Serum metabolic signatures of four types of human arthritis

Molecular portrait of chronic joint diseases: Defining endotypes toward personalized medicine

Joint diseases affect hundreds of millions of people worldwide, and their prevalence is constantly increasing. To date, despite recent advances in the development of therapeutic options for most rheumatic conditions, a significant proportion of patients still lack efficient disease management, considerably impacting their quality of life. Through the spectrum of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and osteoarthritis (OA) as quintessential and common rheumatic diseases, this review first provides an overview of their epidemiological and clinical features before exploring how the better definition of clinical phenotypes has helped their clinical management. It then discusses the recent progress in understanding the diversity of endotypes underlying disease phenotypes. Finally, this review highlights the current challenges of implementing molecular endotypes towards the personalized management of RA, PsA and OA patients in the future.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Association Between Human Metabolomics and Rheumatoid Arthritis: A Systematic Review and Meta-analysis

OBJECTIVE

The underdiagnosis and inadequate treatment of rheumatoid arthritis (RA) can be attributed to the various clinical manifestations presented by patients. To address this concern, we conducted an extensive review and meta-analysis, focusing on RA-related metabolites.

METHODS

A comprehensive literature search was conducted in PubMed, the Cochrane Library, Web of Science, and Embase to identify relevant studies published up to October 5, 2022. The quality of the included articles was evaluated and, subsequently, a meta-analysis was conducted using Review Manager software to analyze the association between metabolites and RA.

RESULTS

Forty nine studies met the inclusion criteria for the systematic review, and six of these studies were meta-analyzed to evaluate the association between 28 reproducible metabolites and RA. The results indicated that, compared to controls, the levels of histidine (RoM = 0.83, 95% CI = 0.79-0.88, I2 = 0%), asparagine (RoM = 0.83, 95% CI = 0.75-0.91, I2 = 0%), methionine (RoM = 0.82, 95% CI = 0.69-0.98, I2 = 85%), and glycine (RoM = 0.81, 95% CI = 0.67-0.97, I2 = 68%) were significantly lower in RA patients, while hypoxanthine levels (RoM = 1.14, 95% CI = 1.09-1.19, I2 = 0%) were significantly higher.

CONCLUSION

This study identified histidine, methionine, asparagine, hypoxanthine, and glycine as significantly correlated with RA, thus offering the potential for the advancement of biomarker discovery and the elucidation of disease mechanisms in RA.

Prediagnostic Glycoprotein Acetyl Levels and Incident and Recurrent Flare Risk Accounting for Serum Urate Levels: A Population-Based, Prospective Study and Mendelian Randomization Analysis

OBJECTIVE

To prospectively investigate population-based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate.

METHODS

We conducted a prediagnostic metabolome-wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006-2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2-sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases.

RESULTS

Correcting for multiple testing, 88 metabolites were associated with risk of incident gout (N = 1,303 cases) before serum urate adjustment, including glutamine and glycine (inversely), and lipids, branched-chain amino acids, and most prominently, glycoprotein acetyls (GlycA; P = 9.17 × 10-32 ). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22-1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36-11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04-1.17) overall and 1.54 (95% CI 1.21-1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27-2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk.

CONCLUSION

This prospective, population-based study implicates GlycA, a stable long-term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate.

Analysis of Metabolites in Gout: A Systematic Review and Meta-Analysis

(1) Background: Many studies have attempted to explore potential biomarkers for the early detection of gout, but consistent and high levels of evidence are lacking. In this study, metabolomics was used to summarize the changes of metabolites in the literature and explore the potential value of metabolites in predicting the occurrence and development of gout. (2) Methods: We searched the databases including the EMBASE, the Cochrane Library, PubMed, Web of Science, VIP Date, Wanfang Data, and CNKI, and the screening was fulfilled on 30 July 2022. The records were screened according to the inclusion criteria and the risk of bias was assessed. Qualitative analysis was performed for all metabolites, and meta-analysis was performed for metabolite concentrations using random effects to calculate the Std mean difference and 95% confidence interval. (3) Results: A total of 2738 records were identified, 33 studies with 3422 participants were included, and 701 metabolites were identified. The qualitative analysis results showed that compared with the healthy control group, the concentration of 56 metabolites increased, and 22 metabolites decreased. The results of the meta-analysis indicated that 17 metabolites were statistically significant. (4) Conclusions: Metabolites are associated with gout. Some specific metabolites such as uric acid, hypoxanthine, xanthine, KYNA, guanosine, adenosine, creatinine, LB4, and DL-2-Aminoadipic acid have been highlighted in the development of gout.

Diagnostic biomarker panels of osteoarthritis: UPLC-QToF/MS-based serum metabolic profiling

Osteoarthritis (OA) is the most common joint disease in the world, characterized by pain and loss of joint function, which has led to a serious reduction in the quality of patients' lives. In this work, ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-QToF/MS) in conjunction with multivariate pattern recognition methods and an univariate statistical analysis scheme were applied to explore the serum metabolic signatures within OA group (n = 31), HC (healthy controls) group (n = 57) and non-OA group (n = 19) for early diagnosis and differential diagnosis of OA. Based on logistic regression analysis and receiver operating characteristic (ROC) curve analysis, seven metabolites, including phosphatidylcholine (18:0/22:6), p-cresol sulfate and so on, were identified as critical metabolites for the diagnosis of OA and HC and yielded an area under the curve (AUC) of 0.978. The other panel of unknown m/z 239.091, phosphatidylcholine (18:0/18:0) and phenylalanine were found to distinguish OA from non-OA and achieved an AUC of 0.888. These potential biomarkers are mainly involved in lipid metabolism, glucose metabolism and amino acid metabolism. It is expected to reveal new insight into OA pathogenesis from changed metabolic pathways.

Metabolomic analysis in spondyloarthritis: A systematic review

Spondyloarthritis (SpA) is a group of rheumatic diseases that cause joint inflammation. Accumulating studies have focused on the metabolomic profiling of SpA in recent years. We conducted a systematic review to provide a collective summary of previous findings on metabolomic profiling associated with SpA. We systematically searched PubMed, Medline, Embase and Web of Science for studies on comparisons of the metabolomic analysis of SpA patients and non-SpA controls. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included articles. From 482 records identified, 31 studies were included in the analysis. A number of metabolites were differentially distributed between SpA and non-SpA cases. SpA patients showed higher levels of glucose, succinic acid, malic acid and lactate in carbohydrate metabolism, higher glycerol levels and lower fatty acid (especially unsaturated fatty acid) levels in lipid metabolism, and lower levels of tryptophan and glutamine in amino acid metabolism than healthy controls. Both conventional and biological therapy of SpA can insufficiently reverse the aberrant metabolism state toward that of the controls. However, the differences in the results of metabolic profiling between patients with SpA and other inflammatory diseases as well as among patients with several subtypes of SpA are inconsistent across studies. Studies on metabolomics have provided insights into etiological factors and biomarkers for SpA. Supplementation with the metabolites that exhibit decreased levels, such as short-chain fatty acids (SCFAs), has good treatment prospects for modulating immunity. Further studies are needed to elucidate the role of disordered metabolic molecules in the pathogenesis of SpA.

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.

Metabolomic profiling in ankylosing spondylitis using time-of-flight mass spectrometry

BACKGROUND & AIMS

Ankylosing spondylitis (AS) is an inflammatory disease associated with destructive changes in the skeleton and joints. The exact molecular mechanism of the disease has not been fully elucidated. This study aimed to determine metabolic differences between active AS patients and healthy controls to understand the molecular mechanism of AS.

PATIENTS AND METHODS

The study included 38 subjects, comprising 18 patients with active AS and 20 healthy controls. Metabolic profiling of the plasma was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC Q-TOF/MS). Data acquisition, classification, and identification were achieved with the METLIN (https://metlin.scripps.edu/) database and XCMS (https://xcmsonline.scripps.edu).

RESULTS

Significant alterations were identified in the unsaturated fatty acids (FA), linoleic acid, alpha-linolenic acid, FA degradation, and FA biosynthesis pathways. Down -regulations were observed in phosphatidylcholine (PC) (16:0/0:0), beta-d-Fructose, stearic acid, trimipramine N-Oxide and muconic acid, and up-regulation were detected in PC (18:2/0:0), 3-Methylindole, palmitic acid (PA), alpha-Tocotrienol, and beta-d-glucopyranoside in active AS patients compared to the healthy control subjects.

CONCLUSION

Pathway analysis revealed that dysregulation in FA metabolism is associated with AS, and therefore, modulation of diet according to PA and PC may be potential therapeutic targets.

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.

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.

Metabolomic Profiling Identifies Exogenous and Microbiota-Derived Metabolites as Markers of Methotrexate Efficacy in Juvenile Idiopathic Arthritis

Variability in methotrexate (MTX) efficacy represents a barrier to early and effective disease control in the treatment of juvenile idiopathic arthritis (JIA). This work seeks to understand the impact of MTX on the plasma metabolome and to identify metabolic biomarkers of MTX efficacy in a prospective cohort of children with JIA. Plasma samples from a cohort of children with JIA (n = 30) collected prior to the initiation of MTX and after 3 months of therapy were analyzed using a semi-targeted global metabolomic platform detecting 673 metabolites across a diversity of biochemical classes. Disease activity was measured using the 71-joint count juvenile arthritis disease activity score (JADAS-71) and clinical response to MTX was based on achievement of ACR Pedi 70 response. Metabolomic analysis identified 50 metabolites from diverse biochemical classes that were altered following the initiation of MTX (p < 0.05) with 15 metabolites reaching a false-discovery rate adjusted p-value (q-value) of less than 0.05. Enrichment analysis identified a class-wide reduction in unsaturated triglycerides following initiation of MTX (q = 0.0009). Twelve of the identified metabolites were significantly associated with disease activity by JADAS-71. Reductions in three metabolites were found to be associated with clinical response by ACR Pedi 70 response criteria and represented several microbiota and exogenously derived metabolites including: dehydrocholic acid, biotin, and 4-picoline. These findings support diverse metabolic changes following initiation of MTX in children with JIA and identify metabolites associated with microbial metabolism and exogenous sources associated with MTX efficacy.

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.

Disease Differentiation and Monitoring of Anti-TNF Treatment in Rheumatoid Arthritis and Spondyloarthropathies

Rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriatic arthritis (PsA) are comprehensive immunological disorders. The treatment of these disorders is limited to ameliorating the symptoms and improving the quality of life of patients. In this study, serum samples from RA, AS, and PsA patients were analyzed with metabolomic tools employing the 1H NMR method in combination with univariate and multivariate analyses. The results obtained in this study showed that the changes in metabolites were the highest for AS > RA > PsA. The study demonstrated that the time until remission or until low disease activity is achieved is shortest (approximately three months) for AS, longer for RA and longest for PsA. The statistically common metabolite that was found to be negatively correlated with the healing processes of these disorders is ethanol, which may indicate the involvement of the gut microflora and/or the breakdown of malondialdehyde as a cell membrane lipid peroxide product.

Transformation of fibroblast-like synoviocytes in rheumatoid arthritis; from a friend to foe

Swelling and the progressive destruction of articular cartilage are major characteristics of rheumatoid arthritis (RA), a systemic autoimmune disease that directly affects the synovial joints and often causes severe disability in the affected positions. Recent studies have shown that type B synoviocytes, which are also called fibroblast-like synoviocytes (FLSs), as the most commonly and chiefly resident cells, play a crucial role in early-onset and disease progression by producing various mediators. During the pathogenesis of RA, the FLSs' phenotype is altered, and represent invasive behavior similar to that observed in tumor conditions. Modified and stressful microenvironment by FLSs leads to the recruitment of other immune cells and, eventually, pannus formation. The origins of this cancerous phenotype stem fundamentally from the significant metabolic changes in glucose, lipids, and oxygen metabolism pathways. Moreover, the genetic abnormalities and epigenetic alterations have recently been implicated in cancer-like behaviors of RA FLSs. In this review, we will focus on the mechanisms underlying the transformation of FLSs to a cancer-like phenotype during RA. A comprehensive understanding of these mechanisms may lead to devising more effective and targeted treatment strategies.

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

INTRODUCTION

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.

OBJECTIVES

Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines.

METHODS

We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint).

RESULTS

Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other.

CONCLUSION

Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.

Predictive biomarkers of IgA vasculitis with nephritis by metabolomic analysis

BACKGROUND

IgA vasculitis (IgAV) is the most common vasculitis of childhood. Renal involvement defines late morbidity of the disease. A better understanding of the pathophysiology of the progression to kidney disease and predictive biomarkers are required for better management of IgAV and its nephritis (IgAVN).

OBJECTIVES

An untargeted metabolomics approach was performed to reveal the underlying molecular mechanism of disease pathogenesis and to define potential biomarkers from plasma samples from IgAV and IgAVN patients.

METHODS

Forty-five active IgAV patients (H) and six healthy controls (C) were enrolled in the study. Plasma samples were collected on the same day of diagnosis and before any immunosuppressive treatment was initiated. At the time of diagnosis and sample collection, none of the patients had renal involvement. We used Quadrupole Time of Flight Mass Spectrometry (Q-TOF LC/MS) to investigate the alterations in plasma metabolomic profiles. Three separate pools were created: healthy controls (group C), active IgAV patients who did not develop renal involvement (group H), and patients who developed IgAVN at follow up (group N). Peak picking, grouping, and comparison parts were performed via XCMS (https://xcmsonline.scripps.edu/) software.

RESULTS

At follow-up, IgAVN developed in 6 out of 45 IgAV patients. The median time of renal involvement development is 23 days (range 5-45 days). Of these, 3 had nephritic proteinuria, one had nephrotic proteinuria, and 2 had microscopic hematuria. There were no significant differences in gender, age, clinical manifestations, and laboratory findings between the six patients who developed renal involvement and those who did not. In multivariate analysis, there was no significant association between any of the defined demographic and clinical characteristics (male sex, gastrointestinal system involvement, joint involvement, CRP, WBC, PLT) and the occurrence of renal involvement. Totally 2618 peaks were detected for group H, N, and C. Among them, 355 peaks were found to be statistically significant and reliable (p<0.05), and 155 of these peaks were found to be changed (fold change >1.5) between the groups C and H, and 66 peaks were found to be changed (fold change >1.5) between the groups H and N. The number of the peaks on the intersection of the peaks found to be different between the groups (C and H) and (H and N) was 39. Based on putative identification results, 15 putatively identified metabolites matched with 11 peaks were presented as biomarker candidates after careful evaluation with a clinical perspective.

CONCLUSION

We suggest that DHAP (18:0), prostaglandin D2/I2, porphobilinogen, 5-methyltetrahydrofolic acid, and N-Acetyl-4-O-acetylneuraminic acid/N-Acetyl-7-O-acetylneuraminic acid may serve as biomarkers for predicting kidney disease. Future studies with larger groups of IgAV patients are needed to validate the identified metabolic profile.

Biomarker development for axial spondyloarthritis

The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.

Changes in Plasma Itaconate Elevation in Early Rheumatoid Arthritis Patients Elucidates Disease Activity Associated Macrophage Activation

Changes in the plasma metabolic profile were characterised in newly diagnosed rheumatoid arthritis (RA) patients upon commencement of conventional disease-modifying anti-rheumatic drug (cDMARD) therapy. Plasma samples collected in an early RA randomised strategy study (NCT00920478) that compared clinical (DAS) disease activity assessment with musculoskeletal ultrasound assessment (MSUS) to drive treatment decisions were subjected to untargeted metabolomic analysis. Metabolic profiles were collected at pre- and three months post-commencement of nonbiologic cDMARD. Metabolites that changed in association with changes in the DAS44 score were identified at the three-month timepoint. A total of nine metabolites exhibited a clear correlation with a reduction in DAS44 score following cDMARD commencement, particularly itaconate, its derived anhydride and a derivative of itaconate CoA. Increasing itaconate correlated with improved DAS44 score and decreasing levels of C-reactive protein (CRP). cDMARD treatment effects invoke consistent changes in plasma detectable metabolites, that in turn implicate clinical disease activity with macrophages. Such changes inform RA pathogenesis and reveal for the first time a link between itaconate production and resolution of inflammatory disease in humans. Quantitative metabolic biomarker-based tests of clinical change in state are feasible and should be developed around the itaconate pathway.

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

OBJECTIVE

A systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.

METHODS

We searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.

RESULTS

A total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n=8), urine (n=1) and synovial fluid (n=1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.

CONCLUSIONS

Metabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.

Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis

Objectives

The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.

Methods

Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.

Results

Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.

Conclusions

PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Plasma Metabolic Profiling Analysis of Gout Party on Acute Gout Arthritis Rats Based on UHPLC-Q-TOF/MS Combined with Multivariate Statistical Analysis

Gout Party is a Chinese medicine prescription composed of Aconiti Lateralis Radix Praeparaia, Aconiti Radix Cocta, Cremastrae Pseudobulbus Pleiones Pseudobulbus, Smilacis Glabrae Rhizoma, Rehmanniae Radix, and Glycyrrhizae Radix et Rhizoma, which can relieve joint pain caused by gouty arthritis (GA) and rheumatoid, and has a therapeutic effect on acute gouty arthritis (AGA). However, little information is available on the molecular biological basis and therapeutic mechanism of Gout Party for the treatment of AGA. AGA model was established by injecting sodium urate, and colchicine served as a positive control drug. We established a metabolomic method based on ultra-high-performance liquid chromatography–tandem quadrupole/time-of-flight mass spectrometry (UHPLC–Q–TOF/MS) to analyze the plasma samples of model group rats and blank group rats. Multiple statistical analyses, including principal component analysis (PCA) and partial least square discrimination analysis (PLS-DA), were used to examine metabolite profile changes in plasma samples. Finally, we identified 2–ketobutyric acid, 3–hexenedioic acid, but–2–enoic acid, and so on; 22 endogenous metabolites associated with AGA. After successful molding, we found that 2–ketobutyric acid, 3–hexenedioic acid, but–2–enoic acid, argininic acid, galactonic acid, lactic acid, equol 4′–O–glucuronide, deoxycholic acid glycine conjugate, glycocholic acid, sphinganine 1–phosphate, LPE (0:0/20:3), LPE (0:0/16:0), LPC (15:0) decreased significantly (p < 0.05 or p < 0.01), alanine, erythrulose, 3–dehydrocarnitine, m–methylhippuric acid, 3–hydroxyoctanoic acid, p–cresol sulfate, estriol 3–sulfate 16–glucuronide, 10–hydroxy–9–(phosphonooxy)octadecenoate, docosahexaenoic acid increased significantly (p < 0.05 or p < 0.01). After Gout Party treatment, 14 biomarkers had a tendency to normal conditions. These above biomarkers were mainly involved in fatty acid metabolism, bile acid metabolism, amino acid metabolism, and energy metabolism pathways. These results suggested that Gout Party exerted therapeutic effects of treating AGA by improving energy metabolism disorder and amino acid metabolism dysfunction, and attenuating fatty acid metabolism abnormal and inflammation. The results of this experiment provided a reference for revealing the metabolic mechanism produced by Gout Party in the treatment of AGA, but the subsequent studies need to be further improved and supported by relevant cell experiments and clinical experiments.

Plasma metabolite profiling reveals potential biomarkers of giant cell tumor of bone by using NMR-based metabolic profiles: A cross-sectional study

Giant cell tumor (GCT) of bone is a locally aggressive bone tumor, which accounts for 4% to 5% of all primary bone tumors. At present, the early diagnosis and postoperative recurrence monitoring are still more difficult due to the lack of effective biomarkers in GCT. As an effective tool, metabolomics has played an essential role in the biomarkers research of many tumors. However, there has been no related study of the metabolomics of GCT up to now. The purpose of this study was to identify several key metabolites as potential biomarkers for GCT by using nuclear magnetic resonance (NMR)-based metabolic profiles.Patients with GCT in our hospital were recruited in this study and their plasma was collected as the research sample, and plasma collected from healthy subjects was considered as the control. NMR was then utilized to detect all samples. Furthermore, based on correlation coefficients, variable importance for the projection values and P values of metabolites obtained from multidimensional statistical analysis, the most critical metabolites were selected as potential biomarkers of GCT. Finally, relevant metabolic pathways involved in these potential biomarkers were determined by database retrieval, based on which the metabolic pathways were plotted.Finally, 28 GCT patients and 26 healthy volunteers agreed to participate in the study. In the multidimensional statistical analysis, all results showed that there was obvious difference between the GCT group and the control group. Ultimately, 18 metabolites with significant differences met the selection condition, which were identified as potential biomarkers. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMD) database searching and literature review, these metabolites were found to be mainly correlated with glucose metabolism, fat metabolism, amino acid metabolism, and intestinal microbial metabolism. These metabolic disorders might, in turn, reflect important pathological processes such as proliferation and migration of tumor cells and immune escape in GCT.Our work showed that these potential biomarkers identified appeared to have early diagnostic and relapse monitoring values for GCT, which deserve to be further investigated. In addition, it also suggested that metabolomics profiling approach is a promising screening tool for the diagnosis and relapse monitoring of GCT patients.

Serum Metabolomic Profiles of Rheumatoid Arthritis Patients With Acute-Onset Diffuse Interstitial Lung Disease

Objective:

Acute-onset diffuse interstitial lung disease (AoDILD) includes acute exacerbation of interstitial lung disease (ILD), drug-induced ILD, and Pneumocystis pneumonia, and frequently occurs in patients with rheumatoid arthritis (RA). Since AoDILD causes a poor prognosis in RA, biomarkers for AoDILD were eagerly desired. Metabolomic analyses were extensively performed in cancer patients and successfully generated better diagnostic biomarkers. In the present study, serum metabolomic profiles of AoDILD in RA were investigated to generate better potential metabolomic biomarkers.

Methods:

Serum samples of 10 RA patients with AoDILD were collected on admission and in a stable state, more than 3 months before the admission. Serum metabolomic analyses were conducted on the samples from these RA patients with AoDILD.

Results:

Apparently distinct serum metabolomic profiles in AoDILD were not observed in univariate or hierarchical cluster analyses. Partial least squares-discriminant analysis (PLS-DA) was performed to select candidate metabolites based on variable importance in projection (VIP) scores. The PLS-DA model generated from the four metabolites with VIP scores more than 2.25 (mannosamine, alliin, kynurenine, and 2-hydroxybutyric acid) could successfully discriminate AoDILD from the stable condition (area under the curve: 0.962, 95% confidence interval: 0.778–1.000).

Conclusion:

It was demonstrated that metabolomic profiling was useful to generate better biomarkers in AoDILD.

A systematic review of the small molecule studies of osteoarthritis using nuclear magnetic resonance and mass spectroscopy

OBJECTIVE

To perform a systematic review of the small molecule metabolism studies of osteoarthritis utilising nuclear magnetic resonance (NMR) or mass spectroscopy (MS) analysis (viz., metabolomics or metabonomics), thereby providing coherent conclusions and reference material for future study.

METHOD

We applied PRISMA guidelines (PROSPERO 95068) with the following MESH terms: 1. "osteoarthritis" AND ("metabolic" OR "metabonomic" OR "metabolomic" OR "metabolism") 2. ("synovial fluid" OR "cartilage" OR "synovium" OR "serum" OR "plasma" OR "urine") AND ("NMR" or "Mass Spectroscopy"). Databases searched were "Medline" and "Embase". Studies were searched in English and excluded review articles not containing original research. Study outcomes were significant or notable metabolites, species (human or animal) and the Newcastle-Ottawa Score.

RESULTS

In the 27 studies meeting the inclusion criteria, there was a shift towards anaerobic and fatty acid metabolism in OA disease, although whether this represents the inflammatory state remains unclear. Lipid structure and composition was altered within disease subclasses including phosphatidyl choline (PC) and the sphingomyelins. Macromolecular proteoglycan destruction was described, but the correlation to disease factors was not demonstrated. Collated results suggested arachidonate signalling pathways and androgen sex hormones as future metabolic pathways for investigation.

CONCLUSION

Our meta-analysis demonstrates significant small molecule differences between sample types, between species (such as human and bovine), with potential OA biomarkers and targets for local or systemic therapies. Studies were limited by numbers and a lack of disease correlation. Future studies should use NMR and MS analysis to further investigate large population subgroups including inflammatory arthropathy, OA subclasses, age and joint differences.

Aqueous Humor Analysis Identifies Higher Branched Chain Amino Acid Metabolism as a Marker for Human Leukocyte Antigen-B27 Acute Anterior Uveitis and Disease Activity

PURPOSE

Human leukocyte antigen-B27 (HLA-B27)-positive acute anterior uveitis (AAU) has a higher recurrence rate and shows more anterior chamber cell infiltration compared with HLA-B27-negative patients, suggesting distinct etiologies of these clinically overlapping conditions. To advance our understanding of the biology of AAU, we characterized the metabolic profile of aqueous humor (AqH) of patients with HLA-B27-associated AAU (B27-AAU) and noninfectious idiopathic AAU (idiopathic AAU).

DESIGN

Experimental laboratory study.

METHODS

AqH samples from 2 independent cohorts totaling 30 patients with B27-AAU, 16 patients with idiopathic AAU, and 20 patients with cataracts underwent 2 individual rounds of direct infusion mass spectrometry. Features predicted by direct infusion mass spectrometry that facilitated maximum separation between the disease groups in regression models were validated by liquid chromatography/tandem mass spectrometry-based quantification with appropriate standards.

RESULTS

Partial least square-discriminant analysis revealed metabolite profiles that were able to separate patients with B27-AAU from those with iodiopathic AAU. Pathway enrichment analysis, based on metabolites on which separation of the groups in the partial least square-discriminant analysis model was based, demonstrated the involvement of branched-chain amino acid biosynthesis, ascorbate and aldarate metabolism, the tricarboxylic acid cycle, and glycolysis-diverting pathways (eg, serine biosynthesis) across all investigated cohorts. Notably, the metabolite ketoleucine was elevated in B27-AAU across all 3 runs and moderately-but robustly-correlated with anterior chamber cell count (correlation coefficient range 0.41-0.81).

CONCLUSIONS

These results illustrate metabolic heterogeneity between HLA-B27-positive and HLA-B27-negative AAU, including an increase of branched-chain amino acid biosynthesis, that reflects disease activity in AAU.

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Metabolic pathways and immunometabolism in rare kidney diseases

OBJECTIVES

To characterise renal tissue metabolic pathway gene expression in different forms of glomerulonephritis.

METHODS

Patients with nephrotic syndrome (NS), antineutrophil cytoplasmic antibody-associated vasculitis (AAV), systemic lupus erythematosus (SLE) and healthy living donors (LD) were studied. Clinically indicated renal biopsies were obtained at time of diagnosis and microdissected into glomerular and tubulointerstitial compartments. Microarray-derived differential gene expression of 88 genes representing critical enzymes of metabolic pathways and 25 genes related to immune cell markers was compared between disease groups. Correlation analyses measured relationships between metabolic pathways, kidney function and cytokine production.

RESULTS

Reduced steady state levels of mRNA species were enriched in pathways of oxidative phosphorylation and increased in the pentose phosphate pathway (PPP) with maximal perturbation in AAV and SLE followed by NS, and least in LD. Transcript regulation was isozymes specific with robust regulation in hexokinases, enolases and glucose transporters. Intercorrelation networks were observed between enzymes of the PPP (eg, transketolase) and macrophage markers (eg, CD68) (r=0.49, p<0.01). Increased PPP transcript levels were associated with reduced glomerular filtration rate in the glomerular (r=-0.49, p<0.01) and tubulointerstitial (r=-0.41, p<0.01) compartments. PPP expression and tumour necrosis factor activation were tightly co-expressed (r=0.70, p<0.01).

CONCLUSION

This study demonstrated concordant alterations of the renal transcriptome consistent with metabolic reprogramming across different forms of glomerulonephritis. Activation of the PPP was tightly linked with intrarenal macrophage marker expression, reduced kidney function and increased production of cytokines. Modulation of glucose metabolism may offer novel immune-modulatory therapeutic approaches in rare kidney diseases.

Gas chromatography-time of flight/mass spectrometry-based metabonomics of changes in the urinary metabolic profile in osteoarthritic rats

The aim of the present study was to explore changes in the urinary metabolic spectrum in rats with knee osteoarthritis, using gas chromatography-time of flight/mass spectrometry (GC-TOF/MS) to determine the metabonomic disease pathogenesis. Sprague-Dawley rats were randomly divided into the control and model groups (n=8/group), and 20 µl of 4% papain and 0.03 M L-cysteine was injected into the right knee on days 1, 3 and 7 to establish the knee osteoarthritis model. Following 14 days, urine was collected over 12 h and cartilage ultrastructural damage was assessed by hematoxylin-eosin staining. GC-TOF/MS, combined with principal component analysis, partial least squares discriminant modeling and orthogonal partial least squares discriminant modeling, was used to analyze the changes in the metabolic spectrum trajectory and to identify potential biomarkers and their related metabolic pathways. Compared with the control group, the synovial cell lining of the knee joint exhibited proliferation, inflammatory cell infiltration and collagen fiber hyperplasia in the knee osteoarthritis group. A total of 23 potential biomarkers were identified, including alanine, α-ketoglutarate, asparagine, maltose and glutamine. Furthermore, metabolomic pathogenesis of osteoarthritis may be related to disorders of amino acid metabolism, energy metabolism, fatty acid metabolism, vitamin B₆ metabolism and nucleic acid metabolism.

Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by progressive joint erosion. Tumor necrosis factor (TNF) antagonists are the most widely used biological disease-modifying anti-rheumatic drug in RA. However, there continue to be one third of RA patients who have poor or no response to TNF antagonists. Following consideration of the uncertainty of therapeutic effects and the high price of TNF antagonists, it is worthy to predict the treatment responses before anti-TNF therapy. According to the comparisons between the responders and non-responders to TNF antagonists by omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, rheumatologists are eager to find significant biomarkers to predict the effect of TNF antagonists in order to optimize the personalized treatment in RA.

Immunometabolism in early and late stages of rheumatoid arthritis

One of the fundamental traits of immune cells in rheumatoid arthritis (RA) is their ability to proliferate, a property shared with the joint-resident cells that form the synovial pannus. The building of biomass imposes high demands for energy and biosynthetic precursors, implicating metabolic control as a basic disease mechanism. During preclinical RA, when autoreactive T cells expand and immunological tolerance is broken, the main sites of disease are the secondary lymphoid tissues. Naive CD4⁺ T cells from patients with RA have a distinct metabolic signature, characterized by dampened glycolysis, low ATP levels and enhanced shunting of glucose into the pentose phosphate pathway. Equipped with high levels of NADPH and depleted of intracellular reactive oxygen species, such T cells hyperproliferate and acquire proinflammatory effector functions. During clinical RA, immune cells coexist with stromal cells in the acidic milieu of the inflamed joint. This microenvironment is rich in metabolic intermediates that are released into the extracellular space to shape cell-cell communication and the functional activity of tissue-resident cells. Increasing awareness of how metabolites regulate signalling pathways, guide post-translational modifications and condition the tissue microenvironment will help to connect environmental factors with the pathogenic behaviour of T cells in RA.

Mixing omics: combining genetics and metabolomics to study rheumatic diseases

Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

We report a chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) method generally applicable for tracking metabolomic changes from samples collected in an animal model for studying disease development and treatment. A rat model of surgically induced osteoarthritis (OA) was used as an example to illustrate the workflow and technical performance. Experimental duplicate analyses of 234 plasma samples were carried out using dansylation labeling LC-MS targeting the amine/phenol submetabolome. These samples composed of 39 groups (6 rats per group) were collected at multiple time points with sham operation, OA control group, and OA rats with treatment, separately, using glucosamine/Celecoxib and three traditional Chinese medicines (Epimedii folium, Chuanxiong Rhizoma and Bushen-Huoxue). In total, 3893 metabolites could be detected and 2923 of them were consistently detected in more than 50% of the runs. This high-coverage submetabolome dataset could be used to track OA progression and treatment. Many differentiating metabolites were found and 11 metabolites including 2-aminoadipic acid, saccharopine and GABA were selected as potential biomarkers of OA progression and OA treatment. This study illustrates that CIL LC-MS is a very useful technique for monitoring incremental metabolomic changes with high coverage and accuracy for studying disease progression and treatment in animal models.

Metabolic Disturbances in Adult-Onset Still's Disease Evaluated Using Liquid Chromatography/Mass Spectrometry-Based Metabolomic Analysis

Objective

Liquid chromatography/mass spectrometry (LC/MS)-based comprehensive analysis of metabolic profiles with metabolomics approach has potential diagnostic and predictive implications. However, no metabolomics data have been reported in adult-onset Still’s disease (AOSD). This study investigated the metabolomic profiles in AOSD patients and examined their association with clinical characteristics and disease outcome.

Methods

Serum metabolite profiles were determined on 32 AOSD patients and 30 healthy controls (HC) using ultra-performance liquid chromatography (UPLC)/MS analysis, and the differentially expressed metabolites were quantified using multiple reactions monitoring (MRM)/MS analysis in 44 patients and 42 HC. Pure standards were utilized to confirm the presence of the differentially expressed metabolites.

Results

Eighteen differentially expressed metabolites were identified in AOSD patents using LC/MS-based analysis, of which 13 metabolites were validated by MRM/MS analysis. Among them, serum levels of lysoPC(18:2), urocanic acid and indole were significantly lower, and L-phenylalanine levels were significantly higher in AOSD patients compared with HC. Moreover, serum levels of lysoPC(18:2), PhePhe, uridine, taurine, L-threonine, and (R)-3-Hydroxy-hexadecanoic acid were significantly correlated with disease activity scores (all p<0.05) in AOSD patients. A different clustering of metabolites was associated with a different disease outcome, with significantly lower levels of isovalerylsarcosine observed in patients with chronic articular pattern (median, 77.0AU/ml) compared with monocyclic (341.5AU/ml, p<0.01) or polycyclic systemic pattern (168.0AU/ml, p<0.05).

Conclusion

Thirteen differentially expressed metabolites identified and validated in AOSD patients were shown to be involved in five metabolic pathways. Significant associations of metabolic profiles with disease activity and outcome of AOSD suggest their involvement in AOSD pathogenesis.

Plasma, urine and ligament tissue metabolite profiling reveals potential biomarkers of ankylosing spondylitis using NMR-based metabolic profiles

Background

Ankylosing spondylitis (AS) is an autoimmune rheumatic disease mostly affecting the axial skeleton. Currently, anti-tumour necrosis factor α (anti-TNF-α) represents an effective treatment for AS that may delay the progression of the disease and alleviate the symptoms if the diagnosis can be made early. Unfortunately, effective diagnostic biomarkers for AS are still lacking; therefore, most patients with AS do not receive timely and effective treatment. The intent of this study was to determine several key metabolites as potential biomarkers of AS using metabolomic methods to facilitate the early diagnosis of AS.

Methods

First, we collected samples of plasma, urine, and ligament tissue around the hip joint from AS and control groups. The samples were examined by nuclear magnetic resonance spectrometry, and multivariate data analysis was performed to find metabolites that differed between the groups. Subsequently, according to the correlation coefficients, variable importance for the projection (VIP) and P values of the metabolites obtained in the multivariate data analysis, the most crucial metabolites were selected as potential biomarkers of AS. Finally, metabolic pathways involving the potential biomarkers were determined using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the metabolic pathway map was drawn.

Results

Forty-four patients with AS agreed to provide plasma and urine samples, and 30 provided ligament tissue samples. An equal number of volunteers were recruited for the control group. Multidimensional statistical analysis suggested significant differences between the patients with AS and control subjects, and the models exhibited good discrimination and predictive ability. A total of 20 different metabolites ultimately met the requirements for potential biomarkers. According to KEGG analysis, these marker metabolites were primarily related to fat metabolism, intestinal microbial metabolism, glucose metabolism and choline metabolism pathways, and they were also probably associated with immune regulation.

Conclusions

Our work demonstrates that the potential biomarkers that were identified appeared to have diagnostic value for AS and deserve to be further investigated. In addition, this work also suggests that the metabolomic profiling approach is a promising screening tool for the diagnosis of patients with AS.

Urine metabolome profiling of immune-mediated inflammatory diseases

BACKGROUND

Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis.

METHODS

Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls.

RESULTS

In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways.

CONCLUSIONS

This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study

INTRODUCTION

Metabolic factors may contribute to osteoarthritis (OA). This study employed metabolomics analyses to determine if differences in metabolite profiles could distinguish people with knee OA who exhibited radiographic progression.

METHODS

Urine samples obtained at baseline and 18 months from overweight and obese adults in the Intensive Diet and Exercise for Arthritis (IDEA) trial were selected from two subgroups (n = 22 each) for metabolomics analysis: a group that exhibited radiographic progression (≥0.7 mm decrease in joint space width, JSW) and an age, gender, and body mass index (BMI) matched group who did not progress (≤0.35 mm decrease in JSW). Multivariate analysis methods, including orthogonal partial least square discriminate analysis, were used to identify metabolite profiles that separated progressors and non-progressors. Plasma levels of IL-6 and C-reactive protein (CRP) were evaluated as inflammatory markers.

RESULTS

Multivariate analysis of the binned metabolomics data distinguished progressors from non-progressors. Library matching revealed that glycolate, hippurate, and trigonelline were among the important metabolites for distinguishing progressors from non-progressors at baseline whereas alanine, N,N-dimethylglycine, glycolate, hippurate, histidine, and trigonelline, were among the metabolites that were important for the discrimination at 18 months. In non-progressors, IL-6 decreased from baseline to 18 months while IL-6 was unchanged in progressors; the change over time in IL-6 was significantly different between groups.

CONCLUSION

These findings support a role for metabolic factors in the progression of knee OA and suggest that measurement of metabolites could be useful to predict progression. Further investigation in a larger sample that would include targeted investigation of specific metabolites is warranted.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

Metabolomics to identify biomarkers and as a predictive tool in inflammatory diseases

There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.

Therapeutic Effects of Chinese Medicine Herb Pair, Huzhang and Guizhi, on Monosodium Urate Crystal-Induced Gouty Arthritis in Rats Revealed by Anti-Inflammatory Assessments and NMR-Based Metabonomics

The present study was undertaken to evaluate the therapeutic effects of Huzhang-Guizhi herb pair (HG), firstly included in Hu-Zhang Power documented in Taiping Shenghui Fang, on monosodium urate (MSU) crystals-induced gouty arthritis in rats. We found that pretreatment with HG in rats with gouty arthritis could significantly attenuate the ankle joint swelling, and this beneficial antigout effect might be mediated, at least in part, by inhibiting tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β) production in synovial fluid as well as nuclear transcription factor-κB p65 (NF-κB p65) protein expression in synovial tissue. Moreover, metabonomic analysis demonstrated that 5 and 6 potential biomarkers associated with gouty arthritis in plasma and urine, respectively, which were mainly involved in energy metabolism, amino acid metabolism, and gut microbe metabolism, were identified. HG could reverse the pathological process of MSU-induced gouty arthritis through regulating the disturbed metabolic pathways. These results provided important mechanistic insights into the protective effects of HG against MSU-induced gouty arthritis in rats.