Human metabolic profiles are stably controlled by genetic and environmental variation.

TitleHuman metabolic profiles are stably controlled by genetic and environmental variation.
Publication TypeJournal Article
Year of Publication2011
AuthorsNicholson G, Rantalainen M, Maher AD, Li JV, Malmodin D, Ahmadi KR, Faber JH, Hallgrímsdóttir IB, Barrett A, Toft H, Krestyaninova M, Viksna J, Neogi S G, Dumas M-E, Sarkans U, Sarkans U, Silverman BW, Donnelly P, Nicholson JK, Allen M, Zondervan KT, Lindon JC, Spector TD, McCarthy MI, Holmes E, Baunsgaard D, Holmes CC
JournalMol Syst Biol
Volume7
Pagination525
Date Published2011
ISSN1744-4292
KeywordsAged, Algorithms, Biological Markers, Databases, Genetic, European Continental Ancestry Group, Female, Gene-Environment Interaction, Genetic Variation, Humans, Metabolome, Middle Aged, Models, Statistical, Nuclear Magnetic Resonance, Biomolecular, Research Design, Sample Size, Systems Biology, Twins, Dizygotic, Twins, Monozygotic
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.

DOI10.1038/msb.2011.57
Alternate JournalMol. Syst. Biol.
PubMed ID21878913