Predict Knee Osteoarthritis Progression Research Articles

Peripheral Blood DNA Methylation-Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression: Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project.

The lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in OA clinical research. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression. Genome-wide buffy coat DNA methylation patterns from 554 individuals from the Osteoarthritis Biomarkers Consortium (OABC) were determined using Illumina Infinium MethylationEPIC 850K arrays. Data were divided into model development and validation sets, and machine learning models were trained to classify future OA progression by knee pain, radiographic imaging, knee pain plus radiographic imaging, and any progression (pain, radiographic, or both). Parsimonious models using the top 13 CpG sites most frequently selected during development were tested on independent samples from participants in the Johnston County Osteoarthritis (JoCo OA) Project (n=128) and a previously published Osteoarthritis Initiative (OAI) data set (n=55). Full models accurately classified future radiographic-only progression (mean ± SEM accuracy 87 ± 0.8%, area under the curve [AUC] 0.94 ± 0.004), pain-only progression (accuracy 89 ± 0.9%, AUC 0.97 ± 0.004), pain plus radiographic progression (accuracy 72 ± 0.7%, AUC 0.79 ± 0.006), and any progression (accuracy 78 ± 0.4%, AUC 0.86 ± 0.004). Pain-only and radiographic-only progressors were not distinguishable (mean ± SEM accuracy 58 ± 1%, AUC 0.62 ± 0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in the OABC cohort and in both validation cohorts (mean ± SEM accuracy 80 ± 0.3%, AUC 0.88 ± 0.003 [using JoCo OA Project data], accuracy 80 ± 0.8%, AUC 0.89 ± 0.002 [using previous OAI data]). Our data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further studies should focus on evaluating the pathophysiologic consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.

Biomarkers of macrophage-associated inflammatory pathways are associated with OA symptoms and radiographic disease

Purpose: Although the role of inflammation in osteoarthritis has been heavily debated, recent evidence has demonstrated macrophage involvement in knee osteoarthritis (OA). Using etarfolatide (EC20) nuclear imaging that detects folate receptors, we have observed activated macrophages in human knee OA joints whose quantity are correlated with both radiographic and symptom severity of knee OA. In addition, we identified synovial fluid (SF) biomarkers, CD163 and CD14, that function as quantitative traits of the etarfolatide-positive phenotype and that predict knee OA progression over 3 years, allowing for the identification of inflammatory OA phenotypes. The purpose of this study was to characterize a molecular biological profile for identifying subsets of subjects with an inflammatory OA phenotype through the investigation of the association of an extensive array of synovial fluid biomarkers with the severity of macrophage infiltration, radiographic knee OA and pain. Methods: The Institutional Review Board approved all procedures and the study was registered at ClinicalTrials.gov (NCT01237405). Participants (n = 25) with radiographic knee OA in at least one knee and knee pain underwent bilateral knee etarfolatide imaging. SF was obtained by either direct aspiration (n = 28 knees) or by lavage (n = 20 knees) when direct aspiration was not possible. Samples were stored at -80 until analyzed. The intensity of etarfolatide imaging of the knee and pain assessment by NHANES criteria were scored semi-quantitatively as normal (score 0), mild (score 1), moderate (score 2), or severe (score 3). Radiographs were scored for Kellgren Lawrence (KL) grade (0-4), joint space narrowing (JSN, 0-3) and osteophyte (OST, 0-3). Biomarkers were quantified by Rules Based Medicine (RBM) using the high sensitivity multiplex immunoassay, Myriad Human InflammationMAP 1.0, including 47 different cytokines, chemokines, and growth factors related to inflammation. Biomarker concentrations in lavage fluid were corrected for dilution by an established urea method. Multivariable linear regression models using generalized estimating equations (GEEs) were performed to evaluate associations of biomarker concentrations with etarfolatide scores, radiographic OA severity, and OA symptoms, adjusting for age, gender and BMI. False discovery rate (FDR) was controlled using the Benjamini-Hochberg method. Spearman correlations were used to test for associations of informative RBM markers with sfCD163 and sfCD14. Results: Participants were 72% female, mean age of 62.4 (range 30-89) and a mean BMI of 29.2 (range 22.5-38.4 kg/m2). The majority (76%) of participants had moderate to severe bilateral radiographic knee OA with 24% having a KL grade 1, 58% KL grade 2-3, and 18% KL grade 4. Significant associations of SF biomarkers included sVCAM-1 and MMP-3 with the presence of activated macrophages (etarfolatide scan), sVCAM-1, sICAM-1, TIMP-1, and VEGF with radiographic OA severity (KL, JSN, and OST), and VEGF, MMP-3, TIMP-1, sICAM-1, sVCAM-1, and MCP-1 with OA symptoms (Table 1). These SF biomarkers were all highly correlated with sfCD163 and sfCD14. Conclusions: This study provides evidence in support of an inflammatory OA subphenotype characterized by the presence of activated macrophages and its association with synovial fluid biomarkers of inflammation and angiogenesis. These processes may interrelate as VEGF, a biomarker of angiogenesis, can increase expression of MMPs and inflammation, and markers of vascular adhesion, VCAM-1 and ICAM-1 correlate with synovitis. This biomarker profile may assist in identifying patients with an inflammatory OA phenotype for targeting of treatment to modulate inflammation and activated macrophages.

What Are the Prognostic Factors for Radiographic Progression of Knee Osteoarthritis? A Meta-analysis.

BackgroundA previous systematic review on prognostic factors for knee osteoarthritis (OA) progression showed associations for generalized OA and hyaluronic acid levels. Knee pain, radiographic severity, sex, quadriceps strength, knee injury, and regular sport activities were not associated. It has been a decade since the literature search of that review and many studies have been performed since then investigating prognostic factors for radiographic knee OA progression.Questions/purposesThe purpose of this study is to provide an updated systematic review of available evidence regarding prognostic factors for radiographic knee OA progression.MethodsWe searched for observational studies in MEDLINE and EMBASE. Key words were: knee, osteoarthritis (or arthritis, or arthrosis, or degenerative joint disease), progression (or prognosis, or precipitate, or predictive), and case-control (or cohort, or longitudinal, or follow-up). Studies fulfilling the inclusion criteria were assessed for methodologic quality according to established criteria for reviews on prognostic factors in musculoskeletal disorders. Data were extracted and results were pooled if possible or summarized according to a best-evidence synthesis. A total of 1912 additional articles were identified; 43 met our inclusion criteria. The previous review contained 36 articles, thus providing a new total of 79 articles. Seventy-two of the included articles were scored high quality, the remaining seven were low quality.ResultsThe pooled odds ratio (OR) of two determinants showed associations with knee OA progression: baseline knee pain (OR, 2.38 [95% CI, 1.74–3.27) and Heberden nodes (OR, 2.66 [95% CI, 1.46–8.84]). Our best-evidence synthesis showed strong evidence that varus alignment, serum hyaluronic acid, and tumor necrosis factor-α are associated with knee OA progression. There is strong evidence that sex, former knee injury, quadriceps strength, smoking, running, and regular performance of sports are not associated with knee OA progression. Evidence for the majority of determined associations, however, was limited, conflicting, or inconclusive.ConclusionsBaseline knee pain, presence of Heberden nodes, varus alignment, and high levels of serum markers hyaluronic acid and tumor necrosis factor-α predict knee OA progression. Sex, knee injury, and quadriceps strength, among others, did not predict knee OA progression. Large variation remains in definitions of knee OA and knee OA progression. Clinical studies should use more consistent definitions of these factors to facilitate data pooling by future meta-analyses.Electronic supplementary materialThe online version of this article (doi:10.1007/s11999-015-4349-z) contains supplementary material, which is available to authorized users.

Biomarkers in osteoarthritis

Biomarkers aid the study of osteoarthritis (OA) in a number of different ways. In this article we summarise briefly their multiple uses and reflect on how the study reported in a previous edition of Arthritis Research & Therapy should promote further investigation of cartilage oligomeric matrix protein (COMP). COMP is foremost among hitherto investigated biomarkers and is most consistently shown to predict knee OA progression. Precisely what role it plays in OA pathogenesis remains unclear and elucidating this may be key to defining, and then targeting, the cellular pathways involved in OA.