Correlation analysis of human urinary metabolites related to gender and obesity using NMR-based metabolic profiling
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Metabolomic studies using human urine have shown that human metabolism is altered by a variety of environmental, cultural, and physiological factors. Comprehensive information about normal human metabolite profiles is necessary for accurate clinical diagnosis of disease and for disease prevention and treatment. In this study, metabolite correlation analyses, using 1H nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics, were performed on human urine to compare metabolic differences based on gender and/or obesity in healthy human subjects. First, we applied partial least squares discriminant analysis to the NMR spectral data set to verify the data’s ability to discriminate by gender and obesity. Then, the differences in metabolite–metabolite correlation between male and female, and between normal and high body mass index (obese) subjects were investigated through pairwise correlations. Creatine and several metabolites, including isoleucine, trans-aconitate, and trimethylamine N-oxide (TMAO), exhibited different quantitative relationships depending on gender. Dimethylamine had a different correlation with glycine and TMAO, based on gender. The correlation of TMAO with amino acids was considerably lower in obese, compared to normal, subjects. We expect that the results will shed light on the metabolic pathways of healthy humans and will assist in the accurate diagnosis of human disease.
Abstract
Metabolomic studies using human urine have shown that human metabolism is altered by a variety of environmental, cultural, and physiological factors. Comprehensive information about normal human metabolite profiles is necessary for accurate clinical diagnosis of disease and for disease prevention and treatment. In this study, metabolite correlation analyses, using 1H nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics, were performed on human urine to compare metabolic differences based on gender and/or obesity in healthy human subjects. First, we applied partial least squares discriminant analysis to the NMR spectral data set to verify the data’s ability to discriminate by gender and obesity. Then, the differences in metabolite–metabolite correlation between male and female, and between normal and high body mass index (obese) subjects were investigated through pairwise correlations. Creatine and several metabolites, including isoleucine, trans-aconitate, and trimethylamine N-oxide (TMAO), exhibited different quantitative relationships depending on gender. Dimethylamine had a different correlation with glycine and TMAO, based on gender. The correlation of TMAO with amino acids was considerably lower in obese, compared to normal, subjects. We expect that the results will shed light on the metabolic pathways of healthy humans and will assist in the accurate diagnosis of human disease.
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- J. Nicholson, , Xenobiotica, 29, pp.1181~ (1999)
- J. Lindon, , Conc. in Magn. Reson, 12, pp.289~ (2000)
- J. Nicholson, , Nat. Rev. Drug Dis, 1, pp.153~ (2002)
- E. Lenz, , J. phar. & biom. Anal, 36, pp.841~ (2004)
- C. Slupsky, , Anal. Chem, 79, pp.6995~ (2007)
- J. Lindon, , Prog. in Nucl. Magn. Reson. Spec, 45, pp.109~ (2004)
- J. Nicholson, , Nat. Rev. Drug Dis, 2, pp.668~ (2003)
- Jeeyoun Jung, "Metabolic profiling study of ketoprofen-induced toxicity", Journal of the Korean Magnetic Resonance Society, 15(1), pp.54~68 (2011), 한국자기공명학회
- 정용재, "^(1)H NMR-based metabolomic study of Cornus officinalis from different geographical origin", Journal of the Korean Magnetic Resonance Society, 15(2), pp.90~103 (2011), 한국자기공명학회
- R. Goodacre, , TRENDS in Biotech, 22, pp.245~ (2004)
- E. Saude, , Metabolomics, 3, pp.439~ (2007)
- J. A. Kraut, , Am. J. Kidney Dis, 45, pp.978~ (2005)
- D. E. Trachtenbarg, , Am. Fam. Physician, 71, pp.1705~ (2005)
- M. Walsh, , Am. J. Clin. Nutr, 84, pp.531~ (2006)
- N. Psihogios, , NMR in Biom, 21, pp.195~ (2008)
- S. Kochhar, , Anal. bioch, 352, pp.274~ (2006)
- E. Lenz, , J. pharm. l & biom. Anal, 33, pp.1103~ (2003)
- C. Zuppi, , Clin. Chim. Acta, 278, pp.75~ (1998)
- J. Nicholson, , Prog. in Nucl. Magn. Reson. Spec, 21, pp.449~ (1989)
- C. Zuppi, , Clin. Chim. Acta, 265, pp.85~ (1997)
- L. Eriksson, "Multi- and Megavariate Data Analysis", pp.43~56 (2001), Umetrics Academy
- M. E. Dumas, , Anal Chem, 78, pp.2199~ (2006)
- E. Holmes, , Anal. Biochem, 220, pp.284~ (1994)
- F. Guneral, , Clin. Chem, 40, pp.862~ (1994)
- D. W. Cockcroft, , Nephron, 16, pp.31~ (1976)
- S. H. Mudd, , Metabolism, 24, pp.721~ (1975)
- K. Oetjen, , Deut. Lebensm. -Rundsch, 95, pp.403~ (1999)
- I. Friis-Liby, , Scand. J. Gastro, 39, pp.864~ (2004)
- T. S. Church, , Gastroenterology, 130, pp.2023~ (2006)
- S. Lowis, , Brit. J. Nutr, 54, pp.43~ (2007)
- S. H. Zeisel, , Annu. Rev. Nutr, 1, pp.95~ (1981)
- S. H. Zeisel, , Cancer Res, 46, pp.6136~ (1986)
- Y. E. Hsia, , J. Clin. Invest, 50, pp.127~ (1971)