Skip to main content

Main menu

  • Home
  • Content
    • First Release
    • Current
    • Archives
    • Collections
    • Audiovisual Rheum
    • COVID-19 and Rheumatology
  • Resources
    • Guide for Authors
    • Submit Manuscript
    • Author Payment
    • Reviewers
    • Advertisers
    • Classified Ads
    • Reprints and Translations
    • Permissions
    • Meetings
    • FAQ
    • Policies
  • Subscribers
    • Subscription Information
    • Purchase Subscription
    • Your Account
    • Terms and Conditions
  • About Us
    • About Us
    • Editorial Board
    • Letter from the Editor
    • Duncan A. Gordon Award
    • GDPR Policy
    • Accessibility
  • Contact Us
  • JRheum Supplements
  • Services

User menu

  • My Cart
  • Log In

Search

  • Advanced search
The Journal of Rheumatology
  • JRheum Supplements
  • Services
  • My Cart
  • Log In
The Journal of Rheumatology

Advanced Search

  • Home
  • Content
    • First Release
    • Current
    • Archives
    • Collections
    • Audiovisual Rheum
    • COVID-19 and Rheumatology
  • Resources
    • Guide for Authors
    • Submit Manuscript
    • Author Payment
    • Reviewers
    • Advertisers
    • Classified Ads
    • Reprints and Translations
    • Permissions
    • Meetings
    • FAQ
    • Policies
  • Subscribers
    • Subscription Information
    • Purchase Subscription
    • Your Account
    • Terms and Conditions
  • About Us
    • About Us
    • Editorial Board
    • Letter from the Editor
    • Duncan A. Gordon Award
    • GDPR Policy
    • Accessibility
  • Contact Us
  • Follow jrheum on Twitter
  • Visit jrheum on Facebook
  • Follow jrheum on LinkedIn
  • Follow jrheum on RSS
Research ArticleArticle

Intraarticular and Systemic Inflammatory Profiles May Identify Patients with Osteoarthritis

Bryan J. Heard, Marvin J. Fritzler, J. Preston Wiley, Jenelle McAllister, Liam Martin, Hani El-Gabalawy, David A. Hart, Cyril B. Frank and Roman Krawetz
The Journal of Rheumatology August 2013, 40 (8) 1379-1387; DOI: https://doi.org/10.3899/jrheum.121204
Bryan J. Heard
From the McCaig Institute for Bone and Joint Health, the Department of Anatomy and Cell Biology, the Department of Surgery, the Department of Medicine, and the Sports Medicine Centre, Faculty of Kinesiology, University of Calgary, Calgary, Alberta; and the Arthritis Centre, University of Manitoba, Winnipeg, Manitoba, Canada.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marvin J. Fritzler
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
J. Preston Wiley
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jenelle McAllister
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Liam Martin
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hani El-Gabalawy
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David A. Hart
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cyril B. Frank
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Roman Krawetz
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: rkrawetz@ucalgary.ca
  • Article
  • Figures & Data
  • References
  • Info & Metrics
  • PDF
  • eLetters
PreviousNext
Loading

Abstract

Objective. To determine whether cytokine/chemokine profiles from synovial fluid and sera discriminate mild/moderate osteoarthritis (OA) from normal and severe OA cohorts.

Methods. Multiplex technology was used to quantify expression levels for 42 cytokines in the synovial fluid of patients diagnosed with severe OA (n = 20) and mild/moderate OA (n = 12), as well as normal controls (n = 34). The same 42 cytokines were examined in serum samples of patients with severe OA (n = 26) and mild/moderate OA (n = 74) and normal individuals (n = 100). Treatment group comparisons followed by principal component analysis (PCA) and K-means clustering of the significantly different cytokines/chemokines revealed groupings of patients by physician diagnosis.

Results. Differences in cytokine/chemokine levels were found between control, mild/moderate OA, and severe OA synovial fluid samples, as well as between normal and mild/moderate OA serum samples, and between control and severe OA serum samples. No differences were observed between mild/moderate and severe OA serum samples. Visual groupings based on PCA were validated by K-means analysis, with the best results obtained from the comparison of normal and mild/moderate OA serum samples with 96% of normal and 93% of mild/moderate OA samples accurately identified.

Conclusion. Our study suggests that comparing the expression levels of cytokines/chemokines in synovial fluid and/or serum of patients with OA may have promise as a diagnostic platform to identify patients early in their disease course. This high-throughput low-cost assay may be able to provide clinicians with a diagnostic test to complement existing clinical and imaging modalities currently used to diagnose OA.

Key Indexing Terms:
  • CYTOKINES
  • OSTEOARTHRITIS
  • INFLAMMATION
  • LUMINEX
  • SYNOVIAL FLUID
  • SERUM

Osteoarthritis (OA) is emerging as the most common disease in the elderly as the global population ages. Following current trends, it has been suggested that OA will become the fourth leading cause of global disability by the year 20201,2. OA is thought to be the result of a destabilization of the balance between synthesis and degradation of intraarticular cartilage, although the exact cause or mechanism remains to be determined. OA is a multifactorial disease that is not limited to destruction of cartilage surfaces of the joint. Indeed, all other joint tissues (synovium, bone, ligament, capsule, muscle) can also be affected by the disease state3,4,5. Most commonly, however, OA is associated with erosion of the cartilage surface of the joint, which presents as pain and loss of mobility, and the majority of research on OA has focused on cartilage and the factors that may regulate its destruction6,7,8,9,10.

Many proteolytic enzymes, including several of the matrix metalloproteinases (MMP), have the ability to degrade the articular cartilage collagen scaffold11,12,13 by recognizing specific sequences in extracellular matrix (ECM) proteins and then cleaving these proteins. A number of studies have used structural fragments of cartilage proteins as potential biomarkers of OA. Procollagen II C-propeptide upregulation, a precursor of collagen (COL) II that appears as a repair response to damaged cartilage, has been localized to the articular cartilage of patients diagnosed with early OA14. Studies have shown this upregulation is detectable in patient synovial fluid and serum, highlighting it as a potential biomarker for early OA5. Conversely, proteolytic COL II breakdown is a key component in cartilage erosions observed in arthritic diseases. Fragments of COL II, or COL II epitopes, have been highlighted as potential OA biomarkers, generally all falling into one of 3 potential categories based on their origin from the native COL II molecule: cleavage neoepitopes, denaturation epitopes, or epitopes from the mature end of the molecule15. To date, however, no COL II/proCOL II early OA diagnostic assays have entered the clinical diagnostic market due to the variability of COL II epitopes within normal and arthritic patient populations. Other historical biomarkers such as cartilage oligomeric protein16 and hyaluronan17,18 have also been identified as early biomarkers, but have not enjoyed wide clinical adoption. One of the key limitations is the use of a single biomarker for a diagnostic assay, because baseline values can vary dramatically between individuals, and this approach can only be definitive in diseases where the product is completely differentially regulated (i.e., present vs absent) between normal and disease states, which has not been the case in OA.

Relevant to these approaches in biomarker development, several MMP have been implicated in arthritis, and in a number of gene expression studies messenger RNA levels of MMP have been shown to be regulated by inflammatory cytokines19,20,21. However, we recently published a study that suggests MMP expression levels are not sufficient to differentiate synovial fluid from normal individuals and from those with “early” OA22.

Multiple cytokines have been demonstrated to inhibit ECM production by chondrocytes, thereby affecting both the synthesis and degradation of the cartilage. In patients with rheumatoid arthritis (RA), a distinct role for cytokines in the destructive pathway has been extensively characterized23,24, but in OA the cytokines involved and their modes of action are less clear, although it was reported that proinflammatory cytokines are indeed expressed in OA synovial membranes and synovial fluids25.

Cytokines are released from cells in response to specific signals and influence target cells by regulating (positively or negatively) gene expression. In general, most cytokines have a relatively short active lifetime and therefore interact with neighboring cells or a population of cells in a micro-environment, in both an autocrine and/or paracrine pathways. Networks of cytokine signaling are complex, with some cytokines being able to amplify their own message and inhibit the expression of competing pathways. In some cases, cells express soluble decoy receptors or receptor antagonists to specific cytokines [e.g., interleukin 1 (IL-1), IL-1 receptor antagonist protein] to attenuate these amplification cascades26,27. With specific relevance to OA and the balance between matrix synthesis and destruction, it is thought that proinflammatory cytokines such as IL-1α/β, and tumor necrosis factor-α (TNF-α) from the synovium induce the expression of MMP to remodel the cartilage surfaces, and that other growth factors (such as transforming growth factor-β) stimulate chondrocytes to produce matrix28,29,30. However, if a dramatic increase in the expression of cytokines occurs, synthesis is inhibited, leading to deregulation of this process, resulting in cartilage destruction within the joint microenvironment29.

The field of biomarker research in OA has greatly expanded over the last decade. However, few, if any, definitive early markers of OA have been validated. We believe that using a multifactorial approach may shed light on potential diagnostic/prognostic biomarkers and possible underlying disease mechanisms. Multiplexing technology has been widely adopted for screening potential biomarkers in serum for many diseases31 and is starting to be used in cell-based assays as well32. This high-throughput, low-volume (roughly 50–100 μl) platform facilitates evaluation of a large number of potential candidate molecules in OA joints and sera. Such analyses have the potential to identify a multicomponent phenotype that best discriminates between “normal” and OA patients. In this study we identified 5 candidate OA biomarkers in synovial fluids and 14 in serum.

MATERIALS AND METHODS

Normal group; synovial fluid

Inclusion criteria for control cadaveric donations [collected from the Southern Alberta Organ and Tissue Donation Program (SAOTDP)] were age ≥ 40 years, no history of arthritis, joint injury or surgery (including visual inspection of the cartilage surfaces during recovery), no prescription antiinflammatory medications, no comorbidities (such as diabetes/cancer), and synovial fluid availability within 4 h of death.

Serum

All controls were specifically questioned about a personal or family history of arthritis or autoimmune diseases, and were excluded if they had a personal or family history of RA, systemic lupus erythematosus, scleroderma, polymyositis, vasculitis, spondyloarthropathies, inflammatory bowel disease, diabetes mellitus type I, and/or thyroid disease.

Mild/moderate OA group; synovial fluid

Inclusion criteria included a diagnosis of mild/moderate OA (preradiographic, Outerbridge score < 2: no evidence of cartilage loss or exposed bone on any joint surface) based on arthroscopic examination performed by a certified orthopedic surgeon at the University of Calgary.

Serum

Inclusion criteria required a diagnosis of mild/moderate OA performed by a sports medicine physician at the University of Calgary based on clinical symptoms for ≥ 3 months with radiographic evidence of changes associated with OA.

Severe OA group; synovial fluid

Inclusion criteria included age ≥ 40 years; OA diagnosed by a rheumatologist at the University of Calgary based on the American College of Rheumatology criteria33,34 including radiographic documentation; and no evidence of autoimmune disease or RA.

Serum

Inclusion criteria required radiographic evidence of OA of any compartment of the knee with collapsed or near-collapsed joint space of any compartment of the knee.

Our study protocol was approved by the University of Calgary Human Research Ethics Board and the Research Ethics Board of the University of Manitoba. All participants provided written consent to participate.

Sample handling

Synovial fluid from control cadaveric individuals was obtained from the SAOTDP. Synovial fluid from patients with severe OA was aspirated from the knee joint by the attending rheumatologist using a conventional sterile technique; early OA synovial fluid samples were obtained by an orthopedic surgeon during arthroscopic surgery. All synovial fluid samples were collected without the use of lavage or any other diluting agent. Native synovial fluid samples were divided into aliquots, centrifuged at 3000 g for 15 min at 4°C, and stored in cryogenic vials at −80°C. For standardization of the protocol, all synovial fluid samples were subjected to only 1 freeze-thaw event prior to assessment.

Serum samples

Serum was collected by standard venipuncture with vacuum tubes from normal individuals at the University of Manitoba and from OA patients (mild/moderate and severe) at the University of Calgary. They were stored at −80°C until required for analysis.

Luminex multiplex array

Synovial fluid and serum aliquots were thawed on ice and 20 μl of fluid was diluted with Milliplex running buffer (Millipore). Sample analysis was performed (Eve Technologies) using the Milliplex MAP Human Cytokine/Chemokine Panel (Millipore) on the Luminex 100 platform (Luminex Corp.), according to the manufacturer’s instructions and a previously published method35. All samples were assayed at least in duplicate and prepared standards were included in all runs.

Briefly, cytokine/chemokine-specific antibodies were precoated onto color-coded microparticles and dispensed into microtiter plates by the manufacturer. The standards and test samples were added to the microtiter wells and after standard incubation any unbound substances were washed away by vacuum filtration in an apparatus provided by the manufacturer. This was followed by the addition of biotinylated antibodies specific to the protein of interest to each well, and after standard incubation protocol any unbound biotinylated antibody was removed by vacuum washes as before. Streptavidin-PE antibodies were then added to each well, followed by a final wash to remove unbound streptavidin-PE. The microparticles were resuspended in Milliplex buffer and read using the Luminex 100 analyzer.

Statistical analysis

Statistical analysis was carried out with Stata 9.2 for Macintosh and Prism GraphPad 5 for Macintosh. Synovial fluid and serum samples were investigated separately to determine which had the greatest diagnostic value. Statistical analyses were completed in 4 steps: (1) each cytokine was examined in patient-group comparisons to acquire a panel of significant cytokines (potential biomarkers) within the synovial fluid and the serum; (2) principal components analysis (PCA) was applied to each panel of significant cytokines (synovial fluid and serum) to reduce the number of variables that define each patient from many to just 2 (the 2 principal components), the contributions of each cytokine to these principal components were reported; (3) patient-group comparisons were made using the principal components to determine whether the collective expression of significantly expressed cytokines was enough to statistically differentiate between patient groups; and (4) using K-means clustering, each patient was then investigated individually, as defined by their principal components, and the accuracy of our diagnostic test was determined.

Step 1: Synovial fluid cytokine expression treatment-group comparisons for normal (n = 34), mild/moderate OA (n = 12), and OA (n = 17), as well as the serum cytokine expression treatment-group comparison of normal (n = 100) and mild/moderate OA (n = 74) and severe OA (n = 26), were made using the Kruskal-Wallis test with Dunn’s multiple comparisons test (significance accepted at p ≤ 0.05). Step 2: Synovial fluid and serum PCA were performed using each specific panel of significantly expressed cytokines for each comparison in question and the weights of each of the cytokines on each component were reported. PCA is a data reduction algorithm that produces components that represent ideally weighted values that are calculated from the contributions of all cytokines investigated, while retaining much of the variability of the dataset. Normally in PCA, principal components (PC) are retained if their Eigen value is ≥ 1; however, in our study only the first 2 PC were investigated visually because they represented the majority of the dataset variability. Step 3: The PC generated from the panels of significantly expressed cytokines within synovial fluid and serum were investigated in patient-group comparisons using the Kruskal-Wallis test with Dunn’s multiple comparison test (significance accepted at p ≤ 0.05). Step 4: To mathematically verify the visual groupings of patients returned by PCA and thus determine the accuracy of the diagnostic test, K-means clustering was implemented using the PC responsible for the separation of treatment-group clusters as inputs. Briefly, K–means clustering is an unguided algorithm that separates data into a predetermined number of groups (k). This is accomplished by allocating each sample through an iterative process that calculates the sample’s shortest distance between k randomly selected centroids (k = 2 centroids in our study). Distances are calculated from the multivariate data that can be associated with each sample. In our study, each patient sample was represented by its score(s) from the first and/or second PC.

RESULTS

Inflammatory profiles of significantly expressed cytokines within synovial fluid and serum samples from normal knee joints, patients with mild/moderate OA, and patients with severe OA were determined. Significant differences (Table 1A; total levels presented in Table 1B) in cytokine expression levels were observed between the patient-group comparisons within synovial fluid. We then examined serum samples collected from individuals with no history of joint disease/injury and compared these to samples of mild/moderate or severe knee OA. The inflammatory profile observed in the synovial fluid was not fully replicated; however, a unique discriminatory profile was observed (Table 1A, 1B). To determine whether the cytokines/chemokines of significance between normal and mild/moderate OA were valuable as biomarkers of disease, they were further examined by multivariate analysis.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table 1.

A. Patient-group comparisons of cytokines and chemokines within synovial fluid samples and serum samples. Arrows represent significant changes in protein levels between groups. M/M-OA: mild/moderate osteoarthritis; S-OA: severe osteoarthritis. B. Means and standard deviations observed within serum and synovial fluid samples from all patient groups.

Using the respective panels of significant cytokines/chemokines from patient groups, PCA was performed. When synovial fluid data from normal, mild/moderate OA, and OA cohorts were analyzed, 3 groupings of data were visualized (Figure 1A). However, when the patient cohorts were investigated in the serum only, 2 groups of data were discerned, because the mild/moderate OA and severe OA samples overlapped to represent a single OA group below the normal group (Figure 1B). Because there was no visual separation along the x axis (PC 1), but there was on the y axis (PC 2), only PC 2 was used as an input to K-means clustering for the serum data. The contribution of each cytokine to each PC is shown in Figure 2 (PC 1 and 2 for synovial fluid, and PC 2 for serum). Also shown in Figure 2, there were statistical differences upon investigation of the PC of each patient group within the synovial fluid and the serum. PC 1 of the synovial fluid returned statistical significance between all 3 treatment groups, while PC 2 showed that normal results were significantly different from the 2 OA cohorts. PC 2 of the serum showed that normal samples were significantly different from those of mild/moderate OA and severe OA. However, no statistical differences were found in any comparisons of mild/moderate and severe OA serum samples.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Cytokines and chemokines that displayed significant differences in protein levels between normal, mild/moderate osteoarthritis (OA), and severe OA synovial fluid or serum were analyzed by principal component analysis (PCA). Three distinct groups were observed in the synovial fluid data, and 2 distinct groups were observed within the serum data. In each of these plots the broken lines are intended to separate each group of data. Each group is labeled with a number that corresponds to the K-means clustering groups described in Table 2.

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Contribution of each cytokine (factor loadings) to each principal component investigated and patient group comparisons of each principal component investigated. Synovial fluid factor loadings on principal components 1 and 2 are displayed in the bottom left and middle, with corresponding patient-group comparisons on the top left and middle. Factor loadings on principal component 2 of the serum are displayed on the bottom right, with corresponding patient-group comparisons shown directly above (on the top right). M/M-OA: mild/moderate osteoarthritis; S-OA: severe osteoarthritis; PC: principal component.

K-means clustering was used to assess the accuracy of our diagnostic test by mathematically verifying the visual grouping of data returned by each PCA conducted (Table 2). The accuracy of the K-means algorithm has been represented as the percentage of subjects of a patient group who were allocated to each PCA group. The serum provided an interesting finding, as we were able to discriminate between normal and OA phenotypes with significant accuracy. Specifically, 96% of the normal samples, 93% of the mild/moderate OA samples and 82% of the severe OA samples were accurately allocated into the correct cluster. It is also important to note that in the comparison investigating normal, mild/moderate OA, and severe OA, a subset (18%) of severe OA patients presented with serum inflammatory profiles that were comparable to those of normal individuals (Figure 2C), this is consistent with the current literature that suggests inflammation normally subsides once the cartilage surface of the joint has been eroded36.

View this table:
  • View inline
  • View popup
Table 2.

K-means clustering analysis.

DISCUSSION

This study demonstrates that multiplex analysis of synovial fluid and serum components has promise as a diagnostic approach to identify a mild/moderate OA phenotype based on the cytokine/chemokine expression in synovial fluid and serum samples, as well as the potential (with further study) to stratify patients with preradiographic and postradiographic OA based on cytokine/chemokine expression in synovial fluid. The reproducible, high-throughput assay that we describe can be easily standardized and performed, and the projected assay cost is < $100/patient. In addition, this diagnostic approach could complement the existing criteria for OA diagnosis, aid in the identification of patients in the early to mild/moderate stage of the disease, and be useful for inclusion criteria for clinical trials of therapies targeted for early to mild/moderate OA. However, the study as presented lacks a longitudinal component, which would strengthen the diagnostic and prognostic capabilities of this assay. Thus, we are assembling a number of longitudinal patient cohorts that will be followed up at regular intervals. Using these cohorts, we will also examine the effects of interventions (pharmaceutical, mechanical, etc.) on the expression of inflammatory biomarkers. However, our study provides the platform on which we can develop a novel and effective diagnostic for OA.

The contemporary clinical diagnosis of OA is based on symptoms of joint pain and swelling, and radiographic changes consistent with OA. These criteria typically identify patients with only moderate to severe OA, at a time when the disease is relatively advanced and treatment options are limited. It is generally accepted that managing patients in the early stages of OA will deliver the best possible outcomes; however, identifying these early individuals remains complicated. Noninvasive imaging such as magnetic resonance imaging and other novel diagnostic modalities are just now entering the clinical arena, but this approach is currently expensive, requires significant medical expertise, and requires access to the technology. Further, it is currently not known whether imaging at a single timepoint is sufficient to make a definitive diagnosis.

In our study, variations in a number of synovial fluid proteins have been identified [basic fibroblast growth factor (FGF-2), Fit-3 ligand, cluster of differentiation 40 ligand (CD40L), IL-5, and macrophage-derived chemokine (MDC)] as potential discriminators (biomarkers) of early OA; while identified serum biomarkers were epidermal growth factor, growth-related oncogene, methyl-accepting chemotaxis protein (MCP-1 and 3), RANTES, eotaxin, interferon-γ, IL-6, IL-8, IL-17, interferon-gamma inducible protein-10 (IP-10), platelet-derived growth factor AA/BB, CD40L, and vascular endothelial growth factor (VEGF). Interestingly, IL-6 and IL-1ra, reported by others to be potential biomarkers of OA37,38,39,40,41, were not significantly different between normal and early OA synovial fluid samples, while IL-6 was identified in serum in our study. To date, many of the potential biomarkers identified by the multiplex technologies have not been extensively reported in OA or in preclinical models of OA. MCP-1 was shown to be the most influential cytokine in the separation of normal and OA samples. As well, a study reported that increased MCP-1 levels correlated with increased knee pain in patients with early joint disease in the absence of RA42, and the authors observed that patients with increased knee pain exhibited increased levels of MCP-1.

It is also notable that the expression of many cytokines [FGF-2, Fit-3 ligand, granulocyte-macrophage colony-stimulating factor (GM-CSF), IL-1α, IL-9, and CD40L] was decreased in synovial fluid in mild/moderate OA knee joints compared to normal joints (Table 1), while only IL-5 and MDC were increased. Some evidence suggests FGF-2 may be chondroprotective within the joints43, and it has been observed that MDC is increased within the synovial fluid and serum of patients with arthritis44. However, in a mouse model of arthritis, it was recently demonstrated that the absence of GM-CSF leads to decreased pain and cartilage damage45. The other factors (except IL-1α) have not been intensely studied in OA, but deserve further attention. When synovial fluid from patients with severe OA was compared to mild/moderate OA or normal synovial fluid, we observed an increase in the expression levels of multiple cytokines (as expected from previous reports). Specifically, IL-6, IL-1ra, IL-12 (p40 and p70), IP-10, MCP-1 and 3, and MIP-1β were all increased in severe OA samples compared to normal and mild/moderate synovial fluid. IL-12 has previously been observed to increase in autoimmune disorders, but not specifically in OA46, while IP-10 levels have been shown to both increase and decrease in synovial fluid, plasma, and serum with OA progression and pain47,48. Therefore, the roles of these proteins in OA are not completely understood, but almost all these cytokines have been linked to pain49,50,51. This introduces the question of how many of these “biomarkers” are associated with osteoarthritic pain, and this should be addressed by looking for correlations with clinical pain scores. One cytokine that has been implicated in progression and pain in OA is TNF-α52; based on previous reports we expected to observe significant differences in TNF-α between our patient groups; however, this was not the case, as only minimal levels of TNF-α were detected within normal and severe OA synovial fluid samples, while mild/moderate OA samples displayed no expression. This suggests that either our test was not sensitive enough to detect TNF-α or that TNF-α may be expressed only in a subset of patients with OA. To test the assay further, we also assessed a number of RA synovial fluid samples and found high levels of TNF-α (data not shown), suggesting that TNF-α is not always expressed in OA (mild/moderate or severe).

In contrast to the synovial fluid cytokine profiles, we observed a number of upregulated and downregulated proteins in patients with OA compared to normal individuals. However, a number of proteins were conserved between synovial fluid and serum (up in OA: eotaxin, IL-6, IL-8, IL-10, MCP-1, TGF-α, and VEGF; down in OA: GM-CSF). While many of these proteins have been implicated in OA, the conservation of expression locally and systemically may suggest they play a role in regulating the process in OA from outside the joint.

Aside from correlating cytokines to pain and other patient outcome measures, groups have linked individual cytokine levels to radiography52,53,54. In serum samples, we did not identify any cytokines that were significantly different between mild/moderate and severe OA (while normal individuals had no radiographic changes), and in synovial fluid we identified a number of cytokines (n = 22) that increased with disease progression (Table 1). It is important to note that mild/moderate patients sampled for synovial fluid exhibited no radiographic evidence of OA. However, only a few of the 22 cytokines that increased with OA progression have been previously correlated to radiographic changes or risk of OA (based on protein and/or gene expression studies), IL-6 being the most studied. Our study suggests that additional proteins may correlate with disease severity; however, longitudinal studies must be carried out to prove this hypothesis.

Our study has limitations. First, a prospective study using cytokines as predictors of OA development would have been helpful. In our study, the cytokine profiles validated an established clinical diagnosis of OA. This is potentially a problem because diagnosis of OA, especially in the early stages, is a particular challenge. Accordingly, we termed our less severe cohort as mild/moderate because the clinical classification of “early OA” had not been achieved unequivocally. The second limitation is that our sample size, while adequate for the statistical methods described, could be increased to strengthen the power of the analysis for both synovial fluid and serum samples.

To overcome these limitations, we are currently recruiting and testing a larger, longitudinal patient cohort that will address the sample size issue, and allow us to perform repeated measures on the same population during their individual and potentially unique progression of disease.

A number of cytokines are differentially expressed in synovial fluid and sera from patients with mild/moderate OA compared to normal individuals and patients with severe OA. These discriminating factors (potential biomarkers) may indeed distinguish among patients with mild/moderate OA (balanced for age and sex) as well as normal individuals with no documented history of joint disease and no visible joint damage. These findings suggest that, with further study and validation, assessment of protein levels for cytokines and chemokines using the multiplex technology could contribute to development of diagnostics for mild/moderate and severe OA, as well as a potential approach to monitor the efficacy of interventions to control or reverse the disease course.

Acknowledgment

We thank Scott Ewald, Gary Rockl, and the Southern Alberta Organ and Tissue Donation Program, as well as Mark Fritzler and Riley Sullivan of Eve Technologies.

Footnotes

  • Supported by an Alberta Innovates — Health Solutions Osteoarthritis Team Grant and a research grant from Pfizer.

  • Accepted for publication March 14, 2013.

REFERENCES

  1. 1.↵
    1. Reginster JY
    . The prevalence and burden of arthritis. Rheumatology 2002;41 Supp 1:3–6.
    OpenUrlAbstract
  2. 2.↵
    1. Woolf AD,
    2. Pfleger B
    . Burden of major musculoskeletal conditions. Bull World Health Organ 2003;81:646–56.
    OpenUrlPubMed
  3. 3.↵
    1. Funakoshi Y,
    2. Hariu M,
    3. Tapper JE,
    4. Marchuk LL,
    5. Shrive NG,
    6. Kanaya F,
    7. et al.
    Periarticular ligament changes following ACL/MCL transection in an ovine stifle joint model of osteoarthritis. J Orthop Res 2007;25:997–1006.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Panula HE,
    2. Helminen HJ,
    3. Kiviranta I
    . Slowly progressive osteoarthritis after tibial valgus osteotomy in young beagle dogs. Clin Orthop Relat Res 1997;343:192–202.
    OpenUrl
  5. 5.↵
    1. Benito MJ,
    2. Veale DJ,
    3. FitzGerald O,
    4. van den Berg WB,
    5. Bresnihan B
    . Synovial tissue inflammation in early and late osteoarthritis. Ann Rheum Dis 2005;64:1263–7.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Garvican ER,
    2. Vaughan-Thomas A,
    3. Innes JF,
    4. Clegg PD
    . Biomarkers of cartilage turnover. Part 1: Markers of collagen degradation and synthesis. Vet J 2010;185:36–42.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Burrage PS,
    2. Mix KS,
    3. Brinckerhoff CE
    . Matrix metalloproteinases: role in arthritis. Front Biosci 2006;11:529–43.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Fan J,
    2. Varshney RR,
    3. Ren L,
    4. Cai D,
    5. Wang DA
    . Synovium-derived mesenchymal stem cells: A new cell source for musculoskeletal regeneration. Tissue Eng Part B Rev 2009;15:75–86.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Galois L,
    2. Freyria AM,
    3. Grossin L,
    4. Hubert P,
    5. Mainard D,
    6. Herbage D,
    7. et al.
    Cartilage repair: Surgical techniques and tissue engineering using polysaccharide- and collagen-based biomaterials. Biorheology 2004;41:433–43.
    OpenUrlPubMed
  10. 10.↵
    1. De Rienzo F,
    2. Saxena P,
    3. Filomia F,
    4. Caselli G,
    5. Colace F,
    6. Stasi L,
    7. et al.
    Progress towards the identification of new aggrecanase inhibitors. Curr Med Chem 2009;16:2395–415.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Flynn BP,
    2. Bhole AP,
    3. Saeidi N,
    4. Liles M,
    5. Dimarzio CA,
    6. Ruberti JW
    . Mechanical strain stabilizes reconstituted collagen fibrils against enzymatic degradation by mammalian collagenase matrix metalloproteinase 8 (MMP-8). PLoS One 2010;5:e12337.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Le Graverand MP,
    2. Eggerer J,
    3. Vignon E,
    4. Otterness IG,
    5. Barclay L,
    6. Hart DA
    . Assessment of specific mRNA levels in cartilage regions in a lapine model of osteoarthritis. J Orthop Res 2002;20:535–44.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Fehr JE,
    2. Trotter GW,
    3. Oxford JT,
    4. Hart DA
    . Comparison of Northern blot hybridization and a reverse transcriptase-polymerase chain reaction technique for measurement of mRNA expression of metalloproteinases and matrix components in articular cartilage and synovial membrane from horses with osteoarthritis. Am J Vet Res 2000;61:900–5.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Elsaid KA,
    2. Chichester CO
    . Review: Collagen markers in early arthritic diseases. Clin Chim Acta 2006;365:68–77.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Deberg M,
    2. Dubuc JE,
    3. Labasse A,
    4. Sanchez C,
    5. Quettier E,
    6. Bosseloir A,
    7. et al.
    One-year follow-up of Coll2-1, Coll2-1NO2 and myeloperoxydase serum levels in osteoarthritis patients after hip or knee replacement. Ann Rheum Dis 2008;67:168–74.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Andriamanalijaona R,
    2. Benateau H,
    3. Barre PE,
    4. Boumediene K,
    5. Labbe D,
    6. Compere JF,
    7. et al.
    Effect of interleukin-1-beta on transforming growth factor-beta and bone morphogenetic protein-2 expression in human periodontal ligament and alveolar bone cells in culture: modulation by avocado and soybean unsaponifiables. J Periodontol 2006;77:1156–66.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Golightly YM,
    2. Marshall SW,
    3. Kraus VB,
    4. Renner JB,
    5. Villaveces A,
    6. Casteel C,
    7. et al.
    Biomarkers of incident radiographic knee osteoarthritis: do they vary by chronic knee symptoms? Arthritis Rheum 2011;63:2276–83.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Chen HC,
    2. Shah S,
    3. Stabler TV,
    4. Li YJ,
    5. Kraus VB
    . Biomarkers associated with clinical phenotypes of hand osteoarthritis in a large multigenerational family: The CARRIAGE Family Study. Osteoarthritis Cartilage 2008;16:1054–9.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Thampatty BP,
    2. Li H,
    3. Im HJ,
    4. Wang JH
    . EP4 receptor regulates collagen type-I, MMP-1, and MMP-3 gene expression in human tendon fibroblasts in response to IL-1 beta treatment. Gene 2007;386:154–61.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Jimbo K,
    2. Park JS,
    3. Yokosuka K,
    4. Sato K,
    5. Nagata K
    . Positive feedback loop of interleukin-1-beta upregulating production of inflammatory mediators in human intervertebral disc cells in vitro. J Neurosurg Spine 2005;2:589–95.
    OpenUrlPubMed
  21. 21.↵
    1. Van Den Steen PE,
    2. Wuyts A,
    3. Husson SJ,
    4. Proost P,
    5. Van Damme J,
    6. Opdenakker G
    . Gelatinase B/MMP-9 and neutrophil collagenase/MMP-8 process the chemokines human GCP-2/CXCL6, ENA-78/CXCL5 and mouse GCP-2/LIX and modulate their physiological activities. Eur J Biochem 2003;270:3739–49.
    OpenUrlPubMed
  22. 22.↵
    1. Heard BJ,
    2. Martin L,
    3. Rattner JB,
    4. Frank CB,
    5. Hart DA,
    6. Krawetz RJ
    . Matrix metalloproteinase protein expression profiles cannot distinguish between normal and early osteoarthritic synovial fluid. BMC Musculoskelet Disord 2012;13:126.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Hueber AJ,
    2. Asquith DL,
    3. McInnes IB,
    4. Miller AM
    . Embracing novel cytokines in RA — complexity grows as does opportunity! Best Pract Res Clin Rheumatol 2010;24:479–87.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Atzeni F,
    2. Sarzi-Puttini P
    . Anti-cytokine antibodies for rheumatic diseases. Curr Opin Investig Drugs 2009;10:1204–11.
    OpenUrlPubMed
  25. 25.↵
    1. Webb GR,
    2. Westacott CI,
    3. Elson CJ
    . Osteoarthritic synovial fluid and synovium supernatants up-regulate tumor necrosis factor receptors on human articular chondrocytes. Osteoarthritis Cartilage 1998;6:167–76.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Jacques C,
    2. Gosset M,
    3. Berenbaum F,
    4. Gabay C
    . The role of IL-1 and IL-1Ra in joint inflammation and cartilage degradation. Vitam Horm 2006;74:371–403.
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Firestein GS,
    2. Berger AE,
    3. Tracey DE,
    4. Chosay JG,
    5. Chapman DL,
    6. Paine MM,
    7. et al.
    IL-1 receptor antagonist protein production and gene expression in rheumatoid arthritis and osteoarthritis synovium. J Immunol 1992;149:1054–62.
    OpenUrlAbstract
  28. 28.↵
    1. Danfelter M,
    2. Onnerfjord P,
    3. Heinegard D
    . Fragmentation of proteins in cartilage treated with interleukin-1: specific cleavage of type IX collagen by matrix metalloproteinase 13 releases the NC4 domain. J Biol Chem 2007;282:36933–41.
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    1. Fernandes JC,
    2. Martel-Pelletier J,
    3. Pelletier JP
    . The role of cytokines in osteoarthritis pathophysiology. Biorheology 2002;39:237–46.
    OpenUrlPubMed
  30. 30.↵
    1. Chen CW,
    2. Tsai YH,
    3. Deng WP,
    4. Shih SN,
    5. Fang CL,
    6. Burch JG,
    7. et al.
    Type I and II collagen regulation of chondrogenic differentiation by mesenchymal progenitor cells. J Orthop Res 2005;23:446–53.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Vistnes M,
    2. Christensen G,
    3. Omland T
    . Multiple cytokine biomarkers in heart failure. Expert Rev Mol Diagn 2010;10:147–57.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Huss RS,
    2. Huddleston JI,
    3. Goodman SB,
    4. Butcher EC,
    5. Zabel BA
    . Synovial tissue-infiltrating natural killer cells in osteoarthritis and periprosthetic inflammation. Arthritis Rheum 2010;62:3799–805.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. Altman R,
    2. Asch E,
    3. Bloch D,
    4. Bole G,
    5. Borenstein D,
    6. Brandt K,
    7. et al.
    Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association. Arthritis Rheum 1986;29:1039–49.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Aletaha D,
    2. Neogi T,
    3. Silman AJ,
    4. Funovits J,
    5. Felson DT,
    6. Bingham CO 3rd.,
    7. et al.
    2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 2010;62:2569–81.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Djoba Siawaya JF,
    2. Roberts T,
    3. Babb C,
    4. Black G,
    5. Golakai HJ,
    6. Stanley K,
    7. et al.
    An evaluation of commercial fluorescent bead-based luminex cytokine assays. PLoS One 2008;3:e2535.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Bonnet CS,
    2. Walsh DA
    . Osteoarthritis, angiogenesis and inflammation. Rheumatology 2005;44:7–16.
    OpenUrlAbstract/FREE Full Text
  37. 37.↵
    1. Westacott CI,
    2. Sharif M
    . Cytokines in osteoarthritis: Mediators or markers of joint destruction? Semin Arthritis Rheum 1996;25:254–72.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Livshits G,
    2. Zhai G,
    3. Hart DJ,
    4. Kato BS,
    5. Wang H,
    6. Williams FM,
    7. et al.
    Interleukin-6 is a significant predictor of radiographic knee osteoarthritis: the Chingford Study. Arthritis Rheum 2009;60:2037–45.
    OpenUrlCrossRefPubMed
  39. 39.↵
    1. Goldring MB,
    2. Otero M,
    3. Tsuchimochi K,
    4. Ijiri K,
    5. Li Y
    . Defining the roles of inflammatory and anabolic cytokines in cartilage metabolism. Ann Rheum Dis 2008;67 Suppl 3:iii75–82.
    OpenUrlAbstract/FREE Full Text
  40. 40.↵
    1. Shlopov BV,
    2. Gumanovskaya ML,
    3. Hasty KA
    . Autocrine regulation of collagenase 3 (matrix metalloproteinase 13) during osteoarthritis. Arthritis Rheum 2000;43:195–205.
    OpenUrlCrossRefPubMed
  41. 41.↵
    1. Attur M,
    2. Wang HY,
    3. Kraus VB,
    4. Bukowski JF,
    5. Aziz N,
    6. Krasnokutsky S,
    7. et al.
    Radiographic severity of knee osteoarthritis is conditional on interleukin 1 receptor antagonist gene variations. Ann Rheum Dis 2010;69:856–61.
    OpenUrlAbstract/FREE Full Text
  42. 42.↵
    1. Cuellar JM,
    2. Scuderi GJ,
    3. Cuellar VG,
    4. Golish SR,
    5. Yeomans DC
    . Diagnostic utility of cytokine biomarkers in the evaluation of acute knee pain. J Bone Joint Surg Am 2009;91:2313–20.
    OpenUrlCrossRef
  43. 43.↵
    1. Chia SL,
    2. Sawaji Y,
    3. Burleigh A,
    4. McLean C,
    5. Inglis J,
    6. Saklatvala J,
    7. et al.
    Fibroblast growth factor 2 is an intrinsic chondroprotective agent that suppresses ADAMTS-5 and delays cartilage degradation in murine osteoarthritis. Arthritis Rheum 2009;60:2019–27.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Flytlie HA,
    2. Hvid M,
    3. Lindgreen E,
    4. Kofod-Olsen E,
    5. Petersen EL,
    6. Jorgensen A,
    7. et al.
    Expression of MDC/CCL22 and its receptor CCR4 in rheumatoid arthritis, psoriatic arthritis and osteoarthritis. Cytokine 2010;49:24–9.
    OpenUrlCrossRefPubMed
  45. 45.↵
    1. Cook AD,
    2. Pobjoy J,
    3. Steidl S,
    4. Durr M,
    5. Braine EL,
    6. Turner AL,
    7. et al.
    Granulocyte-macrophage colony-stimulating factor is a key mediator in experimental osteoarthritis pain and disease development. Arthritis Res Ther 2012;14:R199.
    OpenUrlCrossRefPubMed
  46. 46.↵
    1. Pope RM,
    2. Shahrara S
    . Possible roles of IL-12-family cytokines in rheumatoid arthritis. Nat Rev Rheumatol 2013;9:252–6.
    OpenUrlPubMed
  47. 47.↵
    1. Saetan N,
    2. Honsawek S,
    3. Tanavalee A,
    4. Tantavisut S,
    5. Yuktanandana P,
    6. Parkpian V
    . Association of plasma and synovial fluid interferon-gamma inducible protein-10 with radiographic severity in knee osteoarthritis. Clin Biochem 2011;44:1218–22.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Ceccarelli F,
    2. Perricone C,
    3. Alessandri C,
    4. Modesti M,
    5. Iagnocco A,
    6. Croia C,
    7. et al.
    Exploratory data analysis on the effects of non pharmacological treatment for knee osteoarthritis. Clin Exp Rheumatol 2010;28:250–3.
    OpenUrlPubMed
  49. 49.↵
    1. Miller RE,
    2. Tran PB,
    3. Das R,
    4. Ghoreishi-Haack N,
    5. Ren D,
    6. Miller RJ,
    7. et al.
    CCR2 chemokine receptor signaling mediates pain in experimental osteoarthritis. Proc Natl Acad Sci USA 2012;109:20602–7.
    OpenUrlAbstract/FREE Full Text
  50. 50.↵
    1. Makimura C,
    2. Arao T,
    3. Matsuoka H,
    4. Takeda M,
    5. Kiyota H,
    6. Tsurutani J,
    7. et al.
    Prospective study evaluating the plasma concentrations of twenty-six cytokines and response to morphine treatment in cancer patients. Anticancer Res 2011;31:4561–8.
    OpenUrlAbstract/FREE Full Text
  51. 51.↵
    1. Heitzer E,
    2. Sandner-Kiesling A,
    3. Schippinger W,
    4. Stohscheer I,
    5. Osprian I,
    6. Bitsche S,
    7. et al.
    IL-7, IL-18, MCP-1, MIP1-beta, and OPG as biomarkers for pain treatment response in patients with cancer. Pain Physician 2012;15:499–510.
    OpenUrlPubMed
  52. 52.↵
    1. Orita S,
    2. Koshi T,
    3. Mitsuka T,
    4. Miyagi M,
    5. Inoue G,
    6. Arai G,
    7. et al.
    Associations between proinflammatory cytokines in the synovial fluid and radiographic grading and pain-related scores in 47 consecutive patients with osteoarthritis of the knee. BMC Musculoskelet Disord 2011;12:144.
    OpenUrlCrossRefPubMed
  53. 53.↵
    1. Attur M,
    2. Belitskaya-Levy I,
    3. Oh C,
    4. Krasnokutsky S,
    5. Greenberg J,
    6. Samuels J,
    7. et al.
    Increased interleukin-1-beta gene expression in peripheral blood leukocytes is associated with increased pain and predicts risk for progression of symptomatic knee osteoarthritis. Arthritis Rheum 2011;63:1908–17.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Stannus O,
    2. Jones G,
    3. Cicuttini F,
    4. Parameswaran V,
    5. Quinn S,
    6. Burgess J,
    7. et al.
    Circulating levels of IL-6 and TNF-alpha are associated with knee radiographic osteoarthritis and knee cartilage loss in older adults. Osteoarthritis Cartilage 2010;18:1441–7.
    OpenUrlCrossRefPubMed
View Abstract
PreviousNext
Back to top

In this issue

The Journal of Rheumatology
Vol. 40, Issue 8
1 Aug 2013
  • Table of Contents
  • Table of Contents (PDF)
  • Index by Author
  • Editorial Board (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about The Journal of Rheumatology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Intraarticular and Systemic Inflammatory Profiles May Identify Patients with Osteoarthritis
(Your Name) has forwarded a page to you from The Journal of Rheumatology
(Your Name) thought you would like to see this page from the The Journal of Rheumatology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Intraarticular and Systemic Inflammatory Profiles May Identify Patients with Osteoarthritis
Bryan J. Heard, Marvin J. Fritzler, J. Preston Wiley, Jenelle McAllister, Liam Martin, Hani El-Gabalawy, David A. Hart, Cyril B. Frank, Roman Krawetz
The Journal of Rheumatology Aug 2013, 40 (8) 1379-1387; DOI: 10.3899/jrheum.121204

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

 Request Permissions

Share
Intraarticular and Systemic Inflammatory Profiles May Identify Patients with Osteoarthritis
Bryan J. Heard, Marvin J. Fritzler, J. Preston Wiley, Jenelle McAllister, Liam Martin, Hani El-Gabalawy, David A. Hart, Cyril B. Frank, Roman Krawetz
The Journal of Rheumatology Aug 2013, 40 (8) 1379-1387; DOI: 10.3899/jrheum.121204
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Save to my folders

Jump to section

  • Article
    • Abstract
    • MATERIALS AND METHODS
    • RESULTS
    • DISCUSSION
    • Acknowledgment
    • Footnotes
    • REFERENCES
  • Figures & Data
  • References
  • Info & Metrics
  • PDF
  • eLetters

Keywords

CYTOKINES
OSTEOARTHRITIS
INFLAMMATION
LUMINEX
SYNOVIAL FLUID
SERUM

Related Articles

Cited By...

More in this TOC Section

  • Salivary Gland Focus Score Is Associated With Myocardial Fibrosis in Primary Sjögren Syndrome Assessed by a Cardiac Magnetic Resonance Approach
  • National Priorities for High-quality Rheumatology Transition Care for Youth in Canada
  • The Effect of Creatine Supplementation on Muscle Function in Childhood Myositis: A Randomized, Double-blind, Placebo-controlled Feasibility Study
Show more Articles

Similar Articles

Keywords

  • CYTOKINES
  • osteoarthritis
  • inflammation
  • LUMINEX
  • SYNOVIAL FLUID
  • SERUM

Content

  • First Release
  • Current
  • Archives
  • Collections
  • Audiovisual Rheum
  • COVID-19 and Rheumatology

Resources

  • Guide for Authors
  • Submit Manuscript
  • Author Payment
  • Reviewers
  • Advertisers
  • Classified Ads
  • Reprints and Translations
  • Permissions
  • Meetings
  • FAQ
  • Policies

Subscribers

  • Subscription Information
  • Purchase Subscription
  • Your Account
  • Terms and Conditions

More

  • About Us
  • Contact Us
  • My Alerts
  • My Folders
  • RSS Feeds
The Journal of Rheumatology
The content of this site is intended for health care professionals.
Copyright © 2016 by The Journal of Rheumatology Publishing Co. Ltd.
Print ISSN: 0315-162X; Online ISSN: 1499-2752
Powered by HighWire