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Serum biomarkers are promising minimally invasive outcome measures in clinical studies in Duchenne muscular dystrophy (DMD). However, biomarkers strongly associated with clinical progression and predicting performance decline are lacking. In this study we aimed to identify serum biomarkers associated with clinical performance and able to predict clinical milestones in DMD. Towards this aim we present a retrospective multi-center cohort study including serum samples and clinical data collected in research participants with DMD as part of a natural history study at the University of Florida (UF) and real-world observations at Leiden University Medical Center (LUMC) between 2009-2022. The 7K SomaScan ® assay was used to analyse protein levels in in individual serum samples. Serum biomarkers predicted age at loss of ambulation (LoA), age at loss of overhead reach (OHR) and age at loss of hand to mouth function (HTM). Secondary outcomes were the association of biomarkers with age, corticosteroid (CS) usage, and clinical performance based on the North Star Ambulatory Assessment (NSAA), 10 meter run velocity (10mrv), 6 minute walk (6MWT) and Performance of the Upper Limb (PUL2.0). A total of 716 serum samples were collected in 79 participants at UF and 74 at LUMC (mean[SD] age; 10.9[3.2] vs 8.4[3.4]). 244 serum proteins showed an association with CS usage in both cohorts independent of CS type and regimen, including MMP3 and IGLL1. 318 probes (corresponding to 294 proteins) showed significant associations with NSAA, 10mrv, 6MWT and/or PUL2.0 across both cohorts. The expression of 38 probes corresponding to 36 proteins such as RGMA, EHMT2, ART3, ANTXR2 and DLK1 was associated with risk of both lower and upper limb clinical milestones in both the LUMC and UF cohort. In conclusion, multiple biomarkers were associated with CS use, motor function and upper lower and upper limb disease milestones in DMD. These biomarkers were validated across two independent cohorts, increasing their likelihood of translation for use within the broader DMD population., Competing Interests: Potential Conflicts of Interest SWME, SVH, and KCHH are employees of BioSymetrics, which has a commercial interest in the results. The remaining authors have no competing interests.

Objective: The study objective was to identify differences in gene expression between treatment responders (TRs) and treatment non-responders (TNRs) diagnosed with juvenile dermatomyositis (JDM)., Methods: Gene expression analyses were performed using whole blood messenger RNA sequencing in patients with JDM (n = 17) and healthy controls (HCs; n = 10). Four analyses were performed (A1-4) comparing differential gene expression and pathways analysis exploiting the timing of sample acquisition and the treatments received to perform these comparative analyses. Analyses were done at diagnosis and follow-up, which averaged 7 months later in the cohort., Results: At diagnosis, the expression of 10 genes differed between TRs and TNRs. Hallmark and canonical pathway analysis revealed 11 and 60 pathways enriched in TRs and 3 and 21 pathways enriched in TNRs, respectively. Pathway enrichment at diagnosis in TRs was strongest in pathways involved in metabolism, complement activation, and cell signaling as mediated by IL-8, p38/microtubule associated protein kinases (MAPK)/extracellular signal-regulated kinases (ERK), Phosphatidylinositol 3 Kinase Gamma (PI3Kγ), and the B cell receptor. Follow-up hallmark and canonical pathway analysis showed that 2 and 14 pathways were enriched in TRs, whereas 24 and 123 pathways were enriched in treatment TNRs, respectively. Prior treatment with glucocorticoids significantly altered expression of 13 genes in the analysis of subjects at diagnosis with JDM as compared with HCs., Conclusion: Numerous genes and pathways differ between TRs and TNRs at diagnosis and follow-up. Prior treatment with glucocorticoids prior to specimen acquisition had a small effect on the performed analyses., (© 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Objective: The aim of the Paediatric Rheumatology International Trials Organisation (PRINTO) juvenile idiopathic arthritis (JIA) classification criteria, which is still in development, is to identify homogeneous groups of JIA patients. This study was undertaken to compare International League of Associations for Rheumatology (ILAR) JIA classification criteria and PRINTO JIA classification criteria using data from the ReACCh-Out (Research in Arthritis in Canadian Children, Emphasizing Outcomes) cohort., Methods: We used clinicobiologic data recorded within 7 months of diagnosis to assign a diagnosis of JIA and identify subcategories of JIA among 1,228 patients according to the 2 JIA classification systems. We compared the proportions of patients classified and the alignment of classification categories with clinicobiologic subtypes and adult arthritis types., Results: The PRINTO criteria classified 244 patients (19.9%) as having early-onset antinuclear antibody-positive JIA, 157 (12.8%) as having enthesitis/spondylitis-related JIA, 38 (3.1%) as having systemic JIA, and 10 (0.8%) as having rheumatoid factor-positive JIA. A total of 12% of patients were unclassifiable using the ILAR criteria, while 63.3% were unclassifiable using the PRINTO criteria (777 with other JIA and 2 with unclassified JIA). In sensitivity analyses, >50% of patients remained unclassifiable using the PRINTO criteria. Compared to the PRINTO criteria, ILAR JIA categories aligned better with clinicobiologic subtypes in 131 patients (χ 2 = 44, P = 0.005, versus χ 2 = 15, P = 0.07 for PRINTO), and ILAR categories aligned better with adult types of arthritis in 389 evaluable patients., Conclusion: Currently identified PRINTO disorders can only be used to classify a minority of JIA patients, leaving a large proportion of JIA patients with other disorders requiring further characterization. Current PRINTO JIA classification criteria do not align better with clinicobiologic subtypes or adult forms of arthritis compared with the older ILAR classification system., (© 2022 American College of Rheumatology.)

Objective: To identify discrete clusters comprising clinical features and inflammatory biomarkers in children with JIA and to determine cluster alignment with JIA categories., Methods: A Canadian prospective inception cohort comprising 150 children with JIA was evaluated at baseline (visit 1) and after six months (visit 2). Data included clinical manifestations and inflammation-related biomarkers. Probabilistic principal component analysis identified sets of composite variables, or principal components, from 191 original variables. To discern new clinical-biomarker clusters (clusters), Gaussian mixture models were fit to the data. Newly-defined clusters and JIA categories were compared. Agreement between the two was assessed using Kruskal-Wallis analyses and contingency plots., Results: Three principal components recovered 35% (three clusters) and 40% (five clusters) of the variance in patient profiles in visits 1 and 2, respectively. None of the clusters aligned precisely with any of the seven JIA categories but rather spanned multiple categories. Results demonstrated that the newly defined clinical-biomarker lustres are more homogeneous than JIA categories., Conclusion: Applying unsupervised data mining to clinical and inflammatory biomarker data discerns discrete clusters that intersect multiple JIA categories. Results suggest that certain groups of patients within different JIA categories are more aligned pathobiologically than their separate clinical categorizations suggest. Applying data mining analyses to complex datasets can generate insights into JIA pathogenesis and could contribute to biologically based refinements in JIA classification., (© The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Published predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype., Methods: To identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof-of-concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ 2 tests to link new patient groups with the myositis subtypes., Results: SNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision-recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti-Mi-2, anti-signal recognition peptide (SRP), anti-nuclear matrix protein 2 (NXP2), and anti-synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti-Mi-2 and anti-Syn autoantibodies, JDM being associated with anti-NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti-SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes., Conclusion: Using unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described., (© 2020 The Authors. ACR Open Rheumatology published by Wiley Periodicals, Inc. on behalf of American College of Rheumatology.)

Background: Joint inflammation is the common feature underlying juvenile idiopathic arthritis (JIA). Clinicians recognize patterns of joint involvement currently not part of the International League of Associations for Rheumatology (ILAR) classification. Using unsupervised machine learning, we sought to uncover data-driven joint patterns that predict clinical phenotype and disease trajectories., Methods and Findings: We analyzed prospectively collected clinical data, including joint involvement using a standard 71-joint homunculus, for 640 discovery patients with newly diagnosed JIA enrolled in a Canada-wide study who were followed serially for five years, treatment-naïve except for nonsteroidal anti-inflammatory drugs (NSAIDs) and diagnosed within one year of symptom onset. Twenty-one patients had systemic arthritis, 300 oligoarthritis, 125 rheumatoid factor (RF)-negative polyarthritis, 16 RF-positive polyarthritis, 37 psoriatic arthritis, 78 enthesitis-related arthritis (ERA), and 63 undifferentiated arthritis. At diagnosis, we observed global hierarchical groups of co-involved joints. To characterize these patterns, we developed sparse multilayer non-negative matrix factorization (NMF). Model selection by internal bi-cross-validation identified seven joint patterns at presentation, to which all 640 discovery patients were assigned: pelvic girdle (57 patients), fingers (25), wrists (114), toes (48), ankles (106), knees (283), and indistinct (7). Patterns were distinct from clinical subtypes (P < 0.001 by χ2 test) and reproducible through external data set validation on a 119-patient, prospectively collected independent validation cohort (reconstruction accuracy Q2 = 0.55 for patterns; 0.35 for groups). Some patients matched multiple patterns. To determine whether their disease outcomes differed, we further subdivided the 640 discovery patients into three subgroups by degree of localization-the percentage of their active joints aligning with their assigned pattern: localized (≥90%; 359 patients), partially localized (60%-90%; 124), or extended (<60%; 157). Localized patients more often maintained their baseline patterns (P < 0.05 for five groups by permutation test) than nonlocalized patients (P < 0.05 for three groups by permutation test) over a five-year follow-up period. We modelled time to zero joints in the discovery cohort using a multivariate Cox proportional hazards model considering joint pattern, degree of localization, and ILAR subtype. Despite receiving more intense treatment, 50% of nonlocalized patients had zero joints at one year compared to six months for localized patients. Overall, localized patients required less time to reach zero joints (partial: P = 0.0018 versus localized by log-rank test; extended: P = 0.0057). Potential limitations include the requirement for patients to be treatment naïve (except NSAIDs), which may skew the patient cohorts towards milder disease, and the validation cohort size precluded multivariate analyses of disease trajectories., Conclusions: Multilayer NMF identified patterns of joint involvement that predicted disease trajectory in children with arthritis. Our hierarchical unsupervised approach identified a new clinical feature, degree of localization, which predicted outcomes in both cohorts. Detailed assessment of every joint is already part of every musculoskeletal exam for children with arthritis. Our study supports both the continued collection of detailed joint involvement and the inclusion of patterns and degrees of localization to stratify patients and inform treatment decisions. This will advance pediatric rheumatology from counting joints to realizing the potential of using data available from uncovering patterns of joint involvement., Competing Interests: The authors have declared that no competing interests exist.

mRNA processing, transport, translation, and ultimately degradation involve a series of dedicated protein complexes that often assemble into large membraneless structures such as stress granules (SGs) and processing bodies (PBs). Here, systematic in vivo proximity-dependent biotinylation (BioID) analysis of 119 human proteins associated with different aspects of mRNA biology uncovers 7424 unique proximity interactions with 1,792 proteins. Classical bait-prey analysis reveals connections of hundreds of proteins to distinct mRNA-associated processes or complexes, including the splicing and transcriptional elongation machineries (protein phosphatase 4) and the CCR4-NOT deadenylase complex (CEP85, RNF219, and KIAA0355). Analysis of correlated patterns between endogenous preys uncovers the spatial organization of RNA regulatory structures and enables the definition of 144 core components of SGs and PBs. We report preexisting contacts between most core SG proteins under normal growth conditions and demonstrate that several core SG proteins (UBAP2L, CSDE1, and PRRC2C) are critical for the formation of microscopically visible SGs., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Takayasu arteritis (TAK) is a large vessel vasculitis that rarely affects children. Data on childhood TAK are scarce. The aim of this study was to analyze the presenting features, course and outcome of children with TAK, compare efficacy of treatment regimens and identify high-risk factors for adverse outcome., Methods: A single-center cohort study of consecutive children fulfilling the EULAR/PRINTO/PReS criteria for childhood TAK between 1986 and 2015 was performed. Clinical phenotypes, laboratory markers, imaging features, disease course and treatment were documented. Disease activity was assessed using the Pediatric Vasculitis Disease Activity Score at each visit., Outcome: disease flare defined as new symptoms and/or increased inflammatory markers necessitating therapy escalation and/or new angiographic lesions, or death., Analysis: logistic regression tested relevant variables for flare. Kaplan-Meier analyses compared treatment regimens., Results: Twenty-seven children were included; 74% were female, median age at diagnosis was 12.4 years. Twenty-two (81%) children presented with active disease at diagnosis. Treatment regimens included corticosteroids alone (15%), corticosteroids plus methotrexate (37%), cyclophosphamide (19%), or a biologic agent (11%). Adverse outcomes were documented in 14/27 (52%) children: two (7%) died within 6 months of diagnosis, and 13 (48%) experienced disease flares. The 2-year flare-free survival was 80% with biologic treatments compared to 43% in non-biologic therapies (p = 0.03); at last follow-up, biologic therapies resulted in significantly higher rates of inactive disease (p = 0.02). No additional outcome predictor was identified., Conclusions: Childhood TAK carries a high disease burden; half of the children experienced flares and 7% died. Biologic therapies were associated with better control of disease activity.