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Abstract Background The psychological burden in people with inflammatory arthritis is substantial, yet little is known about the disease-related affect experienced by individuals with axial Spondyloarthritis (axial SpA). The aim of this study was to conduct a qualitative evidence synthesis and a review of social media to explore the emotional impact of living with axial SpA. Methods We searched nine databases for studies reporting qualitative data about participants’ emotional experience of living with axial SpA. In addition, we searched social media platforms for posts from people with axial SpA based in the UK that offered insights into emotional responses to living with the condition. We employed a thematic approach to synthesise the data. Results We included 27 studies (1314 participants; 72% men) in our qualitative evidence synthesis and developed seven descriptive themes from the data: 1) delayed diagnosis: a barrier to emotional wellbeing; 2) disruptive symptoms: a source of mood swings; 3) work disability: a loss of self-esteem; 4) obstacles in interpersonal relationships: a trigger of distress; 5) taking up exercise: personal pride or unwelcomed reminders; 6) anti-TNF therapy: hope reignited despite concerns and 7) a journey of acceptance: worry mixed with hope. Posts extracted from social media fora (537; 48% from women) for the most part supported the seven themes. One additional theme—COVID-19, uncertainty and anxiety during the pandemic, was developed, reflecting common emotions expressed during the UK’s first wave of the coronavirus pandemic. Conclusion This study highlights a preponderance of negative affect experienced by people living with axial SpA, conditioned through existing and anticipated symptoms, failed expectations, and lost sense of self. Given the bidirectional relationships between negative emotions and inflammation, negative emotions and perceptions of pain, and the influence of affect in self-care behaviours, this finding has important implications for treatment and management of people with axial SpA.

BackgroundPrevious studies of radiological damage in rheumatoid arthritis (RA) have used candidate-gene approaches, or evaluated single genome-wide association studies (GWAS). We undertook the first meta-analysis of GWAS of RA radiological damage to: (1) identify novel genetic loci for this trait; and (2) test previously validated variants.MethodsSeven GWAS (2,775 RA cases, of a range of ancestries) were combined in a meta-analysis. Radiological damage was assessed using modified Larsen scores, Sharp van Der Heijde scores, and erosive status. Single nucleotide polymophsim (SNP) associations with radiological damage were tested at a single time-point using regression models. Primary analyses included age and disease duration as covariates. Secondary analyses also included rheumatoid factor (RF). Meta-analyses were undertaken in trans-ethnic and European-only cases.ResultsIn the trans-ethnic primary meta-analysis, one SNP (rs112112734) in close proximity to HLA-DRB1, and strong linkage disequilibrium with the shared-epitope, attained genome-wide significance (P = 4.2x10-8). In the secondary analysis (adjusting for RF) the association was less significant (P = 1.7x10-6). In both trans-ethnic primary and secondary meta-analyses 14 regions contained SNPs with associations reaching PConclusionsOur meta-analysis confirms the known association between the HLA-DRB1 shared epitope and RA radiological damage. The lack of replication of previously validated non-HLA markers highlights a requirement for further research to deliver clinically-useful prognostic genetic markers.

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

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in χ(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.

The Trp(620) allotype of PTPN22 confers susceptibility to rheumatoid arthritis (RA) and certain other classical autoimmune diseases. There has been a report of other variants within the PTPN22 locus that alter risk of RA; protective haplotype '5', haplotype group '6-10' and susceptibility haplotype '4', suggesting the possibility of other PTPN22 variants involved in the pathogenesis of RA independent of R620W (rs2476601). Our aim was to further investigate this possibility.A total of 4,460 RA cases and 4,481 controls, all European, were analysed. Single nucleotide polymorphisms rs3789607, rs12144309, rs3811021 and rs12566340 were genotyped over New Zealand (NZ) and UK samples. Publically-available Wellcome Trust Case Control Consortium (WTCCC) genotype data were used.The protective effect of haplotype 5 was confirmed (rs3789607; (OR = 0.91, P = 0.016), and a second protective effect (possibly of haplotype 6) was observed (rs12144309; OR = 0.90, P = 0.021). The previously reported susceptibility effect of haplotype 4 was not replicated; instead a protective effect was observed (rs3811021; OR = 0.85, P = 1.4×10(-5)). Haplotypes defined by rs3789607, rs12144309 and rs3811021 coalesced with the major allele of rs12566340 within the adjacent BFK (B-cell lymphoma 2 (BCL2) family kin) gene. We, therefore, tested rs12566340 for association with RA conditional on rs2476601; there was no evidence for an independent effect at rs12566340 (P = 0.76). Similarly, there was no evidence for an independent effect at rs12566340 in type 1 diabetes (P = 0.85).We have no evidence for a common variant additional to rs2476601 within the PTPN22 locus that influences the risk of RA. Arg620Trp is almost certainly the single common causal variant.

BackgroundThe acute-phase increase in serum C-reactive protein (CRP) is used to diagnose and monitor infectious and inflammatory diseases. Little is known about the influence of genetics on acute-phase CRP, particularly in patients with chronic inflammation.Methods and findingsWe studied two independent sets of patients with chronic inflammation due to rheumatoid arthritis (total 695 patients). A tagSNP approach captured common variation at the CRP locus and the relationship between genotype and serum CRP was explored by linear modelling. Erythrocyte sedimentation rate (ESR) was incorporated as an independent marker of inflammation to adjust for the varying levels of inflammatory disease activity between patients. Common genetic variants at the CRP locus were associated with acute-phase serum CRP (for the most associated haplotype: p = 0.002, pConclusionsOur findings raise questions about the interpretation of acute-phase serum CRP. In particular, failure to take into account the potential for genetic effects may result in the inappropriate reassurance or suboptimal treatment of patients simply because they carry low-CRP-associated genetic variants. CRP is increasingly being incorporated into clinical algorithms to compare disease activity between patients and to predict future clinical events: our findings impact on the use of these algorithms. For example, where access to effective, but expensive, biological therapies in rheumatoid arthritis is rationed on the basis of a DAS28-CRP clinical activity score, then two patients with identical underlying disease severity could be given, or denied, treatment on the basis of CRP genotype alone. The accuracy and utility of these algorithms might be improved by using a genetically adjusted CRP measurement.