Evidence map›Paper›PMID 41794883›Full record

ArticleScientific reports2026

Research on metabolic characteristics of multiple sclerosis.

Diyuan Wang, Wenguang Feng, Haibin Wang, Xuhua Ying, Wenqiang Cai, Longcheng Guo, Jinteng Hou, Tingjia Yang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Diyuan Wang *Department of Neurology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, Inner Mongolia Autonomous Region, China.
Wenguang Feng *Department of Orthopedics, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, Inner Mongolia Autonomous Region, China.
Haibin Wang *Inner Mongolia Medical University, Hohhot, 010110, Inner Mongolia Autonomous Region, China.
Xuhua YingDepartment of Neurology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, Inner Mongolia Autonomous Region, China.
Wenqiang CaiDepartment of Internal Medicine, Inner Mongolia Autonomous Region General Hospital of the People's Armed Police Force, Hohhot, China.
Longcheng GuoDepartment of Orthopedics, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, Inner Mongolia Autonomous Region, China.
Jinteng HouInner Mongolia Medical University, Hohhot, 010110, Inner Mongolia Autonomous Region, China.
Tingjia YangDepartment of Neurology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, Inner Mongolia Autonomous Region, China. 13190525045@163.com.

Funding

the Inner Mongolia Medical University Affiliated Hospital Young Key Personnel Development Program 2022NYFYFG004
6 · The paper itself

Abstract

By comparing the fecal metabolite profiles of multiple sclerosis patients with those of healthy controls, to screen for significantly different metabolites and provide a basis for the discovery of potential biomarkers; further analyze the molecular functions of differential metabolites and their enriched metabolic pathways, aiming to reveal disease-related metabolic abnormalities and offer clues for the study of multiple sclerosis pathogenesis and treatment strategies. Fecal samples were collected from 37 multiple sclerosis patients and 30 age- and gender-matched healthy controls. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) was used to detect fecal metabolite profiles. Data quality was assessed through total ion chromatograms (TIC) of quality control (QC) samples and principal component analysis (PCA) of all samples. Multivariate statistical analyses, including PCA, partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were performed. Differential metabolites were screened based on variable importance in projection (VIP > 1) in the OPLS-DA model and significant differences (P < 0.05) in t-tests. Further analyses, including cluster analysis, correlation analysis, and KEGG pathway enrichment analysis, were conducted to elucidate the biological functions and metabolic pathway characteristics of the differential metabolites. A total of 552 metabolites were identified in the multiple sclerosis group and the healthy control group, with 56 differential metabolites (P < 0.05, VIP > 1) screened. Among them, 8 metabolites, including L-pyroglutamic acid, 4-pyridoxic acid, 2-oxoadipic acid, L-phenylalanine, and 3-methylphenylacetic acid, were upregulated; 48 metabolites, including propionic acid, isobutyric acid, oleic acid, α-linolenic acid, adenosine, 9,10-DiHOME, palmitic acid, succinate, lithocholic acid, pentadecanoic acid, L-isoleucine, androsterone sulfate, L-leucine, 3-(3-hydroxyphenyl)propionic acid, nicotinic acid, L-arginine, isomaltose, thymidine, 4-aminobutyric acid, uracil, choline, hydroxyarachidonic acid, trehalose, 4-oxoretinol, 3-methylhistidine, DL-norvaline, creatinine, and capsaicin, were downregulated. KEGG pathway analysis revealed that the differential metabolites were mainly enriched in pathways such as protein digestion and absorption, nicotinate and nicotinamide metabolism, central carbon metabolism in cancer, ABC transporters, mTOR signaling pathway, amino acid biosynthesis, and aminoacyl-tRNA biosynthesis. Significant differences exist in the fecal metabolite profiles between multiple sclerosis patients and healthy controls, with amino acids and fatty acids being the most enriched differential metabolite categories, suggesting potential biomarker value. Metabolic pathways such as protein digestion and absorption, central carbon metabolism in cancer, nicotinate and nicotinamide metabolism, ABC transporters, mTOR signaling pathway, amino acid biosynthesis, and aminoacyl-tRNA biosynthesis may be involved in disease development. This study provides metabolomic evidence for uncovering the pathophysiological mechanisms and potential therapeutic targets of multiple sclerosis.

Indexed as

MetabolomeMetabolomicsMultiple SclerosisAdultBiomarkersCase-Control StudiesChromatography, High Pressure LiquidFecesFemaleHumansMaleMetabolic Networks and PathwaysPrincipal Component AnalysisBiomarkersFecesLiquid chromatography-mass spectrometryMetabolomicsMultiple sclerosis

Identifiers

PMID41794883
PMCPMC13086947

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.