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Query: UMLS:C0014070 (encephalomyelitis)
 13,017 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl-experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood-brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease.
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PMID:Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion. 2239 51

Relapsing experimental allergic encephalomyelitis (Cr-EAE) is commonly used to explore the pathogenesis and efficacy of new therapies for MS, but it is unclear whether the metabolome of Cr-EAE is comparable to human multiple sclerosis (MS). For MS, the diagnosis and staging can be achieved by metabolomics on blood using a combination of magnetic resonance spectroscopy and partial least squares discriminant analysis (PLS-DA). Here, we sought to discover whether this approach could be used to differentiate between sequential disease states in Cr-EAE and whether the same metabolites would be discriminatory. Urine and plasma samples were obtained at different time-points from a clinically relevant model of MS. Using PLS-DA modelling for the urine samples furnished some predictive models, but could not discriminate between all disease states. However, PLS-DA modelling of the plasma samples was able to distinguish between animals with clinically silent disease (day 10, 28) and animals with active disease (day 14, 38). We were also able to distinguish Cr-EAE mice from naive mice at all-time points and control mice, treated with complete Freund's adjuvant alone, at day 14 and 38. Key metabolites that underpin these models included fatty acids, glucose and taurine. Two of these metabolites, fatty acids and glucose, were also key metabolites in separating relapsing-remitting MS from secondary-progressive MS in the human study. These results demonstrate the sensitivity of this metabolomics approach for distinguishing between different disease states. Furthermore, some, but not all, of the changes in metabolites were conserved in humans and the mouse model, which could be useful for future drug development.
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PMID:NMR-Based Metabolomics Separates the Distinct Stages of Disease in a Chronic Relapsing Model of Multiple Sclerosis. 2615 56