, Volume 8, Issue 2, pp 323–334 | Cite as

Metabolic profiling of gender: Headspace-SPME/GC–MS and 1H NMR analysis of urine

  • Shucha Zhang
  • Lingyan Liu
  • Debora Steffen
  • Tao Ye
  • Daniel Raftery
Original Article


This study aims to investigate the metabolic difference between male and female healthy adults using a combination of GC–MS and NMR metabolomics techniques. While metabolomics has shown wide applications in characterizing the status and progression of many diseases, physiological factors such as gender often contribute high levels of variability that can hinder the detection of biomarkers of interest, such as in disease detection. We carried out a detailed exploration of gender related metabolic profiling of human urine using a Headspace-SPME/GC–MS approach and detected over two hundred peaks. Fifty-nine metabolites were identified using the NIST library. 1H NMR spectroscopy was also utilized, and resulted in the identification of eighteen metabolites. We find that both GC–MS and NMR are able to capture human gender metabolic differences, and their combination allows a significantly better understanding of this difference. Subtle differences between genders are found to be related to the metabolism of fats, amino acids, and TCA cycle intermediates.


Metabolomics Metabolic profiling GC/MS SPME 1H NMR Gender 



This work was supported by Purdue University. Daniel Raftery is a member of the Purdue Center for Cancer Research and Oncological Sciences Center. The authors thank all the volunteers who contributed their urine samples. Loan of GC–MS instrumentation from the Center for Authentic Science Practice in Education, Discovery Learning Center is gratefully acknowledged.

Supplementary material

11306_2011_315_MOESM1_ESM.doc (241 kb)
Supplementary material 1 (DOC 241 kb)


  1. America, A. H., van Geffern, M. H., et al. (2006). Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional LC-MS. Proteomics, 6(2), 641–653.PubMedCrossRefGoogle Scholar
  2. Arthur, C. L., & Pawliszyn, J. (1990). Solid-phase microextraction with thermal-desorption using fused-silica optical fibers. Analytical Chemistry, 62(19), 2145–2148.CrossRefGoogle Scholar
  3. Asiago, V. M., Alvarado, L. Z., et al. (2010). Early detection of recurrent breast cancer using metabolite profiling. Cancer Research, 70(21), 8309–8318.PubMedCrossRefGoogle Scholar
  4. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57, 289–300.Google Scholar
  5. Blaak, E. (2001). Gender differences in fat metabolism. Current Opinion in Clinical Nutrition and Metabolic Care, 4(6), 499–502.PubMedCrossRefGoogle Scholar
  6. Canto, J. G., Shlipak, M. G., et al. (2000). Prevalence, clinical characteristics, and mortality among patients with myocardial infarction presenting without chest pain. JAMA, 283(24), 3223–3229.PubMedCrossRefGoogle Scholar
  7. Chan, E. C. Y., Koh, P. K., et al. (2009). Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). Journal of Proteome Research, 8(1), 352–361.PubMedCrossRefGoogle Scholar
  8. Coen, M., Holmes, E., et al. (2008). NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chemical Research in Toxicology, 21(1), 9–27.PubMedCrossRefGoogle Scholar
  9. Dalgaard, P. (2002). Introductory statistics with R. Berlin: Springer-Verlag.Google Scholar
  10. Ditscheid, B., Keller, S., et al. (2009). Faecal steroid excretion in humans is affected by calcium supplementation and shows gender-specific differences. European Journal of Nutrition, 48(1), 22–30.PubMedCrossRefGoogle Scholar
  11. Djurendic-Brenesel, M., Mimica-Dukic, N., et al. (2010). Gender-related differences in the pharmacokinetics of opiates. Forensic Science International, 194(1–3), 28–33.PubMedCrossRefGoogle Scholar
  12. Ferraro, R., Lillioja, S., et al. (1992). Lower sedentary metabolic rate in women compared with men. Journal of Clinical Investigation, 90(3), 780–784.PubMedCrossRefGoogle Scholar
  13. Franklin, R. B., Kahng, M. W., et al. (1986). The effect of testosterone on citrate synthesis and citrate oxidation and a proposed mechanism for regulation of net citrate production in prostate. Hormone and Metabolic Research, 18(3), 177–181.PubMedCrossRefGoogle Scholar
  14. Gowda, G. A. N., Zhang, S. C., et al. (2008). Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics, 8(5), 617–633.PubMedCrossRefGoogle Scholar
  15. Gu, H., Pan, Z., et al. (2009) 1H NMR metabolomics study of age profiling in children. NMR in Biomedicine, 22(8), 826–833.Google Scholar
  16. Gu, H. W., Chen, H. W., et al. (2007). Monitoring diet effects via biofluids and their implications for metabolomics studies. Analytical Chemistry, 79(1), 89–97.PubMedCrossRefGoogle Scholar
  17. Guillen, N., Acin, S., et al. (2008). Squalene in a sex-dependent manner modulates atherosclerotic lesion which correlates with hepatic fat content in apoE-knockout male mice. Atherosclerosis, 197(1), 72–83.PubMedCrossRefGoogle Scholar
  18. Hines, A., Yeung, W. H., et al. (2007). Comparison of histological, genetic, metabolomics, and lipid-based methods for sex determination in marine mussels. Analytical Biochemistry, 369(2), 175–186.PubMedCrossRefGoogle Scholar
  19. Hodson, M. P., Dear, G. J., et al. (2007). A gender-specific discriminator in Sprague-Dawley rat urine: the deployment of a metabolic profiling strategy for biomarker discovery and identification. Analytical Biochemistry, 362(2), 182–192.PubMedCrossRefGoogle Scholar
  20. Jia, C. R., Luo, Y. Z., et al. (1998). Solid phase microextraction combined with HPLC for determination of metal ions using crown ether as selective extracting reagent. Journal of Microcolumn Separations, 10(2), 167–173.CrossRefGoogle Scholar
  21. Jones, A. W. (2007). Age- and gender-related differences in blood amphetamine concentrations in apprehended drivers: lack of association with clinical evidence of impairment. Addiction, 102(7), 1085–1091.PubMedCrossRefGoogle Scholar
  22. Jones, A. W., Holmgren, A., et al. (2008). Driving under the influence of cannabis: a 10-year study of age and gender differences in the concentrations of tetrahydrocannabinol in blood. Addiction, 103(3), 452–461.PubMedCrossRefGoogle Scholar
  23. Kanehisa, M. (1997). A database for post-genome analysis. Trends in Genetics, 13(9), 375–376.PubMedCrossRefGoogle Scholar
  24. Kennedy, A., Gettys, T. W., et al. (1997). The metabolic significance of leptin in humans: Gender-based differences in relationship to adiposity, insulin sensitivity, and energy expenditure. Journal of Clinical Endocrinology and Metabolism, 82(4), 1293–1300.PubMedCrossRefGoogle Scholar
  25. Kochhar, S., Jacobs, D. M., et al. (2006). Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics. Analytical Biochemistry, 352(2), 274–281.PubMedCrossRefGoogle Scholar
  26. Krisko, I., & Walker, J. B. (1966). Influence of sex hormones of amidinotransferase levels. metabolic control of creatine biosynthesis. Acta Endocrinologica, 53(4), 655.PubMedGoogle Scholar
  27. Lawton, K. A., Berger, A., et al. (2008). Analysis of the adult human plasma metabolome. Pharmacogenomics, 9(4), 383–397.PubMedCrossRefGoogle Scholar
  28. Lindon, J. C., Holmes, E., et al. (2007). Metabonomics in pharmaceutical R & D. FEBS Journal, 274(5), 1140–1151.PubMedCrossRefGoogle Scholar
  29. Lindon, J. C., Nicholson, J. K., et al. (1999). NMR spectroscopy of biofluids. In G. A. Webb (Ed.), Annual Reports on NMR Spectroscopy (Vol. 38, pp. 1–88). Academic Press: London.Google Scholar
  30. Lostroh, A. J. (1968). Regulation by testosterone and insulin of citrate secretion and protein synthesis in explanted mouse prostates. Proceedings of the National Academy of Sciences of the United States of America, 60(4), 1312.PubMedCrossRefGoogle Scholar
  31. Lu, G., Wang, J., et al. (2006). Study on gender difference based on metabolites in urine by ultra high performance liquid chromatography time of flight mass spectrometry. Chinese Journal of Chromatography, 24(2), 109–113.PubMedCrossRefGoogle Scholar
  32. Lu, X., Zhao, X. J., et al. (2008). LC-MS-based metabonomics analysis. Journal of Chromatography B, 866(1–2), 64–76.CrossRefGoogle Scholar
  33. Mittendorfer, B., Horowitz, J. F., et al. (2002). Effect of gender on lipid kinetics during endurance exercise of moderate intensity in untrained subjects. Amerian Journal of Physiology, Endocrinology and Metabolism, 283(1), E58–E65.Google Scholar
  34. Mo, H. P., Harwood, J., et al. (2009). R: A quantitative measure of NMR signal receiving efficiency. Journal of Magnetic Resonance, 200(2), 239–244.PubMedCrossRefGoogle Scholar
  35. Pan, Z. Z., & Raftery, D. (2007). Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Analytical and Bioanalytical Chemistry, 387(2), 525–527.PubMedCrossRefGoogle Scholar
  36. Penn, D. J., Oberzaucher, E., et al. (2007). Individual and gender fingerprints in human body odour. Journal of the Royal Society Interface, 4(13), 331–340.CrossRefGoogle Scholar
  37. Plumb, R., Granger, J., et al. (2003). Metabonomic analysis of mouse urine by liquid-chromatography-time of flight mass spectrometry (LC-TOFMS): detection of strain, diurnal and gender differences. Analyst, 128(7), 819–823.PubMedCrossRefGoogle Scholar
  38. Plumb, R. S., Granger, J. H., et al. (2005). A rapid screening approach to metabonomics using UPLC and oa-TOF mass spectrometry: application to age, gender and diurnal variation in normal/Zucker obese rats and black, white and nude mice. Analyst, 130(6), 844–849.PubMedCrossRefGoogle Scholar
  39. Priego, T., Sánchez, J., Picó, C., & Palou, A. (2008). Sex-differential expression of metabolism-related genes in response to a high-fat diet. Obesity, 16(4), 819–826.PubMedCrossRefGoogle Scholar
  40. Proteggente, A. R., England, T. G., et al. (2002). Gender differences in steady-state levels of oxidative damage to DNA in healthy individuals. Free Radical Research, 36(2), 157–162.PubMedCrossRefGoogle Scholar
  41. Psihogios, N. G., Gazi, I. F., et al. (2008). Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics. NMR in Biomedicine, 21(3), 195–207.PubMedCrossRefGoogle Scholar
  42. Rezzi, S., Ramadan, Z., et al. (2007). Nutritional metabonomics: Applications and perspectives. Journal of Proteome Research, 6(2), 513–525.PubMedCrossRefGoogle Scholar
  43. Slupsky, C. M., Rankin, K. N., et al. (2007). Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Analytical Chemistry, 79(18), 6995–7004.PubMedCrossRefGoogle Scholar
  44. Sreekumar, A., Poisson, L. M., et al. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457(7231), 910–914.PubMedCrossRefGoogle Scholar
  45. Taylor, R. W., Gold, E., et al. (1997). Gender differences in body fat content are present well before puberty. International Journal of Obesity, 21(11), 1082–1084.PubMedCrossRefGoogle Scholar
  46. Teul, J., Ruperez, F. J., et al. (2009). Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints. Journal of Proteome Research, 8(12), 5580–5589.PubMedCrossRefGoogle Scholar
  47. Tikunov, Y., Lommen, A., et al. (2005). A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles. Plant Physiology, 139(3), 1125–1137.PubMedCrossRefGoogle Scholar
  48. Viant, M. R., Bearden, D. W., et al. (2009). International NMR-based environmental metabolomics intercomparison exercise. Environmental Science and Technology, 43(1), 219–225.PubMedCrossRefGoogle Scholar
  49. Wikoff, W. R., Pendyala, G., et al. (2008). Metabolomic analysis of the cerebrospinal fluid reveals changes in phospholipase expression in the CNS of SIV-infected macaques. Journal of Clinical Investigation, 118(7), 2661–2669.PubMedCrossRefGoogle Scholar
  50. Wishart, D. S., Tzur, D., et al. (2007). HMDB: The human metabolome database. Nucleic Acids Research, 35, D521–D526.PubMedCrossRefGoogle Scholar
  51. Zhang, S., Gowda, G. A. N., et al. (2010). Advances in NMR-based biofluid analysis and metabolite profiling. Analyst, 135(7), 1490–1498.Google Scholar
  52. Zhang, S. C., Nagana Gowda, G. A., et al. (2008). Correlative and quantitative 1H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats. Analytical Biochemistry, 383(1), 76–84.PubMedCrossRefGoogle Scholar
  53. Zhu, Y. F., & Evans, M. I. (2001). Estrogen modulates the expression of l-arginine:glycine amidinotransferase in chick liver. Molecular and Cellular Biochemistry, 221(1–2), 139–145.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Shucha Zhang
    • 2
  • Lingyan Liu
    • 1
  • Debora Steffen
    • 3
  • Tao Ye
    • 1
  • Daniel Raftery
    • 1
  1. 1.Department of ChemistryPurdue UniversityWest LafayetteUSA
  2. 2.Division of Clinical ResearchFred Hutchinson Cancer Research CenterSeattleUSA
  3. 3.Discovery Learning Research Center, Purdue UniversityWest LafayetteUSA

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