Integration of food intake biomarker data with minimal self-reported dietary information to assess eating behaviour and evaluate nutrient intake

For clear understanding of the relationship between food exposure, nutrient status or disease risk there is a need for an accurate assessment of dietary intake combined with measures of nutritional metabotype that account for variability in food composition x individual metabolic handling of nutrients and foods interactions. Dietary exposure is recorded traditionally using self-reported measures such as Food Frequency Questionnaires (FFQs). Unfortunately, self-reporting methods are subjective, and misreporting is common which greatly reduces the value of nutritional research. In addition, capturing self-reported information generally requires substantial input from trained researchers to assist study/survey participants due to the complexity of modern diets which adds cost and significant participant burden to nutrition research. To help counteract these problems and provide scope for digitising and scaling up of research studies, the team in Reading University has developed and validated an online FFQ (eNutri). The FFQ includes food portion photographs and is automatically analysed via a web-application and greatly reduces the burden to both study participants and researchers.
The team in Aberystwyth has demonstrated that metabolites derived from foods present in spot (First Morning Void) urine samples collected in the home and posted to an analytical laboratory provide biomarkers for dietary exposure and individual nutritional metabotype. Although coverage already includes a wide range of foods of high public health significance (including e.g. red meat, oily fish, wholegrain, fruits, leafy vegetables), it is unlikely that biomarkers for exposure to common starchy or high fat foods will be present in urine. Working with collaborators in a range of clinical trials/dietary surveys (including Reading) a biobank of urine samples has been developed, complete with substantial meta-data relating to food intake, that will be ideal for validating the performance of urine biomarkers and investigating the most effective way of sampling populations to assess habitual diet and nutritional metabotype
Common to research in both Aberystwyth and Reading is the drive to develop and validate an objective Diet Quality Index (DQI: similar to the Alternative Healthy Eating Index developed in US) for the UK population based currently on urine biomarker and/or FFQ data. We hypothesise that objective dietary exposure data and a 'Healthy Eating Index' reflective of true nutritional status and individual nutritional metabotype can be developed by integration and analysis of dietary exposure data derived from both urine metabolome and a mini-FFQ developed using the eNutri FFQ database.