Abstract
ObjectiveThe objective of this study was to investigate whether clinical metabolomics, which is increasingly applied in population-based and epidemiological studies, can be used to provide analytical evidence of exposures, and whether such information can be useful to strengthen and/or complement corresponding clinical database entries, taking drug use as an example. Study design and settingLiquid chromatography-mass spectrometry (LC-MS) metabolomics analyses were performed on urine from 100 randomly-selected control subjects (50% females) from the TransplantLines Food and Nutrition Biobank and Cohort Study (NCT identifier ‘NCT02811835’), and drugs were identified through spectral library searching and targeted signal extraction. ResultsIn 83 subjects for whom drug use information was available, 22 expected and 26 unexpected prescription-only drugs were identified, while 28 expected prescription-only drugs remained undetected. In addition, 7 prescription-only drugs were found in 17 subjects for whom drug use information was unavailable, and 58 over-the-counter drugs were identified in all 100 subjects. ConclusionMolecular evidence for many drugs could be retrieved from LC-MS metabolomics data, which could be useful to complement and strengthen epidemiological databases given that considerable discrepancies were found between analytically-identified drugs and drugs listed in the available clinical database.
Highlights
In 83 subjects for whom drug use information was available, 22 expected and 26 unexpected prescription-only drugs were identified, while 28 expected prescription-only drugs remained undetected
Of 83 subjects for whom drug use information was available in the clinical database, drugs were identified in 58 subjects (70%) when including and 35 subjects (42%) when excluding paracetamol
Of the other 20 expected but not identified drugs, some are known to be fully excreted by the liver and not expected in urine [16]; some are extensively metabolised by the liver before renal excretion and not expected in urine in their native form [17,18,19,20]; some cannot be differentiated from an endogenous human metabolite due to structural similarity; and others are too small to be identified with sufficient confidence using the described workflow, with one lacking a reference spectrum in any of the spectral libraries employed in this study
Summary
Epidemiological studies mostly rely on data obtained through anamnesis, questionnaires, and basic measurements (eg, anthropometric parameters, laboratory markers), and more recently, on complex data obtained using “omics” technologies [3,4,5,6]. Omics technologies aim to provide comprehensive understanding of biological systems, for example by studying “stored” genetic information (ie, genomics) and “transcribed” (ie, transcriptomics) or “translated” genetic information (ie, proteomics) [3,4]. More functional understanding can be obtained by studying low-molecularweight compounds (ie, metabolomics), which may reflect biological changes due to genetic and environmental factors, using analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) [7]. The term metabolomics is misleading because metabolomics workflows measure metabolic precursors, intermediates, and end products, and compounds that do not undergo metabolism. While the term metabolomics may intuitively refer to endogenous compounds (eg, amino acids, carbohydrates, fatty acids), exogenous compounds
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