NDLI: Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.

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Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.

Content Provider	Europe PMC
Author	Yue, Tong Tan, Huiling Shi, Yu Xu, Mengyun Luo, Sihui Weng, Jianping Xu, Suowen
Editor	Bhattacharya, Sanjoy
Copyright Year	2022
Abstract	Background: The process of aging and metabolism are intricately linked, thus rendering the identification of reliable biomarkers related to metabolism crucial for delaying the aging process. However, research of reliable markers that reflect aging profiles based on machine learning is scarce. Methods: Serum samples were obtained from aged mice (18-month-old) and young mice (3-month-old). LC-MS was used to perform a comprehensive analysis of the serum metabolome and machine learning was used to screen potential aging-related biomarkers. Results: In total, aging mice were characterized by 54 different metabolites when compared to control mice with criteria: VIP ≥ 1, q-value < 0.05, and Fold-Change ≥ 1.2 or ≤0.83. These metabolites were mostly involved in fatty acid biosynthesis, cysteine and methionine metabolism, D-glutamine and D-glutamate metabolism, and the citrate cycle (TCA cycle). We merged the comprehensive analysis and four algorithms (LR, GNB, SVM, and RF) to screen aging-related biomarkers, leading to the recognition of oleic acid. In addition, five metabolites were identified as novel aging-related indicators, including oleic acid, citric acid, D-glutamine, trypophol, and L-methionine. Conclusions: Changes in the metabolism of fatty acids and conjugates, organic acids, and amino acids were identified as metabolic dysregulation related to aging. This study revealed the metabolic profile of aging and provided insights into novel potential therapeutic targets for delaying the effects of aging.
Journal	Biomolecules
Volume Number	12
PubMed Central reference number	PMC9687663
Issue Number	11
PubMed reference number	36358944
e-ISSN	2218273X
DOI	10.3390/biom12111594
Language	English
Publisher	MDPI
Publisher Date	2022-10-29
Access Restriction	Open
Rights License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). © 2022 by the authors.
Subject Keyword	aging metabolomics biomarkers machine learning
Content Type	Text
Resource Type	Article
Subject	Biochemistry Molecular Biology

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