Skip to main content
SpringerLink
Log in

Metabolomics and Molecular Imaging in the Post-genomic Era

  • Chapter
  • First Online:
P5 Medicine and Justice

  • 630 Accesses

Abstract

Metabolomics and Molecular Imaging are important tools in targeted medicine for better understanding disease pathoetiology and etiopathogenesis, as well as for improved diagnostics and therapy. Advances in analytical biochemistry have recently made it possible to obtain global snapshots of metabolism. In particular, the combination of different molecular omics techniques shows major differentiations in the metabolic make-up of the human population. Metabolites may determine the risk for a certain medical phenotype, the response to a given drug treatment, and the reaction to a nutritional intervention or environmental challenge. Molecular imaging (MI) is based on the idea that diagnostic tracers are concentrated in specific areas because of their interaction with molecular species that are distinctly present in a diseased state. Current molecular imaging techniques include positron emission tomography (PET), magnetic resonance imaging (MRI), ultrasonography (US), and computed tomography (CT). MI is non-invasive, allows serial investigations and can monitor the therapeutic efficacy of drugs during the entire course of treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Daviss B (2005) Growing pains for metabolomics. The Scientist 19(8):25–28

    Google Scholar 

  2. Nicholson JK, Lindon JC (2008) Systems biology: metabonomics. Nature 455(7216):1054–1056. doi:10.1038/4551054a

    Article  CAS  PubMed  Google Scholar 

  3. Wang-Sattler R, Yu Z, Herder C et al (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8:615. doi:10.1038/msb.2012.43

    Article  PubMed  PubMed Central  Google Scholar 

  4. Xu T, Brandmaier S, Messias AC et al (2015) Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes. Diab Care 38(10):1858–1867. doi:10.2337/dc15-0658

    Article  CAS  Google Scholar 

  5. Kastenmüller G, Raffler J, Gieger C, Suhre K (2015) Genetics of human metabolism: an update. Hum Mol Genet 24(R1):R93–R101. doi:10.1093/hmg/ddv263

    Article  PubMed  PubMed Central  Google Scholar 

  6. Link H, Fuhrer T, Gerosa L, Zamboni N, Sauer U (2015) Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat Methods 12(11):1091–1097. doi:10.1038/nmeth.3584

    Article  CAS  PubMed  Google Scholar 

  7. Society of Nuclear Medicine and Molecular Imaging. http://www.snmmi.org/AboutSNMMI/CMIIT.aspx?ItemNumber=6559

  8. Schork NJ, Murray SS, Frazer KA, Topol EJ (2009) Common vs rare allele hypotheses for complex diseases. Curr Opin Genet Dev 19(3):212–219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hindorff LA, Sethupathy P, Junkins HA, Ramosa EM, Mehtac JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:9362–9367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang-Sattler R, Prehn C, Altmaier E, Kastenmüller G, Kato BS, Mewes HW, Meitinger T, de Angelis MH, Kronenberg F, Soranzo N, Wichmann HE, Spector TD, Adamski J, Suhre K (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42(2):137–141

    Article  CAS  PubMed  Google Scholar 

  11. Gieger C, Geistlinger L, Altmaier E, Hrabé de Angelis M, Kronenberg F, Meitinger T, Mewes HW, Wichmann HE, Weinberger KM, Adamski J, Illig T, Suhre K (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4(11):e1000282

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kathiresan S, Melander O, Guiducci C et al (2008) Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 40(2):189–197. doi:10.1038/ng.75

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Willer CJ, Sanna S, Jackson AU et al (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40(2):161–169. doi:10.1038/ng.76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mootha VK, Hirschhorn JN (2010) Inborn variation in metabolism. Nat Genet 42(2):97–98. doi:10.1038/ng0210-97

    Article  CAS  PubMed  Google Scholar 

  15. Mittelstrass K, Ried JS, Yu Z, Krumsiek J, Gieger C, Prehn C, Roemisch-Margl W, Polonikov A, Peters A, Theis FJ, Meitinger T, Kronenberg F, Weidinger S, Wichmann HE, Suhre K, Wang-Sattler R, Adamski J, Illig T (2011) Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet 7(8):e1002215. doi:10.1371/journal.pgen.1002215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hughes TP, Branford S (2009) Monitoring disease response to tyrosine kinase inhibitor therapy in CML. Hematology Am Soc Hematol Educ Program 477–487

    Google Scholar 

  17. Nicholson JK, Wilson ID, Lindon JC (2011) Pharmacometabonomics as an effector for personalized medicine. Pharmacogenomics 12(1):103–111

    Article  CAS  PubMed  Google Scholar 

  18. He Y, Yu Z, Giegling I, Xie L, Hartmann AM, Prehn C, Adamski J, Kahn R, Li Y, Illig T, Wang-Sattler R, Rujescu D (2012) Schizophrenia shows a unique metabolomics signature in plasma. Transl Psychiatry 14(2):e149. doi:10.1038

    Article  Google Scholar 

  19. Altmaier E, Menni C, Heier M, Meisinger C, Thorand B, Quell J, Kobl M, Römisch-Margl w, Valdes AM, Mangino M, Waldenberger M, Strauch K, Illig T, Adamski J, Spector T, Gieger C, Suhre K, Kastenmüller G (2016) The pharmacogenetic footprint of ACE inhibition: a population-based metabolomics study. PLoS ONE 11(4):e0153163

    Article  PubMed  PubMed Central  Google Scholar 

  20. Yu Z, Kastenmüller G, He Y, Belcredi P, Möller G, Prehn C, Mendes J, Wahl S, Roemisch-Margl W, Ceglarek U, Polonikov A, Dahmen N, Prokisch H, Xie L, Li Y, Wichmann HE, Peters A, Kronenberg F, Suhre K, Adamski J, Illig T, Wang-Sattler R (2011) Differences between human plasma and serum metabolite profiles. PLoS ONE 6(7):e21230. doi:10.1371/journal.pone.0021230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C, Römisch-Margl W, Lattka E, Gieger C, Soranzo N, Heinrich J, Standl M, Thiering E, Mittelstraß K, Wichmann HE, Peters A, Suhre K, Li Y, Adamski J, Spector TD, Illig T, Wang-Sattler R (2012) Human serum metabolic profiles are age dependent. Aging Cell 11(6):960–967. doi:10.1111/j.1474-9726.2012.00865.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Anton G, Wilson R, Yu ZH, Prehn C, Zukunft S, Adamski J, Heier M, Meisinger C, Römisch-Margl W, Wang-Sattler R, Hveem K, Wolfenbuttel B, Peters A, Kastenmüller G, Waldenberger M (2015) Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples. PLoS ONE 10(3):e0121495. doi:10.1371/journal.pone.0121495

    Article  PubMed  PubMed Central  Google Scholar 

  23. Gauberti M, Montagne A, Quenault A, Vivien D (2014) Molecular magnetic resonance imaging of brain-immune interactions. Front Cell Neurosci 8:389. doi:10.3389/fncel.2014.00389

    Article  PubMed  PubMed Central  Google Scholar 

  24. Holmes E, Tsang TM, Huang JT, Leweke FM, Koethe D, Gerth CW, Nolden BM, Gross S, Schreiber D, Nicholson JK, Bahn S (2006) Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Med 3(8):e327

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kim SH, Lee JH, Hyun H, Ashitate Y, Park G, Robichaud K, Lunsford E, Lee SJ, Khang G, Choi HS (2013) Near-infrared fluorescence imaging for noninvasive trafficking of scaffold degradation. Sci Rep 3:1198. doi:10.1038/srep01198

    Article  PubMed  PubMed Central  Google Scholar 

  26. Xu R, Huang L, Wei W, Chen X, Zhang X, Zhang X (2016) Real-time imaging and tracking of ultrastable organic dye nanoparticles in living cells. Biomaterials 93:38–47. doi:10.1016/j.biomaterials.2016.03.045

    Article  CAS  PubMed  Google Scholar 

  27. Lüneburg N, Lieb W, Zeller T et al (2014) Genome-wide association study of L-arginine and dimethylarginines reveals novel metabolic pathway for symmetric dimethylarginine. Circ Cardiovasc Genet 7(6):864–872. doi:10.1161/CIRCGENETICS.113.000264

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schaarschmidt H, Ellinghaus D, Rodríguez E, Kretschmer A, Baurecht H, Lipinski S, Meyer-Hoffert U, Harder J, Lieb W, Novak N, Fölster-Holst R, Esparza-Gordillo J, Marenholz I, Ruschendorf F, Hubner N, Reischl E, Waldenberger M, Gieger C, Illig T, Kabesch M, Zhang XJ, Xiao FL, Lee YA, Franke A, Weidinger S (2015) A genome-wide association study reveals 2 new susceptibility loci for atopic dermatitis. J Allergy Clin Immunol 136(3):802–826. doi:10.1016/j.jaci.2015.01.047

    Article  CAS  PubMed  Google Scholar 

  29. Shungin D, Winkler TW, Croteau-Chonka DC et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518(7538):187–196. doi:10.1038/nature14132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kilpeläinen TO, Carli JF, Skowronski AA et al (2016) Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat Commun 7:10494. doi:10.1038/ncomms10494

    Article  PubMed  PubMed Central  Google Scholar 

  31. Maxmen A (2011) Exome sequencing deciphers rare diseases. Cell 144:635–637

    Article  CAS  PubMed  Google Scholar 

  32. Newgard CB, Attie AD (2010) Getting biological about the genetics of diabetes. Nat Med 16(4):388–391. doi:10.1038/nm0410-388

    Article  CAS  PubMed  Google Scholar 

  33. Suhre K, Raffler J, Kastenmüller G (2016) Biochemical insights from population studies with genetics and metabolomics. Arch Biochem Biophys 589:168–176

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hannover Unified Biobank, Clinical Research Center Hannover, Feodor-Lynen-Str. 15, 30625, Hannover, Germany

    Linda Illig & Thomas Illig

Authors
  1. Linda Illig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Illig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Illig .

Editor information

Editors and Affiliations

  1. Department of Legal and Occupational Medicine, Toxicology and Public Health, University-Hospital of Padova, Padova, Italy

    Santo Davide Ferrara

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Illig, L., Illig, T. (2017). Metabolomics and Molecular Imaging in the Post-genomic Era. In: Ferrara, S. (eds) P5 Medicine and Justice. Springer, Cham. https://doi.org/10.1007/978-3-319-67092-8_2

Download citation

Publish with us

Policies and ethics

Search

Navigation