Introduction: The severity of joint damage progression in rheumatoid arthritis (RA) is heritable. Several genetic variants have been identified, but together explain only part of the total genetic effect. Variants in Interleukin-6 (IL-6), Interleukin-10 (IL-10), C5-TRAF1, and Fc-receptor-like-3 (FCRL3) have been described to associate with radiographic progression, but results of different studies were incongruent. We aimed to clarify associations of these variants with radiographic progression by evaluating six independent cohorts. Methods: In total 5,895 sets of radiographs of 2,493 RA-patients included in six different independent datasets from the Netherlands, Sweden, Spain and North-America were studied in relation to rs1800795 (IL-6), rs1800896 (IL-10), rs2900180 (C5-TRAF1) and rs7528684 (FCRL3). Associations were tested in the total RA-populations and in anti-citrullinated peptide antibodies (ACPA)-positive and ACPA-negative subgroups per cohort, followed by meta-analyses. Furthermore, the associated region C5-TRAF1 was fine-mapped in the ACPA-negative Dutch RA-patients. Results: No associations were found for rs1800795 (IL-6), rs1800896 (IL-10) and rs7528684 (FCRL3) in the total RA-population and after stratification for ACPA. Rs2900180 in C5-TRAF1 was associated with radiographic progression in the ACPA-negative population (P-value meta-analysis = 5.85 × 10⁻⁷); the minor allele was associated with more radiographic progression. Fine-mapping revealed a region of 66Kb that was associated; the lowest P-value was for rs7021880 in TRAF1. The P-value for rs7021880 in meta-analysis was 6.35 × 10⁻⁸. Previous studies indicate that the region of rs7021880 was associated with RNA expression of TRAF1 and C5. Conclusion: Variants in IL-6, IL-10 and FCRL3 were not associated with radiographic progression. Rs2900180 in C5-TRAF1 and linked variants in a 66Kb region were associated with radiographic progression in ACPA-negative RA.

With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures almost the complete variety of patients, leading to more generalisable results. For rheumatology, these data are particularly interesting as our diseases are uncommon and often take years to develop. In this review, we discuss the following concepts related to the use of EHR for research and considerations for translation into clinical care: EHR data contain a broad collection of healthcare data covering the multitude of real-life patients and the healthcare processes related to their care. Machine learning (ML) is a powerful method that allows us to leverage a large amount of heterogeneous clinical data for clinical algorithms, but requires extensive training, testing, and validation. Patterns discovered in EHR data using ML are applicable to real life settings, however, are also prone to capturing the local EHR structure and limiting generalisability outside the EHR(s) from which they were developed. Population studies on EHR necessitates knowledge on the factors influencing the data available in the EHR to circumvent biases, for example, access to medical care, insurance status. In summary, EHR data represent a rapidly growing and key resource for real-world studies. However, transforming RWD EHR data for research and for real-world evidence using ML requires knowledge of the EHR system and their differences from existing observational data to ensure that studies incorporate rigorous methods that acknowledge or address factors such as access to care, noise in the data, missingness and indication bias.

Objectives The human leukocyte antigen-shared epitope (HLA-SE) alleles and smoking are the most prominent genetic and environmental risk factors for rheumatoid arthritis (RA). However, at which pre-arthritis stage (asymptomatic/symptomatic) they exert their effect is unknown. We aimed to determine whether HLA-SE and smoking are involved in the onset of autoantibody positivity, symptoms (clinically suspect arthralgia (CSA)) and/or progression to clinical arthritis. Methods We performed meta-analyses on results from the literature on associations of HLA-SE and smoking with anti-citrullinated protein antibodies (ACPAs) in the asymptomatic population. Next, we studied associations of HLA-SE and smoking with autoantibody positivity at CSA onset and with progression to clinical inflammatory arthritis (IA) during follow-up. Associations in ACPA-positive patients with CSA were validated in meta-analyses with other arthralgia cohorts. Analyses were repeated for rheumatoid factor (RF), anti-carbamylated protein antibodies (anti-CarP) and anti-acetylated protein antibodies (AAPA). Results Meta-analyses showed that HLA-SE is not associated with ACPA positivity in the asymptomatic population (OR 1.06 (95% CI:0.69 to 1.64)), whereas smoking was associated (OR 1.37 (95% CI: 1.15 to 1.63)). At CSA onset, both HLA-SE and smoking associated with ACPA positivity (OR 2.08 (95% CI: 1.24 to 3.49), OR 2.41 (95% CI: 1.31 to 4.43)). During follow-up, HLA-SE associated with IA development (HR 1.86 (95% CI: 1.23 to 2.82)), in contrast to smoking. This was confirmed in meta-analyses in ACPA-positive arthralgia (HR 1.52 (95% CI: 1.08 to 2.15)). HLA-SE and smoking were not associated with RF, anti-CarP or AAPA-positivity at CSA onset. Longitudinally, AAPA associated with IA development independent from ACPA and RF (HR 1.79 (95% CI: 1.02 to 3.16)), anti-CarP did not. Conclusions HLA-SE and smoking act at different stages: smoking confers risk for ACPA and symptom development, whereas HLA-SE mediates symptom and IA development. These data enhance the understanding of the timing of the key risk factors in the development of RA.

Background The susceptibility to rheumatoid arthritis (RA) is partly heritable, but whether the severity of RA is also influenced by genetics has not been determined. The evaluation of the heritability of the severity of RA is basic to further studies on genetic factors. A study was undertaken to determine whether joint destruction is heritable. Methods Iceland has an unique comprehensive genealogy database covering today's population and stretching back to ≥1000 years ago, as well as genome-wide single nucleotide polymorphism data for a large part of the population. Hand and feet x-rays of 325 Icelandic patients with RA were scored according to the Sharp-van der Heijde method. The degree of relatedness between patients was estimated in two ways: (1) kinship coefficients (KC) on the genealogical data were expressed; and (2) the identical-by-descent (IBD) was estimated applying long-range phasing of the genetic profile of the patients. The degree of relatedness was tested against the similarity in joint destruction rates by linear regression analysis and the heritability of joint destruction was calculated. Results Significant associations between degree of relatedness and similarity in joint destruction rates were observed for both methods of determining relatedness (p_KC=0.018, p_IBD=0.003). The estimated heritability was 45% using KC and 58% using the estimated IBD data. Conclusions The severity of joint destruction in RA is influenced by genetic factors.

It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis–causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician’s initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% (P < 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice.