Rheumatology

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

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.

BackgroundAntiphospholipid syndrome (APS) complicating pregnancy carries significant obstetric morbidity. Secondary APS, arising in the context of systemic autoimmune disease, may confer worse outcomes than primary APS due to additional inflammatory and immunological mechanisms. This study aimed to compare pregnancy outcomes between autoimmune rheumatic disease-associated secondary APS and primary APS managed at a quaternary care hospital in Chennai. MethodsA retrospective observational study analysed 82 pregnancies (secondary APS n=46; primary APS n=36) managed between January 2025 and March 2026. Outcomes including live birth rate, miscarriage, fetal death, preterm birth, pre-eclampsia, and intrauterine growth restriction (IUGR) were compared using chi-square test, Fisher exact test, and independent t-test. Multivariable logistic regression identified independent predictors of adverse outcomes. ResultsLive birth rate was significantly lower in secondary APS compared to primary APS (69.6% vs 86.1%; p=0.048). Triple antiphospholipid antibody positivity was more prevalent in secondary APS (47.8% vs 25.0%; p=0.032). On multivariable analysis, secondary APS (aOR 2.71; 95% CI 1.08-6.81; p=0.033), triple positivity (aOR 3.45; 95% CI 1.39-8.57; p=0.007), and lupus anticoagulant (aOR 2.62; 95% CI 1.01-6.76; p=0.047) independently predicted adverse outcomes. Hydroxychloroquine (aOR 0.39; p=0.038) and combination aspirin plus low-molecular-weight heparin (aOR 0.31; p=0.019) were independently protective. ConclusionSecondary APS is associated with significantly worse pregnancy outcomes than primary APS. Triple antiphospholipid positivity and lupus anticoagulant independently increase obstetric risk. Hydroxychloroquine and combination antithrombotic therapy significantly improve live birth rates. Early rheumatology referral and multidisciplinary obstetric management are essential.

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.

Background. Polypharmacy is common in autoimmune rheumatic diseases (ARDs) and increases adverse drug events (ADEs), but comparative evidence across diseases is limited. We aimed to quantify ADE burden and identify medications associated with ADE risk across six ARDs, and to examine shared and disease specific patterns across diseases. Methods. We conducted a retrospective cohort study at a tertiary medical center (2010 to 2024). Adults with ankylosing spondylitis (AS), psoriatic arthritis (PsA), rheumatoid arthritis (RA), Sjogren's disease (SjD), systemic lupus erythematosus (SLE), or systemic sclerosis (SSc) were identified using diagnostic codes. ADEs were ascertained using validated case definitions. Medications were mapped to Anatomical Therapeutic Chemical classes; active exposure was defined within 30 days before the index date. Polypharmacy was defined as more than 5 concurrent medications (minor 5 to 10; major >10). Within each ARD, nested case control analyses matched on encounter type (1:4) were performed, and adjusted odds ratios (aORs) were estimated using conditional logistic regression. Findings. Among 10,578 patients, 3,154 (29.8%) experienced at least one ADE. ADE burden varied across diseases, with the highest prevalence observed in SSc (35.9%). Polypharmacy was common (57.3% minor, 39.4% major) and medication burden was consistently higher in ADE cases across encounter types (eg, SLE outpatient median 12 vs 6; inpatient 20 vs 10; emergency 17 vs 8). Across ARDs, the strongest associations with ADEs were observed for supportive and symptom directed therapies (acid suppressors, pain adjuncts, and sedative hypnotic/psychotropic medications), whereas conventional disease-modifying antirheumatic drugs (DMARDs) showed weaker associations. Disease-specific signatures included gastrointestinal agents in SSc (metoclopramide aOR 12.32), antibiotics and respiratory agents in AS (ciprofloxacin aOR 13.71, fluticasone aOR 8.88). Interpretation ADEs affect nearly one third of ARD patients and increase with medication burden. Risk concentrates in supportive and symptom directed therapies rather than DMARDs, with both shared and disease-specific patterns. Optimizing prescribing, particularly for pain management and corticosteroid use, can reduce medication-related harm.

Background: Rheumatoid arthritis (RA) has a preclinical period characterised by elevations in serum autoantibodies. Identifying the timing and magnitude of autoantibody trajectory changes may inform screening strategies and preventative interventions. Methods: Using a Bayesian multivariate segmented regression, we jointly modelled longitudinal autoantibody trajectories from two Department of Defense Serum Repository cohorts (Sample A: 209 matched case-control pairs, 1566 samples, six biomarkers; Sample B: 309 cases with two matched controls each, 2758 samples, eight biomarkers). Change-points and magnitudes of change were estimated simultaneously under a multivariate likelihood with an unstructured residual correlation matrix. Results: In Sample A, five of six biomarkers exhibited pre-diagnostic trajectory shifts with 95% highest posterior density intervals excluding zero. RF-IgM demonstrated the earliest change-point at 8.10 years before diagnosis (95% HPDI: -10.47, -5.73), followed by ACPA-IgG at 7.43 years (95% HPDI: -9.33, -5.76). In Sample B, only the four IgG isotypes showed pre-diagnostic shifts, with anti-CCP3 (IgG) earliest at 7.00 years (95% HPDI: -8.48, -5.29). A composite metric integrating timing and magnitude reordered rankings. Conclusions: This Bayesian framework enables simultaneous estimation of change-points and magnitudes across correlated autoantibodies while fully characterising uncertainty, offering a complementary approach to prior divergence-based methods for understanding preclinical RA autoimmunity.

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.

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.

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.

Background: Rheumatoid arthritis is a chronic inflammatory disorder in which exercise is increasingly recognized as an important component of long-term management. Yet, most reviews in this field evaluate the effects of single exercise modalities, while bibliometric studies primarily identify publication trends and research hotspots without showing whether highly visible themes also represent coherent and comparatively mature evidence domains. Methods: We searched the Web of Science Core Collection for publications on exercise interventions in rheumatoid arthritis from 2016 to 2025. CiteSpace (6.4.1) and VOSviewer (1.6.20) were used to analyze publication growth, collaboration networks, keyword co-occurrence, thematic clusters, and burst terms. We then applied structured content coding in Excel 2021 to classify exercise modalities, outcome domains, and mechanistic topics, and integrated these findings into a visual evidence-distribution profile. Results: Publication output increased from 16 studies in 2016 to 37 in 2025. The United States led in productivity, Karolinska Institutet was the most prolific institution, and Kitas, Duda, and Metsios were among the most influential authors. Keyword analyses identified a shift from function- and disease-focused themes toward quality of life, risk factors, and comprehensive management. The integrated analysis revealed an uneven evidence structure: aerobic and resistance training accounted for the most concentrated and recurrently studied exercise-outcome domains, whereas mind-body and water-based interventions formed visible but methodologically heterogeneous clusters. Newer modalities, including blood flow restriction training and high-intensity interval training, showed growing prominence but limited depth of evidence. Conclusion:Exercise research in rheumatoid arthritis has evolved toward broader and more patient-centered management targets, but the field remains imbalanced across intervention types and outcome domains. This study demonstrates the value of combining bibliometric mapping with structured content analysis to distinguish thematic visibility from evidentiary coherence in heterogeneous intervention fields and may offer a transferable analytical framework for research evaluation beyond rheumatoid arthritis. Keywords: Rheumatoid Arthritis; Exercise Intervention; Bibliometrics; Content Analysis; Rehabilitation

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.

Background. A substantial minority (~20%) of patients fail to achieve meaningful pain reduction following surgery intended to relieve pain. Risk is elevated in patients with nociplastic pain features, but available self-report measures were not designed for pre-surgical screening. We aimed to develop a brief, data- driven screener for poor analgesic response to surgery. Methods. Participants were recruited from tertiary orthopedic and chronic pelvic pain clinics. Total hip arthroplasty participants had Kellgren-Lawrence grades III-IV with hip pain greater than or equal to 1 year; hysterectomy participants had chronic pelvic pain greater than or equal to 6 months. The primary outcome was a 50% reduction in worst pain at six months. Items were selected via elastic net regression with k-fold cross-validation from 68 candidates. Results. Of 428 participants (81% female; mean age 51), 35% failed to achieve a 50% pain reduction. The resulting 11-item screener - the GenerAlized sensory sensitivity for sUrGical rEsponsiveness (GAUGE) - comprises pain across seven body regions and four symptom items measuring interoception (nausea, numbness/tingling) and exteroception (sensitivity to sound, sensitivity to odors). GAUGE outperformed the Central Sensitization Inventory, Fibromyalgia Survey Criteria, and PainDETECT for predicting surgical non-response (RR 1.535, 95% CI 1.342-1.55; AUC 0.738; sensitivity 0.741, specificity 0.635) and for predicting Patient Global Impression of Change. In an independent validation cohort of 54 total knee arthroplasty patients, GAUGE outperformed the Fibromyalgia Survey Criteria in predicting pain severity at six-months. Conclusions. GAUGE is a data-driven, theoretically grounded screener for poor analgesic response to surgery, with potential utility for pre-surgical counseling and clinical trial enrichment.

Background: immune dysregulation underlies cardiovascular risk excess in systemic autoimmune diseases, such as rheumatoid arthritis (RA) and Sjogren disease (SjD). However, exact mediators are unknown. Regulatory autoantibodies targeting G protein coupled receptors, including CXCR3, have emerged as modulators of immune and vascular homeostasis, but their role in autoimmunity remains ill defined. Our aim was to evaluate antiCXCR3 levels in systemic autoimmunity and their potential value as biomarkers. Methods: antiCXCR3 IgG serum levels were quantified in early RA (n=84), clinically suspect arthralgia (n=12), and controls (n=65). Established RA (n=103) and SjD (n=44) were recruited for validation. Atherosclerosis was assessed by carotid ultrasound. Cytokines were measured by multiplex immunoassays. Cardiometabolic related proteins were evaluated using high-throughput targeted proteomics. Publicly available datasets were used for validation. Results: antiCXCR3 antibodies were significantly reduced in early RA and arthralgia compared with controls, independently of disease activity, autoantibodies, or systemic inflammation. This finding was confirmed in validation cohorts. AntiCXCR3 were negatively associated with good therapeutic outcomes upon csDMARD at 6 and 12 months. Lower anti-CXCR3 levels were independently associated with atherosclerosis occurrence and extent across conditions. Incorporating antiCXCR3 into mSCORE improved risk stratification. AntiCXCR3 were related to proteomic signatures linked to immune activation and to apoptosis, chemotaxis, and cell adhesion in an atherosclerosis dependent manner. Transcriptomic analyses indicated compartment specific CXCR3 dysregulation. Conclusion: reduced antiCXCR3 antibodies represent a shared hallmark bridging systemic autoimmunity and atherosclerosis burden, shaping our understanding on the regulatory role of antibodies at the vascular immune interface. Clinical translation of anti-CXCR3 antibodies hold promise to improve risk stratification.

ObjectivesThis study aimed to develop and validate machine learning models to predict in-hospital mortality among systemic lupus erythematosus (SLE) patients using administrative claims data in a tertiary referral center in Indonesia. MethodsWe conducted a retrospective cohort study of 327 SLE hospital admissions between January 2019 and June 2025. Predictor variables included demographics, hospitalisation characteristics, and the ten most frequent comorbidities. We developed Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) models. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique. ResultsThe overall in-hospital mortality rate was 7.7%. While models achieved comparable discrimination (Area Under the Curve ~0.71), XGBoost was selected for its superior sensitivity (0.93) compared to Logistic Regression (0.80) and Random Forest (0.97). Feature importance analysis revealed pneumonia as the most significant predictor, followed by acute kidney failure and length of stay. Hypoalbuminemia and hyponatremia were also identified as key prognostic markers. ConclusionsMachine learning models utilising registry-based administrative data effectively stratify mortality risk in hospitalised SLE patients with high sensitivity. The dominance of pneumonia and renal failure as predictors underscores the critical need for aggressive infection control and renal monitoring in this population.

BackgroundGroup A streptococcus (GAS) infections have been associated with neuropsychiatric disorders in epidemiologic studies and animal models, but data in US health care populations are limited. GAS is also associated with autoimmune sequelae, including acute rheumatic fever (ARF)/Sydenham chorea (SC), poststreptococcal reactive arthritis (PSRA), poststreptococcal glomerulonephritis (PSGN), and guttate psoriasis (GP). Epstein-Barr virus (EBV) has been linked to systemic lupus erythematosus (SLE) and multiple sclerosis (MS) and the complexity of these associations parallels that of GAS-associated conditions, providing a useful comparison. Objectives1) Assess the association between a positive GAS test and incident neuropsychiatric diagnoses within 1 year in a large US health care database. 2) Assess the validity of the same database in detecting well-established disease associations while avoiding false associations. Design, Setting, ParticipantsRetrospective cohort study using TriNetX data from US health care organizations. Patients with positive or negative tests were propensity score-matched (GAS cohort n=178,301; EBV cohort n=64,854). Patients with documented neuropsychiatric diagnoses prior to testing were excluded. To approximate a primary care population, inclusion required at least one well-visit. ExposuresPositive vs negative GAS test; positive vs negative EBV test (separate cohorts). Main Outcomes and ValidationsMain outcome: incident neuropsychiatric diagnoses within 1 year of GAS testing. Positive control outcomes: ARF/SC, PSRA, PSGN, and GP (for GAS cohort); SLE and MS (for EBV cohort). Negative control outcomes: conditions without known association with GAS. ResultsAfter matching, a positive GAS test was associated with attention-deficit/hyperactivity disorder (ADHD) (RR: 1.09; 95% CI: 1.03-1.15). Among established poststreptococcal conditions, only GP was associated with prior GAS (RR: 1.75; 95% CI: 1.06-2.89). Case counts were insufficient to evaluate ARF/SC, PSRA, and PSGN. Negative control outcomes showed no association. In the EBV cohort, no association was observed with SLE, and MS showed a decreased risk. Conclusions and RelevanceA positive GAS test was associated with ADHD but not with other neuropsychiatric disorders. The database detected poststreptococcal GP but did not identify most established postinfectious autoimmune associations, likely reflecting rarity, heterogeneity, and diagnostic complexity. These findings begin to describe the range of real-world health care databases to evaluate postinfectious neuropsychiatric risk.

BackgroundGastroesophageal reflux disease (GERD) is highly prevalent in systemic sclerosis (SSc) and frequently persists despite proton pump inhibitor (PPI) therapy. However, the mechanisms underlying PPI-refractory GERD in SSc remain incompletely understood. MethodsWe conducted a singlel7lcentre, retrospective study of adults with SSc who underwent ambulatory pH-multichannel intraluminal impedance (pH/MII) monitoring while receiving twicel7ldaily PPI therapy (2021-2025). Esophageal motility (highl7lresolution manometry, HREM) and gastric emptying scintigraphy were integrated to examine associations between gastro-esophageal dysmotility and reflux phenotypes. ResultsThirty patients were included, of whom 67% had PPI-refractory reflux symptoms and 33% were undergoing pre-lung transplantation evaluation. Refractory GERD was present in 29/30 patients (97%) based on Lyon 2.0 classification, with conclusive evidence in 53% and borderline evidence in 43%. Esophageal dysmotility was identified in 80%, most commonly absent contractility (67%), and was associated with impaired reflux clearance, reflected by longer acid clearance times (2.20 [1.15-3.75] vs 1.15 [0.43-1.90] min) and prolonged reflux episode duration (16.60 [4.38-40.63] vs 1.95 [0.53-20.43] min). Gastric dysmotility was identified in 60.7% and was associated with an increased reflux episode burden (51.00 [30.00-81.50] vs 25.00 [21.00-54.00] episodes/24h). ConclusionsPPIl7lrefractory GERD is nearly universal in this SSc cohort and reflects heterogeneous, quantifiable abnormalities across the foregut, including impaired esophageal clearance and increased reflux burden related to gastric retention. These findings support integrated physiologic evaluation to define reflux mechanisms, inform risk stratification (including lung transplantation), and guide targeted, mechanism-based therapies beyond acid suppression.

ObjectivesLupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE), leading to progressive renal fibrosis and functional decline. Understanding the interplay between immune cells and stromal cells is needed to develop effective therapeutic strategies. Here, we investigated the landscape of macrophage-fibroblast interactions in human LN and validated these findings in mouse models. MethodsWe characterized distinct fibroblast subsets and their interactions with renal macrophages using single-cell RNA sequencing (scRNAseq) of 156 human LN biopsies and 30 healthy controls from the AMP-SLE cohort, and spatial transcriptomics of biopsies from 6 LN patients. In vitro co-culture studies using mouse models were performed to further define functional consequences of these interactions. ResultsWe identified two myofibroblast subsets: a pro-inflammatory subset (Myofib1) enriched in the tubulointerstitium, and a fibrotic/remodeling subset (Myofib2) in glomeruli, both correlating with the histologic chronicity index. Spatial transcriptomics revealed different colocalization patterns, with Myofib1 interacting with activated resident macrophage (RM) subsets and Myofib2 with glomerular infiltrating disease-associated macrophages. In vitro co-culture studies demonstrated that nephritic RMs promote a pro-inflammatory, remodeling fibroblast phenotype that impairs wound healing and drives a Myofib1-like gene program, whereas disease-associated macrophages generated profibrotic fibroblasts with dysregulated reparative capacity. Cell-cell communication analyses identified key ligand-receptor interactions mediating this crosstalk, including Spp1/integrins, Sema4/PlexinB, and NAMPT/INSR. ConclusionsOur data reveal a spatially and functionally heterogeneous landscape of macrophage-fibroblast crosstalk in LN. These findings advance our understanding of renal fibrogenesis in LN, highlighting specific fibro-inflammatory circuits that may represent therapeutic targets to prevent chronic renal damage.

BackgroundGastrointestinal (GI) involvement in systemic sclerosis (SSc) affects up to 90% of patients and is a major driver of morbidity and mortality. Despite its clinical importance, GI disease in SSc is highly heterogeneous, with upper and lower GI manifestations representing distinct phenotypic extremes whose underlying immunologic basis remains poorly defined. MethodsWe performed unbiased, proteome-wide autoantibody profiling using a human protein microarray comprising >21,000 full-length proteins (>80% of the human proteome). Sera from patients with SSc and isolated upper GI dysmotility (n=23), isolated lower GI dysmotility (n=17), and non-SSc controls (n=20) were analyzed. Enriched autoantibodies were identified using Fishers exact test, and unsupervised clustering was applied to define serology-based patient subsets and relate immune signatures to clinical phenotypes. ResultsDistinct autoantibody profiles differentiated patients with upper versus lower GI disease. Upper GI-predominant SSc was characterized by enrichment of previously unreported autoantibodies, including those targeting TiSSc1/2 (newly identified proteins encoded within the MIRLET7BHG locus), FAM9C, SPATA20, FAM110D, EMILIN1, CARD14, SMN1, KCTD7, and PHYHD1, whereas lower GI disease was associated with antibodies against HAO2, KLHL7, SUFU, APPL1, BNIP2, UCHL3, ZNF385A, LIMD1, MAGEA9, and PPP2R3C. Serology-driven clustering identified four reproducible subgroups with distinct patterns of GI, pulmonary, vascular, and autonomic involvement, defining clinically meaningful disease phenotypes that extend beyond traditional anatomic classification. ConclusionsProteome-scale serological profiling reveals previously unrecognized autoimmune signatures underlying GI heterogeneity in SSc. These findings support a shift from anatomy-based to serology-defined classification of SSc GI disease and provide a foundation for biomarker development, patient stratification, and precision medicine approaches in this population.

Objective: To characterize pregnancy outcomes and menstrual irregularities in Mexican women with systemic lupus erythematosus (SLE) and identify clinical factors associated with adverse pregnancy outcomes and early-onset menopause. Methods: We conducted a cross-sectional study of women with SLE enrolled in the Mexican Lupus Registry (LupusRGMX) between May 2021 and September 2024. Clinical and reproductive data were collected using standardized questionnaires. Menopause was defined as the absence of menstruation for [&ge;]12 consecutive months, and early menopause as onset before age 40. Univariable and multivariable logistic regression analyses were used to identify factors associated with pregnancy complications and early menopause. Results: A total of 210 women were included. Median age was 38 years (IQR 29-46) and median disease duration was 4 years (IQR 1-10). Among women with a history of pregnancy (47%), full-term delivery predominated (61%), while pregnancy loss occurred in 26% and preterm delivery in 13%. Pregnancy complications were reported in 9.6%, most commonly preeclampsia (6.7%). Younger maternal age was independently associated with pregnancy complications (OR 0.89, 95% CI 0.83-0.95) and adverse outcomes (OR 0.95, 95% CI 0.92-0.98). Higher disease activity was associated with complications in univariable analysis. Most pregnancies (68.3%) occurred before diagnosis. Early menopause was observed in 6.2% and independently associated with longer disease duration and older age. Conclusion: Younger maternal age was independently associated with adverse pregnancy outcomes, whereas disease activity showed an association in univariable analysis. Most pregnancies occurred prior to SLE diagnosis. Early menopause was associated with longer disease duration, suggesting impact of cumulative disease burden on ovarian function.