Relevance

  • We could associate a better outcome in the patient’s treatments reducing unpredictability of the management.

  • We made a review searching for the biomarkers associated with the different phases of rheumatoid arthritis.

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disease which is progressive and often disabling characterized by joint inflammation and pain; it requires monitoring of disease activity to decide the optimal treatment. Decreased quality of life, reduction of function and work participation are associated with mental and physical health of the patients. The use of biomarkers as monitors of disease development may aid providers improve non-biologic and biologic drugs [1]. Biomarkers have the capacity to enhance payment for medical and pharmacy policies related with the therapeutic management in immune system disorders and inflammatory diseases; they also has a potential impact on economic and clinical outcomes of treatment choices [2].

The aim of RA therapy is to keep and increase a prolonged state of health associated with quality of life by controlling symptoms, preventing structural joint damage, normalizing physical function, and getting better function in their activities. However, in patients with RA, treated either with methotrexate or biologic disease-modifying anti-rheumatic drugs (DMARDs), only 40–60% of them respond effectively as measured by at least 50% of improvement of signs and symptoms of the disease (by ACR criteria), whereas 15–30% develop adverse drug events [3].

On the other hand, in 2013 it was reported a systematic review of the International Journal of Rheumatology that approximately 50% of RA patients in Europe interrupted their biological therapy of the TNF inhibitor group during the first five years of use as a result of ineffectiveness or adverse drug reactions [4]. Similarly, a study in Boston at BRASS (Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study) showed that 42% of patients with RA reported abandonment of their anti-TNF therapy due to ineffectiveness [5]. Hence, biomarkers could become a tool with greater capacity to predict the health results of therapeutic applications to expand them beyond early detection, timely evaluation of a prognosis, and selection of the most effective and safest therapy, as well as monitoring disease activity, resulting in greater preservation of joint space and motility of the RA patients [6].

RA is a major current public health problem, in terms of deterioration in the quality of life and the generation of high costs for the health system. Although in recent years, better health results have been achieved with the incorporation of disease modifying drugs (synthetic and biological), it is evident that there is a need to better redirect resources and take advantage of the window of opportunity of the disease.

Pharmacokinetics and pharmacodynamics of drugs, becomes a fundamental and additional instrument to those already existing, which allows us to determine the choice of a more effective and safer drug that meets the particular needs of the patient. Genetic information is expressed at the level of proteins and metabolites; therefore, it is necessary to determine specific predictors or biomarkers for different phases of rheumatoid arthritis, especially the OMICS that include pharmacogenomics, metabolomics, and proteomics (Fig. 2).

Omics-based biomarkers classification

Biomarkers are interaction parameters that provide information on an objectively measurable physiological, biochemical, or morphological change that can be evaluable at the molecular, biochemical or cellular level and that acts as an indicator of a functional biological process or a pathogenic state, or as a response to medical treatment [7]. Biological markers are conceived as physiological signals induced by a xenobiotic, which is a cellular exposure, a precocious cellular response, or an inherent or acquired susceptibility [8].

Biomarkers are classified according to the information they provide and according to their nature. These biomarkers are important for identification of individuals in a population, that may be sensitive to a certain health problem. These kinds of biomarkers are classified as biomarkers of exposure, effect and susceptibility.

Exposure biomarkers evaluate the presence in an organism of an exogenous substance, a metabolite or product of the interaction between xenobiotic agent (natural or synthetic compounds of the environment that organism metabolizes and accumulates) and a molecule or target cell.

Prognostic Biomarkers report about progression of disease; this is, if disease improves or worsens after corresponding treatment. EphB4 membrane receptor is a prognostic biomarker of colon cancer [7].

Biomarkers of susceptibility are indicators of the inherited or acquired capacity of a given organism to respond to exposure to xenobiotic substances.

Biomarkers according to their nature are classified into omics that come from the study of genes (genomics), proteins (proteomics) and metabolites (metabolomics); epigenetics that come from changes that occur in DNA and that are related to some pathology, and microRNA molecules that are expressed in different amounts in either normal or cancerous cells (genomics/transcriptomics) [9].

According to the Food and Drug Administration, biomarkers are classified as follows [10, 11]:

  • Diagnostic biomarker: used to detect or confirm presence of a disease or certain condition, or to identify individuals with a subtype of disease. For example: HbA1c is commonly the most used biomarker to diagnose prediabetes and diabetes [12].

  • Prognostic biomarker: used to identify probability of a clinical event, disease recurrence or progression in patients with a diagnosis of a disease or medical condition of interest. For example: increasing prostate-specific antigen (PSA) as predictor of clinical progression for prostate cancer [13].

  • Safety biomarker: used to indicate the likelihood, presence, or extent of toxicity as an adverse effect measured before or after exposure to a medical product or derived from environmental causes. For example: transaminases have been selected as biomarkers for potentially hepatotoxic drugs [14].

  • Monitoring biomarker: measured in series to assess the level of a disease or medical condition, or the evidence of exposure to (or the effect of) a medical product or environmental agent. For example: B-type natriuretic peptide as a measure of vascular and ventricular function in pediatric pulmonary arterial hypertension [15].

  • Pharmacodynamic response biomarker: used to demonstrate that in exposition to medical products or environmental causes in an individual there is a biological reaction. For example: International Standardized Ratio (INR) for anticoagulant treatment, which has special interest in the adjustment of drug [16].

Ideal biomarkers should provide diagnostic, prognostic, and therapeutic information; additionally, they have to be obtainable from patient's clinical data, and should possess chemical-analytical characteristics such as:

  • High specificity: measurement of a biomarker must be specific to a disease.

  • Specimen: collection of samples should be minimally invasive. For example, saliva is better than urine and urine better than blood.

  • Representativeness: levels of biomarkers in the selection sample should be representative of levels of biomarkers in the organism.

  • Stability: kinetics must be known [17].

Regarding pharmacological safety of patients, ideal biomarkers should be aimed at health care processors. So, it is advisable to guide pharmacotherapeutic follow-up and programs for appropriate use of drugs through these pointing elements (tracers or markers). In this sense, detection of these elements is highly recommended, by means of information systems (systematized monitoring of warning signals), such as: identification of some medications, laboratory tests, symptoms or diagnoses and medical notes or phrases in clinical histories, known as markers or bookmarks [18].

Materials and methods

A systematic review was performed in PubMed/Medline databases. We searched articles from August 2014 to September 2019, in English and Spanish, filtered by title, full text, and using the terms "biomarkers" AND "Rheumatoid Arthritis". Inclusion criteria defined articles that reported biomarkers in different phases of rheumatoid arthritis and drug specific uses (Fig. 1).

Data extraction was performed on articles that met the inclusion criteria. Articles were downloaded and analyzed according to predefined eligibility criteria in a systematic review database. A format was created with reference, omics biomarker, phase of the disease, and a short description of potential use in clinical practice.

Results

A total of 122 articles were included, only 92 met established criteria for review purposes and 17 relevant references about the topic were included. 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. Figure 1 shows screening carried out concluding in the identification of different types of omics biomarkers.

Fig. 1
figure 1

Phases of rheumatoid arthritis, treatment phases and omics biomarkers

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In addition, Fig. 2 shows different states of rheumatoid arthritis from the beginning with asymptomatic autoimmunity until joint destruction. Also, six phases of RA treatment are shown, and types of omics biomarkers are identified.

Fig. 2
figure 2

Flow diagram for screening and evidence selection. *The repeated article was excluded due to the biomarker having a different end up, and it may disturb the results of the study. *The articles that had little statistical power were not conclusive, and the statistical evaluation did not confirm the use of the biomarker. *The articles that had many limitations were excluded because the impossibility to find the optimal selection method, and factor omission which can affect the outcome. *The articles related to treating the subject with little force were also excluded due to lack of information between the treatment and the use of the biomarkers.

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Consequently, identifying and monitoring biomarkers in different phases of disease will help to improve diagnosis, treatment, and quality of life of patients with RA.

Thus, this review identified both omic and non-omic biomarkers in each phase of RA. Table 1 shows a brief description of each biomarker and number of biomarkers identified in each phase of rheumatoid arthritis. For example, in phase 1 of RA were identified twenty-three biomarkers, in phase 2 seventeen, in phase 3 forty-one, in phase 4 eighteen, in phase 5 forty-nine, and in phase 6 forty-one.

Table 1 Identification of potential biomarkers for rheumatoid arthritis with omic-approaches
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Finally, Table 1 shows biomarkers that can be used as predictors of response to drugs used for treatment of rheumatoid arthritis.

Discussion

Current overview of omics-based biomarkers of rheumatoid arthritis in clinical practice

In 2010, new criteria for early classification of RA were published as a joint effort of the European League against Rheumatism/American College of Rheumatology (EULAR) and the American College of Rheumatology (ACR), with the objective to improve the classification in diagnosis based on existing criteria. It was also proposed as a tool to take advantage of therapeutic opportunity in early stages of disease. On the other hand, these new criteria highlight the importance of using biomarkers to support therapeutic decisions in the clinical field [126].

Within these new clinical findings, rheumatoid factor (RF), anti-cyclic citrullinated peptide (ACPA) and acute phase reactants (APR) are so far the most commonly used biomarkers in clinical settings to guide diagnosis and prognosis of RA. And since its description in 1940, RF continues being the most used laboratory tool for diagnosis and prognosis of RA in early stages of disease. However, its use and interpretation has been conditioned since the appearance of ACPAs as more specific markers (95% -98% vs. 85%) and similarly sensitive than RF (60–80% vs. 65%-80%). In addition, the value of ACPA can predict development of disease and response to specific therapies (high levels—low levels).

Despite their wide use, both RF and ACPA, as well as APR (they are nonspecific of the RA and sometimes they do not change with progression) have proved to be insufficient to respond satisfactorily to the high heterogeneity of RA. However, in the last five years omic approaches have had a gradual and homogeneous increase in the discovery and proposal of new biomarkers that could solve difficult questions about decision making in clinical settings.

Consequently, the so far approved biomarker is ideal and robust enough to be the only clinical criteria for diagnosis or prediction of disease with high reliability, specificity, and sensitivity. In this case, not only a biomarker but also a panel or group of biomarkers -that reflect the multifactor nature of the disease state of RA—should be considered. At present, the right choice of candidate biomarkers offers additional and objective information that, when used in conjunction with traditional tools and techniques, represents a potential opportunity to make more informed and integrative clinical decisions that lead to a more precise medical care model. This model will allow stratification of RA patients according to the level of risk and degree of therapeutic opportunity.

In this sense, a patient can benefit from previous diagnosis, stratification in response to severity of disease progress, prediction of response to a specific therapy, and prediction of toxicity reactions or identification of prognostic value avoiding ineffective treatments which can favor exposure to side effects. This allows access to timely care that improves the quality of life and better control of disease, offers a valuable degree of prevention, reduces costs to the health system, and contributes to the possibility of developing new therapies (Table 2).

Table 2 Advantage of discovering and using new biomarkers in patients with rheumatoid arthritis
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Among the phase 1 biomarkers possess both protective and present actions in patients at risk for RA, this in RF-positive and RF-negative patients. In particular cases such as calgranulin C provides a differentiating characteristic against other inflammatory arthritis (Table 1). As for those proteomics and metabolomics are characterized by their use as clinical diagnostics, and differentiation between RA patients and healthy patients.

The most common associations found in this phase are associated with rapid joint destruction and disease progression. However, there are some omic-biomarkers related with positive outcomes as the requirement of less therapeutic interventions like M1V variant SNP (Table 1). Most of these changes can be assessed by radiological findings.

The major feature that stands out among the phase 6 biomarkers is that they are correlated to treatment response, both for increasing and decreasing biomarker concentration. Their presence can serve as an indicator and predictor of response to treatment with anti-TNF biologic drugs such as ICAM-1. In particular those genomic biomarkers within this phase have an association with reduced disease activity in early RA, for example sTNFRII which is associated with disease remission after treatment with tocilizumab. Among the phase 6 proteomics, relationships are found between a high concentration and clinical response to infliximab such as MMP-3 (Table 1).

Perspectives and challenges of the use of omics-based biomarkers in clinical practice

Although numerous publications about the discovery of new biomarkers are available, currently, their translation into clinical practice is limited. However, progressive growth of technologies and omic sciences and interest of international organizations such as FDA/EMA, have allowed a better outlook to the use of biomarkers as useful tools to improve quality of healthcare.

Even so, it is necessary to identify and recognize a series of barriers and challenges that must be worked on to have a greater number of biomarkers. In this regard, the following stand out:

  • Omics technologies and costs. Although technology is in rapid and progressive growth, they are still expensive.

  • Application in clinical practice. It is probably the biggest challenge for clinical application of biomarkers. There is currently a large number of scientific publications on the discovery of new biomarkers, however, the number of biomarkers applied in clinical practice is very low.

  • Accessibility, repeatability, and technical validation.

  • Validation times. Time from discovery of a biomarker til its validation in clinical practice is usually extensive due to different established requirements.

  • Results processing and interpretation. As use of omic technologies grows, it becomes necessary to be able to disseminate and handle a large amount of data that increases in parallel and ensures an added value for patients. Normally, physicians usually focus on a single issue since addressing other technologies would be complicated. In most cases, biomarkers are specific to a population, so it is necessary to establish biomarkers for each group of patients.

  • Legal and regulatory matters. Although processes have been initiated to set policies regarding the issue of biomarkers, there are still too many legal gaps to consider.

Process of health care needs parameters to evaluate effectiveness and safety of pharmacotherapy. In this context, biomarkers are an excellent information tool for prevention, diagnosis, identifying progression of disease, selection of treatment and assessment of response to therapy (pharmacodynamics), as well as applications in experimental evaluation [17].

Therefore, it is useful in the application of disease diagnosis, prognostic factor, choice and monitoring of the best possible treatment, and evaluation of therapeutics in a simple, minimally invasive way and without additional risk for the patient [127]. The rapid growth of technological tools, the progression in advances in validation and elucidation of processes and procedures in molecular biology, analytical chemistry and bioinformatics have increased the application of biomarkers in research and later in clinical practice, highlighting omic biomarkers: transcriptomics, genomics, proteomics and metabolomics [128].

Usefulness of using biomarkers for health contributes to selection of medicines, evaluation of progression of diseases and their treatment. In same way, technological developments make it possible for implemented biomarkers to adjust more and more to the concept of an ideal biomarker; that is, they are increasingly specific and fundamental in the development of different biomedical disciplines, Allow the development of strategies and policies that include patients with rheumatoid arthritis and improve their quality of life.

The clinic importance of the biomarkers in RA is still uncertain. This diagnosis is still based on clinical findings and blood tests with non-omic biomarkers. There are many associations that are not totally useful for the diagnosis of RA through omic-biomarkers. Although most of them are related to the characteristics of the disease and their possible outcomes, it has not been possible to perform the diagnostic process with biomarkers alone. However, the usefulness of biomarkers could be established as predictors of disease and outcomes. This could be beneficial in determining the natural history of patients depending on the stage of the disease by personalizing each case.

Therefore, we provide an overview of the pharmacogenomics of RA and the possibility of using omic biomarkers with potential to be used in clinical practice and to support pharmacotherapeutic decisions in order to improve response and safety to treatment.

The relevance of this study lies in providing the possibility to encourage the investigation of omic biomarkers -selected for their biological importance in AR- either in pharmacokinetic and pharmacodynamic processes, to provide additional tools that facilitate the identification of individuals at risk of suffering adverse events or individuals likely to fail treatment. Therefore, it is expected that the information generated can be used in daily clinical practice, helping to choose the best therapeutic option, at the right time with the least possible risk (greater effectiveness and safety) in patients with rheumatoid arthritis [8].

Conclusions

Globally, there is not a totally effective medication in all patients, and each individual has a different response to drug treatment. This could be explained due to a modification in pharmacokinetics and pharmacodynamics properties of drugs related with genetic environmental conditions. In this context, the investigation of omic biomarkers has been more successful in the identification and explanation of the alteration of pharmacological response, compared to investigations of candidate genes of disease. Therefore, this paper should make a contribution to the selection of the best therapeutic management in patients with RA according to the phase of disease and is a basis to continue the research aimed at the identification of omic biomarkers according to stage of RA and treatment phase.

As observed in this systematic review, in the last decade a great effort has been made to find omic biomarkers capable of predicting the response to therapy in a patient with rheumatoid arthritis. Many biomarkers have been explored and, even though several omic biomarkers have been identified, there are limitations with respect to their specificity, ease of sampling, representativeness, and stability to predict response. Thus, more comprehensive research is still needed in the identification of omic biomarkers in different phases of rheumatoid arthritis with promising next-generation sequencing and nuclear magnetic resonance techniques.