Annals of the Rheumatic Diseases

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.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease that causes joint destruction along with extra-articular morbidity and early mortality. Abatacept (CTLA-4 Ig), a blocker of lymphocyte co-stimulation, has become a well-accepted biologic treatment with proven efficacy in established-RA and for preventing disease onset in predisposed individuals. To investigate the immunologic implications of abatacept treatment, we conducted a prospective, open-label trial with multi-omic single-cell analyses of lymphocytes and BCR repertoire profiling at predefined intervals. Treatment-induced low-disease activity correlated with coordinated depletion of circulating peripheral helper cells (Tph), late-activated naive cells (late-aNAV), and of CD27-IgD- (Double negative, DN) Zeb2+CD11c+ T-box transcription factor 21 (Tbet+) DN2 unconventional memory B cells, implicated in the tertiary lymphoid structures responsible for the propagation of pathologic autoimmune responses and joint destruction. Among B-cell subsets, DN2 had the greatest representation of molecular machinery for antigen-uptake, processing, and presentation. Among memory B-cell subsets, DN2 had the lowest representation of somatically generated N-glycosylation sites and somatic hypermutation. Yet abatacept induced DN2 cells to express elevated CXCR4 levels, which normalized upon drug withdrawal, suggesting that abatacept treatment may cause these cells to traffic out of pathologic synovial infiltrates. In conclusion, we have documented that abatacept affects the circulating immune cellular drivers of disease activity, Tph, late-aNAV and DN2. Therapeutic depletion of these pathologic lymphocyte subsets is associated with clinical benefits that can persist after therapy cessation. Hence, levels of these subsets may serve as surrogates for the overall burden of disease and potential response to abatacept therapy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/26348386v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@b44131org.highwire.dtl.DTLVardef@241f4eorg.highwire.dtl.DTLVardef@18361f6org.highwire.dtl.DTLVardef@9470b7_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryMulti-omics analyses showed costimulatory blockade depletes trafficking DN2 B cells and Tph cells that correlates with rheumatoid disease response.

Over half of patients with rheumatoid arthritis (RA) report clinically meaningful pain, despite treatment with disease-modifying antirheumatic drugs (DMARDs). While joint inflammation is a known cause of pain in patients with rheumatic diseases, emerging data indicate that many patients also suffer from centralized or nociplastic pain. There is a critical unmet need to characterize the altered cellular state that distinguishes patients with centralized pain. In the IMPACT study, 39 RA patients with minimal joint inflammation but varying levels of pain underwent quantitative sensory testing (QST) to assess nociplastic pain, completed patient-reported outcome (PRO) surveys, and provided blood samples for immune profiling. Supervised and unsupervised analysis of the multi-parameter spectral flow cytometry data identified immune cell populations correlated with nociplastic pain and patient-reported pain intensity. Moreover, analyses of single-cell RNA-seq from a subset of 22 patients revealed differences in cell type proportions and differential expression between the high and low pain groups. These studies provide novel insights into the role of circulating immune cells in altered central nervous system (CNS) pain regulation in adults with RA.

Individuals who have serum elevations of anti-cyclic citrullinated protein (anti-CCP) antibodies are at risk for developing rheumatoid arthritis (RA), yet immunologic factors that lead to a transition from pre- to clinical RA remain unclear. Here, we used materials from anti-CCP antibody-positive individuals enrolled in a clinical trial that evaluated the efficacy of hydroxychloroquine to prevent clinical RA, and performed multi-modal single-cell profiling (transcriptome, surface proteins, T/B-cell receptor sequencing, and chromatin accessibility) on samples obtained at baseline and at RA onset in those who developed clinical RA (Converters) or follow-up point in matched Nonconverters. At both baseline and follow-up, Converters had expansions of peripheral helper T (Tph) cells and CD8+ T cells expressing GZMK and GZMB, along with elevated potentially autoreactive T-cell receptors in CD4+ T cells compared to Nonconverters. Induction of age-associated B cell signatures was observed in B cells of Converters prior to RA onset. Epigenetic profiling further identified chromatin accessibility changes in Converters over time, particularly within myeloid and NK cells. Lastly, predictive modeling using baseline immune features, including Tph cells, GZMK+XCL1+ CD8+, and GZMB+CD57+ CD8+ T cells, together with clinical features such as anti-CCP3 levels, RF-positivity, and HLA shared epitope status, stratified RA risk and predicted time to onset. These findings define immune endotypes in pre-RA that could serve as targets for future preventive interventions and be used to stratify the risk of developing clinical RA in anti-CCP antibody-positive individuals.

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.

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.

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.

BackgroundChildren with juvenile idiopathic arthritis (JIA) are reported to exhibit increased rates of symptoms affecting emotional regulation and behavior. However, underlying biological mechanisms remain unclear. Neuroinflammation in the central nervous system (CNS) can be triggered by peripheral immune effects and may contribute to these observations. In this study, we aimed to investigate if neurobiological alterations are present in systemic JIA (sJIA), and if CNS neuroinflammation occurs during arthritis, and to explore the potential mechanisms involved. MethodsPlasma samples from patients with active sJIA (n = 16) and sex- and age-matched healthy controls (HCs, n = 16), together with paired samples from the same sJIA patients during inactive disease (n = 12), were analyzed using Olink proteomics to determine the peripheral neurobiological and inflammation protein profiles. Clinical data was retrieved from the Swedish Pediatric Rheumatology Register and medical charts. CNS Neuroinflammatory responses and underlying mechanisms were further explored through in vivo and in vitro experiments. FindingsActive sJIA patients exhibited altered neurobiological protein profiles compared with HCs. These alterations correlated with higher scores of pain and life impact in patients, suggesting that the altered profiles may reflect neurofunctional changes in the patients. Notably, the neurobiological protein profile remained altered even during the inactive phase of the disease. In chronic arthritic mice, microglial activation and impaired neurogenesis were observed in hippocampus, with no significant cortical changes. RNA-seq analysis implicated mitochondrial dysfunction and oxidative stress in mediating neuroinflammation during chronic arthritis in mice. Heme oxygenase 2 (HMOX2) was identified as a peripheral biomarker indicating hippocampal microglia activation. Combined neurobiological and inflammation profiling in sJIA patients implicated Interleukin-6 (IL-6) and Interleukin-18 (IL-18) as key drivers of hippocampal microglia activation during arthritis. InterpretationChronic arthritis is associated with neuroinflammation and altered neurobiological protein profiles in sJIA. HMOX2 emerges as a promising plasma biomarker of CNS changes. IL-6 and especially IL-18 are indicated as key drivers of neuroinflammatory processes. These findings offer insights for clinical monitoring and targeted therapies. FundingThis study was funded by grants from the Swedish Research Council and The Swedish Rheumatism Association. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSChildren with juvenile idiopathic arthritis (JIA) have increased rates of emotional and behavioral disturbances compared with healthy peers. Systemic inflammation and chronic arthritis are suspected to affect the central nervous system, but biological mechanisms in systemic JIA (sJIA) are poorly understood. Added value of this studyIn this study, we demonstrate patients with sJIA have a distinct plasma neurobiological protein profile compared with healthy controls, which correlate with higher pain and life impact scores. In chronic arthritic mice, hippocampal microglial activation, impaired neurogenesis, and mitochondrial dysfunction with oxidative stress are presented. By combining patient and mouse data, we identify heme oxygenase 2 (HMOX2) as a candidate plasma biomarker of hippocampal neuroinflammation and implicate IL-6, and especially IL-18, as key mediators linking chronic arthritis to neurobiological changes. Implications of all the available evidenceThis study provides molecular evidence that neurobiological alterations in sJIA patients and supports incorporating neurobiological and neuropsychiatric monitoring into the clinical follow-up of children with sJIA. We highlight the mechanistic targets and measurable biomarkers (e.g. HMOX2) for future studies and trials aiming to modulate neuroinflammation in chronic arthritis. This study may inform the development of personalized treatment strategies, including IL-18-directed therapies, for patients at risk of neurological or psychosocial complications.

Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of autoimmune conditions characterized by immune system dysregulation leading to chronic inflammation and tissue damage. The overlapping clinical manifestations make differential diagnosis challenging, highlighting the need for novel biomarkers to facilitate early diagnosis, stratification, and personalized treatment. Five SARDs including idiopathic inflammatory myopathies (n=210), rheumatoid arthritis (n=84), systemic sclerosis (n=100), Sjogren disease (n=99), and systemic lupus erythematosus (n=99), as well as healthy controls (n=400) and controls with acute infectious diseases (n=218) were selected for plasma protein profiling using Olink Explore 1536. Proteins with known association to SARDs as well as novel associations were identified through differential abundance analysis and machine learning. This explorative cross-sectional study demonstrates the importance of a pan-disease approach to biomarker identification within and between the five SARDs. NPX boxplots from this study are available open-access through the Human Protein Atlas, facilitating further plasma-proteome research on autoimmune diseases.

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.

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.

Rheumatoid arthritis is a prototypical autoimmune disease, characterised by prolonged systemic autoimmunity prior to organ-specific tissue inflammation. To achieve the contemporary goal of autoimmune disease prevention, a nuanced understanding of the transition from systemic autoimmunity to tissue-specific inflammation is critical. Here, we sought to identify immune signatures associated with the transition to subclinical joint inflammation detected by multi-joint ultrasound in anti-citrullinated protein antibodies (ACPA+)-positive individuals who imminently progress to RA. To achieve this, we performed single-cell transcriptomic and proteomic profiling on prospectively collected blood samples from high-risk ACPA+ imminent progressors, who were further stratified by the presence or absence of ultrasound (US)-detectable subclinical synovitis and compared them with ACPA+ non-progressors. We found type-1 interferon (IFN-I) activation in circulating CD14+ classical monocyte and GZMK+ CD8+ T cells preceding subclinical joint inflammation in ultrasound-negative (USneg) future progressors. In contrast, US-positive (USpos) future progressors exhibited a phenotypic shift in CD14+ classical monocytes towards IL1B+ expression and clonal expansion of GZMB+ cytotoxic CD8+ T cells at the onset of subclinical synovitis. Plasma proteomics also revealed a shift from Toll-like receptor-associated innate pathways in USneg future progressors toward effector and tissue-remodeling signatures in USpos future progressors. These findings suggest IFN-I-driven immune priming in specific immune subsets precedes the onset of subclinical joint inflammation, whereas tissue-directed inflammatory and cytotoxic programmes emerge at the onset of joint inflammation when clinical RA is imminent.

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.

ObjectivesAutoreactive T cells recognizing citrullinated antigens, although rare and difficult to study, are implicated in rheumatoid arthritis (RA). To allow functional studies and manipulation of such T cells, we have generated and characterized transgenic mice expressing a TCR cloned from an RA patient and reactive with a prominent target, citrullinated tenascin C (citTNC). MethodsA humanized TCR transgenic (hTCR-tg) mouse recognizing the citrullinated TNC22 (citTNC22) antigen in an HLA-DRB1*04:01 (HLA-DR4) restricted manner was developed by genetically engineering a chimeric TCR expressing murine constant domains and human V(D)J sequences. hTCR-tg mice were immune phenotyped in murine H-2b and humanized HLA-DR4 backgrounds using full spectrum flow cytometry, cytokine ELISAs and fluorospot assays at steady-state and after antigen challenge. Additionally, we investigated the presence of antibodies to citTNC and arthritis following citTNC protein immunization. ResultsThymic selection of hTCR T cells differed between the two MHC-II alleles, with a normal CD4+ T cell development observed solely under HLA-DR4 restriction. The chimeric hTCR maintained its citTNC22 specificity in vitro and ex vivo, without cross-reactivity to native TNC22 or other citrullinated autoantigens. hTCR-tg CD4+ T cells responded to antigen challenge in vivo and provided support to IgG class-switch and antigen-specific antibody production. Moreover, protein immunized hTCR-tg mice developed arthritis after periarticular challenge with citTNC. ConclusionshTCR-tg mice expressing an RA patient-derived autoreactive TCR develop functional antigen-specific and HLA-restricted CD4+ T cells. The combination of humanized HLA-DR4 and TNC22 mice will be a valuable tool in the development of antigen-specific therapies translatable to human disease.

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

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.

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.

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.

BackgroundBiologics and Janus kinase (JAK) inhibitors carry specific risks for Ankylosing Spondylitis patients at risk of tuberculosis infection or those with contraindications such as a history of cancer, there is an urgent need to explore safe and effective alternative treatment options. AimsTo evaluate the efficacy and safety of Iguratimod combined with Yunke injection in the treatment of ankylosing spondylitis at risk of tuberculosis infection or those with a history of cancer. Study DesignRetrospective cohort study. MethodsA retrospective study was conducted on 48 patients with ankylosing spondylitis who had received treatment over the past 3 years and had a history of tuberculosis infection or malignancy. Their treatment regimens and therapeutic outcomes were analyzed, with particular attention to the progression of tuberculosis and malignancy. ResultsThere was 30 patients receiving Iguratimod combined with Yunke injection treatment, and non-steroidal anti-inflammatory drugs (NSAIDs) were added when pain was severe,referred to as the observation group; 18 patients took Iguratimod and NSAIDs, referred to as the contral group. After treatment of 24 months, both groups showed significant improvements in Ankylosing Spondylitis Disease Activity Score (ASDAS), Bath Ankylosing Spondylitis Functional Index (BASFI), modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), Erythrocyte Sedimentation Rate (ESR), and C-Reactive Protein (CRP), and overall levels could achieve low disease activity. However, the improvement of observation groupin was better than that in the control group, P<0.05. Moreover, the use of NSAIDs in the observation group was significantly less than that in the control group, P<0.001. ConclusionThis study shows that Iguratimod combined with Yunke injection has good efficacy in patients with ankylosing spondylitis who cannot use biologics or JAK inhibitors, not only alleviating pain and morning stiffness but also slowing radiographic progression and reducing the dose of NSAIDs. The combination has a synergistic effect and does not increase adverse reactions. This therapy provides a novel option for patients with specific ankylosing spondylitis.

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.