Human metabolic profiles are stably controlled by genetic and environmental variation

Mol Syst Biol. 2011 Aug 30;7:525. doi: 10.1038/msb.2011.57.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Algorithms
  • Biomarkers* / blood
  • Biomarkers* / urine
  • Databases, Genetic
  • European Continental Ancestry Group / genetics*
  • Female
  • Gene-Environment Interaction*
  • Genetic Variation
  • Humans
  • Metabolome / genetics*
  • Middle Aged
  • Models, Statistical
  • Nuclear Magnetic Resonance, Biomolecular / methods*
  • Research Design
  • Sample Size
  • Systems Biology / methods*
  • Twins, Dizygotic / genetics
  • Twins, Monozygotic / genetics

Substances

  • Biomarkers