Sabrina R. Kapoor

Metabolic Profiling Predicts Response to Anti–Tumor Necrosis Factor α Therapy in Patients With Rheumatoid Arthritis

Objective Anti–tumor necrosis factor (anti-TNF) therapies are highly effective in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), but a significant number of patients exhibit only a partial or no therapeutic response. Inflammation alters local and systemic metabolism, and TNF plays a role in this. We undertook this study to determine if the patients metabolic fingerprint prior to therapy could predict responses to anti-TNF agents. Methods Urine was collected from 16 RA patients and 20 PsA patients before and during therapy with infliximab or etanercept. Urine metabolic profiles were assessed using nuclear magnetic resonance spectroscopy. Discriminating metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed. Results Baseline urine metabolic profiles discriminated between RA patients who did or did not have a good response to anti-TNF therapy according to European League Against Rheumatism criteria, with a sensitivity of 88.9% and a specificity of 85.7%, with several metabolites contributing (in particular histamine, glutamine, xanthurenic acid, and ethanolamine). There was a correlation between baseline metabolic profiles and the magnitude of change in the Disease Activity Score in 28 joints from baseline to 12 months in RA patients (P = 0.04). In both RA and PsA, urinary metabolic profiles changed between baseline and 12 weeks of anti-TNF therapy. Within the responders, urinary metabolite changes distinguished between etanercept and infliximab treatment. Conclusion The clear relationship between urine metabolic profiles of RA patients at baseline and their response to anti-TNF therapy may allow development of novel approaches to the optimization of therapy. Differences in metabolic profiles during treatment with infliximab and etanercept in RA and PsA may reflect distinct mechanisms of action.

Metabolomics in the Analysis of Inflammatory Diseases

Inflammation is a normal and extraordinarily important component of responses to infection and injury. The cardinal features of swelling, redness, stiffness and increasing temperature are strong indicators of the significant changes in tissue metabolism and the ingress of immune cells into the tissues. The increase in blood flow which underlies many of these changes may result in changes to the supply of nutrients and in particular the level of oxygen in the tissues. Inward migration of immune cells, which is also enabled by the increased blood flow, will put further stress on the metabolic environment of the tissues. The activity of macrophages and neutrophils in clearing infection and repairing tissue damage also have significant metabolic consequences particularly because of the production of cytokines and cytotoxic molecules such as reactive oxygen species and reactive nitrogen species, which are required to kill invading organisms. Production of these molecules will consume considerable quantities of oxygen, ATP and NADPH. These antimicrobial agents put considerable stress on host cells in the surrounding and distal tissues and can lead to significant loss of protective metabolites such as glutathione.

Metabolomics in rheumatology

The musculoskeletal system is a highly active metabolic system. Energy consumption is driven by the combined demands of skeletal muscle, turnover and remodelling of bone, cartilage and other structural components in response to changing levels of loading. The high-energy respiratory chain is estimated to result in the turnover of 65 kg/day of adenosine triphosphate for the whole body, increasing during periods of activity [1]. With skeletal muscle accounting for 30% of body mass in a postmenopausal woman (and more in men), it is perhaps not surprising then that changes in metabolic activity are observed in musculoskeletal disease. Overall resting energy use is increased by 8% in RA patients and, interestingly, a similar increase in metabolic activity is seen in patients who smoke, a well-established risk factor for the development of RA [2]. The mechanism underlying this increase in energy metabolism is unclear, but the active role of the immune system in the inflammatory processes in RA suggests that immune cell activation and turnover may contribute. There are also significant changes in liver metabolism as a result of the acute phase response and large shifts in systemic metabolism as a result of rheumatoid cachexia, where muscle degradation occurs along with the related increase in the mass of body fat. Many of these metabolic changes can pre-date obvious joint symptoms, and metabolic pathways may change in response to therapy. It is not surprising therefore that there is increasing interest in using altered metabolites as biomarkers of disease activity and response to therapy. These studies may also provide novel insights into the pathological processes driving complex musculoskeletal diseases. RA was first associated with altered levels of individual metabolites in a report from 1962 describing changes in the metabolism of tryptophan [3]. In recent years a more broad-ranging approach, metabolomics, has been applied to assessment of the disease. Metabolomics is the new kid on the omics block and comparable in scope to genomics, transcriptomics and proteomics. Through the study of small molecules (<1500 Da) within specific or multiple compartments (blood, urine, joints, saliva, eyes, tears, cerebrospinal fluid, intact cells), metabolite profiles or fingerprints, each containing thousands of metabolites, can be identified. In individuals at risk due to genetics, their environment or both, disruptive pathological processes can result in altered metabolite profiles long before overt signs and symptoms of disease appear. The metabolites themselves can be identified and quantified using NMR spectroscopy or mass spectrometry. Metabolite identification is done by reference to metabolite databases or by direct metabolite assay. The Human Metabolome Database lists >41 000 metabolite entries in the latest version, however, just 3000 have been linked with diseases to date [4]. In common with other omics approaches, metabolomics experiments generate prodigious amounts of data, but this is amenable to multivariate analysis techniques, including principal components analysis, partial least squares discriminant analysis, regression methods and genetic algorithms. Metabolomics studies have been reported for virtually all of the main rheumatologic diseases, although notably none yet for SSc [5]. Our own group has demonstrated the use of urinary metabolic fingerprint analysis to predict responses to anti-TNF [6], and we have suggested that metabolites resulting from TNF-driven cachexia are among the useful predictive biomarkers. Serum metabolic profiling can differentiate four types of human arthritis [7], and we have shown the predictive value of the serum metabolite profile in early synovitis patients, with differences between those with self-limiting disease and those who went on to develop persistent RA [8]. The value of combining omics approaches has been demonstrated in a study using proteomics and metabolomics to show alterations in both vitamin D3 metabolites and proteins in patients with AS [9]. The combination of genetic and metabolomic data [10] has shown the potential to identify genotype-influenced metabotypes in a number of chronic diseases. Different cells types vary in their energy and metabolic requirements, with cells undergoing proliferation being very different from those in a stable steady state. This applies to tissue cells such as synovial stroma and to immune cells including macrophages and T lymphocytes. Thus the state of immune activation and tissue hyperplasia may be strongly reflected in the metabolic profiles observed in tissues and in biofluids from patients, providing useful insights into the state of the disease and its aetiopathology. Comprehensive clinical assessment approaches used by BILAG and SLEDAI offer a clinical approach to individualized systems medicine by objectively quantifying components in a disease. Transcriptomics, proteomics and metabolomics are the biological equivalents of these clinical assessments. However, the sole analysis of tissues from the specific site of disease will miss the systemic changes commonly associated with complex diseases that may be responsible for driving the persistence and much of the associated co-morbidity.

THU0098 The Metabolic Profile of Synovial Fibroblasts - Implications for Disease Processes in Early Inflammatory Arthritis

Background Synovial fibroblasts play a key role in the persistence of inflammation and joint destruction in rheumatoid arthritis (RA). Cell function and proliferation are highly dependent on availability of nutrients and their metabolism. In the joint, fibroblasts have limited access to nutrients within a poorly vascularised hypoxic tissue and yet expansion of fibroblast numbers is a feature of the RA joint. This suggests fibroblasts may adapt their metabolism to this environment and this altered function may be involved in preventing the resolution of chronic inflammation. Objectives To use NMR spectroscopy to assess differences in metabolite fingerprints in fibroblasts from patients with established RA, early arthritis and healthy controls, and determine how cytokine production by fibroblasts relates to their metabolic profile. Methods Fibroblasts were cultured from synovial biopsies from 6 newly presenting, disease-modifying drug naive patients with established RA (>12 week symptom duration), 6 healthy controls (HC), and patients with arthritis of ≤12 weeks duration whose disease resolved (n=6) or evolved into RA (n=6) at follow-up. Cell metabolites were extracted for analysis using 1D 1H-NMR spectroscopy and secreted IL6 measured by ELISA. NMR spectra were analysed using partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLS-R) to correlate the metabolite profiles with 1) IL6 production by fibroblasts and 2) the level of CRP, at the time of synovial biopsy, in the serum of patients whose fibroblasts were being assessed. Results We were able to distinguish the metabolic profiles of fibroblasts from HC and early RA (sensitivity 67%, specificity 67%), HC and established RA (sensitivity 67%, specificity 50%) and resolving arthritis and early RA (sensitivity 67%, specificity 83%). The IL6 production of fibroblasts from patients with inflammatory arthritis was clearly distinct from that of healthy controls. There was a strong correlation between the metabolic profile of synovial fibroblasts and their IL6 production (p<0.001) with several metabolites (in particular citrate, carnosine, pyroglutamate, alanine and lactate) contributing. In patients with inflammatory arthritis the fibroblast metabolic profile correlated strongly (p<0.001) with patient serum CRP at the time of synovial biopsy, with several metabolites (in particular cholesterol, fatty acids, leucine, citrate and pyroglutamate) contributing. Conclusions There was a significant association between the metabolomic fingerprint of synovial fibroblasts and their IL 6 production, suggesting that IL6 production drives or is driven by significant changes in metabolism. There was also a significant association between CRP levels in the patients’ serum and the metabolic profile of their synovial fibroblasts suggesting that fibroblasts retain their metabolic fingerprint during culture ex vivo and that this is strongly related to systemic measures of inflammation in patients with clinical synovitis. Disclosure of Interest None Declared

AB0105 The impact of inflammation on metabolomic profiles in patients with arthritis

Background In addition to synovitis, rheumatoid arthritis (RA) is characterised by widespread systemic changes. These changes are largely mediated by pro-inflammatory cytokines that impact on metabolism, particularly that of muscle and fat. Given these widespread metabolic effects we hypothesised that the level of current inflammation would be reflected in changes in the types and levels of metabolites found in the serum of patients with inflammatory arthritis. Objectives We have used NMR-based metabolomic fingerprinting to analyse serum from patients with newly presenting established RA, early arthritis and healthy controls. We sought to assess whether the metabolite fingerprint in patients with established RA differed from that of healthy controls and whether this fingerprint differed in patients with early arthritis in relation to the extent of inflammation and final outcomes. Methods Serum samples were collected from newly presenting, disease-modifying drug naive patients with established RA (16 patients), matched healthy controls (14 patients), and two groups of newly presenting patients with synovitis of ≤3 months’ symptom duration (89 patients and 127 patients) whose outcomes were determined at clinical follow-up. Serum metabolic profiles were assessed using 1D 1H-NMR spectroscopy. Discriminating metabolites were identified, and the relationships between metabolic profiles and clinical variables including CRP and outcomes were examined. Results The serum metabolite fingerprint in established RA was clearly distinct from that of healthy controls. In early arthritis, we were able to stratify the patients according to the level of current inflammation, with CRP correlating with metabolic differences in two separate groups (p<0.001). Lactate, glucose, cholesterol, fatty acids and lipids were important discriminators of inflammatory burden in both early arthritis patient groups. Conclusions The metabolomic fingerprint reflects inflammatory disease activity in patients with synovitis, demonstrating that underlying inflammatory processes drive widespread changes in metabolism that can be measured in the peripheral blood. The identification of novel metabolic alterations may provide insights into disease mechanisms operating in patients with inflammatory arthritis and provide novel variables that may add discriminating value to existing predictive algorithms. Disclosure of Interest None Declared

OP0128 Metabolic profiling of urine samples predicts response to anti-TNF therapy in patients with rheumatoid arthritis

Background Anti-TNFα therapies are highly effective in the treatment of rheumatoid arthritis (RA) but a significant proportion of patients have an inadequate response. TNFα has an important role in regulating systemic and localised metabolism. Objectives We sought to determine if the metabolic profile of patients prior to therapy could be used to predict responses to anti-TNFα agents. Methods Urine was collected from 16 patients with RA before and during therapy with infliximab or etanercept as part of a multicentre study. All patients were female and the mean age was 51.5. 14 patients were positive for rheumatoid factor and 14 for anti-CCP antibody. All had a DAS28>4 at baseline. Urine metabolic profiles were assessed using NMR spectroscopy. Relevant metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed (using partial least square discriminant analysis (PLSDA), principal component analysis using a genetic algorithm data selection (Galgo) and PLS-R (regression) analysis). Results Baseline urine metabolic profiles discriminated between RA patients who did (7 patients) or did not (9 patients) have a good response to anti-TNFα therapy according to EULAR criteria with a sensitivity of 85.9% and specificity of 85.7% with several metabolites (in particular citrate, creatinine and cresol) contributing. A correlation between baseline metabolic profiles in the urine samples and the extent of change in DAS 28 was seen (PLS-R analysis p=0.04). In patients with RA who responded to TNFα antagonists, a good response to therapy was associated with changes in the following urinary metabolites: erythritol, phenylacetic acid, p-cresol, propionic acid, methylamine, citrate, hippuric acid and creatinine. Urine samples from 20 psoriatic arthritis (PsA) patients were also assessed. Similar metabolites were identified in the urine samples of the patients with PsA that responded to TNFα antagonists. We were unable to study the ability of baseline urinary metabolite profiles to predict response in PsA as all but one of the PsA patients responded according to predefined criteria. Conclusions There are clear differences in the baseline urinary metabolic profiles of RA patients who respond well to anti-TNFα therapy. This may be relevant to the development of clinically useful predictive strategies. Disclosure of Interest S. Kapoor: None Declared, A. Filer: None Declared, M. Fitzpatrick: None Declared, B. Fisher Grant/Research support from: Merck provided funding for the clinical study and associated sample collection (but not for the metabolomic analysis reported here)., P. Taylor Grant/Research support from: Merck provided funding for the clinical study and associated sample collection (but not for the metabolomic analysis reported here)., C. Buckley Grant/Research support from: Merck provided funding for the clinical study and associated sample collection (but not for the metabolomic analysis reported here)., I. McInnes Grant/Research support from: Merck provided funding for the clinical study and associated sample collection (but not for the metabolomic analysis reported here)., K. Raza: None Declared, S. Young: None Declared