Use of implementation strategies to promote the uptake of knee osteoarthritis practice guidelines and improve patient outcomes: A Systematic Review.

OBJECTIVE: Translation of knee osteoarthritis (KOA) clinical practice guidelines (CPGs) to practice remains suboptimal. The primary purpose of this systematic review was to describe the use of implementation strategies to promote KOA CPG recommended care. METHODS: MEDLINE (via PubMed), Embase, CINAHL, and Web of Science were searched from inception to February 23, 2023, and subsequently updated and expanded on January 16, 2024. Implementation strategies were mapped per the Expert Recommendations for Implementing Change taxonomy. Risk of bias (RoB) was assessed using the Cochrane Effective Practice and Organization of Care criteria. The review was registered prospectively (CRD42023402383). RESULTS: Nineteen studies were included in the final review. All (100% (n=4) studies that included the domains of provide interactive assistance, train and educate stakeholders (89%(n=18)), engage consumers (87%(n=15)) and support clinicians (79%(n=14)) reported change to provider adherence. Studies that reported a change to disability included train and educate stakeholders, engage consumers and adapt and tailor to context. Studies that used train and educate stakeholders, engage consumers, and support clinicians reported a change in pain and quality of life. Most studies had a low to moderate RoB. CONCLUSION: Implementation strategies have the potential to impact clinician uptake of CPG's and patient reported outcomes (PROs). The implementation context, using an active learning strategy with a patient partner, restructuring funding models, and integrating taxonomies to tailor multifaceted strategies should be prioritized. Further experimental research is recommended to determine which implementation strategies are most effective.

Occupational tasks associated with shoulder pain and upper extremity disability: a cross-sectional study in the Johnston County Osteoarthritis Project.

BACKGROUND: Shoulder pain is a leading cause of disability. Occupations requiring high upper extremity demands may put workers at greater risk of shoulder injury and resulting pain. We examined associations of occupation with shoulder pain and upper extremity disability in the Johnston County Osteoarthritis Project. METHODS: Work industry and occupational tasks for the longest job held were collected from participants. At follow-up ranging from 4-10 years later, participants were asked about shoulder symptoms (pain, aching, or stiffness occurring most days of 1 month in the last year) and given a 9-item, modified Disabilities Arm Shoulder and Hand (DASH) questionnaire to categorize disability from 0-4 (none-worst). Logistic regression and cumulative logit regression models were used to estimate associations with prevalent shoulder symptoms and with worse disability category, respectively. Models were adjusted for cohort, age, sex, race, education and time to follow-up. Sex- and race-stratified associations were evaluated. RESULTS: Among 1560 included participants, mean age was 62 years (standard deviation ± 9 years); 32% were men, and 31% were Black. Compared to the managerial/professional industry, higher odds of both shoulder symptoms and worse upper extremity disability were seen for most industrial groups with physically demanding jobs, particularly the service industry. Work that often or always required lifting/moving > 10 lbs. was associated with higher odds of shoulder symptoms. Work that sometimes or always required heavy work while standing was associated with higher odds of shoulder symptoms, and this association was stronger among men and White workers. CONCLUSION: Physically demanding occupations were associated with increased occurrence of shoulder pain and disability. Mitigating specific physical work demands may reduce shoulder-related disability.

Cohort profile: Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) - an international consortium of prospective cohort studies with individual participant data on hip osteoarthritis.

PURPOSE: Hip osteoarthritis (OA) is a major cause of pain and disability worldwide. Lack of effective therapies may reflect poor knowledge on its aetiology and risk factors, and result in the management of end-stage hip OA with costly joint replacement. The Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) consortium was established to pool and harmonise individual participant data from prospective cohort studies. The consortium aims to better understand determinants and risk factors for the development and progression of hip OA, to optimise and automate methods for (imaging) analysis, and to develop a personalised prediction model for hip OA. PARTICIPANTS: World COACH aimed to include participants of prospective cohort studies with ≥200 participants, that have hip imaging data available from at least 2 time points at least 4 years apart. All individual participant data, including clinical data, imaging (data), biochemical markers, questionnaires and genetic data, were collected and pooled into a single, individual-level database. FINDINGS TO DATE: World COACH currently consists of 9 cohorts, with 38 021 participants aged 18-80 years at baseline. Overall, 71% of the participants were women and mean baseline age was 65.3±8.6 years. Over 34 000 participants had baseline pelvic radiographs available, and over 22 000 had an additional pelvic radiograph after 8-12 years of follow-up. Even longer radiographic follow-up (15-25 years) is available for over 6000 of these participants. FUTURE PLANS: The World COACH consortium offers unique opportunities for studies on the relationship between determinants/risk factors and the development or progression of hip OA, by using harmonised data on clinical findings, imaging, biomarkers, genetics and lifestyle. This provides a unique opportunity to develop a personalised hip OA risk prediction model and to optimise methods for imaging analysis of the hip.

Looking back on 30+ years of the Johnston County Osteoarthritis Project while looking forward with the Johnston County Health Study: A narrative review.

Over the last 30 years, knowledge of the epidemiology of osteoarthritis (OA) has dramatically advanced, and Osteoarthritis and Cartilage has been on the forefront of disseminating research findings from large OA cohort studies, including the Johnston County OA Project (JoCoOA). The JoCoOA is a population-based, prospective longitudinal cohort that began roughly 30 years ago with a key focus on understanding prevalence, incidence, and progression of OA, as well as its risk factors, in a predominantly rural population of Black and White adults 45+ years old in a county in the southeastern United States. Selected OA results that will be discussed in this review include racial differences, lifetime risk, biomarkers, mortality, and OA risk factors. The new Johnston County Health Study will also be introduced. This new cohort study of OA and comorbid conditions builds upon current OA knowledge and JoCoOA infrastructure and is designed to reflect changes in demographics and urbanization in the county and the region.

Epidemiology of osteoarthritis: literature update 2022-2023.

PURPOSE OF REVIEW: This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods. RECENT FINDINGS: Contemporary research has improved our understanding of the burden of OA in typically understudied regions, including ethnic and racial minorities in high-income countries, the Middle East and North Africa (MENA) and Latin America. Efforts have also been made to explore the burden and risk factors in OA in previously understudied joints, such as the hand, foot, and ankle. Advancements in OA imaging techniques have occurred alongside the developments of AI methods aiming to predict disease phenotypes, progression, and outcomes. SUMMARY: Continuing efforts to expand our knowledge around OA in understudied populations will allow for the creation of targeted and specific interventions and inform policy changes aimed at reducing disease burden in these groups. The burden and disability associated with OA is notable in understudied joints, warranting further research efforts that may lead to effective therapeutic options. AI methods show promising results of predicting OA phenotypes and progression, which also may encourage the creation of targeted disease modifying OA drugs (DMOADs).

Tick-Borne Disease Infections and Chronic Musculoskeletal Pain.

Importance: Tick-borne diseases (TBDs) other than Lyme disease, such as spotted fever group rickettsiosis, ehrlichiosis, and galactose-α-1,3-galactose (α-gal) syndrome, are an emerging public health issue. Long-term sequelae secondary to Ehrlichia or Rickettsia infection are uncommon; however, musculoskeletal symptoms are often attributed to prior tick exposure. Objective: To evaluate the potential associations between prior exposure to TBDs and musculoskeletal symptoms, including radiographic osteoarthritis. Design, Setting, and Participants: This cross-sectional study analyzed serum samples from the fourth visit (2017-2018) of the Johnston County Osteoarthritis (JoCo OA) project, an ongoing longitudinal, population-based study in Johnston County, North Carolina. Biospecimen testing and analysis were performed between May 2022 and November 2023. Participants in the JoCo OA project are noninstitutionalized White and Black Johnston County residents 45 years or older. Main Outcome and Measures: The primary outcome was seropositivity with Ehrlichia IgG, Rickettsia IgG, and/or α-gal IgE and musculoskeletal symptoms. Secondary outcomes included risk factors associated with elevated α-gal IgE and weighted population point prevalence rates. Participants completed questionnaires, underwent physical assessments, and provided biospecimens for serological testing. Multivariable models were used to estimate associations of interest. Results: Of the 605 participants who completed the fourth visit of the JoCo OA project, 488 (80.7%) had serum samples available for testing. The 488 participants had a median (IQR) age of 72 (68-78) years and included 336 females (68.9%) and 161 Black (33.0%) and 327 White (67.0%) individuals. The overall weighted point prevalence was 8.6% (95% CI, 5.9%-11.3%) for Ehrlichia IgG, 17.1% (95% CI, 12.6%-21.5%) for Rickettsia IgG, and 19.6% (95% CI, 15.3%-23.8%) for α-gal IgE level greater than 0.1 IU/mL. Only α-gal IgE was associated with knee pain, aching or stiffness (mean ratio, 1.30; 95% CI, 1.09-1.56). Antibodies to Rickettsia, Ehrlichia, and α-gal were not associated with symptomatic radiographic knee osteoarthritis. Male sex (odds ratio [OR], 2.63; 95% CI, 1.55-4.47), current smoker status (OR, 3.55; 95% CI, 1.38-9.18), and an attached tick bite in the past 5 years (OR, 3.99; 95% CI, 2.22-7.15) were all risk factors that were associated with α-gal IgE level greater than 0.1 IU/mL. Despite only 84 individuals (17.2%) recalling a tick bite in the past 5 years, 178 (36.5%) had evidence of prior tick-borne exposure, suggesting frequent human-tick interactions. Conclusions and Relevance: Results of this cross-sectional study indicate no association between Ehrlichia or Rickettsia seropositivity and chronic musculoskeletal symptoms or osteoarthritis. Further investigation is needed into the pathogenesis of α-gal syndrome and interventions to reduce human-tick interactions.

Serum proteomic biomarkers diagnostic of knee osteoarthritis.

OBJECTIVE: To better understand the pathogenesis of knee osteoarthritis (OA) through identification of serum diagnostics. DESIGN: We conducted multiple reaction monitoring mass spectrometry analysis of 107 peptides in baseline sera of two cohorts: the Foundation for National Institutes of Health (NIH) (n = 596 Kellgren-Lawrence (KL) grade 1-3 knee OA participants); and the Johnston County Osteoarthritis Project (n = 127 multi-joint controls free of radiographic OA of the hands, hips, knees (bilateral KL=0), and spine). Data were split into (70%) training and (30%) testing sets. Diagnostic peptide and clinical data predictors were selected by random forest (RF); selection was based on association (p < 0.05) with OA status in multivariable logistic regression models. Model performance was based on area under the curve (AUC) of receiver operating characteristic (ROC) and precision-recall (PR) curves. RESULTS: RF selected 23 peptides (19 proteins) and body mass index (BMI) as diagnostic of OA. BMI weakly diagnosed OA (ROC-AUC 0.57, PR-AUC 0.812) and only symptomatic OA cases. ACTG was the strongest univariable predictor (ROC-AUC 0.705, PR-AUC 0.897). The final model (8 serum peptides) was highly diagnostic (ROC-AUC 0.833, 95% confidence interval [CI] 0.751, 0.905; PR-AUC 0.929, 95% CI 0.876, 0.973) in the testing set and equally diagnostic of non-symptomatic and symptomatic cases (AUCs 0.830-0.835), and not significantly improved with addition of BMI. The STRING database predicted multiple high confidence interactions of the 19 diagnostic OA proteins. CONCLUSIONS: No more than 8 serum protein biomarkers were required to discriminate knee OA from non-OA. These biomarkers lend strong support to the involvement and cross-talk of complement and coagulation pathways in the development of OA.

Multiple joint osteoarthritis (MJOA): What's in a name?

OBJECTIVE: To summarize the current state of the literature regarding multi-joint osteoarthritis (MJOA) and discuss important future directions. DESIGN: A narrative review of the author's work and other key references on this topic with a focus on the Johnston County studies, definitions of MJOA and their impact, multi-site pain in osteoarthritis (OA), genetics and biomarkers in MJOA, and perspectives on future work. RESULTS: MJOA is variably defined and lacks a clear consensus definition, making comprehensive study challenging. Involvement of both symptoms and structural changes of OA in multiple joints in an individual is common, but patterns vary by sex, race/ethnicity, and other factors. Outcomes (e.g., general health, function, falls, mortality) are negatively impacted by a greater whole-body OA burden. Recent genetic and biomarker studies including whole-body OA assessments have begun to shed some light on potentially unique factors in the MJOA population. CONCLUSIONS: Consideration of MJOA is essential for ongoing study of OA phenotypes, epidemiology, risk factors, genetics, biomarkers, and outcomes, to fully understand and eventually limit the negative impact of OA burden on health.

The Critical Role of Physical Activity and Weight Management in Knee and Hip Osteoarthritis: A Narrative Review.

Physical activity (PA) and weight management are critical components of an effective knee and hip osteoarthritis (OA) management plan, yet most people with OA remain insufficiently active and/or overweight. Clinicians and their care teams play an important role in educating patients with OA about PA and weight management, eliciting patient motivation to engage in these strategies, and referring patients to appropriate self-management interventions. The purpose of this review is to educate clinicians about the current public health and clinical OA guidelines for PA and weight management and highlight a variety of evidence-based self-management interventions available in community and clinical settings and online.

Arthritis Foundation/HSS Workshop on Hip Osteoarthritis, Part 1: Epidemiology, Early Development, and Cohorts From Around the World.

Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation, in partnership with the Hospital for Special Surgery, convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations and clinical research gaps gleaned from 5 presentations given in the "how hip osteoarthritis begins" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City.

Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort.

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Features of Knee and Multijoint Osteoarthritis by Sex and Race and Ethnicity: A Preliminary Analysis in the Johnston County Health Study.

OBJECTIVE: To evaluate knee osteoarthritis (KOA) and multijoint osteoarthritis (MJOA), and to compare features by sex and race and ethnicity in a population-based cohort. METHODS: Participants (n = 544) enrolled in the Johnston County Health Study (JoCoHS) as of January 2023 were categorized by radiographic and symptomatic KOA and MJOA phenotypes, and frequencies were compared by sex and race and ethnicity. Symptoms were assessed according to the Knee Injury and Osteoarthritis Outcome Score (KOOS) and pain, aching, and stiffness (PAS) scores at various joints. Models produced estimates (odds ratio [OR] or mean ratios [MR] and 95% CI) adjusted for age, BMI (kg/m2), and education. RESULTS: Men had twice the odds of having MJOA-6 (≥ 3 lower extremity joints affected); there were no significant differences in MJOA phenotypes by race and ethnicity. Women had 50% higher odds of having KOA or having various features of KOA. Women reported significantly worse KOOS Symptoms scores (MR 1.25). Black participants had higher odds of more severe KOA (OR 1.47), subchondral sclerosis (OR 2.06), and medial tibial osteophytes (OR 1.50). Black participants reported worse KOOS Symptoms than White participants (MR 1.18). Although not statistically significant, Hispanic participants (vs non-Hispanic participants) appeared to have lower odds of radiographic changes but reported worse symptoms. CONCLUSION: Preliminary findings in the diverse JoCoHS cohort suggest more lower extremity- predominant MJOA in men compared to women. Women and Black participants had more KOA features and more severe symptoms. Hispanic participants appear to have higher pain and symptoms scores despite having fewer structural changes. Studies in diverse populations are needed to understand the burden of OA.

Precision Medicine-Based Machine Learning Analyses to Explore Optimal Exercise Therapies for Individuals With Knee Osteoarthritis: Random Forest-Informed Tree-Based Learning.

OBJECTIVE: We applied a precision medicine-based machine learning approach to discover underlying patient characteristics associated with differential improvement in knee osteoarthritis symptoms following standard physical therapy (PT), internet-based exercise training (IBET), and a usual care/wait list control condition. METHODS: Participants (n = 303) were from the Physical Therapy vs Internet-Based Training for Patients with Knee Osteoarthritis trial. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score at 12-month follow-up. Random forest-informed tree-based learning was applied to identify patient characteristics that were critical to improving outcomes, and patients with those features were grouped. RESULTS: Age, BMI, and Brief Fear of Movement (BFOM) score, all at baseline, were identified as characteristics that effectively divided participants, creating 6 subgroups. Assigning treatments according to these models, compared to assigning a single best treatment to all patients, resulted in greater improvements of the average WOMAC at 12 months (P = 0.01). Key patterns were that IBET was the optimal treatment for patients of younger age and low BFOM, whereas PT was the optimal treatment for patients of older age, high BFOM, and BMI (kg/m2) between 26.3 and 37.2. CONCLUSION: These results suggest that easily assessed patient characteristics including age, fear of movement, and BMI could be used to guide patients toward either home-based exercise or PT, though additional studies are needed to confirm these findings. (ClinicalTrials.gov: NCT02312713).

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.

PURPOSE OF REVIEW: Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research. RECENT FINDINGS: AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.

Association of Foot Symptoms With Decreased Time to All-Cause Mortality: The Johnston County Osteoarthritis Project.

OBJECTIVE: Adults with foot symptoms (ie, pain, aching, or stiffness) may be at increased risk of reduced time to all-cause mortality. The purpose of this study was to evaluate whether foot symptoms are independently associated with all-cause mortality in older adults. METHODS: We analyzed longitudinal data from 2613 participants from the Johnston County Osteoarthritis Project, a longitudinal population-based cohort of adults 45 years of age and older. Participants completed questionnaires at baseline to determine presence of foot symptoms and covariable status. Baseline walking speed was measured via an 8-foot walk test. To examine the association of foot symptoms with time to mortality, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox regression models, adjusted for potential confounders. RESULTS: We observed 813 deaths over 4 to 14.5 years of follow-up. At baseline, 37% of participants had foot symptoms, mean age was 63 years, mean body mass index was approximately 31 kg/m2 , 65% were women, and 33% were Black. Moderate to severe foot symptoms were associated with reduced time to mortality after adjustment for demographics, comorbidities, physical activity, and knee and hip symptoms (HR = 1.30, 95% CI 1.09-1.54). Importantly, this association was not modified by walking speed or diabetes. CONCLUSION: Individuals with foot symptoms had an increased hazard of all-cause mortality compared with those with no foot symptoms. These effects were independent of key confounders and were not moderated by walking speed. Effective interventions to identify and manage at least moderate foot symptoms may reduce the risk of decreased time to mortality.

Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.

Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. Methods: Baseline data were from 3321 Osteoarthritis Initiative (OAI) participants with available cartilage thickness maps (6265 knees) and 77 clinical features. Cartilage maps were obtained from 3D DESS MR images using a deep-learning based segmentation approach and an atlas-based analysis developed by our group. Angle-based Joint and Individual Variation Explained (AJIVE) was used to capture and quantify variation, both shared among multiple data blocks and individual to each block, and to determine statistical significance. Results: Three major modes of variation were shared across the three data blocks. Mode 1 reflected overall thicker cartilage among men, those with higher education, and greater knee forces; Mode 2 showed associations between worsening Kellgren-Lawrence Grade, medial cartilage thinning, and worsening symptoms; and Mode 3 contrasted lateral and medial-predominant cartilage loss associated with BMI and malalignment. Each data block also demonstrated individual, independent modes of variation consistent with the known discordance between symptoms and structure in knee OA and reflecting the importance of features such as physical function, symptoms, and comorbid conditions independent of structural damage. Conclusions: This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in knee OA phenotyping.

Peripheral Blood DNA Methylation-Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression: Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project.

OBJECTIVE: The lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in OA clinical research. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression. METHODS: Genome-wide buffy coat DNA methylation patterns from 554 individuals from the Osteoarthritis Biomarkers Consortium (OABC) were determined using Illumina Infinium MethylationEPIC 850K arrays. Data were divided into model development and validation sets, and machine learning models were trained to classify future OA progression by knee pain, radiographic imaging, knee pain plus radiographic imaging, and any progression (pain, radiographic, or both). Parsimonious models using the top 13 CpG sites most frequently selected during development were tested on independent samples from participants in the Johnston County Osteoarthritis (JoCo OA) Project (n = 128) and a previously published Osteoarthritis Initiative (OAI) data set (n = 55). RESULTS: Full models accurately classified future radiographic-only progression (mean ± SEM accuracy 87 ± 0.8%, area under the curve [AUC] 0.94 ± 0.004), pain-only progression (accuracy 89 ± 0.9%, AUC 0.97 ± 0.004), pain plus radiographic progression (accuracy 72 ± 0.7%, AUC 0.79 ± 0.006), and any progression (accuracy 78 ± 0.4%, AUC 0.86 ± 0.004). Pain-only and radiographic-only progressors were not distinguishable (mean ± SEM accuracy 58 ± 1%, AUC 0.62 ± 0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in the OABC cohort and in both validation cohorts (mean ± SEM accuracy 80 ± 0.3%, AUC 0.88 ± 0.003 [using JoCo OA Project data], accuracy 80 ± 0.8%, AUC 0.89 ± 0.002 [using previous OAI data]). CONCLUSION: Our data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further studies should focus on evaluating the pathophysiologic consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.

Association of plasma microbial composition with a leaky gut in obesity-related osteoarthritis: An exploratory study.

Objective: To examine the plasma microbiome for differences between obese individuals with and without osteoarthritis (OA) and its association with serum lipopolysaccharide (LPS). Design: Blood samples from 70 participants with body mass index (BMI) â€‹≥ â€‹30kg/m2 and age ≥55 years, with (cases) or without (controls) hand plus knee OA, were analyzed for serum LPS and composition of the plasma microbiome. The Dirichlet-multinominal recursive partitioning model (DM-RPart) was applied to microbiome compositional data to test the hypothesis that LPS levels distinguish plasma microbiome, accounting for BMI and age. Results: No significant differences in alpha diversity, or compositional differences between groups at the genus level, were seen between cases and controls (p â€‹= â€‹0.11). ß-Diversity was significantly associated with serum LPS levels (p â€‹= â€‹0.01). DM-RPart resulted in an optimal tree with 3 divisions: 1) based on age (split at 69 years); 2) those older than 69 were split based on BMI; 3) those with BMI <39 â€‹kg/m2 were split based on LPS level (at 65 EU/ml). This resulted in 4 groups (nodes 2, and 5-7). Participants in node 2 were younger and the majority had no or mild OA. Those in nodes 5 and 6 were comparable in age and BMI but node 6 had higher LPS and more severe OA. Individuals in node 7 were older, had higher BMI, and the most severe OA. Conclusions: Our results suggest a relationship between serum LPS and the plasma microbiome in a subgroup of obese individuals with hand plus knee OA that could reflect differences in intestinal permeability.