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Integrative metabolomics reveals unique metabolic traits in Guillain-Barre Syndrome and its variants

Authors
Park, Soo JinKim, Jong KukKim, Hyun-HwiYoon, Byeol-A.Ji, Dong YoonLee, Chang-WanKim, Ho JinKim, Kyoung HeonShin, Ha YoungPark, Sung JeanLee, Do Yup
Issue Date
31-Jan-2019
Publisher
NATURE PUBLISHING GROUP
Citation
SCIENTIFIC REPORTS, v.9
Journal Title
SCIENTIFIC REPORTS
Volume
9
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/1956
DOI
10.1038/s41598-018-37572-w
ISSN
2045-2322
Abstract
Guillain-Barre syndrome (GBS) is an acute fatal progressive disease caused by autoimmune mechanism mainly affecting peripheral nervous system. Although the syndrome is clinically sub-classified into several variants, specific biomarker and exact pathomechanism of each subtypes are not well elucidated yet. In current study, integrative metabolomic and lipidomic profiles were acquisitioned from cerebrospinal fluid samples of 86 GBS from three variants and 20 disease controls. And the data were systematically compared to our previous result on inflammatory demyelination disorders of central nervous system (IDDs) and healthy controls. Primary metabolite profiles revealed unique metabolic traits in which 9 and 7 compounds were specifically changed in GBS and IDD, respectively. Next, the biomarker panel with 10 primary metabolites showed a fairly good discrimination power among 3 GBS subtypes, healthy controls, and disease controls (AUCs ranged 0.849-0.999). The robustness of the biomarker panel was vigorously validated by multi-step statistical evaluation. Subsequent lipidomics revealed GBS variant-specific alteration where the significant elevations of lyso-phosphatidylcholines and sphingomyelins were unique to AIDP (acute inflammatory demyelinating polyneuropathy) and AMAN (acute motor axonal neuropathy), respectively. And metabolome-wide multivariate correlation analysis identified potential clinical association between GBS disability scale (Hughes score) and CSF lipids (monoacylglycerols, and sphingomyelins). Finally, Bayesian network analysis of covarianced structures of primary metabolites and lipids proposed metabolic hub and potential biochemical linkage associated with the pathology.
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