BackgroundThe adaptive immune response in rheumatoid arthritis (RA) is influenced by an interaction between host genetics and environment, particularly the host microbiome. Association of the gut microbiota with various diseases has been reported, though the specific components of the microbiota that affect the host response leading to disease remain unknown. However, there is limited information on the role of gut microbiota in RA. In this study we aimed to define a microbial and metabolite profile that could predict disease status. In addition, we aimed to generate a humanized model of arthritis to confirm the RA-associated microbe.MethodsTo identify an RA biomarker profile, the 16S ribosomal DNA of fecal samples from RA patients, first-degree relatives (to rule out environment/background as confounding factors), and random healthy non-RA controls were sequenced. Analysis of metabolites and their association with specific taxa was performed to investigate a potential mechanistic link. The role of an RA-associated microbe was confirmed using a human epithelial cell line and a humanized mouse model of arthritis.ResultsPatients with RA exhibited decreased gut microbial diversity compared with controls, which correlated with disease duration and autoantibody levels. A taxon-level analysis suggested an expansion of rare taxa, Actinobacteria, with a decrease in abundant taxa in patients with RA compared with controls. Prediction models based on the random forests algorithm suggested that three genera, Collinsella, Eggerthella, and Faecalibacterium, segregated with RA. The abundance of Collinsella correlated strongly with high levels of alpha-aminoadipic acid and asparagine as well as production of the proinflammatory cytokine IL-17A. A role for Collinsella in altering gut permeability and disease severity was confirmed in experimental arthritis.ConclusionsThese observations suggest dysbiosis in RA patients resulting from the abundance of certain rare bacterial lineages. A correlation between the intestinal microbiota and metabolic signatures could determine a predictive profile for disease causation and progression.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0299-7) contains supplementary material, which is available to authorized users.

Objective. Understanding of the personal risks for rheumatoid arthritis (RA) and other rheumatic diseases remains poor, despite advances in knowledge with regard to their pathogenesis, therapeutics, and clinical impact, in part because the personal lifetime risk of developing these diseases is unknown. This study was undertaken to estimate the lifetime risk of RA, as well as other inflammatory autoimmune rheumatic diseases, including systemic lupus erythematosus, psoriatic arthritis, polymyalgia rheumatica (PMR), giant cell arteritis, ankylosing spondylitis, and Sjögren's syndrome, and to provide an overall estimate of the risk of developing inflammatory autoimmune rheumatic disease over a lifetime.Methods. Using the incidence rates obtained from our population-based studies of rheumatic diseases among residents of Olmsted County, Minnesota, and mortality rates from life tables for the general population, we estimated the sex-specific lifetime risk of rheumatic disease.Results. The lifetime risk of RA developing in US adults was 3.6% for women and 1.7% for men, and the lifetime risk of rheumatoid factor-positive RA was 2.4% for women and 1.1% for men. The second most common inflammatory autoimmune rheumatic disease was PMR, with a lifetime risk of 2.4% for women and 1.7% for men. The overall lifetime risk of inflammatory autoimmune rheumatic disease was 8.4% for women and 5.1% for men.Conclusion. One in 12 women and 1 in 20 men will develop an inflammatory autoimmune rheumatic disease during their lifetime. These results can serve as useful guides in counseling patients regarding their lifetime risk of these conditions and have important implications regarding disease awareness campaigns.

Objective. Overall mortality rates in the general US population have declined substantially over the last 4-5 decades, but it is unclear whether patients with rheumatoid arthritis (RA) have experienced the same improvements in survival. The purpose of this study was to determine the mortality trends among RA patients compared with those in the general population.Methods. A population-based incidence cohort of RA patients was assembled, comprising all residents of Rochester, Minnesota ages >18 years in whom RA was first diagnosed ( Conclusion. Our findings show that RA patients have not experienced improvements in survival over the past 4 decades, despite dramatic improvements in the overall rates of mortality in the general US population. Further research into the causes of the widening gap in mortality between RA patients and the general population, and the influence of current therapeutic strategies on mortality, is needed in order to develop strategies to reduce the excess mortality observed in RA patients.

Background Rheumatic disease and heart disease share common underpinnings involving inflammation. The high levels of inflammation that characterize rheumatic diseases provide a “natural experiment” to help elucidate the mechanisms by which inflammation accelerates heart disease. Rheumatoid arthritis (RA) is the most common of the rheumatic diseases and has the best studied relationships with heart disease. Methods Review of current literature on heart disease and rheumatoid arthritis Results Patients with RA have an increased risk of developing heart disease that is not fully explained by traditional cardiovascular risk factors. Therapies used to treat RA may also affect the development of heart disease; by suppressing inflammation, they may also reduce the risk of heart disease. However, their other effects, as in the case of steroids, may increase heart disease risk. Conclusions Investigations of the innate and adaptive immune responses occurring in RA may delineate novel mechanisms in the pathogenesis of heart disease, and help identify novel therapeutic targets for the prevention and treatment of heart disease.

ObjectivesTo assess the accuracy of dual-energy CT (DECT) for diagnosing gout, and to explore whether it can have any impact on clinical decision making beyond the established diagnostic approach using polarising microscopy of synovial fluid (diagnostic yield).MethodsDiagnostic single-centre study of 40 patients with active gout, and 41 individuals with other types of joint disease. Sensitivity and specificity of DECT for diagnosing gout was calculated against a combined reference standard (polarising and electron microscopy of synovial fluid). To explore the diagnostic yield of DECT scanning, a third cohort was assembled consisting of patients with inflammatory arthritis and risk factors for gout who had negative synovial fluid polarising microscopy results. Among these patients, the proportion of subjects with DECT findings indicating a diagnosis of gout was assessed.ResultsThe sensitivity and specificity of DECT for diagnosing gout was 0.90 (95% CI 0.76 to 0.97) and 0.83 (95% CI 0.68 to 0.93), respectively. All false negative patients were observed among patients with acute, recent-onset gout. All false positive patients had advanced knee osteoarthritis. DECT in the diagnostic yield cohort revealed evidence of uric acid deposition in 14 out of 30 patients (46.7%).ConclusionsDECT provides good diagnostic accuracy for detection of monosodium urate (MSU) deposits in patients with gout. However, sensitivity is lower in patients with recent-onset disease. DECT has a significant impact on clinical decision making when gout is suspected, but polarising microscopy of synovial fluid fails to demonstrate the presence of MSU crystals.

Providing insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independent of the clinical diagnosis. GMHI is formulated upon 50 microbial species associated with healthy gut ecosystems. These species are identified through a multi-study, integrative analysis on 4347 human stool metagenomes from 34 published studies across healthy and 12 different nonhealthy conditions, i.e., disease or abnormal bodyweight. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of disease presence (or absence) compared to α-diversity indices. Validation on 679 samples from 9 additional studies results in a balanced accuracy of 73.7% in distinguishing healthy from non-healthy groups. Our findings suggest that gut taxonomic signatures can predict health status, and highlight how data sharing efforts can provide broadly applicable discoveries.

OBJECTIVE To determine whether the “obesity epidemic” could explain the recent rise in the incidence of RA. BACKGROUND Obesity is an under-recognized risk factor for RA. In recent years both the prevalence of obesity and the incidence of RA have been rising. METHODS An inception cohort of Olmsted County, Minnesota residents who fulfilled 1987 American College of Rheumatology criteria for RA in 1980–2007 was compared to population-based controls (matched on age, sex and calendar year). Heights, weights and smoking status were collected from medical records. Obesity was defined as body mass index (BMI) ≥ 30 kg/m2. Conditional logistic regression was used to assess the influence of obesity on developing RA. Population attributable risk was used to estimate the incidence of RA in the absence of obesity. RESULTS The study included 813 patients with RA and 813 controls. Both groups had extensive medical history available prior to incidence/index date (mean 32.2 years), and approximately 30% of each group were obese at incidence/index date. The history of obesity was a significantly associated with developing RA (OR:1.24; 95 % CI: 1.01, 1.53 adjusted for smoking status). In 1985–2007 the incidence of RA rose by an increment of 9.2 per 100,000 among women. Obesity accounted for 4.8 per 100,000 (or 52%) of this increase. CONCLUSION Obesity is associated with a modest risk for developing RA. Given the rapidly increasing prevalence of obesity, this has had a significant impact on RA incidence and may account for much of the recent increase in incidence of RA.

Objective. To determine the relationship between glucocorticoid exposure and cardiovascular (CV) events in patients with rheumatoid arthritis (RA).Methods. A total of 603 adult residents of Rochester, Minnesota with incident RA between 1955 and 1995 were followed up through their medical records for a median of 13 years (total of 9,066 person-years). Glucocorticoid exposure was defined 3 ways: tertiles of cumulative exposure; recent use (<3 months) versus past use (>3 months); and average daily dosage (<7.5 mg/day or >7.5 mg/day). CV events, including myocardial infarction, heart failure, and death from CV causes, were defined according to validated criteria. Cox regression models were adjusted for demographic features, CV risk factors, and RA characteristics.Results. Rheumatoid factor (RF)-negative patients with exposure to glucocorticoids were not at increased risk of CV events, irrespective of the glucocorticoid dosage or timing of use, as compared with the reference group of RF-negative patients who had never been exposed to glucocorticoids. In contrast, RFpositive patients were at increased risk of CV events, particularly with higher cumulative exposure, higher average daily dosage, and recent use of glucocorticoids. RF-positive patients with high cumulative exposure to glucocorticoids had a 3-fold increased risk of CV events (hazard ratio 3.06 [95% confidence interval 1.81-5.18]), whereas RF-negative patients with high cumulative exposure were not at increased risk (hazard ratio 0.85 [95% confidence interval 0.39-1.87]).Conclusion. RF-positive but not RF-negative patients were at increased risk of CV events following exposure to glucocorticoids. These findings suggest that glucocorticoids interact with RF status to modulate the occurrence of CV events in patients with RA. The mechanisms underlying this interaction are unknown and should be the subject of further research.