Knee Images Digital Analysis (KIDA): a novel method to quantify individual radiographic features of knee osteoarthritis in detail

SummaryObjectiveRadiography is still the golden standard for imaging features of osteoarthritis (OA), such as joint space narrowing, subchondral sclerosis, and osteophyte formation. Objective assessment, however, remains difficult. The goal of the present study was to evaluate a novel digital method to analyse standard knee radiographs.MethodsStandardized radiographs of 20 healthy and 55 OA knees were taken in general practise according to the semi-flexed method by Buckland-Wright. Joint Space Width (JSW), osteophyte area, subchondral bone density, joint angle, and tibial eminence height were measured as continuous variables using newly developed Knee Images Digital Analysis (KIDA) software on a standard PC.Two observers evaluated the radiographs twice, each on two different occasions. The observers were blinded to the source of the radiographs and to their previous measurements. Statistical analysis to compare measurements within and between observers was performed according to Bland and Altman. Correlations between KIDA data and Kellgren & Lawrence (K&L) grade were calculated and data of healthy knees were compared to those of OA knees.ResultsIntra- and inter-observer variations for measurement of JSW, subchondral bone density, osteophytes, tibial eminence, and joint angle were small. Significant correlations were found between KIDA parameters and K&L grade. Furthermore, significant differences were found between healthy and OA knees.ConclusionIn addition to JSW measurement, objective evaluation of osteophyte formation and subchondral bone density is possible on standard radiographs. The measured differences between OA and healthy individuals suggest that KIDA allows detection of changes in time, although sensitivity to change has to be demonstrated in long-term follow-up studies

Українська діаспора Кабардино-Балкарії

Кабардино-Балкарська республіка – суб’єкт Російської Федерації. За даними статистичного управління за 2000 рік, у республіці проживало близько 15 тисяч етнічних українців. З них в Нальчику - 5725 чоловік

Decrease in immunoglobulin free light chains in patients with rheumatoid arthritis upon rituximab (anti-CD20) treatment correlates with decrease in disease activity

Objectives Immunoglobulin (Ig) free light chains (FLCs) are short-lived B cell products that contribute to inflammation in several experimental disease models. In this study, FLC concentrations in inflamed joints of patients with rheumatoid arthritis (RA) as compared to patients with osteoarthritis were investigated. In addition, the relationship of FLCs and disease activity upon B cell depletion (rituximab) in patients with RA was studied. Methods Synovial fluid (SF) and tissue from patients with RA were analysed for local presence of FLCs using ELISA and immunohistochemistry. In addition, FLC concentrations were measured (at baseline, 3 and 6 months after treatment) in 50 patients with RA with active disease who were treated with rituximab. Changes in FLCs were correlated to changes in disease activity and compared to alterations in IgM, IgG, IgA, IgM-rheumatoid factor (RF) and IgG-anti-citrullinated protein antibody (ACPA) concentrations. Results FLCs were detected in synovial tissue from patients with RA, and high FLC concentrations were found in SF from inflamed joints, which positively correlate with serum FLC concentrations. Serum FLC concentrations significantly correlated with disease activity score using 28 joint counts, erythrocyte sedimentation rate (ESR) and C reactive protein, and changes in FLC correlated with clinical improvement after rituximab treatment. Moreover, effect of treatment on FLC concentrations discriminated clinical responders from non-responders, whereas IgM-RF and IgG-ACPA significantly decreased in both patient groups. Conclusions FLCs are abundantly present in inflamed joints and FLC levels correlate with disease activity. The correlation of FLC concentrations and disease activity indicates that FLCs may be relevant biomarkers for treatment response to rituximab in patients with RA and suggests that targeting FLC may be of importance in the therapy of R

Human C-reactive protein aggravates osteoarthritis development in mice on a high-fat diet

Objective: C-reactive protein (CRP) levels can be elevated in osteoarthritis (OA) patients. In addition to indicating systemic inflammation, it is suggested that CRP itself can play a role in OA development. Obesity and metabolic syndrome are important risk factors for OA and also induce elevated CRP levels. Here we evaluated in a human CRP (hCRP)-transgenic mouse model whether CRP itself contributes to the development of ‘metabolic’ OA.Design: Metabolic OA was induced by feeding 12-week-old hCRP-transgenic males (hCRP-tg, n = 30) and wild-type littermates (n = 15) a 45 kcal% high-fat diet (HFD) for 38 weeks. Cartilage degradation, osteophytes and synovitis were graded on Safranin O-stained histological knee joint sections. Inflammatory status was assessed by plasma lipid profiling, flow cytometric analyses of blood immune cell populations and immunohistochemical staining of synovial macrophage subsets.Results: Male hCRP-tg mice showed aggravated OA severity and increased osteophytosis compared with their wild-type littermates. Both classical and non-classical monocytes showed increased expression of CCR2 and CD86 in hCRP-tg males. HFD-induced effects were evident for nearly all lipids measured and indicated a similar low-grade systemic inflammation for both genotypes. Synovitis scores and synovial macrophage subsets were similar in the two groups.Conclusions: Human CRP expression in a background of HFD-induced metabolic dysfunction resulted in the aggravation of OA through increased cartilage degeneration and osteophytosis. Increased recruitment of classical and non-classical monocytes might be a mechanism of action through which CRP is involved in aggravating this process. These findings suggest interventions selectively directed against CRP activity could ameliorate metabolic OA development

The catabolic-to-anabolic shift seen in the canine osteoarthritic cartilage treated with knee joint distraction occurs after the distraction period

Background Cartilage regenerative mechanisms initiated by knee joint distraction (KJD) remain elusive. Animal experiments that are representative for the human osteoarthritic situation and investigate the effects of KJD at consecutive time points could be helpful in this respect but are lacking. This study investigated the effects of KJD on the osteoarthritic joint of dogs on two consecutive timepoints. Methods Osteoarthritis was bilaterally induced for 10 weeks in 12 dogs using the groove model. Subsequently, KJD was applied to the right hindlimb for 8 weeks. The cartilage, subchondral bone and synovial membrane were investigated directly after KJD treatment, and after 10 weeks of follow-up after KJD treatment. Macroscopic and microscopic joint tissue alterations were investigated using the OARSI grading system. Additionally, proteoglycan content and synthesis of the cartilage were assessed biochemically. RT-qPCR analysis was used to explore involved signaling pathways. Results Directly after KJD proteoglycan and collagen type II content were reduced accompanied by decreased proteoglycan synthesis. After 10 weeks of follow-up, proteoglycan and collagen type II content were partly restored and proteoglycan synthesis increased. RT-qPCR analysis of the cartilage suggests involvement of the TGF-β and Notch signalling pathways. Additionally, increased subchondral bone remodelling was found at 10 weeks of follow-up. Conclusion While the catabolic environment in the cartilage is still present directly after KJD, at 10 weeks of follow-up a switch towards a more anabolic joint environment was observed. Further investigation of this timepoint and the pathways involved might elucidate the regenerative mechanisms behind KJD. The Translational Potential of this Article Further elucidation of the regenerative mechanisms behind KJD could improve the existing KJD treatment. Furthermore, these findings could provide input for the discovery or improvement of other joint regenerative treatment strategies

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-alpha Inhibitors in Rheumatoid Arthritis

Analytical BioScience

Differences between serum polar lipid profiles of male and female rheumatoid arthritis patients in response to glucocorticoid treatment

Analytical BioScience

Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis

The goals of this study were to examine whether machine-learning algorithms outper-form multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to in-vestigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Fi-nally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response

Translation of clinical problems in osteoarthritis into pathophysiological research goals

Osteoarthritis (OA) accounts for more disability among the elderly than any other disease and is associated with an increased mortality rate. The prevalence in Europe will rise in the future since this continent has a strongly ageing population and an obesity epidemic; obesity and age both being major risk factors for OA. No adequate therapeutic options, besides joint replacement, are available, although they are greatly needed and should be acquired by adequate research investments. However, the perspective on OA from a researcher's point of view is not always aligned with the perspective of a patient with OA. Researchers base their views on OA mainly on abnormalities in structure and function while patients consider OA as a collection of symptoms. In this viewpoint paper, we discuss the possibility of translating the most important clinical problems into pathophysiological research goals to facilitate the translation from bench to bedside and vice versa. This viewpoint is the outcome of a dialogue within the 'European League Against Rheumatism study group on OA' and People with Arthritis/Rheumatism across Europe (PARE) representatives