Diagnosis and Intervention in Early Psoriatic Arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory disorder that affects approximately 20–30% of patients with psoriasis. PsA causes deformities and joint damage, impairing quality of life and causing long-term functional disability. Several recent studies demonstrated that early diagnosis and intervention for PsA prevents permanent invalidity. However, the clinical features of PsA vary and are shared with other differential diseases, such as reactive arthritis, osteoarthritis, and ankylosing spondylitis. The common and overlapping features among these diseases complicate the accurate early diagnosis and intervention of PsA. Therefore, this review focuses on the current knowledge of the diagnosis of early PsA and discusses the meaning of early intervention for early PsA.

Identification and Evaluation of Serum Protein Biomarkers Which Differentiate Psoriatic from Rheumatoid Arthritis

OBJECTIVES

To identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) and may be used to support appropriate early intervention.

METHODS

The serum proteome of patients with PsA and RA was interrogated using nano-flow liquid chromatography mass spectrometry (nLC-MS/MS) (n=64 patients), an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients) and a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients). Multiple reaction monitoring assays (MRM) were developed to evaluate the performance of putative markers using the discovery cohort (n=60) and subsequently an independent cohort of PsA and RA patients (n=167).

RESULTS

Multivariate machine learning analysis of the protein discovery data from the three platforms revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for bead based immunoassay measurements and 0.73 for aptamer based analysis. Subsequently in the separate verification and evaluation studies, random forest models revealed that a subset of proteins measured by MRM could differentiate PsA and RA patients with AUCs of 0.79 and 0.85 respectively.

CONCLUSION

We report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. With continued evaluation and refinement using additional and larger patient cohorts including those with other arthropathies we suggest the panel identified here could contribute toward improved clinical decision making.

Fatal acute haemolysis and methaemoglobinaemia in a man with renal failure and Alkaptonuria - Is nitisinone the solution?

Haemolysis and methaemoglobinaemia (MetHb) are rare metabolic complications that can occur in Alkaptonuria (AKU), for which there is no curative treatment. Presented is a case of a man who had AKU, and serves as a reminder of life-threatening complications that can occur with haemolysis and MetHb. This case presents an opportunity to revisit important considerations relating to the investigation and treatment of haemolysis and MetHb with a view to raising awareness, and in doing so hopefully reducing the uniformly fatal outcome. Additionally it is proposed that treatment of haemolysis and MetHb with nitisinone is considered as a potentially lifesaving treatment as it is believed that reducing the concentration of circulating homogentisic acid will reduce oxidative stress.

Challenges in the clinical diagnosis of psoriatic arthritis

Psoriatic arthritis (PsA) is a chronic heterogeneous inflammatory musculoskeletal disease. The non-specific and often subtle manifestations make early diagnosis and subsequent treatment challenging. In the absence of diagnostic criteria and biomarkers, the diagnosis is often delayed leading to poor long-term outcomes. In addition, the differential diagnosis of a patient presenting with arthritis in the setting of skin psoriasis is wide due to symptom overlap with many other diseases. Peripheral arthritis, dactylitis, enthesitis and axial arthritis are the 4 domains of musculoskeletal involvement in PsA and careful examination of each domain by a rheumatologist is the first step for a correct diagnosis. Other extra-musculoskeletal features such as the presence of uveitis, inflammatory bowel disease, nail psoriasis and elevated acute phase reactants aid in the diagnosis of PsA. Screening patients with skin psoriasis using validated questionnaires might help in early diagnosis especially when coupled with imaging.

Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease

Background

Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. One of the main obstacles in studying AKU, and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Quality of Life scores (QoL) are a reliable way to monitor patients’ clinical condition and health status. QoL scores allow to monitor the evolution of diseases and assess the suitability of treatments by taking into account patients’ symptoms, general health status and care satisfaction. However, more comprehensive tools to study a complex and multi-systemic disease like AKU are needed. In this study, a Machine Learning (ML) approach was implemented with the aim to perform a prediction of QoL scores based on clinical data deposited in the ApreciseKUre, an AKU- dedicated database.

Method

Data derived from 129 AKU patients have been firstly examined through a preliminary statistical analysis (Pearson correlation coefficient) to measure the linear correlation between 11 QoL scores. The variable importance in QoL scores prediction of 110 ApreciseKUre biomarkers has been then calculated using XGBoost, with K-nearest neighbours algorithm (k-NN) approach. Due to the limited number of data available, this model has been validated using surrogate data analysis.

Results

We identified a direct correlation of 6 (age, Serum Amyloid A, Chitotriosidase, Advanced Oxidation Protein Products, S-thiolated proteins and Body Mass Index) out of 110 biomarkers with the QoL health status, in particular with the KOOS (Knee injury and Osteoarthritis Outcome Score) symptoms (Relative Absolute Error (RAE) 0.25). The error distribution of surrogate-model (RAE 0.38) was unequivocally higher than the true-model one (RAE of 0.25), confirming the consistency of our dataset. Our data showed that inflammation, oxidative stress, amyloidosis and lifestyle of patients correlates with the QoL scores for physical status, while no correlation between the biomarkers and patients’ mental health was present (RAE 1.1).

Conclusions

This proof of principle study for rare diseases confirms the importance of database, allowing data management and analysis, which can be used to predict more effective treatments.

Antibody modified gold nanoparticles for fast colorimetric screening of rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic joint inflammation and one of the main causes of chronic disability worldwide with high prevalence in the ageing population. RA is characterized by autoantibody production, synovial inflammation and bone destruction, and the most accepted biomarker is rheumatoid factor (RF) autoantibodies. In this work, we developed a low-cost approach for the detection and quantification of the RF marker. This colorimetric immunosensor is based on gold nanoprobe crosslinking that results in extensive aggregation in the presence of the pentameric IgM RF. Aggregation of the nanoconjugates yields a color change from red to purple that can be easily observed by the naked eye. The interaction between nanoconjugates and the specific target was confirmed via dynamic light scattering (DLS), Raman spectroscopy and atomic force microscopy (AFM) imaging. This conceptual system shows a LOD of 4.15 UA mL^-1 IgM RF (clinical threshold is set for 20 IU mL^-1). The one-step biosensor strategy herein proposed is much faster than conventional detection techniques, without the need for secondary antibodies, additional complex washing or signal amplification protocols. To the best of our knowledge this is the first report on target induced aggregation of gold nanoprobes for quantitative colorimetric autoantibody detection.

Optimal search strategies for identifying moderators and predictors of treatment effects in PubMed

BACKGROUND

Treatment effects differ across patients. To guide selection of treatments for patients, it is essential to acknowledge these differences and identify moderators or predictors. Our aim was to generate optimal search strategies (commonly known as filters) for PubMed to retrieve papers identifying moderators and predictors of treatment effects.

METHODS

Six journals were hand-searched for articles on moderators or predictors. Selected articles were randomly allocated to a development and validation set. Search terms were extracted from the development set and tested for their performance. Search filters were created from combinations of these terms and tested in the validation set.

RESULTS

Of 4407 articles, 198 were considered to be relevant. The most sensitive filter in the development set '("Epidemiologic Methods" [MeSH] OR assign* OR control*[tiab] OR trial*[tiab]) AND therapy*[sh]' yielded in the validation set a sensitivity of 89% [88%-90%] and a specificity of 80% [79%-82%].

CONCLUSIONS

The search filters created in this study can help to efficiently retrieve evidence on moderators and predictors of treatment effect. Testing of the filters in multiple domains should reveal robustness across disciplines. These filters can facilitate the retrieval of evidence on moderators and predictors of treatment effects, helping the implementation of stratified or personalised health care.

Proteomics in Chronic Arthritis-Will We Finally Have Useful Biomarkers?

PURPOSE OF REVIEW

Current technical advances enable the assessment of the complex changes in body fluid proteomes and thus allow for the discovery of biomarker signatures rather than just following differences of a single marker. In this review, we aim to summarize current approaches to discover and evaluate multi-biomarker panels for improved monitoring of chronic arthritis disease activity.

RECENT FINDINGS

Mass spectrometry and affinity proteomic methodologies have been used to identify biomarker panels in synovial fluid, serum, plasma, or urine of pediatric and adult chronic arthritis patients. Notably, despite the numerous efforts to develop new and better biomarker panels, very few have undergone extensive analytical and clinical validation and been adopted into routine use for patient benefit. There remains a significant gap between discovery of chronic arthritis biomarker signatures and their validation for clinical use.

Genomics and epigenomics in rheumatic diseases: what do they provide in terms of diagnosis and disease management?

Most rheumatic diseases are complex or multifactorial entities with pathogeneses that interact with both multiple genetic factors and a high number of diverse environmental factors. Knowledge of the human genome sequence and its diversity among populations has provided a crucial step forward in our understanding of genetic diseases, identifying many genetic loci or genes associated with diverse phenotypes. In general, susceptibility to autoimmunity is associated with multiple risk factors, but the mechanism of the environmental component influence is poorly understood. Studies in twins have demonstrated that genetics do not explain the totality of the pathogenesis of rheumatic diseases. One method of modulating gene expression through environmental effects is via epigenetic modifications. These techniques open a new field for identifying useful new biomarkers and therapeutic targets. In this context, the development of "-omics" techniques is an opportunity to progress in our knowledge of complex diseases, impacting the discovery of new potential biomarkers suitable for their introduction into clinical practice. In this review, we focus on the recent advances in the fields of genomics and epigenomics in rheumatic diseases and their potential to be useful for the diagnosis, follow-up, and treatment of these diseases. The ultimate aim of genomic studies in any human disease is to understand its pathogenesis, thereby enabling the prediction of the evolution of the disease to establish new treatments and address the development of personalized therapies.

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.

Review of biomarkers in systemic juvenile idiopathic arthritis: helpful tools or just playing tricks?

BACKGROUND

Diagnosing systemic juvenile idiopathic arthritis (SJIA) can be extremely challenging if typical arthritis is lacking. A variety of biomarkers have been described for the diagnosis and management of SJIA. However, very few markers have been well-validated. In addition, increasing numbers of biomarkers are identified by high throughput or multi-marker panels.

METHOD

We identified diagnostic or prognostic biomarkers by systematic literature review, evaluating each according to a predefined level of verification, validation or clinical utility. Diagnostic biomarkers were those identifying SJIA versus (1) non-SJIA conditions or healthy controls (HC) or (2) other non-systemic JIA subtypes. Prognostic biomarkers were those specifically tested for the prediction of (1) disease flare, (2) increased disease activity +/- discrimination of active versus inactive disease, or (3) macrophage activation syndrome (MAS).

RESULTS

Fifty-five studies fulfilled the inclusion criteria identifying 68 unique biomarkers, of which 50/68 (74 %) were investigated by only a single research group. Candidate marker verification and clinical utility was evaluated according to whether markers were readily and reliably measurable, investigated by independent study groups, discovered by more than one method (i.e. verified markers) and validated in independent cohorts. This evaluation revealed diagnostic biomarkers of high interest for further evaluation in the diagnostic approach to SJIA that included heme oxygenase-1, interleukin-6 (IL-6), IL-12, IL-18, osteoprotegerin, S100 calcium-binding protein A12 (S100A12) and S100A8/A9.

CONCLUSION

In summary, a number of biomarkers were identified, though most had limited evidence for their use. However, our findings combined with the identified studies could inform validation studies, whether in single or multi-marker assays, which are urgently needed.

Fibromyalgia diagnosis: a review of the past, present and future

Diagnosis of fibromyalgia (FM) remains controversial even though diverse diagnostic criteria have been developed. This review looks at the history, evolution of diagnostic criteria, endless controversy, recent trends and future perspectives regarding FM diagnosis. Some have criticized that diagnosis of FM could lead to medicalization, whereas others have raised concerns of under-diagnosing FM. With the evolution of diagnosis criteria from American College of Rheumatology 1990 to modified American College of Rheumatology 2010, diagnosis of FM has become simpler. The recent trend of applying patient-reported questionnaires has also increased a simpler FM diagnosis. Reliable biomarkers will not be available for the foreseeable future, so diverse assessment tools will have to be used more pro-actively. After initial diagnosis, multiple and comprehensive assessment measures are needed during the course of treatment in order to better understand type and severity of FM symptoms. These, in turn, could help classify FM based on symptom domain, symptom severity, and comorbidity which would enable more personalized treatment.

Developing clinically relevant biomarkers in inflammatory arthritis: A multiplatform approach for serum candidate protein discovery

PURPOSE

To identify candidate biomarkers that have the potential to distinguish between patients with psoriatic arthritis (PsA) or rheumatoid arthritis (RA) and explore the value of combining different protein discovery platforms for the development of a multiplexed protein biomarker panel.

EXPERIMENTAL DESIGN

Serum samples from 32 patients (PsA; n = 16 and RA; n = 16) defined as active, early onset, and treatment naïve were analyzed using unbiased label-free LC-MS/MS, a microsphere bead-based immunoassay (Luminex xMAP) and an aptamer-based assay (SOMAscan).

RESULTS

LC-MS/MS was used to quantify 324 proteins, while the Luminex xMAP targeted 48 proteins and SOMAscan supported the measurement of 1129 proteins. The combined data from these techniques gave reproducible quantification of 1501 proteins in total. Of these, 42 (LC-MS/MS), 3 (Luminex xMAP), and 127 (SOMAscan) proteins were found to be differentially expressed between PsA and RA (p < 0.05).

CONCLUSION AND CLINICAL RELEVANCE

Using three different and potentially complementary proteomic platforms we identified a total of 172 proteins that are differentially expressed in patients with PsA compared to RA. These proteins collectively represent candidates for inclusion in a protein signature that could be developed as a diagnostic test to discriminate patients with PsA from RA and therefore be of clinical utility.

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate biomarkers in rheumatoid and psoriatic arthritis, evaluated the evidence for their potential as biomarkers of bone (joint) damage, and outlined how mass spectrometry-based targeted and multiplexed measurement of candidate biomarker proteins is likely to accelerate their clinical validation and the development of clinical diagnostic tests.

Current and future trends in biomarker discovery and development of companion diagnostics for arthritis

Musculoskeletal diseases such as rheumatoid arthritis are complex multifactorial disorders that are chronic in nature and debilitating for patients. A number of drug families are available to clinicians to manage these disorders but few tests exist to target these to the most responsive patients. As a consequence, drug failure and switching to drugs with alternate modes of action is common. In parallel, a limited number of laboratory tests are available which measure biological indicators or 'biomarkers' of disease activity, autoimmune status, or joint damage. There is a growing awareness that assimilating the fields of drug selection and diagnostic tests into 'companion diagnostics' could greatly advance disease management and improve outcomes for patients. This review aims to highlight: the current applications of biomarkers in rheumatology with particular focus on companion diagnostics; developments in the fields of proteomics, genomics, microbiomics, imaging and bioinformatics and how integration of these technologies into clinical practice could support therapeutic decisions.

Computer-based diagnostic expert systems in rheumatology: where do we stand in 2014?

Background. The early detection of rheumatic diseases and the treatment to target have become of utmost importance to control the disease and improve its prognosis. However, establishing a diagnosis in early stages is challenging as many diseases initially present with similar symptoms and signs. Expert systems are computer programs designed to support the human decision making and have been developed in almost every field of medicine. Methods. This review focuses on the developments in the field of rheumatology to give a comprehensive insight. Medline, Embase, and Cochrane Library were searched. Results. Reports of 25 expert systems with different design and field of application were found. The performance of 19 of the identified expert systems was evaluated. The proportion of correctly diagnosed cases was between 43.1 and 99.9%. Sensitivity and specificity ranged from 62 to 100 and 88 to 98%, respectively. Conclusions. Promising diagnostic expert systems with moderate to excellent performance were identified. The validation process was in general underappreciated. None of the systems, however, seemed to have succeeded in daily practice. This review identifies optimal characteristics to increase the survival rate of expert systems and may serve as valuable information for future developments in the field.

Redox proteomics gives insights into the role of oxidative stress in alkaptonuria

Alkaptonuria (AKU) is an ultra-rare metabolic disorder of the catabolic pathway of tyrosine and phenylalanine that has been poorly characterized at molecular level. As a genetic disease, AKU is present at birth, but its most severe manifestations are delayed due to the deposition of a dark-brown pigment (ochronosis) in connective tissues. The reasons for such a delayed manifestation have not been clarified yet, though several lines of evidence suggest that the metabolite accumulated in AKU sufferers (homogentisic acid) is prone to auto-oxidation and induction of oxidative stress. The clarification of the pathophysiological molecular mechanisms of AKU would allow a better understanding of the disease, help find a cure for AKU and provide a model for more common rheumatic diseases. With this aim, we have shown how proteomics and redox proteomics might successfully overcome the difficulties of studying a rare disease such as AKU and the limitations of the hitherto adopted approaches.

Exploration of panviral proteome: high-throughput cloning and functional implications in virus-host interactions

Throughout the long history of virus-host co-evolution, viruses have developed delicate strategies to facilitate their invasion and replication of their genome, while silencing the host immune responses through various mechanisms. The systematic characterization of viral protein-host interactions would yield invaluable information in the understanding of viral invasion/evasion, diagnosis and therapeutic treatment of a viral infection, and mechanisms of host biology. With more than 2,000 viral genomes sequenced, only a small percent of them are well investigated. The access of these viral open reading frames (ORFs) in a flexible cloning format would greatly facilitate both in vitro and in vivo virus-host interaction studies. However, the overall progress of viral ORF cloning has been slow. To facilitate viral studies, we are releasing the initiation of our panviral proteome collection of 2,035 ORF clones from 830 viral genes in the Gateway® recombinational cloning system. Here, we demonstrate several uses of our viral collection including highly efficient production of viral proteins using human cell-free expression system in vitro, global identification of host targets for rubella virus using Nucleic Acid Programmable Protein Arrays (NAPPA) containing 10,000 unique human proteins, and detection of host serological responses using micro-fluidic multiplexed immunoassays. The studies presented here begin to elucidate host-viral protein interactions with our systemic utilization of viral ORFs, high-throughput cloning, and proteomic technologies. These valuable plasmid resources will be available to the research community to enable continued viral functional studies.

Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research

There is an urgent need for identifying novel serum biomarkers that can be used to improve diagnosis, predict disease progression or response to therapy, or serve as therapeutic targets for rheumatic diseases. Synovial fluid (SF) is secreted by and remains in direct contact with the synovial membrane, and can reflect the biochemical state of the joint under different physiological and pathological conditions. Therefore, SF is regarded as an excellent source for identifying biomarkers of rheumatologic diseases. The use of high-throughput and/or quantitative proteomics and sophisticated computational software applied to analyze the protein content of SF has been well-adopted as an approach to finding novel arthritis biomarkers. This review will focus on some of the potential pitfalls of biomarker studies using SF, summarize the status of the field of SF proteomics in general, as well as discuss some of the most promising biomarker study approaches using proteomics. A brief status of the biomarker discovery efforts in rheumatoid arthritis, osteoarthritis and juvenile idiopathic arthritis is also provided.

Detection of autoantibodies in a point-of-care rheumatology setting

Autoimmune rheumatic diseases are common and confront society with serious medical, social, and financial burdens imposed by their debilitating nature. Many autoimmune diseases are associated with a particular set of autoantibodies, which have emerged as highly useful to define and classify disease, predict flares, or monitor efficacy of therapy. However, current practice for monitoring autoantibodies is protracted, labor-intensive, and expensive. This review provides an overview on the value of point-of-care (POC) biosensor technology in the diagnosis and management of patients with autoimmune rheumatic diseases. Real-time measurement of autoantibodies will clearly benefit the rheumatology practice in emergency and urgent care settings, where definitive diagnosis is essential for initiation of correct critical care therapy. Immediate serological information in clinic will provide considerable value for long-term patient care and an opportunity for an instant, result-deduced therapeutic action, avoiding delays and improving compliance, especially in field-based and remote areas. We describe the particular autoantibodies that are useful disease and activity markers and would, therefore, be attractive to POC applications. Already existing biosensors and platforms that show promise for autoantibody testing are summarized and comparatively evaluated. As POC assessment is gaining momentum in several areas of patient care, we propose that rheumatology is poised to benefit from this innovative and affordable technology.