Abstract

Urine Biomarker Technology for Human Phenotyping in Community-based Settings – Dietary Exposure

John Draper, Professor, Aberystwyth University

Obtaining objective, self-reported dietary exposure information is challenging as a result of the complexity of the monitoring tools (e.g. Food Frequency Questionnaires), bias and genuine miss-reporting, hindering research efforts to link specific foods to clear population health outcomes. Additionally, within the older members of society there are many vulnerable individuals whom are incapable of precise self-reporting, where providing accurate diagnoses of dietary exposure and nutritional status is particularly important to avoid malnutrition. In controlled interventions we have previously used non-targeted metabolome fingerprinting coupled with semi-automated machine learning data mining to discover food-derived molecules in urine with potential as dietary exposure biomarkers. These data-driven studies revealed that potential biomarkers of more than 20 food component that are strongly represented in the UK diet are candidates for measurement in urine. However collection of urine samples that can accurately represent the eating pattern of an individual in epidemiological studies is challenging as a result of the cyclical nature of eating behaviour and the dynamic, and often rapid, metabolism and excretion of metabolites derived from dietary sources. From a practical perspective it is essential that the sampling methodology not only has minimal impact on the day-to-day activities of participants, but also is data rich in terms of biomarker discovery and/or targeted measurement. The presentation describes progress in development of a simplified, robust, diagnostic urine population screening method acceptable in a community setting that can provide dietary exposure information much closer to point of care.