Meta-analysis: diagnostic accuracy of antibody against peptidylarginine deiminase 4 by ELISA for rheumatoid arthritis

Diagnostic values, association with disease activity and possible risk factors of anti-PAD4 in rheumatoid arthritis: a meta-analysis

OBJECTIVE

Anti-peptidyl arginine deaminase 4 (anti-PAD4) antibody has been a subject of investigation in RA in the last two decades. This meta-analysis investigated the diagnostic values, association with disease activity and possible risk factors of anti-PAD4 antibody in rheumatoid arthritis.

METHOD

We searched studies from five databases up to 1 December 2022. Bivariate mixed-effect models were used to pool the diagnostic accuracy indexes, and the summary receiver operating characteristics (SROC) curve was plotted. The quality of diagnostic studies was assessed using QUADAS-2. Non-diagnostic meta-analyses were conducted using the random-effects model. Sensitivity analysis, meta-regression, subgroup analyses and Deeks' funnel plot asymmetry test were used to address heterogeneity.

RESULT

Finally, 24 journal articles and one letter were included. Anti-PAD4 antibody had a good diagnostic value between RA and healthy individuals, but it might be lower between RA and other rheumatic diseases. Moreover, anti-PAD4 could slightly enhance RA diagnostic sensitivity with a combination of ACPA or ACPA/RF. Anti-PAD4 antibody was positively correlated with HLA-SE and negatively correlated with ever or current smoking in patients with RA. RA patients with anti-PAD4 antibody had higher DAS28, ESR, swollen joint count (SJC) and the possibility of having interstitial lung disease (ILD) and pulmonary fibrosis compared with those without.

CONCLUSION

Our study suggests that anti-PAD4 antibody is a potentially useful diagnostic biomarker and clinical indicator for RA. Further mechanistic studies are required to understand the impact of HLA-SE and smoking on the production of anti-PAD4 antibody.

Anti-Protein-Arginine Deiminase 4 IgG and IgA Delineate Severe Rheumatoid Arthritis

There is a strong need for biomarkers of rheumatoid arthritis (RA) in all phases of the patient’s journey and to enable the implementation of precision medicine strategies to improve patient care. The objective of this study was to evaluate the presence of anti-protein-arginine deiminase (PAD) 4 IgG and IgA in the sera of RA patients and disease controls, and to investigate their association with joint erosion and biological treatment use. Sera from 104 RA and 155 controls were tested for the presence of anti-PAD4 IgG and IgA using a new particle-based multi-analyte technology (PMAT). Information on the erosive disease and biological treatment use was available for 54 of the RA patients, who were also tested for anti-citrullinated protein antibodies (ACPA). An association between the autoantibodies and these clinical features was investigated. Anti-PAD4 showed sensitivity and specificity values of 25.0% and 94.2% for IgG and of 21.2% and 94.8% for IgA for RA, respectively. The levels of these antibodies were also significantly higher in RA patients vs. controls, in erosive RA vs. non-erosive disease, and in patients under biologics vs. patients that were not on this treatment regimen. The anti-PAD4 IgG and IgA levels were correlated (rho = 0.60, p < 0.0001), but individuals that were positive for only one of the two isotypes were also observed. Anti-PAD4 IgG and IgA are associated with severe RA, and they represent valuable biomarkers for prognosis prediction and patient stratification.

Autoantibodies in Rheumatoid Arthritis - Laboratory and Clinical Perspectives

Measurement of two groups of autoantibodies, rheumatoid factor (RF) and anti-citrullinated protein/peptide antibodies (ACPA) have gained increasing significance in the diagnosis and classification of rheumatoid arthritis (RA) over the last 65 years. Despite this rising importance of autoimmune serology in RA, there is a palpable lack of harmonization between different commercial RF and ACPA tests. While a minimal diagnostic specificity has been defined for RF tests, which almost always are related to an international reference preparation, neither of this applies to ACPA. Especially assays with low diagnostic specificity are associated with very low positive predictive values or post-test probabilities in real world settings. In this review we focus on issues of practical bearing for the clinical physician diagnosing patients who potentially have RA, or treating patients diagnosed with RA. We advocate that all clinically used assays for RF and ACPA should be aligned to a common diagnostic specificity of 98-99% compared to healthy controls. This high and rather narrow interval corresponds to the diagnostic specificity seen for many commercial ACPA tests, and represents a specificity that is higher than what is customary for most RF assays. Data on antibody occurrence harmonized in this way should be accompanied by test result-specific likelihood ratios for the target diagnosis RA on an ordinal or interval scale, which will provide the clinical physician with more granular and richer information than merely relating numerical values to a single cut-off point. As many physicians today are used to evaluate autoantibodies as positive or negative on a nominal scale, the introduction of test result-specific likelihood ratios will require a change in clinical mindset. We also discuss the use of autoantibodies to prognosticate future arthritis development in at-risk patients as well as predict severe disease course and outcome of pharmacological treatment.

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.

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.

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

There is an emerging understanding that an individual's risk for future rheumatoid arthritis (RA) can be determined using a combination of factors while they are still in a state where clinically-apparent inflammatory arthritis (IA) is not yet present. Indeed, this concept has underpinned several completed and ongoing prevention trials in RA. Importantly, risk factors can be divided into modifiable (e.g. smoking, exercise, dental care and diet) and non-modifiable factors (e.g. genetics, sex, age). In addition, there are now several biomarkers including autoantibodies, inflammatory markers and imaging techniques that are highly predictive of future clinically-apparent IA/RA. Although none of the prevention studies have yet provided major breakthroughs, several of them have provided valuable insights that can help to improve the design of future clinical trials and enable RA prevention. In aggregate, these findings suggest that the most accurate disease prediction models will require the combination of demographic and clinical information, biomarkers and potentially medical imaging data to identify individuals for intervention. This review summarizes some of the key aspects around precision medicine in RA with special focus on disease prediction and prevention.