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

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.
(1) Use existing biobanked urine samples and dietary records to determine which dietary components are efficiently monitored by biomarkers (e.g. some fatty foods such as dairy and eggs and some starchy foods such as rice, pasta, white bread contain few secondary metabolites and are unlikely to provide urine biomarkers) and which need to be assessed by a mini-FFQ.
(2) Adapt eNutri tool to integrate biomarker data with a mini-FFQ covering foods not likely to be well measured by biomarkers and a few foods already well covered by specific biomarkers to assess reliability of data derived from specific participants.
(3) Validate the performance of the combined tool to assess habitual diet and estimate nutrient intake in a small intervention study and conduct focus groups to determine the acceptability (study participants) and suitability (healthcare professionals and researchers) for widespread use in future nutrition studies or national dietary surveys.
(4) Use metabolite profiling of urine from bio-banked samples to analyse differential metabolism of diet-related metabolites to identify biomarkers likely to be sensitive to nutritional metabotype differences in a study population from e.g. gut microbiome fermentation or liver/endocyte biotransformation.
(5) Develop a novel 'Healthy Eating Index' utilising data generated using the combined tool (biomarker data and mini-FFQ), for use in future large-scale clinical intervention studies.