Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

Severe Allergy as a Chronic Inflammatory Condition From a Systems Biology Perspective

Persistent and unresolved inflammation is a common underlying factor observed in several and seemingly unrelated human diseases, including cardiovascular and neurodegenerative diseases. Particularly, in atopic conditions, acute inflammatory responses such as those triggered by insect venom, food or drug allergies possess also a life-threatening potential. However, respiratory allergies predominantly exhibit late immune responses associated with chronic inflammation, that can eventually progress into a severe phenotype displaying similar features as those observed in other chronic inflammatory diseases, as is the case of uncontrolled severe asthma. This review aims to explore the different facets and systems involved in chronic allergic inflammation, including processes such as tissue remodelling and immune cell dysregulation, as well as genetic, metabolic and microbiota alterations, which are common to other inflammatory conditions. Our goal here was to deepen on the understanding of an entangled disease as is chronic allergic inflammation and expose potential avenues for the development of better diagnostic and intervention strategies.

Relationship Between the Lipidome Profile and Disease Activity in Patients with Rheumatoid Arthritis

Lipid mediators have been suggested to play important roles in the pathogenesis of rheumatoid arthritis (RA). Lipidomics has recently allowed for the comprehensive analysis of lipids and has revealed the potential of lipids as biomarkers for the early diagnosis of RA and prediction of therapeutic responses. However, the relationship between disease activity and the lipid profile in RA remains unclear. In the present study, we performed a plasma lipidomic analysis of 278 patients with RA during treatment and examined relationships with disease activity using the Disease Activity Score in 28 joints (DAS28)-erythrocyte sedimentation rate (ESR). In all patients, five lipids positively correlated and seven lipids negatively correlated with DAS28-ESR. Stearic acid [FA(18:0)] (r = -0.45) and palmitic acid [FA(16:0)] (r = -0.38) showed strong negative correlations. After adjustments for age, body mass index (BMI), and medications, stearic acid, palmitic acid, bilirubin, and lysophosphatidylcholines negatively correlated with disease activity. Stearic acid inhibited osteoclast differentiation from peripheral blood monocytes in in vitro experiments, suggesting its contribution to RA disease activity by affecting bone metabolism. These results indicate that the lipid profile correlates with the disease activity of RA and also that some lipids may be involved in the pathogenesis of RA.

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.

Exploring the metabolomic landscape: Perilla frutescens as a promising enhancer of production, flavor, and nutrition in Tan lamb meat

Addressing health-related concerns linked to the metabolite profile of lamb meat has become paramount, in line with the growing demand for enhanced flavor and taste. We examined the impact of Perilla frutescens seeds on Tan lamb growth, carcass traits, and metabolite profiles. Three diets were employed: a low-concentrate group (LC), a high-concentrate group (HC), and a PFS group (the LC diet supplemented with 3% Perilla frutescens seeds) on a dry matter basis. Forty-five male Tan-lambs (approximately six months) with similar body weights (25.1 kg ± 1.12 SD) were randomly assigned to one of these three groups for 84-day feeding, including an initial 14-day adjustment phase. The supplementation of PFS resulted in increased average daily gain (P < 0.01) and improved carcass quality and meat color (P < 0.05). Additionally, it led to an enhancement in omega-3 polyunsaturated fatty acids (P < 0.05) and a reduction in the omega-6/omega-3 ratio (P < 0.05). Using gas chromatography-mass spectrometry, 369 volatile compounds were identified with enhanced levels of acetaldehyde and 1,2,4-trimethyl-benzene associated with PFS (P < 0.05). Among the 807 compounds identified by ultra-high performance liquid chromatography-mass spectrometry, there were 66 significantly differential compounds (P < 0.05), including 43 hydrophilic metabolites and 23 lipids. PFS supplementation led to significant alterations in 66 metabolites, with three metabolites including 2,5-diisopropyl-3-methylphenol, 3-hydroxydecanoic acid, and lysophosphatidylcholine (15:0) emerging as potential PFS-related biomarkers. The study indicates that PFS supplementation can enhance Tan-lamb growth, feed efficiency, and meat quality, potentially providing lamb meat with improved flavor and nutritional characteristics.

Effect of Salvia miltiorrhiza Bunge extracts on improving the efficacy and reducing the toxicity of Tripterygium wilfordii polyglycosides in the treatment of rheumatoid arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium wilfordii polyglycosides (TWP), extracted from the traditional Chinese herb Tripterygium wilfordii, has been widely used in the treatment of rheumatoid arthritis (RA). However, the toxicity of TWP to a variety of organs such as liver, kidney and testis greatly limits its clinical application. Salvia miltiorrhiza Bunge is often used in the treatment of RA due to its blood circulation promoting, stasis resolving, and anti-inflammatory effects. Salvia miltiorrhiza Bunge has also been reported to possess multiple organ protective effects.

AIM OF THE STUDY

To investigate the influences of two main components of Salviorrhiza miltiorrhiza Bunge, hydrophilic salvianolic acids (SA) and lipophilic tanshinones (Tan), on the efficacy and toxicity of TWP in treating RA and to explore the underlying mechanisms.

MATERIALS AND METHODS

SA and Tan were extracted from Salvia miltiorrhiza Bunge and the extracts were quantitated by HPLC and identified by UPLC-Q/TOF-MS. Then, a collagen-induced arthritis (CIA) rat model was established using bovine type II collagen (CII) and incomplete Freund's adjuvant (IFA). CIA rats were treated with TWP and/or SA/Tan. After 21 days of continuous treatment, arthritis symptoms and organs toxicity were evaluated. Meanwhile, serum metabolomics were investigated by the UPLC-Q/TOF-MS to understand the underlying mechanism.

RESULTS

SA and Tan extracts could significantly alleviate arthritis symptoms in CIA rats and decrease the serum levels of inflammatory factors TNF-α, IL-1β and IL-6 when combined with TWP. Meanwhile, both extracts alleviated injury of liver, kidney and testis caused by TWP, and the hydrophilic extract SA was superior. Moreover, a total of 38 endogenous differential metabolites were identified between the CIA model group and the TWP group, among which 33 metabolites were significantly recovered after the combination of SA or Tan. Metabolic pathway analysis showed that SA and Tan can affect metabolic pathways including linoleic acid metabolism, glycerophospholipid metabolism, sphingolipid metabolism and steroid biosynthesis metabolism pathway.

CONCLUSIONS

Our findings indicated for the first time that two Salviorrhiza miltiorrhiza Bunge extracts could improve the efficacy and reduce the toxicity of TWP in the treatment of RA by adjusting metabolic pathways, and the hydrophilic extract SA was superior.

Refractory inflammatory arthritis definition and model generated through patient and multi-disciplinary professional modified Delphi process

OBJECTIVE

Various definitions have been proposed for Refractory Disease in people with Rheumatoid Arthritis; however, none were generated for Polyarticular Juvenile Idiopathic Arthritis or involving adult and paediatric multidisciplinary healthcare professionals and patients. The study aim is to redefine Refractory Disease, using Delphi methodology.

METHODS

Three rounds of surveys (one nominal group and two online (2019-2020)) to achieve consensus using a predetermined cut-off were conducted voting on: a) name, b) treatment and inflammation, c) symptoms and impact domains, and d) rating of individual components within domains. Theoretical application of the definition was conducted through a scoping exercise.

RESULTS

Votes were collected across three rounds from Patients, Researchers and nine multi-disciplinary healthcare professional groups (n = 106). Refractory Inflammatory Arthritis was the most popular name. Regarding treatment and inflammation, these were voted to be kept broad rather than specifying numbers/cut-offs. From 10 domains identified to capture symptoms and disease impact, six domains reached consensus for inclusion: 1) Disease Activity, 2) Joint Involvement, 3) Pain, 4) Fatigue, 5) Functioning and Quality of Life, and 6) Disease-Modifying Anti-Rheumatic Drug Experiences. Within these domains, 18 components, from an initial pool (n = 73), were identified as related and important to capture multi-faceted presentation of Refractory Inflammatory Arthritis, specifically in Rheumatoid Arthritis and Polyarticular Juvenile Idiopathic Arthritis. Feasibility of the revised definition was established (2022-2023) with good utility as was applied to 82% of datasets (n = 61) incorporating 20 outcome measures, with two further measures added to increase its utility and coverage of Pain and Fatigue.

CONCLUSION

Refractory Inflammatory Arthritis has been found to be broader than not achieving low disease activity, with wider biopsychosocial components and factors incorporating Persistent Inflammation or Symptoms identified as important. This definition needs further refinement to assess utility as a classification tool to identify patients with unmet needs.

Serum metabolomic profiling identifies potential biomarkers in arthritis in older adults: an exploratory study

BACKGROUND

Seronegative elderly-onset rheumatoid arthritis (EORA)neg and polymyalgia rheumatica (PMR) have similar clinical characteristics making them difficult to distinguish based on clinical features. We hypothesized that the study of serum metabolome could identify potential biomarkers of PMR vs. EORAneg.

METHODS

Arthritis in older adults (ARTIEL) is an observational prospective cohort with patients older than 60 years of age with newly diagnosed arthritis. Patients' blood samples were compared at baseline with 18 controls. A thorough clinical examination was conducted. A Bruker Avance 600 MHz spectrometer was used to acquire Nuclear Magnetic Resonance (NMR) spectra of serum samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification.Student t-test, one-way ANOVA, binary linear regression and ROC curve, Pearson's correlation along with pathway analyses were conducted.

RESULTS

Twenty-eight patients were diagnosed with EORAneg and 20 with PMR. EORAneg patients had a mean disease activity score (DAS)-Erythrocyte Sedimentation Rate (ESR) of 6.21 ± 1.00. All PMR patients reported shoulder pain, and 90% reported pelvic pain. Fifty-eight polar metabolites were identified. Of these, 3-hydroxybutyrate, acetate, glucose, glycine, lactate, and o-acetylcholine (o-ACh), were significantly different between groups. Of interest, IL-6 correlated with different metabolites in PMR and EORAneg suggesting different inflammatory activated pathways. Finally, lactate, o-ACh, taurine, and sex (female) were identified as distinguishable factors of PMR from EORAneg with a sensitivity of 90%, specificity of 92.3%, and an AUC of 0.925 (p < 0.001).

CONCLUSION

These results suggest that EORAneg and PMR have different serum metabolomic profiles that might be related to their pathobiology and can be used as biomarker to discriminate between both diseases.

MTHFR c.665C>T and c.1298A>C Polymorphisms in Tailoring Personalized Anti-TNF-α Therapy for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease with a prevalence of 1%. Currently, RA treatment aims to achieve low disease activity or remission. Failure to achieve this goal causes disease progression with a poor prognosis. When treatment with first-line drugs fails, treatment with tumor necrosis factor-α (TNF-α) inhibitors may be prescribed to which many patients do not respond adequately, making the identification of response markers urgent. This study investigated the association of two RA-related genetic polymorphisms, c.665C>T (historically referred to as C677T) and c.1298A>C, in the MTHFR gene as response markers to an anti-TNF-α therapy. A total of 81 patients were enrolled, 60% of whom responded to the therapy. Analyses showed that both polymorphisms were associated with a response to therapy in an allele dose-dependent manner. The association for c.665C>T was significant for a rare genotype (p = 0.01). However, the observed opposite trend of association for c.1298A>C was not significant. An analysis revealed that c.1298A>C, unlike c.665C>T, was also significantly associated with the drug type (p = 0.032). Our preliminary results showed that the genetic polymorphisms in the MTHFR gene were associated with a response to anti-TNF-α therapy, with a potential significance for the anti-TNF-α drug type. This evidence suggests a role for one-carbon metabolism in anti-TNF-α drug efficacy and contributes to further personalized RA interventions.

The change of plasma metabolic profile and gut microbiome dysbiosis in patients with rheumatoid arthritis

Objective

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease, which is associated with progressive disability, systemic complications, and early death. But its etiology and pathogenesis are not fully understood. We aimed to investigate the alterations in plasma metabolite profiles, gut bacteria, and fungi and their role of them in the pathogenesis of RA.

Methods

Metabolomics profiling of plasma from 363 participants including RA (n = 244), systemic lupus erythematosus (SLE, n = 50), and healthy control (HC, n = 69) were performed using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The differentially expressed metabolites were selected among groups and used to explore important metabolic pathways. Gut microbial diversity analysis was performed by 16S rRNA sequencing and ITS sequencing (RA = 195, HC = 269), and the specific microbial floras were identified afterward. The diagnosis models were established based on significant differential metabolites and microbial floras, respectively.

Results

There were 63 differential metabolites discovered between RA and HC groups, mainly significantly enriched in the arginine and proline metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism between RA and HC groups. The core differential metabolites included L-arginine, creatine, D-proline, ornithine, choline, betaine, L-threonine, LysoPC (18:0), phosphorylcholine, and glycerophosphocholine. The L-arginine and phosphorylcholine were increased in the RA group. The AUC of the predictive model was 0.992, based on the combination of the 10 differential metabolites. Compared with the SLE group, 23 metabolites increased and 61 metabolites decreased in the RA group. However, no significant metabolic pathways were enriched between RA and SLE groups. On the genus level, a total of 117 differential bacteria genera and 531 differential fungal genera were identified between RA and HC groups. The results indicated that three bacteria genera (Eubacterium_hallii_group, Escherichia-Shigella, Streptococcus) and two fungal genera (Candida and Debaryomyces) significantly increased in RA patients. The AUC was 0.80 based on a combination of six differential bacterial genera and the AUC was 0.812 based on a combination of seven differential fungal genera. Functional predictive analysis displayed that differential bacterial and differential fungus both were associated with KEGG pathways involving superpathway of L-serine and glycine biosynthesis I, arginine, ornithine, and proline interconversion.

Conclusion

The plasma metabolism profile and gut microbe profile changed markedly in RA. The glycine, serine, and threonine metabolism and arginine and proline metabolism played an important role in RA.

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.

Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis

Anti-TNF therapy has significantly improved disease control in rheumatoid arthritis, but a fraction of rheumatoid arthritis patients do not respond to anti-TNF therapy or lose response over time. Moreover, the mechanisms underlying non-response to anti-TNF therapy remain largely unknown. To date, many single biomarkers of response to anti-TNF therapy have been published but they have not yet been analyzed as a system of interacting nodes. The aim of our study is to systematically elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis using the gene ontologies of previously published predictive biomarkers. Gene networks were constructed based on published biomarkers and then enriched gene ontology terms were elucidated in subgroups using gene ontology software tools. Our results highlight the novel role of proteasome-mediated protein catabolic processes (p = 2.91 × 10⁻¹⁵) and plasma lipoproteins (p = 4.55 × 10⁻¹¹) in anti-TNF therapy response. The results of our gene ontology analysis help elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis and encourage further study of the highlighted processes.

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.

Detection of serum phospholipids by microchannel-integrated black phosphorus-assisted laser desorption/ionization mass spectrometry

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has been widely applied in the analysis of phospholipids in biological samples. However, it remains a challenge to improve the sensitivity and reproducibility and to control the background noise of matrices. In this study, black phosphorus nanomaterial was used as the matrix of MALDI-MS, and microchannel technique was combined. This microchannel-integrated black phosphorus-assisted laser desorption/ionization (BPALDI) MS approach can effectively detect a variety of lipids with a small amount of sample, and has high sensitivity for phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) with a detection limit of 0.2 μg/mL. Compared with traditional matrices, BPALDI-MS has the advantages of high sensitivity, good reproducibility, and high salt tolerance. This method was successfully applied in the detection of serum PC/LPC ratios in children patients with asthma or bronchopneumonia. This work provides a novel application of black phosphorus matrix and microchannel technique, and gives new insights into method development of rapid screening and identification of disease indicators in biological fluids.

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.

Precision Medicine for Rheumatoid Arthritis: The Right Drug for the Right Patient-Companion Diagnostics

Despite the growing number of biologic and JAK inhibitor therapeutic agents available to treat various systemic autoimmune illnesses, the lack of a validated companion diagnostic (CDx) to accurately predict drug responsiveness for an individual results in many patients being treated for years with expensive, ineffective, or toxic drugs. This review will focus primarily on rheumatoid arthritis (RA) therapeutics where the need is greatest due to poor patient outcomes if the optimum drug is delayed. We will review current FDA-approved biologic and small molecule drugs and why RA patients switch these medications. We will discuss the sampling of various tissues for potential CDx and review early results from studies investigating drug responsiveness utilizing advanced technologies including; multiplex testing of cytokines and proteins, autoantibody profiling, genomic analysis, proteomics, miRNA analysis, and metabolomics. By using these new technologies for CDx the goal is to improve RA patient outcomes and achieve similar successes like those seen in oncology using precision medicine guided therapeutics.

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.

Plasma metabolomic profiling in patients with rheumatoid arthritis identifies biochemical features predictive of quantitative disease activity

Background

Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA.

Methods

Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups (“lower”, DAS28-CRP ≤ 3.2; and “higher”, DAS28-CRP > 3.2) and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups (“low-CRP”, CRP ≤ 3.0 mg/L; “high-CRP”, CRP > 3.0 mg/L) were investigated.

Results

We identified 33 metabolites differentially abundant between the lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE] ± SD: 1.51 ± 1.77) compared to a GLM without feature selection (MAE ± SD: 2.02 ± 2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted and actual DAS28-CRP (with feature selection: Spearman’s ρ = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearman’s ρ = 0.18, 95% CI: [−0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with the CRP patient groups while controlling for potential confounders (P < 0.05).

Conclusions

We demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02537-4.

Reliability of urinary charged metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Currently, large-scale cohort studies for metabolome analysis have been launched globally. However, only a few studies have evaluated the reliability of urinary metabolome analysis. This study aimed to establish the reliability of urinary metabolomic profiling in cohort studies. In the Tsuruoka Metabolomics Cohort Study, 123 charged metabolites were identified and routinely quantified using capillary electrophoresis-mass spectrometry (CE-MS). We evaluated approximately 750 quality control (QC) samples and 6,720 participants’ spot urine samples. We calculated inter- and intra-batch coefficients of variation in the QC and participant samples and technical intraclass correlation coefficients (ICC). A correlation of metabolite concentrations between spot and 24-h urine samples obtained from 32 sub-cohort participants was also evaluated. The coefficient of variation (CV) was less than 20% for 87 metabolites (70.7%) and 20–30% for 19 metabolites (15.4%) in the QC samples. There was less than 20% inter-batch CV for 106 metabolites (86.2%). Most urinary metabolites would have reliability for measurement. The 96 metabolites (78.0%) was above 0.75 for the estimated ICC, and those might be useful for epidemiological analysis. Among individuals, the Pearson correlation coefficient of 24-h and spot urine was more than 70% for 59 of the 99 metabolites. These results show that the profiling of charged metabolites using CE-MS in morning spot human urine is suitable for epidemiological metabolomics studies.

Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics

Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30–40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra‐performance liquid chromatography–mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.

Relationship Between Inflammation and Metabolism in Patients With Newly Presenting Rheumatoid Arthritis

Background

Systemic inflammation in rheumatoid arthritis (RA) is associated with metabolic changes. We used nuclear magnetic resonance (NMR) spectroscopy-based metabolomics to assess the relationship between an objective measure of systemic inflammation [C-reactive protein (CRP)] and both the serum and urinary metabolome in patients with newly presenting RA.

Methods

Serum (n=126) and urine (n=83) samples were collected at initial presentation from disease modifying anti-rheumatic drug naïve RA patients for metabolomic profile assessment using 1-dimensional ¹H-NMR spectroscopy. Metabolomics data were analysed using partial least square regression (PLS-R) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) with cross validation.

Results

Using PLS-R analysis, a relationship between the level of inflammation, as assessed by CRP, and the serum (p=0.001) and urinary (p<0.001) metabolome was detectable. Likewise, following categorisation of CRP into tertiles, patients in the lowest CRP tertile and the highest CRP tertile were statistically discriminated using OPLS-DA analysis of both serum (p=0.033) and urinary (p<0.001) metabolome. The most highly weighted metabolites for these models included glucose, amino acids, lactate, and citrate. These findings suggest increased glycolysis, perturbation in the citrate cycle, oxidative stress, protein catabolism and increased urea cycle activity are key characteristics of newly presenting RA patients with elevated CRP.

Conclusions

This study consolidates our understanding of a previously identified relationship between serum metabolite profile and inflammation and provides novel evidence that there is a relationship between urinary metabolite profile and inflammation as measured by CRP. Identification of these metabolic perturbations provides insights into the pathogenesis of RA and may help in the identification of therapeutic targets.

Increased levels of plasma nucleotides in patients with rheumatoid arthritis

Novel biomarkers of rheumatoid arthritis (RA), in addition to antibodies against cyclic citrullinated peptides, are required. Metabolome analysis is a promising approach to identify metabolite biomarkers for clinical diagnosis. We adopted a comprehensive non-targeted metabolomics approach combining capillary electrophoresis time-of-flight mass spectrometry (TOFMS) and liquid chromatography TOFMS. We constructed metabolomics profiling of 286 plasma samples of a Japanese population [92 RA patients, 13 systemic lupus erythematosus (SLE) patients and 181 healthy controls). RA case–control association tests showed that seven metabolites exhibited significantly increased levels in RA samples compared with controls (P < 1.0 × 10⁻⁴; UTP, ethanolamine phosphate, ATP, GDP, ADP, 6-aminohexanoic acid and taurine), whereas one exhibited a decreased level (xanthine). The plasma levels of these eight metabolites were not significantly different between seropositive and seronegative RA patients (P > 0.05; n = 68 and 24, respectively). The four nucleotide levels (UTP, ATP, GDP and ADP) were significantly higher in the non-treatment patients in comparison between patients with and without treatment (P < 0.014; n = 57 and 35, respectively). Furthermore, we found that none of the four nucleotide levels showed significant differences in SLE case–control association tests (P > 0.2; 13 patients with SLE and the 181 shared controls) and psoriatic arthritis (PsA) case–control association tests (P > 0.11; 42 patients with PsA and 38 healthy controls), indicating disease specificity in RA. In conclusion, our large-scale metabolome analysis demonstrated the increased plasma nucleotide levels in RA patients, which could be used as potential clinical biomarkers of RA, especially for seronegative RA.

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.

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

OBJECTIVES

Biologic treatment has recently revolutionized the management of RA. Despite this success, ∼30-40% of the patients undergoing biologic treatment respond insufficiently. The aim of this study was to identify several specific reliable metabolites for predicting the response of RA patients to TNF-α inhibitors (TNFi) and abatacept (ABT), using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS).

METHODS

We collected serum from RA patients with moderate or high disease activity prior to biologic treatment, and obtained the serum metabolomic profiles of these samples using CE-TOFMS. The patients' response was determined 12 weeks after starting biologic treatment, according to the EULAR response criteria. We compared the metabolites between the response and non-response patient groups and analysed their discriminative ability.

RESULTS

Among 43 total patients, 14 of 26 patients in the TNFi group and 6 of 17 patients in the ABT group responded to the biologic treatment. Of the metabolites separated by CE-TOFMS, 196 were identified as known substances. Using an orthogonal partial least-squares discriminant analysis, we identified five metabolites as potential predictors of TNFi responders and three as predictors of ABT responders. Receiver operating characteristic analyses for multiple biomarkers revealed an area under the curve (AUC) of 0.941, with a sensitivity of 85.7% and specificity of 100% for TNFi, and an AUC of 0.985, with a sensitivity of 100% and specificity of 90.9% for ABT.

CONCLUSION

By metabolomic analysis, we identified serum biomarkers that have a high ability to predict the response of RA patients to TNFi or ABT treatment.

Metabolomic Profiling to Identify Molecular Biomarkers of Cellular Response to Methotrexate In Vitro

Variation in methotrexate (MTX) efficacy represents a significant barrier to early and effective disease control in the treatment of autoimmune arthritis. We hypothesize that the utilization of metabolomic techniques will allow for an improved understanding of the biochemical basis for the pharmacological activity of MTX, and can promote the identification and evaluation of novel molecular biomarkers of MTX response. In this work, erythroblastoid cells were exposed to MTX at the physiologic concentration of 1,000 nM and analyzed using three metabolomic platforms to give a broad spectrum of cellular metabolites. MTX pharmacological activity, defined as cellular growth inhibition, was associated with an altered cellular metabolomic profile based on the analysis of 724 identified metabolites. By discriminant analysis, MTX treatment was associated with increases in ketoisovaleric acid, fructose, galactose, and 2-deoxycytidine, and corresponding reductions in 2-deoxyuridine, phosphatidylinositol 32:0, orotic acid, and inosine monophosphate. Inclusion of data from analysis of folate metabolism in combination with chemometric and metabolic network analysis demonstrated that MTX treatment is associated with dysregulated folate metabolism and nucleotide biosynthesis, which is in line with its known mechanism of action. However, MTX treatment was also associated with alterations in a diversity of metabolites, including intermediates of amino acid, carbohydrate, and lipid metabolism. Collectively, these findings support a robust metabolic response following exposure to physiologic concentrations of MTX. They also identify various metabolic intermediates that are associated with the pharmacological activity of MTX, and are, therefore, potential molecular biomarker candidates in future preclinical and clinical studies of MTX efficacy in autoimmune arthritis.

MS-based targeted metabolomics of eicosanoids and other oxylipins: Analytical and inter-individual variabilities

Oxylipins, including the well-known eicosanoids, are potent lipid mediators involved in numerous physiological and pathological processes. Therefore, their quantitative profiling has gained a lot of attention during the last years notably in the active field of health biomarker discovery. Oxylipins include hundreds of structurally and stereochemically distinct lipid species which today are most commonly analyzed by (ultra) high performance liquid chromatography-mass spectrometry based ((U)HPLC-MS) methods. To maximize the utility of oxylipin profiling in clinical research, it is crucial to understand and assess the factors contributing to the analytical and biological variability of oxylipin profiles in humans. In this review, these factors and their impacts are summarized and discussed, providing a framework for recommendations expected to enhance the interlaboratory comparability and biological interpretation of oxylipin profiling in clinical research.

Metabolomic profiles of induced pluripotent stem cells derived from patients with rheumatoid arthritis and osteoarthritis

Background

Metabolomics is the systemic study of the unique fingerprints of metabolites involved in cellular processes and biochemical reactions. The metabolomic approach is useful in diagnosing and predicting the development of rheumatoid arthritis (RA) and osteoarthritis (OA) and is emerging as a useful tool for identifying disease biomarkers. The aim of this study was to compare the metabolic blueprint of fibroblast-like synoviocyte (FLS) cells and induced pluripotent stem cells (iPSCs) derived from RA and OA patients.

Methods

Somatic cells of RA patients (n = 3) and OA patients (n = 3) were isolated, transduced with a lentiviral plasmid, and reprogrammed into iPSCs displaying pluripotency. Metabolic profiling of RA and OA patient–derived FLS cells and iPSCs was performed using liquid chromatography/mass spectrometry and statistical analysis. After normalization by the sum of the peak intensities through LC/MS, 37 metabolites were detected across RA and OA patients.

Results

The metabolites of RA and OA were distinguishable according to the PLS-DA analysis. LysoPC (20:4), 4-methoxychalcone, phosphorylcholine, and nicotinamide (NAM) were significantly higher in RA iPSCs than in OA iPSCs (p < 0.05). The NMNAT-3 enzyme, which catalyzes an important step in the biosynthesis of NAD⁺ from adenosine triphosphate, was also upregulated in RA iPSCs. Interestingly, the proliferation of RA iPSCs was significantly greater than OA iPSC proliferation (p < 0.05). NAM played a critical role in the proliferation of RA iPSCs but not in OA iPSCs. When iPSCs were treated with 100 nM of the NAM inhibitor tannic acid (TA), the proliferation of RA iPSCs was significantly reduced (p < 0.001).

Conclusions

The metabolites of RA and OA FLS cells and RA and OA iPSCs were all clearly distinguishable from each other. NAM played a critical role in the proliferation of RA iPSCs but not in OA iPSCs. TA effectively inhibited the expression of NAM in RA iPSCs and is a possible effective treatment for RA patients.

Metabolomic approach to the exploration of biomarkers associated with disease activity in rheumatoid arthritis

We aimed to investigate metabolites associated with the 28-joint disease activity score based on erythrocyte sedimentation rate (DAS28-ESR) in patients with rheumatoid arthritis (RA) using capillary electrophoresis quadrupole time-of-flight mass spectrometry. Plasma and urine samples were collected from 32 patients with active RA (DAS28-ESR≥3.2) and 17 with inactive RA (DAS28-ESR<3.2). We found 15 metabolites in plasma and 20 metabolites in urine which showed a significant but weak positive or negative correlation with DAS28-ESR. When metabolites between active and inactive patients were compared, 9 metabolites in plasma and 15 in urine were found to be significantly different. Consequently, we selected 11 metabolites in plasma and urine as biomarker candidates which significantly correlated positively or negatively with DAS28-ESR, and significantly differed between active and inactive patients. When a multiple logistic regression model was built to discriminate active and inactive cohorts, three variables-histidine and guanidoacetic acid from plasma and hypotaurine from urine-generated a high area under the receiver operating characteristic (ROC) curve value (AUC = 0.8934). Thus, this metabolomics approach appeared to be useful for investigating biomarkers of RA. Combination of plasma and urine analysis may lead to more precise and reliable understanding of the disease condition. We also considered the pathophysiological significance of the found biomarker candidates.

Can Metabolic Pathways Be Therapeutic Targets in Rheumatoid Arthritis?

The metabolic rewiring of tumor cells and immune cells has been viewed as a promising source of novel drug targets. Many of the molecular pathways implicated in rheumatoid arthritis (RA) directly modify synovium metabolism and transform the resident cells, such as the fibroblast-like synoviocytes (FLS), and the synovial tissue macrophages (STM), toward an overproduction of enzymes, which degrade cartilage and bone, and cytokines, which promote immune cell infiltration. Recent studies have shown metabolic changes in stromal and immune cells from RA patients. Metabolic disruption in the synovium provide the opportunity to use in vivo metabolism-based imaging techniques for patient stratification and to monitor treatment response. In addition, these metabolic changes may be therapeutically targetable. Thus, resetting metabolism of the synovial membrane offers additional opportunities for disease modulation and restoration of homeostasis in RA. In fact, rheumatologists already use the antimetabolite methotrexate, a chemotherapy agent, for the treatment of patients with inflammatory arthritis. Metabolic targets that do not compromise systemic homeostasis or corresponding metabolic functions in normal cells could increase the drug armamentarium in rheumatic diseases for combination therapy independent of systemic immunosuppression. This article summarizes what is known about metabolism in synovial tissue cells and highlights chemotherapies that target metabolism as potential future therapeutic strategies for RA.

Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, debilitating disease associated with reduced quality of life and substantial costs. It is unclear which tests and assessment tools allow the best assessment of prognosis in people with early RA and whether or not variables predict the response of patients to different drug treatments.

OBJECTIVE

To systematically review evidence on the use of selected tests and assessment tools in patients with early RA (1) in the evaluation of a prognosis (review 1) and (2) as predictive markers of treatment response (review 2).

DATA SOURCES

Electronic databases (e.g. MEDLINE, EMBASE, The Cochrane Library, Web of Science Conference Proceedings; searched to September 2016), registers, key websites, hand-searching of reference lists of included studies and key systematic reviews and contact with experts.

STUDY SELECTION

Review 1 - primary studies on the development, external validation and impact of clinical prediction models for selected outcomes in adult early RA patients. Review 2 - primary studies on the interaction between selected baseline covariates and treatment (conventional and biological disease-modifying antirheumatic drugs) on salient outcomes in adult early RA patients.

RESULTS

Review 1 - 22 model development studies and one combined model development/external validation study reporting 39 clinical prediction models were included. Five external validation studies evaluating eight clinical prediction models for radiographic joint damage were also included. c-statistics from internal validation ranged from 0.63 to 0.87 for radiographic progression (different definitions, six studies) and 0.78 to 0.82 for the Health Assessment Questionnaire (HAQ). Predictive performance in external validations varied considerably. Three models [(1) Active controlled Study of Patients receiving Infliximab for the treatment of Rheumatoid arthritis of Early onset (ASPIRE) C-reactive protein (ASPIRE CRP), (2) ASPIRE erythrocyte sedimentation rate (ASPIRE ESR) and (3) Behandelings Strategie (BeSt)] were externally validated using the same outcome definition in more than one population. Results of the random-effects meta-analysis suggested substantial uncertainty in the expected predictive performance of models in a new sample of patients. Review 2 - 12 studies were identified. Covariates examined included anti-citrullinated protein/peptide anti-body (ACPA) status, smoking status, erosions, rheumatoid factor status, C-reactive protein level, erythrocyte sedimentation rate, swollen joint count (SJC), body mass index and vascularity of synovium on power Doppler ultrasound (PDUS). Outcomes examined included erosions/radiographic progression, disease activity, physical function and Disease Activity Score-28 remission. There was statistical evidence to suggest that ACPA status, SJC and PDUS status at baseline may be treatment effect modifiers, but not necessarily that they are prognostic of response for all treatments. Most of the results were subject to considerable uncertainty and were not statistically significant.

LIMITATIONS

The meta-analysis in review 1 was limited by the availability of only a small number of external validation studies. Studies rarely investigated the interaction between predictors and treatment.

SUGGESTED RESEARCH PRIORITIES

Collaborative research (including the use of individual participant data) is needed to further develop and externally validate the clinical prediction models. The clinical prediction models should be validated with respect to individual treatments. Future assessments of treatment by covariate interactions should follow good statistical practice.

CONCLUSIONS

Review 1 - uncertainty remains over the optimal prediction model(s) for use in clinical practice. Review 2 - in general, there was insufficient evidence that the effect of treatment depended on baseline characteristics.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42016042402.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Emerging role of metabolomics in rheumatology

The pursuit for understanding disease pathogenesis, in this age of rapid laboratory diagnostics and fast-paced research, has led scientists worldwide to take recourse in hypothesis-free approaches for molecular diagnosis. Metabolomics is one such powerful tool that explores comprehensibly the metabolic alternations in human diseases. It involves study of small molecules of less than 1 kD in size by either LSMS or nuclear magnetic resonance. Unlike genomics, which tells us what may have happened, metabolomics reflects what did happen. The NMR technique has an advantage of analyzing metabolites without sample preparation, thereby diminishing artifacts, is less cumbersome and with the latest database on Metabolome; about 30 000 metabolites can be identified. The study of metabolomics for several rheumatic diseases, including rheumatoid arthritis, lupus, osteoarthritis and vasculitis, has revealed distinctive metabolic signatures. Thus, metabolomics is a technique that promises precision medicine with better biomarkers, robust predictors of drug response and of disease outcome, discovery of newer metabolites and pathways in disease pathogenesis, and finally, targeted drug development. This review intends to decipher its relevance in common rheumatic diseases.

Baseline metabolic profiles of early rheumatoid arthritis patients achieving sustained drug-free remission after initiating treat-to-target tocilizumab, methotrexate, or the combination: insights from systems biology

Background

We previously identified, in newly diagnosed rheumatoid arthritis (RA) patients, networks of co-expressed genes and proteomic biomarkers associated with achieving sustained drug-free remission (sDFR) after treatment with tocilizumab- or methotrexate-based strategies. The aim of this study was to identify, within the same patients, metabolic pathways important for achieving sDFR and to subsequently study the complex interactions between different components of the biological system and how these interactions might affect the therapeutic response in early RA.

Methods

Serum samples were analyzed of 60 patients who participated in the U-Act-Early trial (ClinicalTrials.gov number NCT01034137) and initiated treatment with methotrexate, tocilizumab, or the combination and who were thereafter able to achieve sDFR (n = 37); as controls, patients were selected who never achieved a drug-free status (n = 23). Metabolomic measurements were performed using mass spectrometry on oxidative stress, amine, and oxylipin platforms covering various compounds. Partial least square discriminant analyses (PLSDA) were performed to identify, per strategy arm, relevant metabolites of which the biological pathways were studied. In addition, integrative analyses were performed correlating the previously identified transcripts and proteins with the relevant metabolites.

Results

In the tocilizumab plus methotrexate, tocilizumab, and methotrexate strategy, respectively, 19, 13, and 12 relevant metabolites were found, which were subsequently used for pathway analyses. The most significant pathway in the tocilizumab plus methotrexate strategy was “histidine metabolism” (p < 0.001); in the tocilizumab strategy it was “arachidonic acid metabolism” (p = 0.018); and in the methotrexate strategy it was “arginine and proline metabolism” (p = 0.022). These pathways have treatment-specific drug interactions with metabolites affecting either the signaling of interleukin-6, which is inhibited by tocilizumab, or affecting protein synthesis from amino acids, which is inhibited by methotrexate.

Conclusion

In early RA patients treated-to-target with a tocilizumab- or methotrexate-based strategy, several metabolites were found to be associated with achieving sDFR. In line with our previous observations, by analyzing relevant transcripts and proteins within the same patients, the metabolic profiles were found to be different between the strategy arms. Our metabolic analysis further supports the hypothesis that achieving sDFR is not only dependent on predisposing biomarkers, but also on the specific treatment that has been initiated.

Trial registration

ClinicalTrials.gov, NCT01034137. Registered on January 2010

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1729-2) contains supplementary material, which is available to authorized users.

1H NMR Metabolomics Identifies Underlying Inflammatory Pathology in Osteoarthritis and Rheumatoid Arthritis Synovial Joints

Despite osteoarthritis (OA) and rheumatoid arthritis (RA) being typically age-related, their underlying etiologies are markedly different. We used ¹H nuclear magnetic resonance (NMR) spectroscopy to identify differences in metabolite profiles in low volumes of OA and RA synovial fluid (SF). SF was aspirated from knee joints of 10 OA and 14 RA patients. 100 μL SF was analyzed using a 700 MHz Avance IIIHD Bruker NMR spectrometer with a TCI cryoprobe. Spectra were analyzed by Chenomx, Bruker TopSpin and AMIX software. Statistical analysis was undertaken using Metaboanalyst. 50 metabolites were annotated, including amino acids, saccharides, nucleotides and soluble lipids. Discriminant analysis identified group separation between OA and RA cohorts, with 32 metabolites significantly different between OA and RA SF (false discovery rate (FDR) < 0.05). Metabolites of glycolysis and the tricarboxylic acid cycle were lower in RA compared to OA; these results concur with higher levels of inflammation, synovial proliferation and hypoxia found in RA compared to OA. Elevated taurine in OA may indicate increased subchondral bone sclerosis. We demonstrate that quantifiable differences in metabolite abundance can be measured in low volumes of SF by ¹H NMR spectroscopy, which may be clinically useful to aid diagnosis and improve understanding of disease pathogenesis.

Review: Synovial Cell Metabolism and Chronic Inflammation in Rheumatoid Arthritis

Metabolomic studies of body fluids show that immune-mediated inflammatory diseases such as rheumatoid arthritis (RA) are associated with metabolic disruption. This is likely to reflect the increased bioenergetic and biosynthetic demands of sustained inflammation and changes in nutrient and oxygen availability in damaged tissue. The synovial membrane lining layer is the principal site of inflammation in RA. Here, the resident cells are fibroblast-like synoviocytes (FLS) and synovial tissue macrophages, which are transformed toward overproduction of enzymes that degrade cartilage and bone and cytokines that promote immune cell infiltration. Recent studies have shown metabolic changes in both FLS and macrophages from RA patients, and these may be therapeutically targetable. However, because the origins and subset-specific functions of synoviocytes are poorly understood, and the signaling modules that control metabolic deviation in RA synovial cells are yet to be explored, significant additional research is needed to translate these findings to clinical application. Furthermore, in many inflamed tissues, different cell types can forge metabolic collaborations through solute carriers in their membranes to meet a high demand for energy or biomolecules. Such relationships are likely to exist in the synovium and have not been studied. Finally, it is not yet known whether metabolic change is a consequence of disease or whether primary changes to cellular metabolism might underlie or contribute to the pathogenesis of early-stage disease. In this review article, we collate what is known about metabolism in synovial tissue cells and highlight future directions of research in this area.

Local and systemic inflammatory lipid profiling in a rat model of osteoarthritis with metabolic dysregulation

Objective

Bioactive oxidised lipids (oxylipins) are important signalling mediators, capable of modulating the inflammatory state of the joint and anticipated to be of importance in joint homeostasis and status of osteoarthritis. The aim of this study was to quantify oxylipin levels in plasma and synovial fluid from rats with experimentally induced osteoarthritis to investigate the potential role of oxylipins as a marker in the disease process of early osteoarthritis.

Design

Forty rats were randomly allocated to a standard or high-fat diet group. After 12 weeks, local cartilage damage was induced in one knee joint in 14 rats of each diet group. The remaining 6 rats per group served as controls. At week 24, samples were collected. Oxylipin levels were quantified by liquid chromatography–mass spectrometry.

Results

Overall, 31 lipid-derived inflammatory mediators were detected in fasted plasma and synovial fluid. Principal component analysis identified four distinct clusters associated with histopathological changes. Diet induced differences were evident for 13 individual plasma oxylipins, as well as 5,6-EET in synovial fluid. Surgical-model induced differences were evident for three oxylipins in synovial fluid (15-HETE, 8,9-DHET and 17R-ResolvinD1) with a different response in lipid concentrations for synovial fluid and plasma.

Conclusions

We demonstrate the quantification of oxidised lipids in rat plasma and synovial fluid in a model of early experimental osteoarthritis. Oxylipins in the synovial fluid that were altered as consequence of the surgically induced osteoarthritis were not represented in the plasma. Our findings suggest differential roles of the oxylipins in the local versus peripheral compartment.

Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis?

Individualising biologic disease-modifying anti-rheumatic drugs (bDMARDs) to maximise outcomes and deliver safe and cost-effective care is a key goal in the management of rheumatoid arthritis (RA). Investigation to identify predictive tools of bDMARD response is a highly active and prolific area of research. In addition to clinical phenotyping, cellular and molecular characterisation of synovial tissue and blood in patients with RA, using different technologies, can facilitate predictive testing. This narrative review will summarise the literature for the available bDMARD classes and focus on where progress has been made. We will also look ahead and consider the increasing use of 'omics' technologies, the potential they hold as well as the challenges, and what is needed in the future to fully realise our ambition of personalised bDMARD treatment.

Obesity is positively associated with arachidonic acid-derived 5- and 11-hydroxyeicosatetraenoic acid (HETE)

BACKGROUND

Oxylipids are oxygenated polyunsaturated fatty acid (PUFA) metabolites that are responsible for the onset and resolution of the inflammatory response. Enzymatic oxygenation through the lipoxygenase (LOX) or cytochrome P450 (CYP) pathways can form oxylipids that have either proinflammatory or proresolving functions depending on the type of PUFA substrate and degree of metabolism. The objective of this study was to determine how PUFA substrates and their corresponding oxylipids are associated with obesity.

METHODS

Plasma non-esterified FA and oxylipids were isolated from 123 Caucasian males using solid phase extraction and quantified using high performance liquid chromatography-tandem mass spectrometry. Statistical analyses included linear regressions and polytomous logistic regressions, and the responses were body mass index (BMI) and waist circumference (WC), and serum leptin, total adiponectin, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-peptide. Models were adjusted for age and smoking, and p-values were corrected for false discovery per Benjamini-Hochberg and Bonferroni.

RESULTS

We report that BMI, WC, and several serum cytokines were highly associated arachidonic acid (ARA)-derived hydroxyeicosatetraenoic acids (HETEs), and vicinal diols (i.e., alcohols on adjacent carbon atoms) derived from several PUFAs. There was a significant linear relationship between BMI, WC, and serum leptin, and ARA-derived 5-, 11-, and 15-HETE. Specifically, BMI and WC were positively associated with proinflammatory 5- and 11-hydroxyeicosatetraenoic acid (HETE), even after normalization to ARA concentrations and false discovery p-value correction. Individuals with 5-HETE concentrations >5.01nmol/L or 11-HETE concentrations and >0.89nmol/L were over 5 times more likely to be obese compared to those with ≤1.86nmol/L and ≤0.39nmol/L, respectively. Vicinal diols from linoleic, eicosapentaenoic, and docosahexaenoic acid were inversely associated with obesity. Across all statistical tests, vicinal diols were inversely associated with obesity whether normalized to parent PUFA concentrations or normalized to precursor epoxides. Interestingly, the proinflammatory cytokines IL-6 and TNF-α were not associated with any oxylipids. Since 5-HETE is a 5LOX product, 11-HETE is marker of lipid peroxidation, and vicinal diols are formed through soluble epoxide hydrolase (sEH) metabolism of CYP epoxygenated PUFAs, therefore, these results indicate that obesity is likely associated with altered metabolism with distinct oxygenating pathways. Taken together, our results indicate that obesity is associated with specific oxylipids indicative of altered PUFA metabolism through several pathways (i.e., LOX, reactive oxygen species, and sEH and CYP epoxygenase), rather than attributed solely to altered dietary PUFA intake.

Differences between serum polar lipid profiles of male and female rheumatoid arthritis patients in response to glucocorticoid treatment

Objective

As there are pharmacological differences between males and females, and glucocorticoid (GC) treatment is associated with increased cardiovascular mortality rate in rheumatoid arthritis (RA) patients, it is important to study serum polar lipid profiles of male and female patients in response to GC therapy. Gender differences may require an adjustment to the treatment strategy for a selection of patients.

Methods

Serum samples from 281 RA patients were analysed using a targeted lipidomics platform. The differences in GC use and gender on polar lipid profiles were cross sectionally examined by multiple linear regressions, while correcting for confounding factors.

Results

Differences in polar lipids between GC users and non-GC users in females and males were merely restricted to lysophospholipids (lysophosphatidylcholines and lysophosphatidylethanolamines). Lysophospholipids in female patients treated with GCs were significantly higher than female patients not treated with GCs (p = 6.0 E−6), whereas no significant difference was observed in male GC users versus non-users (p = 0.397).

Conclusion

The lysophospholipid profiles in response to GCs were significantly different between male and female RA patients, which may have implications for the cardiovascular risk of GC treatment.

Electronic supplementary material

The online version of this article (doi:10.1007/s10787-016-0284-1) contains supplementary material, which is available to authorized users.