Gut microbiome and osteoarthritis: insights from the naturally occurring canine model of OA.

OBJECTIVES: The purpose was to enhance the current knowledge of the relationship between the gut microbiome and osteoarthritis (OA) and associated pain using pet dogs as a clinically relevant translational model. METHODS: Fecal samples were collected from 93 owned pet dogs. Dogs were designated either clinically healthy or OA-pain using validated methods. Metagenomic profiling was performed through shotgun sequencing using the Illumina Novaseq platform. MetaPhlAn2 and HUMAnN2 were used to evaluate bacterial taxonomic and pathway relative abundance. Comparisons between healthy and OA-pain groups were performed individually for each taxa using non-parametric tests following Benjamini and Hochberg adjustment for multiple comparisons. Permutation analysis of variance was performed using Bray-Curtis distance matrices. All downstream analyses were completed in R. RESULTS: No significant differences between healthy and OA-pain dogs were observed for alpha and beta-diversity. We found 13 taxa with nominally significant (p<0.05) associations with OA case status, but none of the associations remained significant after adjustment for multiple comparisons. No differences in alpha, beta diversities or Firmicutes/Bacteroidetes ratio (FBR) were found regarding pain severity, mobility/ activity level, age, or body composition score. CONCLUSIONS: Similar to recent studies in humans, the present study did not demonstrate a significant difference in the fecal microbial communities between dogs with OA pain and healthy control dogs. Future research in this naturally occurring model should expand on these data and relate the gut microbiome to gut permeability and circulating pro- and anti-inflammatory molecules to better understand the influence of the gut microbiome on OA and OA-pain.

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.

Intestinal Helminth Infection Impairs Oral and Parenteral Vaccine Efficacy.

The impact of endemic parasitic infection on vaccine efficacy is an important consideration for vaccine development and deployment. We have examined whether intestinal infection with the natural murine helminth Heligmosomoides polygyrus bakeri alters Ag-specific Ab and cellular immune responses to oral and parenteral vaccination in mice. Oral vaccination of mice with a clinically relevant, live, attenuated, recombinant Salmonella vaccine expressing chicken egg OVA (Salmonella-OVA) induced the accumulation of activated, OVA-specific T effector cells rather than OVA-specific regulatory T cells in the GALT. Intestinal helminth infection significantly reduced Th1-skewed Ab responses to oral vaccination with Salmonella-OVA. Activated, adoptively transferred, OVA-specific CD4+ T cells accumulated in draining mesenteric lymph nodes of vaccinated mice, regardless of their helminth infection status. However, helminth infection increased the frequencies of adoptively transferred OVA-specific CD4+ T cells producing IL-4 and IL-10 in the mesenteric lymph node. Ab responses to the oral Salmonella-OVA vaccine were reduced in helminth-free mice adoptively transferred with OVA-specific CD4+ T cells harvested from mice with intestinal helminth infection. Intestinal helminth infection also significantly reduced Th2-skewed Ab responses to parenteral vaccination with OVA adsorbed to alum. These findings suggest that vaccine-specific CD4+ T cells induced in the context of helminth infection retain durable immunomodulatory properties and may promote blunted Ab responses to vaccination. They also underscore the potential need to treat parasitic infection before mass vaccination campaigns in helminth-endemic areas.

CC-Chemokine Receptor-2 Expression in Osteoblasts Contributes to Cartilage and Bone Damage during Post-Traumatic Osteoarthritis.

In osteoarthritis (OA), bone changes are radiological hallmarks and are considered important for disease progression. The C-C chemokine receptor-2 (CCR2) has been shown to play an important role in bone physiology. In this study, we investigated whether Ccr2 osteoblast-specific inactivation at different times during post-traumatic OA (PTOA) progression improves joint structures, bone parameters, and pain. We used a tamoxifen-inducible Ccr2 inactivation in Collagen1α-expressing cells to obtain osteoblasts lacking Ccr2 (CCR2-Col1αKO). We stimulated PTOA changes in CCR2-Col1αKO and CCR2+/+ mice using the destabilization of the meniscus model (DMM), inducing recombination before or after DMM (early- vs. late-inactivation). Joint damage was evaluated at two, four, eight, and twelve weeks post-DMM using multiple scores: articular-cartilage structure (ACS), Safranin-O, histomorphometry, osteophyte size/maturity, subchondral bone thickness and synovial hyperplasia. Spontaneous and evoked pain were assessed for up to 20 weeks. We found that early osteoblast-Ccr2 inactivation delayed articular cartilage damage and matrix degeneration compared to CCR2+/+, as well as DMM-induced bone thickness. Osteophyte formation and maturation were only minimally affected. Late Collagen1α-Ccr2 deletion led to less evident improvements. Osteoblast-Ccr2 deletion also improved static measures of pain, while evoked pain did not change. Our study demonstrates that Ccr2 expression in osteoblasts contributes to PTOA disease progression and pain by affecting both cartilage and bone tissues.

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.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Point prevalence of Hip Symptoms, Radiographic, And Symptomatic OA at Five Time Points: The Johnston County Osteoarthritis Project, 1991-2018.

Objective: To describe the point prevalence of hip symptoms, radiographic hip osteoarthritis (rHOA), severe rHOA, and symptomatic rHOA (sxHOA) at five time points in the longitudinal, population-based Johnston County Osteoarthritis Project (JoCoOA). Design: Data were from 3068 JoCoOA participants who attended up to five study visits (1991-2018). Standardized supine pelvis radiographs were read by a single, expert musculoskeletal radiologist with high reliability. The four outcomes were: 1) self-reported hip symptoms: "On most days, do you have pain, aching, or stiffness in your right/left hip?"; 2) rHOA: Kellgren-Lawrence grade (KLG) of 2-4; 3) severe rHOA: KLG of 3-4; and 4) sxHOA: both symptoms and rHOA in the same joint. Weighted point prevalence and 95% confidence intervals (CI) were generated overall and by age group (45-54, 55-64, 65-74, 75+ years), sex, race (Black/White), and body mass index (BMI; 18.5-24.9; 25-29.9; 30+ kg/m2). Results: At the most recent follow-up (2017-2018), the point prevalence (%) of hip symptoms, rHOA, severe rHOA, and sxHOA were 30% (95% CI 25%, 35%), 53% (95% CI 48%, 58%), 9% (95% CI 6%, 12%), and 15% (95% CI 11%, 19%), respectively. RHOA and severe rHOA were most prevalent in those 75+ years. Women were more likely than men to have hip symptoms and sxHOA. No consistent trends were noted by race or BMI. Conclusion: These updated point prevalence estimates demonstrate a large and increasing burden of HOA in the general population, particularly with aging. Black and White individuals were affected similarly in this cohort.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.

There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative.

OBJECTIVE: To apply biclustering, a methodology originally developed for analysis of gene expression data, to simultaneously cluster observations and clinical features to explore candidate phenotypes of knee osteoarthritis (KOA) for the first time. METHODS: Data from the baseline Osteoarthritis Initiative (OAI) visit were cleaned, transformed, and standardized as indicated (leaving 6461 knees with 86 features). Biclustering produced submatrices of the overall data matrix, representing similar observations across a subset of variables. Statistical validation was determined using the novel SigClust procedure. After identifying biclusters, relationships with key outcome measures were assessed, including progression of radiographic KOA, total knee arthroplasty, loss of joint space width, and worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, over 96 months of follow-up. RESULTS: The final analytic set included 6461 knees from 3330 individuals (mean age 61 years, mean body mass index 28 kg/m2, 57% women and 86% White). We identified 6 mutually exclusive biclusters characterized by different feature profiles at baseline, particularly related to symptoms and function. Biclusters represented overall better (#1), similar (#2, 3, 6), and poorer (#4, 5) prognosis compared to the overall cohort of knees, respectively. In general, knees in biclusters #4 and 5 had more structural progression (based on Kellgren-Lawrence grade, total knee arthroplasty, and loss of joint space width) but tended to have an improvement in WOMAC pain scores over time. In contrast, knees in bicluster #1 had less incident and progressive KOA, fewer total knee arthroplasties, less loss of joint space width, and stable pain scores compared with the overall cohort. SIGNIFICANCE: We identified six biclusters within the baseline OAI dataset which have varying relationships with key outcomes in KOA. Such biclusters represent potential phenotypes within the larger cohort and may suggest subgroups at greater or lesser risk of progression over time.

Application of Heterogeneity of Treatment-Effects Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee Osteoarthritis.

OBJECTIVE: To evaluate heterogeneity of treatment effects in a trial of exercise-based interventions for knee osteoarthritis (OA). METHODS: Participants (n = 350) were randomized to standard physical therapy (PT; n = 140), internet-based exercise training (IBET; n = 142), or wait list (WL; n = 68) control. We applied qualitative interaction trees (QUINT), a sequential partitioning method, and generalized unbiased interaction detection and estimation (GUIDE), a regression tree approach, to identify subgroups with greater improvements in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score over 4 months. Predictors included 24 demographic, clinical, and psychosocial characteristics. We conducted internal validation to estimate optimism (bias) in the range of mean outcome differences among arms. RESULTS: Both QUINT and GUIDE indicated that for participants with lower body mass index (BMI), IBET was better than PT (improvements of WOMAC ranged from 6.3 to 9.1 points lower), and for those with higher BMI and a longer duration of knee OA, PT was better than IBET (WOMAC improvement was 6.3 points). In GUIDE analyses comparing PT or IBET to WL, participants not employed had improvements in WOMAC ranging from 1.8 to 6.8 points lower with PT or IBT versus WL. From internal validation, there were large corrections to the mean outcome differences among arms; however, after correction, some differences remained in the clinically meaningful range. CONCLUSION: Results suggest there may be subgroups who experience greater improvement in symptoms from PT or IBET, and this finding could guide referrals and future trials. However, uncertainty persists for specific treatment-effects size estimates and how they apply beyond this study sample.

Accelerometer-Based Physical Activity Patterns and Associations With Outcomes Among Individuals With Osteoarthritis.

BACKGROUND: This study examined patterns of physical activity and associations with pain, function, fatigue, and sleep disturbance among individuals with knee or hip osteoarthritis. METHODS: Participants (n = 54) were enrolled in a telephone-based physical activity coaching intervention trial; all data were collected at baseline. Self-reported measures of pain and function (WOMAC [Western Ontario and McMaster Universities Osteoarthritis Index] subscales), fatigue (10-point numeric rating scale), and PROMIS (Patient-Reported Outcomes Information System) Sleep Disturbance were collected via telephone. Accelerometers were mailed to participants and were worn for at least 3 days. Proportion of time participants spent in sedentary behavior during the morning (from wake until 12:00 pm), afternoon (12:00 pm until 5:59 pm) and evening (6:00 pm until sleep) each day was averaged across all days of wear. Pearson correlations assessed associations between activity and self-reported measures. RESULTS: Participants spent a large proportion of time in sedentary behavior: 65.6% of mornings, 70.0% of afternoons, and 76.6% of evenings. Associations between proportion of time spent in sedentary behavior and reported outcomes were generally strongest in the afternoon, strongest for WOMAC function, and lowest for PROMIS Sleep Disturbance. In the evening hours, sedentary time was most strongly associated with fatigue. CONCLUSIONS: Overall, findings stress the importance of reducing sedentary behavior among adults with osteoarthritis and suggest behavioral interventions may be strengthened by considering patients' within-day variation in symptoms and activity.

Association of Increased Serum Lipopolysaccharide, But Not Microbial Dysbiosis, With Obesity-Related Osteoarthritis.

OBJECTIVE: To test the hypothesis that an altered gut microbiota (dysbiosis) plays a role in obesity-associated osteoarthritis (OA). METHODS: Stool and blood samples were collected from 92 participants with a body mass index (BMI) ≥30 kg/m2 , recruited from the Johnston County Osteoarthritis Project. OA patients (n = 50) had hand and knee OA (Kellgren/Lawrence [K/L] grade ≥2 or arthroplasty). Controls (n = 42) had no hand OA and a K/L grade of 0-1 for the knees. Compositional analysis of stool samples was carried out by 16S ribosomal RNA amplicon sequencing. Alpha- and beta-diversity and differences in taxa relative abundances were determined. Blood samples were used for multiplex cytokine analysis and measures of lipopolysaccharide (LPS) and LPS binding protein. Germ-free mice were gavaged with patient- or control-pooled fecal samples and fed a 40% fat, high-sucrose diet for 40 weeks. Knee OA was evaluated histologically. RESULTS: On average, OA patients were slightly older than the controls, consisted of more women, and had a higher mean BMI, higher mean Western Ontario and McMaster Universities Osteoarthritis Index pain score, and higher mean K/L grade. There were no significant differences in α- or ß-diversity or genus level composition between patients and controls. Patients had higher plasma levels of osteopontin (P = 0.01) and serum LPS (P < 0.0001) compared to controls. Mice transplanted with patient or control microbiota exhibited a significant difference in α-diversity (P = 0.02) and ß-diversity, but no differences in OA severity were observed. CONCLUSION: The lack of differences in the gut microbiota, but increased serum LPS levels, suggest the possibility that increased intestinal permeability allowing for greater absorption of LPS, rather than a dysbiotic microbiota, may contribute to the development of OA associated with obesity.

Associations Between Baseline and Longitudinal Semiautomated Quantitative Joint Space Width at the Hip and Incident Hip Osteoarthritis: Data From a Community-Based Cohort.

OBJECTIVE: To evaluate quantitative joint space width (JSW) at 10-, 30-, and 50-degree locations in relation to incident radiographic and symptomatic hip osteoarthritis (HOA) in a community-based cohort. METHODS: Data were from Johnston County OA Project participants with supine hip radiographs at each of 4 time points; all had Kellgren/Lawrence (K/L) grades and quantitative JSW. We assessed covariates (age, race, height, weight, body mass index [BMI]) associated with quantitative JSW and hip-level associations between quantitative JSW and HOA over time using sex-stratified and multivariable-adjusted linear mixed models. A cluster analysis with logistic regression estimated associations between quantitative JSW trajectory groups and incident radiographic HOA and symptomatic HOA. RESULTS: At baseline, 397 participants (784 hips, 41% men, 24% Black, mean age 57 years) had a mean BMI of 29 kg/m2 . Over a mean of 18 years, 20% and 12% developed incident K/L grade-defined radiographic HOA or symptomatic HOA, respectively. Quantitative JSW was more sensitive to changes over time at 50 degrees. Values were stable among men but declined over time in women. Heavier women lost more quantitative JSW; changes in quantitative JSW were not significantly associated with race, education, or injury in women or men. In women only, loss of quantitative JSW over time was associated with 2-3 times higher odds of radiographic HOA and symptomatic HOA; among women and men, narrower baseline quantitative JSW was associated with these outcomes. CONCLUSION: Hip quantitative JSW demonstrates marked differences in respect to sex, with significant loss over time only in women. Loss of quantitative JSW over time in women and narrower baseline quantitative JSW in men and women were associated with incident radiographic HOA and symptomatic HOA.

The Prevalence of Knee Symptoms, Radiographic, and Symptomatic Osteoarthritis at Four Time Points: The Johnston County Osteoarthritis Project, 1999-2018.

OBJECTIVE: To describe point prevalence of knee symptoms, radiographic knee osteoarthritis (rKOA), severe rKOA, and symptomatic rKOA at four time points in the longitudinal, population-based Johnston County Osteoarthritis Project (JoCo OA). METHODS: Data were from 2573 JoCo OA participants with up to 18 years of follow-up (1999-2018) and standardized fixed-flexion knee radiographs read by a single, reliable expert musculoskeletal radiologist. The four outcomes were 1) self-reported knee symptoms, defined by "On most days, do you have pain, aching, or stiffness in your right/left knee?"; 2) rKOA, defined as a Kellgren-Lawrence grade (KLG) of 2 to 4); 3) severe rKOA, defined as a KLG of 3 or 4; and 4) symptomatic rKOA, defined as both symptoms and rKOA in the same joint. Weighted prevalence estimates and 95% confidence intervals (CIs) were generated overall and by age group, sex, race, and body mass index (BMI). RESULTS: Most recently (2017-2018, T4), the overall prevalence (percentage) of knee symptoms, rKOA, severe rKOA, and symptomatic rKOA was 41% (95% CI: 35-47%), 61% (95% CI: 56-67%), 35% (95% CI: 30-40%), and 30% (95% CI: 24-35%), respectively. From time point T1 to T4, prevalence increased for rKOA, severe rKOA, and symptomatic rKOA but not for knee symptoms. The prevalence of both severe rKOA (17-39%) and symptomatic rKOA (23-30%) was consistently higher among women. The prevalence of all outcomes was higher among those with higher BMI and among Black participants at all time points, particularly rKOA (35-69%) and severe rKOA (22-46%). CONCLUSION: These updated estimates demonstrate a large and increasing burden of knee OA, particularly among women and Black individuals.

Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort.

OBJECTIVE: To examine relationships between knee osteoarthritis (KOA) and obesity, diabetes mellitus (DM), and cardiovascular disease (CVD). METHODS: Associations of time-dependent obesity, DM, and CVD with KOA transition states over approximately 18 years were examined among 4093 participants from a community-based cohort. Transition states were 1) no knee symptoms and no radiographic KOA (rKOA; Kellgren-Lawrence grade ≥2 in at least one knee), 2) asymptomatic rKOA, 3) knee symptoms only, 4) symptomatic rKOA (sxKOA; rKOA and symptoms in same knee). Markov multistate models estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for associations between comorbid conditions and transitions across states, adjusting for baseline age, sex, race, education, enrollment cohort, birth year, and time-dependent knee injury history. RESULTS: At baseline, 40% of participants had obesity, 13% had DM, and 22% had CVD (mean age = 61 years; 34% Black; 37% male). Compared with those without obesity, those with obesity had a higher hazard of worsening from no rKOA/no symptoms to asymptomatic rKOA (aHR = 1.7; 95% CI = 1.3-2.2) and from knee symptoms to sxKOA (aHR = 1.7; 95% CI = 1.3-2.3), as well as a lower hazard of symptom resolution from sxKOA to asymptomatic rKOA (aHR = 0.5 [95% = CI 0.4-0.7]). Compared with those without CVD, those with CVD had a higher hazard of worsening from no rKOA/symptoms to knee symptoms (aHR = 1.5; 95% CI = 1.1-2.1). DM was not associated with transitions of rKOA. CONCLUSION: Prevention of obesity and CVD may limit the development or worsening of rKOA and symptoms.

Discrimination Experiences and Depressive Symptoms among African Americans with Osteoarthritis Enrolled in a Pain Coping Skills Training Randomized Controlled Trial.

African Americans are more likely than members of other racial groups to report perceived discrimination in health care settings, and discrimination is linked to depression. Using data from a randomized controlled trial of pain coping skills training (PCST) for African Americans with osteoarthritis (N=164), we evaluated the interaction between discrimination experiences and experimental condition (PCST or control group) in linear regression models predicting depressive symptoms. There was a significant interaction between personal discrimination and experimental condition on depressive symptoms (interaction term coefficient: b=-3.2, 95% CI [- 6.4, - .02], p=.05). Discrimination was associated with depressive symptoms among those in the control group but not among those who received PCST. Participation in a PCST intervention may have reduced the association between discrimination experiences and depressive symptoms among participants in this sample. Future research should explore whether interventions aimed at teaching coping skills may be effective in ameliorating the harmful mental health effects of perceived discrimination.

Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight-Loss Treatments for Overweight and Obese Adults With Knee Osteoarthritis: Data From a Single-Center Randomized Trial.

OBJECTIVE: To apply a precision medicine approach to determine the optimal treatment regime for participants in an exercise (E), dietary weight loss (D), and D + E trial for knee osteoarthritis that would maximize their expected outcomes. METHODS: Using data from 343 participants of the Intensive Diet and Exercise for Arthritis (IDEA) trial, we applied 24 machine-learning models to develop individualized treatment rules on 7 outcomes: Short Form 36 physical component score, weight loss, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain/function/stiffness scores, compressive force, and interleukin-6 level. The optimal model was selected based on jackknife value function estimates that indicate improvement in the outcomes if future participants follow the estimated decision rule compared to the optimal single, fixed treatment model. RESULTS: Multiple outcome random forest was the optimal model for the WOMAC outcomes. For the other outcomes, list-based models were optimal. For example, the estimated optimal decision rule for weight loss indicated assigning the D + E intervention to participants with baseline weight not exceeding 109.35 kg and waist circumference above 90.25 cm, and assigning D to all other participants except those with a history of a heart attack. If applied to future participants, the optimal rule for weight loss is estimated to increase average weight loss to 11.2 kg at 18 months, contrasted with 9.8 kg if all participants received D + E (P = 0.01). CONCLUSION: The precision medicine models supported the overall findings from IDEA that the D + E intervention was optimal for most participants, but there was evidence that a subgroup of participants would likely benefit more from diet alone for 2 outcomes.