Detection of Urinary Drug Metabolite (Xenometabolome) Signatures in Molecular Epidemiology Studies via Statistical Total Correlation (NMR) Spectroscopy

Elaine Holmes; Ruey Leng Loo; Olivier Cloarec; Muireann Coen; Huiru Tang; Elaine Maibaum; Stephen Bruce; Queenie Chan; Paul Elliott; Jeremiah Stamler; Ian D. Wilson; John C. Lindon; Jeremy K. Nicholson

doi:10.1021/ac062305n

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Detection of Urinary Drug Metabolite (Xenometabolome) Signatures in Molecular Epidemiology Studies via Statistical Total Correlation (NMR) Spectroscopy

Journal article

Peer reviewed

Detection of Urinary Drug Metabolite (Xenometabolome) Signatures in Molecular Epidemiology Studies via Statistical Total Correlation (NMR) Spectroscopy

Elaine Holmes, Ruey Leng Loo, Olivier Cloarec, Muireann Coen, Huiru Tang, Elaine Maibaum, Stephen Bruce, Queenie Chan, Paul Elliott, Jeremiah Stamler, …

Analytical chemistry (Washington), Vol.79(7), pp.2629-2640

2007

DOI: https://doi.org/10.1021/ac062305n

PMCID: PMC6688492

PMID: 17323917

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Abstract

Conjugate acid-base pairs

Metabolism

Nuclear magnetic resonance spectroscopy

Pharmaceuticals

Western populations use prescription and nonprescription drugs extensively, but large-scale population usage is rarely assessed objectively in epidemiological studies. Here we apply statistical methods to characterize structural pathway connectivities of metabolites of commonly used drugs detected routinely in 1H NMR spectra of urine in a human population study. 1H NMR spectra were measured for two groups of urine samples obtained from U.S. participants in a known population study. The novel application of a statistical total correlation spectroscopy (STOCSY) approach enabled rapid identification of the major and certain minor drug metabolites in common use in the population, in particular, from acetaminophen and ibuprofen metabolites. This work shows that statistical connectivities between drug metabolites can be established in routine “high-throughput” NMR screening of human samples from participants who have randomly self-administered drugs. This approach should be of value in considering interpopulation patterns of drug metabolism in epidemiological and pharmacogenetic studies.

Details

Title: Detection of Urinary Drug Metabolite (Xenometabolome) Signatures in Molecular Epidemiology Studies via Statistical Total Correlation (NMR) Spectroscopy
Authors/Creators: Elaine Holmes - Imperial College London
Ruey Leng Loo - Wuhan Institute of Physics and Mathematics
Olivier Cloarec - Imperial College London
Muireann Coen - Imperial College London
Huiru Tang - Wuhan Institute of Physics and Mathematics
Elaine Maibaum - Wuhan Institute of Physics and Mathematics
Stephen Bruce - Wuhan Institute of Physics and Mathematics
Queenie Chan - Imperial College London
Paul Elliott - Imperial College London
Jeremiah Stamler - Wuhan Institute of Physics and Mathematics
Ian D. Wilson - Imperial College London
John C. Lindon - Wuhan Institute of Physics and Mathematics
Jeremy K. Nicholson - Wuhan Institute of Physics and Mathematics
Publication Details: Analytical chemistry (Washington), Vol.79(7), pp.2629-2640
Identifiers: 991005598618907891
Murdoch Affiliation: Centre for Computational and Systems Medicine
Language: English
Resource Type: Journal article

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Collaboration types: Industry collaboration; Domestic collaboration; International collaboration
Citation topics: 2 Chemistry; 2.211 Mass Spectrometry; 2.211.990 Metabolomics
Web Of Science research areas: Chemistry, Analytical
ESI research areas: Chemistry