Biomarkers identified by serum metabolomic analysis to predict biologic treatment response in rheumatoid arthritis patients

Towards Personalized Medicine in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Metabolomics in rheumatoid arthritis: Advances and review

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.

Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity

Immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis (Ps), represent autoinflammatory and autoimmune disorders, as well as conditions that have an overlap of both categories. Understanding the underlying pathogeneses, making diagnoses, and choosing individualized treatments remain challenging due to heterogeneous disease phenotypes and the lack of reliable biomarkers that drive the treatment choice. In this review, we provide an overview of the low-molecular-weight metabolites that might be employed as biomarkers for various applications, e.g., early diagnosis, disease activity monitoring, and treatment-response prediction, in RA, PsA, and Ps. The literature was evaluated, and putative biomarkers in different matrices were identified, categorized, and summarized. While some of these candidate biomarkers appeared to be disease-specific, others were shared across multiple IMIDs, indicating common underlying disease mechanisms. However, there is still a long way to go for their application in a routine clinical setting. We propose that studies integrating omics analyses of large patient cohorts from different IMIDs should be performed to further elucidate their pathomechanisms and treatment options. This could lead to the identification and validation of biomarkers that might be applied in the context of precision medicine to improve the clinical outcomes of these IMID patients.

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

A Review of Metabolomic Profiling in Rheumatoid Arthritis: Bringing New Insights in Disease Pathogenesis, Treatment and Comorbidities

Metabolomic analysis provides a wealth of information that can be predictive of distinctive phenotypes of pathogenic processes and has been applied to better understand disease development. Rheumatoid arthritis (RA) is an autoimmune disease with the establishment of chronic synovial inflammation that affects joints and peripheral tissues such as skeletal muscle and bone. There is a lack of useful disease biomarkers to track disease activity, drug response and follow-up in RA. In this review, we describe potential metabolic biomarkers that might be helpful in the study of RA pathogenesis, drug response and risk of comorbidities. TMAO (choline and trimethylamine oxide) and TCA (tricarboxylic acid) cycle products have been suggested to modulate metabolic profiles during the early stages of RA and are present systemically, which is a relevant characteristic for biomarkers. Moreover, the analysis of lipids such as cholesterol, FFAs and PUFAs may provide important information before disease onset to predict disease activity and treatment response. Regarding therapeutics, TNF inhibitors may increase the levels of tryptophan, valine, lysine, creatinine and alanine, whereas JAK/STAT inhibitors may modulate exclusively fatty acids. These observations indicate that different disease modifying antirheumatic drugs have specific metabolic profiles and can reveal differences between responders and non-responders. In terms of comorbidities, physical impairment represented by higher fatigue scores and muscle wasting has been associated with an increase in urea cycle, FFAs, tocopherols and BCAAs. In conclusion, synovial fluid, blood and urine samples from RA patients seem to provide critical information about the metabolic profile related to drug response, disease activity and comorbidities.

Serum metabolite profiling yields insights into health promoting effect of A. muciniphila in human volunteers with a metabolic syndrome

Reduction of A. muciniphila relative abundance in the gut microbiota is a widely accepted signature associated with obesity-related metabolic disorders. Using untargeted metabolomics profiling of fasting plasma, our study aimed at identifying metabolic signatures associated with beneficial properties of alive and pasteurized A. muciniphila when administrated to a cohort of insulin-resistant individuals with metabolic syndrome. Our data highlighted either shared or specific alterations in the metabolome according to the form of A. muciniphila administered with respect to a control group. Common responses encompassed modulation of amino acid metabolism, characterized by reduced levels of arginine and alanine, alongside several intermediates of tyrosine, phenylalanine, tryptophan, and glutathione metabolism. The global increase in levels of acylcarnitines together with specific modulation of acetoacetate also suggested induction of ketogenesis through enhanced β-oxidation. Moreover, our data pinpointed some metabolites of interest considering their emergence as substantial compounds pertaining to health and diseases in the more recent literature.

Metabolites and metabolic pathways associated with rheumatoid arthritis and systemic lupus erythematosus

Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are chronic autoimmune diseases that result from the combined influence of genetic and environmental factors that promotes the loss of tolerance to cellular components. The complexity of these diseases converts them into a major challenge at the diagnostic and treatment level. Therefore, it is convenient to implement the use of tools for a better understanding of the physiopathology of these diseases to propose reliable biomarkers. The "omics" disciplines like metabolomics and lipidomics allow to study RA and SLE in a higher degree of detail since they evaluate the metabolites and metabolic pathways involved in disease pathogenesis. This review has compiled the information of metabolomics and lipidomics studies where samples obtained from RA and SLE patients were evaluated to find the metabolites and pathways differences between patients and healthy controls. In both diseases, there is a decrease in several amino acids and oxidative stress-related metabolites like glutathione. These findings may be useful for functional metabolomics studies aiming to reprogram the metabolism in a disease setting to recover normal immune cell homeostasis and function.

Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy

Diabetic retinopathy (DR) is a universal microvascular complication of diabetes mellitus (DM), which is the main reason for global sight damage/loss in middle-aged and/or older people. Current clinical analyses, like hemoglobin A1c, possess some importance as prognostic indicators for DR severity, but no effective circulating biomarkers are used for DR in the clinic currently, and studies on the latent pathophysiology remain lacking. Recent developments in omics, especially metabolomics, continue to disclose novel potential biomarkers in several fields, including but not limited to DR. Therefore, based on the overview of metabolomics, we reviewed progress in analytical technology of metabolomics, the prominent roles and the current status of biomarkers in DR, and the update of potential biomarkers in various DR-related samples via metabolomics, including tear as well as vitreous humor, aqueous humor, retina, plasma, serum, cerebrospinal fluid, urine, and feces. In this review, we underscored the in-depth analysis and elucidation of the common biomarkers in different biological samples based on integrated results, namely, alanine, lactate, and glutamine. Alanine may participate in and regulate glucose metabolism through stimulating N-methyl-D-aspartate receptors and subsequently suppressing insulin secretion, which is the potential pathogenesis of DR. Abnormal lactate could cause extensive oxidative stress and neuroinflammation, eventually leading to retinal hypoxia and metabolic dysfunction; on the other hand, high-level lactate may damage the structure and function of the retinal endothelial cell barrier via the G protein-coupled receptor 81. Abnormal glutamine indicates a disturbance of glutamate recycling, which may affect the activation of Müller cells and proliferation via the PPP1CA-YAP-GS-Gln-mTORC1 pathway.

Jintiange Capsule Alleviates Rheumatoid Arthritis and Reverses Changes of Serum Metabolic Profile in Collagen-Induced Arthritic Rats

Purpose

Jintiange capsule (JTG), an approved drug developed as a substitute for tiger bone (TB), has been clinically applied for osteoporosis therapy since 2003. The drug is composed of bionic TB powder, in which peptides and proteins are primarily enriched from other bone extracts. However, as a precious material of traditional Chinese medicine (TCM), TB has been mainly understood and used in TCM to relieve osteoporosis, rheumatoid arthritis and bone injury. Inspired by those, the purpose of this study was to investigate whether JTG also had an effect on relieving rheumatoid arthritis in collagen-induced arthritic (CIA) rats and explore potential mechanism from the perspective of serum metabolic profile changes.

Methods

JTG was analyzed using Nano LC-MS/MS and orally administered in CIA rats for 6 weeks. After administration, intervention effects of JTG on synovial inflammation, bone micro-architecture and bone metabolism were studied, and the impact of JTG on serum metabolic profiles in CIA rats was investigated by metabolomics.

Results

Nine bioactive peptides were identified in JTG. In animal treatments, JTG alleviated paw swelling (P < 0.01), arthritic severity (P < 0.01) and synovial tissue proliferation, as well as inflammatory cell infiltration of ankle joint, decreased bone loss, improved microstructure of bone in CIA rats by regulating bone absorption and formation, specifically increasing bone mineral density (BMD) (P < 0.05), bone volume fraction (BVF) (P < 0.05), trabecular number (Tb.N) (P < 0.05) and decreasing trabecular separation (Tb.Sp) (P < 0.05). Besides, serum IL-6 was down-regulated remarkably in CIA rats (P < 0.05). Furthermore, metabolomics analysis revealed that 32 metabolites were regulated significantly (P < 0.05) by comparison between CIA model and JTG in 360 mg/kg dose. The pathway analysis implied that JTG was involved in regulation of biosynthesis of phenylalanine.

Conclusion

JTG alleviates rheumatoid arthritis and reverses changes in serum metabolic profile in CIA rats.

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Toward Overcoming Treatment Failure in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

919 Syrup Alleviates Postpartum Depression by Modulating the Structure and Metabolism of Gut Microbes and Affecting the Function of the Hippocampal GABA/Glutamate System

Postpartum depression (PPD) is a mental disorder that affects pregnant women around the world, with serious consequences for mothers, families, and children. Its pathogenesis remains unclear, and medications for treating PPD that can be used during lactation remain to be identified. 919 syrup (919 TJ) is a Chinese herbal medicine that has been shown to be beneficial in the treatment of postpartum depression in both clinical and experimental studies. The mechanism of action of 919 TJ is unclear. 919 syrup is ingested orally, making the potential interaction between the drug and the gut microbiome impossible to ignore. We therefore hypothesized that 919 syrup could improve the symptoms of postpartum depression by affecting the structure and function of the intestinal flora, thereby altering hippocampal metabolism. We compared changes in hippocampal metabolism, fecal metabolism, and intestinal microflora of control BALB/c mice, mice with induced untreated PPD, and mice with induced PPD treated with 919 TJ, and found that 4-aminobutyric acid (GABA) in the hippocampus corresponded with PPD behaviors. Based on changes in GABA levels, multiple key gut bacterial species (Mucispirillum schaedleri, Bifidobacterium pseudolongum, Desulfovibrio piger, Alloprevotella tannerae, Bacteroides sp.2.1.33B and Prevotella sp. CAG:755) were associated with PPD. Metabolic markers that may represent the function of the intestinal microbiota in mice with PPD were identified (Met-Arg, urocanic acid, thioetheramide-PC, L-pipecolic acid, and linoleoyl ethanolamide). The relationship between these factors is not a simple one-to-one correspondence, but more likely a network of staggered functions. We therefore believe that the composition and function of the entire intestinal flora should be emphasized in research studying the gut and PPD, rather than changes in the abundance of individual bacterial species. The introduction of this concept of “GutBalance” may help clarify the relationship between gut bacteria and systemic disease.

Metabolomics profiling predicts outcome of tocilizumab in rheumatoid arthritis: an exploratory study

INTRODUCTION

To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response.

OBJECTIVES

We hypothesized that the characterization of a patients' metabolic profile, utilizing one-dimensional nuclear magnetic resonance (¹H-NMR), may predict a response to tocilizumab in patients with rheumatoid arthritis (RA).

METHODS

40 active RA patients meeting the 2010 ACR/EULAR classification criteria initiating treatment with tocilizumab were recruited. Clinical outcomes were determined at baseline, and after six and twelve months of treatment. EULAR response criteria at 6 and 12 months to categorize patients as responders and non-responders. Blood was collected at baseline and after six months of tocilizumab therapy. ¹H-NMR was used to acquire a spectra of plasma samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. SPSS v.27 and MetaboAnalyst 4.0 were used for statistical and pathway analysis.

RESULTS

Isobutyrate, 3-hydroxybutyrate, lysine, phenylalanine, sn-glycero-3-phosphocholine, tryptophan and tyrosine were significantly elevated in responders at the baseline. OPLS-DA at baseline partially discriminated between RA responders and non-responders. A multivariate diagnostic model showed that concentrations of 3-hydroxybutyrate and phenylalanine improved the ability to specifically predict responders classifying 77.1% of the patients correctly. At 6 months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders.

CONCLUSION

The relationship between plasma metabolic profiles and the clinical response to tocilizumab suggests that ¹H-NMR may be a promising tool for RA therapy optimization. More studies are needed to determine if metabolic profiling can predict the response to biological therapies in RA patients.

Adenosine deaminase modulates metabolic remodeling and orchestrates joint destruction in rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic autoimmune disease associated with inflammation and joint remodeling. Adenosine deaminase (ADA), a risk factor in RA, degrades adenosine, an anti-inflammatory molecule, resulting in an inflammatory bias. We present an integrative analysis of clinical data, cytokines, serum metabolomics in RA patients and mechanistic studies on ADA-mediated effects on in vitro cell culture models. ADA activity differentiated patients into low and high ADA sets. The levels of the cytokines TNFα, IFNγ, IL-10, TGFβ and sRANKL were elevated in RA and more pronounced in high ADA sets. Serum metabolomic analysis shows altered metabolic pathways in RA which were distinct between low and high ADA sets. Comparative analysis with previous studies shows similar pathways are modulated by DMARDs and biologics. Random forest analysis distinguished RA from control by methyl-histidine and hydroxyisocaproic acid, while hexose-phosphate and fructose-6-phosphate distinguished high ADA from low ADA. The deregulated metabolic pathways of High ADA datasets significantly overlapped with high ADA expressing PBMCs GEO transcriptomics dataset. ADA induced the death of chondrocytes, synoviocyte proliferation, both inflammation in macrophages and their differentiation into osteoclasts and impaired differentiation of mesenchymal stem cells to osteoblasts and mineralization. PBMCs expressing elevated ADA had increased expression of cytokines and P2 receptors compared to synovial macrophages which has low expression of ADA. Our data demonstrates increased cytokine levels and distinct metabolic signatures of RA based on the ADA activity, suggests an important role for ADA in the pathophysiology of RA joints and as a potential marker and therapeutic target in RA patients.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

CE-MS for metabolomics: Developments and applications in the period 2018-2020

Capillary electrophoresis-mass spectrometry (CE-MS) is now a mature analytical technique in metabolomics, notably for the efficient profiling of polar and charged metabolites. Over the past few years, (further) progress has been made in the design of improved interfacing techniques for coupling CE to MS; also, in the development of CE-MS approaches for profiling metabolites in volume-restricted samples, and in strategies that further enhance the metabolic coverage. In this article, which is a follow-up of a previous review article covering the years 2016-2018 (Electrophoresis 2019, 40, 165-179), the main (technological) developments in CE-MS methods and strategies for metabolomics are discussed covering the literature from July 2018 to June 2020. Representative examples highlight the utility of CE-MS in the fields of biomedical, clinical, microbial, plant and food metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, some general conclusions and perspectives are given.

Identification of Metabolic Biomarkers in Relation to Methotrexate Response in Early Rheumatoid Arthritis

This study aimed to identify baseline metabolic biomarkers for response to methotrexate (MTX) therapy in rheumatoid arthritis (RA) using an untargeted method. In total, 82 baseline plasma samples (41 insufficient responders and 41 sufficient responders to MTX) were selected from the Treatment in the Rotterdam Early Arthritis Cohort (tREACH, trial number: ISRCTN26791028) based on patients' EULAR response at 3 months. Metabolites were assessed using high-performance liquid chromatography-quadrupole time of flight mass spectrometry. Differences in metabolite concentrations between insufficient and sufficient responders were assessed using partial least square regression discriminant analysis (PLS-DA) and Welch's t-test. The predictive performance of the most significant findings was assessed in a receiver operating characteristic plot with area under the curve (AUC), sensitivity and specificity. Finally, overrepresentation analysis was performed to assess if the best discriminating metabolites were enriched in specific metabolic events. Baseline concentrations of homocystine, taurine, adenosine triphosphate, guanosine diphosphate and uric acid were significantly lower in plasma of insufficient responders versus sufficient responders, while glycolytic intermediates 1,3-/2,3-diphosphoglyceric acid, glycerol-3-phosphate and phosphoenolpyruvate were significantly higher in insufficient responders. Homocystine, glycerol-3-phosphate and 1,3-/2,3-diphosphoglyceric acid were independent predictors and together showed a high AUC of 0.81 (95% CI: 0.72-0.91) for the prediction of insufficient response, with corresponding sensitivity of 0.78 and specificity of 0.76. The Warburg effect, glycolysis and amino acid metabolism were identified as underlying metabolic events playing a role in clinical response to MTX in early RA. New metabolites and potential underlying metabolic events correlating with MTX response in early RA were identified, which warrant validation in external cohorts.