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A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis

A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis

Everett, Jeremy R. ORCID: 0000-0003-1550-4482, Holmes, Elaine, Veselkov, Kirill A., Lindon, John C. and Nicholson, Jeremy K. (2019) A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis. Trends in Pharmacological Sciences. ISSN 0165-6147 (Print), 1873-3735 (Online) (doi:https://doi.org/10.1016/j.tips.2019.08.004)

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Abstract

Understanding metabotype (multicomponent metabolic characteristics) variation can help generate new diagnostic and prognostic biomarkers and models with the potential to impact patient management. Here we present a suite of conceptual approaches for the generation, analysis and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of: i) metabolite levels; ii) metabolic trajectories; iii) metabolic entropies and iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalised medicine, with the aim of improving clinical outcomes for patients, but it will have value and utility in all areas of metabolic profiling well beyond this exemplar.

Item Type: Article
Uncontrolled Keywords: metabonomics, metabolomics, metabotypes, metabolic entropy, systems medicine, personalised medicine, pharmacometabonomics, precision medicine
Subjects: R Medicine > RS Pharmacy and materia medica
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Pharmaceutical, Chemical and Environmental Sciences
Last Modified: 02 Oct 2019 11:41
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24971

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