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<p>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 &gt; 1) in the OPLS-DA model and significant differences (<i>P</i> &lt; 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 (<i>P</i> &lt; 0.05, VIP &gt; 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.</p>

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Research on metabolic characteristics of multiple sclerosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月7日

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

摘要

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.