Everett, Jeremy 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, 40 (10). pp. 763-773. ISSN 0165-6147 (Print), 1873-3735 (Online) (doi:https://doi.org/10.1016/j.tips.2019.08.004)

Preview

PDF (Author's Accepted Manuscript) 24971 EVERETT_Unified_Conceptual_Framework_Metabolic_Phenotyping_(AAM)_2019.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview

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 / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Science (SCI)
Last Modified:	19 Nov 2024 14:26
URI:	http://gala.gre.ac.uk/id/eprint/24971

Actions (login required)

View Item

Downloads