biostudies-literatureYue TProgram for Innovative Research Team of The First Affiliated Hospital of USTCStrategic Priority Research Program of the Chinese Academy of SciencesNational Natural Science Foundation of ChinaHefei Comprehensive National Science CenterAnhui Province Science Fund for Distinguished Young ScholarsAnhui Provincial Key Research and Development Program1594https://www.ebi.ac.uk/biostudies/studies/S-EPMC9687663biostudies-literatureUnknown12(11)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. 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. 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. 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.BiomoleculesSerum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.PMC968766381530025XDB38010100202104j07020051BJ910000000581941022CXGG02820704642208085J08Yue TXu MTan HWeng JXu SLuo SShi YfalseSerum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.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. 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. 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. 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.2022-01-01T00:00:00Z2022 Oct2025-04-19T14:44:28.907Z2025-04-19T14:44:28.907ZS-EPMC96876633635894410.3390/biom12111594