Precision medicine in the care of rheumatoid arthritis: Focus on prediction and prevention of future clinically-apparent disease

Machine learning prediction and explanatory models of serious infections in patients with rheumatoid arthritis treated with tofacitinib

BACKGROUND

Patients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program.

METHODS

This analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model.

RESULTS

A total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only.

CONCLUSIONS

Baseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.

The role of anti-citrullinated protein antibody in pathogenesis of RA

Rheumatoid arthritis (RA) is a common autoimmune rheumatic disease that causes chronic synovitis, bone erosion, and joint destruction. The autoantigens in RA include a wide array of posttranslational modified proteins, such as citrullinated proteins catalyzed by peptidyl arginine deiminase4a. Pathogenic anti-citrullinated protein antibodies (ACPAs) directed against a variety of citrullinated epitopes are abundant both in plasma and synovial fluid of RA patients. ACPAs play an important role in the onset and progression of RA. Intensive and extensive studies are being conducted to unveil the mechanisms of RA pathogenesis and evaluate the efficacy of some investigative drugs. In this review, we focus on the formation and pathogenic function of ACPAs.

CXCR4/CXCL12 axis: "old" pathway as "novel" target for anti-inflammatory drug discovery

Inflammation is the body's defense response to exogenous or endogenous stimuli, involving complex regulatory mechanisms. Discovering anti-inflammatory drugs with both effectiveness and long-term use safety is still the direction of researchers' efforts. The inflammatory pathway was initially identified to be involved in tumor metastasis and HIV infection. However, research in recent years has proved that the CXC chemokine receptor type 4 (CXCR4)/CXC motif chemokine ligand 12 (CXCL12) axis plays a critical role in the upstream of the inflammatory pathway due to its chemotaxis to inflammatory cells. Blocking the chemotaxis of inflammatory cells by CXCL12 at the inflammatory site may block and alleviate the inflammatory response. Therefore, developing CXCR4 antagonists has become a novel strategy for anti-inflammatory therapy. This review aimed to systematically summarize and analyze the mechanisms of action of the CXCR4/CXCL12 axis in more than 20 inflammatory diseases, highlighting its crucial role in inflammation. Additionally, the anti-inflammatory activities of CXCR4 antagonists were discussed. The findings might help generate new perspectives for developing anti-inflammatory drugs targeting the CXCR4/CXCL12 axis.

Utility of testing for third-generation anticyclic citrullinated peptide (anti-CCP3) antibodies in individuals who present with new musculoskeletal symptoms but have a negative second-generation anticyclic citrullinated peptide (anti-CCP2) antibody test

OBJECTIVES

To investigate the role of third-generation anticyclic citrullinated peptide (anti-CCP3) antibodies in predicting progression to inflammatory arthritis (IA) in individuals with new musculoskeletal (MSK) symptoms and a negative second-generation anti-CCP antibody test (anti-CCP2-).

METHODS

469 anti-CCP2- individuals underwent baseline anti-CCP3 testing (QUANTA Lite CCP3; Inova Diagnostics) and received a post enrolment 12-month questionnaire. A rheumatologist confirmed or excluded diagnosis of IA. Univariable/multivariable analyses were performed to assess the value of anti-CCP3 in predicting IA development in these anti-CCP2- individuals.

RESULTS

Only 16/469 (3.4%) anti-CCP2- individuals had a positive anti-CCP3 test. Of these 16 individuals, 4 developed IA. In addition, 61/469 (13.0%) anti-CCP2- individuals self-reported, to have developed, IA. Progression was confirmed in 43/61 of them (70.5%); of whom 30/43 (69.8%) and 13/43 (30.2%) were given a diagnosis of IA and rheumatoid arthritis (RA), respectively. In qualitative univariable analysis, anti-CCP3 positivity was associated with self-reported progression (p<0.01) and IA (p=0.03), but not with RA. Anti-CCP3 levels differed significantly between progressors and non-progressors (p<0.01) for all three categories. At the manufacturer's cut-off, OR for progression ranged from 2.4 (95% CI 0.5 to 18.6; RA) to 7.5 (95% CI 2.3 to 24.0; self-reported progression). Interestingly, when cut-offs for anti-CCP3 were optimised, lower values (≥5 units) significantly increased the OR for progression in all three categories. In multivariable analysis, anti-CCP3 positivity at the manufacturer's cut-off did not remain associated with IA progression, while this lower cut-off value (≥5 units) was associated with diagnosis of RA (p=0.02).

CONCLUSIONS

Anti-CCP3 testing could improve the prediction of IA development in anti-CCP2- individuals with new MSK symptoms.

Integrative non-pharmacological care for individuals at risk of rheumatoid arthritis

There is increasing knowledge in the recognition of individuals at risk for progression to rheumatoid arthritis (RA) before the clinical manifestation of the disease. This prodromal phase preceding the manifestation of RA may represent a "window of opportunity" for preventive interventions that may transform the clinical approach to this disease. However, limited evidence exists in support of effective interventions to delay the onset or even halt the manifestation of RA. Given the multifactorial nature of RA development and disease progression, the latest guidelines for established RA stress the use of integrative interventions and multidisciplinary care strategies, combining pharmacologic treatment with non-pharmacological approaches. Accordingly, individuals at risk of RA could be offered an integrative, multifactorial intervention approach. Current data point toward pharmacological intervention reverting the subclinical inflammation and delay in the disease onset. In addition, targeting life style modifiable factors (smoking cessation, dental health, physical activity, and diet) may presumably improve RA prognosis in individuals at risk, mainly by changes in epigenetics, autoantibodies, cytokines profiles, and microbiome. Nonetheless, the benefits of multidisciplinary interventions to halt the manifestation of RA in at-risk individuals remain unknown. As there is a growing knowledge of possible pharmacological intervention in the preclinical phase, this narrative review aims to provide a comprehensive overview of non-pharmacological treatments in individuals at risk of RA. Considering the mechanisms preceding the clinical manifestation of RA we explored all aspects that would be worth modifying and that would represent an integrative non-pharmacological care for individuals at risk of RA.

Different Biomarkers of Response to Treatment with Selective Jak-1 Inhibitors in Rheumatoid Arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a systemic autoimmune disease that causes progressive joint damage. The Janus kinase (JAK) inhibitors (JAK-I) represent a new therapeutic option for RA patients, blocking the intracellular JAK-STAT pathway. Today, no studies have been conducted to determine whether new biomarkers could better reflect disease activity in patients treated with JAK-I than traditional disease activity indicators. Thus, the aim of our study was to determine additional disease activity biomarkers in RA patients receiving selective JAK-1 inhibitors.

METHODS

we enrolled 57 patients with RA: 34 patients were treated with Upadacitinib (UPA) and 23 patients with Filgotinib (FIL). All patients were evaluated for clinimetry with DAS28 and Crohn's Disease Activity Index (CDAI), number of tender and swollen joints, Visual Analogic Scale (VAS), Physician Global Assessment (PhGA), and Health Assessment Questionnaire (HAQ), at baseline and at the 12th week of treatment. Lymphocyte subpopulations, complete blood count, erythrocyte sedimentation rate (ESR), C-Reactive Protein (CRP), anti-cyclic citrullinated peptide antibodies (APCA), rheumatoid factor (RF) IgM, interleukin 6 (IL-6), circulating calprotectin (cCLP), tumor necrosis factor α (TNFα), soluble urokinase Plasminogen Activator Receptor (suPAR), complement functional activity were measured at baseline and after the 12th week of treatment.

RESULTS

in both groups of patients, we documented a significant reduction in the clinimetric parameters DAS28, CDAI, number of tender joints, number of swollen joints, VAS, PhGA, and HAQ. Moreover, significant differences were reported for laboratory parameters of ESR, CRP, IL-6, suPAR, cCLP, and PLT/L ratio in both groups. However, no difference was demonstrated between the two groups for changes in renal, hepatic, and lipid parameters.

CONCLUSIONS

the suPAR and cCLP levels may lead towards a different therapeutic choice between UPA and FIL, with the expression of two different RA pathophenotypes directing FIL towards a lymphocyte-poor form and UPA towards a myeloid form of RA.

Autoantibodies testing in autoimmunity: Diagnostic, prognostic and classification value

Diagnosis of autoimmune diseases is in most cases challenging for clinicians as there is not a single specific laboratory or histological marker to diagnose or exclude the presence of the conditions. This review focused on the current knowledge of the role of autoantibodies' testing in various diseases, such as systemic lupus erythematosus, rheumatoid arthritis, antiphospholipid syndrome, undifferentiated connective tissues disease, primary biliary cirrhosis and primary sclerosing cholangitis. Similarly, the prognostic and diagnostic values of autoantibodies testing in patients with interstitial lung disease have been reviewed. In-depth research on the molecular action of these autoantibodies on immune regulation and diseases pathogenesis has been explored beyond their correlation with disease phenotypes, highlighting the impact of autoantibodies targeting on disease outcomes and etiopathogenesis.

Predicting the onset of rheumatoid arthritis

Various scores have attempted to predict the onset of rheumatoid arthritis (RA). In particular, EULAR proposed a simple rule to identify new-onset arthralgia suspicious for progression to RA. However, its specificity would likely be higher if serological tests were included. In patients with clinical arthritis, reliable predictive criteria for progression to RA have also been identified. Overall, the validity of the available scores is still being debated. Such scores do not fully account for the interactions between risk factors in specific subpopulations. New technologies could help to overcome these limitations, but we need databases containing a sufficient number of RA and pre-RA patients, including pre-diagnostic monitoring. Today, the existing predictive rules cannot compete with expert opinions.

Detection of Autoantibodies in Saliva as New Avenue for the Diagnosis and Management of Autoimmune Patients

(1) Background: Autoimmune diseases are characterized by autoantibodies directed to a large number of antigenic targets and are measured using serum as sample matrix. Although serum is a very common specimen type, it comes with certain drawbacks. Most importantly, it depends on venous puncture and requires medical personnel for sampling. This is of particular importance in light of the limited healthcare access of patients with autoimmune diseases during the COVID-19 pandemic. Consequently, alternative sample matrices are being explored for the measurement of autoantibodies. Our study aimed to establish the feasibility of measuring autoantibodies in saliva samples using a novel and highly sensitive method for the detection of autoantibodies. (2) Methods: A total of 48 serum/saliva pairs were collected and tested using a novel particle-based multi-analyte technology (PMAT) system for the presence of a wide range of autoantibodies. (3) Results: A high level of correlation was observed between the results obtained with serum and saliva (Spearman’s rho = 0.725). Study participants clearly preferred saliva over serum sampling as part of the usability assessment. (4) Conclusions: Saliva represents a promising alternative sample matrix for the detection of autoantibodies. The usability study showed a clear preference of saliva over serum as a sample matrix.

Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis

Objective

The study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.

Method

Serum antigens were captured from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), together with sex- and age-matched 30 osteoarthritis (OA) patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then performed. The significantly upregulated and downregulated proteins with fold change > 1.5 (p < 0.05) were selected. Based on these differentially expressed proteins (DEPs), a machine learning model was trained and validated to classify RA, ACPA-positive RA, and ACPA-negative RA.

Results

We identified 62, 71, and 49 DEPs in RA, ACPA-positive RA, and ACPA-negative RA, respectively, as compared to OA and healthy controls. Typical pathway enrichment and protein-protein interaction networks were shown among these DEPs. Three panels were constructed to classify RA, ACPA-positive RA, and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792-1), 0.9913 (95% CI = 0.9653-1), and 1.0 (95% CI = 1-1).

Conclusion

This study illustrated the serum auto-antigen profiling of RA. Among them, three panels of antigens were identified as diagnostic biomarkers to classify RA, ACPA-positive, and ACPA-negative RA patients.

Multicentre study to improve clinical interpretation of rheumatoid factor and anti-citrullinated protein/peptide antibodies test results

BACKGROUND

Rheumatoid factor (RF) and anti-citrullinated protein/peptide antibodies (ACPA) are important biomarkers for diagnosis of rheumatoid arthritis (RA). However, there is poor harmonisation of RF and ACPA assays. The aim of this study was to refine RF and ACPA interpretation across commercial assays.

MATERIALS AND METHODS

Six total RF isotype-non-specific assays, 3 RF IgM isotype-specific assays and 9 ACPA immunoglobulin G assays of 13 different companies were evaluated using 398 diagnostic samples from patients with RA and 1073 disease controls.

RESULTS

Using cut-offs proposed by the manufacturer, there was a large variability in diagnostic sensitivity and specificity between assays. Thresholds of antibody levels were determined based on predefined specificities and used to define test result intervals. Test result interval-specific likelihood ratios (LRs) were concordant across the different RF and ACPA assays. For all assays, the LR for RA increased with increasing antibody level. Higher LRs were found for ACPA than for RF. ACPA levels associated with LRs >80 were found in a substantial fraction (>22%) of patients with RA.

CONCLUSION

Defining thresholds for antibody levels and assigning test result interval-specific LRs allows alignment of clinical interpretation for all RF and ACPA assays.

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

Etiology and Risk Factors for Rheumatoid Arthritis: A State-of-the-Art Review

Rheumatoid arthritis (RA) is the most common systemic inflammatory rheumatic disease. It is associated with significant burden at the patient and societal level. Extensive efforts have been devoted to identifying a potential cause for the development of RA. Epidemiological studies have thoroughly investigated the association of several factors with the risk and course of RA. Although a precise etiology remains elusive, the current understanding is that RA is a multifactorial disease, wherein complex interactions between host and environmental factors determine the overall risk of disease susceptibility, persistence and severity. Risk factors related to the host that have been associated with RA development may be divided into genetic; epigenetic; hormonal, reproductive and neuroendocrine; and comorbid host factors. In turn, environmental risk factors include smoking and other airborne exposures; microbiota and infectious agents; diet; and socioeconomic factors. In the present narrative review, aimed at clinicians and researchers in the field of RA, we provide a state-of-the-art overview of the current knowledge on this topic, focusing on recent progresses that have improved our comprehension of disease risk and development.

Epigenome association study for DNA methylation biomarkers in buccal and monocyte cells for female rheumatoid arthritis

Genetics (i.e., mutations) has been assumed to be the major factor in rheumatoid arthritis (RA) etiology, but accounts for a minority of the variance in disease risk for RA. In contrast to genetics, the environment can have dramatic impacts on epigenetics that associate with disease etiology. The current study used buccal cells and purified blood monocytes from two different clinical cohorts involving Caucasian or African American female populations with or without arthritis. The differential DNA methylation regions (DMRs) between the control and RA populations were identified with an epigenome-wide association study. The DMRs (i.e., epimutations) identified in the buccal cells and monocytes were found to be distinct. The DMR associated genes were identified and many have previously been shown to be associated with arthritis. Observations demonstrate DNA methylation epimutation RA biomarkers are cell type specific and similar findings were observed with the two racial background populations. Rheumatoid arthritis susceptibility epigenetic diagnosis appears feasible and may improve the clinical management of RA and allowpreventative medicine considerations.

Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data

INTRODUCTION

In rheumatoid arthritis, time spent using ineffective medications may lead to irreversible disease progression. Despite availability of targeted treatments, only a minority of patients achieve sustained remission, and little evidence exists to direct the choice of biologic disease-modifying antirheumatic drugs in individual patients. Machine learning was used to identify a rule to predict the response to sarilumab and discriminate between responses to sarilumab versus adalimumab, with a focus on clinically feasible blood biomarkers.

METHODS

The decision tree model GUIDE was trained using a data subset from the sarilumab trial with the most biomarker data, MOBILITY, to identify a rule to predict disease activity after sarilumab 200 mg. The training set comprised 18 categorical and 24 continuous baseline variables; some data were omitted from training and used for validation by the algorithm (cross-validation). The rule was tested using full datasets from four trials (MOBILITY, MONARCH, TARGET, and ASCERTAIN), focusing on the recommended sarilumab dose of 200 mg.

RESULTS

In the training set, the presence of anti-cyclic citrullinated peptide antibodies, combined with C-reactive protein > 12.3 mg/l, was identified as the "rule" that predicts American College of Rheumatology 20% response (ACR20) to sarilumab. In testing, the rule reliably predicted response to sarilumab in MOBILITY, MONARCH, and ASCERTAIN for many efficacy parameters (e.g., ACR70 and the 28-joint disease activity score using CRP [DAS28-CRP] remission). The rule applied less to TARGET, which recruited individuals refractory to tumor necrosis factor inhibitors. The potential clinical benefit of the rule was highlighted in a clinical scenario based on MONARCH data, which found that increased ACR70 rates could be achieved by treating either rule-positive patients with sarilumab or rule-negative patients with adalimumab.

CONCLUSIONS

Well-established and clinically feasible blood biomarkers can guide individual treatment choice. Real-world validation of the rule identified in this post hoc analysis is merited.

CLINICAL TRIAL REGISTRATION

NCT01061736, NCT02332590, NCT01709578, NCT01768572.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Serum levels of the soluble urokinase plasminogen activator receptor (suPAR) correlates with disease activity in early rheumatoid arthritis and reflects joint damage over time

Soluble urokinase plasminogen activator receptor (suPAR) is intensively studied as a biomarker of inflammation and disease outcome in various diseases. In rheumatoid arthritis (RA), suPAR have shown an association with inflammation and swollen joints, but data on suPAR in relation to early disease course and disease progression are lacking. This study investigates the potential of suPAR to predict or reflect disease outcome in early RA. Serum suPAR was measured by enzyme-linked immunosorbent assay at disease onset and after 3 and 36 months in 252 patients from a Swedish prospective observational early RA cohort. Levels and changes of suPAR were analyzed in relation to the 28-joint disease activity score (DAS28) and joint damage according to the Larsen score at inclusion and during follow-up. 100 healthy blood donors served as controls. Circulating levels of suPAR were higher in RA patients at all time points as compared to healthy controls. Baseline suPAR was significantly associated with baseline disease activity whereas suPAR levels at 36 months were associated with joint damage at 36 months. No predictive value of suPAR levels or changes in suPAR levels over time were found. In conclusion, suPAR levels associate with disease activity in early untreated RA and reflects joint damage at later stages. Increased suPAR in established RA could indicate patients in need of frequent monitoring of joint status, irrespective of disease activity. In the view of suPAR as a rapidly emerging biomarker, it is important to be aware of its ability to reflect both inflammation and subsequent damage.

Autoantibody Discovery, Assay Development and Adoption: Death Valley, the Sea of Survival and Beyond

Dating to the discovery of the Lupus Erythematosus (LE) cell in 1948, there has been a dramatic growth in the discovery of unique autoantibodies and their cognate targets, all of which has led to the availability and use of autoantibody testing for a broad spectrum of autoimmune diseases. Most studies of the sensitivity, specificity, commutability, and harmonization of autoantibody testing have focused on widely available, commercially developed and agency-certified autoantibody kits. However, this is only a small part of the spectrum of autoantibody tests that are provided through laboratories world-wide. This manuscript will review the wider spectrum of testing by exploring the innovation pathway that begins with autoantibody discovery followed by assessment of clinical relevance, accuracy, validation, and then consideration of regulatory requirements as an approved diagnostic test. Some tests are offered as "Research Use Only (RUO)", some as "Laboratory Developed Tests (LDT)", some enter Health Technology Assessment (HTA) pathways, while others are relegated to a "death valley" of autoantibody discovery and become "orphan" autoantibodies. Those that achieve regulatory approval are further threatened by the business world's "Darwinian Sea of Survival". As one example of the trappings of autoantibody progression or failure, it is reported that more than 200 different autoantibodies have been described in systemic lupus erythematosus (SLE), a small handful (~10%) of these have achieved regulatory approval and are widely available as commercial diagnostic kits, while a few others may be available as RUO or LDT assays. However, the vast majority (90%) are orphaned and languish in an autoantibody 'death valley'. This review proposes that it is important to keep an inventory of these "orphan autoantibodies" in 'death valley' because, with the increasing availability of multi-analyte arrays and artificial intelligence (MAAI), some can be rescued to achieve a useful role in clinical diagnostic especially in light of patient stratification and precision medicine.

Digital Twins for Multiple Sclerosis

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

The pre-clinical phase of rheumatoid arthritis: From risk factors to prevention of arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease considered as a multistep process spanning from the interaction of genetic (e.g., shared epitope or non-HLA loci), environmental and behavioral risk factors (e.g., smoking) leading to breaking immune tolerance and autoimmune processes such as the production of autoantibodies (e.g., antibodies against citrullinated proteins ACPA or rheumatoid factors, RF), development of the first symptoms without clinical arthritis, and, finally, the manifestation of arthritis. Despite the typical joint involvement in established RA, the pathogenesis of the disease likely begins far from joint structures: in the lungs or periodontium in association with citrullination, intestinal microbiome, or adipose tissue, which supports normal findings in synovial tissue in ACPA+ patients with arthralgia. The presence of ACPA is detectable even years before the first manifestation of RA. The pre-clinical phase of RA is the period preceding clinically apparent RA with ACPA contributing to the symptoms without subclinical inflammation. While the combination of ACPA and RF increases the risk of progression to RA by up to 10 times, increasing numbers of novel autoantibodies are to be investigated to contribute to the increased risk and pathogenesis of RA. With growing knowledge about the course of RA, new aspiration emerges to cure and even prevent RA, shifting the "window of opportunity" to the pre-clinical phases of RA. The clinical definition of individuals at risk of developing RA (clinically suspect arthralgia, CSA) makes it possible to unify these at-risk individuals' clinical characteristics for "preventive" treatment in ongoing clinical trials using mostly biological or conventional synthetic disease-modifying drugs. However, the combination of symptoms, laboratory, and imaging biomarkers may be the best approach to select the correct target at-risk population. The current review aims to explore different phases of RA and discuss the potential of (non)pharmacological intervention aiming to prevent RA.

Consensus terminology for preclinical phases of psoriatic arthritis for use in research studies: results from a Delphi consensus study

The concept of psoriatic arthritis (PsA) prevention is gaining increased interest owing to the physical limitation, poor quality of life and low remission rates that are achieved with current therapies for PsA. The psoriasis-to-PsA transition offers a unique opportunity to identify individuals at increased risk of developing PsA and to implement preventive strategies. However, identifying individuals at increased risk of developing PsA is challenging as there is no consensus on how this population should be defined. This Consensus Statement puts forward recommended terminology from the Psoriasis and Psoriatic Arthritis Clinics Multicenter Advancement Network (PPACMAN) for defining specific subgroups of individuals during the preclinical and early clinical phases of PsA to be used in research studies. Following a three-round Delphi process, consensus was reached for three terms and definitions: 'increased risk for PsA', 'psoriasis with asymptomatic synovio-entheseal imaging abnormalities' and 'psoriasis with musculoskeletal symptoms not explained by other diagnosis'. These terms and their definitions will enable improved identification and standardization of study populations in clinical research. In the future, as increasing evidence emerges regarding the molecular and clinical features of the psoriasis-to-PsA continuum, these terms and definitions will be further refined and updated.

From Rheumatoid Factor to Anti-Citrullinated Protein Antibodies and Anti-Carbamylated Protein Antibodies for Diagnosis and Prognosis Prediction in Patients with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease mainly involving synovial inflammation and articular bone destruction. RA is a heterogeneous disease with diverse clinical presentations, prognoses and therapeutic responses. Following the first discovery of rheumatoid factors (RFs) 80 years ago, the identification of both anti-citrullinated protein antibodies (ACPAs) and anti-carbamylated protein antibodies (anti-CarP Abs) has greatly facilitated approaches toward RA, especially in the fields of early diagnosis and prognosis prediction of the disease. Although these antibodies share many common features and can function synergistically to promote disease progression, they differ mechanistically and have unique clinical relevance. Specifically, these three RA associating auto-antibodies (autoAbs) all precede the development of RA by years. However, while the current evidence suggests a synergic effect of RF and ACPA in predicting the development of RA and an erosive phenotype, controversies exist regarding the additive value of anti-CarP Abs. In the present review, we critically summarize the characteristics of these autoantibodies and focus on their distinct clinical applications in the early identification, clinical manifestations and prognosis prediction of RA. With the advancement of treatment options in the era of biologics, we also discuss the relevance of these autoantibodies in association with RA patient response to therapy.

The immunology of rheumatoid arthritis

The immunopathogenesis of rheumatoid arthritis (RA) spans decades, beginning with the production of autoantibodies against post-translationally modified proteins (checkpoint 1). After years of asymptomatic autoimmunity and progressive immune system remodeling, tissue tolerance erodes and joint inflammation ensues as tissue-invasive effector T cells emerge and protective joint-resident macrophages fail (checkpoint 2). The transition of synovial stromal cells into autoaggressive effector cells converts synovitis from acute to chronic destructive (checkpoint 3). The loss of T cell tolerance derives from defective DNA repair, causing abnormal cell cycle dynamics, telomere fragility and instability of mitochondrial DNA. Mitochondrial and lysosomal anomalies culminate in the generation of short-lived tissue-invasive effector T cells. This differentiation defect builds on a metabolic platform that shunts glucose away from energy generation toward the cell building and motility programs. The next frontier in RA is the development of curative interventions, for example, reprogramming T cell defects during the period of asymptomatic autoimmunity.

Beyond the joints, the extra-articular manifestations in rheumatoid arthritis

Rheumatoid arthritis (RA) is an inflammatory disease typically affecting the joints, but the systemic inflammatory process may involve other tissues and organs. Many extra-articular manifestations are recognized, which are related to worse long outcomes. Rheumatoid nodules are the most common extra-articular feature, found in about 30% of patients. Secondary Sjögren's syndrome and pulmonary manifestations are observed in almost 10% of patients, also in the early disease. Active RA with high disease activity has been associated with an increased risk of such features. Male gender, smoking habit, severe joint disease, worse function, high pro-inflammatory markers levels, high titer of rheumatoid factor, and HLA-related shared epitope have been reported as clinical predictors of occurrence of these rheumatoid complications. In addition, there is a little evidence deriving from randomized controlled trials in this field, thus the therapeutic strategy is mainly empiric and based on small case series and retrospective studies. However, considering that these extra-articular manifestations are usually related to the more active and severe RA, an aggressive therapeutic strategy is usually employed in view of the poor outcomes of these patients. The extra-articular features of RA remain, despite the improvement of joint damage, a major diagnostic and therapeutic challenge, since these are associated with a poor prognosis and need to be early recognized and promptly managed.

The growing role of precision medicine for the treatment of autoimmune diseases; results of a systematic review of literature and Experts' Consensus

Autoimmune diseases (AIDs) share similar serological, clinical, and radiological findings, but, behind these common features, there are different pathogenic mechanisms, immune cells dysfunctions, and targeted organs. In this context, multiple lines of evidence suggest the application of precision medicine principles to AIDs to reduce the treatment failure. Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient, thus it could be a new approach for management of AIDS which considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. In this work, the growing body of evidence is summarized regarding the predictive factors for drug response in patients with AIDs, applying the precision medicine principles to provide high-quality evidence for therapeutic opportunities in improving the management of these patients.

An updated advance of autoantibodies in autoimmune diseases

Autoantibodies are abnormal antibodies which are generated by pathogenic B cells when targeting an individual's own tissue. Autoantibodies have been identified as a symbol of autoimmune disorders and are frequently considered a clinical marker of these disorders. Autoimmune diseases, including system lupus erythematosus and rheumatoid arthritis, consist of a series of disorders that share some similarities and differences. They are characterized by chronic, systemic, excessive immune activation and inflammation and involve in almost all body tissues. Autoimmune diseases occur more frequently in women than men due to hormonal impacts. In this review we systemically introduce and summarize the latest advances of various autoantibodies in multiple autoimmune diseases.

Contribution of Multiplex Immunoassays to Rheumatoid Arthritis Management: From Biomarker Discovery to Personalized Medicine

Rheumatoid arthritis (RA) is a multifactorial, inflammatory and progressive autoimmune disease that affects approximately 1% of the population worldwide. RA primarily involves the joints and causes local inflammation and cartilage destruction. Immediate and effective therapies are crucial to control inflammation and prevent deterioration, functional disability and unfavourable progression in RA patients. Thus, early diagnosis is critical to prevent joint damage and physical disability, increasing the chance of achieving remission. A large number of biomarkers have been investigated in RA, although only a few have made it through the discovery and validation phases and reached the clinic. The single biomarker approach mostly used in clinical laboratories is not sufficiently accurate due to its low sensitivity and specificity. Multiplex immunoassays could provide a more complete picture of the disease and the pathways involved. In this review, we discuss the latest proposed protein biomarkers and the advantages of using protein panels for the clinical management of RA. Simultaneous analysis of multiple proteins could yield biomarker signatures of RA subtypes to enable patients to benefit from personalized medicine.