Advertisement

Metabolomics

, Volume 10, Issue 6, pp 1268–1292 | Cite as

Metabolomic approaches to the normal aging process

  • Soo Hyun Lee
  • Sungha Park
  • Han-Soo Kim
  • Byung Hwa JungEmail author
Original Article

Abstract

Aging is a multifaceted process involving the accumulation of diverse deleterious changes in biological systems over time, so significant alterations in cellular metabolism are detected throughout aging. In the present study, the metabolic processes relevant to the normal aging process were investigated via non-targeted metabolomics using liquid chromatography–mass spectrometry. To exclude physiological and environmental differences, the metabolic profiles and the relevant metabolic pathways were analyzed in plasma from two separate study groups comprising two distinctly aged cohorts of healthy individuals, the elderly and the younger. The first group was recruited from an urban hospital, and the second group was recruited from a rural community. Alterations in fatty acid beta-oxidation, glycerophospholipid metabolism, and sphingolipid metabolism were identified as significant metabolic pathways relevant to normal aging. It was also found that sphingosine in sphingolipid metabolism, long-chain acylcarnitines in beta-oxidation, and lysophosphatidylcholines (LysoPCs) in glycerophospholipid metabolism could be critical candidate metabolites in the aging process. These results suggest that the metabolic profile of the healthiest individuals could be associated with the normal function of mitochondria, the primary organelle of redox homeostasis, as indicated by their low acylcarnitine to l-carnitine ratio and low levels of LysoPCs and sphingosine in plasma. The present study provides a critical contribution to the entire picture of the aging process.

Keywords

Metabolomics Aging UPLC–QTOF-MS Acylcarnitines Lysophospholipids Sphingolipid metabolism 

Notes

Acknowledgments

This study was supported by the Creative Fusion Research Program through the Creative Allied Project funded by the Korea Research Council of Fundamental Science and Technology (CAP-12-1), the Bio-Synergy Research Project (NRF-2013M3A9C4078145) of the Ministry of Science, ICT and Future Planning through the National Research Foundation and the Korea Institute of Science and Technology (KIST). We would like to especially thank Prof. Hyun Ok Kim (Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, Korea) for her help with the serum biochemical analyses.

Supplementary material

11306_2014_663_MOESM1_ESM.docx (2 mb)
Supplementary material 1 (DOCX 2084 kb)

References

  1. Beekman, M., Blanche, H., Perola, M., Hervonen, A., Bezrukov, V., Sikora, E., et al. (2013). Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study. Aging Cell, 12(2), 184–193. doi: 10.1111/acel.12039.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bruunsgaard, H., Pedersen, M., & Pedersen, B. K. (2001). Aging and proinflammatory cytokines. Current Opinion in Hematology, 8(3), 131–136.CrossRefPubMedGoogle Scholar
  3. Bylesjo, M., Eriksson, D., Sjodin, A., Jansson, S., Moritz, T., & Trygg, J. (2007). Orthogonal projections to latent structures as a strategy for microarray data normalization. BMC Bioinformatics, 8, 207. doi: 10.1186/1471-2105-8-207.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chalmers, R. A., Roe, C. R., Stacey, T. E., & Hoppel, C. L. (1984). Urinary excretion of l-carnitine and acylcarnitines by patients with disorders of organic acid metabolism: Evidence for secondary insufficiency of l-carnitine. Pediatric Research, 18(12), 1325–1328.CrossRefPubMedGoogle Scholar
  5. Choe, M., Jackson, C., & Yu, B. P. (1995). Lipid peroxidation contributes to age-related membrane rigidity. Free Radical Biology and Medicine, 18(6), 977–984.CrossRefPubMedGoogle Scholar
  6. Chung, H. Y., Cesari, M., Anton, S., Marzetti, E., Giovannini, S., Seo, A. Y., et al. (2009). Molecular inflammation: Underpinnings of aging and age-related diseases. Ageing Research Reviews, 8(1), 18–30. doi: 10.1016/j.arr.2008.07.002.CrossRefPubMedGoogle Scholar
  7. Cooper, R. A. (1978). Influence of increased membrane cholesterol on membrane fluidity and cell function in human red blood cells. Journal of Supramolecular Structure, 8(4), 413–430. doi: 10.1002/jss.400080404.CrossRefPubMedGoogle Scholar
  8. D’Adamo, P., Ulivi, S., Beneduci, A., Pontoni, G., Capasso, G., Lanzara, C., et al. (2010). Metabonomics and population studies: Age-related amino acids excretion and inferring networks through the study of urine samples in two Italian isolated populations. Amino Acids, 38(1), 65–73. doi: 10.1007/s00726-008-0205-8.CrossRefPubMedGoogle Scholar
  9. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7), 1060–1083. doi: 10.1038/nprot.2011.335.CrossRefPubMedGoogle Scholar
  10. Edsall, L. C., Van Brocklyn, J. R., Cuvillier, O., Kleuser, B., & Spiegel, S. (1998). N,N-dimethylsphingosine is a potent competitive inhibitor of sphingosine kinase but not of protein kinase C: Modulation of cellular levels of sphingosine 1-phosphate and ceramide. Biochemistry, 37(37), 12892–12898. doi: 10.1021/bi980744d.CrossRefPubMedGoogle Scholar
  11. Findeisen, H. M., Pearson, K. J., Gizard, F., Zhao, Y., Qing, H., Jones, K. L., et al. (2011). Oxidative stress accumulates in adipose tissue during aging and inhibits adipogenesis. PLoS ONE, 6(4), e18532. doi: 10.1371/journal.pone.0018532.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Fouque, D., Holt, S., Guebre-Egziabher, F., Nakamura, K., Vianey-Saban, C., Hadj-Aissa, A., et al. (2006). Relationship between serum carnitine, acylcarnitines, and renal function in patients with chronic renal disease. Journal of Renal Nutrition, 16(2), 125–131. doi: 10.1053/j.jrn.2006.01.004.CrossRefPubMedGoogle Scholar
  13. Gelinas, D. S., & McLaurin, J. (2005). PPAR-alpha expression inversely correlates with inflammatory cytokines IL-1beta and TNF-alpha in aging rats. Neurochemical Research, 30(11), 1369–1375. doi: 10.1007/s11064-005-8341-y.CrossRefPubMedGoogle Scholar
  14. Gika, H. G., Macpherson, E., Theodoridis, G. A., & Wilson, I. D. (2008a). Evaluation of the repeatability of ultra-performance liquid chromatography–TOF-MS for global metabolic profiling of human urine samples. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 871(2), 299–305. doi: 10.1016/j.jchromb.2008.05.048.CrossRefPubMedGoogle Scholar
  15. Gika, H. G., Theodoridis, G. A., Earll, M., & Wilson, I. D. (2012). A QC approach to the determination of day-to-day reproducibility and robustness of LC–MS methods for global metabolite profiling in metabonomics/metabolomics. Bioanalysis, 4(18), 2239–2247. doi: 10.4155/bio.12.212.CrossRefPubMedGoogle Scholar
  16. Gika, H. G., Theodoridis, G. A., & Wilson, I. D. (2008b). Liquid chromatography and ultra-performance liquid chromatography–mass spectrometry fingerprinting of human urine: Sample stability under different handling and storage conditions for metabonomics studies. Journal of Chromatography A, 1189(1–2), 314–322. doi: 10.1016/j.chroma.2007.10.066.CrossRefPubMedGoogle Scholar
  17. Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. (2007). Within-day reproducibility of an HPLC–MS-based method for metabonomic analysis: Application to human urine. Journal of Proteome Research, 6(8), 3291–3303. doi: 10.1021/pr070183p.CrossRefPubMedGoogle Scholar
  18. Gonzalez-Covarrubias, V., Beekman, M., Uh, H. W., Dane, A., Troost, J., Paliukhovich, I., et al. (2013). Lipidomics of familial longevity. Aging Cell, 12(3), 426–434. doi: 10.1111/acel.12064.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Gregersen, N. (1985). The acyl-CoA dehydrogenation deficiencies. Recent advances in the enzymic characterization and understanding of the metabolic and pathophysiological disturbances in patients with acyl-CoA dehydrogenation deficiencies. Scandinavian Journal of Clinical and Laboratory Investigation. Supplementum, 174, 1–60.PubMedGoogle Scholar
  20. Grolleau-Julius, A., Ray, D., & Yung, R. L. (2010). The role of epigenetics in aging and autoimmunity. Clinical Reviews in Allergy and Immunology, 39(1), 42–50. doi: 10.1007/s12016-009-8169-3.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Harman, D. (1956). Aging: A theory based on free radical and radiation chemistry. The Journal of Gerontology, 11(3), 298–300.CrossRefPubMedGoogle Scholar
  22. Hiona, A., & Leeuwenburgh, C. (2008). The role of mitochondrial DNA mutations in aging and sarcopenia: Implications for the mitochondrial vicious cycle theory of aging. Experimental Gerontology, 43(1), 24–33. doi: 10.1016/j.exger.2007.10.001.CrossRefPubMedGoogle Scholar
  23. Hong, S. E., Heo, H. S., Kim, D. H., Kim, M. S., Kim, C. H., Lee, J., et al. (2010). Revealing system-level correlations between aging and calorie restriction using a mouse transcriptome. Age, 32(1), 15–30. doi: 10.1007/s11357-009-9106-3.CrossRefPubMedGoogle Scholar
  24. Knight, J. A. (2000). The biochemistry of aging. Advances in Clinical Chemistry, 35, 1–62.CrossRefPubMedGoogle Scholar
  25. Kregel, K. C., & Zhang, H. J. (2007). An integrated view of oxidative stress in aging: Basic mechanisms, functional effects, and pathological considerations. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 292(1), R18–R36. doi: 10.1152/ajpregu.00327.2006.CrossRefPubMedGoogle Scholar
  26. Kristal, B. S., & Shurubor, Y. I. (2005). Metabolomics: Opening another window into aging. Science of Aging Knowledge Environment, 2005(26), pe19. doi: 10.1126/sageke.2005.26.pe19.CrossRefPubMedGoogle Scholar
  27. Lai, L., Michopoulos, F., Gika, H., Theodoridis, G., Wilkinson, R. W., Odedra, R., et al. (2010). Methodological considerations in the development of HPLC–MS methods for the analysis of rodent plasma for metabonomic studies. Molecular Biosystems, 6(1), 108–120. doi: 10.1039/b910482h.CrossRefPubMedGoogle Scholar
  28. Lawton, K. A., Berger, A., Mitchell, M., Milgram, K. E., Evans, A. M., Guo, L., et al. (2008). Analysis of the adult human plasma metabolome. Pharmacogenomics, 9(4), 383–397. doi: 10.2217/14622416.9.4.383.CrossRefPubMedGoogle Scholar
  29. Lehmann, R., Zhao, X., Weigert, C., Simon, P., Fehrenbach, E., Fritsche, J., et al. (2010). Medium chain acylcarnitines dominate the metabolite pattern in humans under moderate intensity exercise and support lipid oxidation. PLoS ONE, 5(7), e11519. doi: 10.1371/journal.pone.0011519.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Leslie, C. C. (1997). Properties and regulation of cytosolic phospholipase A2. The Journal of Biological Chemistry, 272(27), 16709–16712.CrossRefPubMedGoogle Scholar
  31. Moyes, K. M., Drackley, J. K., Morin, D. E., Bionaz, M., Rodriguez-Zas, S. L., Everts, R. E., et al. (2009). Gene network and pathway analysis of bovine mammary tissue challenged with Streptococcus uberis reveals induction of cell proliferation and inhibition of PPARgamma signaling as potential mechanism for the negative relationships between immune response and lipid metabolism. BMC Genomics, 10, 542. doi: 10.1186/1471-2164-10-542.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Pearson, K. (1909). Determination of the coefficient of correlation. Science, 30(757), 23–25. doi: 10.1126/science.30.757.23.CrossRefPubMedGoogle Scholar
  33. Pellkofer, R., & Sandhoff, K. (1980). Halothane increases membrane fluidity and stimulates sphingomyelin degradation by membrane-bound neutral sphingomyelinase of synaptosomal plasma membranes from calf brain already at clinical concentrations. Journal of Neurochemistry, 34(4), 988–992.CrossRefPubMedGoogle Scholar
  34. Pohjantahti-Maaroos, H., Palomaki, A., Kankkunen, P., Laitinen, R., Husgafvel, S., & Oksanen, K. (2010). Circulating oxidized low-density lipoproteins and arterial elasticity: Comparison between men with metabolic syndrome and physically active counterparts. Cardiovascular Diabetology, 9, 41. doi: 10.1186/1475-2840-9-41.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Psihogios, N. G., Gazi, I. F., Elisaf, M. S., Seferiadis, K. I., & Bairaktari, E. T. (2008). Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics. NMR in Biomedicine, 21(3), 195–207. doi: 10.1002/nbm.1176.CrossRefPubMedGoogle Scholar
  36. Qin, Z. X., Zhu, H. Y., & Hu, Y. H. (2009). Effects of lysophosphatidylcholine on beta-amyloid-induced neuronal apoptosis. Acta Pharmacologica Sinica, 30(4), 388–395. doi: 10.1038/aps.2009.25.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Rebouche, C. J., & Seim, H. (1998). Carnitine metabolism and its regulation in microorganisms and mammals. Annual Review of Nutrition, 18, 39–61. doi: 10.1146/annurev.nutr.18.1.39.CrossRefPubMedGoogle Scholar
  38. Sacket, S. J., Chung, H. Y., Okajima, F., & Im, D. S. (2009). Increase in sphingolipid catabolic enzyme activity during aging. Acta Pharmacologica Sinica, 30(10), 1454–1461. doi: 10.1038/aps.2009.136.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Sangster, T., Major, H., Plumb, R., Wilson, A. J., & Wilson, I. D. (2006). A pragmatic and readily implemented quality control strategy for HPLC–MS and GC–MS-based metabonomic analysis. The Analyst, 131(10), 1075–1078. doi: 10.1039/b604498k.CrossRefPubMedGoogle Scholar
  40. Sheikh, A. M., & Nagai, A. (2011). Lysophosphatidylcholine modulates fibril formation of amyloid beta peptide. The FEBS Journal, 278(4), 634–642. doi: 10.1111/j.1742-4658.2010.07984.x.CrossRefPubMedGoogle Scholar
  41. Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., et al. (2007). Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Analytical Chemistry, 79(18), 6995–7004. doi: 10.1021/ac0708588.CrossRefPubMedGoogle Scholar
  42. Steinbrecher, U. P., Parthasarathy, S., Leake, D. S., Witztum, J. L., & Steinberg, D. (1984). Modification of low density lipoprotein by endothelial cells involves lipid peroxidation and degradation of low density lipoprotein phospholipids. Proceedings of the National Academy of Sciences of the United States of America, 81(12), 3883–3887.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Suhre, K., & Gieger, C. (2012). Genetic variation in metabolic phenotypes: Study designs and applications. Nature Reviews Genetics, 13(11), 759–769. doi: 10.1038/nrg3314.CrossRefPubMedGoogle Scholar
  44. Sung, B., Park, S., Yu, B. P., & Chung, H. Y. (2006). Amelioration of age-related inflammation and oxidative stress by PPARgamma activator: Suppression of NF-kappaB by 2,4-thiazolidinedione. Experimental Gerontology, 41(6), 590–599. doi: 10.1016/j.exger.2006.04.005.CrossRefPubMedGoogle Scholar
  45. Wick, G., & Grubeck-Loebenstein, B. (1997). Primary and secondary alterations of immune reactivity in the elderly: Impact of dietary factors and disease. Immunological Reviews, 160, 171–184.CrossRefPubMedGoogle Scholar
  46. Wick, G., Huber, L. A., Xu, Q. B., Jarosch, E., Schonitzer, D., & Jurgens, G. (1991). The decline of the immune response during aging: The role of an altered lipid metabolism. Annals of the New York Academy of Sciences, 621, 277–290.CrossRefPubMedGoogle Scholar
  47. Won, J. S., & Singh, I. (2006). Sphingolipid signaling and redox regulation. Free Radical Biology and Medicine, 40(11), 1875–1888. doi: 10.1016/j.freeradbiomed.2006.01.035.CrossRefPubMedGoogle Scholar
  48. Woodward, M., Rumley, A., Lowe, G. D., & Tunstall-Pedoe, H. (2003). C-reactive protein: Associations with haematological variables, cardiovascular risk factors and prevalent cardiovascular disease. British Journal of Haematology, 122(1), 135–141.CrossRefPubMedGoogle Scholar
  49. Wu, D., Ren, Z., Pae, M., Guo, W., Cui, X., Merrill, A. H., et al. (2007). Aging up-regulates expression of inflammatory mediators in mouse adipose tissue. Journal of Immunology, 179(7), 4829–4839.CrossRefGoogle Scholar
  50. Xia, J., Mandal, R., Sinelnikov, I. V., Broadhurst, D., & Wishart, D. S. (2012). MetaboAnalyst 2.0—A comprehensive server for metabolomic data analysis. Nucleic Acids Research, 40(Web Server issue), W127–W133. doi: 10.1093/nar/gks374.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Xing, J., Chen, X., Sun, Y., Luan, Y., & Zhong, D. (2005). Interaction of baicalin and baicalein with antibiotics in the gastrointestinal tract. The Journal of Pharmacy and Pharmacology, 57(6), 743–750. doi: 10.1211/0022357056244.CrossRefPubMedGoogle Scholar
  52. Yu, B. P., Suescun, E. A., & Yang, S. Y. (1992). Effect of age-related lipid peroxidation on membrane fluidity and phospholipase A2: Modulation by dietary restriction. Mechanisms of Ageing and Development, 65(1), 17–33.CrossRefPubMedGoogle Scholar
  53. Yu, Z., Zhai, G., Singmann, P., He, Y., Xu, T., Prehn, C., et al. (2012). Human serum metabolic profiles are age dependent. Aging Cell, 11(6), 960–967. doi: 10.1111/j.1474-9726.2012.00865.x.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Zou, Y., Kim, D. H., Jung, K. J., Heo, H. S., Kim, C. H., Baik, H. S., et al. (2009). Lysophosphatidylcholine enhances oxidative stress via the 5-lipoxygenase pathway in rat aorta during aging. Rejuvenation Research, 12(1), 15–24. doi: 10.1089/rej.2008.0807.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Soo Hyun Lee
    • 1
  • Sungha Park
    • 2
    • 3
    • 4
  • Han-Soo Kim
    • 4
  • Byung Hwa Jung
    • 5
    • 6
    Email author
  1. 1.Department of Medical Records and Health Information Management, College of Nursing and HealthKongju National UniversityKongjuRepublic of Korea
  2. 2.Division of Cardiology, Yonsei Cardiovascular CenterYonsei University College of MedicineSeoulRepublic of Korea
  3. 3.Laboratory of Immunology and Infectious Diseases, Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and TechnologyTaejonRepublic of Korea
  4. 4.Department of Laboratory Medicine and Cell Therapy CenterYonsei University College of MedicineSeoulRepublic of Korea
  5. 5.Molecular Recognition Research CenterKorea Institute of Science and TechnologySeoulRepublic of Korea
  6. 6.University of Science and TechnologyTaejonRepublic of Korea

Personalised recommendations