Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography—Mass Spectrometry

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.

Multi-Omics Analysis Reveals Age-Related Microbial and Metabolite Alterations in Non-Human Primates

“…Our multi-omics results indicated age-related differences in amino acid metabolism, with a notable decline in several amino acids crucial for protein synthesis and a decrease in the relative abundance of nutrient-absorbing genera during aging. Above all, levels of L-leucine, L-methionine, L-serine, L-tyrosine, and 4-hydroxyproline in serum showed significant negative correlation with age, in line with previous studies [42]. A key charac-teristic of aging is the decline in overall protein synthesis [43].…”

FBXO22 is a potential therapeutic target for recurrent chondrosarcoma

“…1 A ). In the proteomic analysis results, we referenced literature related to protein expression studies [23] , [24] , [25] , [26] , [27] , [28] , [29] , using a fold change greater than 1.2 or less than 0.83 as the selection criteria for differential protein genes. A total of 166 differentially expressed (DE) proteins were identified, including 117 upregulated proteins and 49 downregulated proteins (FC > 1.2 or FC < 0.83, P < 0.05) ( Fig.…”

Untargeted Analysis of Serum Metabolomes in Dogs with Exocrine Pancreatic Insufficiency

“…There were small differences in the mean ages of the EPI and healthy controls groups, though the differences were not statistically significant. Serum metabolomes are known to vary with age in dogs and other mammals [55][56][57]. To understand whether differences in age were likely to have influenced our results, we attempted to detect correlations among relative abundances of serum metabolites and age.…”