Association Between Anti-Citrullinated Fibrinogen Antibodies and Coronary Artery Disease in Rheumatoid Arthritis

Anti-apolipoprotein A-1 IgG, incident cardiovascular events, and lipid paradox in rheumatoid arthritis

Objective

To validate the prognostic accuracy of anti-apolipoprotein A-1 (AAA1) IgG for incident major adverse cardiovascular (CV) events (MACE) in rheumatoid arthritis (RA) and study their associations with the lipid paradox at a multicentric scale.

Method

Baseline AAA1 IgG, lipid profile, atherogenic indexes, and cardiac biomarkers were measured on the serum of 1,472 patients with RA included in the prospective Swiss Clinical Quality Management registry with a median follow-up duration of 4.4 years. MACE was the primary endpoint defined as CV death, incident fatal or non-fatal stroke, or myocardial infarction (MI), while elective coronary revascularization (ECR) was the secondary endpoint. Discriminant accuracy and incidence rate ratios (IRR) were respectively assessed using C-statistics and Poisson regression models.

Results

During follow-up, 2.4% (35/1,472) of patients had a MACE, consisting of 6 CV deaths, 11 MIs, and 18 strokes; ECR occurred in 2.1% (31/1,472) of patients. C-statistics indicated that AAA1 had a significant discriminant accuracy for incident MACE [C-statistics: 0.60, 95% confidence interval (95% CI): 0.57-0.98, p = 0.03], mostly driven by CV deaths (C-statistics: 0.77; 95% CI: 0.57-0.98, p = 0.01). IRR indicated that each unit of AAA1 IgG increase was associated with a fivefold incident CV death rate, independent of models' adjustments. At the predefined and validated cut-off, AAA1 displayed negative predictive values above 97% for MACE. AAA1 inversely correlated with total and HDL cholesterol.

Conclusions

AAA1 independently predicts CV deaths, and marginally MACE in RA. Further investigations are requested to ascertain whether AAA1 could enhance CV risk stratification by identifying patients with RA at low CV risk.

Are the integrin binding motifs within SARS CoV-2 spike protein and MHC class II alleles playing the key role in COVID-19?

The previous studies on the RGD motif (aa403-405) within the SARS CoV-2 spike (S) protein receptor binding domain (RBD) suggest that the RGD motif binding integrin(s) may play an important role in infection of the host cells. We also discussed the possible role of two other integrin binding motifs that are present in S protein: LDI (aa585-587) and ECD (661-663), the motifs used by some other viruses in the course of infection. The MultiFOLD models for protein structure analysis have shown that the ECD motif is clearly accessible in the S protein, whereas the RGD and LDI motifs are partially accessible. Furthermore, the amino acids that are present in Epstein-Barr virus protein (EBV) gp42 playing very important role in binding to the HLA-DRB1 molecule and in the subsequent immune response evasion, are also present in the S protein heptad repeat-2. Our MultiFOLD model analyses have shown that these amino acids are clearly accessible on the surface in each S protein chain as monomers and in the homotrimer complex and bind to HLA-DRB1 β chain. Therefore, they may have the identical role in SARS CoV-2 immune evasion as in EBV infection. The prediction analyses of the MHC class II binding peptides within the S protein have shown that the RGD motif is present in the core 9-mer peptide IRGDEVRQI within the two HLA-DRB1*03:01 and HLA-DRB3*01.01 strong binding 15-mer peptides suggesting that RGD motif may be the potential immune epitope. Accordingly, infected HLA-DRB1*03:01 or HLA-DRB3*01.01 positive individuals may develop high affinity anti-RGD motif antibodies that react with the RGD motif in the host proteins, like fibrinogen, thrombin or von Willebrand factor, affecting haemostasis or participating in autoimmune disorders.

Role of protein deimination in cardiovascular diseases: potential new avenues for diagnostic and prognostic biomarkers

INTRODUCTION

Arginine deimination (citrullination) is a post-translational modification catalyzed by a family of peptidyl arginine deiminase (PAD) enzymes. Cell-based functional studies and animal models have manifested the key role of PADs in various cardiovascular diseases (CVDs).

AREA COVERED

This review summarizes the latest developments in the role of PADs in CVD pathogenesis. It focuses on the PAD functions and diverse citrullinated proteins in cardiovascular conditions like deep vein thrombosis, ischemia/reperfusion, and atherosclerosis. Identification of PAD isoforms and citrullinated targets are essential for directing diagnosis and clinical intervention. Finally, anti-citrullinated protein antibodies (ACPAs) are addressed as an independent risk factor for cardiovascular events. A search of PubMed biomedical literature from the past ten years was performed with a combination of the following keywords: PAD/PADI, deimination/citrullination, autoimmune, fibrosis, NET, neutrophil, macrophage, inflammation, inflammasome, cardiovascular, heart disease, myocardial infarction, ischemia, atherosclerosis, thrombosis, and aging. Additional papers from retrieved articles were also considered.

EXPERT OPINION

PADs are unique family of enzymes that converts peptidyl-arginine to -citrulline in protein permanently. Overexpression or increased activity of PAD has been observed in various CVDs with acute and chronic inflammation as the background. Importantly, far beyond being simply involved in forming neutrophil extracellular traps (NETs), accumulating evidence indicated PAD activation as a trigger for numerous processes, such as transcriptional regulation, endothelial dysfunction, and thrombus formation. In summary, the findings so far have testified the important role of deimination in cardiovascular biology, while more basic and translational studies are essential to further exploration.

Anti-Cyclic Citrullinated Peptide Antibodies Correlate to Ultrasound Synovitis in Rheumatoid Arthritis Better Than C-Reactive Protein

OBJECTIVES

The aim of this study was to compare ultrasound (US) grading and laboratory measures in patients with rheumatoid arthritis.

METHODS

Two-hundred four patients with rheumatoid arthritis who received US evaluation for synovitis were included after excluding those using tocilizumab. Ultrasound grading of synovial hypertrophy (SH) and power Doppler (PD) at the most severe site were recorded. An assessment of the correlation of laboratory measures and US grading was conducted by reviewing the electronic medical records.

RESULTS

High-titer anti-cyclic citrullinated peptide (anti-CCP) antibodies positivity was associated with SH grade ≥2 (odds ratio [OR], 6.00; 95% confidence interval [CI], 1.78-20.2) and PD grade ≥2 (OR, 5.56; 95% CI, 1.82-16.9). Recent C-reactive protein (CRP) levels ≥0.3 mg/dL were associated with SH grade ≥2 (OR, 3.13; 95% CI, 1.38-7.10) and PD grade ≥2 (OR, 2.38; 95% CI, 1.31-4.31). Anti-CCP antibody levels correlated with US scores better than the levels of CRP with higher Spearman ρ correlation coefficients. Most of the patients with recent CRP levels <0.3 mg/dL had US synovitis. In logistic regression, high levels of anti-CCP antibodies and CRP were both independently associated with SH grade ≥2 and PD grade ≥2.

CONCLUSIONS

Higher levels of anti-CCP antibodies and CRP may predict synovitis on US, whereas discrepancies existed between inflammatory markers and US grading. These findings suggest that US has a role in the comprehensive assessment of disease activity, especially for patients with high-titer positive anti-CCP antibodies.

Cardiovascular Disease in Rheumatoid Arthritis: Risk Factors, Autoantibodies, and the Effect of Antirheumatic Therapies

Objective:

To scope the current published evidence on cardiovascular risk factors in rheumatoid arthritis (RA) focusing on the role of autoantibodies and the effect of antirheumatic agents.

Methods:

Two reviews were conducted in parallel: A targeted literature review (TLR) describing the risk factors associated with cardiovascular disease (CVD) in RA patients; and a systematic literature review (SLR) identifying and characterizing the association between autoantibody status and CVD risk in RA. A narrative synthesis of the evidence was carried out.

Results:

A total of 69 publications (49 in the TLR and 20 in the SLR) were included in the qualitative evidence synthesis. The most prevalent topic related to CVD risks in RA was inflammation as a shared mechanism behind both RA morbidity and atherosclerotic processes. Published evidence indicated that most of RA patients already had significant CV pathologies at the time of diagnosis, suggesting subclinical CVD may be developing before patients become symptomatic. Four types of autoantibodies (rheumatoid factor, anti-citrullinated peptide antibodies, anti-phospholipid autoantibodies, anti-lipoprotein autoantibodies) showed increased risk of specific cardiovascular events, such as higher risk of cardiovascular death in rheumatoid factor positive patients and higher risk of thrombosis in anti-phospholipid autoantibody positive patients.

Conclusion:

Autoantibodies appear to increase CVD risk; however, the magnitude of the increase and the types of CVD outcomes affected are still unclear. Prospective studies with larger populations are required to further understand and quantify the association, including the causal pathway, between specific risk factors and CVD outcomes in RA patients.

Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records

OBJECTIVES

To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes.

METHODS

An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms-on a training set of 127 axSpA cases and 423 non-cases-and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only.

RESULTS

NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80-0.87).

CONCLUSION

Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

Investigating the impact of disease and health record duration on the eMERGE algorithm for rheumatoid arthritis

OBJECTIVE

The study sought to determine the dependence of the Electronic Medical Records and Genomics (eMERGE) rheumatoid arthritis (RA) algorithm on both RA and electronic health record (EHR) duration.

MATERIALS AND METHODS

Using a population-based cohort from the Mayo Clinic Biobank, we identified 497 patients with at least 1 RA diagnosis code. RA case status was manually determined using validated criteria for RA. RA duration was defined as time from first RA code to the index date of biobank enrollment. To simulate EHR duration, various years of EHR lookback were applied, starting at the index date and going backward. Model performance was determined by sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC).

RESULTS

The eMERGE algorithm performed well in this cohort, with overall sensitivity 53%, specificity 99%, positive predictive value 97%, negative predictive value 74%, and AUC 76%. Among patients with RA duration <2 years, sensitivity and AUC were only 9% and 54%, respectively, but increased to 71% and 85% among patients with RA duration >10 years. Longer EHR lookback also improved model performance up to a threshold of 10 years, in which sensitivity reached 52% and AUC 75%. However, optimal EHR lookback varied by RA duration; an EHR lookback of 3 years was best able to identify recently diagnosed RA cases.

CONCLUSIONS

eMERGE algorithm performance improves with longer RA duration as well as EHR duration up to 10 years, though shorter EHR lookback can improve identification of recently diagnosed RA cases.

The Utility and Experience with Disease Biomarkers in Juvenile Onset Arthritis vs. Adult Onset Arthritis

Juvenile idiopathic arthritis (JIA) is the most common but extremely heterogeneous group of rheumatic diseases of childhood. There are no reliable, well-researched and published biomarkers for diagnosis or monitoring in juvenile idiopathic arthritis as there are for rheumatoid arthritis (RA) in adults. Biomarkers are not utilized in classifying JIA as they are in adult RA, making the JIA classifications less clinically effective and informative. The situation presents a lost opportunity for early aggressive therapy in JIA patients. Various researchers have used diverse biomarkers anecdotally in JIA and more systematically in RA patients and have drawn inferences on their utility from their experiences. The experience with biomarkers from RA patients cannot necessarily be extrapolated for JIA patients because they are dissimilar diseases. This article reconnoiters the comparative utility of various arthritis biomarkers in adult as well as in JIA patients. In contrast to RA, JIA is in itself a diverse group of arthritis with clinically overlapping subgroups with diverse etiology. The difference in the etiopathogenesis of arthritis subgroups demands identifying subgroup-specific biomarkers for diagnosis/monitoring and subgroup-specific therapies for management. The diagnostic/prognostic value of the individual biomarker could be different in different types of arthritis and in different types of hosts. Understanding the utility of individual biomarkers and careful selection of the assay are important to achieve the best disease outcomes.