Objectives To assess the ability of baseline serum biomarkers to predict disease activity and remission status in juvenile idiopathic arthritis (JIA) at 18-year follow-up (FU) in a population-based setting.Methods Clinical data and serum levels of inflammatory biomarkers were assessed in the longitudinal population-based Nordic JIA cohort study at baseline and at 18-year FU. A panel of 16 inflammatory biomarkers was determined by multiplexed bead array assay. We estimated both univariate and multivariate logistic regression models on binary outcomes of disease activity and remission with baseline variables as explanatory variables.Results Out of 349 patients eligible for the Nordic JIA cohort study, 236 (68%) had available serum samples at baseline. We measured significantly higher serum levels of interleukin 1β (IL-1β), IL-6, IL-12p70, IL-13, MMP-3, S100A9 and S100A12 at baseline in patients with active disease at 18-year FU than in patients with inactive disease. Computing receiver operating characteristics illustrating the area under the curve (AUC), we compared a conventional prediction model (gender, age, joint counts, erythrocyte sedimentation rate, C reactive protein) with an extended model that also incorporated the 16 baseline biomarkers. Biomarker addition significantly improved the ability of the model to predict activity/inactivity at the 18-year FU, as evidenced by an increase in the AUC from 0.59 to 0.80 (p=0.02). Multiple regression analysis revealed that S100A9 was the strongest predictor of inactive disease 18 years after disease onset.Conclusion Biomarkers indicating inflammation at baseline have the potential to improve evaluation of disease activity and prediction of long-term outcomes.

Background Acute and chronic pancreatitis constitute a continuum of inflammatory disease of the pancreas with an increasing incidence in most high-income countries. A subset of patients with a history of pancreatitis suffer from recurrence of acute pancreatitis attacks, which accelerate disease progression towards end-stage chronic pancreatitis with loss of exocrine and endocrine function. There is currently no available prophylactic treatment for recurrent acute pancreatitis apart from removing risk factors, which is not always possible. Pain is the primary symptom of acute pancreatitis, which induces the endogenous release of opioids. This may further be potentiated by opioid administration for pain management. Increased exposure to opioids leads to potentially harmful effects on the gastrointestinal tract, including, e.g. increased sphincter tones and decreased fluid secretion, which may impair pancreatic ductal clearance and elevate the risk for new pancreatitis attacks and accelerate disease progression. Peripherally acting µ-opioid receptor antagonists (PAMORAs) have been developed to counteract the adverse effects of opioids on the gastrointestinal tract. We hypothesize that the PAMORA naldemedine will reduce the risk of new pancreatitis attacks in patients with recurrent acute pancreatitis and hence decelerate disease progression. Methods The study is a double-blind, randomized controlled trial with allocation of patients to either 0.2 mg naldemedine daily or matching placebo for 12 months. A total of 120 outpatients will be enrolled from five specialist centres in Denmark and Sweden. The main inclusion criteria is a history of recurrent acute pancreatitis (minimum of two confirmed pancreatitis attacks). The primary endpoint is time to acute pancreatitis recurrence after randomization. Secondary outcomes include changes in quality of life, gastrointestinal symptom scores, new-onset diabetes, exocrine pancreatic insufficiency, disease severity, health care utilization, adherence to treatment, and frequency of adverse events. Exploratory outcomes are included for mechanistic linkage and include the progression of chronic pancreatitis-related findings on magnetic resonance imaging (MRI) and changes in circulating blood markers of inflammation and fibrosis. Discussion This study investigates if naldemedine can change the natural course of pancreatitis in patients with recurrent acute pancreatitis and improve patient outcomes. Trial registration EudraCT no. 2021–000069-34. ClinicalTrials.gov NCT04966559. Registered on July 8, 2021.

OBJECTIVE:To estimate the cumulative incidences of orofacial conditions related to temporomandibular joint (TMJ) juvenile idiopathic arthritis (JIA) between diagnosis in childhood to transition into adult care, and to identify features in JIA associated with TMJ involvement.METHODS:A population-based cohort analysis was conducted of patients with JIA involving longitudinal data on orofacial health from 2000 to 2018. Regardless of TMJ status, the patients were referred to the Regional Specialist Craniofacial Clinic of Western Denmark for routine orofacial examinations. Data collection included information about disease-specific background characteristics, TMJ involvement, JIA-induced dentofacial deformity, and orofacial symptoms and dysfunction.RESULTS:A total of 613 patients were followed up with a mean clinical TMJ observation time of 4.0 years. From JIA onset to transition into adult care, the cumulative incidence of patients with JIA involvement of the TMJ was 30.1%. Furthermore, 20.6% of the cohort had developed arthritis-induced dentofacial deformity. A substantial proportion of the cohort experienced several events with orofacial symptoms (23.5%) and dentofacial dysfunction (52%). Young age at diagnosis (<9 years), female gender, and antinuclear antibody positivity were significantly associated with TMJ involvement.CONCLUSION:Orofacial signs and symptoms were frequent findings in children and adolescents with JIA. TMJ involvement was seen in 30.1% of the cohort; and 20.6% of the total cohort developed JIA-related dentofacial deformity before transition into adult care. This is the first population-based study in the era of available biologic treatments to document these frequent orofacial complications in children with JIA.

Background This retrospective, cross-sectional study aimed to assess the pharyngeal airway dimensions of patients with juvenile idiopathic arthritis (JIA) and moderate/severe JIA-related dentofacial deformity (mandibular retrognathia/micrognathia), and compare the results with JIA patients with a normal mandibular appearance and a group of non-JIA patients. Methods Seventy-eight patients were retrospectively included in a 1:1:1 manner as specified below. All patients had previously been treated at the Section of Orthodontics, Aarhus University, Denmark. All had a pretreatment cone beam computed tomography (CBCT). Group 1 (JIA+); 26 JIA patients with severe arthritis-related dentofacial deformity and mandibular retrognathia/micrognathia. Group 2 (JIA-); 26 JIA patients with normal mandibular morphology/position. Group 3 (Controls); 26 non-JIA subjects. Dentofacial morphology and upper airway dimensions, excluding the nasal cavity, were assessed in a validated three-dimensional (3D) fashion. Assessment of dentofacial deformity comprised six morphometric measures. Assessment of airway dimensions comprised nine measures. Results Five morphometric measures of dentofacial deformity were significantly deviating in the JIA+ group compared with the JIA- and control groups: Posterior mandibular height, anterior facial height, mandibular inclination, mandibular occlusal inclination, and mandibular sagittal position. Five of the airway measurements showed significant inter-group differences: JIA+ had a significantly smaller nasopharyngeal airway dimension (ad2-PNS), a smaller velopharyngeal volume, a smaller minimal cross-sectional area and a smaller minimal hydraulic diameter than JIA- and controls. No significant differences in upper airway dimensions were seen between JIA- and controls. Conclusion JIA patients with severe arthritis-related dentofacial deformity and mandibular micrognathia had significantly restricted upper airway dimensions compared with JIA patients without dentofacial deformity and controls. The restrictions of upper airway dimension seen in the JIA+ group herein were previously associated with sleep-disordered breathing in the non-JIA background population. Further studies are needed to elucidate the role of dentofacial deformity and restricted airways in the development of sleep-disordered breathing in JIA.

A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer's Disease.

We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An l(2)-penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other l(2) -penalized estimators as special cases. In addition, the approach allows specification of target matrices through which prior knowledge may be incorporated and which can stabilize the estimation procedure in high-dimensional settings. The result is a targeted fused ridge estimator that is of use when the precision matrices of the constituent classes are believed to chiefly share the same structure while potentially differing in a number of locations of interest. It has many applications in (multi)factorial study designs. We focus on the graphical interpretation of precision matrices with the proposed estimator then serving as a basis for integrative or meta-analytic Gaussian graphical modeling. Situations are considered in which the classes are defined by data sets and subtypes of diseases. The performance of the proposed estimator in the graphical modeling setting is assessed through extensive simulation experiments. Its practical usability is illustrated by the differential network modeling of 12 large-scale gene expression data sets of diffuse large B-cell lymphoma subtypes. The estimator and its related procedures are incorporated into the R-package rags2ridges.

Methods for unsupervised clustering is an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian Mixture Copula Models (GMCM) for general unsupervised clustering. Li, Brown, Huang, and Bickel (2011) independently discussed a special case of these GMCMs as a novel approach to meta-analysis in high-dimensional settings. GMCMs have attractive properties which make them highly flexible and therefore interesting alternatives to well-established methods. However, parameter estimation is hard because of intrinsic identifiability issues and intractable likelihood functions. Both aforementioned papers discuss similar expectation-maximization-like (EM) algorithms as their pseudo maximum likelihood estimation procedure. We present and discuss an improved implementation in R of both classes of GMCMs along with various alternative optimization routines to the EM algorithm. The software is freely available through the accompanying R package GMCM. The implementation is fast, general, and optimized for very large numbers of observations. We demonstrate the use of GMCM through different applications.

Objective To estimate the cumulative incidence of arthritis-induced orofacial symptoms, dysfunction, and dentofacial deformities in growing individuals with juvenile idiopathic arthritis (JIA) in a 36-month regional cohort study and to identify predictors for the development of arthritis-induced dentofacial deformities. Methods Data were retrieved from the AarhusJIAtemporomandibular joint (TMJ) cohort register, which contains standardized, longitudinal, observational data regarding orofacial conditions in patients withJIA(n = 1,040). This regional cohort represents the majority of all subjects withJIAfrom the western part of Denmark between 1990 and 2016, regardless ofTMJarthritis status. Cumulative incidences of orofacial conditions were reported using Kaplan-Meier methods, and predictors for dentofacial deformity were identified using Cox proportional hazards analysis. Results Follow-up data from 351 subjects for 36 months were included in this study. Median age at first clinical examination was 6.6 years (interquartile range 4.8-9.9 years). Orofacial symptoms and dysfunctions were common findings at 36 months after the first clinical examination and 5 years afterJIAonset, with a cumulative incidence of 38% and 53%, respectively. Dentofacial deformities were found in 35% of subjects at the 36-month follow-up and were significantly associated with the presence of orofacial dysfunction. Conclusion Orofacial conditions were frequently observed in individuals withJIAand were represented in allJIAsubcategories in this regional study. One-third of subjects had arthritis-induced dentofacial deformities that required orthopedic appliance treatment at the 36-month follow-up.

The estimation of covariance matrices of gene expressions has many applications in cancer systems biology.Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies.Motivated by the traditional meta-analysis using random effects models, we present a hierarchical random covariance model and use it for the meta-analysis of gene correlation networks across 11 large-scale gene expression studies of diffuse large B-cell lymphoma (DLBCL).We suggest to use a maximum likelihood estimator for the underlying common covariance matrix and introduce an EM algorithm for estimation.By simulation experiments comparing the estimated covariance matrices by cophenetic correlation and Kullback-Leibler divergence the suggested estimator showed to perform better or not worse than a simple pooled estimator.In a posthoc analysis of the estimated common covariance matrix for the DLBCL data we were able to identify novel biologically meaningful gene correlation networks with eigengenes of prognostic value.In conclusion, the method seems to provide a generally applicable framework for meta-analysis, when multiple features are measured and believed to share a common covariance matrix obscured by study dependent noise.

BACKGROUND:Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments. The most commonly used correction strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the Δ Δ C q . Currently, it is recommended to determine the AE with high precision as this plug-in approach does not account for the AE uncertainty, implicitly assuming an infinitely precise AE estimate. Determining the AE with such precision, however, requires tedious laboratory work and vast amounts of biological material. Violation of the assumption leads to overly optimistic standard errors of the Δ Δ C q , confidence intervals, and p-values which ultimately increase the type I error rate beyond the expected significance level. As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level.RESULTS:We suggest and benchmark different methods to obtain the standard error of the efficiency adjusted Δ Δ C q using the statistical delta method, Monte Carlo integration, or bootstrapping. Our suggested methods are founded in a linear mixed effects model (LMM) framework, but the problem and ideas apply in all qPCR experiments. The methods and impact of the AE uncertainty are illustrated in three qPCR applications and a simulation study. In addition, we validate findings suggesting that MGST1 is differentially expressed between high and low abundance culture initiating cells in multiple myeloma and that microRNA-127 is differentially expressed between testicular and nodal lymphomas.CONCLUSIONS:We conclude, that the commonly used efficiency corrected quantities disregard the uncertainty of the AE, which can drastically impact the standard error and lead to increased false positive rates. Our suggestions show that it is possible to easily perform statistical inference of Δ Δ C q , whilst properly accounting for the AE uncertainty and better controlling the false positive rate.

Background Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. Results This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. Conclusions The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

The standard treatment for patients with diffuse large B-cell lymphoma (DLBCL) is the immunochemotherapy-based R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone). Resistance to treatment, intrinsic or acquired, is observed in approximately 40% of patients with DLBCL, who thus require novel interventions to survive. To identify biomarkers for cytotoxic response assessment, microRNAs (miRNAs) associated with doxorubicin sensitivity were determined by combining global miRNA expression profiling with systematic dose-response screens in 15 human DLBCL cell lines. One candidate, miR-34a, was tested in functional in vitro studies and in vivo in a retrospective clinical cohort. High expression of miR-34a was observed in cell lines sensitive to doxorubicin, and upregulation of miR-34a is documented here to increase doxorubicin sensitivity in in vitro lentiviral transduction assays. High expression of miR-34a had a prognostic impact using overall survival as outcome. With risk stratification of DLBCL samples based on resistance gene signatures (REGS), doxorubicin-responsive samples had statistically significant upregulated miR-34a expression. Classification of the DLBCL samples into subset-specific B cell-associated gene signatures (BAGS) revealed differentiation-specific expression of miR-34a. Our data further support FOXP1 as a target of miR-34a, suggesting that downregulation of FOXP1 may sensitize DLBCL cells to doxorubicin. We conclude that miRNAs, in particular miR-34a, may have clinical utility in DLBCL patients as both predictive and prognostic biomarkers.

Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general unsupervised learning based on clustering. Li, Brown, Huang, and Bickel (2011) independently discussed a special case of these GMCMs as a novel approach to meta-analysis in highdimensional settings. GMCMs have attractive properties which make them highly flexible and therefore interesting alternatives to other well-established methods. However, parameter estimation is hard because of intrinsic identifiability issues and intractable likelihood functions. Both aforementioned papers discuss similar expectation-maximization-like algorithms as their pseudo maximum likelihood estimation procedure. We present and discuss an improved implementation in R of both classes of GMCMs along with various alternative optimization routines to the EM algorithm. The software is freely available in the R package GMCM. The implementation is fast, general, and optimized for very large numbers of observations. We demonstrate the use of package GMCM through different applications.

Today's diagnostic tests for multiple myeloma (MM) reflect the criteria of the updated WHO classification based on biomarkers and clinicopathologic heterogeneity. To that end, we propose a new biological subtyping of myeloma plasma cells (PC) by B-cell subset associated gene signatures (BAGS), from the normal B-cell hierarchy in the bone marrow (BM). Here we document the prognostic and biological value of subtyping, as shown for DLBCL (JCO 2015 Apr 20;33:1379).

We propose a hierarchical random effects model for a common covariance matrix in cases where multiple classes are present. It is applicable where the classes are believed to share a common covariance matrix of interest obscured by class-dependent noise. As such, it provides a basis for integrative or meta-analysis of covariance matrices where the classes are formed by datasets. Our approach is inspired by traditional meta-analysis using random effects models but the model is also shown to be applicable as an intermediate between linear and quadratic discriminant analysis. We derive basic properties and estimators of the model and compare their properties. Simple inference and interpretation of the introduced parameter measuring the inter-class homogeneity is suggested.

High-dose therapy (HDT) followed by autologous stem cell transplant (ASCT) has been adopted as the standard second-line approach for patients with relapsed chemosensitive aggressive non-Hodgkin lym...

Purpose Current diagnostic tests for diffuse large B-cell lymphoma use the updated WHO criteria based on biologic, morphologic, and clinical heterogeneity. We propose a refined classification system based on subset-specific B-cell-associated gene signatures (BAGS) in the normal B-cell hierarchy, hypothesizing that it can provide new biologic insight and diagnostic and prognostic value.Patients and Methods We combined fluorescence-activated cell sorting, gene expression profiling, and statistical modeling to generate BAGS for naive, centrocyte, centroblast, memory, and plasmablast B cells from normal human tonsils. The impact of BAGS-assigned subtyping was analyzed using five clinical cohorts (treated with cyclophosphamide, doxorubicin, vincristine, and prednisone [CHOP], n = 270; treated with rituximab plus CHOP [R-CHOP], n = 869) gathered across geographic regions, time eras, and sampling methods. The analysis estimated subtype frequencies and drug-specific resistance and included a prognostic meta-analysis of patients treated with first-line R-CHOP therapy.Results Similar BAGS subtype frequencies were assigned across 1,139 samples from five different cohorts. Among R-CHOP-treated patients, BAGS assignment was significantly associated with overall survival and progression-free survival within the germinal center B-cell-like subclass; the centrocyte subtype had a superior prognosis compared with the centroblast subtype. In agreement with the observed therapeutic outcome, centrocyte subtypes were estimated as being less resistant than the centroblast subtype to doxorubicin and vincristine. The centroblast subtype had a complex genotype, whereas the centrocyte subtype had high TP53 mutation and insertion/deletion frequencies and expressed LMO2, CD58, and stromal-1-signature and major histocompatibility complex class II-signature genes, which are known to have a positive impact on prognosis.Conclusion Further development of a diagnostic platform using BAGS-assigned subtypes may allow pathogenetic studies to improve disease management.