Osteoarthritis year in review 2023: metabolite and protein biomarkers

Therapeutic Controlled Release Strategies for Human Osteoarthritis

Osteoarthritis is a progressive, irreversible debilitating whole joint disease that affects millions of people worldwide. Despite the availability of various options (non-pharmacological and pharmacological treatments and therapy, orthobiologics, and surgical interventions), none of them can definitively cure osteoarthritis in patients. Strategies based on the controlled release of therapeutic compounds via biocompatible materials may provide powerful tools to enhance the spatiotemporal delivery, expression, and activities of the candidate agents as a means to durably manage the pathological progression of osteoarthritis in the affected joints upon convenient intra-articular (injectable) delivery while reducing their clearance, dissemination, or side effects. The goal of this review is to describe the current knowledge and advancements of controlled release to treat osteoarthritis, from basic principles to applications in vivo using therapeutic recombinant molecules and drugs and more innovatively gene sequences, providing a degree of confidence to manage the disease in patients in a close future.

Fracture-related infection blood-based biomarkers: Diagnostic strategies

Fracture-related infections are significant postoperative complications that carry substantial patient burden and additional healthcare costs. Despite their impact on outcome, early diagnosis of these infections remains challenging due to current available tests lacking acceptable diagnostic parameters. This review compiles existing information on blood-based biomarkers that have been evaluated as early diagnostic tools and highlights the challenges in their reliability. To begin to overcome these challenges new avenues of biomarker discovery utilizing "omics" technologies and novel analytical methods are being investigated in recent years. It appears that, despite their complexity, these newer approaches may be the future in biomarker discovery for fracture-related infection diagnosis.

Exploring Trends and Gaps in Osteoarthritis Biomarker Research (1999-2024): A Citation Analysis of Top 50 Cited Articles

PURPOSE

This study aimed to comprehensively analyze the landscape of osteoarthritis (OA) biomarker research through the citation analysis of top-cited articles, identifying trends and gaps in this field.

METHODS

The Web of Science Core Collection was utilized to retrieve the top 50 cited articles on OA biomarkers. Data extraction included publication characteristics, citation metrics, and biomarker categorization. Statistical analyses were conducted to discern correlations and assess significance.

RESULTS

The top 50 cited articles spanned the years 1999 to 2020, and collectively cited 4849 articles, accumulating a total of 6177 citations, resulting in an average of 123.5 citations per document. Citations per article varied between 78 and 359, with a citation density ranging from 3.9 to 23.93. Analysis of the top 50 cited articles revealed comparable impact between recent and older publications. Predominant trends included cartilage-related and blood-based biomarkers, while inflammation-related, radiomics, and multi-omics emerged as potential future research directions. In BIPEDS classification, gaps were identified in biomarkers evaluating intervention efficacy and safety.

CONCLUSION

Despite significant advancements, there is no universally acknowledged biomarker for OA. Addressing gaps in biomarker exploration is crucial for enhancing OA management strategies. This study provides insights into prevailing trends and future research directions in OA biomarkers, guiding future investigations and therapeutic development.

Synovial microenvironment in temporomandibular joint osteoarthritis: crosstalk with chondrocytes and potential therapeutic targets

Temporomandibular joint osteoarthritis (TMJOA) is considered to be a low-grade inflammatory disease involving multiple joint tissues. The crosstalk between synovium and cartilage plays an important role in TMJOA. Synovial cells are a group of heterogeneous cells and synovial microenvironment is mainly composed of synovial fibroblasts (SF) and synovial macrophages. In TMJOA, SF and synovial macrophages release a large number of inflammatory cytokines and extracellular vesicles and promote cartilage destruction. Cartilage wear particles stimulate SF proliferation and macrophages activation and exacerbate synovitis. In TMJOA, chondrocytes and synovial cells exhibit increased glycolytic activity and lactate secretion, leading to impaired chondrocyte matrix synthesis. Additionally, the synovium contains mesenchymal stem cells, which are the seed cells for cartilage repair in TMJOA. Co-culture of chondrocytes and synovial mesenchymal stem cells enhances the chondrogenic differentiation of stem cells. This review discusses the pathological changes of synovium in TMJOA, the means of crosstalk between synovium and cartilage, and their influence on each other. Based on the crosstalk between synovium and cartilage in TMJOA, we illustrate the treatment strategies for improving synovial microenvironment, including reducing cell adhesion, utilizing extracellular vesicles to deliver biomolecules, regulating cellular metabolism and targeting inflammatory cytokines.

Cartilage Oligomeric Matrix Protein in Osteoarthritis and Obesity-Do New Considerations Emerge?

The diagnosis of osteoarthritis (OA) is based on radiological changes that are delayed, along with clinical symptoms. Early and very early diagnosis at the stage of molecular pathology may eventually offer an opportunity for early therapeutic intervention that may retard and prevent future damage. Cartilage oligomeric matrix protein (COMP) is a non-collagenous extracellular matrix protein that promotes the secretion and aggregation of collagen and contributes to the stability of the extracellular matrix. There are contradictory literature data and currently, the parameter is used only for scientific purposes and its significance is not well-determined. The serum level of COMP in patients with metabolic type OA of the knee has not been evaluated. The aim of the study was to analyze serum COMP levels in metabolic knee OA and controls with different BMI. Our results showed that the mean COMP values were significantly higher in the control group (1518.69 ± 232.76 ng/mL) compared to the knee OA patients (1294.58 ± 360.77 ng/mL) (p = 0.0012). This may be related to the smaller cartilage volume in OA patients. Additionally, COMP levels negatively correlated with disease duration (p = 0.04). The COMP level in knee OA with BMI below 30 kg/m2 (n = 61, 1304.50 ± 350.60 ng/mL) was higher compared to cases with BMI ≥ 30 kg/m2 (n = 76, 1286.63 ± 370.86 ng/mL), but the difference was not significant (p = 0.68). Whether this finding is related to specific features in the evolution of the metabolic type of knee OA remains to be determined. Interestingly, comparison of COMP levels in the controls with different BMI revealed significantly higher values in overweight and obese individuals (1618.36 ± 203.76 ng/mL in controls with BMI ≥ 25 kg/m2, n = 18, 1406.61 ± 216.41 ng/mL, n = 16; p = 0.0092). Whether this finding is associated with increased expression of COMP in the adipose tissue or with more intensive cartilage metabolism in relation to higher biomechanical overload in obese patients, considering the earlier development of metabolic type knee OA as an isolated finding, remains to be determined.

Metabolic profiling of synovial fluid in human temporomandibular joint osteoarthritis

Introduction

Temporomandibular joint (TMJ) osteoarthritis (OA) is a common TMJ degenerative disease with an unclear mechanism. Synovial fluid (SF), an important component of TMJ, contains various proteins and metabolites that may directly contribute to OA. The present study aimed to investigate the influence of SF in TMJOA at the metabolite level.

Methods

Untargeted and widely targeted metabolic profiling were employed to identify metabolic changes in SF of 90 patients with different TMJOA grades according to TMJ magnetic resonance imaging.

Results

A total 1498 metabolites were detected. Most of the metabolites were amino acids and associated metabolites, benzene and substituted derivatives, and lipids. Among patients with mild, moderate and severe TMJOA, 164 gradually increasing and 176 gradually decreasing metabolites were identified, indicating that biosynthesis of cofactors, choline metabolism, mineral absorption and selenocompound metabolism are closely related to TMJOA grade. Combined metabolomics and clinical examination revealed 37 upregulated metabolites and 16 downregulated metabolites in patients with pain, of which 19 and 26 metabolites were positively and negatively correlated, respectively, with maximum interincisal opening. A model was constructed to diagnose TMJOA grade and nine biomarkers were identified. The identified metabolites are key to exploring the mechanism of TMJOA.

Discussion

In the present study, a metabolic profile was constructed and assessed using a much larger number of human SF samples from patients with TMJOA, and a model was established to contribute to the diagnosis of TMJOA grade. The findings expand our knowledge of metabolites in human SF of TMJOA patients, and provide an important basis for further research on the pathogenesis and treatment of TMJOA.

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value.

METHODS

OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs.

RESULTS

We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated.

CONCLUSIONS

Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.