Presence of MRI-defined inflammation particularly in overweight and obese women increases risk of radiographic knee osteoarthritis: the POMA Study

Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease

OBJECTIVES

To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.

METHODS

We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary. Key predictors of progression were identified through advanced interpretability techniques, and subgroup analyses were conducted by age, sex and ethnicity with a focus on early-stage disease.

RESULTS

Although the most reliable models incorporated all available features, simpler models including only clinical variables achieved robust external validation performance, with area under the precision-recall curve (AUC-PRC) 0.727 (95% CI: 0.726 to 0.728) for multiclass predictions; and AUC-PRC 0.764 (95% CI: 0.762 to 0.766) for binary predictions. Multiclass models performed best in patients with early-stage OA (AUC-PRC 0.724-0.806) whereas binary models were more reliable in patients younger than 60 (AUC-PRC 0.617-0.693). Patient-reported outcomes and MRI features emerged as key predictors of progression, though subgroup differences were noted. Finally, we developed web-based applications to visualise our personalised predictions.

CONCLUSIONS

Our novel tool's transparency and reliability in predicting rapid knee OA progression distinguish it from conventional 'black-box' methods and are more likely to facilitate its acceptance by clinicians and patients, enabling effective implementation in clinical practice.

Sexual dimorphism in peri-articular tissue anatomy - More keys to understanding sex-differences in osteoarthritis?

Objective

Osteoarthritis prevalence differs between women and men; whether this is the result of differences in pre-morbid articular or peri-articular anatomical morphotypes remains enigmatic. Albeit sex within humans cannot be reduced to female/male, this review focusses to the sexual dimorphism of peri-articular tissues, given lack of literature on non-binary subjects.

Methods

Based on a Pubmed search and input from experts, we selected relevant articles based on the authors' judgement of relevance, interest, and quality; no "hard" bibliometric measures were used to evaluate the quality or importance of the work. Emphasis was on clinical studies, with most (imaging) data being available for the knee and thigh.

Results

The literature on sexual dimorphism of peri-articular tissues is reviewed: 1) bone size/shape, 2) subchondral/subarticular bone, 3) synovial membrane and infra-patellar fad-pad (IPFP), 4) muscle/adipose tissue, and 5) peri-articular tissue response to treatment.

Conclusions

Relevant sex-specific differences exist for 3D bone shape and IPFP size, even after normalization to body weight. Presence of effusion- and Hoffa-synovitis is associated with greater risk of incident knee osteoarthritis in overweight women, but not in men. When normalized to bone size, men exhibit greater muscle, and women greater adipose tissue measures relative to the opposite sex. Reduced thigh muscle specific strength is associated with incident knee osteoarthritis and knee replacement in women, but not in men. These observations may explain why women with muscle strength deficits have a poorer prognosis than men with similar deficits. A "one size/sex fits all" approach must be urgently abandoned in osteoarthritis research.

Survival analysis on subchondral bone length for total knee replacement

OBJECTIVE

Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR).

METHODS

We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk.

RESULTS

The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827.

CONCLUSION

SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

The mediating role of synovitis in meniscus pathology and knee osteoarthritis radiographic progression

Meniscus pathologies (damage, extrusion) and synovitis are associated with knee osteoarthritis (KOA); however, whether synovitis mediates the relationship between meniscus pathologies and KOA radiographic progression remains unclear. We conducted an observational study in the Osteoarthritis Initiative (OAI) cohort, with a 48-month follow-up. Meniscus pathology and synovitis were measured by MRI osteoarthritis knee score (MOAKS) at baseline and 24 months, and a comprehensive synovitis score was calculated using effusion and Hoffa synovitis scores. The knee osteoarthritis radiographic progression was considered that Kellgren-Lawrence (KL) grade and joint space narrowing (JSN) grade at 48 months were increased compared to those at baseline. This study included a total of 589 participants, with KL grades mainly being KL1 (26.5%), KL2 (34.1%), and KL3 (30.2%) at baseline, while JSN grades were mostly 0 at baseline. A logistic regression model was used to analyze the relationship between meniscus pathology, synovitis, and KOA progression. Mediation analysis was used to evaluate the mediation effect of synovitis. The average age of the participants was 61 years old, 62% of which were female. The medial meniscus extrusion was longitudinally correlated with the progression of KL (odds ratio [OR]: 2.271, 95% confidence interval [CI]: 1.412-3.694) and medial JSN (OR: 3.211, 95% CI: 2.040-5.054). Additionally, the longitudinal correlation between medial meniscus damage and progression of KOA (OR: 1.853, 95% CI: 1.177-2.941) and medial JSN (OR: 1.655, 95% CI: 1.053-2.602) was significant. Synovitis was found to mediate the relationship between medial meniscus extrusion and KL and medial JSN progression at baseline (β: 0.029, 95% CI: 0.010-0.053; β: 0.022, 95% CI: 0.005-0.046) and beyond 24 months (β: 0.039, 95% CI: 0.016-0.068; β: 0.047, 95% CI: 0.020-0.078). However, we did not find evidence of synovitis mediating the relationship between meniscal damage and KOA progression. Synovitis mediates the relationship between medial meniscus extrusion (rather than meniscus damage) and KOA progression.

Effect of weight loss on knee joint synovitis over 48 months and mediation by subcutaneous fat around the knee: data from the Osteoarthritis Initiative

BACKGROUND

Obesity influences the development of osteoarthritis via low-grade inflammation. Progression of local inflammation (= synovitis) increased with weight gain in overweight and obese women compared to stable weight. Synovitis could be associated with subcutaneous fat (SCF) around the knee. Purpose of the study was to investigate the effect of weight loss on synovitis progression and to assess whether SCF around the knee mediates the relationship between weight loss and synovitis progression.

METHODS

We included 234 overweight and obese participants (body mass index [BMI] ≥ 25 kg/m2) from the Osteoarthritis Initiative (OAI) with > 10% weight loss (n = 117) or stable overweight (< ± 3% change, n = 117) over 48 months matched for age and sex. In magnetic resonance imaging (MRI) at baseline and 48 months, effusion-synovitis and Hoffa-synovitis using the MRI Osteoarthritis Knee Score (MOAKS) and average joint-adjacent SCF (ajSCF) were assessed. Odds-ratios (ORs) for synovitis progression over 48 months (≥ 1 score increase) were calculated in logistic regression models adjusting for age, sex, baseline BMI, Physical Activity Scale for the Elderly (PASE), and baseline SCF measurements. Mediation of the effect of weight loss on synovitis progression by local SCF change was assessed.

RESULTS

Odds for effusion-synovitis progression decreased with weight loss and ajSCF decrease (odds ratio [OR] = 0.61 and 0.56 per standard deviation [SD] change, 95% confidence interval [CI] 0.44, 0.83 and 0.40, 0.79, p = 0.002 and 0.001, respectively), whereas odds for Hoffa-synovitis progression increased with weight loss and ajSCF decrease (OR = 1.47 and 1.48, CI 1.05, 2.04 and 1.02, 2.13, p = 0.024 and 0.038, respectively). AjSCF decrease mediated 39% of the effect of weight loss on effusion-synovitis progression.

CONCLUSIONS

Effusion-synovitis progression was slowed by weight loss and decrease in local subcutaneous fat. Hoffa-synovitis characterized by fluid in the infrapatellar fat pad increased at the same time, suggesting a decreasing fat pad rather than active synovitis. Decrease in local subcutaneous fat partially mediated the systemic effect of weight loss on synovitis.

Update on recent developments in imaging of inflammation in osteoarthritis: a narrative review

Synovitis is an important component of the osteoarthritis (OA) disease process, particularly regarding the "inflammatory phenotype" of OA. Imaging plays an important role in the assessment of synovitis in OA with MRI and ultrasound being the most deployed imaging modalities. Contrast-enhanced (CE) MRI, particularly dynamic CEMRI (DCEMRI) is the ideal method for synovitis assessment, but for several reasons CEMRI is not commonly performed for OA imaging in general. Effusion-synovitis and Hoffa-synovitis are commonly used as surrogate markers of synovitis on non-contrast-enhanced (NCE) MRI and have been used in many epidemiological observational studies of knee OA. Several semiquantitative MRI scoring systems are available for the evaluation of synovitis in knee OA. Synovitis can be a target tissue for disease-modifying OA drug (DMOAD) clinical trials. Both MRI and ultrasound may be used to determine the eligibility and assess the therapeutic efficacy of DMOAD approaches. Ultrasound is mostly used for evaluation of synovitis in hand OA, while MRI is typically used for larger joints, namely knees and hips. The role of other modalities such as CT (including dual-energy CT) and nuclear medicine imaging (such as positron-emission tomography (PET) and its hybrid imaging) is limited in the context of synovitis assessment in OA. Despite research efforts to develop NCEMRI-based synovitis evaluation methods, these typically underestimate the severity of synovitis compared to CEMRI, and thus more research is needed before we can rely only on NCEMRI.

Identification and validation of hub genes and potential drugs involved in osteoarthritis through bioinformatics analysis

Purpose: Osteoarthritis (OA) is a common degenerative disease, which still lacks specific therapeutic drugs. Synovitis is one of the most important pathological process in OA. Therefore, we aim to identify and analyze the hub genes and their related networks of OA synovium with bioinformatics tools to provide theoretical basis for potential drugs. Materials and methods: Two datasets were obtained from GEO. DEGs and hub genes of OA synovial tissue were screened through Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment as well as protein-protein interaction (PPI) network analysis. Subsequently, the correlation between expression of hub genes and ferroptosis or pyroptosis was analyzed. CeRNA regulatory network was constructed after predicting the upstream miRNAs and lncRNAs. The validation of hub genes was undertook through RT-qPCR and ELISA. Finally, potential drugs targeting pathways and hub genes were identified, followed by the validation of the effect of two potential drugs on OA. Results: A total of 161 commom DEGs were obtained, of which 8 genes were finally identified as hub genes through GO and KEGG enrichment analysis as well as PPI network analysis. Eight genes related to ferroptosis and pyroptosis respectively were significantly correlated to the expression of hub genes. 24 miRNAs and 69 lncRNAs were identified to construct the ceRNA regulatory network. The validation of EGR1, JUN, MYC, FOSL1, and FOSL2 met the trend of bioinformatics analysis. Etanercept and Iguratimod reduced the secretion of MMP-13 and ADAMTS5 of fibroblast-like synoviocyte. Conclusion: EGR1, JUN, MYC, FOSL1, and FOSL2 were identified as hub genes in the development of OA after series of bioinformatics analysis and validation. Etanercept and Iguratimod seemed to have opportunities to be novel drugs for OA.

Osteoarthritis Bone Marrow Lesions

Assessment and treatment of Bone Marrow Lesions (BMLs) could ultimately make step changes to the lives of people with osteoarthritis (OA). We here review the imaging and pathological characteristics of OA-BMLs, their differential diagnosis and measurement, and cross-sectional and longitudinal associations with pain and OA structural progression. We discuss how biomechanical and cellular factors may contribute to BML pathogenesis, and how pharmacological and non-pharmacological interventions that target BMLs might reduce pain and OA structural progression. We critically appraise semiquantitative and quantitative methods for assessing BMLs, and their potential utilities for identifying people at risk of symptomatic and structural OA progression, and evaluating treatment responses. New interventions that target OA-BMLs should both confirm their importance, and reduce the unacceptable burden of OA.

Effects of Greenshell™ mussel intervention on biomarkers of cartilage metabolism, inflammatory markers and joint symptoms in overweight/obese postmenopausal women: A randomized, double-blind, and placebo-controlled trial

Objective

To investigate the effect of whole greenshell mussel (GSM) powder on biomarkers of cartilage metabolism, inflammatory cytokines, and joint symptoms in postmenopausal women with overweight/obesity and joint discomfort.

Design

Fifty-five postmenopausal women with overweight/obesity were randomly assigned to receive 3 g/day whole GSM powder or placebo for 12 weeks. Cartilage turnover biomarkers urinary C-telopeptide of type II collagen (CTX-II) and serum cartilage oligomeric matrix protein (COMP) were measured at baseline, week 6 and 12. Plasma cytokines were measured at baseline and week 12. Joint pain and knee-related problems were assessed at baseline and week 12 using a 100 mm Visual Analogue Scale (VAS) and the Knee injury and Osteoarthritis Outcome Score (KOOS) questionnaire, respectively.

Results

Forty-nine participants completed the study (GSM n = 25, placebo n = 24). After 12 weeks, urinary CTX-II showed no significant change over time or between the groups (interaction effect P = 0.1). However, in women with symptomatic knees, a significant difference was noted between the group (treatment effect P = 0.04), as it was lower in the GSM group compared to placebo group at week 6 (P = 0.04) and week 12 (P = 0.03). Serum COMP and plasma cytokines were not affected. GSM supplementation showed greater reduction in the VAS pain score than placebo (-13.2 ± 20.3 vs. -2.9 ± 15.9; P = 0.04). No significant change in KOOS domains between the two groups was observed.

Conclusion

Oral supplementation of whole GSM powder at 3 g/day may slow down the degradation of type II collagen in postmenopausal women with symptomatic knees. GSM treatment conferred clinical benefit on overall joint pain. No significant effect was noted for inflammatory cytokines, suggesting that GSM may act within the joint microenvironment rather than at the systemic level.

Clinical trial registration

[www.australianclinicaltrials.gov.au/clinical-trialregistries], identifier [ACTRN12620000413921p].

Fundamentals of OA. An initiative of Osteoarthritis and Cartilage. Obesity and metabolic factors in OA

OBJECTIVE

Obesity was once considered a risk factor for knee osteoarthritis (OA) primarily for biomechanical reasons. Here we provide an additional perspective by discussing how obesity also increases OA risk by altering metabolism and inflammation.

DESIGN

This narrative review is presented in four sections: 1) metabolic syndrome and OA, 2) metabolic biomarkers of OA, 3) evidence for dysregulated chondrocyte metabolism in OA, and 4) metabolic inflammation: joint tissue mediators and mechanisms.

RESULTS

Metabolic syndrome and its components are strongly associated with OA. However, evidence for a causal relationship is context dependent, varying by joint, gender, diagnostic criteria, and demographics, with additional environmental and genetic interactions yet to be fully defined. Importantly, some aspects of the etiology of obesity-induced OA appear to be distinct between men and women, especially regarding the role of adipose tissue. Metabolomic analyses of serum and synovial fluid have identified potential diagnostic biomarkers of knee OA and prognostic biomarkers of disease progression. Connecting these biomarkers to cellular pathophysiology will require future in vivo studies of joint tissue metabolism. Such studies will help reveal when a metabolic process or a metabolite itself is a causal factor in disease progression. Current evidence points towards impaired chondrocyte metabolic homeostasis and metabolic-immune dysregulation as likely factors connecting obesity to the increased risk of OA.

CONCLUSIONS

A deeper understanding of how obesity alters metabolic and inflammatory pathways in synovial joint tissues is expected to provide new therapeutic targets and an improved definition of "metabolic" and "obesity" OA phenotypes.

Osteoarthritis year in review 2021: imaging

PURPOSE

To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging.

METHODS

The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further.

RESULTS

The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging.

CONCLUSION

Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.

Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis

Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.