RMD Open

Autoimmune inflammatory myopathies (AIM) with prominent B cell aggregates (BCM) on muscle biopsy is an uncommon finding. We aimed to compare the characteristics and clinical course of patients with BCM on muscle biopsy to those without and characterize B cell infiltrates in the muscle of these patients. We performed a retrospective study of subjects with AIM in the Canada Inflammatory Myopathy Study (CIMS) cohort to identify cases with BCM on muscle biopsy, which was defined as [≥]30 CD20+ cells/aggregate. AIM cases without BCM that encompassed the broader spectrum of AIM, namely dermatomyositis, overlap myositis and inclusion body myositis were selected as controls. Descriptive statistics were used to compare BCM cases and controls. Cyclic immunofluorescence (Cyc-IF) was performed to characterize inflammatory infiltrates and B cell structures. We included 69 subjects (mean age at diagnosis 51{+/-}16 years, 77% females): 22 BCM, 24 dermatomyositis, 14 overlap myositis, and inclusion body myositis. Most BCM subjects (18/22, 82%) had an associated autoimmune disease: 12 (55%) had systemic sclerosis, 5 (23%) rheumatoid arthritis and one (5%) systemic lupus erythematosus/systemic sclerosis overlap. Extra-muscular features found in BCM patients included arthritis (50%), interstitial lung disease (43%), Raynauds phenomenon (32%), and dermatomyositis rash (27%). Two patients (9%) had facial muscle weakness and one (5%) had positive anti-AChR autoantibodies. In BCM subjects, upper extremities were weaker than lower extremities in 7/21 (33%) of cases. Neck flexor weakness was frequent (17/22, 77%), while neck extensor weakness was uncommon (1/15, 7%). Cyclic immunofluorescence (Cyc-IF) spatial analysis of BCM biopsies displayed many features akin to tertiary lymphoid structures. Findings suggest possible involvement of both the traditional germinal center reaction and the extrafollicular pathway in BCM. In conclusion, in this series of myositis with B cell aggregates reported to date we found clinical similarities (i.e., associated with overlapping autoimmune diseases) and differences (i.e., muscle weakness distribution) with previous reports. The discovery of tertiary lymphoid structures on spatial analysis of muscle biopsies of BCM patients provides novel insight into its pathophysiology and potential therapeutic targets.

Background and AimFibromyalgia is an idiopathic chronic widespread pain syndrome affecting 2-4% of the general population globally. Besides widespread fibromyalgia pain, morning stiffness, associated neurologic as well as sleep problems are also reported. Disease is more prevalent in females of middle-age group with low socioeconomic status, thus deteriorating overall productivity and psychosocial health. There is no permanent cure of the disease. This study aimed to explore, validate and assess the effect of four weeks of supervised yogic intervention on pain status, quality of life, sleep, cortical excitability, flexibility and range of motion in fibromyalgia patients, as compared to standard therapy. MethodCase-control study, interventional study and assessor-blined randomized controlled trial, conducted in 120 fibromyalgia patients (60 yoga group: 60 waitlisted controls) and 60 age-matched healthy controls. Pain was assessed subjectively, using questionnaires and objectively, using quantitative sensory testing and ELISA. Sleep and quality of life were assessed using common and disease specific decsiptors. Flexibility and range of motion was assessed using sit and reach box, lateral goniometry and modified Schobers test. Transcranial magnetic stimulation on M1 was used to assess corticomotor excitability of participants. Study parameters were assessed at baseline and after four weeks of the intervention. ResultsA significantly poor sleep, flexibility and quality of life was reported in the fibromyalgia patients due to excruciating pain (VAS = 6.92{+/-}0.12); corticomotor function was also abnormal in the patients, which were restored after four weeks of yogic intervention. On subjective and objective assessment of pain, we found significant relief and improvement in pain status in the yoga group as compared to the waitlisted controls. Fibromyalgia impact, sleep, quality of life and flexibility were also found solely better in fibromyalgia patients undergoing yogic interventions. Cortical parameters, specifically RMT, MEPs and MEP recruitment curves showed a significant improvement in yoga group as compared to waitlisted controls. ConclusionFour weeks of regular and supervised yogic intervention may ameliorate pain, improve flexibility and range of motion and changes cortical plasticity in the Indian cohort of fibromyalgia patients, as compared to standard therapy. Yoga-based interventions can also improve overall quality of life and sleep impairmentsby reducing catastrophization and fibromyalgia impact.

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

ObjectiveTo clinically evaluate a digital biomarker, the Finger Fold Index (FFI), derived from the ratio of joint diameter to finger fold surface area in hand photographs, for assessing joint swelling in inflammatory arthritis. MethodsSmartphone hand photographs from two routine care cohorts of patients with rheumatoid (RA) and psoriatic arthritis (PsA) were analyzed using a machine learning pipeline for automated detection and processing of proximal interphalangeal (PIP) joints. The FFI was clinically evaluated by correlation with joint swelling scores (0-3) and DAS28-CRP. A healthy cohort was used to establish FFI reference ranges, which were then compared to the arthritis cohorts. ResultsA total of 1275 PIP joint images of 124 arthritis patients and 53 healthy individuals were included. FFI values correlated with swelling scores in the arthritis population with r = 0.443 (95% CI 0.384-0.498). A correlation was observed between the mean FFI and DAS28-CRP dichotomized at 3.2 (r = 0.310, 95% CI 0.123-0.475). FFI values exceeding the healthy reference ranges were associated with swelling (Cramers V = 0.400-0.631; p < 0.001). ConclusionFFI values derived from hand photographs showed a significant association with clinical joint swelling and disease activity in RA and PsA patients. Longitudinal studies are needed to assess sensitivity to change and to establish whether this biomarker can be reliably used for remote patient monitoring.

BackgroundCardiovascular (CV) disease risk is increased in rheumatoid arthritis (RA) and is the leading cause of mortality. Improved CV risk stratification tools in RA could enhance use of preventative care and improve outcomes. MethodsWe previously studied biomarkers of CV disease - adiponectin, hsCRP, Lp(a), osteoprotegerin (OPG), high-sensitivity cardiac troponin T (hsTnT), serum amyloid A (SAA), YKL-40, soluble TNF receptor1 (sTNFR1) -- that were associated with CV risk. In the current study, these biomarkers were tested in an unrelated external cohort of RA patients followed at a single academic medical center without a history of CV events. CV events were identified through Medicare and Medicaid administrative data or through medical record review of self-reported events. Biomarkers were assessed at cohort entry among a nested cohort of cases and controls, matched 1:1 on sex and age. Analyses were conducted using conditional logistic regression. We examined whether the candidate biomarkers added to clinical CV risk factors improved model prediction, using the area under the curve (AUC) as well as the net reclassification index (NRI). ResultsFrom a cohort of 1,345 eligible patients with RA, we identified 123 patients with confirmed CV events. Cases and matched controls were typical of RA: median age 63 years, 77% women, RA disease duration 11 years, 72% seropositive, 85% used a biologic or conventional disease modifying anti-rheumatic drug, 58% non-steroidal anti-inflammatory drugs, and 30% oral glucocorticoids. From the candidate biomarkers, LASSO regression selected hsTnT and sTNFR1 as associated with CV events. The AUC for models that included only clinical risk factors was 0.758 (95% CI 0.689-0.829); after adding hsTnT and sTNFR1, the AUC increased to 0.802 (95% CI 0.718-0.998). The NRI of the model with biomarkers was 16.3%, with improvement only observed in patients who did not have CV events during follow-up. ConclusionsAdding selected biomarkers to clinical risk factors enhances the discrimination of models predicting CV events among patients with RA. These risk models require prospective testing to see if they have value in clinical practice decision-making regarding preventative care.

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

ObjectiveTo apply large language models (LLMs) to Reddit posts referencing systemic lupus erythematosus (SLE) to identify patient-expressed unmet medical needs, symptom experiences, and healthcare challenges, demonstrating how AI-enabled social media listening complements traditional patient-experience research. MethodsWe extracted 4,633 posts from ten SLE-related or health-focused Reddit communities using the public Reddit API (October-November 2025). After removing duplicates, promotional content, and posts with insufficient information, 2,603 posts remained. A thematic codebook was developed through manual review of 300 posts and iteratively refined. Two LLMs (Gemini 3.0 and GPT-5.2) were evaluated for automated thematic labeling using percent agreement, Cohens {kappa}, and a human-annotated reference set (n=100). The best-performing model was used to quantify theme prevalence, followed by qualitative review of representative narratives. ResultsGPT-5.2 demonstrated higher performance (F1=0.844) than Gemini 3.0 (F1=0.811), with substantial inter-model agreement across main themes (mean {kappa}=0.71). Posts reflected multidimensional experiences. The most frequent subtheme was Advice Seeking (84.1%), followed by Emotional Coping (55.6%). Common symptom-related themes included Pain (37.2%), Other Symptom Presentations (37.6%), Fatigue (24.7%), and Acute or Worsening Flares (30.2%). Diagnostic uncertainty was prominent, including confusion about laboratory results (24.0%) and emotional impact of uncertainty (33.0%). Qualitative review highlighted emotional distress, reliance on peer communities for interpretation of symptoms and labs, and difficulty managing complex treatment regimens. ConclusionLLM-enabled social media listening offers a scalable method for synthesizing large volumes of unstructured patient narratives, providing timely insights into lived experiences and unmet needs among individuals discussing lupus online. Findings align with established qualitative literature while highlighting persistent gaps in patient education, communication, and care coordination. This analytical framework can be applied across disease areas to support patient-centered care, measurement development, and evidence generation relevant to therapeutic and health-services research. What is already known on this topicO_LIPeople living with systemic lupus erythematosus (SLE) experience substantial unmet needs related to diagnostic uncertainty, symptom burden, emotional distress, medication challenges, and healthcare system barriers. C_LIO_LITraditional qualitative methods (e.g., interviews, focus groups, surveys) capture valuable patient perspectives but are limited by small sample sizes, recall bias, and restricted question frameworks. C_LIO_LISocial media listening has emerged as a promising way to collect real-time patient insights, and recent regulatory guidance acknowledges its value as patient experience data. However, systematic, scalable analysis of large patient-generated datasets has historically been constrained by analytic burden and variability. C_LI What this study addsO_LIThis study is among the first to apply state-of-the-art large language models (LLMs) to a large corpus of SLE-related social media posts, enabling scalable thematic analysis of thousands of patient narratives. C_LIO_LIIt provides a validated methodological framework for using dual-LLM agreement, human-annotated references, and performance benchmarking (precision, recall, F1) to ensure reliability in automated thematic labeling. C_LIO_LIFindings reveal a multidimensional patient burden consistent with prior studies while uncovering persistent gaps in patient education, confusion around laboratory testing, care coordination challenges, and heavy reliance on peer communities for advice. C_LIO_LIThe approach demonstrates that LLM-enabled social media listening can generate timely, granular, patient-prioritized insights at a scale unattainable by traditional methods. C_LI How this study might affect research, practice, or policyO_LIResearch: Establishes a reproducible, scalable framework for integrating LLM-based thematic analysis into patient-focused evidence generation, accelerating insight extraction from large unstructured datasets across disease areas. C_LIO_LIClinical practice: Highlights actionable gaps in patient education, communication, and care coordination, informing interventions to improve clinical encounters, shared decision-making, and symptom management support. C_LIO_LIPolicy and regulatory science: Demonstrates how social media-derived patient experience data, when paired with rigorous quality controls, can complement formal qualitative studies and support patient-focused drug development, measurement development, and health-services planning. C_LI

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

Gout is driven by an interleukin-1{beta}-mediated intense innate immune reaction to monosodium urate (MSU) crystals (MSUc). In cell culture models of inflammatory gout there is a synergistic effect of phagocytosis of MSUc and TLR2 and TLR4 activation by agonists such as free fatty acid and lipopolysaccharide (LPS) in NLRP3-inflammasome activation and IL-1{beta} secretion. A substantial number of gout patients do not report a dietary trigger, and observational studies associate airborne particulate matter with incident gout and flares. Airborne particulate matter contains LPS and airborne-derived particulate matter stimulates IL-1{beta} secretion in cell culture. We hypothesized that air-borne particulate matter could co-stimulate, with MSUc, IL-1{beta} secretion and inflammation. We tested the hypothesis using MSUc with extracted airborne PM4 in human cells (the THP-1 monocyte cell line, primary human monocytes and PBMCs) or carbon black particles with ozone (CB+O3) in a murine foot-pad injection model of gout. There was strong NLRP3-inflammasome-dependent co-stimulation of IL-1{beta} secretion in THP-1 cells with PM4+MSUc and a moderate additive effect in primary human PBMCs. However, there was no added effect on IL-1{beta} secretion of PM4 in isolated primary human monocytes. Inhalation of CB+O3 persistently exacerbated MSUc-induced murine paw inflammation, with an increase of alveolar/lavage macrophages that contained CB+O3 particles and increased lavage expression of IL-1{beta}. In conclusion, airborne-derived PM4 particulate matter enhanced MSUc-induced IL-1{beta} secretion in THP-1 cells and PBMCs. Combined with exacerbation of MSUc-induced inflammation by fine particulate matter in in vivo experiments, these data provide evidence that exposure to fine particulate matter may play a role in the etiology of gout.

BackgroundTotal knee arthroplasty (TKA), partial knee arthroplasty (pKA), and arthroscopic meniscectomy are among the most commonly performed procedures for knee osteoarthritis and degenerative meniscal tears in the United States, yet concerns persist regarding overuse, variable clinical benefit, and high costs. Orthobiologic treatments, including platelet-rich plasma (PRP) and bone marrow aspirate concentrate (BMAC), have emerged as less invasive alternatives, but downstream health care resource use (HCRU) and costs associated with these treatments relative to surgery are not well established. MethodsWe conducted a retrospective, observational cohort study using linked commercial insurance claims data and a national orthobiologic treatment registry to compare downstream HCRU and costs following orthobiologic versus surgical treatment of knee conditions. Two comparisons were evaluated separately: (1) PRP versus arthroscopic meniscectomy among patients with degenerative meniscal pathology and minimal osteoarthritis, and (2) BMAC with or without PRP versus TKA or pKA among patients with knee osteoarthritis. Eligible procedures occurred between 2016 and 2023. Propensity score matching was used to balance demographic and clinical confounders. Co-primary outcomes were total health care costs at 12 and 24 months post-procedure, with exploratory analyses at 36 and 48 months. Costs were estimated using multiple approaches, including Medicare-based estimates, commercial payer estimates, and aggregate allowed amounts. HCRU outcomes included outpatient visits, physical therapy, imaging, opioid use, repeat injections, and subsequent surgery. ResultsAfter matching, analyses included 167 PRP-treated patients matched to 1,670 meniscectomy patients and 165 BMAC/PRP-treated patients matched to 1,650 TKA/pKA patients, with good balance across pre-specified confounders. Progression to subsequent surgery after orthobiologic treatment was rare at 12 and 24 months in both cohorts. Compared with TKA/pKA, BMAC/PRP was associated with lower overall health care use for several services, including outpatient visits, physical therapy, knee radiographs, and opioid prescriptions, although magnetic resonance imaging was more frequent following orthobiologic treatment. Total costs at 12 and 24 months were consistently higher for TKA/pKA than for BMAC/PRP across all costing methods. In the PRP versus meniscectomy comparison, differences in health care use were modest, and costs were similar or lower for PRP depending on the costing approach. Exploratory analyses through 48 months showed similar patterns, with persistently low rates of subsequent surgery after orthobiologic treatment and generally higher cumulative costs following surgical intervention. ConclusionsIn this real-world, propensity-matched analysis of commercially insured patients, orthobiologic treatments with PRP or BMAC were associated with similar or lower downstream health care costs compared with commonly performed surgical alternatives for selected patients with degenerative meniscal tears or knee osteoarthritis. Progression to surgery following orthobiologic treatment was uncommon through two years and remained low in longer-term exploratory analyses. These findings support the consideration of orthobiologic therapies as potentially lower-cost alternatives to surgery for appropriately selected patients and may inform shared decision-making and payer policy.

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.

Anti-{beta}2-glycoprotein I (anti-{beta}2GPI) antibodies are central to the pathogenesis of antiphospholipid syndrome (APS), an autoimmune disease characterized by a strong predisposition to venous thromboembolism (VTE). In this study, we conducted a multi-ancestry genome-wide association study (GWAS) of quantitative total anti-{beta}2GPI levels in 5,969 participants enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) and identified a genome-wide significant association at the APOH locus. Paradoxically, genetically determined increases in anti-{beta}2GPI levels at this locus were associated with lower VTE risk. Fine-mapping and functional genomics prioritized the missense variant rs1801690 (W335S) in {beta}2GPI (apolipoprotein H, [APOH]) as the most likely causal variant. This variant has an allele frequency of 5-6% in European and East Asian ancestries but only 1% in African ancestries. Integrating prior experimental studies, molecular dynamics simulations and structure-based epitope prediction, we propose a dual-effect mechanism whereby W335S reduces thrombotic risk by disrupting phospholipid binding in Domain V, yet increases autoantibody production through conformational changes that enhance epitope exposure in Domains I and II. These findings mechanistically uncouple autoantibody formation from thrombotic risk in carriers of the W335S variant, and suggest that APOH genotype may represent a clinically relevant genetic biomarker with potential utility for thrombotic risk stratification in anti-{beta}2GPI-positive individuals.

Autoantibody internalization has been implicated in autoimmune disease pathogenesis, yet its mechanisms, and generality across different diseases, cell types, and affected tissues remain poorly defined. Using bulk RNA sequencing, we identified reproducible, autoantibody-specific transcriptomic signatures consistent with autoantigen dysfunction in muscle biopsies from patients with anti-Mi2 dermatomyositis and anti-PM/Scl scleromyositis across independent cohorts. Electroporation of purified patient IgG into primary cultures of healthy cells was sufficient to induce the corresponding transcriptomic programs in vitro. Direct immunofluorescence demonstrated immunoglobulin internalization into subcellular compartments matching the localization of the autoantigen in different affected tissues. Spatial transcriptomic analyses revealed that antibody-secreting cells translocated cytoplasmic material (i.e., immunoglobulin RNA) into adjacent affected cells expressing autoantibody-specific transcripts. The disease-specific transcripts were present not only in muscle fibers, but also in other cells, including macrophages, endothelial cells, and fibroblasts. Autoantibody-induced transcriptomic programs were associated with cell damage and autoantibody-specific reactive inflammatory programs, including activation of type I interferon and TGF-{beta}1 signaling in anti-Mi2 dermatomyositis and activation of type II interferon in anti-PM/Scl scleromyositis. Antibody internalization was also observed in different tissues from patients with other autoimmune diseases, including anti-U1RNP mixed connective tissue disease, anti-Ku overlap syndrome, and anti-Scl70 systemic sclerosis. Together, these findings establish autoantibody internalization as a shared pathogenic mechanism across diverse autoimmune diseases, providing a unifying framework for conditions driven by autoantibodies against intracellular antigens.

Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies combine male and female datasets to train a single model, introducing bias since osteoporosis risk and progression differ by gender. This study aims to develop gender-specific machine learning models that leverage longitudinal data to predict osteoporosis risk, providing tailored insights for men and women. Data were obtained from two large longitudinal cohorts: the Study of Osteoporotic Fractures (SOF) for women and the Osteoporotic Fractures in Men Study (MrOS) for men. Multiple ML algorithms were trained and evaluated for each sex, with model performance assessed using the area under the receiver operating characteristic curve (AUC-ROC). Among the tested models, the XGBoost model demonstrated the best performance for women, achieving an AUC-ROC of 0.93 using SOF data. For men, the Random Forest model achieved an AUC-ROC of 0.89 using MrOS data. Feature importance analysis identified sex-specific osteoporosis risk factors, underscoring the need for tailored prediction and management. By revealing male and female risk factors and reducing bias from combined datasets, the work advances personalized care and supports earlier, effective clinical intervention to prevent fractures and improve health outcomes.

ObjectiveMyositis-associated interstitial lung disease (myositis-ILD) consists of two predominant radiologic patterns of lung injury--nonspecific interstitial pneumonia (NSIP) and organizing pneumonia (OP)--that oftentimes coexist. However, it remains unclear whether either is associated with clinical outcomes. We aimed to assess the therapeutic response in patients with NSIP-compared to those with OP-predominant myositis-ILD. MethodsThis retrospective, single-center cohort study recruited participants from the Northwestern University ILD Registry with a circulating myositis-associated antibody, ILD, and at least 6 months of follow-up while on immunomodulatory therapy during a 24-month observation period after diagnosis. Two thoracic radiologists determined the predominant radiologic pattern (NSIP or OP). The primary outcome was the absolute change in forced vital capacity (FVC) at 24 months post-diagnosis. Secondary outcomes included changes in the diffusing capacity of the lung for carbon monoxide (DLCO) and radiologic qualitative and quantitative measures of lung injury. ResultsForty-one participants were included in analyses. 71% had an OP-predominant while 29% had an NSIP-predominant radiologic pattern of lung injury. Both exposure cohorts had improvement in mean absolute FVC (OP cohort = +0.18L [p=0.005], NSIP cohort = +0.24L [p=0.07]) over the 24-month observation period. The OP (p<0.05) but not the NSIP cohort (p=0.20) had an increase in DLCO. The OP cohort demonstrated improvement in the qualitative assessment of follow-up imaging (p<0.05), driven by quantitative improvement in groundglass/consolidative opacities (p=0.006). A subset of participants demonstrated features of NSIP/OP overlap and had greater baseline radiologic severity of lung injury. ConclusionPatients with circulating myositis-associated antibodies and an OP-predominant pattern of lung injury may have a more favorable response to therapy than those with NSIP. Further studies are needed to validate our findings and delineate other features cognate with these associations. Significance and InnovationsO_LIRadiologic phenotyping may predict therapeutic response in myositis-ILD. This study demonstrates that an OP-predominant computed tomography (CT) pattern of lung injury is associated with greater improvement in lung function and radiologic signs of inflammation over 24 months on at least 6 months of immunomodulatory therapy compared with an NSIP-predominant pattern, suggesting that CT pattern may provide clinically meaningful prognostic information. C_LIO_LIFirst study to integrate blinded qualitative radiologic adjudication with quantitative CT scoring in myositis-ILD. By combining dual-radiologist review with Kazerooni quantitative scoring and longitudinal pulmonary function testing, this study offers a rigorous and multidimensional assessment of treatment response. C_LIO_LIExpands risk stratification beyond antibody-based toward imaging-based phenotyping strategies. In a heterogeneous population defined by diverse myositis-associated antibodies, this work introduces radiologic pattern as a practical and accessible framework for anticipating treatment responsiveness. C_LIO_LIProvides hypothesis-generating data for precision management in myositis-ILD. The findings support the concept that imaging-defined subgroups may exhibit differential therapeutic trajectories, laying groundwork for future multicenter studies integrating CT phenotype, antibody profile, and treatment strategy. C_LI

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

ObjectiveThis study explored the recovery experiences of individuals who report having (largely) recovered from long covid and who attributed their improvement to mind-body approaches. Design, setting and participantsWe conducted an explorative qualitative study using purposive recruitment through social media and snowball sampling. Eighteen adult women (aged 37-62 years), who self-identified as having had long covid and having substantially recovered through mind-body approaches participated in semi-structured interviews. Data were analysed using Saunders practical thematic analysis. ResultsDespite variation in personal narratives, a common trajectory emerged: participants moved away from a biomedical explanatory model towards one centred on nervous system dysregulation. This shift, sometimes following initial scepticism, was often described as a turning point, sparking hope and motivation to engage in self-directed strategies. Recovery was not linear but an iterative process, involving cycles of practice, reflection (especially when progress stagnated) and adaptation of mind-body techniques. Over time, participants gained insights into contributing factors and, in many cases, made intentional life changes to support ongoing recovery. These patterns echo findings from previous research on mind-body approaches in chronic pain and chronic fatigue, and align with neuroscientific perspectives on symptom generation. Most participants navigated this process without formal clinical support, relying instead on online communities and actively avoiding sources of (biomedical) information that conflicted with their new understanding. ConclusionsWhile causal inferences cannot be drawn from qualitative data, this study highlights potential mechanisms that may underpin recovery for people with long covid using mind-body approaches. Further research is needed to develop structured interventions, and to evaluate their efficacy and safety. Future research should also explore how prevailing narratives within healthcare and society influence treatment engagement and recovery trajectories. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIThis is the first study exploring experiences of recovery from long covid using mind-body approaches. C_LIO_LIIn-depth, real-world accounts capture the lived-experiences over time and allow in-depth exploration if the recovery process, while the semi-structured design facilitates the emergence of themes rarely captured in clinical research. C_LIO_LIGeneralisability is limited due to self-identified long covid status, lack of formal diagnostic verification, absence of strict definitions of mind-body approaches and recovery, and a relatively homogenous sample (mostly highly educated women). C_LI

BackgroundReliable identification of early predictors of adverse outcomes was essential during the pre-vaccination phase of the COVID-19 pandemic. Few studies have comprehensively integrated clinical presentation, laboratory parameters including arterial blood gas analysis, and chest computed tomography (CT) findings within a single well-characterized cohort, particularly in underrepresented regions of Brazil. MethodsThis retrospective cohort study included 482 consecutive adults (median age 61 years [IQR 49-73]; 64.3% men) with RT-PCR-confirmed SARS-CoV-2 infection hospitalized at a tertiary cardiac center in Central-West Brazil between March 2020 and January 2021. Demographic, clinical, laboratory (including arterial blood gas analysis), and chest CT data obtained within 48 hours of admission were analyzed. Univariable logistic regression was performed for 76 variables. Multivariable models were constructed using an a priori variable selection strategy based on clinical relevance, representation of distinct pathophysiological domains, and adherence to events-per-variable principles. Complete-case analyses were performed without imputation. ResultsIn-hospital mortality was 9.3% (45/482). Invasive mechanical ventilation was required in 74 patients (15.4%), with a mortality rate of 58.1% among those ventilated. In univariable analysis, 42 variables were associated with mortality (p < 0.05). In multivariable analysis (n = 438), five independent predictors of death were identified: age (adjusted OR 1.66 per 10 years; 95% CI 1.19-2.32; p = 0.003), arterial pH (adjusted OR 0.47 per 0.1-unit increase; 95% CI 0.25-0.89; p = 0.021), neutrophil-to-lymphocyte ratio (adjusted OR 1.30; 95% CI 1.18-1.44; p < 0.001), number of comorbidities (adjusted OR 1.59; 95% CI 1.25-2.02; p < 0.001), and serum creatinine (adjusted OR 1.37; 95% CI 1.05-1.77; p = 0.019). The model demonstrated good calibration (Hosmer-Lemeshow p > 0.05) and moderate-to-high explanatory power (Nagelkerke R{superscript 2} = 0.43). For the composite outcome of death or invasive mechanical ventilation (74 events; 15.4%), four predictors remained independently associated; serum creatinine showed a non-significant trend (p = 0.069). On chest CT (n = 424), analyzed descriptively and in univariable models only, pulmonary involvement > 50% was associated with increased odds of death (OR 2.87; 95% CI 1.42-5.79; p = 0.003). ConclusionsFive admission variables representing distinct pathophysiological domains--age, arterial pH, neutrophil-to-lymphocyte ratio, comorbidity burden, and serum creatinine--were independently associated with in-hospital mortality in this pre-vaccination cohort. Arterial pH provided independent prognostic information beyond inflammatory and renal markers. These findings support early risk stratification using routinely available clinical data.

BackgroundHospital-acquired bacterial pneumonia (HABP) and ventilator-associated bacterial pneumonia (VABP), particularly those caused by multi-drug resistant organisms (MDROs), often require newer antibiotic treatment. The efficacy and safety of newer antibiotics compared to generic antibiotics in randomized controlled trials (RCTs) have not been evaluated before. MethodsIn this systematic review, we searched RCTs in the United States National Library of Medicine (PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Ovid MEDLINE, Clinical Trials.gov and Google Scholar databases published between 2013 and 2025. The primary efficacy endpoint was 28-day all-cause mortality. Secondary efficacy endpoints were clinical and microbiological response. Safety endpoint was nephrotoxicity. ResultsWe identified eight eligible RCTs involving 2,881 patients (1,450 patients treated with newer antibiotics and 1,431 patients treated with generic antibiotics) with HABP/VABP. The meta-analysis did not reveal any significant differences between newer and generic antibiotics for all-cause mortality at day 28 (risk ratio (RR) 0.97, 95% confidence interval (CI) 0.72-1.30), clinical response (RR 1.04, 95%CI 0.93-1.17), and microbiological response (RR 1.05, 95%CI 0.89-1.24). However, newer antibiotics showed significant lower occurrences of nephrotoxicity compared to colistin component (RR 0.30, 95%CI 0.11-0.79). In subgroup analysis, newer antibiotic regimens demonstrated significant improvement in microbiological eradication of carbapenem-resistant Gram-negative bacilli (RR 1.50, 95%CI 1.18-1.90). ConclusionsNewer antibiotics showed similar efficacy and safety in treating HABP/VABP compared to generic drugs. The superiority in microbiological eradication of carbapenem-resistant Gram-negative bacilli of newer antibiotics could suggest that future trials should be targeted for those patients to improve understanding of their therapeutic use and pathophysiology of these conditions. Key pointsNewer antibiotics, despite broader antimicrobial coverage, have not significantly outperformed generic comparators in terms of 28-day all-cause mortality, clinical, or microbiological response in patients with Gram-negative HABP/VABP. This may reflect limitations in current trial designs focused primarily on regulatory approval.

BackgroundSevere Fever with Thrombocytopenia Syndrome (SFTS) is an acute infectious disease with high mortality. This study aimed to develop a quantitative scoring system for grading SFTS severity using dynamic clinical data. MethodsA retrospective study included 547 confirmed SFTS patients from two hospitals. Clinical data were collected over a 14-day course (divided into four phases). Patients were grouped into survivors (n=451) and non-survivors (n=96). Statistical analyses, including Kaplan-Meier curves and log-rank tests, were performed. An external validation cohort of 44 new patients was used to validate the scoring system via C-statistic, calibration curves, and decision curve analysis (DCA). ResultsOf 547 patients, 96 (17.55%) were non-survivors. Multivariate logistic regression identified six independent prognostic factors across phases: age, platelet (PLT), aspartate aminotransferase (AST), and creatinine (Cr) (days 5-7); age, red blood cell distribution width (RDW), Cr, and lactate dehydrogenase (LDH) (days 8-10); Cr and LDH (days 11-14). A scoring system (0-11 points) was developed, stratifying patients into low (0-3), intermediate (4-7), and high (8-11) risk groups, with adverse outcome rates of 1.04%, 22.92%, and 76.04%, respectively. Kaplan-Meier curves showed significant prognostic differences (log-rank P<0.001). External validation (44 cases) confirmed excellent performance: AUC 0.810-0.952, good calibration (Hosmer-Lemeshow P>0.05), and net clinical benefit (DCA Eavg 0.068-0.098, Emax 0.422-0.559). ConclusionA dynamic SFTS severity scoring system was developed and validated. Internal and external validation confirmed its reliability and clinical utility, providing a simple, practical tool for timely assessment and early intervention.