Metabolic Changes in Autoimmune Diseases

Seeger, Karsten

doi:10.2174/157016309789869074

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…Nevertheless, studying the metabolome could not only improve the diagnosis of a disease but also predict the tolerance and effectiveness of certain treatments [31]. For example, analysis of the urinary metabolome was able to predict the digestive toxicity of non-steroidal inflammatory drugs in rats [32].…”

Section: Discussionmentioning

confidence: 99%

Variations in the metabolome in response to disease activity of rheumatoid arthritis

Tatar¹,

Migné

Petera

et al. 2016

BMC Musculoskelet Disord

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BackgroundAnti-Tumor Necrosis Factor (TNF) therapies are able to control rheumatoid arthritis (RA) disease activity and limit structural damage. Yet no predictive factor of response to anti-TNF has been identified. Metabolomic profile is known to vary in response to different inflammatory rheumatisms so determining it could substantially improve diagnosis and, consequently, prognosis.The aim of this study was to use mass spectrometry to determine whether there is variation in the metabolome in patients treated with anti-TNF and whether any particular metabolomic profile can serve as a predictor of therapeutic response.MethodsBlood samples were analyzed in 140 patients with active RA before initiation of anti-TNF treatment and after 6 months of Anti-TNF treatment (100 good responders and 40 non-responders). Plasma was deproteinized, extracted and analyzed by reverse-phase chromatography–QToF mass spectrometry. Extracted and normalized ions were tested by univariate and ANOVA analysis followed by partial least-squares regression-discriminant analysis (PLS-DA). Orthogonal Signal Correction (OSC) was also used to filter data from unwanted non-related effects. Disease activity scores (DAS 28) obtained at 6 months were correlated with metabolome variation findings to identify a metabolite that is predictive of therapeutic response to anti-TNF.ResultsAfter 6 months of anti-TNF therapy, 100 patients rated as good responders and 40 patients as non-responders according to EULAR criteria. Metabolomic investigations suggested two different metabolic fingerprints splitting the good-responders group and the non-responders group, without differences in anti-TNF therapies. Univariate analysis revealed 24 significant ions in positive mode (p < 0.05) and 31 significant ions in negative mode (p < 0.05). Once intersected with PLS results, only 35 ions remained. Carbohydrate derivates emerged as strong candidate determinants of therapeutic response.ConclusionsThis is the first study describing metabolic profiling in response to anti-TNF treatments using plasma samples. The study highlighted two different metabolic profiles splitting good responders from non-responders.

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Section: Discussionmentioning

confidence: 99%

Variations in the metabolome in response to disease activity of rheumatoid arthritis

Tatar¹,

Migné

Petera

et al. 2016

BMC Musculoskelet Disord

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“…Activated 13,105,106,171 or malignant 172–175 cells shift from oxidative phosphorylation to glycolysis for ATP generation (FIG. 2).…”

Section: Figurementioning

confidence: 99%

Metabolomics in rheumatic diseases: desperately seeking biomarkers

2016

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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.

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“…Metabolome studies were already performed using serum, plasma or urine samples (Illig et al 2010;Lv et al 2010;Seeger 2009;Zhao et al 2010). However, the use of dried blood has been shown to be advantageous for large scale studies due to the easy sample management, storage and shipment (Leichtle et al 2010;Michopoulos et al 2010).…”

Section: Specimen Collection Conditionsmentioning

confidence: 99%

Preanalytical standardization of amino acid and acylcarnitine metabolite profiling in human blood using tandem mass spectrometry

et al. 2010

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Quantitative metabolite profiling in biological samples has the potential to reflect physiological status and to identify disease associated disturbances in metabolic networks. However, this approach is hampered by a wide range of preanalytical variables. Hence, the aim of our study was to develop a standardized preanalytical protocol for metabolite profiling of amino acids and acylcarnitines in human blood. Amino acids and acylcarnitines were simultaneous analyzed after butylation of 3 lL dried blood or 10 lL whole blood, serum and anticoagulated plasma using electrospray tandem-mass spectrometry. The influence of exogenous and endogenous preanalytical variables was investigated in healthy volunteers. Different sampling materials and anticoagulants for blood taking were investigated. Concentrations of long-chain acylcarnitines were 5-fold higher in EDTA-whole blood or dried whole blood compared to serum and anticoagulated plasma. Significant differences in amino acid concentrations were found for capillary versus venous blood taking. Fasting for 8 h before specimen collection minimized the nutritional influence. Physical activity significantly alters amino acid and short chain acylcarnitine concentrations. As a result of our preanalytical investigation we developed a pre-treatment protocol based on EDTA whole blood dried on filter paper to reduce the preanalytical variability and facilitate reproducible quantitative metabolite profiling in clinical trials.

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Metabolic Changes in Autoimmune Diseases

Cited by 17 publications

References 42 publications

Variations in the metabolome in response to disease activity of rheumatoid arthritis

Variations in the metabolome in response to disease activity of rheumatoid arthritis

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Preanalytical standardization of amino acid and acylcarnitine metabolite profiling in human blood using tandem mass spectrometry

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