Association of a Serum Protein Signature With Rheumatoid Arthritis Development

The Impact of Rheumatoid Arthritis on First Nations and How We Can Work With Communities to Prevent It

Rheumatoid arthritis (RA) is prevalent in many Indigenous North American First Nations (FN) and tends to be seropositive, familial, and disabling, as well as associated with highly unfavorable outcomes such as early mortality. The risk of developing RA is based on a perfect storm of gene-environment interactions underpinning this risk. The gene-environment interactions include a high frequency of shared epitope encoding HLA alleles, particularly HLA-DRB1*1402, in the background population, and prevalent predisposing environmental factors such as smoking and periodontal disease. Together, these provide a compelling rationale for an RA prevention agenda in FN communities. Our research team has worked in partnership with several FN communities to prospectively follow the first-degree relatives of FN patients with RA, with the aim of better understanding the preclinical stages of RA in this population. We have focused on specific features of the anticitrullinated protein antibodies (ACPA) and other proteomic biomarkers as predictors of future development of RA. These studies have now led us to consider interventions having a favorable risk-benefit ratio if applied at a stage prior to a hypothetical "point of no return," when the autoimmunity potentially becomes irreversible. Based on a supportive mouse model and available human studies of curcumin, omega-3, and vitamin D supplements, we are undertaking studies where we screen communities using dried blood spot technology adapted for the detection of ACPA, and then enrolling ACPA-positive individuals in studies that use a combination of these supplements. These studies are guided by shared decision-making principles.

Role of inter-alpha-trypsin inhibitor heavy chain 4 and its citrullinated form in experimental arthritis murine models

Inter-α-trypsin inhibitor heavy chain 4 (ITIH4) is a major protein in serum and reported to be upregulated at the onset of rheumatoid arthritis (RA). Its citrullinated form, cit-ITIH4, is specifically found in the serum and synovial fluid of patients with RA. However, the detailed function of ITIH4 in arthritis remains unknown. The aim of this study was to clarify the role of ITIH4 and cit-ITIH4 using experimental arthritis models. ITIH4 and cit-ITIH4 expression was examined in steady-state mice and two different arthritis models, and their pathological effects were examined in Itih4-deficient mice. In naïve C57BL/6 (WT) mice, ITIH4 was expressed as mRNA in the liver and the lung and was expressed as protein in serum and hepatocytes. In K/BxN serum transferred arthritis (K/BxN-STA) and collagen-induced arthritis (CIA), ITIH4 and cit-ITIH4 in sera were increased before the onset of arthritis, and cit-ITIH4 was further increased at the peak of arthritis. In Itih4-deficient mice, citrullinated proteins in serum and joints, especially 120 kDa protein, were clearly diminished; however, there was no significant difference in arthritis severity between WT and itih-/- mice either in the K/BxN-STA or CIA model. CIA mice also exhibited pulmonary lesions and itih4-/- mice tended to show enhanced inflammatory cell aggregation compared to WT mice. Neutrophils in the lungs of itih4-/- mice were significantly increased compared to WT mice. In summary, ITIH4 itself did not alter the severity of arthritis but may inhibit autoimmune inflammation via suppression of neutrophil recruitment.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.

The application of Aptamer in biomarker discovery

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Physical pain among Indigenous Peoples in Canada: a scoping review

PURPOSE

Pain is a multifaceted experience shaped by various factors including context of pain, previous life events, and ongoing ethnocultural circumstances. Moreover, the definition of pain is inconsistent across cultures. Western medicine views physical pain (e.g., associated with a bone fracture) and nonphysical mental pain (e.g., depression) as two distinct conditions. Indigenous perspectives are often more wholistic, encompassing mental, spiritual, emotional, and physical hurt. The subjective nature of pain invites ample opportunity for discrimination in both its assessment and management. As such, it is important to consider Indigenous perspectives of pain in research and clinical practice. To investigate which aspects of Indigenous pain knowledge are currently considered by Western research, we conducted a scoping review of the literature on pain in Indigenous Peoples of Canada.

SOURCE

In June 2021, we searched nine databases and downloaded 8,220 papers after removal of duplicates. Two independent reviewers screened abstracts and full-text articles.

PRINCIPLE FINDINGS

Seventy-seven papers were included in the analysis. Using grounded theory, five themes emerged: pain measures/scales (n = 7), interventions (n = 13), pharmaceuticals (n = 17), pain expression/experiences (n = 45), and pain conditions (n = 70).

CONCLUSION

This scoping review shows that there is a paucity of research on pain measurement in Indigenous Peoples of Canada. This finding is concerning in light of numerous studies reporting that Indigenous Peoples experience their pain as ignored, minimized, or disbelieved. Furthermore, a clear disconnect emerged between pain expression in Indigenous Peoples and assessment in medical professionals. We hope that this scoping review will serve to translate current knowledge to other non-Indigenous academics and to initiate meaningful collaboration with Indigenous partners. Future research led by Indigenous academics and community partners is critically needed to better address pain needs in Canada.

The regulation and functions of the matricellular CCN proteins induced by shear stress

Shear stress is a frictional drag generated by the flow of fluid, such as blood or interstitial fluid, and plays a critical role in regulating cellular gene expression and functional phenotype. The matricellular CCN family proteins are dynamically regulated by shear stress of different flow patterns, and their expression significantly alters the microenvironment of cells. Secreted CCN proteins mainly bind to several cell surface integrin receptors to mediate their diverse functions in regulating cell survival, function, and behavior. Gene-knockout studies indicate major functions of CCN proteins in the cardiovascular and skeletal systems, the two primary systems in which CCN expressions are regulated by shear stress. In the cardiovascular system, the endothelium is directly exposed to vascular shear stress. Unidirectional laminar blood flow generates laminar shear stress, which promotes a mature endothelial phenotype and upregulates anti-inflammatory CCN3 expression. In contrast, disturbed flow generates oscillatory shear stress, which induces endothelial dysfunction through the induction of CCN1 and CCN2. Shear-induced CCN1 binds to integrin α6β1 and promotes superoxide production, NF-κB activation, and inflammatory gene expression in endothelial cells. Although the interaction between shear stress and CCN4-6 is not clear, CCN 4 exhibits a proinflammatory property and CCN5 inhibits vascular cell growth and migration. The crucial roles of CCN proteins in cardiovascular development, homeostasis, and disease are evident but not fully understood. In the skeletal system, mechanical loading on bone generates shear stress from interstitial fluid in the lacuna-canalicular system and promotes osteoblast differentiation and bone formation. CCN1 and CCN2 are induced and potentially mediate fluid shear stress mechanosensing in osteocytes. However, the exact roles of interstitial shear stress-induced CCN1 and CCN2 in bone are still not clear. In contrast to other CCN family proteins, CCN3 inhibits osteoblast differentiation, although its regulation by interstitial shear stress in osteocytes has not been reported. The induction of CCN proteins by shear stress in bone and their functions remain largely unknown and merit further investigation. This review discusses the expression and functions of CCN proteins regulated by shear stress in physiological conditions, diseases, and cell culture models. The roles between CCN family proteins can be compensatory or counteractive in tissue remodeling and homeostasis.

Serum proteomic networks associate with pre-clinical rheumatoid arthritis autoantibodies and longitudinal outcomes

Objectives

The development of autoantibody directed towards citrullinated proteins (ACPA) are predictive of RA in at-risk individuals. The biological events that underpin loss of immune tolerance and progression into inflammatory arthritis are not known. We sought to identify serum proteomic alterations that drive autoantibody formation, persistence and progression into inflammatory arthritis in a cohort of first-degree relatives (FDR) of RA patients.

Methods

We studied baseline serum samples from a cohort of Indigenous FDR (n = 147) and quantified serum proteins using a 48-plex platform. Longitudinal outcomes were defined on the basis of ACPA status and progression into inflammatory arthritis (IA). K-means clustering, differential expression, and principal components analyze group differences. A co-expression module analysis was used to identify enriched networks. Random forest was used to classify ACPA positive samples, while network analysis was used to understand underlying biological processes based on protein expression.

Results

We defined 6 proteomic clusters, with enrichment of ACPA positive samples in one of the clusters. 23 of 24 differentially expressed proteins in ACPA positive samples were upregulated. A co-expression network was enriched in ACPA positive sera and individuals who progressed into IA. Random Forest achieved an area under the curve of 0.767 to classify ACPA positive sera in a test dataset. Network analysis revealed upregulation of JAK-STAT signalling as being activated in those at highest risk to develop future IA.

Conclusions

The serum proteome provides a rich dataset to understand biological processes in ACPA seropositive individuals. A combination of serum biomarkers, including ACPA, may predict future arthritis onset in at-risk individuals.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

A Roadmap for Investigating Preclinical Autoimmunity Using Patient-Oriented and Epidemiologic Study Designs: Example of Rheumatoid Arthritis

Background & Aims

Rheumatoid arthritis (RA) is a prototypic autoimmune disease causing inflammatory polyarthritis that affects nearly 1% of the population. RA can lead to joint destruction and disability along with increased morbidity and mortality. Similar to other autoimmune diseases, RA has distinct preclinical phases corresponding to genetic risk, lifestyle risk factors, autoantibody development, and non-specific symptoms prior to clinical diagnosis. This narrative review will detail observational studies for RA risk and clinical trials for RA prevention as a roadmap to investigating preclinical autoimmunity that could be applied to other diseases.

Methods

In this narrative review, we summarized previous and ongoing research studies investigating RA risk and prevention, categorizing them related to their design and preclinical phases.

Results

We detailed the following types of studies investigating RA risk and prevention: retrospective population-based and administrative datasets; prospective studies (case-control and cohort; some enrolling based on genetics, first-degree relative status, elevated biomarkers, or early symptoms/arthritis); and randomized clinical trials. These correspond to all preclinical RA phases (genetic, lifestyle, autoimmunity, early signs/symptoms). Previous and ongoing randomized controlled trials have enrolled individuals at very elevated risk for RA based on biomarkers, symptoms, imaging abnormalities, or early signs/symptoms.

Conclusion

We detailed the rich variety of study designs that is necessary to investigate distinct preclinical phases of an autoimmune disease such as RA. However, further progress is needed to fully elucidate the pathogenesis of RA that may ultimately lead to prevention or delay of disease onset.

Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis

Objectives

Patients with Rheumatoid Arthritis (RA) are increasingly achieving stable disease remission, yet the mechanisms that govern ongoing clinical disease and subsequent risk of future flare are not well understood. We sought to identify serum proteomic alterations that dictate clinically important features of stable RA, and couple broad-based proteomics with machine learning to predict future flare.

Methods

We studied baseline serum samples from a cohort of stable RA patients (RETRO, n = 130) in clinical remission (DAS28<2.6) and quantified 1307 serum proteins using the SOMAscan platform. Unsupervised hierarchical clustering and supervised classification were applied to identify proteomic-driven clusters and model biomarkers that were associated with future disease flare after 12 months of follow-up and RA medication withdrawal. Network analysis was used to define pathways that were enriched in proteomic datasets.

Results

We defined 4 proteomic clusters, with one cluster (Cluster 4) displaying a lower mean DAS28 score (p = 0.03), with DAS28 associating with humoral immune responses and complement activation. Clustering did not clearly predict future risk of flare, however an XGboost machine learning algorithm classified patients who relapsed with an AUC (area under the receiver operating characteristic curve) of 0.80 using only baseline serum proteomics.

Conclusions

The serum proteome provides a rich dataset to understand stable RA and its clinical heterogeneity. Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy.

Functional disability to evaluate the risk of arthritis in First-degree relatives of Rheumatoid Arthritis patients

OBJECTIVE

The events that occur prior to the onset of rheumatoid arthritis (RA) continue to be delineated. We examined the relationship between self-reported joint symptoms, functional disability, and anticitrullinated protein antibody (ACPA) status in a cohort of first-degree relatives (FDR) of RA patients who are at risk of future disease development.

METHODS

We studied a cohort of 607 FDR of First Nations (FN) RA patients who are at increased risk for future RA development, and analyzed data collected at their enrollment study visit. In parallel, we analyzed data from 279 FN People with no family history of RA. A subset of FDR developed inflammatory arthritis and we analyzed longitudinal data in this group.

RESULTS

The prevalence of joint symptoms and functional disability was higher in FDR compared to non- FDR (all p<0.001). Difficulty walking (37.3% vs 18.0%) and mHAQ were higher in ACPA positive FDR compared to ACPA negative FDR, and mHAQ was independently associated with ACPA seropositivity (OR: 2.79, 1.56-5.00). Longitudinally, in individuals who developed ACPA+ RA, ACPA level and mHAQ score were significantly associated (R = 0.43, p< 0.001) in the preclinical period.

CONCLUSION

Compared to population-based controls, FDR have a high burden of joint symptoms and functional disability. Functional disability was most closely associated with ACPA seropositivity in the FDR, suggesting a direct role for ACPA outside of the context of clinically detectable synovitis. mHAQ appears to be particularly valuable in the assessment of individuals at risk for future RA development.

Lyme arthritis: linking infection, inflammation and autoimmunity

Infectious agents can trigger autoimmune responses in a number of chronic inflammatory diseases. Lyme arthritis, which is caused by the tick-transmitted spirochaete Borrelia burgdorferi, is effectively treated in most patients with antibiotic therapy; however, in a subset of patients, arthritis can persist and worsen after the spirochaete has been killed (known as post-infectious Lyme arthritis). This Review details the current understanding of the pathogenetic events in Lyme arthritis, from initial infection in the skin, through infection of the joints, to post-infectious chronic inflammatory arthritis. The central feature of post-infectious Lyme arthritis is an excessive, dysregulated pro-inflammatory immune response during the infection phase that persists into the post-infectious period. This response is characterized by high amounts of IFNγ and inadequate amounts of the anti-inflammatory cytokine IL-10. The consequences of this dysregulated pro-inflammatory response in the synovium include impaired tissue repair, vascular damage, autoimmune and cytotoxic processes, and fibroblast proliferation and fibrosis. These synovial characteristics are similar to those in other chronic inflammatory arthritides, including rheumatoid arthritis. Thus, post-infectious Lyme arthritis provides a model for other chronic autoimmune or autoinflammatory arthritides in which complex immune responses can be triggered and shaped by an infectious agent in concert with host genetic factors.

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.