A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

An Expert Panel convened by the National Lipid Association previously developed a consensus set of recommendations for the patient-centered management of dyslipidemia in clinical medicine (part 1). These were guided by the principle that reducing elevated levels of atherogenic cholesterol (non-high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) reduces the risk for atherosclerotic cardiovascular disease. This document represents a continuation of the National Lipid Association recommendations developed by a diverse panel of experts who examined the evidence base and provided recommendations regarding the following topics: (1) lifestyle therapies; (2) groups with special considerations, including children and adolescents, women, older patients, certain ethnic and racial groups, patients infected with human immunodeficiency virus, patients with rheumatoid arthritis, and patients with residual risk despite statin and lifestyle therapies; and (3) strategies to improve patient outcomes by increasing adherence and using team-based collaborative care.

Objective. Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone. Methods. Subjects with >1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti-cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an "RA Mart" (n ‫؍‬ 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms. Results. A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions). Conclusion. We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone.

Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

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.

No abstract

Introduction Vitamin D may have an immunological role in Crohn’s disease (CD) and ulcerative colitis (UC). Retrospective studies suggested a weak association between vitamin D status and disease activity but have significant limitations. Methods Using a multi-institution inflammatory bowel disease (IBD) cohort, we identified all CD and UC patients who had at least one measured plasma 25-hydroxy vitamin D [25(OH)D]. Plasma 25(OH)D was considered sufficient at levels ≥ 30ng/mL. Logistic regression models adjusting for potential confounders were used to identify impact of measured plasma 25(OH)D on subsequent risk of IBD-related surgery or hospitalization. In a subset of patients where multiple measures of 25(OH)D were available, we examined impact of normalization of vitamin D status on study outcomes. Results Our study included 3,217 patients (55% CD, mean age 49 yrs). The median lowest plasma 25(OH)D was 26ng/ml (IQR 17–35ng/ml). In CD, on multivariable analysis, plasma 25(OH)D < 20ng/ml was associated with an increased risk of surgery (OR 1.76 (1.24 – 2.51) and IBD-related hospitalization (OR 2.07, 95% CI 1.59 – 2.68) compared to those with 25(OH)D ≥ 30ng/ml. Similar estimates were also seen for UC. Furthermore, CD patients who had initial levels < 30ng/ml but subsequently normalized their 25(OH)D had a reduced likelihood of surgery (OR 0.56, 95% CI 0.32 – 0.98) compared to those who remained deficient. Conclusion Low plasma 25(OH)D is associated with increased risk of surgery and hospitalizations in both CD and UC and normalization of 25(OH)D status is associated with a reduction in the risk of CD-related surgery.

Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.