Osteoarthritis year in review 2023: metabolite and protein biomarkers

Infrapatellar fat pad as a source of biomarkers and therapeutic target for knee osteoarthritis

BACKGROUND AND OBJECTIVE

Osteoarthritis (OA) is a multifactorial and highly prevalent disease in elderly adults; however, its pathogenesis, diagnosis, and treatment are unmet needs nowadays. Research efforts have focused on elucidating the molecular mechanisms involved in the pathogenesis, onset, and progression of OA to facilitate early detection and effective therapeutic approaches. Infrapatellar fat pad (IPFP) represents a promising novel source of OA biomarkers given that it is an active player in OA. This review aims to investigate the current literature regarding the potential of the IPFP as a source of diagnostic and prognostic biomarkers for OA as well as potential target for novel therapies.

METHODS

A literature search was conducted in the PubMed database in June 2024. We included cross-sectional and longitudinal studies based on IPFP from human OA patients, oriented in the identification of imaging, biochemical, and molecular biomarkers in the IPFP.

RESULTS

After screening and evaluation, we included a total of 61 studies. Most of the imaging publications (n = 47) on IPFP are based on magnetic resonance imaging (MRI) that revealed potential semiquantitative and quantitative imaging biomarkers linked to inflammation, fibrosis, pain, and joint degeneration imaging parameters. Biochemical and molecular studies (n = 14) pointed out an increase in interleukin-6 (IL-6), fatty acid-binding protein 4 (FABP4), adiponectin, and lysophosphatidylcholine (LysoPC) in the IPFP during OA progression.

CONCLUSIONS

Imaging, biochemical, and molecular studies indicate OA potential biomarkers in the IPFP related to inflammation, lipid dysregulation, and fibrosis. The combination of imaging and biochemical biomarkers could provide a better prediction of OA onset and the identification of OA progressors at an early stage. The IPFP study could also reveal potential therapeutic targets with the vision of better precision medicine.

Exploration and verification of circulating diagnostic biomarkers in osteoarthritis based on machine learning

Background

Osteoarthritis (OA) is a prevalent chronic joint condition. This study sought to explore potential diagnostic biomarkers for OA and assess their relevance in clinical samples.

Methods

We searched the GEO database for peripheral blood leukocytes expression profiles of OA patients as a training set to conduct differentially expressed gene (DEG) analysis. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE), were employed to identify candidate biomarkers for OA diagnosis. The performance was assessed using receiver operating characteristic (ROC) curves, and the areas under the curve (AUCs) with 95% confidence interval (CI) were calculated. Furthermore, we gathered clinical peripheral blood samples from healthy donors and OA patients (validation set) to validate our findings. Small interfering RNA and CCK8 proliferation assay were used for experimental verification.

Results

A total of 31 DEGs were discovered, and the machine learning screening found five DEGs that were considered to be candidate biomarkers. Notably, BIRC2 had a very good discriminatory effect among the five candidate biomarkers, with an AUC of 0.814 (95% CI: 0.697-0.915). In our validation set, results showed that the levels of BIRC2 and SEH1L were remarkably higher in healthy donors than OA patients, consistent with the results of the training set. SEH1L owned the largest AUC of 0.964 (95% CI: 0.855-1.000). BIRC2 also displayed a larger AUC of 0.836 (95% CI: 0.618-1.000) in the training set. Knockdown of these two genes could significantly suppress human chondrocyte proliferation.

Conclusion

Two novel biomarkers, SEH1L and BIRC2, were indicated to have the capacity to differentiate healthy people from OA patients at the peripheral level. Experiments have shown that knockdown of these two genes could inhibit human chondrocyte proliferation, as verified by cell proliferation assays.

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.