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Objective To obtain updated estimates on the incidence and prevalence of systemic lupus erythematosus (SLE) in the UK, over the period 1990–2020, using the Clinical Practice Research Datalink (CPRD).Methods This was a retrospective cohort study using the CPRD covering the period 1990–2020. A case ascertainment algorithm was developed in line with best practice recommendations for observational research. Incidence was calculated per 100 000 person-years and point prevalence (at the mid-year point) calculated per 100 000. Results were stratified by sex.Results 9443 SLE cases were identified. 5278 incident cases were identified (4538 women, 740 men). The overall incidence rate was 5.47 (95% CI 5.33 to 5.62) cases per 100 000 person-years. Incidence rates decreased slightly across the study period, which was more pronounced for women than men. Point prevalence increased over time, from 21.4 (95% CI 17.68 to 25.67) per 100 000 in 1990 to 107.14 (95% CI 103.26 to 111.12) per 100 000 in 2020.Conclusions The observed fivefold increase in prevalence of SLE over the last 30 years, in the context of a modest decline in incidence rate, may suggest improved outcomes in SLE and has important implications for healthcare service delivery and planning in the UK.

Abstract To assess current evidence of effectiveness of sequential lines of biologic and targeted small molecule drugs for psoriasis beyond first line. A systematic search of the literature (Medline, Embase and bibliographic) was undertaken in October and December 2022 to find all studies assessing effectiveness of biologics and targeted small molecules when used beyond first‐line in adults with psoriasis (PROSPERO CRD42022365298). Data extraction and a bias assessment (Risk Of Bias In Non‐randomized Studies—of Interventions/Cochrane RoB2) were undertaken for all included studies. A random effects proportional meta‐analysis was undertaken for PASI75/90/100 at 12–16 weeks for each line of treatment (1st to 4th). Of 2666 abstracts identified, a full text review was undertaken of 177 studies; 20 manuscripts met eligibility criteria. Twenty studies were included in the analysis: 19 observational studies and one sub analysis of a RCT; n = 6495 (average age 49.7 years, female 35.1%). Eleven studies assessed second line biologic, nine assessed third + line. A meta‐analysis of PASI75 at 12–16 weeks found pooled effect percentage achieving PASI75 of 61%, 56%, 79% and 61% in 1st, 2nd, 3rd and 4th line biologics respectively. Meta‐analyses of PASI90/100 also found no evidence of diminished effectiveness with sequential lines (PASI90 46.1%, 39.9%, 55.8% and 33.7% and PASI100 36.7%, 30.3%, 46.7% and 30.4% in 1st to 4th line respectively). Available evidence for effectiveness of biologics beyond first line in psoriasis is predominantly observational, at high risk of bias and of low quality. There is very limited data for effectiveness beyond second line. Evidence indicates that biologics can be effective to fourth‐line.

Abstract Background Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use. Methods Patients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past). Results Predicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains. Conclusion MMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.

People on low-incomes in the UK develop multiple long-term health conditions over 10 years earlier than affluent individuals. Financial diaries -new to public health- are used to explore the lived experiences of financially-vulnerable individuals, diagnosed with at least one long-term condition, living in two inner-city London Boroughs. Findings show that the health status of these individuals is a key barrier to work opportunities, undermining their income. Their precarious and uncertain financial situation, sometimes combined with housing issues, increased stress and anxiety which, in turn, contributed to further deteriorate participants' health. Long-term health conditions limited the strategies to overcome moments of financial crisis and diarists frequently used credit to cope. Restrictions to access reliable services and timely support were connected to the progression of multiple long-term conditions. Models that integrate healthcare, public health, welfare and financial support are needed to slow down the progression from one to many long-term health conditions.

Abstract Background Health inequalities are persistent and widening with transformative policy change needed. Radically shifting policy to tackle upstream causes of inequalities is likely to require public participation to provide a mandate, evidence and to address questions of co-design, implementation and acceptability. The aim of this paper is to explore perceptions among policy actors on why and how the public should be involved in policymaking for health inequalities. Methods In 2019–2020, we conducted exploratory, in-depth, semi-structured interviews with 21 Scottish policy actors from a range of public sector bodies and agencies and third sector organisations that work in, or across, health and non-health sectors. Data were analysed thematically and used to examine implications for the development of participatory policymaking. Results Policy actors viewed public participation in policymaking as intrinsically valuable for democratic reasons, but the main, and more challenging, concern was with how it could affect positive policy change. Participation was seen as instrumental in two overlapping ways: as evidence to improve policies to tackle health inequalities and to achieve public acceptance for implementing more transformative policies. However, our analysis suggests a paradox: whilst policy actors place importance on the instrumental value of public participation, they simultaneously believe the public hold views about health inequalities that would prevent transformative change. Finally, despite broad agreement on the need to improve public participation in policy development, policy actors were uncertain about how to make the necessary changes due to conceptual, methodological and practical challenges. Conclusions Policy actors believe in the importance of public participation in policy to address health inequalities for intrinsic and instrumental reasons. Yet, there is an evident tension between seeing public participation as a route to upstream policies and a belief that public views might be misinformed, individualistic, short-term or self-interested and doubts about how to make public participation meaningful. We lack good insight into what the public think about policy solutions to health inequalities. We propose that research needs to shift from describing the problem to focusing more on potential solutions and outline a potential way forward to undertake effective public participation to tackle health inequalities.

Summary: Background: In patients with idiopathic inflammatory myopathies (IIM), autoantibodies are associated with specific clinical phenotypes suggesting a pathogenic role of adaptive immunity. We explored if autoantibody profiles are associated with specific HLA genetic variants and clinical manifestations in IIM. Methods: We included 1348 IIM patients and determined the occurrence of 14 myositis-specific or –associated autoantibodies. We used unsupervised cluster analysis to identify autoantibody-defined subgroups and logistic regression to estimate associations with clinical manifestations, HLA-DRB1, HLA-DQA1, HLA-DQB1 alleles, and amino acids imputed from genetic information of HLA class II and I molecules. Findings: We identified eight subgroups with the following dominant autoantibodies: anti-Ro52, -U1RNP, -PM/Scl, -Mi2, -Jo1, -Jo1/Ro52, -TIF1γ or negative for all analysed autoantibodies. Associations with HLA-DRB1∗11, HLA-DRB1∗15, HLA-DQA1∗03, and HLA-DQB1∗03 were present in the anti-U1RNP-dominated subgroup. HLA-DRB1∗03, HLA-DQA1∗05, and HLA-DQB1∗02 alleles were overrepresented in the anti-PM/Scl and anti-Jo1/Ro52-dominated subgroups. HLA-DRB1∗16, HLA-DRB1∗07 alleles were most frequent in anti-Mi2 and HLA-DRB1∗01 and HLA-DRB1∗07 alleles in the anti-TIF1γ subgroup. The HLA-DRB1∗13, HLA-DQA1∗01 and HLA-DQB1∗06 alleles were overrepresented in the negative subgroup. Significant signals from variations in class I molecules were detected in the subgroups dominated by anti-Mi2, anti-Jo1/Ro52, anti-TIF1γ, and the negative subgroup. Interpretation: Distinct HLA class II and I associations were observed for almost all autoantibody-defined subgroups. The associations support autoantibody profiles use for classifying IIM which would likely reflect underlying pathogenic mechanisms better than classifications based on clinical symptoms and/or histopathological features. Funding: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

Abstract In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

Abstract Background Rural health outcomes are often worse than their urban counterparts. While rural health theory recognizes the importance of the social determinants of health, there is a lack of insight into public perspectives for improving rural health beyond the provision of health‐care services. Gaining insight into perceived solutions, that include and go beyond health‐ care, can help to inform resource allocation decisions to improve rural health. Objective To identify and describe shared perspectives within a remote‐rural community on how to improve rural health. Method Using Q methodology, a set of 40 statements were developed representing different perceptions of how to improve rural health. Residents of one remote‐rural island community ranked this statement set according to their level of agreement. Card‐sorts were analysed using factor analysis to identify shared points of view and interpreted alongside post‐sort qualitative interviews. Results Sixty‐two respondents participated in the study. Four shared perspectives were identified, labelled: Local economic activity; Protect and care for the community; Redistribution of resources; and Investing in people. Factors converged on the need to relieve poverty and ensure access to amenities and services. Discussion and conclusions Factors represent different elements of a multifaceted theory of rural health, indicating that ‘lay’ respondents are capable of comprehending various approaches to health improvement and perspectives are not homogenous within rural communities. Respondents diverged on the role of individuals, the public sector and ‘empowered’ community‐based organizations in delivering these solutions, with implications for policy and practice. Public Contribution Members of the public were involved in the development and piloting of the statement set.

Objectives To identify predictors of overall lupus and lupus nephritis (LN) responses in patients with LN.Methods Data from the Aspreva Lupus Management Study (ALMS) trial cohort was used to identify baseline predictors of response at 6 months. Endpoints were major clinical response (MCR), improvement, complete renal response (CRR) and partial renal response (PRR). Univariate and multivariate logistic regressions with least absolute shrinkage and selection operator (LASSO) and cross-validation in randomly split samples were utilised. Predictors were ranked by the percentage of times selected by LASSO and prediction performance was assessed by the area under the receiver operating characteristics (AUROC) curve.Results We studied 370 patients in the ALMS induction trial. Improvement at 6 months was associated with older age (OR=1.03 (95% CI: 1.01 to 1.05) per year), normal haemoglobin (1.85 (1.16 to 2.95) vs low haemoglobin), active lupus (British Isles Lupus Assessment Group A or B) in haematological and mucocutaneous domains (0.61 (0.39 to 0.97) and 0.50 (0.31 to 0.81)), baseline damage (SDI>1 vs =0) (0.38 (0.16 to 0.91)) and 24-hour urine protein (0.63 (0.50 to 0.80)). LN duration 2–4 years (0.43 (0.19 to 0.97) vs

Abstract Background Psoriasis is a chronic inflammatory disease of the skin affecting 2–3% of UK population. 30% of people affected by psoriasis will develop a distinct form of arthritis within 10 years of the skin condition onset. Although the pathogenesis of psoriatic arthritis is still unknown, there is a genetic predisposition triggered by environmental factors. Limited but convincing evidence link the gut microbiome to psoriatic arthritis. The Microbiome in Psoriatic ARThritis (Mi-PART) study propose is to characterise the microbiome-metabolic interface in patients affected by psoriatic arthritis to deepen our understanding of the pathogenesis of the disease. Methods This is a multicentre, prospective, observational study. Psoriatic arthritis (n = 65) and ankylosing spondylitis (n = 30) patients will be recruited in addition to a control group of healthy volunteers (n = 30). Patients eligibility will be evaluated against the Criteria for Psoriatic Arthritis (CASPAR), the Bath Ankylosing Spondylitis Activity Index (BASDAI) and the healthy volunteers who fulfil study inclusion and exclusion criteria. Information regarding their medical and medication history, demographics, diet and lifestyle will be collected. All the participants in the study will be asked to complete a 7-day food diary, to provide stool samples and to complete quality of life questionnaires. Routine clinical laboratory tests will be performed on blood and urine samples. Patients and healthy volunteers with gastrointestinal symptoms, previous history of cancer, gastrointestinal surgery in the previous 6 months or alcohol abuse will be excluded from the study. Discussion The aim of this trial is to characterise the microbiome of psoriatic arthritis patients and to compare it with microbiome of healthy volunteers and of patient with ankylosing spondylitis in order to define if different rheumatologic conditions are associated with characteristic microbiome profiles. Investigating the role of the microbiome in the development of psoriatic arthritis could deepen our understanding of the pathogenesis of the disease and potentially open the way to new therapies.

(1) Background: Myositis specific antibodies (MSA) are important diagnostic biomarkers. Among the rarest and most challenging MSA are anti-OJ antibodies which are associated with anti-synthetase syndrome (ASS). In contrast to the other tRNA synthetases that are targets of ASS autoantibodies (e.g Jo-1, PL-7, PL-12, EJ, KS, Zo), OJ represents a macromolecular complex with several ribonucleoprotein subunits. Therefore, the choice of the antigen in autoantibody assays can be challenging. (2) Methods: We collected two independent cohorts with anti-OJ antibodies, one based on a commercial line immunoassay (LIA) (n = 39), the second based on protein immunoprecipitation (IP) (n = 15). Samples were tested using a particle-based multi-analyte technology (PMAT) system that allows for the simultaneous detection of antibodies to various autoantigens. For the detection of anti-OJ antibodies, two different antigens were deployed (KARS, IARS) on PMAT. The reactivity to the two antigens KARS and IARS was analyzed individually and combined in a score (sum of the median fluorescence intensities). (3) Results: In the cohort selection based on LIA, 3/39 (7.7%) samples were positive for anti-KARS and 7/39 (17.9%) for anti-IARS and 14/39 (35.9%) when the two antigens were combined. In contrast, in samples selected by IP the sensitivity of anti-KARS was higher: 6/15 (40.0%) samples were positive for anti-KARS, 4/15 (26.7%) for anti-IARS and 12/15 (80.0%) for the combination of the two antigens. 18/39 (46.2%) of the LIA samples generated a cytoplasmic IIF pattern (compatible with anti-synthetase antibodies), but there was no association with the antibody levels, neither with LIA nor with PMAT. (4) Conclusions: The combination of IARS and KARS might represent a promising approach for the detection of anti-OJ antibodies on a fully automated platform.

Abstract Background Health inequalities in the UK have proved to be stubborn, and health gaps between best and worst-off are widening. While there is growing understanding of how the main causes of poor health are perceived among different stakeholders, similar insight is lacking regarding what solutions should be prioritised. Furthermore, we do not know the relationship between perceived causes and solutions to health inequalities, whether there is agreement between professional stakeholders and people living in low-income communities or agreement within these groups. Methods Q methodology was used to identify and describe the shared perspectives (‘subjectivities’) that exist on i) why health is worse in low-income communities (‘Causes’) and ii) the ways that health could be improved in these same communities (‘Solutions’). Purposively selected individuals (n = 53) from low-income communities (n = 25) and professional stakeholder groups (n = 28) ranked ordered sets of statements – 34 ‘Causes’ and 39 ‘Solutions’ – onto quasi-normal shaped grids according to their point of view. Factor analysis was used to identify shared points of view. ‘Causes’ and ‘Solutions’ were analysed independently, before examining correlations between perspectives on causes and perspectives on solutions. Results Analysis produced three factor solutions for both the ‘Causes’ and ‘Solutions’. Broadly summarised these accounts for ‘Causes’ are: i) ‘Unfair Society’, ii) ‘Dependent, workless and lazy’, iii) ‘Intergenerational hardships’ and for ‘Solutions’: i) ‘Empower communities’, ii) ‘Paternalism’, iii) ‘Redistribution’. No professionals defined (i.e. had a significant association with one factor only) the ‘Causes’ factor ‘Dependent, workless and lazy’ and the ‘Solutions’ factor ‘Paternalism’. No community participants defined the ‘Solutions’ factor ‘Redistribution’. The direction of correlations between the two sets of factor solutions – ‘Causes’ and ‘Solutions’ – appear to be intuitive, given the accounts identified. Conclusions Despite the plurality of views there was broad agreement across accounts about issues relating to money. This is important as it points a way forward for tackling health inequalities, highlighting areas for policy and future research to focus on.

Objectives To report on fatigue in patients from the United Kingdom primary Sjögren’s syndrome (pSS) registry identifying factors associated with fatigue and robust to assignable causes such as comorbidities and medications associated with drowsiness.Methods From our cohort (n = 608), we identified those with comorbidities associated with fatigue, and those taking medications associated with drowsiness. We constructed dummy variables, permitting the contribution of these potentially assignable causes of fatigue to be assessed. Using multiple regression analysis, we modelled the relationship between Profile of Fatigue and Discomfort physical and mental fatigue scores and potentially related variables.Results Pain, depression and daytime sleepiness scores were closely associated with both physical and mental fatigue (all p ≤ 0.0001). In addition, dryness was strongly associated with physical fatigue (p ≤ 0.0001). These effects were observed even after adjustment for comorbidities associated with fatigue or medications associated with drowsiness.Conclusions These findings support further research and clinical interventions targeting pain, dryness, depression and sleep to improve fatigue in patients with pSS.This finding is robust to both the effect of other comorbidities associated with fatigue and medications associated with drowsiness.

Objectives: Standardization of myositis specific antibody (MSA) detection is of high importance because these antibodies are relevant for diagnosis and stratification of patients with idiopathic inflammatory myositis (IIM) and have the potential to be used in classification criteria. Many laboratories rely on immunoprecipitation (IP) for the detection of MSA but this approach is compromised by logistic, standardization, and regulatory challenges. Therefore, reliable alternatives to IP are mandatory. Here we aimed to compare three methods for the detection of MSA.Methods: Our study initiated from a cohort of 1,619 IIM patients (BIRD/University of Bath serology service and UKMyoNet cohorts) and resulted in 157 unique serum samples enriched for higher prevalence of MSA characterized by the laboratory's routine methods, IP and line immunoassay (LIA: Euroimmun). All samples were tested using a novel fully automated particle-based multi-analyte technology (PMAT, Inova Diagnostics, research use only). Analyses included antibodies to PL-7, PL-12, SRP, NXP2, Mi-2, SAE, EJ, MDA5, TIF1γ, SRP, NXP2.Results: Overall high agreements were observed between novel methods (LIA and PMAT) and IP (Cohen's kappa 0.46–0.96) for the detection of MSA. Lowest level of agreement was found for EJ and highest for SAE.Conclusion: The data hold promise for advancements in standardization of MSA assays as well as for the potential inclusion of MSA in future classification criteria.

OBJECTIVES To assess the use of the Clinical EULAR Sjögren’s Syndrome Disease Activity Index (ClinESSDAI), a version of the ESSDAI without the biological domain, for assessing potential eligibility and outcomes for clinical trials in patients with primary Sjögren’s syndrome (pSS), according to the new ACR-EULAR classification criteria, from the UK Primary Sjögren’s Syndrome Registry (UKPSSR). METHODS A total of 665 patients from the UKPSSR cohort were analysed at their time of inclusion in the registry. ESSDAI and ClinESSDAI were calculated for each patient. RESULTS For different disease activity index cut-off values, more potentially eligible participants were found when ClinESSDAI was used than with ESSDAI. The distribution of patients according to defined disease activity levels did not differ statistically (chi2 p = 0.57) between ESSDAI and ClinESSDAI for moderate disease activity (score ≥5 and

BackgroundFatigue is a debilitating condition with a significant impact on patients' quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren's Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms.MethodsWhole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren's Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W).ResultsAlthough no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively.ConclusionsSystematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS.