Molecular Systems Biology (Aug 2011)

Human metabolic profiles are stably controlled by genetic and environmental variation

  • George Nicholson,
  • Mattias Rantalainen,
  • Anthony D Maher,
  • Jia V Li,
  • Daniel Malmodin,
  • Kourosh R Ahmadi,
  • Johan H Faber,
  • Ingileif B Hallgrímsdóttir,
  • Amy Barrett,
  • Henrik Toft,
  • Maria Krestyaninova,
  • Juris Viksna,
  • Sudeshna Guha Neogi,
  • Marc‐Emmanuel Dumas,
  • Ugis Sarkans,
  • The MolPAGE Consortium,
  • Bernard W Silverman,
  • Peter Donnelly,
  • Jeremy K Nicholson,
  • Maxine Allen,
  • Krina T Zondervan,
  • John C Lindon,
  • Tim D Spector,
  • Mark I McCarthy,
  • Elaine Holmes,
  • Dorrit Baunsgaard,
  • Chris C Holmes

DOI
https://doi.org/10.1038/msb.2011.57
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 12

Abstract

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Abstract 1H Nuclear Magnetic Resonance spectroscopy (1H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top‐down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non‐identical twin pairs donated plasma and urine samples longitudinally. We acquired 1H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common‐environmental), individual‐environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual‐environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in 1H NMR‐detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker‐discovery studies. We provide a power‐calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect 1H NMR‐based biomarkers quantifying predisposition to disease.

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