models. The developed X-ray-based framework may pave the way for a clinically feasible approach for knee joint FE modeling.

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

Biomechanics of the medial meniscus in the osteoarthritic knee joint

Background

Increased mechanical loading and pathological response of joint tissue to the abnormal mechanical stress can cause degradation of cartilage characteristic of knee osteoarthritis (OA). Despite osteoarthritis is risk factor for the development of meniscal lesions the mechanism of degenerative meniscal lesions is still unclear. Therefore, the aim of the study is to investigate the influence of medial compartment knee OA on the stress state and deformation of the medial meniscus.

Methods

The finite element method was used to simulate the stance phase of the gait cycle. An intact knee model was prepared based on magnetic resonance scans of the left knee joint of a healthy volunteer. Degenerative changes in the medial knee OA model were simulated by nonuniform reduction in articular cartilage thickness in specific areas and by a decrease in the material parameters of cartilage and menisci. Two additional models were created to separately evaluate the effect of alterations in articular cartilage geometry and material parameters of the soft tissues on the results. A nonlinear dynamic analysis was performed for standardized knee loads applied to the tibia bone.

Results

The maximum von Mises stress of 26.8 MPa was observed in the posterior part of the medial meniscus body in the OA model. The maximal hoop stress for the first peak of total force was 83% greater in the posterior horn and only 11% greater in the anterior horn of the medial meniscus in the OA model than in the intact model. The reduction in cartilage thickness caused an increase of 57% in medial translation of the medial meniscus body. A decrease in the compressive modulus of menisci resulted in a 2.5-fold greater reduction in the meniscal body width compared to the intact model.

Conclusions

Higher hoop stress levels on the inner edge of the posterior part of the medial meniscus in the OA model than in the intact model are associated with a greater medial translation of the meniscus body and a greater reduction in its width. The considerable increase in hoop stresses shows that medial knee OA may contribute to the initiation of meniscal radial tears.

Tibiofemoral Cartilage Contact Pressures in Athletes During Landing: A Dynamic Finite Element Study

Cartilage defects are common in the knee joint of active athletes and remain a problem as a strong risk factor for osteoarthritis. We hypothesized that landing during sport activities, implication for subfailure ACL loading, would generate greater contact pressures (CP) at the lateral knee compartment. The purpose of this study is to investigate tibiofemoral cartilage CP of athletes during landing. Tibiofemoral cartilage contact pressures (TCCP) under clinically relevant anterior cruciate ligament subfailure external loadings were predicted using four dynamic explicit finite element (FE) models (2 males and 2 females) of the knee. Bipedal landing from a jump for five cases of varying magnitudes of external loadings (knee abduction moment, internal tibial torque, and anterior tibial shear) followed by an impact load were simulated. Lateral TCCP from meniscus (area under meniscus) and from femur (area under femur) increased by up to 94% and %30 respectively when external loads were incorporated with impact load in all the models compared to impact-only case. In addition, FE model predicted higher CP in lateral compartment by up to 37% (11.87 MPa versus 8.67 MPa) and 52% (20.19 MPa versus 13.29 MPa) for 90% and 50% percentile models, respectively. For the same percentile populations, CPs were higher by up to 25% and 82% in smaller size models than larger size models. We showed that subfailure ACL loadings obtained from previously conducted in vivo study led to high pressures on the tibiofemoral cartilage. This knowledge is helpful in enhancing neuromuscular training for athletes to prevent cartilage damage.

An in silico Framework of Cartilage Degeneration That Integrates Fibril Reorientation and Degradation Along With Altered Hydration and Fixed Charge Density Loss

Injurious mechanical loading of articular cartilage and associated lesions compromise the mechanical and structural integrity of joints and contribute to the onset and progression of cartilage degeneration leading to osteoarthritis (OA). Despite extensive in vitro and in vivo research, it remains unclear how the changes in cartilage composition and structure that occur during cartilage degeneration after injury, interact. Recently, in silico techniques provide a unique integrated platform to investigate the causal mechanisms by which the local mechanical environment of injured cartilage drives cartilage degeneration. Here, we introduce a novel integrated Cartilage Adaptive REorientation Degeneration (CARED) algorithm to predict the interaction between degenerative variations in main cartilage constituents, namely collagen fibril disorganization and degradation, proteoglycan (PG) loss, and change in water content. The algorithm iteratively interacts with a finite element (FE) model of a cartilage explant, with and without variable depth to full-thickness defects. In these FE models, intact and injured explants were subjected to normal (2 MPa unconfined compression in 0.1 s) and injurious mechanical loading (4 MPa unconfined compression in 0.1 s). Depending on the mechanical response of the FE model, the collagen fibril orientation and density, PG and water content were iteratively updated. In the CARED model, fixed charge density (FCD) loss and increased water content were related to decrease in PG content. Our model predictions were consistent with earlier experimental studies. In the intact explant model, minimal degenerative changes were observed under normal loading, while the injurious loading caused a reorientation of collagen fibrils toward the direction perpendicular to the surface, intense collagen degradation at the surface, and intense PG loss in the superficial and middle zones. In the injured explant models, normal loading induced intense collagen degradation, collagen reorientation, and PG depletion both on the surface and around the lesion. Our results confirm that the cartilage lesion depth is a crucial parameter affecting tissue degeneration, even under physiological loading conditions. The results suggest that potential fibril reorientation might prevent or slow down fibril degradation under conditions in which the tissue mechanical homeostasis is perturbed like the presence of defects or injurious loading.

Towards understanding mechanistic subgroups of osteoarthritis: 8-year cartilage thickness trajectory analysis

Many studies have validated cartilage thickness as a biomarker for knee osteoarthritis (OA); however, few studies investigate beyond cross-sectional observations or comparisons across two timepoints. By characterizing the trajectory of cartilage thickness changes over 8 years in healthy individuals from the OA initiative data set, this study discovers associations between the dynamics of cartilage changes and OA incidence. A fully automated cartilage segmentation and thickness measurement method were developed and validated against manual measurements: mean absolute error = 0.11-0.14 mm (n = 4129 knees) and automatic reproducibility = 0.04-0.07 mm (n = 316 knees). The mean thickness for the medial and lateral tibia (MT, LT), central weight-bearing medial and lateral femur (cMF, cLF), and patella (P) cartilage compartments were quantified for 1453 knees at seven timepoints. Trajectory subgroups were defined per cartilage compartment such as stable, thinning to thickening, accelerated thickening, plateaued thickening, thickening to thinning, accelerated thinning, or plateaued thinning. For tibiofemoral compartments, the stable (22%-36%) and plateaued thinning (22%-37%) trajectories were the most common, with an average initial velocity (μm/month), acceleration (μm/month² ) for the plateaued thinning trajectories LT: -2.66, 0.0326; MT: -2.49, 0.0365; cMF: -3.51, 0.0509; and cLF: -2.68, 0.041. In the patella compartment, the plateaued thinning (35%) and thickening to thinning (24%) trajectories were the most common, with an average initial velocity, acceleration for each -4.17, 0.0424; 1.95, -0.0835. Knees with nonstable trajectories had higher adjusted odds of OA incidence than stable trajectories: accelerated thickening, accelerated thinning, and plateaued thinning trajectories of the MT had adjusted odds ratio (OR) of 18.9, 5.48, and 1.47, respectively; in the cMF, adjusted OR of 8.55, 10.1, and 2.61, respectively.

12 Degrees of Freedom Muscle Force Driven Fibril-Reinforced Poroviscoelastic Finite Element Model of the Knee Joint

Accurate knowledge of the joint kinematics, kinetics, and soft tissue mechanical responses is essential in the evaluation of musculoskeletal (MS) disorders. Since in vivo measurement of these quantities requires invasive methods, musculoskeletal finite element (MSFE) models are widely used for simulations. There are, however, limitations in the current approaches. Sequentially linked MSFE models benefit from complex MS and FE models; however, MS model's outputs are independent of the FE model calculations. On the other hand, due to the computational burden, embedded (concurrent) MSFE models are limited to simple material models and cannot estimate detailed responses of the soft tissue. Thus, first we developed a MSFE model of the knee with a subject-specific MS model utilizing an embedded 12 degrees of freedom (DoFs) knee joint with elastic cartilages in which included both secondary kinematic and soft tissue deformations in the muscle force estimation (inverse dynamics). Then, a muscle-force-driven FE model with fibril-reinforced poroviscoelastic cartilages and fibril-reinforced poroelastic menisci was used in series to calculate detailed tissue mechanical responses (forward dynamics). Second, to demonstrate that our workflow improves the simulation results, outputs were compared to results from the same FE models which were driven by conventional MS models with a 1 DoF knee, with and without electromyography (EMG) assistance. The FE model driven by both the embedded and the EMG-assisted MS models estimated similar results and consistent with experiments from literature, compared to the results estimated by the FE model driven by the MS model with 1 DoF knee without EMG assistance.

Identification of locations susceptible to osteoarthritis in patients with anterior cruciate ligament reconstruction: Combining knee joint computational modelling with follow-up T1ρ and T2 imaging

BACKGROUND

Finite element modelling can be used to evaluate altered loading conditions and failure locations in knee joint tissues. One limitation of this modelling approach has been experimental comparison. The aims of this proof-of-concept study were: 1) identify areas susceptible to osteoarthritis progression in anterior cruciate ligament reconstructed patients using finite element modelling; 2) compare the identified areas against changes in T₂ and T_1ρ values between 1-year and 3-year follow-up timepoints.

METHODS

Two patient-specific finite element models of knee joints with anterior cruciate ligament reconstruction were created. The knee geometry was based on clinical magnetic resonance imaging and joint loading was obtained via motion capture. We evaluated biomechanical parameters linked with cartilage degeneration and compared the identified risk areas against T₂ and T_1ρ maps.

FINDINGS

The risk areas identified by the finite element models matched the follow-up magnetic resonance imaging findings. For Patient 1, excessive values of maximum principal stresses and shear strains were observed in the posterior side of the lateral tibial and femoral cartilage. For Patient 2, high values of maximum principal stresses and shear strains of cartilage were observed in the posterior side of the medial joint compartment. For both patients, increased T₂ and T_1ρ values between the follow-up times were observed in the same areas.

INTERPRETATION

Finite element models with patient-specific geometries and motions and relatively simple material models of tissues were able to identify areas susceptible to post-traumatic knee osteoarthritis. We suggest that the methodology presented here may be applied in large cohort studies.

Mechanobiological model for simulation of injured cartilage degradation via pro-inflammatory cytokines and mechanical stimulus

Post-traumatic osteoarthritis (PTOA) is associated with cartilage degradation, ultimately leading to disability and decrease of quality of life. Two key mechanisms have been suggested to occur in PTOA: tissue inflammation and abnormal biomechanical loading. Both mechanisms have been suggested to result in loss of cartilage proteoglycans, the source of tissue fixed charge density (FCD). In order to predict the simultaneous effect of these degrading mechanisms on FCD content, a computational model has been developed. We simulated spatial and temporal changes of FCD content in injured cartilage using a novel finite element model that incorporates (1) diffusion of the pro-inflammatory cytokine interleukin-1 into tissue, and (2) the effect of excessive levels of shear strain near chondral defects during physiologically relevant loading. Cytokine-induced biochemical cartilage explant degradation occurs near the sides, top, and lesion, consistent with the literature. In turn, biomechanically-driven FCD loss is predicted near the lesion, in accordance with experimental findings: regions near lesions showed significantly more FCD depletion compared to regions away from lesions (p<0.01). Combined biochemical and biomechanical degradation is found near the free surfaces and especially near the lesion, and the corresponding bulk FCD loss agrees with experiments. We suggest that the presence of lesions plays a role in cytokine diffusion-driven degradation, and also predisposes cartilage for further biomechanical degradation. Models considering both these cartilage degradation pathways concomitantly are promising in silico tools for predicting disease progression, recognizing lesions at high risk, simulating treatments, and ultimately optimizing treatments to postpone the development of PTOA.

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

Use of Computational Modeling to Study Joint Degeneration: A Review

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient’s individualized risk assessment as screening tool for use in clinical practice.

EMG-Assisted Muscle Force Driven Finite Element Model of the Knee Joint with Fibril-Reinforced Poroelastic Cartilages and Menisci

Abnormal mechanical loading is essential in the onset and progression of knee osteoarthritis. Combined musculoskeletal (MS) and finite element (FE) modeling is a typical method to estimate load distribution and tissue responses in the knee joint. However, earlier combined models mostly utilize static-optimization based MS models and muscle force driven FE models typically use elastic materials for soft tissues or analyze specific time points of gait. Therefore, here we develop an electromyography-assisted muscle force driven FE model with fibril-reinforced poro(visco)elastic cartilages and menisci to analyze knee joint loading during the stance phase of gait. Moreover, since ligament pre-strains are one of the important uncertainties in joint modeling, we conducted a sensitivity analysis on the pre-strains of anterior and posterior cruciate ligaments (ACL and PCL) as well as medial and lateral collateral ligaments (MCL and LCL). The model produced kinematics and kinetics consistent with previous experimental data. Joint contact forces and contact areas were highly sensitive to ACL and PCL pre-strains, while those changed less cartilage stresses, fibril strains, and fluid pressures. The presented workflow could be used in a wide range of applications related to the aetiology of cartilage degeneration, optimization of rehabilitation exercises, and simulation of knee surgeries.

EMG-Assisted Muscle Force Driven Finite Element Model of the Knee Joint with Fibril-Reinforced Poroelastic Cartilages and Menisci

Abnormal mechanical loading is essential in the onset and progression of knee osteoarthritis. Combined musculoskeletal (MS) and finite element (FE) modeling is a typical method to estimate load distribution and tissue responses in the knee joint. However, earlier combined models mostly utilize static-optimization based MS models and muscle force driven FE models typically use elastic materials for soft tissues or analyze specific time points of gait. Therefore, here we develop an electromyography-assisted muscle force driven FE model with fibril-reinforced poro(visco)elastic cartilages and menisci to analyze knee joint loading during the stance phase of gait. Moreover, since ligament pre-strains are one of the important uncertainties in joint modeling, we conducted a sensitivity analysis on the pre-strains of anterior and posterior cruciate ligaments (ACL and PCL) as well as medial and lateral collateral ligaments (MCL and LCL). The model produced kinematics and kinetics consistent with previous experimental data. Joint contact forces and contact areas were highly sensitive to ACL and PCL pre-strains, while those changed less cartilage stresses, fibril strains, and fluid pressures. The presented workflow could be used in a wide range of applications related to the aetiology of cartilage degeneration, optimization of rehabilitation exercises, and simulation of knee surgeries.

The Effect of Articular Cartilage Focal Defect Size and Location in Whole Knee Biomechanics Models

Articular cartilage focal defects are common soft tissue injuries potentially linked to osteoarthritis (OA) development. Although several defect characteristics likely contribute to osteoarthritis, their relationship to local tissue deformation remains unclear. Using finite element models with various femoral cartilage geometries, we explore how defects change cartilage deformation and joint kinematics assuming loading representative of the maximum joint compression during the stance phase of gait. We show how defects, in combination with location-dependent cartilage mechanics, alter deformation in affected and opposing cartilages, as well as joint kinematics. Small and average sized defects increased maximum compressive strains by approximately 50% and 100%, respectively, compared to healthy cartilage. Shifts in the spatial locations of maximum compressive strains of defect containing models were also observed, resulting in loading of cartilage regions with reduced initial stiffnesses supporting the new, elevated loading environments. Simulated osteoarthritis (modeled as a global reduction in mean cartilage stiffness) did not significantly alter joint kinematics, but exacerbated tissue deformation. Femoral defects were also found to affect healthy tibial cartilage deformations. Lateral femoral defects increased tibial cartilage maximum compressive strains by 25%, while small and average sized medial defects exhibited decreases of 6% and 15%, respectively, compared to healthy cartilage. Femoral defects also affected the spatial distributions of deformation across the articular surfaces. These deviations are especially meaningful in the context of cartilage with location-dependent mechanics, leading to increases in peak contact stresses supported by the cartilage of between 11% and 34% over healthy cartilage.

Evaluation of the Effect of Bariatric Surgery-Induced Weight Loss on Knee Gait and Cartilage Degeneration

The objective of the study was to investigate the effects of bariatric surgery-induced weight loss on knee gait and cartilage degeneration in osteoarthritis (OA) by combining magnetic resonance imaging (MRI), gait analysis, finite element (FE) modeling, and cartilage degeneration algorithm. Gait analyses were performed for obese subjects before and one-year after the bariatric surgery. FE models were created before and after weight loss for those subjects who did not have severe tibio-femoral knee cartilage loss. Knee cartilage degenerations were predicted using an adaptive cartilage degeneration algorithm which is based on cumulative overloading of cartilage, leading to iteratively altered cartilage properties during OA. The average weight loss was 25.7±11.0 kg corresponding to a 9.2±3.9 kg/m2 decrease in body mass index (BMI). External knee rotation moment increased, and minimum knee flexion angle decreased significantly (p < 0.05) after weight loss. Moreover, weight loss decreased maximum cartilage degeneration by 5±23% and 13±11% on the medial and lateral tibial cartilage surfaces, respectively. Average degenerated volumes in the medial and lateral tibial cartilage decreased by 3±31% and 7±32%, respectively, after weight loss. However, increased degeneration levels could also be observed due to altered knee kinetics. The present results suggest that moderate weight loss changes knee kinetics and kinematics and can slow-down cartilage degeneration for certain patients. Simulation results also suggest that prediction of cartilage degeneration is subject-specific and highly depend on the altered gait loading, not just the patient's weight.

Phenotypes of osteoarthritis: current state and future implications

In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic development and unravelling the pathogenesis of this complex condition. Several phenotypes and endotypes have been proposed albeit none has been sufficiently validated for clinical or research use as yet. This review discusses the latest advances in OA phenotyping including how new modern statistical strategies based on machine learning and big data can help advance this field of research.

Eight-year trajectories of changes in health-related quality of life in knee osteoarthritis: Data from the Osteoarthritis Initiative (OAI)

Background

Knee osteoarthritis (OA) worsens health-related quality of life (HRQoL) but the symptom pathway varies from person to person. We aimed to identify groups of people with knee OA or at its increased risk whose HRQoL changed similarly. Our secondary aim was to evaluate if patient-related characteristics, incidence of knee replacement (KR) and prevalence of pain medication use differed between the identified HRQoL trajectory groups.

Methods

Eight-year follow-up data of 3053 persons with mild knee OA or at increased risk were obtained from the public Osteoarthritis Initiative (OAI) database. Group-based trajectory modeling was used to identify patterns of experiencing a decrease of ≥10 points (Minimal Important Change, MIC) in the Quality of Life subscale of the Knee injury and Osteoarthritis Outcome Score compared to baseline. Multinomial logistic regression, Cox regression and generalized estimating equation models were used to study secondary aims.

Results

Four HRQoL trajectory groups were identified. Persons in the ‘no change’ group (62.9%) experienced no worsening in HRQoL. ‘Rapidly’ (9.5%) and ‘slowly’ worsening (17.1%) groups displayed an increasing probability of experiencing the MIC in HRQoL. The fourth group (10.4%) had ‘improving’ HRQoL. Female gender, higher body mass index, smoking, knee pain, and lower income at baseline were associated with belonging to the ‘rapidly worsening’ group. People in ‘rapidly’ (hazard ratio (HR) 6.2, 95% confidence interval (CI) 3.6–10.7) and ‘slowly’ worsening (HR 3.4, 95% CI 2.0–5.9) groups had an increased risk of requiring knee replacement. Pain medication was more rarely used in the ‘no change’ than in the other groups.

Conclusions

HRQoL worsening was associated with several risk factors; surgical and pharmacological interventions were more common in the poorer HRQoL trajectory groups indicating that HRQoL does reflect the need for OA treatment. These findings may have implications for targeting interventions to specific knee OA patient groups.

Neuro-musculoskeletal flexible multibody simulation yields a framework for efficient bone failure risk assessment

Fragility fractures are a major socioeconomic problem. A non-invasive, computationally-efficient method for the identification of fracture risk scenarios under the representation of neuro-musculoskeletal dynamics does not exist. We introduce a computational workflow that integrates modally-reduced, quantitative CT-based finite-element models into neuro-musculoskeletal flexible multibody simulation (NfMBS) for early bone fracture risk assessment. Our workflow quantifies the bone strength via the osteogenic stresses and strains that arise due to the physiological-like loading of the bone under the representation of patient-specific neuro-musculoskeletal dynamics. This allows for non-invasive, computationally-efficient dynamic analysis over the enormous parameter space of fracture risk scenarios, while requiring only sparse clinical data. Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. Our novel workflow could offer clinicians with decision-making guidance by enabling the first combined in-silico analysis tool using NfMBS and BMD measurements for optimized bone fracture risk assessment.

Computational evaluation of altered biomechanics related to articular cartilage lesions observed in vivo

Chondral lesions provide a potential risk factor for development of osteoarthritis. Despite the variety of in vitro studies on lesion degeneration, in vivo studies that evaluate relation between lesion characteristics and the risk for the possible progression of OA are lacking. Here, we aimed to characterize different lesions and quantify biomechanical responses experienced by surrounding cartilage tissue. We generated computational knee joint models with nine chondral injuries based on clinical in vivo arthrographic computed tomography images. Finite element models with fibril-reinforced poro(visco)elastic cartilage and menisci were constructed to simulate physiological loading. Systematically, the lesions experienced increased peak values of maximum principal strain, maximum shear strain, and minimum principal strain in the surrounding chondral tissue (p < 0.01) compared with intact tissue. Depth, volume, and area of the lesion correlated with the maximum shear strain (p < 0.05, Spearman rank correlation coefficient ρ = 0.733-0.917). Depth and volume of the lesion correlated also with the maximum principal strain (p < 0.05, ρ = 0.767, and ρ = 0.717, respectively). However, the lesion area had non-significant correlation with this strain parameter (p = 0.06, ρ = 0.65). Potentially, the introduced approach could be developed for clinical evaluation of biomechanical risks of a chondral lesion and planning an intervention. Statement of Clinical Relevance: In this study, we computationally characterized different in vivo chondral lesions and evaluated their risk of cartilage degeneration. This information is vital in decision-making for intervention in order to prevent post-traumatic osteoarthritis. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Trajectories of femorotibial cartilage thickness among persons with or at risk of knee osteoarthritis: development of a prediction model to identify progressors

OBJECTIVE

There is significant variability in the trajectory of structural progression across people with knee osteoarthritis (OA). We aimed to identify distinct trajectories of femorotibial cartilage thickness over 2 years and develop a prediction model to identify individuals experiencing progressive cartilage loss.

METHODS

We analysed data from the Osteoarthritis Initiative (OAI) (n = 1,014). Latent class growth analysis (LCGA) was used to identify trajectories of medial femorotibial cartilage thickness assessed on magnetic resonance imaging (MRI) at baseline, 1 and 2 years. Baseline characteristics were compared between trajectory-based subgroups and a prediction model was developed including those with frequent knee symptoms at baseline (n = 686). To examine clinical relevance of the trajectories, we assessed their association with concurrent changes in knee pain and incidence of total knee replacement (TKR) over 4 years.

RESULTS

The optimal model identified three distinct trajectories: (1) stable (87.7% of the population, mean change -0.08 mm, SD 0.19); (2) moderate cartilage loss (10.0%, -0.75 mm, SD 0.16) and (3) substantial cartilage loss (2.2%, -1.38 mm, SD 0.23). Higher Western Ontario & McMaster Universities Osteoarthritis Index (WOMAC) pain scores, family history of TKR, obesity, radiographic medial joint space narrowing (JSN) ≥1 and pain duration ≤1 year were predictive of belonging to either the moderate or substantial cartilage loss trajectory [area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.74, 0.84]. The two progression trajectories combined were associated with pain progression (OR 1.99, 95% CI 1.34, 2.97) and incidence of TKR (OR 4.34, 1.62, 11.62).

CONCLUSIONS

A minority of individuals follow a progressive cartilage loss trajectory which was strongly associated with poorer clinical outcomes. If externally validated, the prediction model may help to select individuals who may benefit from cartilage-targeted therapies.

Maximum shear strain-based algorithm can predict proteoglycan loss in damaged articular cartilage

Post-traumatic osteoarthritis (PTOA) is a common disease, where the mechanical integrity of articular cartilage is compromised. PTOA can be a result of chondral defects formed due to injurious loading. One of the first changes around defects is proteoglycan depletion. Since there are no methods to restore injured cartilage fully back to its healthy state, preventing the onset and progression of the disease is advisable. However, this is problematic if the disease progression cannot be predicted. Thus, we developed an algorithm to predict proteoglycan loss of injured cartilage by decreasing the fixed charge density (FCD) concentration. We tested several mechanisms based on the local strains or stresses in the tissue for the FCD loss. By choosing the degeneration threshold suggested for inducing chondrocyte apoptosis and cartilage matrix damage, the algorithm driven by the maximum shear strain showed the most substantial FCD losses around the lesion. This is consistent with experimental findings in the literature. We also observed that by using coordinate system-independent strain measures and selecting the degeneration threshold in an ad hoc manner, all the resulting FCD distributions would appear qualitatively similar, i.e., the greatest FCD losses are found at the tissue adjacent to the lesion. The proposed strain-based FCD degeneration algorithm shows a great potential for predicting the progression of PTOA via biomechanical stimuli. This could allow identification of high-risk defects with an increased risk of PTOA progression.

Comparison between kinetic and kinetic-kinematic driven knee joint finite element models

Use of knee joint finite element models for diagnostic purposes is challenging due to their complexity. Therefore, simpler models are needed for studies where a high number of patients need to be analyzed, without compromising the results of the model. In this study, more complex, kinetic (forces and moments) and simpler, kinetic-kinematic (forces and angles) driven finite element models were compared during the stance phase of gait. Patella and tendons were included in the most complex model, while they were absent in the simplest model. The greatest difference between the most complex and simplest models was observed in the internal-external rotation and axial joint reaction force, while all other rotations, translations and joint reaction forces were similar to one another. In terms of cartilage stresses and strains, the simpler models behaved similarly with the more complex models in the lateral joint compartment, while minor differences were observed in the medial compartment at the beginning of the stance phase. We suggest that it is feasible to use kinetic-kinematic driven knee joint models with a simpler geometry in studies with a large cohort size, particularly when analyzing cartilage responses and failures related to potential overloads.