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Serum metabolomics identifies unique inflammatory signatures to distinguish rheumatoid arthritis responders and non-responders to TNF inhibitor therapy

ABSTRACT: Rheumatoid arthritis (RA) is an auto-immune disease which causes irreversible damage to tissue and cartilage within synovial joints. Rapid diagnosis and treatment with disease-modifying therapies is essential to reduce inflammation and prevent destruction of the joint. RA is a heterogeneous disease, and many patients do not respond to front-line therapies, requiring escalation of treatment onto biologics, of which TNF inhibitors (TNF-i) are the most common. In this study we determined whether serum metabolomics, using nuclear magnetic resonance (NMR) and Fourier transform infarred (FTIR) spectroscopy, could discriminate RA blood sera from healthy human controls and whether serum metabolomics could be used to predict response or non-response to TNF inhibitor (TNF-i) therapy. NMR spectroscopy identified 35 metabolites in RA sera, with acetic acid being significantly lower in RA sera compared to healthy controls (HC, FDR<0.05). PLS-DA modelling identified 2-hydroxyisovalericacetic acid, acetoacetic acid, mobile lipids, alanine and leucine as important metabolites for discrimination of RA and HC sera by 1H NMR spectroscopy (averaged 83.1% balanced accuracy, VIP score >1). FTIR spectroscopy identified a significant difference between RA and HC sera in the 1000-1200 cm-1 spectral area, representing the mixed region of carbohydrates and nucleic acids (FDR<0.05). Sera from RA patients who responded to TNF-i were significantly different from TNF-i non-responder sera in the 1600-1700 cm-1 region (FDR<0.05). We propose that NMR and FTIR serum metabolomics could be used as a diagnostic tool alongside current clinical parameters to diagnose RA and to predict whether a patient with severe RA will respond to TNF-i.

INSTRUMENT(S): Nuclear Magnetic Resonance (NMR) -

PROVIDER: MTBLS6213 | MetaboLights | 2025-08-14

REPOSITORIES: MetaboLights

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Serum metabolomics identifies unique inflammatory signatures to distinguish rheumatoid arthritis responders and non-responders to TNF inhibitor therapy.

Fresneda Alarcon Michele M Xu Yun Y Lima Cassio C Ford Susanna S Grosman Rudi R Goodacre Royston R Phelan Marie M MM Wright Helen L HL

Metabolomics : Official journal of the Metabolomic Society 20250812 5

<h4>Introduction</h4>Rheumatoid arthritis (RA) is an auto-immune disease which causes irreversible damage to tissue and cartilage within synovial joints. Rapid diagnosis and treatment with disease-modifying therapies is essential to reduce inflammation and prevent joint destruction. RA is a heterogeneous disease, and many patients do not respond to front-line therapies, requiring escalation of treatment onto biologics, of which TNF inhibitors (TNF-i) are the most common.<h4>Objectives/methods</h ...[more]

PMID: 40794325

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Project description:The purpose of this study was (1) to identify novel genes involved in the pathogenesis of Rheumatoid Arthritis (RA) disease, which may provide additional targets for therapeutic intervention and (2) to examine the molecular mechanisms associated with the response to anti-TNF treatment. Microarray analysis of LPS-stimulated whole blood from RA patients pre and post anti-TNF treatment was conducted. This study identified 818 transcripts, differentially expressed in RA patients pre-treatment compared to non-RA control samples. While a number of these genes were associated with RA in previous studies, validating our data, a number of novel genes with possible functions in RA disease were also identified. The number of transcripts (1051) significantly altered post anti-TNF treatment indicates the impact of anti-TNF therapy on systemic gene expression. A number of these transcripts were confirmed to be altered in a larger patient group and may represent potential genetic markers of a patientâs clinical response to anti-TNF treatment. Experiment Overall Design: Transcriptome profiling was performed on two RA patients, pre- and three months post-Enbrel therapy and two matched non-RA controls. The whole blood model described by Wang et al (2000) was used with minor modifications. Briefly, to EDTA anti-coagulated blood, LPS (30ng/ml) was added immediately after drawing, and incubated at 37oC for 4 hours, mixing at 80s. After 4 hours red blood cells were lysed and pelleted, white cells were washed once in PBS (Gibco) and frozen at â70oC for subsequent RNA extraction. Total RNA was extracted using the RNeasy kit (QIAGEN) as per manufacturerâs instructions. Samples were treated with DNA-Free (Ambion) to remove genomic DNA and checked using intron-overlapping primers prior to generating cRNA probes or cDNA (data not shown). Affymetrix array data was normalised (globally) and statistically (t-test) analysed using GeneSight software (Biodiscovery). Gene expression changes with a p value of < 0.05 and a fold change of 3 or greater were considered differentially expressed.

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