Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Targeted metabolomics approach for identification of relapsing-remitting multiple sclerosis markers and evaluation of diagnostic models. / Kasakin, Marat F.; Rogachev, Artem D.; Predtechenskaya, Elena V. и др.
в: MedChemComm, Том 10, № 10, 01.10.2019, стр. 1803-1809.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Targeted metabolomics approach for identification of relapsing-remitting multiple sclerosis markers and evaluation of diagnostic models
AU - Kasakin, Marat F.
AU - Rogachev, Artem D.
AU - Predtechenskaya, Elena V.
AU - Zaigraev, Vladimir J.
AU - Koval, Vladimir V.
AU - Pokrovsky, Andrey G.
N1 - Publisher Copyright: © 2019 The Royal Society of Chemistry. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Multiple sclerosis (MS) is an inflammatory autoimmune disease that causes demyelination of nerve cell axons. This paper is devoted to the study of relapsing-remitting multiple sclerosis (RRMS) biomarkers using an LC-MS/MS-based targeted metabolomics approach and the assessment of changes in the profile of 13 amino acids and 29 acylcarnitines in plasma during the relapse of the disease. A significant increase (p < 0.05) in the concentration of glutamate in plasma in patients with RRMS was detected, while the sum of leucine and isoleucine was reduced. A decrease in the concentration of decenoylcarnitine (C10:1, p < 0.05) was observed among acylcarnitines, and this metabolite was detected as a biomarker for the disease for the first time. Several models based on a single marker or multiple pre-selected markers and multivariate analysis with a dimension reduction technique were compared in their effectiveness for the classification of RRMS and healthy controls. The best results for cross-validation showed models of general linear regression (GLM, AUC = 0.783) and random forest model (RF, AUC = 0.769) based on pre-selected biomarkers. Validation of the models on the test set showed that the RF model based on selected metabolites was the most effective (AUC = 0.72). The results obtained are promising for further development of the system of clinical decision support for the diagnosis of RRMS based on metabolic data.
AB - Multiple sclerosis (MS) is an inflammatory autoimmune disease that causes demyelination of nerve cell axons. This paper is devoted to the study of relapsing-remitting multiple sclerosis (RRMS) biomarkers using an LC-MS/MS-based targeted metabolomics approach and the assessment of changes in the profile of 13 amino acids and 29 acylcarnitines in plasma during the relapse of the disease. A significant increase (p < 0.05) in the concentration of glutamate in plasma in patients with RRMS was detected, while the sum of leucine and isoleucine was reduced. A decrease in the concentration of decenoylcarnitine (C10:1, p < 0.05) was observed among acylcarnitines, and this metabolite was detected as a biomarker for the disease for the first time. Several models based on a single marker or multiple pre-selected markers and multivariate analysis with a dimension reduction technique were compared in their effectiveness for the classification of RRMS and healthy controls. The best results for cross-validation showed models of general linear regression (GLM, AUC = 0.783) and random forest model (RF, AUC = 0.769) based on pre-selected biomarkers. Validation of the models on the test set showed that the RF model based on selected metabolites was the most effective (AUC = 0.72). The results obtained are promising for further development of the system of clinical decision support for the diagnosis of RRMS based on metabolic data.
KW - MECHANISMS
KW - TOXICITY
KW - IMMUNE
UR - http://www.scopus.com/inward/record.url?scp=85073787187&partnerID=8YFLogxK
U2 - 10.1039/c9md00253g
DO - 10.1039/c9md00253g
M3 - Article
C2 - 31803396
AN - SCOPUS:85073787187
VL - 10
SP - 1803
EP - 1809
JO - MedChemComm
JF - MedChemComm
SN - 2040-2503
IS - 10
ER -
ID: 22047296