Appraisal of quality and analysis of the similarities and differences between osteoarthritis Clinical Practice Guideline recommendations: A systematic review.

OBJECTIVE: Clinical Practice Guidelines (CPGs) aim to support management of hip and knee osteoarthritis (OA), but recommendations are often conflicting and implementation is poor, contributing to evidence-to-practice gaps. This systematic review investigated the contextual and methodological factors contributing to conflicting recommendations for hip and knee OA. METHOD: Our systematic review appraised CPGs for managing hip and knee OA in adults ≥18 years (PROSPERO CRD42021276635). We used AGREE-II and AGREE-REX to assess quality and extracted data on treatment gaps, conflicts, biases, and consensus. Heterogeneity of recommendations was determined using Weighted Fleiss Kappa (K). The relationship between (K) and AGREE-II/AGREE-REX scores was explored. RESULTS: We identified 25 CPGs across eight countries and four international organisations. The ACR, EULAR, NICE, OARSI and RACGP guidelines scored highest for overall AGREE-II quality (83%). The highest overall AGREE-REX scores were for BMJ Arthroscopy (80%), RACGP (78%) and NICE (76%). CPGs with the least agreement for pharmacological recommendations were ESCEO and NICE (-0.14), ACR (-0.08), and RACGP (-0.01). The highest agreements were between RACGP and NICE (0.53), RACGP and ACR (0.61), and NICE and ACR (0.91). Decreased internal validity determined by low-quality AGREE scores(<60%) in editorial independence were associated with less agreement for pharmacological recommendations. CONCLUSION: There were associations between guideline quality and agreement scores. Future guideline development should be informed by robust evidence, editorial independence and methodological rigour to ensure a harmonisation of recommendations. End-users of CPGs must recognise the contextual factors associated with the development of OA CPGs and balance these factors with available evidence.

Gender and age differences in the associations between cortical thickness and hand osteoarthritis severity: data from the Osteoarthritis Initiative.

OBJECTIVE: To evaluate gender differences in the association between metacarpal cortical thickness (Tcort)-a surrogate for bone density-and severity of radiographic hand osteoarthritis (HOA) in a longitudinal observational study. METHOD: Hand radiographs of 3575 participants (2039 F/1536 M) from the Osteoarthritis Initiative were assessed at baseline and 48 months. A reader used a semi-automated software tool to calculate Tcort, a measurement of the cortical thickness, for metacarpals 2-4. Average Tcort at baseline and change in Tcort from baseline to 48 months was determined and stratified by gender and age for 7 5-year age groups. Spearman's rank correlation coefficients were calculated for the association of baseline Tcort and 2 measures of baseline HOA severity: the sum of Kellgren-Lawrence (KL) grade and total number of joints with radiographic HOA. Longitudinally, logistic regression was used to assess the relationship of Tcort loss to new finger joint radiographic HOA, increase in KL grades, and incident hand pain. RESULTS: Male Tcort was higher than females. Significant correlations between Tcort and radiographic severity were noted for women but not men, with stronger associations among women >60 years (rho = -0.25; 95% confidence interval (CI) = -0.31 to -0.19). Statistically significant associations were seen between Tcort change and radiographic osteoarthritis change among women but not men, with substantial gender differences for Tcort change, particularly ages 50 to 70 years (p < 0.01; e.g., Tcort change ages 55 to <60: males = -0.182 (0.118), females = -0.219 (0.124)). CONCLUSION: We found significant HOA-related gender differences in Tcort, suggesting the involvement of female bone loss during and after menopause.

Associations of inflammatory and metabolic biomarkers with incident erosive hand osteoarthritis in the osteoarthritis initiative cohort.

OBJECTIVE: Erosive hand osteoarthritis (eHOA) is a subtype of hand osteoarthritis (OA) that develops in finger joints with pre-existing OA and is differentiated by clinical characteristics (hand pain/disability, inflammation, and erosions) that suggest inflammatory or metabolic processes. METHOD: This was a longitudinal nested case-cohort design among Osteoarthritis Initiative participants who had hand radiographs at baseline and 48-months, and biospecimens collected at baseline. We classified incident radiographic eHOA in individuals with ≥1 joint with Kellgren-Lawrence ≥2 and a central erosion present at 48-months but not at baseline. We used a random representative sample (n = 1282) for comparison. We measured serum biomarkers of inflammation, insulin resistance and dysglycemia, and adipokines using immunoassays and enzymatic colorimetric procedures, blinded to case status. RESULTS: Eighty-six participants developed incident radiographic eHOA. In the multivariate analyses adjusted for age, gender, race, smoking, and body mass index, and after adjustment for multiple analyses, incident radiographic eHOA was associated with elevated levels of interleukin-7 (risk ratio (RR) per SD = 1.30 [95% confidence interval (CI) 1.09, 1.55] p trend 0.01). CONCLUSION: This exploratory study suggests an association of elevated interleukin-7, an inflammatory cytokine, with incident eHOA, while other cytokines or biomarkers of metabolic inflammation were not associated. Interleukin-7 may mediate inflammation and tissue damage in susceptible osteoarthritic finger joints and participate in erosive progression.

Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning.

OBJECTIVE: Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous factors influencing TKR decisions. Acknowledging these limitations, our study identifies a critical need for a more robust metric to assess severe KOA. The end-stage KOA (esKOA) measure, which combines symptomatic and radiographic criteria, serves as a solid indicator. To enhance future trials that use esKOA as an endpoint, our study focuses on developing and validating a machine-learning tool to identify individuals likely to develop esKOA within 2 to 5 years. DESIGN: Utilizing the Osteoarthritis Initiative (OAI) data, we trained models on 3,114 participants and validated them with 606 participants for the right knee, and similarly for the left knee, with external validation from the Multicentre Osteoarthritis Study (MOST) involving 1,602 participants. We aimed to predict esKOA onset at 2-to-2.5 years and 4-to-5 years, defining esKOA by severe radiographic KOA with moderate/severe symptoms or mild/moderate radiographic KOA with persistent/intense symptoms. Our analysis considered 51 candidate predictors, including demographics, clinical history, physical examination, and X-ray evaluations. An online tool predicting esKOA progression, based on models with ten and nine predictors for the right and left knees, respectively, was developed. RESULTS: External validation (MOST) for the right knee at 2.5 years yielded an Area Under Curve (AUC) of 0.847 (95 % CI 0.811 to 0.882), and at 5 years, 0.853 (95 % CI 0.823 to 0.881); for the left knee at 2.5 years, AUC was 0.824 (95 % CI 0.782 to 0.857), and at 5 years, 0.807 (95 % CI 0.768 to 0.843). Models with fewer predictors demonstrated comparable performance. The online tool is available at: https://eskoa.shinyapps.io/webapp/. CONCLUSION: Our study unveils a robust, externally validated machine learning tool proficient in predicting the onset of esKOA over the next 2 to 5 years. Our tool can lead to more efficient KOA trials.

Evaluation of a measure of end-stage knee osteoarthritis compared to total knee replacement: An observational study using multicohort data.

OBJECTIVES: To determine if an end-stage knee osteoarthritis (esKOA) measure, based on symptomatic and radiographic criteria, can indicate progression to severe KOA earlier and with fewer research participants than total knee replacement (TKR). We employed both interventional and observational study designs as examples to estimate the required sample sizes. EsKOA in a knee was declared if either of the following two conditions were met: 1) moderate, intense, or severe symptoms of KOA indicated by pain and disability measurement and severe KOA indicated by radiographically-assessed knee structure; 2) intense or severe symptoms of KOA indicated by pain and disability measurement and frequent knee pain with mild or moderate KOA as indicated by radiographically-assessed knee structure. METHODS: We examined the association between weight loss from baseline to 2-to-2.5-year and 4-to-5-year follow-ups and the odds of esKOA and TKR in 5,593 participants (10,357 knees) from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST). We also estimated the sample sizes needed for interventional and observational study designs to detect a 10, 20, or 50% reduction in the incidence of esKOA and TKR. RESULTS: The association of weight loss with both esKOA and TKR was detected at the 4-to-5-year follow-up. However, at the 2-to-2.5-year follow-up, the association was detected for esKOA but not TKR. The required sample sizes for detecting associations of weight loss with the incidence of esKOA were 85% to 93% smaller than those for TKR at the 4-to-5-year and 2-to-2.5-year follow-ups, respectively. CONCLUSION: The esKOA measure enables shorter and smaller studies compared to using TKR as an outcome.

Association of Weight Loss and Weight Gain With Structural Defects and Pain in Hand Osteoarthritis: Data From the Osteoarthritis Initiative.

OBJECTIVE: Our aim was to define the association of weight change (weight loss or weight gain) with the incidence and progression of hand osteoarthritis (OA), assessed either by radiography or by pain, using data from the Osteoarthritis Initiative. METHODS: Among the 4,796 participants, we selected 4,598 participants, excluding those with cancer or rheumatoid arthritis or a body mass index under 18.5 kg/m2. We investigated the association of weight change with incidence and progression of radiographic hand OA and the development and resolution of hand pain. Using multivariable logistic regression, we investigated the association of weight change from baseline to the 4-year follow-up with the incidence and progression of radiographic hand OA at the 4-year follow-up. Additionally, multivariable repeated-measure mixed-effects logistic regression analyzed the association of weight change with the development and resolution of hand pain across 2-year, 4-year, 6-year, and 8-year follow-ups. RESULTS: No statistically significant associations were observed between weight change and the investigated outcomes. Specifically, for each 5% weight loss, the odds ratios for the incidence and progression of radiographic hand OA were 0.90 (95% confidence interval [95% CI] 0.67-1.23) and 0.92 (95% CI 0.84-1.00), respectively. Similarly, for each 5% weight loss, the odds ratios for the development and resolution of hand pain at the 8-year follow-up were 1.00 (95% CI 0.92-1.09) and 1.07 (95% CI 0.91-1.25), respectively. CONCLUSION: Our study found no evidence of an association between weight change and the odds of incidence or progression of radiographic hand OA over 4 years, nor the development or resolution of hand pain over 8 years.

Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson's correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

Radiomic features of the medial meniscus predicts incident destabilizing meniscal tears: Data from the osteoarthritis initiative.

The objective of this study was to determine the optimal meniscal radiomic features to classify people who will develop an incident destabilizing medial meniscal tear. We used magnetic resonance (MR) images from an existing case-control study that includes images from the first 4 years of the Osteoarthritis Initiative (OAI). For this exploratory analysis (n = 215), we limited our study sample to people with (1) intact menisci at the OAI baseline visit, (2) 4-year meniscal status data, and (3) complete meniscal data from each region of interest. Incident destabilizing meniscal tear was defined as progressing from an intact meniscus to a destabilizing tear by the 48-month visit using intermediate-weighted fat-suppressed MR images. One reader manually segmented each participant's anterior and posterior horn of the medial menisci at the OAI baseline visit. Next, 61 different radiomic features were extracted from each medial meniscus horn. We performed a classification and regression tree (CART) analysis to determine the classification rules and important variables that predict incident destabilizing meniscal tear. The CART correctly classified 24 of the 34 cases and 172 out of 181 controls with a sensitivity of 70.6% and a specificity of 95.0%. The CART identified large zone high gray level emphasis (i.e., more coarse texture) from the posterior horn as the most important variable to classify who would develop an incident destabilizing medial meniscal tear. The use of radiomic features provides sensitive and quantitative measures of meniscal alterations, allowing us to intervene and prevent destabilizing meniscal tears.

Single Injection AAV2-FGF18 Gene Therapy Reduces Cartilage Loss and Subchondral Bone Damage in a Mechanically Induced Model of Osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a highly debilitating, degenerative pathology of cartilaginous joints affecting over 500 million people worldwide. The global economic burden of OA is estimated at $260-519 billion and growing, driven by aging global population and increasing rates of obesity. To date, only the multi-injection chondroanabolic treatment regimen of Fibroblast Growth Factor 18 (FGF18) has demonstrated clinically meaningful disease-modifying efficacy in placebo-controlled human trials. Our work focuses on the development of a novel single injection disease-modifying gene therapy, based on FGF18's chondroanabolic activity. METHODS: OA was induced in Sprague-Dawley rats using destabilization of the medial meniscus (DMM) (3 weeks), followed by intra-articular treatment with 3 dose levels of AAV2-FGF18, rh- FGF18 protein, and PBS. Durability, redosability, and biodistribution were measured by quantifying nLuc reporter bioluminescence. Transcriptomic analysis was performed by RNA-seq on cultured human chondrocytes and rat knee joints. Morphological analysis was performed on knee joints stained with Safranin O/Fast Green and anti-PRG antibody. RESULTS: Dose-dependent reductions in cartilage defect size were observed in the AAV2-FGF18- treated joints relative to the vehicle control. Total defect width was reduced by up to 76% and cartilage thickness in the thinnest zone was increased by up to 106%. Morphologically, the vehicle- treated joints exhibited pronounced degeneration, ranging from severe cartilage erosion and bone void formation, to subchondral bone remodeling and near-complete subchondral bone collapse. In contrast, AAV2-FGF18-treated joints appeared more anatomically normal, with only regional glycosaminoglycan loss and marginal cartilage erosion. While effective at reducing cartilage lesions, treatment with rhFGF18 injections resulted in significant joint swelling (19% increase in diameter), as well as a decrease in PRG4 staining uniformity and intensity. In contrast to early-timepoint in vitro RNA-seq analysis, which showed a high degree of concordance between protein- and gene therapy-treated chondrocytes, in vivo transcriptomic analysis, revealed few gene expression changes following protein treatment. On the other hand, the gene therapy treatment exhibited a high degree of durability and localization over the study period, upregulating several chondroanabolic genes while downregulating OA- and fibrocartilage-associated markers. CONCLUSION: FGF18 gene therapy treatment of OA joints can provide benefits to both cartilage and subchondral bone, with a high degree of localization and durability.

Gardening/yardwork in people with knee osteoarthritis is not associated with symptom or structural progression over 48 months: data from the Osteoarthritis Initiative.

OBJECTIVE: To evaluate the relationship of gardening/yardwork with symptomatic and structural progression in those with pre-existing radiographic knee osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), an observational study designed to evaluate potential and known biomarkers and risk factors of knee OA. METHODS: We conducted a cohort study nested within the OAI, including participants ≥ 50 years old with radiographic OA in at least one knee at the time of OAI enrollment. A participant reported the level of gardening/yardwork activity in a self-administered survey. Logistic regression analyses were used to evaluate the association of gardening/yardwork on new frequent knee pain, Kellgren-Lawrence (KL) worsening, medial joint space narrowing (JSN) worsening, and improved frequent knee pain. RESULTS: Of 1808 knees (1203 participants), over 60% of knees had KL grade = 2, 65% had medial JSN, and slightly more than a third had frequent knee symptoms. Gardeners/yardworkers and non-gardners/yardworkers had similar "worsening" outcomes for new knee pain (29% vs. 29%), KL worsening (19% vs. 18%), and medial JSN (23% vs. 24%). The adjusted odds ratio (OR) for the "worsening" outcomes of new knee pain, KL worsening, and medial JSN worsening were 1.0 (0.7-1.3), 1.0 (0.8-1.3), and 1.1 (0.9-1.4), respectively. The gardeners/yardworkers had an adjusted OR of 1.2 (0.9-1.7) for improved knee pain compared with non-gardners/yardworkers. CONCLUSION: Gardening/yardwork is not associated with knee OA progression and should not be discouraged in those with knee OA. Key Points • Gardening/yardwork is not associated with knee OA symptomatic or structural progression. • Gardening/yardwork should not be discouraged in people with knee OA.

Bicycling over a Lifetime Is Associated with Less Symptomatic Knee Osteoarthritis: Data from the Osteoarthritis Initiative.

INTRODUCTION: This study aimed to evaluate the relationship between a history of bicycling and symptomatic and structural outcomes of knee osteoarthritis (OA), the most common form of arthritis. METHODS: This was a retrospective, cross-sectional study within the Osteoarthritis Initiative (OAI), where we investigated OAI participants with complete data on bicycling, knee pain, and radiographic evidence of knee OA. We used a self-administered questionnaire at the 96-month OAI visit to identify participation in bicycling during four time periods throughout a participant's lifetime (ages 12-18, 19-34, 35-49, and >50 yr). Using logistic regression, we evaluated the influence of prior bicycling status (any history, history for each time period, number of periods cycling) on three outcomes at the 48-month OAI visit: frequent knee pain, radiographic OA (ROA), and symptomatic radiographic OA (SOA), adjusting for age and gender. RESULTS: A total of 2607 participants were included: 44.2% were male, mean age was 64.3 (SD, 9.0) yr, and body mass index was 28.5 (SD, 4.9) kg·m -2 . The adjusted risk ratios for the outcome of frequent knee pain, ROA, and SOA among those who reported any history of bicycling compared with non-bicyclers were 0.83 (0.73-0.92), 0.91 (0.85-0.98), and 0.79 (0.68-0.90), respectively. We observed a dose-response among those who participated in bicycling during more time periods. CONCLUSIONS: People who participated in bicycling had a lower prevalence of frequent knee pain, ROA, and SOA. The benefit appeared cumulative. This study indicates that bicycling may be favorable to knee health and should be encouraged.