Contribution of collagen degradation and proteoglycan depletion to cartilage degeneration in primary and secondary osteoarthritis: an in silico study

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

The interplay between biochemical mediators and mechanotransduction in chondrocytes: Unravelling the differential responses in primary knee osteoarthritis

In primary or idiopathic osteoarthritis (OA), it is unclear which factors trigger the shift of articular chondrocyte activity from pro-anabolic to pro-catabolic. In fact, there is a controversy about the aetiology of primary OA, either mechanical or inflammatory. Chondrocytes are mechanosensitive cells, that integrate mechanical stimuli into cellular responses in a process known as mechanotransduction. Mechanotransduction occurs thanks to the activation of mechanosensors, a set of specialized proteins that convert physical cues into intracellular signalling cascades. Moderate levels of mechanical loads maintain normal tissue function and have anti-inflammatory effects. In contrast, mechanical over- or under-loading might lead to cartilage destruction and increased expression of pro-inflammatory cytokines. Simultaneously, mechanotransduction processes can regulate and be regulated by pro- and anti-inflammatory soluble mediators, both local (cells of the same joint, i.e., the chondrocytes themselves, infiltrating macrophages, fibroblasts or osteoclasts) and systemic (from other tissues, e.g., adipokines). Thus, the complex process of mechanotransduction might be altered in OA, so that cartilage-preserving chondrocytes adopt a different sensitivity to mechanical signals, and mechanic stimuli positively transduced in the healthy cartilage may become deleterious under OA conditions. This review aims to provide an overview of how the biochemical exposome of chondrocytes can alter important mechanotransduction processes in these cells. Four principal mechanosensors, i.e., integrins, Ca2+ channels, primary cilium and Wnt signalling (canonical and non-canonical) were targeted. For each of these mechanosensors, a brief summary of the response to mechanical loads under healthy or OA conditions is followed by a concise overview of published works that focus on the further regulation of the mechanotransduction pathways by biochemical factors. In conclusion, this paper discusses and explores how biological mediators influence the differential behaviour of chondrocytes under mechanical loads in healthy and primary OA.

Osteoarthritis year in review 2023: Biomechanics

Biomechanics plays a significant yet complex role in osteoarthritis (OA) onset and progression. Identifying alterations in biomechanical factors and their complex interactions is critical for gaining new insights into OA pathophysiology and identification of clearly defined and modifiable mechanical treatment targets. This review synthesized biomechanics studies from March 2022 to April 2023, from which three themes relating to human gait emerged: (1) new insights into the pathogenesis of OA using computational modeling and machine learning, (2) technology-enhanced biomechanical interventions for OA, and (3) out-of-lab biomechanical assessments of OA. We further highlighted future-focused areas which may continue to advance the field of biomechanics in OA, with a particular emphasis on exploiting technology to understand and treat biomechanical mechanisms of OA outside the laboratory. The breadth of studies included in this review highlights the complex role of biomechanics in OA and showcase numerous innovative and outstanding contributions to the field. Exciting cross-disciplinary efforts integrating computational modeling, mobile sensors, and machine learning methods show great promise for streamlining in vivo multi-scale biomechanics workflows and are expected to underpin future breakthroughs in the understanding and treatment of biomechanics in OA.

Injectable Bioactive Antioxidative One-Component Polycitrate Hydrogel with Anti-Inflammatory Effects for Osteoarthritis Alleviation and Cartilage Protection

Chronic inflammation in osteoarthritis (OA) can destroy the cartilage extracellular matrix (ECM), causing cartilage damage and further exacerbating the inflammation. Effective regulation of the inflammatory microenvironment has important clinical significance for OA alleviation and cartilage protection. Polycitrate-based polymers have good antioxidant and anti-inflammatory abilities but cannot self-polymerize to form hydrogels. Herein, a one-component multifunctional polycitrate-based (PCCGA) hydrogel for OA alleviation and cartilage protection is reported. The PCCGA hydrogel is prepared using only the PCCGA polymer by self-polymerization and exhibits multifunctional properties such as injectability, adhesion, controllable pore size and elasticity, self-healing ability, and photoluminescence. Moreover, the PCCGA hydrogel exhibits good biocompatibility, biodegradability, antioxidation by scavenging intracellular reactive oxygen species, and anti-inflammatory ability by downregulating the expression of proinflammatory cytokines and promoting the proliferation and migration of stem cells. In vivo results from an OA rat model show that the PCCGA hydrogel can effectively alleviate OA and protect the cartilage by restoring uniform articular surface and cartilage ECM levels, as well as inhibiting cartilage resorption and matrix metalloproteinase-13 levels. These results indicate that the PCCGA hydrogel, as a novel bioactive material, is an effective strategy for OA treatment and has broad application prospects in inflammation-related biomedicine.

Signatures of disease progression in knee osteoarthritis: insights from an integrated multi-scale modeling approach, a proof of concept

Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts.