Knee osteoarthritis phenotypes based on synovial fluid immune cells correlate with clinical outcome trajectories

Unveiling the macrophage dynamics in osteoarthritic joints: From inflammation to therapeutic strategies

Osteoarthritis (OA) is an incurable, painful, and debilitating joint disease affecting over 500 million people worldwide. The OA joint tissues are infiltrated by various immune cells, particularly macrophages, which are able to induce or perpetuate inflammation. Notably, synovitis and its macrophage component represent a target of interest for developing treatments. In this review, we describe the latest advances in understanding the heterogeneity of macrophage origins, phenotypes, and functions in the OA joint and the effect of current symptomatic therapies on these cells. We then highlight the therapeutic potential of anticytokines/chemokines, nano- and microdrug delivery, and future strategies to modulate macrophage functions in OA.

Synovial fluid monocyte-to-lymphocyte ratio in knee osteoarthritis patients predicts patient response to conservative treatment: a retrospective cohort study

BACKGROUND

Biomarkers that predict the treatment response in patients with knee osteoarthritis are scarce. This study aimed to investigate the potential role of synovial fluid cell counts and their ratios as biomarkers of primary knee osteoarthritis.

METHODS

This retrospective study investigated 96 consecutive knee osteoarthritis patients with knee effusion who underwent joint fluid aspiration analysis and received concomitant intra-articular corticosteroid injections and blood tests. The monocyte-to-lymphocyte ratio (MLR) and neutrophil-to-lymphocyte ratio (NLR) were calculated. After 6 months of treatment, patients were divided into two groups: the responder group showing symptom resolution, defined by a visual analog scale (VAS) score of ≤ 3, without additional treatment, and the non-responder group showing residual symptoms, defined by a VAS score of > 3 and requiring further intervention, such as additional medication, repeated injections, or surgical treatment. Unpaired t-tests and univariate and multivariate logistic regression analyses were conducted between the two groups to predict treatment response after conservative treatment. The predictive value was calculated using the area under the receiver operating characteristic curve, and the optimal cutoff value was determined.

RESULTS

Synovial fluid MLR was significantly higher in the non-responder group compared to the responder group (1.86 ± 1.64 vs. 1.11 ± 1.37, respectively; p = 0.02). After accounting for confounding variables, odds ratio of non-responder due to increased MLR were 1.63 (95% confidence interval: 1.11-2.39). The optimal MLR cutoff value for predicting patient response to conservative treatment was 0.941.

CONCLUSIONS

MLR may be a potential biomarker for predicting the response to conservative treatment in patients with primary knee osteoarthritis.

Osteoarthritis year in review 2023: metabolite and protein biomarkers

OBJECTIVE

To highlight the advances over the past year in metabolite/protein biomarkers for osteoarthritis (OA).

METHOD

A literature search of five databases including PubMed, Web of Science, Scopus, Ovid Medline, and Embase was performed for studies on metabolite/protein/peptide/biochemical markers for OA published between April 1st, 2022 and March 31st, 2023. Records were then screened to include only original research articles using directly collected human specimens, in English language, and with full text available. Data from eligible studies were systematically extracted and summarized.

RESULTS

A total of 1600 unique records were extracted, out of which 46 fulfilled the inclusion criteria and were used for data extraction. Forty-one of these 46 studies focused on biomarkers for OA/OA severity/progression, four on OA clustering, and one on OA treatment outcomes. Twenty-nine studied protein markers for OA, thirteen studied metabolite markers, and four studied both. While many studies were the validation of the previously reported biomarkers, a number of novel metabolite/protein biomarkers and biomarker panels were reported in the past year. Biomarker panels might be useful to subset OA patients.

CONCLUSION

The number of studies on OA clustering is rising. Although validation in larger cohorts is needed in order to utilize reported biomarkers in clinical practice, these discoveries help better understand the pathogenesis of OA, provide insights into possible mechanisms underlying poor treatment outcomes, and aid in developing personalized treatment based on OA subtypes.

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

PURPOSE OF REVIEW

Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.

RECENT FINDINGS

AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.

Synovial fluid analysis: Relevance for daily clinical practice

Synovial fluid analysis can provide a prompt and definite diagnosis of crystal-induced arthritis, the most common acute inflammatory arthritis and a cause of chronic arthritis that may mimic rheumatoid, psoriatic, or peripheral spondyloarthritis. In many patients the diagnosis of gout or calcium pyrophosphate arthritis cannot be made with certainty without synovial fluid analysis. Additional information from fluid analysis can assist the clinician in honing the differential diagnosis of non-crystalline arthritis.

Healthy and Osteoarthritis-Affected Joints Facing the Cellular Crosstalk

Osteoarthritis (OA) is a chronic, progressive, severely debilitating, and multifactorial joint disease that is recognized as the most common type of arthritis. During the last decade, it shows an incremental global rise in prevalence and incidence. The interaction between etiologic factors that mediate joint degradation has been explored in numerous studies. However, the underlying processes that induce OA remain obscure, largely due to the variety and complexity of these mechanisms. During synovial joint dysfunction, the osteochondral unit undergoes cellular phenotypic and functional alterations. At the cellular level, the synovial membrane is influenced by cartilage and subchondral bone cleavage fragments and extracellular matrix (ECM) degradation products from apoptotic and necrotic cells. These "foreign bodies" serve as danger-associated molecular patterns (DAMPs) that trigger innate immunity, eliciting and sustaining low-grade inflammation in the synovium. In this review, we explore the cellular and molecular communication networks established between the major joint compartments-the synovial membrane, cartilage, and subchondral bone of normal and OA-affected joints.

Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2

Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.