Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.769377
Title: Targeted and untargeted liquid chromatography-mass spectrometry (LC-MS) metabolic profiling in population cohort studies
Author: Sood, Deepti
ISNI:       0000 0004 7657 5062
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Abstract:
Incorporating liquid chromatography-mass spectrometry (LC-MS) based metabolite profiling into molecular epidemiological studies holds the potential to identify biomarkers that link genetics, environmental exposures (the exposome) and phenotypes to multifactorial diseases. This thesis applied and examined three LC-MS platforms for three different kinds of studies in the context of exposome research. A targeted, fully quantitative LC-MS/MS method was applied to quantify 14 urinary endogenous oestrogen metabolites; the results demonstrated identification of metabolic phenotype for the genetic polymorphism in CYP3A gene associated with breast cancer in pre-menopausal women. A combined targeted LC-MS/MS and direct infusion-MS/MS method were used to explore the metabolic response to prenatal exposures to persistent organic pollutants (POPs) in 1st trimester maternal and cord serum. The results showed an association of POPs with concentration variation in citrulline and diacyl phosphatidylcholines in both maternal and cord serum and kynurenine/tryptophan ratio in mothers indicating altered indoleamine 2, 3-dioxygenase enzyme activity. Finally, an untargeted LC-MS metabolite profiling pilot study was conducted to extend the limited knowledge of the urinary metabolites level temporal variability in healthy children. Despite high precision, the data demonstrated large inter- and intra-individual variation in metabolite levels. Through these studies, the broader challenges for the successful application of LC-MS metabolome analysis in identifying molecular biomarkers from population studies are explored. These specifically include data comparability, lack of longitudinal variability data and pre-analytical biases. In summary, this thesis illustrates the value of LC-MS to exposome research and identifies the strengths and weakness of different experimental strategies used to characterise metabolic phenotypes in population studies.
Supervisor: Keun, Hector ; Want, Elizabeth ; Coen, Muireann Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.769377  DOI:
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