FOODMETCH

Development of obesity and risk of chronic diseases is largely influenced by the diet, but the association is hard to grasp because of the challenges of dietary assessment with traditional methods based on questionnaires. Progress in the development of metabolomics and the measurement of the food metabolome in human biological samples led to the identification of new dietary biomarkers as more objective indicators of diet. But to date few dietary biomarkers have been validated in population studies, particularly in children, where they would be most useful. Therefore, the present project aims to comprehensively measure the food metabolome in urine samples from two children cohorts, in order (1) to identify food metabolites related to short-term food intake, (2) to investigate variability of the level of these metabolites (3) to identify biomarkers best predicting habitual food intake and (4) to evaluate longitudinal associations between food metabolites and health outcomes.

The present study is based on the established IDEFICS/I.Family and DONALD cohorts with unique longitudinal data including repeated examinations and repeated biosample collections. In the IDEFICS/I.Family cohort children aged 2 to 9.9 years at baseline were deeply phenotyped at 3 study examinations over a 7 year follow-up period. In the DONALD birth cohort children were annually examined from age 3 months to adulthood. Urine samples repeatedly collected from these cohorts will be analyzed by high-resolution mass spectrometry. Out of several thousand signals detected in 1800 spot urine samples in the IDEFICS/I.Family study and 600 24h-urine samples in the DONALD study, metabolites that are linked to short-term food intake will be identified through analysis of associations with dietary intake data obtained from 24hdietary recall (24h-DR) (IDEFICS/I.Family) and 3d-weighed dietary records (3d-DR) (DONALD) collected on the days of urine collections. We will focus on the most frequently consumed fruit and vegetables, sweet and fatty snacks and sugar-sweetened beverages that have been linked to obesity risk. Temporal variation of food metabolites over 1-4 years will be evaluated from up to 3 repeated urine sample collections. A comparison of 24h-urine versus spot urine samples and use of 24h-DR versus 3d-DR to identify relevant metabolites will be conducted. Next, food metabolites to estimate habitual food intake based on the combined information of different traditional instruments will be studied. In the last step, the prospective association between food metabolites and anthropometric and clinical risk markers will be investigated, and the performance of dietary biomarkers compared with that of traditional approaches based on questionnaires. As two independent cohorts are studied simultaneously results are directly replicated. The exploitation of the food metabolome as proposed here should constitute a major breakthrough to further identify novel and valid dietary biomarkers.