Metabolomic study of aging in mouse plasma by gas chromatography–mass spectrometry

• Metabolic profiling analysis of amino acids, organic acids and fatty acids in plasma of young (8 weeks) and old (72 weeks) mice by GC–MS.
• Total 68 metabolites of 27 amino acids, 17 organic acids and 24 fatty acids were screened in plasma samples of young and old mice.
• Levels of six amino acids, five organic acids and three n-3 polyunsaturated fatty acids were significantly different in young and old mice.
• Star graphic patterns were readily distinguishable for young and old groups.
• The present metabolic profiling and star graphic pattern methods will be useful for understanding the complexity of biochemical events in aging.

Metabolomic analysis of aging was performed in plasma samples of young (8 weeks) and old (72 weeks) mice as ethoxycarbonyl/methoxime/tert-butyldimethylsilyl derivatives by gas chromatography–mass spectrometry (GC–MS). As new approaches, study of altered metabolism from aging was attempted by simultaneous profiling analysis of amino acids (AAs), organic acids (OAs) and fatty acids (FAs) by GC–MS in a single run combined with pattern analysis. As a result, 27 amino acids (AAs), 17 organic acids (OAs) and 24 fatty acids (FAs) were positively screened with large variations in plasma samples. Among altered metabolites, levels of six AAs (proline, methionine, 4-hydroxyproline, pipecolic acid, glutamic acid, α-aminoadipic acid) as neurotransmetters and nutrients, five OAs (2-hydroxybutyric acid, 2-hydroxyglutaric acid, cis-aconitic acid citric acid, isocitric acid) including intermediate metabolites in the TCA cycle, and three n-3 polyunsaturated FAs (PUFAs) of α-octadecatrienoic acid, eicosapentaenoic acid and docosahexaenoic acid as potential biomarkers were significantly different between young and old groups. Their levels were normalized to the corresponding mean values of the young group and then plotted into star symbol patterns, which were clearly distinct compared with numerical data and readily distinguishable for young and old groups. Thus, the present metabolomic screening and the star pattern recognition method might be useful for understanding the complexity of biochemical events in aging.