Predicting knee replacement in participants eligible for disease-modifying osteoarthritis drug treatment with structural endpoints

30 Years of MRI-based cartilage & bone morphometry in knee osteoarthritis: From correlation to clinical trials

OBJECTIVE

The first publication on morphometric analysis of articular cartilage using magnetic resonance imaging (MRI) in 1994 set the scene for a game change in osteoarthritis (OA) research. The current review highlights milestones in cartilage and bone morphometry, summarizing the rapid progress made in imaging, its application to understanding joint (patho-)physiology, and its use in interventional clinical trials.

METHODS

Based on a Pubmed search of articles from 1994 to 2023, the authors subjectively selected representative work illustrating important steps in the development or application of magnetic resonance-based cartilage and bone morphometry, with a focus on studies in humans, and on the knee. Research on OA-pathophysiology is addressed only briefly, given length constraints. Compositional and semi-quantitative assessment are not covered here.

RESULTS

The selected articles are presented in historical order as well as by content. We review progress in the technical aspects of image acquisition, segmentation and analysis, advances in understanding tissue growth, physiology, function, and adaptation, and a selection of clinical trials examining the efficacy of interventions on knee cartilage and bone. A perspective is provided of how lessons learned may be applied to future research and clinical management.

CONCLUSIONS

Over the past 30 years, MRI-based morphometry of cartilage and bone has contributed to a paradigm shift in understanding articular tissue physiology and OA pathophysiology, and to the development of new treatment strategies. It is likely that these technologies will continue to play a key role in the development and (accelerated) approval of therapy, potentially targeted to different OA phenotypes.

CartiMorph: A framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

Bibliometric Analysis of Publications in Clinical Trials on Knee Osteoarthritis Between 2001 and 2022

Purpose

Clinical treatment of knee osteoarthritis has gained great interest, and numerous clinical studies have been reported. Few studies have described the detailed characterizations of clinical trials on knee osteoarthritis. This study is to identify, visualize, and characterize the clinical trials in knee osteoarthritis research.

Design and Methods

Articles published in recent two decades were extracted from the Web of Science core collection database using a query based on MeSH terms and topics of knee osteoarthritis and clinical trial. Basic characteristics of publications were analyzed based on the publication year, authors, institutions, counties, and keywords covered in the article. CiteSpace and VOS viewer were used for data visualization. The data were retrieved on May 28, 2022.

Results

A total of 1972 trials on knee osteoarthritis were identified. The number of publications has experienced rapid growth in the past two decades. America, England, and China had strong contributions in publication. Osteoarthritis and Cartilage, Annals of the Rheumatic Diseases and American Journal of Sports Medicine were highly cited and bellwether journals. Clustering mapping of the collaborative network, co-citation, and co-occurrence analyses showed that the research hotspots are mainly focused on disease-modifying medications, intra-articular injections, physical therapy for symptom control, lifestyle intervention, Chinese medicine treatment, and knee replacement.

Conclusion

Clinical treatments of knee OA are evolving. Pharmacologic therapy, intra-articular therapy, nonpharmacologic therapy including exercise or diet, self-management programs, Chinese medicine treatment, and knee replacement were highly represented in clinical trials of knee OA. Adjustment of combination therapy may be the next study focus in the future.

A joint multi-modal learning method for early-stage knee osteoarthritis disease classification

Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease is important for the treatment and care of patients. However, most state-of-the-art methods only use single-modal data to predict disease status, so that these methods usually ignore complementary information in multi-modal data. In this study, we develop an integrated multi-modal learning method (MMLM) that uses an interpretable strategy to select and fuse clinical, imaging, and demographic features to classify the grade of early-stage knee OA disease. MMLM applies XGboost and ResNet50 to extract two heterogeneous features from the clinical data and imaging data, respectively. And then we integrate these extracted features with demographic data. To avoid the negative effects of redundant features in a direct integration of multiple features, we propose a L1-norm-based optimization method (MMLM) to regularize the inter-correlations among the multiple features. MMLM was assessed using the Osteoarthritis Initiative (OAI) data set with machine learning classifiers. Extensive experiments demonstrate that MMLM improves the performance of the classifiers. Furthermore, a visual analysis of the important features in the multimodal data verified the relations among the modalities when classifying the grade of knee OA disease.

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren-Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models' performance were not possible due to the lack of a unique definition of each of the three outcomes.

Novel Framework for Measuring Whole Knee Osteoarthritis Progression Using Magnetic Resonance Imaging

OBJECTIVE

We developed and validated a set of composite scores that combine quantitative magnetic resonance imaging (MRI)-based measurements of hyaline cartilage damage, bone marrow lesions (BMLs), and effusion-synovitis into composite scores.

METHODS

We selected 300 participants (n = 100 for development cohort; n = 200 for validation cohort) from the Osteoarthritis Initiative with complete clinical, radiographic, and MRI data at baseline and 24 months. We used semiautomated programs to quantify tibiofemoral and patellar cartilage damage, BML volume, and whole-knee effusion-synovitis volume. The candidate composite scores were formed by summing changes from baseline to 24 months based on prespecified methods. We evaluated the candidate composite scores for 1) the ability to differentiate groups with and without knee osteoarthritis progression (17 radiographic and patient-reported definitions), 2) sensitivity to change (standardized response means), and 3) relative performance relating to legacy outcome measures of knee osteoarthritis progression.

RESULTS

Three of 13 developed composite scores qualified for testing in the validation cohort (ranked by sensitivity to change): whole-knee cumulative cartilage damage, unweighted total knee score, and BML plus effusion-synovitis volume. Change in cumulative cartilage damage associated with radiographic progression (Kellgren/Lawrence grade: odds ratio [OR] 1.84; joint space width progression: OR 2.11). Changes in the unweighted total knee score (OR 1.97) and BML plus effusion-synovitis score (OR 1.92) associated with Western Ontario and McMaster Universities Osteoarthritis Index knee pain progression.

CONCLUSION

Two composite scores emerged, reflecting discrete domains of knee osteoarthritis progression. First, cumulative damage, which is measured by a whole-knee cartilage damage score, reflects the damage accrued over time. Second, dynamic disease activity, which is measured by a BML plus effusion-synovitis score, relates to changes in a patient's state of disease and symptoms.

Low levels of type II collagen formation (PRO-C2) are associated with response to sprifermin: a pre-defined, exploratory biomarker analysis from the FORWARD study

OBJECTIVE

Osteoarthritis (OA) is characterized by the gradual loss of cartilage. Sprifermin, a recombinant FGF18, is being developed as a cartilage anabolic drug. PRO-C2 is a serum marker of type II collagen formation and low levels have been shown to be prognostic of radiographic progression. The aim of the study was to investigate whether the patient groups with either high or low PRO-C2 levels responded differently to sprifermin.

DESIGN

PRO-C2 was measured in synovial fluid (SF) (n = 59) and serum samples (n = 225) from participants of the FORWARD study, a 2-year phase IIb clinical trial testing the efficacy of intra-articular (IA) sprifermin over placebo. The difference between sprifermin and placebo in respect to in change cartilage thickness (measured by quantitative (q) MRI) was analyzed in groups with either high or low (3^rd vs 1^st-2^nd tertiles) baseline serum PRO-C2 levels.

RESULTS

SF levels of PRO-C2 increased over time in response to sprifermin, but not to placebo. In the placebo arm, significantly (p = 0.005) more cartilage was lost in the low vs high PRO-C2 group over the 2-year period. The contrast between sprifermin and placebo was significant (p < 0.001), ranging from 0.104 mm at week 26 to 0.229 mm at week 104 in the low PRO-C2 group. This result was not significant in the high PRO-C2 group ranging from -0.034 to 0.142.

CONCLUSIONS

Patients with low serum PRO-C2 levels lost more cartilage thickness over time and grew more cartilage in response to sprifermin vs a placebo when compared to patients with high PRO-C2 levels.

RIP2 knockdown inhibits cartilage degradation and oxidative stress in IL-1β-treated chondrocytes via regulating TRAF3 and inhibiting p38 MAPK pathway

Receptor-interacting protein 2 (RIP2) is a key mediator implicated in multiple cellular processes, and its dysregulation has been recently reported in colitis, asthma and other inflammatory diseases. However, the effects of RIP2 on osteoarthritis (OA) and the underlying mechanisms remain unclear. In this study, we found that RIP2 expression was upregulated in human articular cartilage tissues with OA and interleukin-1β (IL-1β)-treated chondrocytes. Knockdown of RIP2 inhibited IL-1β-induced extracellular matrix (ECM) and oxidative stress. Moreover, knockdown of TRAF3 reversed the effects of RIP2 silencing on cartilage degradation and oxidative stress in IL-1β-induced chondrocytes. In addition, p38 mitogen-activated protein kinase (MAPK) activator dehydrocorydalmine chloride (Dc) also reversed the effects of RIP2 silencing on IL-1β-induced chondrocytes. Taken together, our data reveal that RIP2 knockdown inhibits cartilage degradation and oxidative stress in IL-1β-treated chondrocytes by regulating TRAF3 expression and p38 MAPK pathway activation.

The effects of sprifermin on symptoms and structure in a subgroup at risk of progression in the FORWARD knee osteoarthritis trial

OBJECTIVE

To assess pain outcomes and cartilage thickness change in a subgroup at risk (SAR) of further progression in the FORWARD trial of knee osteoarthritis patients treated with sprifermin.

METHODS

Patients were randomised 1:1:1:1:1 to: sprifermin 100 µg every 6 months (q6mo), 100 µg q12mo, 30 µg q6mo, 30 µg q12mo, or placebo for 18 months. SAR was defined as baseline medial or lateral minimum joint-space width (mJSW) 1.5-3.5 mm and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score 40-90 units. Follow-up to 3 years was included in the analysis. Treatment benefit was explored by repeated measures, linear dose-effect trends by timepoint.

RESULTS

The SAR comprised 161 (29%) of 549 patients. Mean difference (95% CI) in WOMAC pain at year 3 for sprifermin 100 µg q6mo vs placebo SAR was -8.75 (-22.42, 4.92) for SAR vs 0.97 (-6.22, 8.16) for the intent-to-treat population. SAR placebo patients lost more cartilage over 2 years than the modified ITT (mITT) placebo arm (mean change from baseline, mm [SD]: -0.05 [0.10] vs -0.02 [0.07]). Net total femorotibial joint thickness gain with sprifermin 100 µg q6mo (adjusted mean difference from placebo [95% CI] was similar in the SAR and in the mITT group: 0.06 [0.01, 0.11] vs 0.05 [0.03, 0.07]).

CONCLUSIONS

Selection for low mJSW and moderate-to-high pain at baseline resulted in more rapid disease progression and demonstrated translation of structure modification (with maintained net benefit on total cartilage thickness) into symptomatic benefit. This subgroup may represent a target population for future trials.

CLINICAL TRIAL REGISTRATION

NCT01919164.

A Decline in Walking Speed Is Associated With Incident Knee Replacement in Adults With and at Risk for Knee Osteoarthritis

OBJECTIVE

To determine if a 1-year change in walking speed is associated with receiving an incident knee replacement during the following year in adults with and at risk for knee osteoarthritis (OA).

METHODS

Using data from the Osteoarthritis Initiative, we determined a 1-year change in the 20-meter walk speed from 3 observation periods (i.e., 0-12, 12-24, and 24-36 months). We operationally defined 1-year change in walking speed as either (1) decline: ≤ -0.1 m/s change, (2) no change: between -0.1 and 0.1 m/s change, and (3) increase: ≥ 0.1 m/s change. Incident knee replacement was defined using each subsequent 1-year period (i.e., 12-24, 24-36, and 36-48 months). Combining data from the 3 observation periods, we performed a Poisson regression with robust error variance to determine the relative risk between a change in walking speed (exposure) and incident knee replacement over the following year (outcome).

RESULTS

Of the 4264 participants included within this analysis (11,311 total person visits), 115 (3%) adults received a knee replacement. Decline in walking speed was associated with a 104% increase in risk [adjusted relative risk (RR) 2.04, 95% CI 1.40-2.98], while an increase in walking speed associated with a 55% decrease in risk (RR 0.45; 95% CI 0.22-0.93) of incident knee replacement in the following year compared to a person with no change in walking speed.

CONCLUSION

A 1-year decline in walking speed is associated with an increased risk, while a 1-year increase in walking speed is associated with a decreased risk of future incident knee replacement.