Osteoarthritis Biomarkers Research Articles

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

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. 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. 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. 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.

Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model

Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model

Osteoarthritis Year in Review 2024: Molecular Biomarkers of Osteoarthritis

ObjectiveTo provide a comprehensive and insightful summary of studies on molecular biomarkers at the gene, protein, and metabolite levels across different sample types and joints affected by osteoarthritis (OA). MethodsA literature search using the PubMed database for publications on OA biomarkers published between April 1, 2023 and April 30, 2024 was performed. Publications were then screened, examined at length, and summarized in a narrative review. ResultsOut of the 364 papers initially identified, 44 publications met inclusion criteria, were relevant to OA, and were further examined for data extraction and discussion. These studies included 1 genomic analysis, 22 on protein markers, 6 on metabolite markers, 9 on inflammatory mediators, and 6 integrating multiple molecular levels. ConclusionsSignificant advancements have been made in identifying molecular biomarkers for OA, encompassing various joints, sample types, and molecular levels. Despite this progress, gaps remain, particularly in the need for validation, larger sample sizes, the integration of more clinical data, and consideration of covariates. For early detection and improved treatment of OA, continued efforts in biomarker identification are needed. This effort should seek to identify effective biomarkers that advance early detection, support prevention, evaluate interventions, and improve patient outcomes.

Vitamin D Deficiency after Anterior Cruciate Ligament Reconstruction Associates with Knee Osteoarthritis: A Retrospective Study.

The objective of this study was to test the hypothesis that vitamin D deficiency (i.e., serum 25-hydroxyvitamin D (25(OH)D) ≤ 20 ng/mL) associates with the increased occurrence and shortened time to a knee osteoarthritis (OA) diagnosis after anterior cruciate ligament reconstruction (ACLR). This study consisted of a retrospective, case-control design. The inclusion criteria consisted of (1) patients (≥18 y) who underwent arthroscopic ACLR with (cases; n = 28) and without (controls; n = 56) a subsequent knee OA diagnosis (≥90 d from the date of ACLR) and (2) with a documented serum 25(OH)D concentration after ACLR (and before a knee OA diagnosis for the cases). Controls were matched (2:1) to cases based on sex, age at ACLR, date of ACLR, and body mass index. After matching, patients were separated into two groups: (1) vitamin D deficient (serum 25(OH)D ≤ 20 ng/mL) or (2) non-vitamin D deficient (serum 25(OH)D > 20 ng/mL). Data were extracted from the medical records. Thirty-one percent (n = 26) of patients included were vitamin D deficient. Fifty percent (n = 13) of the vitamin D deficient and twenty-six percent (n = 15) of the non-vitamin D deficient patients were subsequently diagnosed with knee OA (p = 0.03). Time from ACLR to a knee OA diagnosis was significantly (p = 0.02) decreased in the vitamin D deficient (OA-free interval, 95% confidence interval [CI] = 7.9 to 10.9 y) compared to the non-vitamin D deficient group (OA-free interval, 95% CI = 10.5 to 12.5 y). Vitamin D deficiency after ACLR may serve as a prognostic biomarker for knee OA following ACLR.

Assessment of whole cartilage surface damage in an osteoarthritis rat model: The Cartilage Roughness Score (CRS) utilizing microcomputed tomography

ObjectiveThis study aims to establish an accurate and robust imaging biomarker for pre-clinical osteoarthritis (OA) research, focusing on early detection of cartilage surface degeneration. MethodUsing 50 male Wistar rats, this study aims to observe Collagenase Induced Osteoarthritis (CIOA) progression through microcomputed x-ray tomography (µCT), histopathological analysis, and gait analysis. A novel parameter, Cartilage Roughness Score (CRS), was developed for assessing cartilage structural damage from µCT data and was compared with histological OARSI Cartilage Degeneration Score (OARSI CDS). Additionally, as CRS maps the full surface, it was used to simulate the level of uncertainty in histological sampling. ResultsCRS and OARSI CDS have a linear relationship. CRS for healthy cartilage is 2.75 (95% CI: 1.14-4.36) and with every 1 unit increase in OARSI, CRS is expected to increase by 0.64 (95% CI: 0.35-0.92). Cartilage degeneration due to CIOA was evident in both histopathological scoring and CRS. However, only CRS was sensitive enough to show consistent damage progression from day 10 to day 60. Furthermore, our simulation for histological sampling suggested that up to 16 coronal slices with 200µm spacing would be needed to accurately represent the full extent of cartilage surface degeneration in a slice wise manner. Gait analysis showed changes solely at eight days post-collagenase injection, normalizing by day 60. ConclusionThe CRS analysis method emerges as a robust tool for cartilage surface damage assessment. This study demonstrates the potential of automatic 3D analysis over the traditional 2D histological approach when evaluating cartilage surface damage. Data statementMicro-computed tomography data is available via ida.fairdata.fi46 (https://doi.org/10.23729/f33d3ba8-01b8-47af-90dd-94b7c7861247)

Two Members of Vitamin-K-Dependent Proteins, Gla-Rich Protein (GRP) and Matrix Gla Protein (MGP), as Possible New Players in the Molecular Mechanism of Osteoarthritis.

Objectives: The pathophysiology of osteoarthritis is mainly unknown. Matrix Gla protein (MGP) and Gla-rich protein (GRP) are both vitamin-K-dependent mineralization inhibitors. In this study, we aimed to compare the levels of MGP and GRP in the synovial fluid of osteoarthritic (OA) and non-osteoarthritic (non-OA) knee joints. Materials and Methods: Two groups were formed, with one consisting of patients with OA and the other non-OA, serving as a control group. The non-OA group included individuals who had arthroscopic surgery for non-cartilage-related issues. In the OA group, all participants had undergone total knee arthroplasty because of grade 4 primary degenerative osteoarthritis. During the operation, at least 1 mL of knee synovial fluid was collected. The GRP and MGP levels in the synovial fluid were measured using an ELISA kit. Results: The mean age in the OA group (62.03 ± 11.53 years) was significantly higher than that in the non-OA group (47.70 ± 14.49 years; p = 0.0001). GRP levels were significantly higher in the OA group (419.61 ± 70.14 ng/mL) compared to the non-OA group (382.18 ± 62.34 ng/mL; p = 0.037). MGP levels were significantly higher in the OA group (67.76 ± 11.36 ng/mL) compared to the non-OA group (53.49 ± 18.28 ng/mL; p = 0.001). Calcium levels (Ca++) were also significantly higher in the OA group (12.89 ± 3.43 mg/dL) compared to the non-OA group (9.51 ± 2.15 mg/dL; p = 0.0001). There was a significantly positive correlation between MGP levels and age (p = 0.011, R = +0.335). Linear regression analysis was performed to determine the effect of age on MGP levels (p = 0.011, R-Square = 0.112). The dependent variable in this analysis was MGP (ng/mL), and age was the predictor. Conclusions: In conclusion, both GRP and MGP are potentially usable biomarkers in osteoarthritis. However, GRP seems to be more valuable because it is not associated with age. In the future, both proteins could provide important contributions to the diagnosis and treatment of osteoarthritis.

DNA methylation of bone morphogenetic protein 7 in leukocytes as a possible biomarker for hand osteoarthritis: A pilot study.

Hand osteoarthritis (HOA), characterized by an earlier onset age and reduced susceptibility to mechanical stress compared with knee and hip osteoarthritis, is considered a suitable disease for identifying predictive biomarkers of osteoarthritis. In particular, DNA methylation variants, expected to contribute to HOA susceptibility, hold potential as osteoarthritis biomarkers. In this study, leukocyte DNA methylation patterns were analyzed in blood samples from patients with HOA, aiming to identify disease-specific biomarkers for osteoarthritis. Using DNA methylation microarrays, we analyzed samples from three subjects with HOA and three age- and gender-matched healthy individuals. For validation, pyrosequencing analysis was conducted using samples from 16 to 9 subjects with and without HOA, respectively. From 735,026 probes in the DNA methylation array, the Top 100 CpG sites associated with HOA, based on low adjusted P-values, including those targeting bone morphogenetic protein 7 (BMP7), SBF2-AS1, PLOD2, ICOS, and CSF1R were identified. Validation analysis revealed significantly higher methylation levels in the BMP7-related site in the HOA group compared with the control group, even after adjusting for age, gender, and body mass index (p = 0.037). In contrast, no significant difference was observed in the other selected CpG sites between the HOA and control groups. This study highlights the significantly increased frequency of methylation at the specific BMP7 site in leukocytes of patients with HOA, suggesting its potential as a biomarker for HOA. Measurement of methylation levels at the CpG sites identified in this study offers a potential approach to prevent future osteoarthritis progression, providing valuable insights into disease management.

Transcriptome combined with single cell to explore hypoxia-related biomarkers in osteoarthritis

Osteoarthritis (OA) is a prevalent degenerative condition among the elderly on a global scale. Research has demonstrated that hypoxia can promote chondrocyte apoptosis and autophagy leading to OA. Hence, it was vital to screen the hypoxia related biomarkers in OA. We introduced transcriptome data to screen out differentially expressed genes (DEGs) in GSE114007 and GSE57218 (OA samples vs control samples). We performed differential expression analysis in key annotated cell to obtain differentially expressed marker genes at the single-cell level (GSE169454). Venn diagram was executed to identify hypoxia related differentially expressed genes (HR-DEGs) associated with OA. Further, feature genes were obtained through the application of least absolute shrinkage and selection operator (LASSO) regression and the Random Forest (RF) algorithm. Receiver operating characteristic (ROC) and expression level analysis were used to identify hypoxia related biomarkers in OA. We further performed immune infiltration and gene set enrichment analysis (GSEA) based on hypoxia related biomarkers. Finally, we analyzed the expression of biomarkers in single-cell level. We identified 2351 DEGs associated with OA. At the single-cell level, 242 differentially expressed marker genes were obtained. 12 HR-DEGs were retained venn diagram. Subsequently, three hypoxia related biomarkers (ADM, DDIT3 and MAFF) were identified. Moreover, we got 15 significantly different immune cells. Finally, we found a lower expression of ADM, DDIT3 and MAFF in OA group compared to the control group in ECs. Overall, we obtained three hypoxia related biomarkers (ADM, DDIT3 and MAFF) associated with OA, which established a theoretical basis for addressing OA.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BackgroundOur study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.MethodsWe utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.ResultsWGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.ConclusionsHTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Association of serum biomarkers with radiographic knee osteoarthritis, knee pain and function in a young, male, trauma-exposed population – Findings from the ADVANCE study

ObjectiveThe ArmeD SerVices TrAuma RehabilitatioN OutComE (ADVANCE) study is investigating long-term combat-injury outcomes; this sub-study aims to understand the association of osteoarthritis (OA) biomarkers with knee radiographic OA (rOA), pain and function in this high-risk population for post-traumatic OA. DesignADVANCE compares combat-injured participants with age, rank, deployment and job-role frequency-matched uninjured participants. Post-injury immunoassay-measured serum biomarkers, knee radiographs, knee injury and osteoarthritis outcome scale (KOOS), and six-minute walk-tests (6MWT) are reported. The primary analysis, adjusted for age, body mass, socioeconomic status, and ethnicity, was to determine any differences in biomarkers between those with/without combat-injury, rOA and pain. Secondary analyses were performed to compare post-traumatic/idiopathic OA, painful/painfree rOA and injury patterns. Results1145 male participants were recruited, aged 34.1±5.4, 8.9±2.2 years post-injury (n=579 trauma-exposed, of which, traumatic-amputation n=161) or deployment (n=566 matched). Cartilage oligomeric matrix protein (COMP) was significantly higher in the combat-injured group compared to uninjured (p=0.01). Notably, COMP was significantly lower in the traumatic-amputation group compared to non-amputees (p<0.001), decreasing relative to number of amputations (p<0.001). Leptin was higher (p=0.005) and adiponectin lower (p=0.017) in those with v without knee pain, associated with an increased risk of 22% and 17% for pain, and 46% and 34% for painful rOA, respectively. There were no significant differences between trauma-exposed and unexposed participants with rOA. ConclusionsThe most notable findings of this large, unique study are the similarities between those with rOA regardless of trauma-exposure, the injury-pattern and traumatic-amputation-associated differences in COMP, and the relationship between adipokines and pain.

Synovium-Derived and Bone-Derived Mesenchymal Stem/Stromal Cells from Early OA Patients Show Comparable In Vitro Properties to Those of Non-OA Patients.

Degenerative disorders like osteoarthritis (OA) might impair the ability of tissue-resident mesenchymal stem/stromal cells (MSCs) for tissue regeneration. As primary cells with MSC-like properties are exploited for patient-derived stem cell therapies, a detailed evaluation of their in vitro properties is needed. Here, we aimed to compare synovium-derived and bone-derived MSCs in early hip OA with those of patients without OA (non-OA). Tissues from three synovial sites of the hip (paralabral synovium, cotyloid fossa, inner surface of peripheral capsule) were collected along with peripheral trabecular bone from 16 patients undergoing hip arthroscopy (8 early OA and 8 non-OA patients). Primary cells isolated from tissues were compared using detailed in vitro analyses. Gene expression profiling was performed for the skeletal stem cell markers podoplanin (PDPN), CD73, CD164 and CD146 as well as for immune-related molecules to assess their immunomodulatory potential. Synovium-derived and bone-derived MSCs from early OA patients showed comparable clonogenicity, cumulative population doublings, osteogenic, adipogenic and chondrogenic potential, and immunophenotype to those of non-OA patients. High PDPN/low CD146 profile (reminiscent of skeletal stem cells) was identified mainly for non-OA MSCs, while low PDPN/high CD146 mainly defined early OA MSCs. These data suggest that MSCs from early OA patients are not affected by degenerative changes in the hip. Moreover, the synovium represents an alternative source of MSCs for patient-derived stem cell therapies, which is comparable to bone. The expression profile reminiscent of skeletal stem cells suggests the combination of low PDPN and high CD146 as potential biomarkers in early OA.

Serum Sirt6 as a Potential Biomarker for Osteoarthritis and its Correlation with IL-6 Alterations.

The study aimed to assess Sirt6 levels in serum of knee osteoarthritis (OA) patients compared to healthy individuals to evaluate its correlation with OA and to understand how Sirt6 is linked with the change in IL-6 levels. The cross-sectional study involved 50 knee OA patients clinically diagnosed as per the American College of Rheumatology guidelines and 50 healthy controls. Radiological examination as per Kellgren-Lawrence (KL) criteria was done to determine the disease severity. Peripheral blood samples were collected from each participant, and serum Sirt6 and IL-6 levels were measured using ELISA. The serum Sirt6 levels in knee OA patients were significantly lower as compared to healthy controls (p = 0.023). Patients with knee OA of KL grade 4 had significantly lower Sirt6 levels as compared to those with KL grade 2 OA (p = 0.031). Individuals of younger age group had higher Sirt6 levels compared to older age group. IL-6 levels in knee OA patients were significantly higher as compared to controls (p = 0.007). A negative correlation was observed between serum Sirt6 and IL-6 levels (r = -0.407; p = 0.035). The study concludes that serum Sirt6 levels are inversely associated with knee OA and may serve as a potential biomarker for the disease. Moreover, a negative correlation between Sirt6 and IL-6 levels was observed in this study. Further investigations are necessary to confirm these findings and to explore the mechanisms by which Sirt6 and IL-6 are involved in OA.

A novel method to quantify TSG‐6 protein levels in synovial fluid from osteoarthritis patients

AbstractTSG‐6, the protein product of Tumor Necrosis Factor‐stimulated gene‐6, is a potential biomarker of disease activity/progression in osteoarthritis (OA). By ELISA, TSG‐6 is elevated in synovial fluid (SF) from OA patients and its catalytic activity, for the covalent transfer of inter‐α‐inhibitor heavy chains onto hyaluronan (HA), is reported to correlate with progression to total knee replacement. Here, quantitative western blot (qWB) analysis, following sequential digestion with hyaluronidase and chondroitinase enzymes, was developed for the accurate determination of TSG‐6 levels in SFs. qWB analysis of 125 OA SFs yielded values of 1.0–45.2 μg/mL (median: 3.9 μg/mL), 100–1000‐fold higher than those previously reported; TSG‐6 concentration was positively correlated with age and pain/outcome scores in male patients. The values determined, by ELISA or a heavy chain transfer activity assay, following the same digestion conditions (in 23 SFs), were both ≥10‐fold lower. Apparent TSG‐6 catalytic activity was significantly affected by the concentration of HA present in SF or added exogenously. Thus, the interactions of TSG‐6 with components of OA SF prevent accurate determination of its concentration/activity when using existing assays. However, the quantitative western blot method developed here appears more suitable for further evaluation of the utility of TSG‐6 as a biomarker in OA.

Bioinformatic analysis combined with immune infiltration to explore osteoarthritis biomarkers and drug prediction.

Along with global aging, osteoarthritis (OA) appears to have a high incidence and disability rate, which seriously affects the quality of life of patients, making age a major risk factor. However, the pathology of OA is under-researched, and there is no obvious and effective treatment. Research has demonstrated the importance of aging, inflammation, and immunology in the onset and course of OA. This study aims to anticipate therapeutic drugs based on critical genes associated with OA and to elucidate the roles of genes and possible biomarkers associated with inflammation, immunology, and cellular senescence in OA. The OA gene expression matrix was first obtained from the Gene Expression Omnibus database. Screening for OA significant differentially expressed genes by bioinformatics identification. Specific biological processes and related signaling pathways of the differential genes were enriched. Then elucidate the status of immune cell involvement in OA based on immune infiltration analysis. Finally predict therapeutic agents based on pivotal genes. A total of 198 differentially expressed genes were identified in OA, and TP53, EGFR, TGFB1, LEP, CD4, MAPK8, SCARB1, ADIPOQ, JAK2, and SERPINE1 were further identified as important hub genes. The enrichment results showed that the development of arthritis was mainly related to immune cell differentiation, amino acid metabolism and cellular senescence process. The validation of immune infiltration results indicated that NK_cells, CD4_Tcells, Macrophages, Monocytic_lineage, Dendritic_cells, Basophils, CD8+_naive_T-cells may play an important role in the immune process of OA. Key Drug Prediction of Hub Genes found that Halicin, Ruxolitinib, Tofacitinib, Clenoliximab, Baricitinib may be a key drug or component in the treatment of OA.

Gold Nanoparticles as a Tool to Detect Biomarkers in Osteoarthritis: New Insights

Gold Nanoparticles as a Tool to Detect Biomarkers in Osteoarthritis: New Insights

Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment

BackgroundOsteoarthritis (OA) is a prevalent joint disorder characterized by cartilage degeneration and joint inflammation. Liquid-liquid phase separation (LLPS), a biophysical process involved in cellular organization, has recently gained attention in OA research. However, the relationship between LLPS and OA remains poorly understood. MethodsWe analyzed gene expression data from the GSE48556 dataset to identify LLPS-related genes associated with OA. Differential expression analysis, enrichment analyses, and machine learning algorithms were employed to explore the functional significance of LLPS-related genes in OA and then construct a diagnostic model for OA. In addition, IL-1β as a pro-inflammatory factor to establish an in vitro OA model, and the protein expression levels of OA biomarkers were detected by western blot. ResultsA total of 145 LLPS-related genes were screened in OA samples. Enrichment analyses revealed these genes were mainly enriched in mRNA metabolic processes, cytoplasmic granules, and insulin resistance. Four characteristic genes for OA were selected by using machine learning algorithms, including ADRB2, H3F3B, GNL3L, and PELO. These genes showed satisfactory diagnostic values. Furthermore, there were association between these biomarkers and immune cells, including T cells CD8 and monocytes. In vitro experiments showed that IL-1β stimulation significantly inhibited the cell viability of chondrocytes and enhanced the levels of pro-inflammatory factors, that could mimic the inflammatory state of OA. The expression levels of GNL3L and H3F3B proteins in IL-1β group were obviously lower than those in control group, while levels of ADRB2 and PELO were higher, which was consistent with the results of bioinformatics analysis. ConclusionOur study identifies LLPS-related genes as potential diagnostic biomarkers for OA. These findings provide insights into the molecular mechanisms underlying OA pathogenesis and offer opportunities for the development of novel therapeutic strategies.

The Role of MicroRNAs in the Pathophysiology of Osteoarthritis.

Worldwide, osteoarthritis (OA) is the most common cause of joint pain in older people. Many factors contribute to osteoarthritis' development and progression, including secondary osteoarthritis' underlying causes. It is important to note that osteoarthritis affects all four tissues: cartilage, bone, joint capsule, and articular apparatus. An increasingly prominent area of research in osteoarthritis regulation is microRNAs (miRNAs), a small, single-stranded RNA molecule that controls gene expression in eukaryotes. We aimed to assess and summarize current knowledge about the mechanisms of the action of miRNAs and their clinical significance. Osteoarthritis (OA) is affected by the interaction between miRNAs and inflammatory processes, as well as cartilage metabolism. MiRNAs also influence cartilage cell apoptosis, contributing to the degradation of the cartilage in OA. Studies have shown that miRNAs may have both an inhibitory and promoting effect on osteoporosis progression through their influence on molecular mechanisms. By identifying these regulators, targeted treatments for osteoarthritis may be developed. In addition, microRNA may also serve as a biomarker for osteoarthritis. By using these biomarkers, the disease could be detected faster, and early intervention can be instituted to prevent mobility loss and slow deterioration.

Cartilage oligomeric matrix protein as a potential biomarker for knee osteoarthritis.

This study aimed to determine the expression and clinical significance of a cartilage protein, cartilage oligomeric matrix protein (COMP), in knee osteoarthritis (OA) patients. A total of 270 knee OA patients and 93 healthy controls were recruited. COMP messenger RNA (mRNA) and protein levels in serum, synovial fluid, synovial tissue, and fibroblast-like synoviocytes (FLSs) of knee OA patients were determined using enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and immunohistochemistry. COMP protein levels were significantly elevated in serum and synovial fluid of knee OA patients, especially those in the advanced stages of the disease. Serum COMP was significantly correlated with radiological severity as well as measures of body composition, physical performance, knee pain, and disability. Receiver operating characteristic curve analysis unveiled a diagnostic value of serum COMP as a biomarker of knee OA (41.64 ng/ml, area under the curve (AUC) = 1.00), with a sensitivity of 99.6% and a specificity of 100.0%. Further analysis uncovered that COMP mRNA expression was markedly upregulated in the inflamed synovium of knee OA, consistent with immunohistochemical staining revealing localization of COMP protein in the lining and sub-lining layers of knee OA inflamed synovium. Most notably, relative COMP mRNA expression in knee OA synovium was positively associated with its protein levels in serum and synovial fluid of knee OA patients. In human knee OA FLSs activated with tumour necrosis factor-alpha, COMP mRNA expression was considerably up-regulated in a time-dependent manner. All results indicate that COMP might serve as a supportive diagnostic marker for knee OA in conjunction with the standard diagnostic methods.

Predictive models of radiographic progression and pain progression in patients with knee osteoarthritis: data from the FNIH OA biomarkers consortium project

ObjectivesThe progression of knee osteoarthritis (OA) can be defined as either radiographic progression or pain progression. This study aimed to construct models to predict radiographic progression and pain progression in patients with knee OA.MethodsWe retrieved data from the FNIH OA Biomarkers Consortium project, a nested case-control study. A total of 600 subjects with mild to moderate OA (Kellgren-Lawrence grade of 1, 2, or 3) in one target knee were enrolled. The patients were classified as radiographic progressors (n = 297), non-radiographic progressors (n = 303), pain progressors (n = 297), or non-pain progressors (n = 303) according to the change in the minimum joint space width of the medial compartment and the WOMAC pain score during the follow-up period of 24–48 months. Initially, 376 variables concerning demographics, clinical questionnaires, imaging measurements, and biochemical markers were included. We developed predictive models based on multivariate logistic regression analysis and visualized the models with nomograms. We also tested whether adding changes in predictors from baseline to 24 months would improve the predictive efficacy of the models.ResultsThe predictive models of radiographic progression and pain progression consisted of 8 and 10 variables, respectively, with area under curve (AUC) values of 0.77 and 0.76, respectively. Incorporating the change in the WOMAC pain score from baseline to 24 months into the pain progression predictive model significantly improved the predictive effectiveness (AUC = 0.86).ConclusionsWe identified risk factors for imaging progression and pain progression in patients with knee OA over a 2- to 4-year period, and provided effective predictive models, which could help identify patients at high risk of progression.

Identification of plasma and urinary inflammatory markers in severe knee osteoarthritis: Relations with synovial fluid markers

BackgroundThis study aimed to identify plasma and urinary cytokines as potential biomarkers for severe knee osteoarthritis (OA). It also investigated associations between these cytokines and cartilage markers, as well as their connections with synovial fluid (SF) markers.MethodsSamples of plasma, urine, and SF were obtained from patients (n = 40) undergoing total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA) due to severe knee OA. Control samples of plasma and urine were collected from non-OA individuals (n = 15). We used a Luminex immunoassay for the simultaneous measurement of 19 cytokines, MMP-1, and MMP-3 levels. COMP, CTX-II, and hyaluronan (HA) levels were quantified using enzyme-linked immunosorbent assay (ELISA) kits. Receiver operating characteristic (ROC) curves were utilized to analyze each biomarker’s performance. Correlations among these biomarkers were evaluated via Spearman’s correlation.ResultsThe levels of plasma (p)CCL11, pCXCL16, pIL-8, pIL-15, pHA, urinary (u)CCL2, uCCL11, uCCL19, uCXCL16, uIL-1β, uIL-6, uIL-8, uIL-12p70, uIL-15, uIL-33, uMMP-3, uHA, uCTX-II, and uCOMP were significantly elevated in individuals with severe knee OA. Notably, specific correlations were observed between the plasma/urine biomarkers and SF biomarkers: pCCL11 with sfHA (r = 0.56) and sfTNF-α (r = 0.58), pIL-15 with sfCCL19 (r = 0.43) and sfCCL20 (r = 0.44), and uCCL19 with sfCCL11 (r = 0.45) and sfIL-33 (r = 0.51). Positive correlations were also observed between uCCL11 and its corresponding sfCCL11(r = 0.49), as well as between sfCCL11 and other cytokines, namely sfCCL4, sfCCL19, sfCCL20, sfIL-33, and sfTNF-α (r = 0.46–0.63).ConclusionThis study provides an extensive profile of systemic inflammatory mediators in plasma of knee OA and identified four inflammatory markers (pCCL11, pIL-15, uCCL11, and uCCL19) reflecting joint inflammation.