Evidence of Diagnostic and Treatment Delay in Seronegative Rheumatoid Arthritis: Missing the Window of Opportunity

Clinical Features of ACPA-Negative and ACPA-Positive Variants of Rheumatoid Arthritis

The aim of the study was to investigate the features of the clinical picture of the disease in patients with ACPA-negative and ACPA-positive variants of rheumatoid arthritis.

MATERIALS AND METHODS

: The study included patients with a reliable diagnosis of rheumatoid arthritis (RA) according to the criteria of ACR/EULAR 2010. Depending on the ACPA values, two groups of patients were recruited: ACPA-positive and ACPA-negative, comparable in gender, age, duration of the disease, and therapy. The nature of the onset and course of the disease and the activity of RA were evaluated (according to the DAS28, SDAI, CDAI indices).

RESULTS AND DISCUSSION

: The study involved 79 patients with ACPA-negative variant of RA and 79 ACPA-positive patients. The age of patients (Me [IR] (in years)) with the ACPA(-) variant was 52 [39; 62]; with the ACPA(+) variant, 54 [42; 62]; the duration of the disease (in months) was 59 [23; 122] and 48 [17; 84], respectively. In ACPA(+) patients, a higher disease activity was determined (by the indices DAS 28crp, DAS28esr, SDAI, CDAI), higher values of C-reactive protein and erythrocyte sedimentation rate, and a greater number of painful and swollen joints (p < 0.05). According to the localization of the involved joints, arthritis of the proximal interphalangeal, metacarpal, wrist and shoulder joints was more often determined in ACPA(+) patients. Systemic manifestations of RA at the time of examination and in the anamnesis were statistically significantly more common in ACPA(+) (32.9%) than in ACPA(-) (17.7%) patients. Of the systemic manifestations, rheumatoid nodules were more common in ACPA(+) patients, whereas a tendency to a higher frequency of neuropathy, sclerites, and episcleritis was revealed in ACPA(-) patients.

CONCLUSIONS

. In patients with ACPA(-) subtype, clinical signs of joint damage and the inflammatory component are less pronounced compared to ACPA(+). However, the mixed picture of manifestation, the less "bright" course of the disease, the absence of characteristic immunological biomarkers necessitate long-term and careful monitoring of this group of patients. At the same time, the subjective severity of the disease and dysfunction due to ankylosing joints do not differ from the ACPA(+) variant of RA.

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis

Objective

Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA.

Methods

We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation.

Results

The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA.

Conclusion

Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.

Evaluation of Choroidal Thickness and Choroidal Vascularity Index in Patients with Rheumatoid Arthritis

PURPOSE

To evaluate the choroidal vascularity index and choroidal thickness in patients with rheumatoid arthritis.

METHOD

This study is a case control study. Our study consists of a total of two groups, with 32 individuals diagnosed with rheumatoid arthritis (RA) and 32 healthy volunteers. The thickness of the subfoveal choroid was measured from the 500 micron (µm), 1000 µm, 1500 µm nasal aspect of the fovea, and 500µm, 1000µm, 1500 µm temporal and subfoveal thickness of the fovea. ImageJ version 1.53i (National Institutes of Health, Bethesda, MD, USA) from open access was used for choroidal vascular index calculation.

RESULTS

The mean age (p = 0.064) and gender distribution (p = 0.522) were not statistically different between these two groups. There was no difference between the groups in terms of visual acuity (p = 0.060), intraocular pressures (p=0.056), refractive errors (p = 0.418), and axial lengths (p = 0.280). Temporal 500 µm CT (p = 0.038), temporal 1000 µm CT (p = 0.010), and temporal 1500 µm CT (p = 0.005) differed significantly between the groups. The luminal area was significantly different between the RA group (842.71 ± 192.77) and the control group (957.78 ± 230.83) (p = 0.034). The choroidal vascularity index showed a significant difference between the RA group (64.99 ± 4.71) and the control group (67.34 ± 3.40) (p = 0.026). A significant difference was observed between the seronegative RA and the control group with temporal 1500 µm CT (p = 0.030), temporal 1000 µm CT (p = 0.023), and luminal area (p = 0.034).

CONCLUSION

We demonstrated thinning in CT and decreased CVI for the first time in RA patients by comparing it with the control group.

Identification of serum exosomal miRNA biomarkers for diagnosis of Rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation-induced joint damage, which can cause lasting disability. Therefore, early diagnosis and treatment of RA are crucial. Herein, we evaluated whether exosomal microRNAs (miRNAs) could be served as promising biomarkers that can accelerate the diagnosis of RA and development of therapies for RA.

METHODS

First, we performed small RNA sequencing to determine the miRNA profiles of serum exosomes within a screening cohort comprised of 18 untreated active RA patients, along with 18 age and gender-matched healthy controls (HCs). Subsequently, the miRNA profiles were then validated in a training cohort consisting of 24 RA patients and 24 HCs by RT-qPCR. Finally, the selected exosomal miRNAs were validated in a larger cohort comprising 108 RA patients and 103 HCs. The diagnostic efficacy of the exosomal miRNAs was evaluated by receiver operating characteristic (ROC) curve analysis. Biological functions of the miRNAs were determined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.

RESULTS

Our results first demonstrated a noteworthy upregulation of three candidate miRNAs (miR-885-5p, miR-6894-3p, and miR-1268a) in the RA patients' serum exosomes compared to HCs. The combination of three miRNAs along with anti- citrullinated peptide antibodies (ACPA) exhibited excellent diagnostic accuracy, yielding an area under the curve (AUC) of 0.963 (95 % CI : 0.941-0.984), sensitivity of 87.96 %, and specificity of 93.20 %. Notably, miR-885-5p exhibited remarkable discriminatory capacity by itself in indistinguishing ACPA- negative RA patients from HCs, with an AUC of 0.993 (95 % CI : 0.978-1.000), sensitivity of 96.67 %, and specificity of 100 %. Moreover, the expression of miR-1268a in the assessment of therapeutic effectiveness displayed significant reduction on 29th day of Methotrexate (MTX) treatment in RA patients. This decreased expression paralleled with trends observed in tender 28-joint count (TJC28), swollen 28-joint count (SJC28), and disease activity score with 28-joint count using C-reactive protein (DAS28-CRP), all of which are indicative of RA disease activity. Finally, predictive analysis indicated that, these three exosomal miRNAs target pivotal signaling molecules involved in inflammatory pathways, thereby demonstrating effective modulation of the immune system.

CONCLUSIONS

In this study, we successfully demonstrated the promising potential for serum exosomal miRNAs, particularly miR-885-5p, miR-6894-3p and miR-1268a as biomarkers for early diagnosis and prediction of RA for the first time.

Performance of Anti-Carbamylated Protein Antibody Testing in the Routine Evaluation of Rheumatoid Arthritis from a Single Center

BACKGROUND

Detection of anticyclic citrullinated peptide antibodies (anti-CCP) and rheumatoid factors (RF) in sera support the diagnosis of rheumatoid arthritis (RA); however, these markers are not detected in about 20% of RA patients. More recently, antibodies against carbamylated proteins (anti-CarP) have emerged with implications for preclinical RA diagnosis. The objective of this study was to assess the clinical performance of anti-CarP and correlate with disease severity in routine clinical practice.

METHODS

Retrospective chart review of 331 subjects submitted for RA panel serology: 136 clinically defined RA-positive and 195 RA-negative patients. Fifty additional individuals were recruited for healthy controls. Patients' sera were tested for anti-CCP, anti-CarP, and RF antibodies. Clinical performance characteristics were evaluated for anti-CarP individually and in combination with anti-CCP and RF. Documented erosions and synovitis were correlated with anti-CarP positivity.

RESULTS

Anti-CarP had a clinical sensitivity and specificity of 27% and 94%, respectively, for established RA. This sensitivity was lower than anti-CCP (79%) and RF (85%). The specificity of anti-CarP was similar to anti-CCP (93%) and higher than RF (69%). Anti-CarP in combination with anti-CCP and RF increased specificity (100%) but decreased sensitivity (21%). There was no correlation of anti-CarP positivity with presence of bone erosions; however, there was an increase in anti-CarP positivity among patients with synovitis.

CONCLUSIONS

Anti-CarP demonstrates high specificity in diagnosis of established RA but lacks clinical sensitivity. In combination, anti-CarP does not improve clinical performance of anti-CCP and RF but may be useful in anti-CCP negative patients and in identifying patients with more active disease.

Association between sinusitis and incident rheumatic diseases: a population-based study

OBJECTIVES

To determine whether antecedent sinusitis is associated with incident rheumatic disease.

METHODS

This population-based case-control study included all individuals meeting classification criteria for rheumatic diseases between 1995 and 2014. We matched three controls to each case on age, sex and length of prior electronic health record history. The primary exposure was presence of sinusitis, ascertained by diagnosis codes (positive predictive value 96%). We fit logistic regression models to estimate ORs for incident rheumatic diseases and disease groups, adjusted for confounders.

RESULTS

We identified 1729 incident rheumatic disease cases and 5187 matched controls (mean age 63, 67% women, median 14 years electronic health record history). After adjustment, preceding sinusitis was associated with increased risk of several rheumatic diseases, including antiphospholipid syndrome (OR 7.0, 95% CI 1.8 to 27), Sjögren's disease (OR 2.4, 95% CI 1.1 to 5.3), vasculitis (OR 1.4, 95% CI 1.1 to 1.9) and polymyalgia rheumatica (OR 1.4, 95% CI 1.0 to 2.0). Acute sinusitis was also associated with increased risk of seronegative rheumatoid arthritis (OR 1.8, 95% CI 1.1 to 3.1). Sinusitis was most associated with any rheumatic disease in the 5-10 years before disease onset (OR 1.7, 95% CI 1.3 to 2.3). Individuals with seven or more codes for sinusitis had the highest risk for rheumatic disease (OR 1.7, 95% CI 1.3 to 2.4). In addition, the association between sinusitis and incident rheumatic diseases showed the highest point estimates for never smokers (OR 1.7, 95% CI 1.3 to 2.2).

CONCLUSIONS

Preceding sinusitis is associated with increased incidence of rheumatic diseases, suggesting a possible role for sinus inflammation in their pathogenesis.

Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography

PURPOSE

To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance.

MATERIALS AND METHODS

A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients. Binary discriminatory capacity (no arthritis versus arthritis; RA versus not RA) and three-way classification (no arthritis versus OA versus RA) were evaluated. The effects of additional pretraining using musculoskeletal radiographs, using all views as opposed to only the posteroanterior view, and varying image resolution on model performance were also investigated. Area under the receiver operating characteristic curve (AUC) and Cohen's kappa coefficient were used to evaluate diagnostic performance.

RESULTS

For no arthritis versus arthritis, the model achieved an AUC of 0.975 (95% CI: 0.957, 0.989). For RA versus not RA, the model achieved an AUC of 0.955 (95% CI: 0.919, 0.983). For three-way classification, the model achieved a kappa of 0.806 (95% CI: 0.742, 0.866) and accuracy of 87.2% (95% CI: 83.2%, 91.2%) on the test set. Increasing image resolution increased performance up to 1024 × 1024 pixels. Additional pretraining on musculoskeletal radiographs and using all views did not significantly affect performance.

CONCLUSION

A deep learning model can be used to distinguish no arthritis, OA, and RA on hand radiographs with high performance.

Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools.

METHODS

The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes.

RESULTS

There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs' differences among subtypes using the ssGSEA algorithm and subtype grouping.

CONCLUSIONS

The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.

Diagnostic Value of Short Course Low-dose Prednisolone in Patients with Clinically Suspected Seronegative Inflammatory Arthritis - A Retrospective Study

OBJECTIVE

We aim to establish the utility of a trial of low-dose systemic glucocorticoid therapy in the assessment of new clinically suspected inflammatory arthritis patients.

METHODS

We retrospectively identified patients from a private rheumatology practice in Melbourne, Australia between January 1st, 2019, and December 31st, 2021, who presented with clinically suspected inflammatory arthritis and subsequently underwent a trial of low-dose prednisolone (15 mg daily weaned over three weeks in 5 mg increments). We excluded patients with known autoimmune/ inflammatory disorders or concurrent immunosuppression at presentation. We collected basic participant demographic details and clinical details of their presentation, glucocorticoid response, investigations, and treatment.

RESULTS

We recruited 177 participants with a median age of 52, and 69.5% were female gender. The median symptom time to presentation was 12 months. Hands were the most affected joint in 63.3% and 85% had bilateral disease. Among the participants, 29.4% had synovitis on clinical review and 75.7% had imaging performed as part of the initial assessment. At presentation, the median CRP was 11 and the median ESR was 16. 79.7% of the cohort experienced significant improvement in their arthritis symptoms from low-dose glucocorticoids and 83.6% of the cohort required long-term immunosuppression for an underlying inflammatory condition. Of those who responded to glucocorticoids, 92.1% were diagnosed with an inflammatory condition. Rheumatoid arthritis was the most common overall diagnosis in 28%.

CONCLUSION

An initial trial of low-dose glucocorticoids in undifferentiated arthritis patients is useful in predicting the diagnosis of inflammatory arthritis. It is also a predictor of further long-term steroid-sparing therapy.

When Autoantibodies Are Missing: The Challenge of Seronegative Rheumatoid Arthritis

Seronegative rheumatoid arthritis (SNRA) is characterized by the absence of both rheumatoid factor (RF) and antibodies against the cyclic citrullinated protein (ACPA) in serum. However, the differences between the two forms of RA are more complex and have not yet been definitively characterized. Several lines of evidences support the idea that there are specific elements of the two forms, including genetic background, epidemiology, pathogenesis, severity of progression over time, and response to therapy. Clinical features that may differentiate SNRA from SPRA are also suggested by data obtained from classical radiology and newer imaging techniques. Although new evidence seems to provide additional help in differentiating the two forms of RA, their distinguishing features remain largely elusive. It should also be emphasized that the distinctive features of RA forms, if not properly recognized, can lead to the underdiagnosis of SNRA, potentially missing the period called the "window of opportunity" that is critical for early diagnosis, timely treatment, and better prognosis. This review aims to summarize the data provided in the scientific literature with the goal of helping clinicians diagnose SNRA as accurately as possible, with emphasis on the most recent findings available.

Importance of tenosynovitis in preventing the progression through rheumatoid arthritis continuum

Rheumatoid arthritis (RA) has long been characterized by synovitis and bone erosions typically developing symmetrically in small joints. However, recent advances in imaging modalities have indicated frequent association of tenosynovitis with RA, and some consider tenosynovitis to be not just a complication but a major trait of RA. Furthermore, as there are cases with tenosynovitis preceding the clinical detection of inflammatory arthritis in predisposed individuals, tenosynovitis may constitute an important biomarker in defining the pre-RA phase of disease development. Tenosynovitis itself must be treated as it causes functional impairment and physical as well as socioeconomic burden, and its treatment may result in effective prevention of RA development at a pre-arthritic stage. Thus, further efforts need to be taken in detecting and treating tenosynovitis in the pre-RA stage, which can be facilitated by ultrasonography and magnetic resonance imaging.

Rheumatoid Arthritis from Easy to Complex Disease: From the "2022 GISEA International Symposium"

Rheumatoid Arthritis (RA) is a systemic disease with many different clinical phenotypes. RA could be classified according to disease duration, seropositivity for rheumatoid factor (RF) and/or anti-citrullinated protein antibodies (ACPA), joint subtype, clinical behaviourbehavior and many other subgroups. In this review, we summarize and discuss the multifaceted aspects of RA, focusing on the relationship between autoimmunity status and clinical outcome, achievement of remission and influence on treatment response, from the 2022 International GISEA/OEG Symposium.

Patients with ACPA-positive and ACPA-negative rheumatoid arthritis show different serological autoantibody repertoires and autoantibody associations with disease activity

Patients with rheumatoid arthritis (RA) can test either positive or negative for circulating anti-citrullinated protein antibodies (ACPA) and are thereby categorized as ACPA-positive (ACPA+) or ACPA-negative (ACPA-), respectively. In this study, we aimed to elucidate a broader range of serological autoantibodies that could further explain immunological differences between patients with ACPA+ RA and ACPA- RA. On serum collected from adult patients with ACPA+ RA (n = 32), ACPA- RA (n = 30), and matched healthy controls (n = 30), we used a highly multiplex autoantibody profiling assay to screen for over 1600 IgG autoantibodies that target full-length, correctly folded, native human proteins. We identified differences in serum autoantibodies between patients with ACPA+ RA and ACPA- RA compared with healthy controls. Specifically, we found 22 and 19 autoantibodies with significantly higher abundances in ACPA+ RA patients and ACPA- RA patients, respectively. Among these two sets of autoantibodies, only one autoantibody (anti-GTF2A2) was common in both comparisons; this provides further evidence of immunological differences between these two RA subgroups despite sharing similar symptoms. On the other hand, we identified 30 and 25 autoantibodies with lower abundances in ACPA+ RA and ACPA- RA, respectively, of which 8 autoantibodies were common in both comparisons; we report for the first time that the depletion of certain autoantibodies may be linked to this autoimmune disease. Functional enrichment analysis of the protein antigens targeted by these autoantibodies showed an over-representation of a range of essential biological processes, including programmed cell death, metabolism, and signal transduction. Lastly, we found that autoantibodies correlate with Clinical Disease Activity Index, but associate differently depending on patients' ACPA status. In all, we present candidate autoantibody biomarker signatures associated with ACPA status and disease activity in RA, providing a promising avenue for patient stratification and diagnostics.

Barriers to the Diagnosis of Early Inflammatory Arthritis: A Literature Review

The early identification of patients with inflammatory arthritis and their referral to rheumatologists in order to establish a diagnosis and to start treatment plays a crucial role in patient outcomes. However, it is recognized that a large proportion of patients with inflammatory arthritis are diagnosed very late, losing the opportunity to start treatment in the very early stages of disease, resulting in a worse prognosis. This delay depends on several factors related to the patient, the disease, socio-demographic and health system aspects. Over time, several strategies have been developed and implemented at different levels aiming to overcome such barriers and to reduce the time from the onset of the symptoms until the diagnosis and start of adequate treatment. In this non-systematic comprehensive review, we will describe the main barriers in the identification of patients with inflammatory arthritis at different levels. We will also discuss the different strategies that have been implemented with the objective to overcome the recognized barriers and their impact in the reduction of delays.

High-frequency ultrasound in patients with seronegative rheumatoid arthritis

This study aimed to investigate the value of high-frequency ultrasound (HFUS) in differentiation of the seronegative rheumatoid arthritis (SNRA) and osteoarthritis (OA) and in the diagnosis of SNRA. 83 patients diagnosed with SNRA (SNRA group) and 40 diagnosed with OA (OA group) who received HFUS were retrospectively analyzed. The grayscale (GS) scores, power Doppler (PD) scores, and bone erosion (BE)scores were recorded, and added up to calculate the total scores of US variables. The correlations of the total scores of US variables with the 28-joint disease activity score (DAS28), erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) were analyzed. The diagnostic efficacy of the total scores of US variables for SNRA was assessed. In the SNRA group, the detection rate of abnormal US findings in the joints and tendons by GS and PD as well as BE was higher than those in the OA group. There were significant differences between the two groups in GS scores and PD scores of joints and tendons, and BE scores of joints (P < 0.05). In the SNRA group, the total scores of most US variables were positively correlated with CRP, ESR, and DAS28 (P < 0.05), while such correlations were not observed in the OA group (P > 0.05). Among different US variables, the diagnostic value of total PD scores of the joints was the highest for SNRA. HFUS could be used to differentiate SNRA from OA and make a diagnosis of SNRA based on joint and tendon synovial sheath assessment.

Comparison of glucocorticoids and painkiller prescribed days between rheumatoid arthritis patients receiving early and late treatment with a biological agent via a population-based cohort study

Comparison between early biologics treatment and late biologics treatment of rheumatoid arthritis (RA) patients in decreasing prescription days of glucocorticoids and painkillers by using the Taiwan National Health Insurance Research database from January 1, 1997 to December 31, 2013. We defined early use of biologics as biologics prescribed within 2.24 years after the RA diagnosis, and the late use of biologics was defined as those prescribed after 2.24 years of the RA diagnosis. These definitions are based on previous studies defining early arthritis as arthritis within 2 years of diagnosis, while we needed another 3 months for application biologics here in Taiwan, which equals a total of 2.24 years. Among the 821 patients, 410 patients (50%) were classified in the Early group, and the other 411 patients (50%) were classified in the Late group. The use of any of these 3 types of medication, including steroids, disease modifying antirhuematic drugs, and nonsteroid anti-inflammatory drug (NSAID) was changed significantly after biologics treatment. Comparing between before and after biologics treatment, oral medication was significantly tapered (all P < .0001). The results show that men are 1.81 times more likely than women to taper oral glucocorticoids and NSAIDs. Younger age (<45) patients are 1.91 times more likely to taper steroids and NSAIDs than those aged over 65 years old. Both gender and age were found to be independent factors that could decrease days of prescription of both steroids and NSAIDs in early use of biologics agents. This study indicates that younger patients only need short-term (2.53 ± 1.92 years, P = .03) and early treatment with biologics (within 2.24 years of diagnosis of RA), just in order to taper steroids and NSAIDs to less than 50% compared to the steroids and NSAIDs doses before biologics treatment.

Impact of autoimmune serology test results on RA classification and diagnosis

Rheumatoid arthritis (RA) is the most common systemic autoimmune disease and also the most severe arthritic disorder. The measurement of rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) in serum supports the diagnosis of RA, which gained increasing significance over the last 65 years. However, a high variability between RF and ACPA methods has been described, impacting the diagnostic performance of the current ACR/EULAR RA classification criteria.

The great number of commercially available assays, often lacking traceability to an international standard, is a major factor attributing to this in-between assay variability. The adoption of an international standard for ACPA, as is since long available for rheumatoid factor, is therefore highly desirable.

Further harmonization in clinical interpretation of RF/ACPA assays could be obtained by harmonization of the cut-offs, for both the low and high antibody levels, based on predefined specificity in disease controls. Reporting test result specific likelihood ratios (LR) adds value in the interpretation of autoantibody tests. However, a good understanding of the control population used to define antibody test result interval-associated LRs is crucial in defining the diagnostic performance characteristics of antibody serology.

Finally, specificity in RA classification can be improved by refining serological weight scoring taking into account the nature of the antibody, the antibody level and double RF + ACPA positivity.

Factors associated with treatment satisfaction in patients with rheumatoid arthritis: data from the biological register RABBIT

Objective

To assess satisfaction with the effectiveness and tolerability of treatments in patients with rheumatoid arthritis (RA).

Methods

Patients from the RABBIT register, starting a biological (b) or targeted synthetic (ts) disease-modifying antirheumatic drug (DMARD), or a conventional synthetic (cs)DMARD treatment after ≥1 csDMARD failure, were included. Treatment satisfaction was measured after 1 year of treatment in four categories and binarised for analysis. Logistic regression models were performed to calculate ORs for factors associated with treatment satisfaction.

Results

Data of 10 646 patients (74% women, mean 58 years) were analysed. At baseline, 55% of the patients were satisfied with the efficacy and 68% with the tolerability of their previously given treatments. After 1 year, 85% of the patients were satisfied with treatment effectiveness and 90% with tolerability. Baseline satisfaction (OR 2.98, 95% CI 2.58 to 3.44), seropositivity (OR 1.36, 95% CI 1.17 to 1.57), reduction of DAS28 (OR 1.38, 95% CI 1.31 to 1.46) and pain (OR 1.26, 95% CI 1.22 to 1.31), and the improvement of physical capacity (OR 1.22, 95% CI 1.17 to 1.29) were positively associated with treatment satisfaction at follow-up while glucocorticoids (GCs) >5 mg/day, depression, fibromyalgia, obesity, prior bDMARDs and therapy changes were negatively associated. The impact of GC on satisfaction was dose-dependent, becoming strongest for GC >15 mg (OR 0.24, 95% CI 0.16 to 0.34). A 5 mg/day reduction within 12 months was positively associated with satisfaction regarding efficacy (OR 1.19, 95% CI 1.11 to 1.27) and tolerability (OR 1.11, 95% CI 1.03 to 1.21).

Conclusion

Most patients were satisfied with their treatment’s effectiveness and tolerability after 1 year of treatment. Tapering GCs was positively associated with the improvement of patients’ satisfaction.

Comparison of healthcare resource utilization and medical costs between patients with seropositive and seronegative rheumatoid arthritis

Objectives:

To compare healthcare utilization and medical costs between patients with seronegative (SN) and seropositive (SP) rheumatoid arthritis (RA).

Methods:

We conducted a nationwide population study using the Korean health insurance claims database in 2016. We divided patients with RA into SN and SP groups and compared healthcare utilization including medications, medical utilization, and direct medical costs for 1 year between the groups in a cross-sectional analysis. Differences in costs between patients with SPRA and SNRA were assessed using the quantile regression model. We performed longitudinal analysis using data from 2012 and 2016 to examine changes over time.

Results:

A total of 103,815 SPRA and 75,809 SNRA patients were included in the analyses. The SPRA group used significantly more methotrexate (73.2% versus 30.3%) and biologic agents (7.9% versus 2.9%) than the SNRA group. The number of RA-related outpatient visits [6.0 ± 3.7 versus 4.4 ± 4.0 times/year, standardized difference (SD) = 0.41] and annual medical costs per patient ($1027 versus $450/year, SD = 0.25) were higher in the SPRA group than the SNRA group. Quantile regression results indicated that the incremental cost of seropositivity on total medical costs of RA patients gradually increased as medical costs approached the upper quantile. The annual direct medical costs for each patient between 2012 and 2016 increased in both groups: by 25.1% in the SPRA group and 37.6% in the SNRA group.

Conclusion:

Annual RA-related direct medical costs and RA-related healthcare utilization per patient are higher in patients with SPRA than those with SNRA.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

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.

Biomarkers in Rheumatoid Arthritis

The utilization and identification of biomarkers in rheumatoid arthritis (RA) to facilitate timely diagnosis and the optimal management of the disease is an area of active investigation. This review focuses on biomarkers available for routine clinical use, details potential investigational biomarkers, and raises outstanding clinical questions.

Multimorbidity Burden in Rheumatoid Arthritis: A Population-based Cohort Study

OBJECTIVE

To estimate the prevalence and incidence of multimorbidity (MM) in a population-based cohort of patients with rheumatoid arthritis (RA) compared to subjects without RA.

METHODS

Between 1999-2013, residents of Olmsted County, Minnesota with incident RA who met the 1987 American College of Rheumatology criteria were compared to age- and sex-matched non-RA subjects from the same population. Twenty-five chronic comorbidities from a combination of the Charlson, Elixhauser, and Rheumatic Disease Comorbidity Indices were included, excluding rheumatic comorbidities. The Aalen-Johansen method was used to estimate the cumulative incidence of MM (MM2+; ≥ 2 chronic comorbidities) or substantial MM (MM5+; ≥ 5), adjusting for the competing risk of death.

RESULTS

The study included 597 patients with RA and 594 non-RA subjects (70% female, 90% White, mean age 55.5 yrs). At incidence/index date, the prevalence of MM2+ was higher in RA than non-RA subjects (38% RA vs 32% non-RA, P = 0.02), whereas prevalence of MM5+ was similar (5% RA vs. 4% non-RA, P = 0.68). During follow-up (median 11.6 yrs RA, 11.3 yrs non-RA), more patients with RA developed MM2+ (214 RA vs 188 non-RA; adjusted HR 1.39, 95% CI 1.14-1.69). By 10 years after RA incidence/index, the cumulative incidence of MM2+ was 56.5% among the patients with RA (95% CI 56.5-62.3%) compared with 47.9% among the non-RA (95% CI 42.8-53.7%). Patients with RA showed no evidence of increase in incidence of MM5+ (adjusted HR 1.17, 95% CI 0.93-1.47).

CONCLUSION

Patients with RA have both a higher prevalence of MM at the time of RA incidence as well as increased incidence thereafter.

A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning

Background

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA.

Methods

The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes.

Results

Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC.

Conclusion

The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-020-02180-w.

Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify critical biomarkers and possible pathogenesis of RA. Three gene expression datasets profiled by microarray were obtained from GEO database. Dataset GSE55235 and GSE55457 were merged for subsequent analyses. We identified differentially expressed genes (DEGs) in RStudio with limma package, performing functional enrichment analysis based on GSEA software and clusterProfiler package. Next, protein-protein interaction (PPI) network was set up through STRING database and Cytoscape. Moreover, CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE77298. As a result, we identified 106 DEGs (72 upregulated and 34 downregulated genes). Through GO, KEGG, and GSEA analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway. With the help of Cytoscape software and MCODE plug-in, the most prominent subnetwork was screened out, containing 14 genes and 45 edges. For ROC curve analysis, eight genes with AUC >0.80 were considered as hub genes of RA. In conclusion, compared with healthy controls, the DEGs and their closely related biological functions were analyzed, and we held that chemokines and immune cells infiltration promote the progression of rheumatoid arthritis. Targeting the eight biomarkers we identified may be useful for the diagnosis and treatment of rheumatoid arthritis.

Is the epidemiology of rheumatoid arthritis changing? Results from a population-based incidence study, 1985-2014

OBJECTIVES

To examine trends in the incidence of rheumatoid arthritis (RA) from 2005 to 2014 overall and by serological status as compared with 1995-2004 and 1985-1994.

METHODS

We evaluated RA incidence trends in a population-based inception cohort of individuals aged ≥18 years who first fulfilled the 1987 American College of Rheumatology (ACR) criteria for RA between 1 January 1985 and 31 December 2014. Incidence rates were estimated and were age-adjusted and sex-adjusted to the white population in the USA in 2010. Trends in incidence were examined using Poisson regression methods.

RESULTS

The 2005-2014 incidence cohort comprised 427 patients: mean age 55.4 years, 68% female, 51% rheumatoid factor (RF) positive and 50% anti-cyclic citrullinated peptide antibody positive. The overall age-adjusted and sex-adjusted annual RA incidence in 2005-2014 was 41/100 000 population (age-adjusted incidence: 53/100 000 in women and 29/100 000 in men). While these estimates were similar to the 1995-2004 decade, there was a decline in the incidence of RF-positive RA in 2005-2014 compared with the previous two decades (p=0.004), with a corresponding increase in RF-negative cases (p<0.001). Smoking rates declined and obesity rates increased from earlier decades to more recent years.

CONCLUSIONS

Significant increase in incidence of RF-negative RA and decrease in RF-positive RA in 2005-2014 compared with previous decades was found using 1987 ACR criteria. The incidence of RA overall during this period remained similar to the previous decade. The changing prevalence of environmental factors, such as smoking, obesity and others, may have contributed to these trends. Whether these trends represent a changing serological profile of RA requires further investigation.