Urine metabolomics in rats after administration of ketamine.
In this study, we developed a urine metabonomic method, based on gas chromatography-mass spectrometry (GC-MS), to evaluate the effect of ketamine on rats. Pattern recognition analysis, including both principal component analysis and partial least squares discriminate analysis revealed that ketamine (50 mg/kg) induced metabolic perturbations. Compared with the control group, at day 7, the level of alanine, butanoic acid, glutamine, butanedioic, trimethylsiloxy, L-aspartic acid, D-glucose, cholesterol, acetamide, and oleic acid of the ketamine group was increased, while the level of 2,3,4-trihydroxybutyric acid, benzeneacetic acid, threitol, ribitol, xylitol, and glycine decreased. At day 14, the level of alanine, ethanedioic acid, L-proline, glycerol, tetradecanoic acid, l-serine, l-phenylalanine, L-aspartic acid, d-glucose, cholesterol, heptadecanoic acid, and acetamide in rat urine of the ketamine group was increased, while the 2,3,4-trihydroxybutyric acid, benzeneacetic acid, d-ribose, threitol, ribitol, glycine, pyrazine, and oleic acid levels decreased. Our results indicate that metabonomic methods based on GC-MS may be useful to elucidate ketamine abuse, through the exploration of biomarkers.
['Wen|Congcong|C|', 'Zhang|Meiling|M|', 'Ma|Jianshe|J|', 'Hu|Lufeng|L|', 'Wang|Xianqin|X|', 'Lin|Guanyang|G|']
D000818:Animals D015415:Biomarkers D001711:Biotransformation D018691:Excitatory Amino Acid Antagonists D008401:Gas Chromatography-Mass Spectrometry D007274:Injections, Intraperitoneal D007649:Ketamine D016018:Least-Squares Analysis D008297:Male D055432:Metabolomics D010363:Pattern Recognition, Automated D025341:Principal Component Analysis D017207:Rats, Sprague-Dawley D015813:Substance Abuse Detection
GC-MS abuse biomarker metabolite