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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2629-2640
Number of pages12
JournalAnalytical Chemistry
Volume79
Issue number7
DOIs
StatePublished - Apr 1 2007

ASJC Scopus subject areas

  • Analytical Chemistry

Fingerprint Dive into the research topics of 'Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy'. Together they form a unique fingerprint.

Cite this