Something Old, Something New: Biomarkers in Rheumatoid Arthritis

Potential clinical biomarkers in rheumatoid arthritis with an omic approach

Objective

To aid in the selection of the most suitable therapeutic option in patients with diagnosis of rheumatoid arthritis according to the phase of disease, through the review of articles that identify omics biological markers.

Methods

A systematic review in PubMed/Medline databases was performed. We searched articles from August 2014 to September 2019, in English and Spanish, filtered by title and full text; and using the terms "Biomarkers" AND “Rheumatoid arthritis".

Results

This article supplies an exhaustive review from research of objective measurement, omics biomarkers and how disease activity appraise decrease unpredictability in treatment determinations, and finally, economic, and clinical outcomes of treatment options by biomarkers’ potential influence. A total of 122 articles were included. Only 92 met the established criteria for review purposes and 17 relevant references about the topic were included as well. Therefore, it was possible to identify 196 potential clinical biomarkers: 22 non-omics, 20 epigenomics, 33 genomics, 21 transcriptomics, 78 proteomics, 4 glycomics, 1 lipidomics and 17 metabolomics.

Conclusion

A biomarker is a measurable indicator of some, biochemical, physiological, or morphological condition; evaluable at a molecular, biochemical, or cellular level. Biomarkers work as indicators of physiological or pathological processes, or as a result of a therapeutic management. In the last five years, new biomarkers have been identified, especially the omics, which are those that proceed from the investigation of genes (genomics), metabolites (metabolomics), and proteins (proteomics). These biomarkers contribute to the physician choosing the best therapeutic option in patients with rheumatoid arthritis.

Biomarker Approach Towards Rheumatoid Arthritis Treatment

Rheumatoid arthritis is an auto-immune disorder, recognized by cartilage as well as bone destruction, which causes irreversible joint deformities, which further results in functional limitations in the patient. Genes like HLA-DRB1 and PTPN22 are likely implicated in the genetic predisposition of rheumatoid arthritis pathology. The first and foremost clinical manifestation in a person with rheumatoid arthritis is joint destruction followed by cartilage and bone destruction caused by cell-cell interactions. The cell-cell interactions are thought to be initialized through the contact of antigen-presenting cells (APC) with CD4+ cells, leading to the progression of the disease. APC includes a complex of class ІІ major histocompatibility complex molecules along with peptide antigens and binds to the receptors present on the surface of T-cells. Further, the activation of macrophages is followed by the release of various pro-inflammatory cytokines such as IL-1 and TNF-α, which lead to the secretion of enzymes that degrade proteoglycan and collagen, which in turn, increase tissue degradation. Biomarkers like IL-6, IL-12, IL-8 and IL-18, 14-3-3η, RANKL, IFN-γ, IFN-β and TGF-β have been designated as key biomarkers in disease development and progression. The study of these biomarkers is very important as they act as a molecular indicator of pathological processes that aggravate the disease.

The significance of serum 14-3-3η level in rheumatoid arthritis patients

BACKGROUND

RA is a systemic inflammatory condition characterized by chronic arthritis and often associated with irreversible joint damage.

OBJECTIVES

To assess the significance of serum level of 14-3-3η in RA and its association with clinical and serological features of the disease.

METHODS

This is a case-control study done on 80 participants. They were divided into 2 groups. Group 1: 40 rheumatoid arthritis patients compared to group 2: 40 healthy participants matched for age and sex. Laboratory investigations including complete blood count (CBC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) rheumatoid factor (RF), anti-citrullinated peptide antibodies (ACPAs), and serum 14-3-3η were done to all participants. Radiological examination in the form of plain X-ray for hands and feet was done to all patients.

RESULTS

Serum levels of 14-3-3η were significantly higher in RA patients compared to the control group (p < 0.001). Serum 14-3-3η was the only predictor of high Larsen's score (p = 0.013) on using linear regression analysis. Serum 14-3-3η can predict RA in healthy controls in univariate (p = 0.001) and multivariate (p = 0.004) analyses. The receiver operating characteristic (ROC) curve of 14-3-3η was constructed for discrimination between RA and control subjects. The best cut-off value was 61.9 ng/mL, with fair AUC (0.773, p < 0.001), 95% CI (0.656-0.889), and the sensitivity and specificity of 14-3-3η for RA diagnosis as 65% and 95% respectively. Also, we constructed ROC curves for RF, ACPA, 14-3-3η, and their combinations; we found that the highest test sensitivity of 95.7% appeared on adding the 3 markers together, and the highest test specificity of 100% was detected on adding RF to ACPA, 14-3-3η to ACPA or the 3 molecules together.

CONCLUSION

14-3-3η could be a valuable marker for the diagnosis of RA patients and it may have prognostic value. Key Points • 14-3-3η is a valuable marker for the diagnosis of RA patients. • 14-3-3η reflects disease severity and joint damage in RA patients.

A Retrospective Study: The Significance of Combined Testing of Serum Markers for Diagnosis of Rheumatoid Arthritis

Background

There have been few studies on the value of various antibody combinations in rheumatoid arthritis (RA) diagnosis, and a lack of studies with large sample sizes, especially in the Chinese population. This study retrospectively evaluated the diagnostic value of a combined assay of five auto-antibodies [anti-cyclic citrullinated peptide (anti-CCP), anti-keratin (AKA), anti-RA 33, glucose-6-phosphate isomerase (GPI), and rheumatoid factor (RF)] for RA.

Material/Methods

Data were obtained from 5,725 patients with rheumatic diseases in Southwest Hospital of Chongqing from 2011 to 2014. Detection of the five serological markers was performed for all study patients using the appropriate method for each antibody.

Results

It was found that of the 5,725 patients, the positive rates for RF, anti-CCP, anti-RA 33, AKA, and GPI were 52.5%, 40.1%, 12.8%, 12.0%, and 50.0% respectively. In RA patients, the positive rates were 83.3%, 68.5%, 16.6%, 20.8%, and 77.9% respectively, which were all significantly higher than those detected in patients with the other diseases (p<0.01). The areas under the receiver operator characteristic (ROC) curve for RF, anti-CCP, anti-RA 33, AKA, and GPI were 0.857, 0.831, 0.528, 0.602, and 0.822 respectively, indicating that these five serological markers display favorable diagnostic value for RA. There were positive correlations between anti-CCP antibody and RF and GPI (p<0.01) and between RF and GPI (p<0.01), but no correlation between anti-RA 33 and AKA (p<0.01). The specificity of the combination of anti-CCP, AKA, and GPI was 100% for RA diagnosis.

Conclusions

The combined assay of serological markers significantly improved the diagnostic specificity for RA. The diagnostic value of RF for RA was the highest and the combined assay for anti-CCP, AKA, and GPI had the highest specificity for RA diagnosis.

Biomarkers to guide clinical therapeutics in rheumatology?

PURPOSE OF REVIEW

The use of biomarkers in rheumatology can help identify disease risk, improve diagnosis and prognosis, target therapy, assess response to treatment, and further our understanding of the underlying pathogenesis of disease. Here, we discuss the recent advances in biomarkers for rheumatic disorders, existing impediments to progress in this field, and the potential of biomarkers to enable precision medicine and thereby transform rheumatology.

RECENT FINDINGS

Although significant challenges remain, progress continues to be made in biomarker discovery and development for rheumatic diseases. The use of next-generation technologies, including large-scale sequencing, proteomic technologies, metabolomic technologies, mass cytometry, and other single-cell analysis and multianalyte analysis technologies, has yielded a slew of new candidate biomarkers. Nevertheless, these biomarkers still require rigorous validation and have yet to make their way into clinical practice and therapeutic development. This review focuses on advances in the biomarker field in the last 12 months as well as the challenges that remain.

SUMMARY

Better biomarkers, ideally mechanistic ones, are needed to guide clinical decision making in rheumatology. Although the use of next-generation techniques for biomarker discovery is making headway, it is imperative that the roadblocks in our search for new biomarkers are overcome to enable identification of biomarkers with greater diagnostic and predictive utility. Identification of biomarkers with robust diagnostic and predictive utility would enable precision medicine in rheumatology.