Global burden of osteoarthritis in adults aged 30 to 44 years, 1990 to 2019: results from the Global Burden of Disease Study 2019.

BACKGROUND: Osteoarthritis (OA) is a common orthopedic disorder, and its incidence has been increasing among young adults in recent years. The purpose of this study is to investigate the global, regional, and national trends in OA burden and variation among individuals aged 30 to 44 from 1990 to 2019. METHODS: Data on the incidence, prevalence, and years lived with disability (YLDs) related to OA were sourced from the Global Burden of Disease Study 2019 among individuals aged 30 to 44. These measures were stratified by gender, region, country, and socio-demographic index (SDI). Additionally, we analyzed YLDs attributable to risk factors. RESULTS: In 2019, there were a total of 32,971,701 cases of OA among individuals aged 30 to 44 years worldwide, with an additional 7,794,008 new incident cases reported. OA of the knee was the primary contributor to both incidence and prevalence rates over the past three decades. From 1990 to 2019, both males and females in countries with high SDI and high-middle SDI showed upward trends in age-standardized incidence, prevalence, and YLDs rates. In 2019, the United States of America had the highest age-standardized incidence, prevalence, and YLDs rates. Elevated body-mass index (BMI) was found to be the most prevalent risk factor for osteoarthritis-related YLDs. Age-standardized YLDs rates were positively associated with SDI. CONCLUSIONS: OA remains a significant disease burden on individuals aged 30 to 44, with modifiable risk factors such as unhealthy lifestyle and obesity representing key targets for future interventions aimed at reducing the impact of this condition on younger generations.

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning.

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-ß signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

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Objective: To identify inflamm-aging related biomarkers in osteoarthritis (OA). Methods: Microarray gene profiles of young and aging OA patients were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) were obtained from the Human Aging Genome Resource (HAGR) database. The differentially expressed genes of young OA and older OA patients were screened and then intersected with ARGs to obtain the aging-related genes of OA. Enrichment analysis was performed to reveal the potential mechanisms of aging-related markers in OA. Three machine learning methods were used to identify core senescence markers of OA and the receiver operating characteristic (ROC) curve was used to assess their diagnostic performance. Peripheral blood mononuclear cells were collected from clinical OA patients to verify the expression of senescence-associated secretory phenotype (SASP) factors and senescence markers. Results: A total of 45 senescence-related markers were obtained, which were mainly involved in the regulation of cellular senescence, the cell cycle, inflammatory response, etc. Through the screening with the three machine learning methods, 5 core senescence biomarkers, including FOXO3, MCL1, SIRT3, STAG1, and S100A13, were obtained. A total of 20 cases of normal controls and 40 cases of OA patients, including 20 cases in the young patient group and 20 in the elderly patient group, were enrolled. Compared with those of the young patient group, C-reactive protein (CRP), interleukin (IL)-6, and IL-1ß levels increased and IL-4 levels decreased in the elderly OA patient group (P<0.01); FOXO3, MCL1, and SIRT3 mRNA expression decreased and STAG1 and S100A13 mRNA expression increased (P<0.01). Pearson correlation analysis demonstrated that the selected markers were associated with some indicators, including erythrocyte sedimentation rate (ESR), IL-1ß, IL-4, CRP, and IL-6. The area under the ROC curve of the 5 core aging genes was always greater than 0.8 and the C-index of the calibration curve in the nomogram prediction model was 0.755, which suggested the good calibration ability of the model. Conclusion: FOXO3, MCL1, SIRT3, STAG1, and S100A13 may serve as novel diagnostic biomolecular markers and potential therapeutic targets for OA inflamm-aging.

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value. METHODS: OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs. RESULTS: We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated. CONCLUSIONS: Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.

Identification of clinical characteristics biomarkers for rheumatoid arthritis through targeted DNA methylation sequencing.

OBJECTIVES: Rheumatoid arthritis (RA) is a complex disease with a challenging diagnosis, especially in seronegative patients. The aim of this study is to investigate whether the methylation sites associated with the overall immune response in RA can assist in clinical diagnosis, using targeted methylation sequencing technology on peripheral venous blood samples. METHODS: The study enrolled 241 RA patients, 30 osteoarthritis patients (OA), and 30 healthy volunteers control (HC). Fifty significant cytosine guanine (CG) sites between undifferentiated arthritis and RA were selected and analyzed using targeted DNA methylation sequencing. Logistic regression models were used to establish diagnostic models for different clinical features of RA, and six machine learning methods (logit model, random forest, support vector machine, adaboost, naive bayes, and learning vector quantization) were used to construct clinical diagnostic models for different subtypes of RA. Least absolute shrinkage and selection operator regression and detrended correspondence analysis were utilized to screen for important CGs. Spearman correlation was used to calculate the correlation coefficient. RESULTS: The study identified 16 important CG sites, including tumor necrosis factort receptor associated factor 5 (TRAF5) (chr1:211500151), mothers against decapentaplegic homolog 3 (SMAD3) (chr15:67357339), tumor endothelial marker 1 (CD248) (chr11:66083766), lysosomal trafficking regulator (LYST) (chr1:235998714), PR domain zinc finger protein 16 (PRDM16) (chr1:3307069), A-kinase anchoring protein 10 (AKAP10) (chr17:19850460), G protein subunit gamma 7 (GNG7) (chr19:2546620), yes1 associated transcriptional regulator (YAP1) (chr11:101980632), PRDM16 (chr1:3163969), histone deacetylase complex subunit sin3a (SIN3A) (chr15:75747445), prenylated rab acceptor protein 2 (ARL6IP5) (chr3:69134502), mitogen-activated protein kinase kinase kinase 4 (MAP3K4) (chr6:161412392), wnt family member 7A (WNT7A) (chr3:13895991), inhibin subunit beta B (INHBB) (chr2:121107018), deoxyribonucleic acid replication helicase/nuclease 2 (DNA2) (chr10:70231628) and chromosome 14 open reading frame 180 (C14orf180) (chr14:105055171). Seven CG sites showed abnormal changes between the three groups (P < 0.05), and 16 CG sites were significantly correlated with common clinical indicators (P < 0.05). Diagnostic models constructed using different CG sites had an area under the receiver operating characteristic curve (AUC) range of 0.64-0.78 for high-level clinical indicators of high clinical value, with specificity ranging from 0.42 to 0.77 and sensitivity ranging from 0.57 to 0.88. The AUC range for low-level clinical indicators of high clinical value was 0.63-0.72, with specificity ranging from 0.48 to 0.74 and sensitivity ranging from 0.72 to 0.88. Diagnostic models constructed using different CG sites showed good overall diagnostic accuracy for the four subtypes of RA, with an accuracy range of 0.61-0.96, a balanced accuracy range of 0.46-0.94, and an AUC range of 0.46-0.94. CONCLUSIONS: This study identified potential clinical diagnostic biomarkers for RA and provided novel insights into the diagnosis and subtyping of RA. The use of targeted deoxyribonucleic acid (DNA) methylation sequencing and machine learning methods for establishing diagnostic models for different clinical features and subtypes of RA is innovative and can improve the accuracy and efficiency of RA diagnosis.

Quantitative sensory testing in canine musculoskeletal pain: Findings from a systematic review, meta-analysis feasibility assessment, and limitations.

Quantitative sensory testing (QST) allows the study of pain mechanisms, patient phenotyping, and response to therapy. The goals of this study were to conduct a systematic review of the use of QST in dogs with musculoskeletal disease including osteoarthritis (OA), and to assess, by means of a meta-analysis, the ability of QST to differentiate affected dogs from healthy controls. The study protocol was registered; three bibliographic databases were screened. Studies involving QST in healthy dogs and those with musculoskeletal disease were included. Data were extracted using a standardized form. Assessment of quality and risk of bias were performed using the CAMARADES critical assessment tool. Twenty-nine articles met the inclusion criteria [systematic review (n = 11); meta-analysis (n = 28)]. In the systematic review, ten studies performed static QST: mechanical [punctate tactile (n = 6); mechanical pressure (n = 5)]; thermal [cold (n = 3); hot (n = 4)]; electrical (n = 1); and one study performed dynamic QST [conditioned pain modulation (n = 1)]. Most studies were of good scientific quality and showed low to moderate risk of bias. A meta-analysis was not possible due to numerous and severe issues of heterogeneity of data among studies. Methods to reduce risk of bias and use of reporting guidelines are some of the most needed improvements in QST research in dogs. Standardization of QST methodology is urgently needed in future studies to allow for data synthesis and a clear understanding of the sensory phenotype of dogs with and without chronic pain including OA.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications.

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

microRNAs are differentially expressed in equine plasma of horses with osteoarthritis and osteochondritis dissecans versus control horses.

Osteoarthritis (OA) is a leading cause of lameness in horses with no effective disease-modifying treatment and challenging early diagnosis. OA is considered a disease of the joint involving the articular cartilage, subchondral bone, synovial membrane, and ligaments. Osteochondritis dissecans (OCD) is a joint disease consisting of focal defects in the osteochondral unit which may progress to OA later in life. MicroRNAs (miRNAs) have been recognized as small non-coding RNAs that regulate a variety of biological processes and have been detected in biological fluids. MiRNAs are currently investigated for their utility as biomarkers and druggable targets for a variety of diseases. The current study hypothesizes that miRNA profiles can be used to actively monitor joint health and differences in miRNA profiles will be found in healthy vs diseased joints and that differences will be detectable in blood plasma of tested horses. Five horses with OA, OCD, and 4 controls (C) had blood plasma and synovial fluid collected. Total RNA, including miRNA was isolated before generating miRNA libraries from the plasma of the horses. Libraries were sequenced at the Schroeder Arthritis Institute (Toronto). Differential expression analysis was done using DESeq2 and validated using ddPCR. KEGG pathway analysis was done using mirPath v.3 (Diana Tools). 57 differentially expressed miRNAs were identified in OA vs C plasma, 45 differentially expressed miRNAs in OC vs C plasma, and 21 differentially expressed miRNAs in OA vs OCD plasma. Notably, miR-140-5p expression was observed to be elevated in OA synovial fluid suggesting that miR-140-5p may serve as a protective marker early on to attenuate OA progression. KEGG pathway analysis of differentially expressed plasma miRNAs showed relationships with glycan degradation, glycosaminoglycan degradation, and hippo signaling pathway. Interestingly, ddPCR was unable to validate the NGS data suggesting that isomiRs may play an integral role in miRNA expression when assessed using NGS technologies.

Soluble signal inhibitory receptor on leukocytes-1 reflects disease activity and assists diagnosis of patients with rheumatoid arthritis.

BACKGROUND: SIRL-1, an immunosuppressive receptor encoded by the VSTM1 gene, has recently been linked to rheumatoid arthritis (RA) due to its association with activated polymorphonuclear neutrophils (PMNs). Considering that the activated PMNs play a crucial role in the pathogenesis of rheumatoid arthritis (RA), we aimed to measure the levels of soluble SIRL-1, investigating whether they add value to RA in the clinical diagnosis. METHODS: Utilizing an enzyme-linked immunosorbent assay, the concentration of sSIRL-1 was measured in serum samples from cohort 1 diagnosed with RA (n = 96), gout (n = 54), osteoarthritis (n = 47), healthy controls (n = 86) and synovial fluid samples from OA (n = 8) and RA (n = 8) patients, respectively. Additionally, an external validation in cohort 2 (n = 156) comprising various inflammatory diseases was employed. RESULTS: The study revealed a distinctive upregulation of sSIRL-1 in the serum of RA compared to HC and other arthralgia diseases (p < 0.0001), which also displayed a significant elevation in synovial fluid from RA compared to OA (p < 0.05). Notably, sSIRL-1 levels exhibited a significant decrease in patients who achieved disease remission (p < 0.05). Furthermore, the diagnostic accuracy of RA was enhanced when sSIRL-1 was combined with anti-CCP and RF, yielding an impressive AUC value of 0.950. CONCLUSION: The expression pattern of sSIRL-1 in RA, coupled with its correlation with disease activity, underscores its potential clinical utility for both diagnosis and disease monitoring in RA patients. This study offers valuable insights into the evolving diagnostic landscape of RA.

Comparative study of 1H-NMR metabolomic profile of canine synovial fluid in patients affected by four progressive stages of spontaneous osteoarthritis.

The study aimed to assess the metabolomic profile of the synovial fluid (SF) of dogs affected by spontaneous osteoarthritis (OA) and compare any differences based on disease progression. Sixty client-owned dogs affected by spontaneous OA underwent clinical, radiographic, and cytologic evaluations to confirm the diagnosis. The affected joints were divided into four study groups based on the Kallgreen-Lawrence classification: OA1 (mild), OA2 (moderate), OA3 (severe), and OA4 (extremely severe/deforming). The osteoarthritic joint's SF was subjected to cytologic examination and 1H-NMR analysis. The metabolomic profiles of the study groups' SF samples were statistically compared using one-way ANOVA. Sixty osteoarthritic joints (45 stifles, 10 shoulders and 5 elbows) were included in the study. Fourteen, 28, and 18 joints were included in the OA1, OA2, and OA3 groups, respectively (0 joints in the OA4 group). Metabolomic analysis identified 48 metabolites, five of which were significantly different between study groups: Mannose and betaine were elevated in the OA1 group compared with the OA2 group, and the 2-hydroxyisobutyrate concentration decreased with OA progression; in contrast, isoleucine was less concentrated in mild vs. moderate OA, and lactate increased in severe OA. This study identified different 1H-NMR metabolomic profiles of canine SF in patients with progressive degrees of spontaneous OA, suggesting 1H-NMR metabolomic analysis as a potential alternative method for monitoring OA progression. In addition, the results suggest the therapeutic potentials of the metabolomic pathways that involve mannose, betaine, 2-hydroxyisobutyrate, isoleucine, and lactate.

Comprehensive analysis of anoikis-related genes in diagnosis osteoarthritis: based on machine learning and single-cell RNA sequencing data.

Osteoarthritis (OA) is a degenerative disease closely associated with Anoikis. The objective of this work was to discover novel transcriptome-based anoikis-related biomarkers and pathways for OA progression.The microarray datasets GSE114007 and GSE89408 were downloaded using the Gene Expression Omnibus (GEO) database. A collection of genes linked to anoikis has been collected from the GeneCards database. The intersection genes of the differential anoikis-related genes (DEARGs) were identified using a Venn diagram. Infiltration analyses were used to identify and study the differentially expressed genes (DEGs). Anoikis clustering was used to identify the DEGs. By using gene clustering, two OA subgroups were formed using the DEGs. GSE152805 was used to analyse OA cartilage on a single cell level. 10 DEARGs were identified by lasso analysis, and two Anoikis subtypes were constructed. MEgreen module was found in disease WGCNA analysis, and MEturquoise module was most significant in gene clusters WGCNA. The XGB, SVM, RF, and GLM models identified five hub genes (CDH2, SHCBP1, SCG2, C10orf10, P FKFB3), and the diagnostic model built using these five genes performed well in the training and validation cohorts. analysing single-cell RNA sequencing data from GSE152805, including 25,852 cells of 6 OA cartilage.

Investigation of periodontitis, halitosis, xerostomia, and serological characteristics of patients with osteoarthritis and rheumatoid arthritis and identification of new biomarkers.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two different types of arthritis. Within RA, the subsets between seronegative RA (snRA) and seropositive RA (spRA) represent distinct disease entities; however, identifying clear distinguishing markers between them remains a challenge. This study investigated and compared the oral health conditions in patients with RA and OA to clarify the differences from healthy controls. In addition, we investigated the serological characteristics of the patients, the factors that distinguished patients with RA from those with OA, and the main factors that differentiated between snRA and spRA patients. A total of 161 participants (mean age: 52.52 ± 14.57 years, 32 males and 129 females) were enrolled in this study and categorized as: normal (n = 33), OA (n = 31), and RA (n = 97). Patients with RA were divided into the following two subtypes: snRA (n = 18) and spRA (n = 79). Demographics, oral health, and serological characteristics of these patients were compared. The prevalence of periodontal diseases was significantly higher in patients with OA (100%) and RA (92.8%) than in healthy controls (0.0%). However, the presence of periodontal diseases was not utilized as a distinguishing factor between OA and RA. Xerostomia occurred more frequently in patients with RA (84.5%) than in patients with OA (3.2%) and healthy controls (0.0%) (all p < 0.001). ROC analysis revealed that periodontal disease was a very strong predictor in the diagnosis of OA compared to healthy controls, with an AUC value of 1.00 (p < 0.001). Additionally, halitosis (AUC = 0.746, 95% CI 0.621-0.871, p < 0.001) and female sex (AUC = 0.663, 95% CI 0.529-0.797, p < 0.05) were also significant predictors of OA. The strongest predictors of RA diagnosis compared to healthy controls were periodontal diseases (AUC = 0.964), followed by xerostomia (AUC = 0.923), age (AUC = 0.923), female sex (AUC = 0.660), and halitosis (AUC = 0.615) (all p < 0.05). Significant serological predictors of RA were anti-CCP Ab (AUC = 0.808), and RF (AUC = 0.746) (all p < 0.05). In multiple logistic regression analysis, xerostomia (odds ratio, OR: 8124.88, 95% CI 10.37-6368261.97, p-value = 0.008) and Anti-CCP Ab (OR: 671.33, 95% CI 2.18-207,074.02, p = 0.026) were significant predictors for RA compared to OA. When diagnosing spRA compared to snRA, anti-CCP Ab (AUC = 1.000, p < 0.001) and RF (AUC = 0.910, 95%CI 0.854-0.967, p < 0.001) had outstanding predictive performances. Therefore, clinicians and researchers should thoroughly evaluate the oral status of both OA and RA patients, alongside serological factors, and consider these elements as potential predictors.

From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers.

INTRODUCTION: Osteoarthritis (OA) affects over 500 million people worldwide. OA patients are symptomatically treated, and current therapies exhibit marginal efficacy and frequently carry safety-risks associated with chronic use. No disease-modifying therapies have been approved to date leaving surgical joint replacement as a last resort. To enable effective patient care and successful drug development there is an urgent need to uncover the pathobiological drivers of OA and how these translate into disease endotypes. Endotypes provide a more precise and mechanistic definition of disease subgroups than observable phenotypes, and a panel of tissue- and pathology-specific biochemical markers may uncover treatable endotypes of OA. AREAS COVERED: We have searched PubMed for full-text articles written in English to provide an in-depth narrative review of a panel of validated biochemical markers utilized for endotyping of OA and their association to key OA pathologies. EXPERT OPINION: As utilized in IMI-APPROACH and validated in OAI-FNIH, a panel of biochemical markers may uncover disease subgroups and facilitate the enrichment of treatable molecular endotypes for recruitment in therapeutic clinical trials. Understanding the link between biochemical markers and patient-reported outcomes and treatable endotypes that may respond to given therapies will pave the way for new drug development in OA.

Exploring m6A-linked aging genes in osteoarthritis and broad cancer spectrum: Prospects for diagnostic and therapeutic advancements.

Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts individuals and healthcare systems worldwide. However, the exploration of N6-methyladenosine (m6A)-related aging genes in OA pathogenesis remains largely underexplored. This study aimed to elucidate the role of m6A-related aging genes in OA and to develop a robust diagnostic model based on their expression profiles. Leveraging publicly available gene expression datasets, we conducted consensus clustering to categorize OA into distinct subtypes, guided by the expression patterns of m6A-related aging genes. Utilizing XGBoost, a cutting-edge machine learning approach, we identified key diagnostic genes and constructed a predictive model. Our investigation extended to the immune functions of these genes, shedding light on potential therapeutic targets and underlying regulatory mechanisms. Our analysis unveiled specific OA subtypes, each marked by unique expression profiles of m6A-related aging genes. We pinpointed a set of pivotal diagnostic genes, offering potential therapeutic avenues. The developed diagnostic model exhibited exceptional capability in distinguishing OA patients from healthy controls. To corroborate our computational findings, we performed quantitative real-time polymerase chain reaction analyses on two cell lines: HC-OA (representing adult osteoarthritis cells) and C-28/I2 (representative of normal human chondrocytes). The gene expression patterns observed were consistent with our bioinformatics predictions, further validating our initial results. In conclusion, this study underscores the significance of m6A-related aging genes as promising biomarkers for diagnosis and prognosis, as well as potential therapeutic targets in OA. Although these findings are encouraging, further validation and functional analyses are crucial for their clinical application.

Co-development and testing of an extended community pharmacy model of service delivery for managing osteoarthritis: protocol for a sequential, multi-methods study (PharmOA).

BACKGROUND: Osteoarthritis is a common, painful and disabling long-term condition. Delivery of high-quality guideline-informed osteoarthritis care that successfully promotes and maintains supported self-management is imperative. However, osteoarthritis care remains inconsistent, including under use of core non-pharmacological approaches of education, exercise and weight loss. Community pharmacies are an accessible healthcare provider. United Kingdom government initiatives are promoting their involvement in a range of long-term conditions, including musculoskeletal conditions. It is not known what an enhanced community pharmacy role for osteoarthritis care should include, what support is needed to deliver such a role, and whether it would be feasible and acceptable to community pharmacy teams. In this (PharmOA) study, we aim to address these gaps, and co-design and test an evidence-based extended community pharmacy model of service delivery for managing osteoarthritis. METHODS: Informed by the Theoretical Domains Framework, Normalisation Process Theory, and the Medical Research Council (MRC) framework for developing complex interventions, we will undertake a multi-methods study involving five phases: 1. Systematic review to summarise currently available evidence on community pharmacy roles in supporting adults with osteoarthritis and other chronic (non-cancer) pain. 2. Cross-sectional surveys and one-to-one qualitative interviews with patients, healthcare professionals and pharmacy staff to explore experiences of current, and potential extended community pharmacy roles, in delivering osteoarthritis care. 3. Stakeholder co-design to: a) agree on the extended role of community pharmacies in osteoarthritis care; b) develop a model of osteoarthritis care within which the extended roles could be delivered (PharmOA model of service delivery); and c) refine existing tools to support community pharmacies to deliver extended osteoarthritis care roles (PharmOA tools). 4. Feasibility study to explore the acceptability and feasibility of the PharmOA model of service delivery and PharmOA tools to community pharmacy teams. 5. Final stakeholder workshop to: a) finalise the PharmOA model of service delivery and PharmOA tools, and b) if applicable, prioritise recommendations for its wider future implementation. DISCUSSION: This novel study paves the way to improving access to and availability of high-quality guideline-informed, consistent care for people with osteoarthritis from within community pharmacies.

Community ambulation in older adults and people with OA - a model verification using Canadian Longitudinal Study on Aging (CLSA) data.

BACKGROUND: There are health and well-being benefits of community ambulation; however, many older adults do not regularly walk outside of their home. Objectives were to estimate the associations between latent constructs related to community ambulation in older adults aged 65-85 (65+), and in adults with osteoarthritis (OA) aged 45-85. METHODS: Secondary data analysis of the comprehensive baseline and maintaining contact questionnaire data from the Canadian Longitudinal Study of Aging (CLSA) was completed. Based on a previous model of community ambulation post-stroke, structural equation modeling (SEM) was used to develop measurement and structural models for two groups: older adults 65+ and people with OA. Multi-group SEM was conducted to test measurement invariance across sex and age groups. Measurement models were developed for the following latent factors: ambulation (frequency of walking outside/week, hours walked/day, ability to walk without help, frequency and aids used in different settings); health perceptions (general health, pain frequency/intensity); timed functional mobility (gait speed, timed up-and-go, sit-to-stand, balance). Variables of depression, falls, age, sex, and fear of walking alone at night were covariates in the structural models. RESULTS: Data were used from 11,619 individuals in the 65+ group (mean age 73 years ±6, 49% female) and 5546 individuals in the OA group (mean age 67 ± 10, 60% female). The final 65+ model had a close fit with RMSEA (90% CI) = 0.018 (0.017, 0.019), CFI = 0.91, SRMR = 0.09. For the OA group, RMSEA (90% CI) = 0.021 (0.020, 0.023), CFI = 0.92, SRMR = 0.07. Health perceptions and timed functional mobility had a positive association with ambulation. Depression was associated with ambulation through negative associations with health perceptions and timed functional mobility. Multi-group SEM results reveal the measurement model was retained for males and females in the 65+ group, for males and females and for age groups (65+, < 65) in the OA group. CONCLUSIONS: The community ambulation model post-stroke was verified with adults aged 65+ and for those with OA. The models of community ambulation can be used to frame and conceptualize community ambulation research and clinical interventions.

Is it possible to predict which patients are most likely to benefit from intra-articular corticosteroid injections? A systematic review.

AIM: Intra-articular corticosteroid injections (IACIs) can reduce osteoarthritis-related pain, with differing levels of response across patient groups. This systematic review investigates what is known about the positive and negative predictors of outcomes in patients with osteoarthritis who undergo IACIs. METHODS: We systematically searched the Medline, Embase, and Cochrane databases to May 2023 for studies that evaluated patients undergoing IACIs for osteoarthritis and reported on predictors of outcomes in these patients. RESULTS: Eight studies were included. Two were placebo-controlled trials, six were observational studies. Due to the heterogeneity of outcomes and variables between the studies, it was not possible to pool the results for formal meta-analysis. Higher baseline pain, older age, higher BMI, lower range of movement, higher Kellgren-Lawrence radiographic score, joint effusion, and aspiration were shown to be predictors of a positive response to IACIs in some of the included studies. However, other studies showed no difference in response with these variables, or a negative correlation with response. Sex, smoking, mental health status, hypertension/ischaemic heart disease, diabetes mellitus, duration of symptoms, and socioeconomic status did not demonstrate any correlation with the prediction of positive or negative outcomes after IACIs. CONCLUSION: Several patient features have been identified as positive predictors of outcomes following IACIs. However, this systematic review has identified inconsistent and variable findings across the existing literature. Further research with standardization of IACI administration and outcome measures is required to facilitate further analysis of the reliability and significance of predictive factors for response to IACIs.

Identification of aging-related genes in diagnosing osteoarthritis via integrating bioinformatics analysis and machine learning.

BACKGROUND: Osteoarthritis (OA) is one of the main causes of pain and disability in the world, it may be caused by many factors. Aging plays a significant role in the onset and progression of OA. However, the mechanisms underlying it remain unknown. Our research aimed to uncover the role of aging-related genes in the progression of OA. METHODS: In Human OA datasets and aging-related genes were obtained from the GEO database and the HAGR website, respectively. Bioinformatics methods including Gene Ontology (GO), Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment, and Protein-protein interaction (PPI) network analysis were used to analyze differentially expressed aging-related genes (DEARGs) in the normal control group and the OA group. And then weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) regression, and the Random Forest (RF) machine learning algorithms were used to find the hub genes. RESULTS: Four overlapping hub genes: HMGB2, CDKN1A, JUN, and DDIT3 were identified. According to the nomogram model and receiver operating characteristic (ROC) curve analysis, four hub DEARGs had good diagnostic value in distinguishing normal from OA. Furthermore, the qRT-PCR test demonstrated that HMGB2, CDKN1A, JUN, and DDIT3 mRNA expression levels were lower in OA group than in normal group. CONCLUSION: Finally, these four-hub aging-related genes may help us understand the underlying mechanism of aging in osteoarthritis and could be used as possible diagnostic and therapeutic targets.

Identification and Verification of Hub Mitochondrial Dysfunction Genes in Osteoarthritis Based on Bioinformatics Analysis.

Objective: Age-related mitochondrial dysfunction and associated oxidative stress may contribute to the development of osteoarthritis. The aim of this study was to identify hub genes associated with mitochondrial dysfunction in osteoarthritis (OA) patients, helping predict the risk of OA, and revealing the mechanism of OA progression. Methods: OA expression data and mitochondrial dysfunction genes were downloaded from GEO (GSE55235, GSE82107, and GSE114007) and GeneCard databases. The differentially expressed mitochondrial dysfunction genes (DEMDFGs) between OA and control samples were screened. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathways were analyzed for DEMDFGs. The hub genes were determined by WGCNA and LASSO regression analysis. ROC curves manifested the diagnostic efficacy of each hub gene. A nomogram model was constructed and validated to predict OA risk. The expression of hub genes in OA and normal chondrocytes was verified by external datasets, qRT-PCR and western blotting. Results: A total of 31 DEMDFGs were identified, with 15 genes upregulated and 16 genes downregulated. GO functional enrichment analysis revealed that DEMDFGs were enriched in biological processes related to energy metabolism and cellular respiration. By employing weighted gene coexpression network analysis, we identified four distinct coexpression modules, among which the blue module exhibited the strongest correlation with OA. The intersection between DEMDFGs and this module yielded eight candidate genes. After LASSO analysis of the data, four hub genes (ACADL, CYBA, SLC19A2, and UCP2) were identified as potential biomarkers for OA. The expression levels of these four genes were externally validated in the GSE114007 dataset. And the biologically differential expression of these four genes has been verified in OA and normal chondrocytes. Moreover, the four hub genes had good sensitivity and specificity by ROC curve analysis, and the risk model constructed with these four genes showed promising performance. In conclusion, our study may provide novel mitochondrial dysfunction hub genes with potential clinical applications for understanding the pathology, diagnosis, and treatment of OA.