Rheumatology

BackgroundInterleukin-6 (IL-6) is a cytokine that plays a key role in systemic hyperinflammation and may mediate the relationship between acute COVID-19 and severe long-term outcomes such as Long COVID or death. IL-6 modulating drugs may reduce patients risk of severe post-COVID-19 outcomes. MethodsWe conducted an emulated target trial in a retrospective cohort of patients with moderate-to-severe rheumatoid arthritis who were prescribed IL-6 receptor antagonists (sarilumab or tocilizumab, pooled treatment) or other biologic agents (anakinra or baricitinib, pooled comparator) in 2022. We compared the 12-month cumulative incidence of mortality and Long COVID (diagnosed and probable) between groups using Super Learner and targeted maximum likelihood estimation, adjusting for covariates of interest. ResultsIn our cohort of 3,553 patients, we found that prescription of IL-6 receptor antagonists was associated with a lower 12-month cumulative mortality (adjusted relative risk (aRR) 0.40, 95% CI 0.27, 0.59), diagnosed Long COVID aRR 0.42, 95% CI 0.23, 0.78), and probable Long COVID (aRR 0.71, 95% CI 0.61, 0.83), compared to prescription of other biologic agents, among rheumatoid arthritis patients. ConclusionsIL-6 receptor antagonists may prevent the incidence of severe post-COVID-19 outcomes, such as Long COVID or mortality. This supports the hypothesis that IL-6 may be a mechanistic biomarker of COVID-19 sequelae and that acute COVID-19 severity may mediate this relationship.

ObjectivesPatients with osteoarthritis (OA) affecting multiple joints have poorer health outcomes than those without, yet most research examines isolated joints, leaving a gap in multi-joint disease. This study aimed to describe radiographically defined hip (rHOA) and knee OA (rKOA) within UK Biobank (UKB), exploring interrelationships across joints, and associations with joint pain, obesity, race and deprivation. MethodsAutomated machine learning was applied to left and right hip and knee dual-energy X-ray absorptiometry scans. Radiographic OA (rOA) was defined as custom grades [≥]2. Joint pain was assessed through self-reported questionnaires. Descriptive statistics summarised the population characteristics. Logistic regression models examined bilateral and cross-joint associations, as well as associations with joint pain. Adjustments were made for age, sex, race, height, weight and deprivation. Other models examined the associations between body size and OA. ResultsAmong 59,475 individuals (mean age 65 years; 52.8% female), rHOA prevalence was 4,098 (6.9%)) and 4,841 (8.1%) for the right and left joints, respectively. The corresponding estimates for rKOA were 3,750 (6.3%) and 4,220 (7.1%). Overall, increasing grades of rOA and number of joints affected were more strongly associated with joint pain. Regarding joint-interrelationships, bilateral associations were stronger at the knee, whereas cross-joint associations (hip-knee) were weaker. Associations with BMI and height differed between the hip and knee. ConclusionsRadiographic hip and knee OA exhibit distinct patterns of interrelationship, associations with symptoms and risk factors, suggesting heterogeneity in disease process and the need for joint-specific treatment. Key MessagesO_ST_ABSWhat is already known on this topic?C_ST_ABSO_LIOsteoarthritis (OA) commonly affects the hip and knee and is associated with pain and disability, with recognised risk factors such as age, obesity and deprivation. C_LIO_LIIncreasing interest in multi-joint OA challenges the traditional concept of lower-limb OA as a monoarthritis, but most research examines joints in isolation. C_LIO_LIGenetic evidence suggests that hip and knee OA may differ in underlying mechanisms, yet population-scale comparisons are limited. C_LI What this study adds?O_LIAmong 59,574 individuals, this study identifies that radiographic OA captures structurally and clinically relevant disease with increasing severity and greater number of joints affected, positively associated with chronic joint pain. C_LIO_LIRadiographic hip and knee OA demonstrated strong bilateral but weaker cross-joint associations, indicating preferential within-joint symmetry. C_LIO_LIRisk factors differed by anatomical site with BMI and weight strongly associated with knee OA and weakly associated with hip OA. Height showed the opposite associations. C_LI How this study might affect research, practice or policy?O_LIThese findings support that hip and knee OA may partially represent different disease processes rather than a single condition. C_LIO_LIClinical practice should consider cumulative joint involvement and joint-specific risk factors. C_LIO_LIFuture research should consider the development of more targeted treatment to prevent multi-joint progression. C_LI

ObjectivesThe aetiopathogenesis of SLE encompasses genetic, environmental and epigenetic factors. We investigated associations between an SLE methylation risk score (MRS), HLA-DRB1*03:01, a non-HLA polygenic risk score (PRS) and clinical and immunological phenotypes. MethodsDNA methylation in whole blood from patients fulfilling [&ge;]4 ACR-82 criteria and controls were investigated using the Illumina HM450K array. The discovery cohort included 311 patients and 400 controls, and the replication cohort comprised 175 patients and 187 controls. Seventeen independent, top differentially methylated CpG sites ({Delta}{beta} of [&ge;]0.1) from case-control comparisons, were used to calculate the MRS. Genotyping was performed using the Immunochip, and the PRS included 57 non-HLA SLE SNVs. Clinical data were collected from patient charts, and serum IFN-2 was measured using Simoa. ResultsHigher MRS was strongly associated with serum IFN-2 levels (p=1.04x10-14). In both cohorts, higher MRS associated with discoid lupus, immunologic involvement, and anti-SSA/SSB/RNP/Sm autoantibodies (all p<0.05), and with higher disease activity in the discovery cohort (p=1.50x10-). MRS was also elevated in patients with multiple autoantibodies (p<1.0x10-15) and in HLA-DRB1*03:01 carriers (p<1.0x10-3). In contrast, higher PRS was associated with nephritis, anti-dsDNA positivity, and lower prevalence of anti-SSB antibodies (all p<0.05). No correlation was observed between the MRS and the PRS (p=0.35). ConclusionThe MRS defines an interferon-high, HLA-DRB1*03:01-linked SLE subset with multiple autoantibodies, partly distinct from PRS-associated nephritis risk, highlighting potentially divergent pathogenic pathways. These findings underscore the value of integrating genetic and epigenetic data to better understand underlying disease mechanisms in SLE. Key MessagesO_LIHigher MRS, but not PRS, correlated with increased levels of serum IFN-. C_LIO_LIThe MRS was associated with discoid rash, hematologic disorder, hypocomplementemia, antibodies including anti-SSA and HLA-DRB1*03:01. C_LIO_LIHigher PRS was linked to nephritis and anti-dsDNA positivity, and did not associate with the MRS. C_LI

Objectives: Juvenile systemic lupus erythematosus (JSLE) and juvenile dermatomyositis (JDM) are systemic autoimmune rheumatic diseases (RMDs) with childhood-onset associated with increased risk of damage accumulation and cardiovascular disease (CVD) over the life course. Methods: Damage associated with JSLE and JDM has been assessed using validated outcome measures in a longitudinal single-centre cohort study with long-term follow-up, involving data collected both retrospectively and prospectively. Descriptive statistics, sensitivity and regression analyses have been used to evaluate predictors of damage and CVD-risk. Results: We assessed comparatively a JSLE cohort (n=76), with a mean age of 24.3 +/- 4.2 years and a JDM cohort (n=79) with a mean 20.1 +/-5.0 years (p<0.001), with matched duration of follow-up (10.0 +/- 4.2 vs. 11.0 +/- 5.1, respectively, p=0.68). Traditional CVD-risk factors, including hypertension (p=0.02), dyslipidaemia (p=0.0005), and higher total cholesterol (p=0.01) and LDL-cholesterol (p=0.02) levels at the last assessment were higher in JSLE vs. JDM. Over the disease course, 39 (51.3%) AYA with JSLE vs. 47 (59.4%) AYA with JDM accumulated damage (p=0.307), which was independently predicted by the body mass index in both cohorts (p=0.038 and p=0.026, respectively). The PDAY score was the only tool able to stratify AYA based on CVD-risk (median = 5 (4-13) points in JSLE vs. 0 (0-3) points in JDM, p=0.0001), as all the adult CVD-risk scores were very low in both cohorts. Conclusions: This is the first comparative evaluation of JSLE vs. JDM in adulthood, which highlighted increased damage burden and CVD-risk in JSLE that warrants further investigation.

ObjectivesPatients with Systemic lupus erythematosus (SLE) who carry a high genetic burden often experience more severe disease. To understand the molecular consequences of polygenic risk, we analyzed single-cell gene expression profiles in SLE patients stratified by genetic risk. MethodsSingle-cell RNA sequencing (scRNA-seq) was performed on fresh peripheral blood mononuclear cells (PBMCs) from 16 female SLE patients, stratified by a weighted polygenic risk score (PRS), and 6 healthy controls (HCs). All patients were in low disease activity (LLDAS) and treated with antimalarials only. We assessed differential gene expression, interferon (IFN) signatures, transcription factor (TF) activity, and pathway enrichment across groups. ResultsPatients with High-PRS had significantly elevated IFN scores compared to HCs (p<0.001), whereas no significant difference was observed between Low-PRS patients and HCs (p>0.05) This pattern held across multiple immune cell types, including T cells, NK cells, and monocytes. Notable genes with increased expression in High-PRS patients included ISG15 and USP18 in plasmacytoid dendritic cells (pDCs), and IFI27 and RSAD2 in monocytes. IFN-related pathways were enriched in pDCs and monocytes in High-PRS patients, and only in monocytes in Low-PRS patients. TF analysis identified IRF7 and BATF3 as key candidate regulators in High-PRS of both cell types. ConclusionsHigh polygenic risk in SLE is associated with persistent activation of IFN signaling pathways, indicating that antimalarial treatment alone is insufficient to fully suppress IFN activity, even during remission or low disease activity.

ObjectivesRheumatoid arthritis (RA) exhibits clinical and biological heterogeneity, with synovial tissue stratified into histological pathotypes: lympho-myeloid, diffuse-myeloid, and pauci-immune fibroid. Although GWAS have uncovered RA risk loci, how genetic risk relates to synovial immunopathology remains unclear. To better understand how genetic predisposition may shape divergent early disease mechanisms, we characterised the expression patterns of GWAS-identified RA susceptibility genes and related rheumatic diseases across the synovial pathotypes. MethodsRNA-sequencing data from synovium of 87 treatment-naive, early RA patients from the Pathobiology of Early Arthritis Cohort. Differential gene expression between pathotypes and pathway enrichment analyses were performed using susceptibility genes for RA, osteoarthritis (OA), ankylosing spondylitis, psoriatic arthritis and systemic lupus erythematosus. ResultsRA susceptibility gene expression in synovial tissue separated patients by pathotype and correlated with markers of disease activity. RA susceptibility genes were significantly enriched among genes upregulated in lympho-myeloid synovium and linked to lymphocyte activation and differentiation pathways. In contrast, OA susceptibility genes were upregulated in diffuse-myeloid and fibroid synovium. Both patterns were most pronounced in ACPA-positive and directionally consistent in ACPA-negative patients. ConclusionRA genetic susceptibility is not evenly distributed across synovial pathotypes but is strongly biased toward the lympho-myeloid pathotype, indicating that current GWAS signals preferentially capture immune-driven disease mechanisms. Enrichment of OA susceptibility genes in diffuse-myeloid and fibroid pathotypes, even among ACPA-positive patients, suggests shared biological features between auto-immune and non-inflammatory degenerative joint diseases in certain RA subtypes. Synovial pathotype stratification is therefore essential for interpreting genetic risk and understanding disease heterogeneity. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABS- Rheumatoid arthritis (RA) is clinically and biologically heterogeneous, and its affected synovial tissue can be stratified into distinct immunohistological pathotypes. - GWAS have identified numerous genetic risk loci for RA and related rheumatic and inflammatory diseases. - It remains poorly understood how RA genetic risk relates to synovial tissue heterogeneity. What this study adds- GWAS-identified RA susceptibility genes show strong, pathotype-specific expression in synovial tissue, with marked enrichment in the lympho-myeloid pathotype. - OA susceptibility genes are primarily upregulated in diffuse-myeloid and pauci-immune fibroid RA synovium, indicating shared fibroblast- and remodelling-related pathways. - These gene expression patterns are most pronounced in ACPA-positive RA but remain directionally consistent in ACPA-negative RA. How this study might affect research, practice or policy- Synovial pathotype stratification should be incorporated into genetic studies of RA. - Pathotype-aware genetic studies may improve patient stratification and guide development of more targeted therapeutic strategies.

BackgroundDespite advances in therapy, optimal management of juvenile idiopathic arthritis (JIA) remains challenging. The ability to predict disease progression in JIA can improve personalized treatment decisions, but few reliable clinical predictors have been identified. We developed machine learning approaches to predict disease trajectories in children with JIA. MethodsUsing data from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry (years 2015-2024), we developed machine learning models to predict attainment of inactive disease in children with non-systemic JIA. We applied Dynamic Bayesian Networks (DBN) to model temporal dependencies and causal relationships, and Convolutional Neural Networks (CNN) to capture complex non-linear patterns. Model input included demographic factors, longitudinal clinical factors, and medication use in the preceding 12 months. FindingsA total of 8,093 participants were included. When tested on an independent test cohort, both DBN (AUC:0.76; precision:0.73; recall:0.83; F1-score:0.78; accuracy:0.71) and CNN (AUC:0.76; precision:0.71; recall:0.63; F1-score:0.67; accuracy:0.70) models achieved comparable performance in predicting inactive disease. Disease activity levels in the preceding 12 months, presence of enthesitis and uveitis were the strongest predictors. Causal relationships captured in the DBN model revealed suboptimal care patterns, likely shaped by insurance constraints and a predominantly reactive approach to JIA management. InterpretationOur study demonstrates that machine learning approaches can predict disease trajectories in JIA with good discriminative performance. Unlike prior studies that predict outcomes at single timepoints, our models are the first to predict inactive disease longitudinally. However, suboptimal care patterns in retrospective data limit models capacity to learn treatment-outcome relationships, underscoring critical opportunities to improve JIA care and the need for prospective comparative studies to better inform prediction models. FundingPatient-Centered Outcomes Research Institute (PCORI) Award (ME-2022C2-25573-IC). RESEARCH IN CONTEXT Evidence before this studyNumerous studies have sought to identify clinical predictors of JIA progression and outcomes. However, few reliable predictors have emerged and existing prediction models demonstrate limited performance. As a result, our ability to personalize treatment decisions based on individual risk of severe disease course remains limited. Added value of this studyWe developed novel machine learning models that predict individualized disease trajectories in children with polyarticular and oligoarticular JIA using data from their preceding 12-month clinical course. These models demonstrated strong discriminative performance and outperformed previously published machine learning approaches in JIA. Unlike prior studies limited to single time-point predictions, our models are the first to predict inactive disease longitudinally, enabling a patient-specific projection of disease progression over time. Importantly, our findings also bright to light patterns of suboptimal care, likely driven by insurance constraints and a reactive treatment paradigm, underscoring critical opportunities to improve JIA management. Implications of all the available evidenceOur models have the potential to support clinical decision-making by enabling early identification of children with JIA at risk for unfavorable disease trajectories. In addition, the suboptimal care patterns and systems-level barriers identified through our analyses highlight priority areas for quality improvement initiatives and policy interventions to reduce gaps in JIA care delivery.

ObjectiveQuantitative computed tomography (QCT) can automatically quantify parenchymal abnormalities on chest CT imaging using deep learning. We leveraged QCT to detect pulmonary abnormalities in patients with early rheumatoid arthritis (RA) compared to healthy controls. MethodsWe analyzed high-resolution CT chest imaging from participants with early RA in the prospective, multicenter, SAIL-RA study and healthy non-smoking controls from the COPDGene study. A deep learning classifier quantified the percentage of normal lung, interstitial abnormalities, and emphysema for each participant. We compared the percentage of QCT features between early RA participants and healthy comparators and examined associations using multivariable linear regression. ResultsWe analyzed 200 participants with early RA (median RA duration 8.3 months, mean age 55.7 years, 74.5% female) and 104 healthy controls (mean age 62.0 years, 68.3% female). The median percentage of interstitial abnormalities on QCT was 3.7% (IQR 2.1, 6.1%) for early RA and 1.6% (IQR 0.8, 2.4%) for healthy controls (p<0.0001). Early RA was associated with 9.3% less normal lung on QCT than healthy controls, adjusted for age and sex (p<0.0001). Among RA participants, QCT interstitial abnormalities were associated with older age (multivariable {beta}=0.1 per year, 95%CI 0.07-0.2, p<0.0001) and higher DAS28-ESR (multivariable {beta}=0.6 per unit, 95%CI 0.01-1.3, p=0.046). ConclusionParticipants with early RA had less normal lung and more interstitial abnormalities on a deep learning-derived QCT measure than healthy controls. These results suggest that loss of normal lung is already present in early RA and emphasizes the urgent need for strategies to preserve lung health in RA.

ImportanceCardiovascular disease (CVD) is a major cause of morbidity/mortality in juvenile-onset systemic lupus erythematosus (JSLE), yet no reliable tools exist to stratify CVD-risk. ObjectiveTo identify serum biomarkers associated with atherosclerosis progression and response to atorvastatin. Design/SettingWe used data/samples from a sub-cohort of the APPLE trial (2009) which investigated atorvastatin vs. placebo to reduce atherosclerosis progression in JSLE, measured by change in carotid intima-media thickness (CIMT), and conducted a baseline autoantibody diagnostic-accuracy biomarker study. Participants/ExposureAPPLE trial participants (randomized 1:1 to atorvastatin vs. placebo) with matched baseline serum samples and stratified based on 36-month CIMT progression were included in the analysis. Main Outcomes and MeasuresBaseline serum autoantibodies were profiled using a functional proteomic platform (Sengenics, N=94). Empirical Bayes moderated t-test and Receiver Operating Characteristic (ROC) based logistic regression were applied to identify autoantibody signatures predictive of high vs. low atherosclerosis progression. ResultsNinety-four children and young people with JSLE (age mean [SD] =15.3 [2.4] years; 73 [78%] female, 8 [8.5%] Asian, 23 [24.5%] Black, 43 [45.7%] White, and 20 [21.3%] Other) were evaluated. Autoantibody levels against six novel autoantigens (STK24, RAD23B, HDAC4, STAT4, SEPTIN9, NFIA) classified high vs. low CIMT progression in the placebo arm (combined AUC 0.87, 95% CI -0.75 to 0.96). In the atorvastatin arm, autoantibodies to eight autoantigens (ABI1, ATP5B, CSNK2A2, NRIP3, PRKAR1A, PDK4, BATF, NUDT2), distinguished the statin responders vs. non-responders (combined AUC 0.96, 95% CI -0.88 to 1). An additional 27-autoantibody signature predicted response/partial response to atorvastatin (AUC 0.88, 95% CI - 0.76 to 0.97). Protein-protein interaction analysis identified endothelial disruption and lipid infiltration as key atherosclerosis mechanisms in atorvastatin non-responders. Combining the autoantibody prediction models with disease parameters and a metabolic signature did not increase model performance in either placebo (AUC 0.81, 95% CI - 0.68 to 0.94 vs. 0.87, 95% CI -0.75 to 0.96) or sttin arms (AUC 0.84, 95% CI -0.73 to 0.95 vs. 0.88, 95% CI -0.76 to 0.97). Conclusions and RelevanceThis study identified novel autoantibody signatures for atherosclerosis progression and statin response in JSLE, with potential utility for precision medicine approaches for CVD-risk management. Key PointsO_ST_ABSQuestionC_ST_ABSCan functional proteomic analyses identify autoantibody signatures predictive of atherosclerosis progression and response to statin treatment in children and young people with juvenile-onset systemic lupus erythematosus? FindingsUsing baseline samples from the APPLE trial (1:1 RCT of atorvastatin vs placebo), we identified novel autoantibody profiles that accurately distinguished individuals with high versus low carotid intima-media thickness progression over three years in both placebo (AUC 0.87, 95% CI-0.75 to 0.96) and atorvastatin groups (AUC 0.96, 95% CI-0.88 to 1). MeaningAutoantibody signatures show strong potential for early risk stratification and for identifying those most likely to benefit from statin therapy.

OBJECTIVESOsteoarthritis is a heterogeneous disease, with diverse structural patterns likely reflecting distinct genetic drivers. Robust, data-driven methods to identify and characterise such phenotypes are lacking. This study leveraged the UK Biobank to define machine learning-derived structural osteoarthritis phenotypes and evaluate their clinical and genetic profiles. METHODSMachine learning models were applied to knee and hip DXA scans to derive osteophyte area, minimum joint space width, and B-scores (a combined shape vector predictive of osteoarthritis). Imaging and demographic features were clustered using k-means to classify individuals with at least one osteoarthritis feature. Phenotypes were compared with healthy controls for associations with joint pain and total joint replacement (TJR). Genetic correlations, osteoarthritis risk loci, and polygenic risk scores were analysed to define shared and distinct genetic mechanisms between phenotypes. RESULTSAmong 59,539 participants (mean age 65 years; 53% female), nine reproducible phenotypes were identified, spanning joint-specific and multi-joint patterns. Hypertrophic and end-stage knee phenotypes showed the highest odds of pain (OR 7.8 [95% CI 7.1,8.7], 13.4 [9.5,19.0]) and TJR (66.0 [46.6,93.5], 127.6 [72.6,224.1]). A novel increased-cartilage phenotype was associated with greater odds of hip (3.5 [2.4,5.2]) and knee replacement (4.1 [2.6,6.6]). Distinct genetic architectures were observed; increased- and atrophic-cartilage phenotypes were inversely genetically correlated (rg -0.46 [-0.9,-0.2]) with opposing effects at DOT1L and COL27A1. CONCLUSIONSMachine learning revealed nine reproducible osteoarthritis structural phenotypes with divergent clinical and genetic signatures. These findings demonstrate that simple imaging and demographic data can stratify patients into biologically distinct phenotypes likely to require tailored treatments. Key messagesWhat is already known on this topic? O_LIDifferent osteoarthritis phenotypes have been proposed, which could guide patient stratification for drug trials and pharmacotherapy. However, these proposals have mainly been based on analysis of small numbers of patients that are focused on the knee joint alone. C_LIO_LITo our knowledge, no systematic, hypothesis-free approach has been applied to classify different osteoarthritis phenotypes using structural features derived from large numbers of individuals. C_LI What this study adds? O_LIThis study identifies and characterises nine reproducible structural phenotypes of osteoarthritis across both the hip and knee using high-resolution DXA imaging in UK Biobank. C_LIO_LIIt demonstrates that these phenotypes have distinct clinical profiles, with widely varying risks of joint pain and subsequent joint replacement. C_LIO_LIIt provides robust evidence that the phenotypes differ in their genetic architecture, supporting the existence of genetically determined endotypes within osteoarthritis. C_LI How this study might affect research, practice or policy? O_LIThe findings advance understanding of the structural heterogeneity of osteoarthritis and highlight that distinct phenotypes represent different biological pathways guiding research into future disease modifying therapeutics. C_LIO_LIThe automated, scalable methods used here could support patient stratification in clinical trials, enabling targeted evaluation of treatments in phenotypes most likely to benefit, an essential step towards a precision medicine approach in osteoarthritis. C_LI

ObjectiveIdentifying risk factors enables stratification of patients susceptibility to inflammatory arthritis immune-related adverse events (IA-irAE). This retrospective study examines whether preexisting osteoarthritis (OA) increases the likelihood of de novo IA in patients treated with immune checkpoint inhibitors (ICIs). MethodsThe prevalence of OA among ICI-treated patients who developed IA-irAE, those who developed other types of irAEs but not IA (non-IA irAE), and those who did not develop any irAEs (non-irAE) were compared. Electronic medical records were reviewed to extract demographic, clinical and laboratory data. Group comparisons and logistic regression analyses were performed. Results181 de novo IA-irAE patients, 140 non-IA irAE patients and 170 non-irAE patients were included. The prevalence of OA was significantly higher in the IA-irAE group (69%) than the non-IA irAE group (48%) and the non-irAE group (48%) (both p < 0.001). The IA-irAE group demonstrated a higher frequency of multisite OA, with predominant hand involvement (62%) than the non-IA irAE with OA group (13%) and the non-irAE with OA group (13%) (both p < 0.001). A family history of autoimmune disease (AID) (OR 2.03, 95% CI 1.02-4.05), preexisting OA (OR 2.88, 95% CI 1.85-4.52) and melanoma (OR 2.63, 95% CI 1.56-4.47) were identified as risk factors for the development of IA-irAE. ConclusionsOA was more prevalent among ICI-treated patients developing IA-irAE than those who did not. Hand OA was the most common OA pattern in IA-irAE patients. Preexisting OA, melanoma and a family history of AID were risk factors for IA-irAE.

Background: Itch in systemic sclerosis (SSc) is thought to be most significant in early disease, but no longitudinal studies have examined itch course. We estimated itch presence and severity from SSc disease onset, accounting for participant age and time since onset at each assessment. Methods: People with SSc from the multinational Scleroderma Patient-centred Intervention Network Cohort completed past-week itch severity assessments (0 to 10 numerical rating scale) at enrolment and longitudinally at 3-month intervals. To estimate itch probability (score > 0) and, if present, itch severity, we used two-stage mixed effects models with basis splines to address non-linearity. The primary predictor was age at each assessment, partitioned into age at non-Raynaud phenomenon symptom onset and time since onset. We estimated prevalence and severity for onset ages of 20, 30, 40, 50 and 60 years and, for each onset age, at 2 years, 3 years, 4 years, 5 years, 7 years, and 5-year intervals 10 years to 35 years post-onset. Findings: We included 2173 participants with 19 733 itch assessments (mean [standard deviation] 9.1 [6.9] assessments). 1896 of 2173 (87.3%) participants were women. Mean age at enrolment was 54.7 (SD 12.7) years. 873 (40.2%) participants had diffuse cutaneous SSc. Predicted itch probability was between 35.0% (95% CI 31.8% to 38.5%) and 36.8% (95% CI 33.3% to 40.4%) at all onset age and disease duration combinations. Mean itch severity, when present, was moderate, between 4.1 (95% CI 4.1 to 4.1) and 4.4 (95% CI 4.3 to 4.4), for all age and duration combinations. Interpretation: Itch prevalence and mean severity were stable across onset ages and over time within onset ages. Findings suggest that itch is common in SSc and not as closely related to disease duration as previously thought. Research is needed to elucidate itch pathophysiology and identify effective management strategies.

BackgroundJuvenile idiopathic arthritis (JIA) shows recognised sex differences, but their impact on treatment and early outcomes remains uncertain. This study assesses sex-specific patterns in onset, phenotype, treatment timing, and short- and medium-term outcomes in JIA. MethodsData were drawn from the Childhood Arthritis Prospective Study (CAPS), a UK multicentre inception cohort of 1,789 children presenting with a new episode of arthritis. Demographics, subtype distribution, clinical features, and 6- and 12-month outcomes were stratified by sex. Cox, Kaplan-Meier, and linear regression models assessed associations between sex and treatment initiation and 12-month outcomes. ResultsThe cohort was predominantly female (64.3%), with a median age at symptom onset of 6.8 years. Girls were younger than boys at onset (6.1 vs 7.8 years, p<0.0001) and diagnosis (7.0 vs 9.1 years, p<0.0001) and demonstrated a distinct bimodal age distribution. Diagnostic delay was short and comparable (median 4.4 months, p=0.8932). At diagnosis, girls had slightly higher active joint counts (p=0.0080, while inflammatory markers were similar except in psoriatic JIA, where females had higher ESR and CRP. After adjustment, sex was not associated with time to methotrexate (HR 0.89, 95% CI 0.74-1.06) or biologic initiation (HR 0.91, 95% CI 0.72-1.16). Outcomes at 6- and 12-month were largely comparable, with only ESR showing a modest male-favoured improvement at 12 months (p=0.0480). ConclusionsSex shaped age at onset and subtype distribution but did not independently influence treatment timing or early outcomes, underscoring the value of sex-aware analyses while confirming broadly comparable short-term trajectories in JIA. Evidence before this studyRecent evidence on sex effects in JIA is genuinely mixed: several cohorts have reported that girls, despite more severe onset, show greater resolution of objective inflammation, while a newer, large network analysis found females had poorer outcomes across composite disease activity and pain, pointing to potential inequities or phenotype-driven differences. In parallel, boys, especially in enthesitis-related arthritis (ERA), often exhibit more persistent activity and risk of damage. Overall, the picture is controversial: sex appears to shape biology, trajectory, and patient-reported burden in different ways across subtypes and settings, reinforcing the need for sex-stratified analyses, careful adjustment for confounders, and precision approaches that integrate biomarkers, subtype, and social context in JIA care. Added value of this studyThe study establishes that, although sex is closely linked to JIA subtype distribution and baseline clinical features, it does not independently determine the timing of methotrexate or biologic initiation within a UK inception cohort. By analysing one of Europes largest prospective multicentre datasets, it provides strong evidence that treatment decisions appear to be guided by disease characteristics rather than demographic bias. Within the context of the UK National Health Service (NHS), where universal access to paediatric rheumatology care is a core principle, this study provides important epidemiological evidence on sex and equity in JIA. Although clear sex differences were observed in age at onset, subtype distribution, and certain diagnostic features, these did not translate into disparities in treatment timing or medium-term disease burden. The absence of sex-based differences in 6 and 12-month outcomes, despite variation in baseline presentation, suggests that the NHS model of coordinated, guideline-driven care may help buffer against inequities that might otherwise emerge in systems with variable access. These findings reinforce the value of population-based cohorts in evaluating equity within healthcare delivery and highlight that, in this setting, sex does not appear to drive differential treatment or short-term clinical trajectories. Implications of all the available evidence.This study underscores sex as an important biological variable in JIA. Although treatment initiation was equitable and disease-driven, baseline phenotype differences and isolated effects on 12-month outcomes highlight how sex interacts with JIA subtype and initial disease burden. Prior work shows that females often present earlier with higher inflammatory burden, while males are more frequently affected by ERA, a subtype linked to treatment resistance and poorer long-term outcomes. Yet published findings remain inconsistent, with some cohorts reporting better resolution of inflammation in females and others suggesting poorer outcomes. Our findings suggest that coordinated and guideline-driven care may minimise sex-related disparities in JIA, even when underlying biological or phenotypic differences exist. The comparable medium-term trajectories observed across sexes support equitable paediatric rheumatology care in the UK and highlight the need to continue monitoring for structural or access-related inequities beyond clinical measures.

ObjectivesResearch of refractory disease in juvenile idiopathic arthritis (JIA) is limited, and a potential genetic contribution has yet to be investigated. This study aimed to explore the presence of rare monogenic disease gene coding variants in a refractory JIA population. MethodsCases were included with a record of inefficacy for methotrexate and [&ge;]1 biologic drug or exposure to methotrexate and [&ge;]2 biologic drugs for any reason. Whole exome sequencing data were analysed using VarSeq. rarity and pathogenicity filters were applied. Variants within an OMIM curated paediatric monogenic gene list, arthritis OMIM gene list, primary immunodeficiency gene panel (PanelApp) or gene reported for JIA drug response or toxicity (ClinPGX) were retained. ACMG classification excluded benign or likely benign variants. ResultsIn total, 83 individuals were included. Twelve variants were previously reported in other paediatric onset diseases with similar phenotypes to JIA. Seventeen variants were detected in twelve genes with an arthritis OMIM phenotype. Seventeen variants were detected within fourteen genes that were reported on the primary immunodeficiency panel (PanelApp) and were previously reported in a publication. A total of 39 variants were detected in genes from a JIA drug response or toxicity gene list (ClinPGX). ConclusionsThis study evidences that 66 individuals with refractory JIA carry rare variants associated with paediatric diseases, JIA susceptibility loci or drug response and toxicity. These variants could contribute to refractory disease, mimics of JIA/complicated phenotypes or effect treatment response. Longitudinal data are needed to confirm these findings.

BackgroundRheumatoid arthritis (RA) is a chronic inflammatory disease characterized by metabolic dysregulation, including altered lipid metabolism. While polyunsaturated fatty acids have been studied, the plasma levels, endogenous synthesis, and relevance of monounsaturated fatty acids (MUFAs) in RA remain unclear. This study examined plasma MUFA levels in RA and their associations with disease activity, adiposity, and intake. MethodsIn this cross-sectional study, 59 individuals with rheumatoid arthritis (RA) and 33 non-RA controls frequency-matched on age, sex, and BMI were recruited between 2017 and 2022. Clinical assessments included disease activity (DAS28), body composition, and metabolic parameters. Dietary intake was assessed using a 4-day food journal, and plasma fatty acids were quantified by gas chromatography in 82 participants with available samples. The stearoyl-CoA desaturase-1 (SCD-1) index was used as a proxy for endogenous MUFA synthesis. Associations between MUFAs and clinical variables were evaluated using univariate and multivariable regression (p<0.05). ResultsRA participants had higher waist-to-hip ratio, fat mass, fasting triglycerides, and lower physical activity than controls. Plasma palmitoleic and oleic acids and the SCD-1 index were higher in RA, whereas linoleic and arachidonic acids were lower. Saturated and omega-3 fatty acids were similar. Higher oleic and gondoic acids were independently associated with greater disease activity; oleic acid was linked to central adiposity, and palmitoleic acid was higher in women, suggesting sex- and adiposity-specific regulation. ConclusionsHigher plasma MUFAs in RA are associated with disease activity, adiposity, and sex, highlighting altered MUFA metabolism as a feature of RA and a potential target for metabolic intervention. Key MessagesO_ST_ABSWhat is already known on this topicC_ST_ABSRheumatoid arthritis (RA) involves systemic inflammation and altered lipid metabolism. While polyunsaturated fatty acids have been studied extensively, the plasma levels, endogenous synthesis, and clinical relevance of monounsaturated fatty acids (MUFAs) in RA remain unclear. What this study addsPatients with RA have higher plasma MUFAs, including oleic and palmitoleic acids, and an elevated SCD-1 index, a marker of endogenous MUFA synthesis. Higher MUFAs are associated with disease activity, central adiposity, and sex-specific patterns, independent of dietary intake. How this study might affect research, practice or policyPlasma MUFAs could serve as potential biomarkers of RA disease activity and metabolic dysregulation. These findings suggest that altered MUFA metabolism contributes to inflammatory pathways, highlighting a potential target for future research, nutritional interventions, or therapeutic strategies.

BackgroundThe safety of Paxlovid and Molnupiravir in COVID-19 patients with autoimmune diseases remains unclear, particularly concerning the risk of interstitial lung disease (ILD). ObjectiveTo evaluate the risk of developing ILD among COVID-19 patients with autoimmune diseases treated with Paxlovid or Molnupiravir. DesignRetrospective cohort study. SettingData were Based on data from the US Collaborative Network in TriNetX Patients: 18,384 COVID-19 patients with pre-existing autoimmune diseases. InterventionsTreatment with Paxlovid or Molnupiravir within five days of COVID-19 diagnosis. MeasurementsILD diagnosis confirmed by ICD-10-CM codes and radiographic evidence. ResultsILD occurred in 54 patients in the Paxlovid group and 79 patients in the Molnupiravir group (HR: 0.73, 95% CI: 0.52-1.03), indicating no statistically significant difference. Subgroup analyses by age, sex, and race showed consistent results. LimitationsObservational design limits causal inference; potential residual confounding. ConclusionTreatment with Paxlovid or Molnupiravir does not significantly increase ILD risk in COVID-19 patients with autoimmune diseases. Primary Funding SourceChung Shan Medical University Hospital. RegistrationNot applicable. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIThe safety of COVID-19 antiviral treatments, particularly nirmatrelvir-ritonavir (Paxlovid) and Molnupiravir, in patients with autoimmune diseases has been poorly understood. Patients with autoimmune conditions are considered high-risk due to compromised immune systems and potential susceptibility to complications like interstitial lung disease (interstitial lung disease). C_LIO_LIPrevious studies have generally excluded or underrepresented patients with autoimmune diseases, creating a significant knowledge gap regarding the safety of these treatments for this vulnerable population. C_LI What this study addsO_LIThis study provides evidence that there is no statistically significant difference in the risk of developing interstitial lung disease between COVID-19 patients with autoimmune diseases treated with nirmatrelvir-ritonavir or Molnupiravir. C_LIO_LIIt reinforces that both antiviral treatments can be used safely regarding the risk of interstitial lung disease in patients with pre-existing autoimmune conditions, addressing an important gap in the literature. C_LI How this study might affect research, practice, or policyO_LIThe findings could influence clinical guidelines and policy decisions regarding the management of COVID-19 in patients with autoimmune diseases, suggesting that healthcare providers can use either nirmatrelvir-ritonavir or Molnupiravir without an increased risk of interstitial lung disease. C_LIO_LIThis study might prompt further research into the long-term effects of COVID-19 treatments on different subpopulations, particularly those with chronic underlying conditions. C_LIO_LIPolicymakers might consider these results when developing targeted recommendations for COVID-19 treatment in populations at increased risk for severe outcomes. C_LI

BackgroundOsteoarthritis (OA) is a leading cause of disability worldwide, yet no licensed therapies can prevent or slow its progression. We aimed to identify potential targets for disease-modifying OA drugs (DMOADs) by integrating genetic and differential protein expression (DPE) evidence. MethodsWe evaluated genetically predicted perturbations of plasma protein levels using cis-protein quantitative trait loci (cis-pQTLs) across three large European cohorts (UK Biobank Pharma Proteomics Project, deCODE, and Fenland) and outcome data from the Genetics of Osteoarthritis Consortium, covering 11 OA phenotypes. DPE analyses were performed in 44,789 UKB participants, comparing 2,920 protein measurements between OA cases and controls, supported by sensitivity analyses. Proteins identified through genetic and/or DPE approaches were further assessed in downstream analyses. FindingsIn total, 305 proteins showed evidence of association with OA through genetically predicted perturbations, with 81 supported by colocalisation across datasets. DPE analyses identified 605 proteins associated with at least one OA phenotype, of which 450 (74{middle dot}4%) remained robust after sensitivity testing. Several novel targets were identified, including PPP1R9B, PCSK7, and ITIH4. Integration of both approaches prioritised 5 proteins, 4 of which demonstrated druggable potential, including 3 high-confidence candidates DLK1, TNFRSF9, and OGN. Downstream analyses highlighted key biological pathways and candidate compounds with potential for repurposing. InterpretationThis large-scale study combines genetic and DPE evidence to prioritise candidate DMOAD targets. Findings reinforce established biology while revealing novel proteins and pathways, providing a foundation for therapeutic development in OA. FundingWL is supported by the Guangzhou Elite Project (project no. JY202314). SSZ is supported by The University of Manchester Deans Prize, Arthritis UK Career Development Fellowship (grant no. 23258). This work is supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSCirculating proteins have been linked to osteoarthritis (OA) in observational studies, supporting their potential as biomarkers and drug targets. However, differential protein expression analyses are vulnerable to confounding and reverse causation. Mendelian randomisation (MR) studies using proteomic GWAS instruments have suggested causal roles for several circulating proteins in OA-related traits and highlighted druggable candidates. However, many analyses relied on earlier OA GWAS data (e.g., Genetics of Osteoarthritis Consortium 1{middle dot}0) and smaller proteomic GWAS datasets, and typically did not integrate MR findings with large-scale differential protein expression. As a result, it remains unclear how well genetically predicted protein effects align with observed protein expression in OA, and how robust prioritised targets are when replicated across proteomic data from multiple cohorts. Added value of this studyThis study integrates large-scale proteomic MR and differential protein expression (DPE) analyses across multiple OA phenotypes using the largest datasets to date. By combining genetic evidence with observed protein dysregulation in population-based cohorts, we strengthen causal inference and improve robustness of target prioritisation. This approach allows us to distinguish proteins that are likely to play a causal role in OA from those that reflect downstream disease processes, and to highlight targets with greater translational relevance than identified by either method alone. Implications of all the available evidenceTaken together, our findings support a causal role for a subset of circulating proteins in OA and demonstrates the value of integrating genetic and observational proteomic data for target prioritisation. Proteins supported by both MR and DPE are more likely to represent biologically relevant drivers of disease and actionable therapeutic targets. This integrated framework reduces false positives arising from confounding or reverse causation and provides a more reliable basis for drug development, biomarker discovery, and patient stratification in OA.

ObjectivesMosaic chromosomal alterations (mCAs) increase with age and are associated with many diseases, including autoimmune diseases. The associations between mCAs and systemic sclerosis (SSc) and its clinical subtypes have not been explored. MethodsWe recruited study subjects from two independent datasets (Set 1: 635 SSc, 4,401 controls; Set 2: 347 SSc, 2,170 controls) and detected mCAs (Loss, LOH, Gain, and mLOX) from their peripheral blood samples. Logistic regression analyses were conducted with covariates in each cohort, and the results were meta-analyzed. We also conducted stratified analyses by age groups, the age at disease onset, clinical phenotypes based on the skin lesions, autoantibody profiles, the presence of complications. ResultsWe observed a trend of increased Loss in SSc, especially in old age (P=0.0063). The association of Loss was strengthened in certain subtypes of SSc, including lcSSc (OR=2.22, P=0.019) and SSc with vascular complications (digital ulcers, pulmonary hypertension, or renal crisis, OR=3.30, P=0.0054). The effect sizes of Loss increased in patients with high cell fractions (CFs). We also observed that mLOX was significantly associated with SSc, lcSSc, and ACA-SSc only for subjects with high CFs. mLOX was significantly associated with lcSSc and ACA-SSc even compared with dcSSc and ATA-SSc, respectively. These associations were consistently observed in each of the two data sets. Finally, we identified majority of the associations of Loss were mainly driven by SSc with late age at onset. ConclusionsLoss and mLOX were significantly and differentially associated with SSc and its subtypes, underscoring potential phenotype-specific contributions of mCAs. WHAT IS ALREADY KNOWN ON THIS TOPICO_LISystemic sclerosis (SSc) is a heterogeneous disease, with its phenotypes and disease outcomes varying among patients. C_LIO_LIAge-related mosaic chromosomal alterations (mCAs) in blood and subsequent clonal haematopoiesis are associated with various adverse health outcomes. C_LIO_LImCAs have also been linked to several immune-mediated diseases, such as LORA, and hence may influence immune cells and their functions. C_LI WHAT THIS STUDY ADDSO_LIAutosomal copy-number loss (Loss) is increased in SSc in aged subjects. C_LIO_LILoss was associated with lcSSc, ACA-SSc. ILD-SSc, and VC-SSc in a dose-dependent manner of cell fraction. C_LIO_LImLOX was associated with SSc and its subtypes only in patients with high cell fraction. C_LIO_LILate-onset SSc and its subtypes show stronger associations with Loss with higher effect sizes compared to non-late onset SSc. C_LI HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYO_LIOur study facilitates further research to recapitulate the current findings in independent cohorts as well as in different ancestries. C_LIO_LIIncorporating profiles of Loss and mLOX in blood into conventional clinical information may enable a better stratification of SSc patients and the development of a better management strategy. C_LIO_LIFurther experimental approaches, such as whole genome sequences and single-cell C_LI RNA sequences, that investigate the underlying molecular mechanisms of phenotypic heterogeneity of SSc driven by Loss and mLOX are also warranted.

PurposeYoung-onset inflammatory arthritis (YOA; disease onset at age <25) incidence has risen since 1990, coinciding with the childhood obesity epidemic. Traditional epidemiology cannot easily quantify obesitys contribution because early-life exposures can precede clinical onset by years and may be poorly measured at diagnosis. We developed a perturbation-based simulation to estimate young-age inflammatory arthritis burden by calibrating to age-stratified RA estimates from GBD (GBD does not report JIA) under varying obesity scenarios - an approach that allows counterfactual testing, difficult to achieve in observational studies. MethodsWe built a Monte Carlo model generating arthritis prevalence estimates for 1,000,000 individuals. The model incorporated published odds ratios: BMI per SD, smoking, HLA-DR shared epitope allele dose (0/1/2), and interactions. We systematically perturbed average BMI in the <25 stratum (weighted towards adolescents and young adults) from 25-29 kg/m2 as a stress-test while holding other factors constant, then compared predicted prevalence against Global Burden of Disease data. Each scenario ran 2,500 iterations to propagate parameter uncertainty. ResultsOur model predicted the current YOA prevalence of 0.07% (observed: 0.06%, 95% CI: 0.05%-0.07%). Under perturbation analysis, each unit increase in average BMI yielded an additional 0.005% (95% CI: 0.0025%-0.0075%) in YOA cases - small but meaningful given the rarity of young-onset inflammatory arthritis. The relationship was locally linear. Significantly, the model saw that returning average BMI toward 1990 levels (<26) predicted around a 30% drop in BMI-attributable diagnoses and a 3% decrease in YOA prevalence. ConclusionsPerturbation modeling identifies childhood obesity as a potentially modifiable driver of young-onset RA, accounting for upwards of 5% of prevalence increases since 1990. This approach uniquely enables testing of prevention scenarios: our model predicts that lowering average BMI by one to two units over the next decade could prevent 3-5% of YOA cases in <25 under the modeled scenarios. These estimates provide a quantitative basis for incorporating arthritis prevention in childhood and adolescent obesity intervention cost-effectiveness analyses.

BackgroundPain is one of the most prevalent and distressing symptoms in juvenile idiopathic arthritis (JIA) and often persists despite treatment. Damage-associated molecular patterns (DAMPs), such as high mobility group box 1 (HMGB1) and S100A8/A9, have been implicated in inflammatory activation and nociceptive sensitization, but their associations with pain are not fully characterized in JIA. MethodsPlasma and paired synovial fluid (SF) samples were obtained from patients with oligoarticular and polyarticular JIA from the Juvenile Arthritis Biobank (JABBA). A discovery cohort (n = 79) was used to investigate associations between biomarkers and pain, and these associations were subsequently examined in a validation cohort (n = 38). Levels of HMGB1, S100A8/A9, IL-6, IL-8, C2C, and TRAP5b were measured using ELISA. Associations between biomarkers and patient-reported pain scores were assessed using multivariable linear regression analyses. ResultsPlasma and SF levels of most biomarkers did not show significant correlations, except for TRAP5b, which demonstrated a moderate correlation. In the discovery cohort, as multivariable linear regression analyses, both CRP and SF HMGB1 ({beta} = 1.14, 95% CI: 0.21-2.08; {beta} = 1.54, 95% CI: 0.06-3.01 respectively in fully adjusted model) were independently associated with higher pain scores. SF S100A8/A9 ({beta} = 1.00, 95% CI: 0.10-1.89) was additionally associated with pain in fully adjusted models. Sensitivity analyses confirmed the robustness of these findings. These associations were further supported in the validation cohort. ConclusionsPain in JIA is associated with both systemic CRP and local alarmin markers, with SF HMGB1 showing a particularly robust association. These findings highlight the importance of local joint HMGB1 in pain mechanisms and suggest a potential role for DAMP-mediated pathways in persistent pain in JIA.