OA Phenotypes Research Articles

Interferon-gamma signaling promotes cartilage regeneration after injury

Osteoarthritis is a common chronic disease and major cause of disability and chronic pain in ageing populations. In this pathology, the entire joint is involved, and the regeneration of articular cartilage still remains one of the main challenges. Here, we investigated the molecular mechanisms underlying cartilage regeneration in young mice using a full-thickness cartilage injury (FTCI) model. FTCI-induced cartilage defects were created in the femoral trochlea of young and adult C57BL/6 mice. To identify key molecules and pathways involved in the early response to cartilage injury, we performed RNA sequencing (RNA-seq) analysis of cartilage RNA at 3 days after injury. Young mice showed superior cartilage regeneration compared to adult mice after cartilage injury. RNA-seq analysis revealed significant upregulation of genes associated with the immune response, particularly in the IFN-γ signaling pathway and qRT-PCR analysis showed macrophage polarization in the early phase of cartilage regeneration (3 days) in young mice after injury, which might promote the removal of damaged or necrotic cells and initiate cartilage regeneration in response to injury. IFN-γR1- and IFN-γ-deficient mice exhibited impaired cartilage regeneration following cartilage injury. DMM-induced and spontaneous OA phenotypes were exacerbated in IFN-γR1−/− mice than in wild-type mice. Our data support the hypothesis that IFN-γ signaling is necessary for cartilage regeneration, as well as for the amelioration of post-traumatic and age-induced OA.

Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study

BackgroundOsteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and is the leading cause of pain and disability in the aged population. However, the underlying biological mechanism has not been fully understood. This study aims to reveal the causal effect of circulation metabolites on OA susceptibility.MethodsA two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of GDMs on OA. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas 8 different OA phenotypes, including any-site OA (All OA), knee and/or hip OA (knee/hip OA), knee OA, hip OA, spine OA, finger and/or thumb OA (hand OA), finger OA, thumb OA, were set the outcomes. Inverse-variance weighted (IVW) was used for calculating causal estimates. Methods including weight mode, weight median, MR-egger, and MR-PRESSO were used for the sensitive analysis. Furthermore, metabolic pathway analysis was performed via the web-based Metaconflict 4.0. All statistical analyses were performed in R software.ResultsIn this MR analysis, a total of 235 causative associations between metabolites and different OA phenotypes were observed. After false discovery rate (FDR) correction and sensitive analysis, 9 robust causative associations between 7 metabolites (e.g., arginine, kynurenine, and isovalerylcarnitine) and 5 OA phenotypes were finally identified. Additionally, eleven significant metabolic pathways in 4 OA phenotypes were identified by metabolic pathway analysis.ConclusionThe finding of our study suggested that identified metabolites and metabolic pathways can be considered useful circulating metabolic biomarkers for OA screening and prevention in clinical practice, and can also serve as candidate molecules for future mechanism exploration and drug target selection.

CircSPI1_005 ameliorates osteoarthritis by sponging miR-370-3p to regulate the expression of MAP3K9

BackgroundOsteoarthritis (OA), caused by the destruction of joint cartilage, is the most prevalent form of arthritis, causing pain and stiffness in joints among millions of patients worldwide. Increasing evidence suggests that non-coding RNAs, including circular RNAs, play important roles in the pathogenesis of OA, but the precise signaling pathway is still unclear. MethodsTo study OA, we established a mouse model by the destabilized medial meniscus (DMM) surgery and used IL-1β stimulated human cell line C28/I2 as an in vitro study. To further study the role of circSPI1_005 in regulating cell proliferation and apoptosis, EdU staining and FACS-based (fluorescence-activated cell sorting) apoptosis examination were performed after the manipulation of the expression of circSPI1_005. Also, bioinformatics predictions were conducted to analyze the downstream microRNAs of circSPI1_005 and the protein regulated by circSPI1_005. The luciferase assay and the RNA immunoprecipitation (RIP) assay were used to confirm the binding between circSPI1_005 and the predicted microRNA. To verify the role of circSPI1_005 in regulating OA in vivo, we also over-expressed circSPI1_005 by injecting AAV into previously injured knees to improve the OA symptoms. ResultsIn this study, we found that circSPI1_005 was significantly down-regulated in IL-1β treated chondrocyte cell lines and cartilage tissues of the OA mouse model. Overexpression of circSPI1_005 ameliorated OA by increasing proliferation and inhibiting apoptosis, and knockdown of circSPI1_005 in chondrocytes mimicked OA phenotypes. Bioinformatics study showed circSPI1_005 could sponge to miR-370-3p, and mechanistic studies confirmed the functional binding between circSPI1_005 and miR-370-3p. Furthermore, we conducted a TargetScan analysis and found that MAP3K9 (mitogen-activated protein kinase kinase kinase 9) could be the downstream protein effector. The expression level of MAP3K9 was regulated by miR-370-3p and overexpression of MAP3K9 could efficiently ameliorate OA. Also, we over-expressed circSPI1_005 in vivo and found that the cartilage surface in the OA mouse model was improved with overexpression of circSPI1_005. ConclusionsCollectively, circSPI1_005 could sponge to miR-370-3p to regulate the expression of MAP3K9, ameliorating the progression of osteoarthritis.

Osteoarthritis and intestinal microbiota: pathogenetic significance of the joint — gut — microbiome axis

Introduction. Osteoarthritis (ОА) is a disease leading to joint degeneration, accompanied by constant pain, inflammation, and functional failure of the joints. Although many factors contribute to the development of ОА, the gut microbiome has recently emerged as an important pathogenic factor in ОА initiation and progression. The purpose of the study was to analyze modern literature data regarding the link between the gut microbiome and ОА. Materials and methods. The available data of clinical studies and scientific reviews were analyzed, and modern meta-analyses on the influence of gut microbiota on the development and progression of ОА were evaluated. Results. Gut microbiota is responsible for a number of metabolic, immunological, and structural and neurological functions, potentially elucidating the heterogeneity of OA phenotypes and formation of individual features of the course of the disease. Numerous studies support the hypothesis of the existence of a gut – joint axis and the interaction between gut microbiota and OA-relevant risk factors. The proposed concept begins with intestinal disruption and dysbacteriosis, disruption of microbiota homeostasis, continuous changes in microbial composition and genomic plasticity for optimal adaptation of bacteria to the host environment, accompanied by both adaptive and innate immune responses due to translocation of bacteria and bacterial products into the bloodstream to the joint. This cascade ultimately leads to inflammation in the joint and contributes to the development and progression of OA. Interpretion of the potential mechanisms of OA pathogenesis is essential for the development of new preventive and disease-modifying therapeutic interventions. In addition, gut microbiota is also a potential biomarker related to inflammation and gut dysbiosis to predict the progression of ОА and monitor the effectiveness of therapeutic interventions. Conclusions. In this review, we summarized research data that are supporting the hypothesis of a “joint – gut – microbiota axis” and the interaction between gut microbiota and the OA-relevant factors, including age, gender, metabolism, obesity.

Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative

To apply biclustering, a methodology originally developed for analysis of gene expression data, to simultaneously cluster observations and clinical features to explore candidate phenotypes of knee osteoarthritis (KOA) for the first time. Data from the baseline Osteoarthritis Initiative (OAI) visit were cleaned, transformed, and standardized as indicated (leaving 6461 knees with 86 features). Biclustering produced submatrices of the overall data matrix, representing similar observations across a subset of variables. Statistical validation was determined using the novel SigClust procedure. After identifying biclusters, relationships with key outcome measures were assessed, including progression of radiographic KOA, total knee arthroplasty, loss of joint space width, and worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, over 96 months of follow-up. The final analytic set included 6461 knees from 3330 individuals (mean age 61 years, mean body mass index 28 kg/m2, 57% women and 86% White). We identified 6 mutually exclusive biclusters characterized by different feature profiles at baseline, particularly related to symptoms and function. Biclusters represented overall better (#1), similar (#2, 3, 6), and poorer (#4, 5) prognosis compared to the overall cohort of knees, respectively. In general, knees in biclusters #4 and 5 had more structural progression (based on Kellgren-Lawrence grade, total knee arthroplasty, and loss of joint space width) but tended to have an improvement in WOMAC pain scores over time. In contrast, knees in bicluster #1 had less incident and progressive KOA, fewer total knee arthroplasties, less loss of joint space width, and stable pain scores compared with the overall cohort. We identified six biclusters within the baseline OAI dataset which have varying relationships with key outcomes in KOA. Such biclusters represent potential phenotypes within the larger cohort and may suggest subgroups at greater or lesser risk of progression over time.

PREDICTORS OF LONGITUDINAL MRI-BASED CARTILAGE THICKNESS CHANGE IN THE OBSERVATIONAL MULTICENTER APPROACH COHORT

Purpose: APPROACH (Applied public-private research enabling osteoarthritis clinical headway) is an exploratory, European, 5-centre, 2-year prospective follow-up cohort project. It was designed for identifying outcome measures that can identify patients with different OA phenotypes who may benefit form specific therapies and collected clinical, imaging, biomechanical and biochemical parameters for this purpose. The patients were selected by the rankings produced by machine learning models trained to estimate a high likelihood of joint space width loss and/or increased or sustained knee pain over the course of the study from demographic data, pain scores, and radiographic features.

NON-CARTILAGINOUS STRUCTURAL JOINT DAMAGE AS ASSESSED BY MRI INCREASES RATES OF QUANTITATIVE MRI-DETECTED CARTILAGE THICKNESS LOSS OVER 24 MONTHS: THE APPROACH STUDY

Purpose: The Innovative Medicines Initiative - Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) consortium is a longitudinal cohort study to combine conventional and new disease markers and to identify different OA phenotypes. Historical data were used to train machine learning models to provide a structural and pain progression score for each individual, which was used as basis for inclusion 1. Study parameters used to assess structural progression include quantitative MRI of cartilage including thickness and semi-quantitative MRI scoring of cartilaginous and non-cartilaginous features of OA.

NON-CHONDRAL MULTI-TISSUE PATHOLOGY AS ASSESSED BY MRI INCREASES RATES OF QUANTITATIVE MRI-DEFINED CARTILAGE THICKNESS LOSS OVER 24 MONTHS: THE APPROACH STUDY

NON-CHONDRAL MULTI-TISSUE PATHOLOGY AS ASSESSED BY MRI INCREASES RATES OF QUANTITATIVE MRI-DEFINED CARTILAGE THICKNESS LOSS OVER 24 MONTHS: THE APPROACH STUDY

QUANTITATIVE CT OF THE KNEE IN THE IMI-APPROACH OSTEOARTHRITIS COHORT: ASSOCIATION OF BONE MINERAL DENSITY WITH KELLGREN-LAWRENCE GRADES AND MENISCAL EXTRUSION

QUANTITATIVE CT OF THE KNEE IN THE IMI-APPROACH OSTEOARTHRITIS COHORT: ASSOCIATION OF BONE MINERAL DENSITY WITH KELLGREN-LAWRENCE GRADES AND MENISCAL EXTRUSION

Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative

Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative

Synovial Fluid Cytokines, Chemokines and MMP Levels in Osteoarthritis Patients with Knee Pain Display a Profile Similar to Many Rheumatoid Arthritis Patients.

Background: There are currently no effective disease-modifying drugs to prevent cartilage loss in osteoarthritis and synovial fluid is a potentially valuable source of biomarkers to understand the pathogenesis of different types of arthritis and identify drug responsiveness. The aim of this study was to compare the differences between SF cytokines and other proteins in patients with OA (n = 21) to those with RA (n = 27) and normal knees (n = 3). Methods: SF was obtained using ultrasound (US) guidance and an external pneumatic compression device. RA patients were categorized as active (n = 20) or controlled (n = 7) based upon SF white blood cell counts (> or <300 cells/mm3). Samples were cryopreserved and analyzed by multiplex fluorescent bead assays (Luminex). Between-group differences of 16 separate biomarker proteins were identified using ANOVA on log10-transformed concentrations with p values adjusted for multiple testing. Results: Only six biomarkers were significantly higher in SF from active RA compared to OA—TNF-α, IL-1-β IL-7, MMP-1, MMP-2, and MMP-3. Only MMP-8 levels in RA patients correlated with SF WBC counts (p < 0.0001). Among OA patients, simultaneous SF IL-4, IL-6, IL-8, and IL-15 levels were higher than serum levels, whereas MMP-8, MMP-9, and IL-18 levels were higher in serum (p < 0.05). Conclusion: These results support the growing evidence that OA patients have a pro-inflammatory/catabolic SF environment. SF biomarker analysis using multiplex testing and US guidance may distinguish OA phenotypes and identify treatment options based upon targeted inflammatory pathways similar to patients with RA.

Quantifying the contribution of genetic variation in bone regulatory pathways to osteoarthritis risk

Quantifying the contribution of genetic variation in bone regulatory pathways to osteoarthritis risk

Identification of TGFβ signatures in six murine models mimicking different osteoarthritis clinical phenotypes

TGFβ is a key player in cartilage homeostasis and OA pathology. However, few data are available on the role of TGFβ signalling in the different OA phenotypes. Here, we analysed the TGFβ pathway by transcriptomic analysis in six mouse models of OA. We have brought together seven expert laboratories in OA pathophysiology and, used inter-laboratories standard operating procedures and quality controls to increase experimental reproducibility and decrease bias. As none of the available OA models covers the complexity and heterogeneity of the human disease, we used six different murine models of knee OA: from post-traumatic/mechanical models (meniscectomy (MNX), MNX and hypergravity (HG-MNX), MNX and high fat diet (HF-MNX), MNX and seipin knock-out (SP-MNX)) to aging-related OA and inflammatory OA (collagenase-induced OA (CIOA)). Four controls (MNX-sham, young, SP-sham, CIOA-sham) were added. OsteoArthritis Research Society International (OARSI)-based scoring of femoral condyles and ribonucleic acid (RNA) extraction from tibial plateau samples were done by single operators as well as the transcriptomic analysis of the TGFβ family pathway by Custom TaqMan® Array Microfluidic Cards. The transcriptomic analysis revealed specific gene signatures in each of the six models; however, no gene was deregulated in all six OA models. Of interest, we found that the combinatorial Gdf5-Cd36-Ltbp4 signature might discriminate distinct subgroups of OA: Cd36 upregulation is a hallmark of MNX-related OA while Gdf5 and Ltbp4 upregulation is related to MNX-induced OA and CIOA. These findings stress the OA animal model heterogeneity and the need of caution when extrapolating results from one model to another.

The contribution of age and obesity to the number of painful joint sites in individuals reporting osteoarthritis: a population-based study.

ObjectiveTo investigate the association of OA risk factors with number of painful joint sites in a representative population sample.MethodsAnalysis of the 2009 Survey on Living with Chronic Diseases in Canada – Arthritis Component (n = 1614) for respondents reporting symptomatic OA. Variables: painful joints sites (hands, wrists, elbows, shoulders, hips, knees, ankles, feet, back, neck), joint symptom duration, sociodemographic characteristics, smoking, comorbidities and BMI. Zero-truncated negative binomial regressions were used to investigate the association between number of painful joint sites and the variables. Generalizability of findings was assessed by a similar analysis in a clinical hip/knee OA sample.ResultsThe sample comprised 73% women and 56% were aged <65 years. The mean number of painful joint sites was 3.8: 84% reported pain at ≥2 sites, and 45% at ≥4 sites. Age, BMI, education and smoking were not associated with the number of joint sites. Significant associations were found with being female [rate ratio (RR) = 1.23, 95% CI 1.09, 1.39], having more comorbidities (RR = 1.11, 95% CI 1.07, 1.15) and longer symptom duration (RR = 1.16, 95% CI 1.09, 1.24), although the increase in joint sites with duration was small. Similar regression results were found with the clinical OA sample.ConclusionThe lack of an association of age and BMI (obesity) with number of painful joint sites in OA raises questions about the role of these risk factors and our understanding of OA as a multi-joint disease. Filling this knowledge gap is critical to making progress with defining OA phenotypes and identifying potential aetiological mechanisms.

Erosive hand osteoarthritis phenotype: clinical and proteomic characterization

Purpose: Erosive hand osteoarthritis (EHOA) is often considered a more severe form of hand OA that includes pain and inflammation. However, more data are needed regarding the different hand OA phenotypes, especially erosive and non-erosive, to conclude if they represent two different subsets of hand OA or even different degrees of affectation of the same disease. The purpose of this work is to define a specific clinical pattern of patients with EHOA and identify putative biochemical markers associated with this phenotype. Methods: This cross-sectional study was conducted in the Prospective Cohort of Osteoarthritis A Coruna (PROCOAC). This cohort consists of 1136 subjects, from which 834 were diagnosed of hand OA following ACR criteria. After reviewing the x-rays, the cohort was split into patients with and without erosive hand OA. Then, both clinical and demographic data within each group were subsequently analyzed. A Univariate analysis comparing different variables between both groups, followed by a stepwise regression logistic regression analysis was performed to characterize the clinical pattern of EHOA. The identification of biochemical markers was carried out following a two-step workflow: in a first discovery phase, a proteomic approach based on peptide labelling with Isobaric tags for relative and absolute quantitation (iTRAQ) was employed using two different sets of sera (n=20) from EHOA or non-EHOA patients. In a second validation phase an independent set of 346 individual serum samples from the PROCOAC cohort (EHOA, n=127; non-EHOA, n= 219) was used to quantify serum concentrations of the extracellular matrix protein 1 (ECM1) by ELISA. The Mann-Whitney test was carried out in order to look for significant differences between EHOA and non-EHOA groups. All the statistical analyses were performed using SPSS software v.24. Results: The univariate analysis showed that EHOA patients were younger (p<0.001), smokers (p=0.011), with lower both body mass index (p=0.006) and arterial hypertension (p=0.002); increased frequency of inflammatory symptoms in hands (p<0.001), more presence of nodular hand OA (p=0.001) but lower frequency of other forms of hand OA, specially thumb-base (p=0.005) and metacarpophalangeal (p=0.036). In addition, these patients also showed a lower number of damaged joints (p<0.001), specially the knee (p<0.001). The stepwise logistic regression model (Table 1) confirmed the strong association of age (OR=0.963;95%CI=0.937-0.989;p=0.005), inflammatory symptoms (OR=4.575;95%CI=2.621-7.985;p<0.001), increased prevalence of nodal OA (OR=2.125;95%CI=1.118-4.037;p=0.021) and, in patients over 64 years old, MetS (OR=2.029;95%CI=1.183-3.478;p=0.010) with the erosive phenotype. In addition, erosive patients show a lower prevalence of knee OA (OR=0.416;95%CI=0.252-0.687;p=0.001) and to carry this phenotype at baseline does not confer a significant increased risk for radiographic knee OA progression over time). Regarding the proteomic analysis for the identification of potential biomarkers associated with the EHOA phenotype, a total of 257 different proteins were identified in the serum samples with more than two peptides and a total score ≥2 at 95% confidence. Thirty-six proteins were found quantitatively altered between those patients with or without EHOA. A significant reduction (p=0.029) of ECM1 concentrations was found in the serum from patients with the EHOA phenotype (0.154 ± 0.010 μg/ml vs 0.169 ± 0.007 μg/ml) (Figure 1). Conclusions: A specific clinical pattern of patients with EHOA has been defined. It has been associated with altered levels of 36 proteins in serum. Particularly, decreased amounts of ECM1 have been found in patients with this erosive phenotype.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Is osteoarthritis one disease or a collection of many?

OA is a multifaceted and heterogeneous syndrome that may be amenable to tailored treatment. There has been an increasing focus within the OA research community on the identification of meaningful OA phenotypes with potential implications for prognosis and treatment. Experimental and clinical data combined with sophisticated statistical approaches have been used to characterize and define phenotypes from the symptomatic and structural perspectives. An improved understanding of the existing phenotypes based on underlying disease mechanisms may shed light on the distinct entities that make up the disease. This narrative review provides an updated summary of the most recent advances in this field as well as limitations from previous approaches that can be addressed in future studies.

Glycosylation of DMP1 Is Essential for Chondrogenesis of Condylar Cartilage

The mandibular condylar cartilage (MCC) shoulders force for the subchondral bone during mastication. The cartilage matrix contains various large molecules, such as type I, II, and X collagens and proteoglycans (PGs), which jointly play essential roles in maintaining cartilage characteristics. PGs play key roles in maintaining the elasticity of cartilage and providing a cushion against mastication forces. In addition to the well-known PGs, DMP1-PG, which is the PG form of dentin matrix protein 1 (DMP1), is a newly identified PG. DMP1 is proteolytically processed in vivo, and the N-terminus is glycosylated into its PG form—that is, DMP1-PG, which is highly expressed not only in tooth and bone but also in the matrix of the MCC. However, the specific functions of DMP1-PG in the MCC remain unclear. In human temporomandibular joint osteoarthritis and hyperocclusion model rat specimens, PGs are significantly downregulated, and DMP1-PG is the most prominently affected PG. To further investigate the role of DMP1-PG in condylar chondrogenesis, a glycosylation site mutant (S89-G89) mouse model was established with knock-in methods. In the MCC of the S89G-DMP1 mice, the glycosylation level of DMP1 was significantly downregulated, and a series of abnormal developmental and pathologic changes could be observed. The morphologic changes included thinner cartilage layers, deformations of the MCC, and disordered arrangements of the chondrocytes, and an earlier onset of temporomandibular joint osteoarthritis–like changes was observed. In addition, markers of chondrogenesis were downregulated, and the matrix of the MCC displayed OA phenotypes in the S89G-DMP1 mice. Further investigations showed that the transforming growth factor β signaling molecules were affected in the MCC after the loss of DMP1-PG. In addition, the loss of DMP1-PG significantly accelerated the progression of cartilage injuries in the hyperocclusion models. Given these findings, we investigated the significant role of DMP1-PG in the chondrogenesis and maintenance of MCC.

Biomarkers reflect differences in osteoarthritis phenotypes of the lumbar spine: the Johnston County Osteoarthritis Project.

To determine differences in biomarker levels between radiographic phenotypes of facet joint osteoarthritis (FOA) only, spine OA only ((disc space narrowing (DSN) and vertebral osteophytes (OST)) or the combination of FOA and spine OA. A cross-sectional analysis of data from 555 participants in the Johnston County Osteoarthritis Project was performed. Lumbar spine levels were graded by severity (OST and DSN) and presence (FOA) of degeneration. Biomarkers included hyaluronan (HA) and type II collagen (CTX-II). Adjusted risk ratios (aRRR) were estimated using multinomial regression, with adjustment for age, race, sex, body mass index (BMI), and radiographic OA (knee, hip, hand). Interactions were tested between sex, race and low back symptoms. FOA only was present in 22.4%, 14.5% had spine OA only, and 34.6% had the combination of FOA and spine OA. Compared to the reference group of neither FOA or spine OA, a one unit higher ln HA level was associated with 31% higher relative risk ratio (RRR=1.31 (95% 1.03, 1.67)) of having FOA only, while, a one unit higher lnuCTX-II level was associated with 84% higher relative risk ratio (RRR=1.84 (95% CI 1.19, 2.84)) of having spine OA only. No significant interactions were identified. Interestingly, OA affecting the synovial facet joint was associated with a marker of inflammation (HA). Spine OA, affecting intervertebral discs that contain collagen type II, was associated with a marker reflecting collagen type II degradation (CTX-II). These findings suggest that biomarkers may reflect the different pathophysiologic processes of lumbar spine OA phenotypes.

An update on the pathophysiology of osteoarthritis

IntroductionOsteoarthritis (OA) is one of the most common forms of arthritis. There is accumulating evidence to suggest that OA is an inflammatory disease of the entire synovial joint and has multiple phenotypes. This presents the OA research community with new challenges and opportunities. The main challenge is to understand the root cause of the disease and identify differences and similarities between OA phenotypes. The key opportunity is the possibility of developing personalized and individualized prevention and treatment strategies for OA patients with different phenotypes of the disease. Indeed, it has been suggested that this is the era of ‘personalized prevention’ for OA. The aim of this mini-review paper is to focus on the pathophysiological aspects of OA development and progression, review the current concepts and discuss the future of personalized medicine for OA. MethodThe PubMed/MEDLINE bibliographic database was searched using the keywords ‘pathophysiology’ and ‘osteoarthritis’. ResultsThe PubMed/MEDLINE search yielded more than 12,000 relevant papers. A selection of these papers is reviewed here. ConclusionThere has been slow but steady progress in our understanding of the pathophysiology of OA over the last two decades. However, large gaps remain in our knowledge of OA pathogenesis and this impacts negatively on patients and drug development pipeline. In the absence of new pharmaceutical agents and disease modifying osteoarthritis drugs (DMOADs) it is clear that lifestyle modification and physical activity are important and may delay the need for surgical intervention.

How can disturbances of the nervous systems translate in OA phenotypes?

How can disturbances of the nervous systems translate in OA phenotypes?