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Objective We aimed to perform a systematic review and meta-analysis of all-cause and cause-specific mortality of patients with benign endogenous Cushing's syndrome (CS). Methods The protocol was registered in PROSPERO (CRD42017067530). PubMed, EMBASE, CINHAL, Web of Science and Cochrane Central searches were undertaken from inception to January 2021. Outcomes were the standardized mortality ratio (SMR), proportion and cause of deaths. The I 2 test, subgroup analysis and meta-regression were used to assess heterogeneity across studies. Results SMR was reported in 14 articles including 3,691 patients (13 Cushing's disease (CD) and 7 adrenal CS (ACS) cohorts). Overall SMR was 3.0 (95%CI 2.3-3.9; I 2=80.5%) for all CS, 2.8 (95%CI 2.1-3.7 I 2=81.2%) for CD and 3.3 (95%CI 0.5-6.6; I 2=77.9%) for ACS. Proportion of deaths, reported in 87 articles including 19,181 CS patients (53 CD, 24 ACS, and 20 combined CS cohorts) was 0.05 (95%CI 0.03, 0.06) for all CS subtypes with meta-regression analysis revealing no differences between CS subtypes (P=0.052). The proportion of deaths was 0.1 (10%) in articles published before 2000 and 0.03 (3%) in 2000 until the last search for CS (P

Background The aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography–Computed Tomography (FDG PET–CT) images. Methods The aorta was manually segmented on FDG PET–CT in 50 patients with aortitis and 25 controls. Radiomic features (RF) (n = 107), including SUV (Standardized Uptake Value) metrics, were extracted from the segmented data and harmonized using the ComBat technique. Individual RFs and groups of RFs (i.e., signatures) were used as input in Machine Learning classifiers. The diagnostic utility of these classifiers was evaluated with area under the receiver operating characteristic curve (AUC) and accuracy using the clinical diagnosis as the ground truth. Results Several RFs had high accuracy, 84% to 86%, and AUC scores 0.83 to 0.97 when used individually. Radiomic signatures performed similarly, AUC 0.80 to 1.00. Conclusion A methodological framework for a radiomic-based approach to support diagnosis of aortitis was outlined. Selected RFs, individually or in combination, showed similar performance to the current standard of qualitative assessment in terms of AUC for identifying active aortitis. This framework could support development of a clinical decision-making tool for a more objective and standardized assessment of aortitis.

Background We aimed to identify clinical, socio-demographic and genetic risk factors for severe COVID-19 (hospitalization, critical care admission or death) in the general population. Methods In this observational study, we identified 9560 UK Biobank participants diagnosed with COVID-19 during 2020. A polygenic risk score (PRS) for severe COVID-19 was derived and optimized using publicly available European and trans-ethnic COVID-19 genome-wide summary statistics. We estimated the risk of hospital or critical care admission within 28 days or death within 100 days following COVID-19 diagnosis, and assessed associations with socio-demographic factors, immunosuppressant use and morbidities reported at UK Biobank enrolment (2006–2010) and the PRS. To improve biological understanding, pathway analysis was performed using genetic variants comprising the PRS. Results We included 9560 patients followed for a median of 61 (interquartile range = 34–88) days since COVID-19 diagnosis. The risk of severe COVID-19 increased with age and obesity, and was higher in men, current smokers, those living in socio-economically deprived areas, those with historic immunosuppressant use and individuals with morbidities and higher co-morbidity count. An optimized PRS, enriched for single-nucleotide polymorphisms in multiple immune-related pathways, including the ‘oligoadenylate synthetase antiviral response’ and ‘interleukin-10 signalling’ pathways, was associated with severe COVID-19 (adjusted odds ratio 1.32, 95% CI 1.11–1.58 for the highest compared with the lowest PRS quintile). Conclusion This study conducted in the pre-SARS-CoV-2-vaccination era, emphasizes the novel insights to be gained from using genetic data alongside commonly considered clinical and socio-demographic factors to develop greater biological understanding of severe COVID-19 outcomes.

Background Non-communicable diseases (NCDs) have been highlighted as important risk factors for COVID-19 mortality. However, insufficient data exist on the wider context of infectious diseases in people with NCDs. We aimed to investigate the association between NCDs and the risk of death from any infection before the COVID-19 pandemic (up to Dec 31, 2019). Methods For this observational study, we used data from the UK Biobank observational cohort study to explore factors associated with infection death. We excluded participants if data were missing for comorbidities, body-mass index, smoking status, ethnicity, and socioeconomic deprivation, and if they were lost to follow-up or withdrew consent. Deaths were censored up to Dec 31, 2019. We used Poisson regression models including NCDs present at recruitment to the UK Biobank (obesity [defined by use of body-mass index] and self-reported hypertension, chronic heart disease, chronic respiratory disease, diabetes, cancer, chronic liver disease, chronic kidney disease, previous stroke or transient ischaemic attack, other neurological disease, psychiatric disorder, and chronic inflammatory and autoimmune rheumatological disease), age, sex, ethnicity, smoking status, and socioeconomic deprivation. Separate models were constructed with individual NCDs replaced by the total number of prevalent NCDs to define associations with multimorbidity. All analyses were repeated with non-infection-related death as an alternate outcome measure to establish differential associations of infection death and non-infection death. Associations are reported as incidence rate ratios (IRR) accompanied by 95% CIs. Findings After exclusion of 9210 (1·8%) of the 502 505 participants in the UK Biobank cohort, our study sample comprised 493 295 individuals. During 5 273 731 person-years of follow-up (median 10·9 years [IQR 10·1–11·6] per participant), 27 729 deaths occurred, of which 1385 (5%) were related to infection. Advancing age, male sex, smoking, socioeconomic deprivation, and all studied NCDs were independently associated with the rate of both infection death and non-infection death. Compared with White ethnicity, a pooled Black, Asian, and minority ethnicity group was associated with a reduced risk of infection death (IRR 0·64, 95% CI 0·46–0·87) and non-infection death (0·80, 0·75–0·86). Stronger associations with infection death than with non-infection death were observed for advancing age (age 65 years vs 45 years: 7·59, 95% CI 5·92–9·73, for infection death vs 5·21, 4·97–5·48, for non-infection death), current smoking (vs never smoking: 3·69, 3·19–4·26, vs 2·52, 2·44–2·61), socioeconomic deprivation (most vs least deprived quintile: 2·13, 1·78–2·56, vs 1·38, 1·33–1·43), class 3 obesity (vs non-obese: 2·21, 1·74–2·82, vs 1·55, 1·44–1·66), hypertension (1·36, 1·22–1·53, vs 1·15, 1·12–1·18), respiratory disease (2·21, 1·96–2·50, vs 1·28, 1·24–1·32), chronic kidney disease (5·04, 4·28–7·31, vs 2·50, 2·20–2·84), psychiatric disease (1·56, 1·30–1·86, vs 1·23, 1·18–1·29), and chronic inflammatory and autoimmune rheumatological disease (2·45, 1·99–3·02, vs 1·41, 1·32–1·51). Accrual of multimorbidity was also more strongly associated with risk of infection death (five or more comorbidities vs none: 9·53, 6·97–13·03) than of non-infection death (5·26, 4·84–5·72). Interpretation Several NCDs are associated with an increased risk of infection death, suggesting that some of the reported associations with COVID-19 mortality might be non-specific. Only a subset of NCDs, together with the accrual of multimorbidity, advancing age, smoking, and socioeconomic deprivation, were associated with a greater IRR for infection death than for other causes of death. Further research is needed to define why these risk factors are more strongly associated with infection death, so that more effective preventive strategies can be targeted to high-risk groups. Funding British Heart Foundation.

Objectives: To determine whether using a re‐weighted disease activity score that better reflects joint synovitis, the 2‐component DAS28 (2C‐DAS28), based on 28‐swollen joint count and C‐reactive protein, produces more clinically relevant treatment outcome trajectories compared with the standard 4 component DAS28 (4C‐DAS28). Methods: Latent class mixed modelling (LCMM) of response to biologic treatment was applied to 2,991 patients with RA about to commence bDMARD treatment from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) cohort using both 4C‐DAS28 and 2C‐DAS28 as outcomes. Identified patient groups with similar trajectories were compared in terms of pre‐treatment baseline characteristics – including disability, comorbidities ‐ and follow‐up characteristics – including anti‐drug antibody (ADAb) events, adherence to treatments and blood drug levels, We compared the trajectories obtained using the 4C and 2C scores to determine which characteristics were better captured by the 2CDAS28 compared with the 4C‐DAS28 trajectories. Results: Using the 4C‐DAS28 we identified 3 trajectory groups, which is consistent with previous reports. We show that the 4C‐DAS28 captures information relating to depression. Using the 2C‐DAS28, 7 trajectory groups were identified; among them, distinct groups of non‐responders had a higher incidence of respiratory comorbidities and a higher proportion of ADAb events. We also identified a group of participants for which the 2C‐DAS28 disease activity score remained relatively low and which was enriched for patients who were non‐adherent to treatment. This highlights the utility of both the 4C and 2C‐DAS28 for monitoring different components of disease activity. Conclusions: Here we show that the 2C‐DAS28 modified disease activity score defines important biological and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.

Objectives To evaluate (i) the association between random certolizumab drug levels, antidrug antibodies (ADAbs) and treatment response in patients with rheumatoid arthritis (RA); (ii) longitudinal factors associated with ADAbs and certolizumab drug levels. Methods This prospective cohort included 115 patients with RA treated with certolizumab. Serum samples were collected at 3, 6 and 12 months following treatment initiation. Drug levels and ADAbs were measured using ELISA and radioimmunoassay, respectively, at 3, 6 and 12 months. Disease Activity Score in 28 joints (DAS28) were measured at each visit and 12 months European League Against Rheumatism (EULAR) response was calculated. Patient self-reported adherence was collected longitudinally. Ordinal logistic regression and generalised estimating equation were used to test the association: (i) between drug levels, from serum sampled and treatment response; (ii) between ADAbs and drug levels; (iii) patient-centred factors and drug levels. Results ADAbs were detected in 37% (42/112 patients by 12 months). The presence of ADAbs were significantly associated with lower drug levels over 12 months (β=−0.037, 95% CI −0.055 to 0.018, p Conclusions This is the first study to demonstrate that higher certolizumab drug levels are associated with better 12 months EULAR response. ADAbs in certolizumab-treated patients with RA were detected at higher levels than previous studies and help determine the aetiology of a low drug level.

Objectives: Identifying blood-based biomarkers that predict treatment response in RA is a clinical priority. We investigated differential DNA methylation as a candidate biomarker of response for the first-line drug used in RA, MTX. Methods: DNA methylation was measured in DNA samples from individuals recruited to the Rheumatoid Arthritis Medication Study. Differentially methylated positions were compared between whole blood samples collected at baseline and at 4 weeks from patients who, by 6 months, had a good (n = 34) or poor response (n = 34) to MTX using linear modelling, adjusting for gender, age, cell composition, baseline 28-joint disease activity score (DAS28) and smoking status. Analyses also compared methylation with changes in DAS28 and changes in swollen joint count and tender joint count, and changes in CRP over the initial 6 months after MTX commencement. Differentially methylated positions showing significant differences with any response parameter were tested using pyrosequencing in an independent group of 100 patients from the Rheumatoid Arthritis Medication Study. Results: In the discovery group, two CpG sites showed methylation changes at 4 weeks associated with clinical EULAR response by 6 months. Significant changes in methylation for three differentially methylated positions associated with change in tender joint counts, three with change in swollen joint count and a further four with change in CRP. Of the 12 CpGs, four showed replicated association in an independent dataset of samples from the Rheumatoid Arthritis Medication Study. Conclusion: These data represent an advance on current practice by contributing to a personalized medicine strategy allowing an escalation or change in therapy as early as 4 weeks.

Objectives: Imaging of joint inflammation provides a standard against which to derive an updated DAS for RA. Our objectives were to develop and validate a DAS based on reweighting the DAS28 components to maximize association with US-assessed synovitis. Methods: Early RA patients from two observational cohorts (n = 434 and n = 117) and a clinical trial (n = 59) were assessed at intervals up to 104 weeks from baseline; all US scans were within 1 week of clinical exam. There were 899, 163 and 183 visits in each cohort. Associations of combined US grey scale and power Doppler scores (GSPD) with 28 tender joint count and 28 swollen joint count (SJC28), CRP, ESR and general health visual analogue scale were examined in linear mixed model regressions. Cross-validation evaluated model predictive ability. Coefficients learned from training data defined a re-weighted DAS28 that was validated against radiographic progression in independent data (3037 observations; 717 patients). Results: Of the conventional DAS28 components only SJC28 and CRP were associated with GSPD in all three development cohorts. A two-component model including SJC28 and CRP outperformed a four-component model (R2 = 0.235, 0.392, 0.380 vs 0.232, 0.380, 0.375, respectively). The re-weighted two-component DAS28CRP outperformed conventional DAS28 definitions in predicting GSPD (Δtest log-likelihood

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

This is a correction to: Rheumatology, Volume 58, Issue 7, July 2019, Pages 1123–1125, https://doi.org/10.1093/rheumatology/key326

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10‾⁷ for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.

BACKGROUND: The TNFSF13B (TNF superfamily member 13b) gene encodes BAFF, a cytokine with a crucial role in the differentiation and activation of B cells. An insertion-deletion variant (GCTGT→A) of this gene, leading to increased levels of BAFF, has been recently implicated in the genetic predisposition to several autoimmune diseases, including multiple sclerosis, systemic lupus erythematosus, and rheumatoid arthritis. Based on the elevated levels of this cytokine found in patients with giant cell arteritis (GCA) and systemic sclerosis (SSc), we aimed to assess whether this functional variant also represents a novel genetic risk factor for these two disorders. METHODS: A total of 1,728 biopsy-proven GCA patients from 4 European cohorts, 4,584 SSc patients from 3 European cohorts and 5,160 ethnically-matched healthy controls were included in the study. The single nucleotide polymorphism (SNP) rs374039502, which colocalizes with the genetic variant previously implicated in autoimmunity, was genotyped using a custom TaqMan assay. First, association analysis was conducted in each independent cohort using χ2 test in Plink (v1.9). Subsequently, different case/control sets were meta-analyzed by the inverse variance method. RESULTS: No statistically significant differences were found when allele distributions were compared between cases and controls for any of the analyzed cohorts. Similarly, combined analysis of the different sets evidenced a lack of association of the rs374039502 variant with GCA (P = 0.421; OR (95% CI) = 0.92 (0.75-1.13)) and SSc (P = 0.500; OR (95% CI) = 1.05 (0.91-1.22)). The stratified analysis considering the main clinical subphenotypes of these diseases yielded similar negative results. CONCLUSION: Our data suggest that the TNFSF13B functional variant does not contribute to the genetic network underlying GCA and SSc., Funding: This work was supported by the following grants: P12-BIO-1395 from Consejería de Innovación, Ciencia y Tecnología, Junta de Andalucía (Spain) (JM), and the Cooperative Research Thematic Network (RETICS) programme (RD16/0012/0013) (RIER) (JM), from Instituto de Salud Carlos III (ISCIII, Spanish Ministry of Economy, Industry and Competitiveness). AM is recipient of a Miguel Servet fellowship (CP17/00008) from ISCIII (Spanish Ministry of Economy, Industry and Competitiveness) (AM).

Giant cell arteritis (GCA) is the most common form of vasculitis in individuals older than 50 years in Western countries. To shed light onto the genetic background influencing susceptibility for GCA, we performed a genome-wide association screening in a well-powered study cohort. After imputation, 1,844,133 genetic variants were analyzed in 2,134 case subjects and 9,125 unaffected individuals from ten independent populations of European ancestry. Our data confirmed HLA class II as the strongest associated region (independent signals: rs9268905, p = 1.94 × 10−54, per-allele OR = 1.79; and rs9275592, p = 1.14 × 10−40, OR = 2.08). Additionally, PLG and P4HA2 were identified as GCA risk genes at the genome-wide level of significance (rs4252134, p = 1.23 × 10−10, OR = 1.28; and rs128738, p = 4.60 × 10−9, OR = 1.32, respectively). Interestingly, we observed that the association peaks overlapped with different regulatory elements related to cell types and tissues involved in the pathophysiology of GCA. PLG and P4HA2 are involved in vascular remodelling and angiogenesis, suggesting a high relevance of these processes for the pathogenic mechanisms underlying this type of vasculitis.

OBJECTIVE: A recent high-density fine-mapping (ImmunoChip) study of genetic associations in rheumatoid arthritis (RA) identified 14 risk loci with validated genome-wide significance, as well as a number of loci showing associations suggestive of significance (P = 5 × 10(-5) < 5 × 10(-8)), but these have yet to be replicated. The aim of this study was to determine whether these potentially significant loci are involved in the pathogenesis of RA, and to explore whether any of the loci are associated with a specific RA serotype. METHODS: A total of 16 single-nucleotide polymorphisms (SNPs) were selected for genotyping and association analyses in 2 independent validation cohorts, comprising 6,106 RA cases and 4,290 controls. A meta-analysis of the data from the original ImmunoChip discovery cohort and from both validation cohorts was carried out, for a combined total of 17,581 RA cases and 20,160 controls. In addition, stratified analysis of patient subsets, defined according to their anti-cyclic citrullinated peptide (anti-CCP) antibody status, was performed. RESULTS: A significant association with RA risk (P < 0.05) was replicated for 6 of the SNPs assessed in the validation cohorts. All SNPs in the validation study had odds ratios (ORs) for RA susceptibility in the same direction as those in the ImmunoChip discovery study. One SNP, rs72928038, mapping to an intron of BACH2, achieved genome-wide significance in the meta-analysis (P = 1.2 × 10(-8), OR 1.12), and a second SNP, rs911263, mapping to an intron of RAD51B, was significantly associated in the anti-CCP-positive RA subgroup (P = 4 × 10(-8), OR 0.89), confirming that both are RA susceptibility loci. CONCLUSION: This study provides robust evidence for an association of RA susceptibility with genes involved in B cell differentiation (BACH2) and DNA repair (RAD51B). The finding that the RAD51B gene exhibited different associations based on serologic subtype adds to the expanding knowledge base in defining subgroups of RA.

Aim: A genetic variant has recently reached genome-wide significance for association with TNF-inhibitor response in rheumatoid arthritis patients. Here we undertake a replication study in a UK Caucasian population to test for association with TNFinhibitor response. Materials & methods: The genetic variant, rs3794271, located within the PDE3A–SLCO1C1 locus was analyzed for correlation with treatment response using both the EULAR classification criteria and absolute change in (Δ) DAS28 scores as outcome measures. Results: Genotype data were available from 1750 TNF-inhibitor treated individuals. However, no evidence for association was observed (EULAR: p = 0.91 and ΔDAS28: p = 0.93). Furthermore, no significant associations were observed upon stratification by the anti-TNF received (p > 0.05). Conclusion: In the largest replication cohort conducted to date, no evidence for association was observed.

Objective: To determine the concordance between RIA and bridging ELISA at detecting anti-drug antibodies (ADAbs) in the context of random adalimumab levels and investigate the additional clinical utility of detecting ADAbs in RA patients who test ADAb positive by RIA and negative by ELISA. Methods: ADAb levels were determined using RIA and bridging ELISA in 63 adalimumab treated RA patients (159 samples). Immunogenicity concordance was determined using receiver-operating characteristic (ROC) curves. To determine the additional clinical value provided by a positive RIA in the presence of negative ELISA, association between treatment response (ΔDAS28), adalimumab drug levels and ADAbs was evaluated longitudinally using generalised estimating equation. Results: Of the 60 RIA+ samples (n=31 patients), 19 (n=10 patients) were also ELISA+, corresponding to 31.7% of samples. Area under the curve (AUC) for detecting ADAbs using ELISA (compared with RIA) using ROC curves was 0.65 (95% CI: 0.59-0.71); this increased to 0.91 (95% CI: 0.81-0.99) if ADAbs were ≥100 AU/ml using RIA. In RIA+/ELISA- patients, adalimumab levels were associated with ΔDAS28 over 12 months [regression coefficient: 0.098 (0.043-0.15), p

Introduction: Giant cell arteritis (GCA) is an autoimmune disease commonest in Northern Europe and Scandinavia. Previous studies report various associations with HLA-DRB1*04 and HLA-DRB1*01; HLA-DRB1 alleles show a gradient in population prevalence within Europe. Our aims were (1) to determine which amino acid residues within HLA-DRB1 best explained HLA-DRB1 allele susceptibility and protective effects in GCA, seen in UK data combined in meta-analysis with previously published data, and (2) to determine whether the incidence of GCA in different countries is associated with the population prevalence of the HLA-DRB1 alleles that we identified in our meta-analysis. Methods: GCA patients from the UK GCA Consortium were genotyped by using single-strand oligonucleotide polymerization, allele-specific polymerase chain reaction, and direct sequencing. Meta-analysis was used to compare and combine our results with published data, and public databases were used to identify amino acid residues that may explain observed susceptibility/protective effects. Finally, we determined the relationship of HLA-DRB1*04 population carrier frequency and latitude to GCA incidence reported in different countries. Results: In our UK data (225 cases and 1378 controls), HLA-DRB1*04 carriage was associated with GCA susceptibility (odds ratio (OR) = 2.69, P = 1.5×10 −11 ), but HLA-DRB1*01 was protective (adjusted OR = 0.55, P = 0.0046). In meta-analysis combined with 14 published studies (an additional 691 cases and 4038 controls), protective effects were seen from HLA-DR2, which comprises HLA-DRB1*15 and HLA-DRB1*16 (OR = 0.65, P = 8.2×10 −6 ) and possibly from HLA-DRB1*01 (OR = 0.73, P = 0.037). GCA incidence (n = 17 countries) was associated with population HLA-DRB1*04 allele frequency (P = 0.008; adjusted R 2 = 0.51 on univariable analysis, adjusted R 2 = 0.62 after also including latitude); latitude also made an independent contribution. Conclusions: We confirm that HLA-DRB1*04 is a GCA susceptibility allele. The susceptibility data are best explained by amino acid risk residues V, H, and H at positions 11, 13, and 33, contrary to previous suggestions of amino acids in the second hypervariable region. Worldwide, GCA incidence was independently associated both with population frequency of HLA-DRB1*04 and with latitude itself. We conclude that variation in population HLA-DRB1*04 frequency may partly explain variations in GCA incidence and that HLA-DRB1*04 may warrant investigation as a potential prognostic or predictive biomarker.

We conducted a large-scale genetic analysis on giant cell arteritis (GCA), a polygenic immune-mediated vasculitis. A case-control cohort, comprising 1,651 case subjects with GCA and 15,306 unrelated control subjects from six different countries of European ancestry, was genotyped by the Immunochip array. We also imputed HLA data with a previously validated imputation method to perform a more comprehensive analysis of this genomic region. The strongest association signals were observed in the HLA region, with rs477515 representing the highest peak (p = 4.05 × 10(-40), OR = 1.73). A multivariate model including class II amino acids of HLA-DRβ1 and HLA-DQα1 and one class I amino acid of HLA-B explained most of the HLA association with GCA, consistent with previously reported associations of classical HLA alleles like HLA-DRB1(∗)04. An omnibus test on polymorphic amino acid positions highlighted DRβ1 13 (p = 4.08 × 10(-43)) and HLA-DQα1 47 (p = 4.02 × 10(-46)), 56, and 76 (both p = 1.84 × 10(-45)) as relevant positions for disease susceptibility. Outside the HLA region, the most significant loci included PTPN22 (rs2476601, p = 1.73 × 10(-6), OR = 1.38), LRRC32 (rs10160518, p = 4.39 × 10(-6), OR = 1.20), and REL (rs115674477, p = 1.10 × 10(-5), OR = 1.63). Our study provides evidence of a strong contribution of HLA class I and II molecules to susceptibility to GCA. In the non-HLA region, we confirmed a key role for the functional PTPN22 rs2476601 variant and proposed other putative risk loci for GCA involved in Th1, Th17, and Treg cell function.

Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis and, despite the larger estimated heritability for PsA, the majority of genetic susceptibility loci identified to date are shared with psoriasis. Here, we present results from a case–control association study on 1,962 PsA patients and 8,923 controls using the Immunochip genotyping array. We identify eight loci passing genome-wide significance, secondary independent effects at three loci and a distinct PsA-specific variant at the IL23R locus. We report two novel loci and evidence of a novel PsA-specific association at chromosome 5q31. Imputation of classical HLA alleles, amino acids and SNPs across the MHC region highlights three independent associations to class I genes. Finally, we find an enrichment of associated variants to markers of open chromatin in CD8+ memory primary T cells. This study identifies key insights into the genetics of PsA that could begin to explain fundamental differences between psoriasis and PsA., Psoriatic arthritis (PsA) is a chronic inflammatory arthritis with a significant genetic component. Here, the authors analyse immune-related genetic markers in 1,962 PsA patients and 8,923 controls to identify novel PsA risk loci and highlight distinct genetic differences between psoriasis and PsA.

BACKGROUND: The NLRP3-inflammasome, implicated in the pathogenesis of several inflammatory disorders, has been analysed in rheumatoid arthritis (RA). METHODS: Relative gene expression of NLRP3-inflammasome components was characterised in PBMCs of 29 patients receiving infliximab. A total of 1278 Caucasian patients with RA from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) cohort receiving tumour necrosis factor (TNF) antagonists (infliximab, adalimumab and etanercept) were genotyped for 34 single nucleotide polymorphisms (SNPs), spanning the genes NLRP3, MEFV and CARD8. Regression analyses were performed to test for association between genotype and susceptibility and treatment response (disease activity score across 28 joints (DAS28) and EULAR improvement criteria) at 6 months, with secondary expression quantitative trait loci (eQTL) analyses. RESULTS: At baseline, gene expression of ASC, MEFV, NLRP3-FL, NLRP3-SL and CASP1 were significantly higher compared with controls whereas CARD8 was lower in the patients. Caspase-1 and interleukin-18 levels were significantly raised in patients with RA. SNPs in NLRP3 showed association with RA susceptibility and EULAR response to anti-TNF in the BRAGGSS cohort, and in monocytes but not B cells, in eQTL analysis of 283 healthy controls. CARD8 SNPs were associated with RA susceptibility and DAS28 improvement in response to anti-TNF and eQTL effects in monocytes and B cells. CONCLUSIONS: This study found evidence of modulation of the NLRP3-inflammasome in patients with RA prior to receiving infliximab and some evidence of association for SNPs at NLRP3 and CARD8 loci with RA susceptibility and response to anti-TNF. The SNPs associated with susceptibility/response are not the main eQTL variants for either locus, and the associations with treatment response require replication in an independent cohort.

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P, Author Summary Rheumatoid arthritis (RA) is a common, incurable disease with major individual and health service costs. Preventing its development is therefore an important goal. Being able to predict who will develop RA would allow researchers to look at ways to prevent it. Many factors have been found that increase someone's risk of RA. These are divided into genetic and environmental (such as smoking) factors. The risk of RA associated with each factor has previously been reported. Here, we demonstrate a method that combines these risk factors in a process called “prediction modelling” to estimate someone's lifetime risk of RA. We show that firstly, our prediction models can identify people with very high-risks of RA and secondly, they can be used to identify people at risk of developing RA at a younger age. Although these findings are an important first step towards preventing RA, as only a minority of people tested had substantially increased disease risks our models could not be used to screen the general population. Instead they need testing in people already at risk of RA such as relatives of affected patients. In this context they could identify enough numbers of high-risk people to allow preventive methods to be evaluated.

OBJECTIVES: Rheumatoid arthritis (RA) shares some similar clinical and pathological features with juvenile idiopathic arthritis (JIA); indeed, the strategy of investigating whether RA susceptibility loci also confer susceptibility to JIA has already proved highly successful in identifying novel JIA loci. A plethora of newly validated RA loci has been reported in the past year. Therefore, the aim of this study was to investigate these single nucleotide polymorphisms (SNP) to determine if they were also associated with JIA. METHODS: Thirty-four SNP that showed validated association with RA and had not been investigated previously in the UK JIA cohort were genotyped in JIA cases (n=1242), healthy controls (n=4281), and data were extracted for approximately 5380 UK Caucasian controls from the Wellcome Trust Case-Control Consortium 2. Genotype and allele frequencies were compared between cases with JIA and controls using PLINK. A replication cohort of 813 JIA cases and 3058 controls from the USA was available for validation of any significant findings. RESULTS: Thirteen SNP showed significant association (p

INTRODUCTION: This study investigated five confirmed rheumatoid arthritis (RA) susceptibility genes/loci (HLA-DRB1, PTPN22, STAT4, OLIG3/TNFAIP3 and TRAF1/C5) for association with susceptibility and severity in an inception cohort. METHODS: The magnitude of association for each genotype was assessed in 1,046 RA subjects from the Yorkshire Early RA cohort and in 5,968 healthy UK controls. Additional exploratory subanalyses were undertaken in subgroups defined by autoantibody status (rheumatoid factor and anti-cyclic citrullinated peptide) or disease severity (baseline articular erosions, Health Assessment Questionnaire (HAQ) score and swollen joint count (SJC)). RESULTS: In the total RA inception cohort, the HLA-DRB1 shared epitope (per-allele odds ratio (OR) = 2.1, trend P < 0.0001), PTPN22 (per-allele OR = 1.5, trend P < 0.0001), OLIG3/TNFAIP3 locus (per-allele OR = 1.2, trend P = 0.009) and TRAF1/C5 locus (per-allele OR = 1.1, trend P = 0.04) were associated with RA. The magnitude of association for these loci was increased in those patients who were autoantibody-positive. PTPN22 was associated with autoantibody-negative RA (per-allele OR = 1.3, trend P = 0.04). There was no evidence of association between these five genetic loci and baseline erosions or SJC in the total RA cohort, after adjustment for symptom duration. TRAF1/C5 was significantly associated with baseline HAQ, however, following adjustment for symptom duration (P trend = 0.03). CONCLUSIONS: These findings support the mounting evidence that different genetic loci are associated with autoantibody-positive and autoantibody-negative RA, possibly suggesting that many of the genes identified to date are associated with autoantibody production. Additional studies with a specific focus on autoantibody-negative RA will be needed to identify the genes predisposing to this RA subgroup. The TRAF1/C5 locus in particular warrants further investigation in RA as a potential disease severity locus.

Montes, Ariana et al., [Introduction] We have hypothesized that incompatibility between the G1m genotype of the patient and the G1m1 and G1m17 allotypes carried by infliximab (INX) and adalimumab (ADM) could decrease the efficacy of these anti-tumor necrosis factor (anti-TNF) antibodies in the treatment of rheumatoid arthritis (RA)., [Methods] The G1m genotypes were analyzed in three collections of patients with RA totaling 1037 subjects. The first, used for discovery, comprised 215 Spanish patients. The second and third were successively used for replication. They included 429 British and Greek patients and 393 Spanish and British patients, respectively. Two outcomes were considered: change in the Disease Activity Score in 28 joint (ΔDAS28) and the European League Against Rheumatism (EULAR) response criteria., [Results] An association between less response to INX and incompatibility of the G1m1,17 allotype was found in the discovery collection at 6 months of treatment (P = 0.03). This association was confirmed in the replications (P = 0.02 and 0.08, respectively) leading to a global association (P = 0.001) that involved a mean difference in ΔDAS28 of 0.4 units between compatible and incompatible patients (2.3 ± 1.5 in compatible patients vs. 1.9 ± 1.5 in incompatible patients) and an increase in responders and decrease in non-responders according to the EULAR criteria (P = 0.03). A similar association was suggested for patients treated with ADM in the discovery collection, but it was not supported by replication., [Conclusions] Our results suggest that G1m1,17 allotypes are associated with response to INX and could aid improved therapeutic targeting in RA., Funding was provided by grants PI11/01048, PI12/01909 and by RETICS Program, RD08/0075/0019 and RD12/0009/0008 (RIER) of the Instituto de Salud Carlos III (Spain) that are partially financed by the European Regional Development Fund of the European Union.