Monitoring Diet Effects via Biofluids and Their Implications for Metabolomics Studies

• Published in: Analytical chemistry (Washington) 2007-01, Vol.79 (1), p.89-97
• Main Authors: Gu, Haiwei, Chen, Huanwen, Pan, Zhengzheng, Jackson, Ayanna U, Talaty, Nari, Xi, Bowei, Kissinger, Candice, Duda, Chester, Mann, Doug, Raftery, Daniel, Cooks, R. Graham
• Format: Article
• Language: English
• Subjects: Alloxan - metabolism; Amino Acids - metabolism; Analytical chemistry; Animal Feed; Animals; Biochemistry; Biomarkers - urine; Chemistry; Diet; Exact sciences and technology; Fasting; Gluconates - metabolism; Glucose - metabolism; Kynurenine - analogs & derivatives; Kynurenine - metabolism; Magnetic Resonance Spectroscopy - methods; Metabolic Networks and Pathways; Metabolism; Multivariate Analysis; NMR; Nuclear magnetic resonance; Principal Component Analysis; Rats; Rats, Inbred Strains; Rodents; Spectrometric and optical methods; Spectrometry, Mass, Electrospray Ionization - methods; Urea - metabolism; Urine
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac060946c

The effect of diet on metabolites found in rat urine samples has been investigated using nuclear magnetic resonance (NMR) and a new ambient ionization mass spectrometry experiment, extractive electrospray ionization mass spectrometry (EESI-MS). Urine samples from rats with three different dietary regimens were readily distinguished using multivariate statistical analysis on metabolites detected by NMR and MS. To observe the effect of diet on metabolic pathways, metabolites related to specific pathways were also investigated using multivariate statistical analysis. Discrimination is increased by making observations on restricted compound sets. Changes in diet at 24-h intervals led to predictable changes in the spectral data. Principal component analysis was used to separate the rats into groups according to their different dietary regimens using the full NMR, EESI-MS data or restricted sets of peaks in the mass spectra corresponding only to metabolites found in the urea cycle and metabolism of amino groups pathway. By contrast, multivariate analysis of variance from the score plots showed that metabolites of purine metabolism obscure the classification relative to the full metabolite set. These results suggest that it may be possible to reduce the number of statistical variables used by monitoring the biochemical variability of particular pathways. It should also be possible by this procedure to reduce the effect of diet in the biofluid samples for such purposes as disease detection.