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Novel statistical and bioinformatic tools for identifying predictive metabolic biomarkers in molecular epidemiology studies

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Title: Novel statistical and bioinformatic tools for identifying predictive metabolic biomarkers in molecular epidemiology studies
Authors: Posma, Joram Matthias
Item Type: Thesis or dissertation
Abstract: A top-down systems biology approach investigating metabolic responses to external stimuli or physiological processes requires multivariate statistical tools to identify metabolites associated with the global biochemical changes in a supra-organism. In this thesis I describe several tools I have developed to improve or supplement currently used methods in molecular epidemiology studies. First, I describe the MetaboNetworks toolbox which is able to create custom, multi-compartmental metabolic reaction networks for a supra-organism, combining both mammalian and microbial reactions. These networks are essentially a summary of the supra-organisms homeostatic signature. Second, I describe a novel statistical spectroscopy approach called STORM which aids in the elucidation of unknown biomarker signals in 1H NMR spectra. Third, I describe the Metabolome-Wide Association Study on obesity in U.S. and U.K. populations. Many novel metabolic associations with obesity are described in a systems framework, among which metabolites associated with energy, skeletal muscle, lipid, amino acid and gut microbial metabolism. Last, I describe a new multivariate approach to adjust for confounders, CA-OPLS. Correcting for confounders is an essential aspect in molecular epidemiology studies as metabolites can be related to a variety of factors such as lifestyle, diet and environmental exposures which or may not be causally related to disease risk. In developing CA-OPLS another aim was to simultaneously eliminate/minimize the effects of different types of sampling bias which are often not taken into account in modelling metabonomics data with current methods.
Content Version: Open Access
Issue Date: Aug-2014
Date Awarded: Nov-2014
URI: http://hdl.handle.net/10044/1/27250
DOI: https://doi.org/10.25560/27250
Supervisor: Nicholson, Jeremy
Elliott, Paul
Sponsor/Funder: Medical Research Council (Great Britain)
Public Health England
Department: Department of Surgery and Cancer
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Medicine PhD theses

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