ABSTRACT: This data set is downloaded from MetaboLights (http://www.ebi.ac.uk/metabolights/) accession number MTBLS263 Abstract:Metabolites present in human blood document individual physiological states influenced by genetic, epigenetic, and lifestyle factors. Using high-resolution liquid chromatography-mass spectrometry (LC-MS), we performed nontargeted, quantitative metabolomics analysis in blood of 15 young (29 ± 4 y of age) and 15 elderly (81 ± 7 y of age) individuals. Coefficients of variation (CV = SD/mean) were obtained for 126 blood metabolites of all 30 donors. Fifty-five RBC-enriched metabolites, for which metabolomics studies have been scarce, are highlighted here. We found 14 blood compounds that show remarkable age-related increases or decreases; they include 1,5-anhydroglucitol, dimethyl-guanosine, acetyl-carnosine, carnosine, ophthalmic acid, UDP-acetyl-glucosamine, N-acetyl-arginine, N6-acetyl-lysine, pantothenate, citrulline, leucine, isoleucine, NAD+, and NADP+. Six of them are RBC-enriched, suggesting that RBC metabolomics is highly valuable for human aging research. Age differences are partly explained by a decrease in antioxidant production or increasing inefficiency of urea metabolism among the elderly. Pearson’s coefficients demonstrated that some age-related compounds are correlated, suggesting that aging affects them concomitantly. Although our CV values are mostly consistent with those CVs previously published, we here report previously unidentified CVs of 51 blood compounds. Compounds having moderate to high CV values (0.4–2.5) are often modified. Compounds having low CV values, such as ATP and glutathione, may be related to various diseases because their concentrations are strictly controlled, and changes in them would compromise health. Thus, human blood is a rich source of information about individual metabolic differences. Validation of experimental procedures was performed as follows. First, we evaluated the contribution of sample handling to within-sample variation. The same blood sample preparation was injected 3x into the LC-MS at 80-min intervals. We thus obtained within-sample CVs (designated as CVwi), which were less than 0.1 in 107 of 126 compounds (85%). Only 10 compounds showed CVwi of 0.1-0.2, while 9 had CVwi >0.2. Second, we also examined sample-to-sample variation caused by sample preparation. Three samples were independently prepared from the same blood sample (one person), and CVs thus determined were designated as CVss. CVss values of HEPES and PIPES in the blood samples were very small (0.06~0.08 for HEPES and 0.04~0.08 for PIPES). The great majority (116/126=92%) of CVss were <0.3. Blood samples drawn from four volunteers four times within 24 h are available under accession number MTBLS264. Whole blood metabolomic data from all 30 subjects are available under accession number MTBLS265. Plasma and RBC data from all 30 subjects can be found under MTBLS266 and MTBLS267, respectively.
Chaleckis 