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.

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.

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.

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.

Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain.

Back pain is the #1 cause of years lived with disability worldwide, yet surprisingly little is known regarding the biology underlying this symptom. We conducted a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP). Adults of European ancestry were included from 15 cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and from the UK Biobank interim data release. CBP cases were defined as those reporting back pain present for ≥3-6 months; non-cases were included as comparisons ("controls"). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5×10(-8). Suggestive (p<5×10(-7)) and genome-wide significant (p<5×10(-8)) variants were carried forward for replication or further investigation in the remaining UK Biobank participants not included in the discovery sample. The discovery sample comprised 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p = 7.2×10(-10)). This was subsequently replicated in 283,752 UK Biobank participants not included in the discovery sample, including 50,915 cases (OR 1.06, p = 5.3×10(-11)), and exceeded genome-wide significance in joint meta-analysis (OR 1.07, p = 4.5×10(-19)). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p = 4.4×10(-13)), and an intronic variant, rs4384683, in DCC (OR 0.97, p = 2.4×10(-10)). In this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC).

Acceptability of telephone-based pain coping skills training among African Americans with osteoarthritis enrolled in a randomized controlled trial: a mixed methods analysis.

BACKGROUND: Osteoarthritis (OA) disproportionately impacts African Americans compared to Caucasians, including greater pain severity. The Pain Coping Skills Training for African Americans with Osteoarthritis (STAART) study examined a culturally enhanced Pain Coping Skills Training (CST) program among African Americans with OA. This mixed methods study evaluated the acceptability of the Pain CST program among STAART participants. METHODS: STAART was a randomized controlled trial evaluating the effectiveness of an 11-session, telephone-based pain CST program, compared to a usual care control group. Participants were from the University of North Carolina and Durham Veterans Affairs Healthcare Systems. The present analyses included 93 participants in the CST group who completed a questionnaire about experiences with the program. Descriptive statistics of the questionnaire responses were calculated using SAS software. Thematic analysis was applied to open-response data using Dedoose software. RESULTS: Participants' mean rating of overall helpfulness of the pain CST program for managing arthritis symptoms was 8.0 (SD = 2.2) on a scale of 0-10. A majority of participants reported the program made a positive difference in their experience with arthritis (83.1%). Mean ratings of helpfulness of the specific skills ranged from 7.7 to 8.8 (all scales 0-10). Qualitative analysis of the open-response data identified four prominent themes: Improved Pain Coping, Mood and Emotional Benefits, Improved Physical Functioning, and experiences related to Intervention Delivery. CONCLUSIONS: The high ratings of helpfulness demonstrate acceptability of this culturally enhanced pain CST program by African Americans with OA. Increasing access to cognitive-behavioral therapy-based programs may be a promising strategy to address racial disparities in OA-related pain and associated outcomes. TRIAL REGISTRATION: NCT02560922 , registered September 25, 2015.

Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality.

Gaining insights into genetic predisposition to age-related diseases and lifespan is a challenging task complicated by the elusive role of evolution in these phenotypes. To gain more insights, we combined methods of genome-wide and candidate-gene studies. Genome-wide scan in the Atherosclerosis Risk in Communities (ARIC) Study (N = 9,573) was used to pre-select promising loci. Candidate-gene methods were used to comprehensively analyze associations of novel uncommon variants in Caucasians (minor allele frequency~2.5%) located in band 2q22.3 with risks of coronary heart disease (CHD), heart failure (HF), stroke, diabetes, cancer, neurodegenerative diseases (ND), and mortality in the ARIC study, the Framingham Heart Study (N = 4,434), and the Health and Retirement Study (N = 9,676). We leveraged the analyses of pleiotropy, age-related heterogeneity, and causal inferences. Meta-analysis of the results from these comprehensive analyses shows that the minor allele increases risks of death by about 50% (p = 4.6×10-9), CHD by 35% (p = 8.9×10-6), HF by 55% (p = 9.7×10-5), stroke by 25% (p = 4.0×10-2), and ND by 100% (p = 1.3×10-3). This allele also significantly influences each of two diseases, diabetes and cancer, in antagonistic fashion in different populations. Combined significance of the pleiotropic effects was p = 6.6×10-21. Causal mediation analyses show that endophenotypes explained only small fractions of these effects. This locus harbors an evolutionary conserved gene-desert region with non-coding intergenic sequences likely involved in regulation of protein-coding flanking genes ZEB2 and ACVR2A. This region is intensively studied for mutations causing severe developmental/genetic disorders. Our analyses indicate a promising target region for interventions aimed to reduce risks of many major human diseases and mortality.

Pain coping skills training for African Americans with osteoarthritis study: baseline participant characteristics and comparison to prior studies.

BACKGROUND: The Pain Coping Skills Training for African Americans with OsteoaRTthritis (STAART) trial is examining the effectiveness of a culturally enhanced pain coping skills training (CST) program for African Americans with osteoarthritis (OA). This disparities-focused trial aimed to reach a population with greater symptom severity and risk factors for poor pain-related outcomes than previous studies. This paper compares characteristics of STAART participants with prior studies of CST or cognitive behavioral therapy (CBT)-informed training in pain coping strategies for OA. METHODS: A literature search identified 10 prior trials of pain CST or CBT-informed pain coping training among individuals with OA. We descriptively compared characteristics of STAART participants with other studies, in 3 domains of the National Institutes of Minority Health and Health Disparities' Research Framework: Sociocultural Environment (e.g., age, education, marital status), Biological Vulnerability and Mechanisms (e.g, pain and function, body mass index), and Health Behaviors and Coping (e.g., pain catastrophizing). Means and standard deviations (SDs) or proportions were calculated for STAART participants and extracted from published manuscripts for comparator studies. RESULTS: The mean age of STAART participants, 59 years (SD = 10.3), was lower than 9 of 10 comparator studies; the proportion of individuals with some education beyond high school, 75%, was comparable to comparator studies (61-86%); and the proportion of individuals who are married or living with a partner, 42%, was lower than comparator studies (62-66%). Comparator studies had less than about 1/3 African American participants. Mean scores on the Western Ontario and McMaster Universities Osteoarthritis Index pain and function scales were higher (worse) for STAART participants than for other studies, and mean body mass index of STAART participants, 35.2 kg/m2 (SD = 8.2), was higher than all other studies (30-34 kg/m2). STAART participants' mean score on the Pain Catastrophizing scale, 19.8 (SD = 12.3), was higher (worse) than other studies reporting this measure (7-17). CONCLUSIONS: Compared with prior studies with predominantly white samples, STAART participants have worse pain and function and more risk factors for negative pain-related outcomes across several domains. Given STAART participants' high mean pain catastrophizing scores, this sample may particularly benefit from the CST intervention approach. TRIAL REGISTRATION: NCT02560922.

Level of participation in physical therapy or an internet-based exercise training program: associations with outcomes for patients with knee osteoarthritis.

BACKGROUND: To examine whether number of physical therapy (PT) visits or amount of use of an internet-based exercise training (IBET) program is associated with differential improvement in outcomes for participants with knee osteoarthritis (OA). METHODS: A secondary analysis was performed using data from participants in 2 arms of a randomized control trial for individuals with symptomatic knee OA: PT (N = 135) or IBET (N = 124). We examined associations of number of PT visits attended (up to 8) or number of days the IBET website was accessed during the initial 4-month study period with changes in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total, pain and function subscales, as well as a 2-min Step Test, at 4-month and 12-month follow-up. RESULTS: Participants with more PT visits experienced greater improvement in WOMAC total score (estimate per additional visit = - 1.18, CI 95% = - 1.91, 0.46, p <  0.001) and function subscore (estimate = - 0.80, CI 95% = - 1.33, - 0.28, p <  0.001) across follow-up periods. For WOMAC pain subscale, the association with number of PT visits varied significantly between 4- and 12-month follow-up, with a stronger relationship at 4-months. There was a non-significant trend for more PT visits to be associated with greater improvement in 2-min Step Test. More frequent use of the IBET website was not associated with greater improvement for any outcome, at either time point. CONCLUSION: Increased number of PT visits was associated with improved outcomes, and some of this benefit persisted 8 months after PT ended. This provides guidance for PT clinical practice and policies. TRIAL REGISTRATION: NCT02312713 , posted 9/25/2015.

Randomized Controlled Trial of a Home-Based Walking Program to Reduce Moderate to Severe Aromatase Inhibitor-Associated Arthralgia in Breast Cancer Survivors.

BACKGROUND: In postmenopausal women diagnosed with breast cancer (BC), most BC tumors are hormone receptor positive and guidelines recommend adjuvant endocrine therapy that includes an aromatase inhibitor (AI). This study investigates the impact of a 6-week, home-based, self-directed walking program on the commonly reported side effect of AI-associated arthralgia (AIAA). MATERIALS AND METHODS: In this phase II trial, consented BC patients were randomized to walking Intervention (n = 31) or Wait List Control (WLC; n = 31). Eligibility criteria included: stage 0-III BC, on AI for at least 4 weeks, ≥3 on a 5-point scale inquiring about joint symptom intensity "at its worst," and exercising ≤150 minutes per week. Outcomes were self-reported joint symptoms and psychosocial measures. Analyses comparing Intervention and WLC groups were conducted on an intention-to-treat basis to assess intervention impact at 6 weeks (postintervention) and at 6-months follow-up. Adjusted means were calculated to assess differences in two groups. RESULTS: In our final sample (n = 62), mean age was 64 years, 74% were white, and 63% had a body mass index of 30 or higher. At postintervention, Intervention group participants reported significantly increased walking minutes per week, reduced stiffness, less difficulty with activities of daily living (ADL), and less perceived helplessness in managing joint symptoms. At 6-months follow-up (postwalking period in both Intervention and WLC), walking minutes per week had decreased significantly; however, improvements in stiffness and difficulty with ADLs were maintained. CONCLUSION: This study adds to the growing evidence base suggesting exercise as a safe alternative or adjunct to medications for the management of AIAA. IMPLICATIONS FOR PRACTICE: Breast cancer survivors whose adjuvant endocrine treatment includes an aromatase inhibitor (AI) often experience the side effect of AI-associated arthralgia (AIAA). This study investigates the impact of a 6-week, home-based, self-directed walking program in the management of AIAA. Compared with Wait List Control, women in the Intervention group reported significantly increased walking minutes per week, reduced stiffness, less difficulty with activities of daily living, and less perceived helplessness in managing joint symptoms. This study adds to the growing evidence base suggesting exercise as a safe alternative or adjunct to medications for the management of AIAA.

Age, gender, and cancer but not neurodegenerative and cardiovascular diseases strongly modulate systemic effect of the Apolipoprotein E4 allele on lifespan.

Enduring interest in the Apolipoprotein E (ApoE) polymorphism is ensured by its evolutionary-driven uniqueness in humans and its prominent role in geriatrics and gerontology. We use large samples of longitudinally followed populations from the Framingham Heart Study (FHS) original and offspring cohorts and the Long Life Family Study (LLFS) to investigate gender-specific effects of the ApoE4 allele on human survival in a wide range of ages from midlife to extreme old ages, and the sensitivity of these effects to cardiovascular disease (CVD), cancer, and neurodegenerative disorders (ND). The analyses show that women's lifespan is more sensitive to the e4 allele than men's in all these populations. A highly significant adverse effect of the e4 allele is limited to women with moderate lifespan of about 70 to 95 years in two FHS cohorts and the LLFS with relative risk of death RR = 1.48 (p = 3.6 × 10(-6)) in the FHS cohorts. Major human diseases including CVD, ND, and cancer, whose risks can be sensitive to the e4 allele, do not mediate the association of this allele with lifespan in large FHS samples. Non-skin cancer non-additively increases mortality of the FHS women with moderate lifespans increasing the risks of death of the e4 carriers with cancer two-fold compared to the non-e4 carriers, i.e., RR = 2.07 (p = 5.0 × 10(-7)). The results suggest a pivotal role of non-sex-specific cancer as a nonlinear modulator of survival in this sample that increases the risk of death of the ApoE4 carriers by 150% (p = 5.3 × 10(-8)) compared to the non-carriers. This risk explains the 4.2 year shorter life expectancy of the e4 carriers compared to the non-carriers in this sample. The analyses suggest the existence of age- and gender-sensitive systemic mechanisms linking the e4 allele to lifespan which can non-additively interfere with cancer-related mechanisms.

Protective role of the apolipoprotein E2 allele in age-related disease traits and survival: evidence from the Long Life Family Study.

The apolipoprotein E (apoE) is a classic example of a gene exhibiting pleiotropism. We examine potential pleiotropic associations of the apoE2 allele in three biodemographic cohorts of long-living individuals, offspring, and spouses from the Long Life Family Study, and intermediate mechanisms, which can link this allele with age-related phenotypes. We focused on age-related macular degeneration, bronchitis, asthma, pneumonia, stroke, creatinine, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, diseases of heart (HD), cancer, and survival. Our analysis detected favorable associations of the ε2 allele with lower LDL-C levels, lower risks of HD, and better survival. The ε2 allele was associated with LDL-C in each gender and biodemographic cohort, including long-living individuals, offspring, and spouses, resulting in highly significant association in the entire sample (ß = -7.1, p = 6.6 × 10-44). This allele was significantly associated with HD in long-living individuals and offspring (relative risk [RR] = 0.60, p = 3.1 × 10-6) but this association was not mediated by LDL-C. The protective effect on survival was specific for long-living women but it was not explained by LDL-C and HD in the adjusted model (RR = 0.70, p = 2.1 × 10-2). These results show that ε2 allele may favorably influence LDL-C, HD, and survival through three mechanisms. Two of them (HD- and survival-related) are pronounced in the long-living parents and their offspring; the survival-related mechanism is also sensitive to gender. The LDL-C-related mechanism appears to be independent of these factors. Insights into mechanisms linking ε2 allele with age-related phenotypes given biodemographic structure of the population studied may benefit translation of genetic discoveries to health care and personalized medicine.

How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity.

Increasing proportions of elderly individuals in developed countries combined with substantial increases in related medical expenditures make the improvement of the health of the elderly a high priority today. If the process of aging by individuals is a major cause of age related health declines then postponing aging could be an efficient strategy for improving the health of the elderly. Implementing this strategy requires a better understanding of genetic and non-genetic connections among aging, health, and longevity. We review progress and problems in research areas whose development may contribute to analyses of such connections. These include genetic studies of human aging and longevity, the heterogeneity of populations with respect to their susceptibility to disease and death, forces that shape age patterns of human mortality, secular trends in mortality decline, and integrative mortality modeling using longitudinal data. The dynamic involvement of genetic factors in (i) morbidity/mortality risks, (ii) responses to stresses of life, (iii) multi-morbidities of many elderly individuals, (iv) trade-offs for diseases, (v) genetic heterogeneity, and (vi) other relevant aging-related health declines, underscores the need for a comprehensive, integrated approach to analyze the genetic connections for all of the above aspects of aging-related changes. The dynamic relationships among aging, health, and longevity traits would be better understood if one linked several research fields within one conceptual framework that allowed for efficient analyses of available longitudinal data using the wealth of available knowledge about aging, health, and longevity already accumulated in the research field.

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).

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.

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.

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.

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.

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.