Dietary effects on type 2 diabetes risk – Using metabolomics networks to elucidate the underlying mechanisms

• Applicant: Dr. Clemens Wittenbecher
• Subject Area: Nutritional Sciences
• Term: from 2019 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 423863258

A healthful diet protects against type 2 diabetes but the underlying mechanisms are poorly understood. This project aims to identify metabolic pathways that mediate the effects of dietary exposures on diabetes risk.The Nurses‘ Health Study is a prospective cohort study including 121 700 nurses. In this population study conducted by the Harvard University, dietary habits and disease incidence were continuously assessed over the last 40 years. Nowadays, new metabolomics techniques cover a broad spectrum of metabolites in blood specimens and generate accurate snapshots of metabolism. Recently, metabolomics profiles were assessed in a case-control sample nested within the Nurses‘ Health Study, including baseline-healthy women who developed type 2 diabetes in course of the study and a control group that remained disease-free. Such detailed longitudinal information on metabolism, dietary habits and disease incidence implies the unique possibility to observe diet-dependent mechanisms of diabetes development under real-life conditions.This systems epidemiology study comprises four work packages. First, data-driven metabolomics networks will be constructed based on the correlation between metabolites. Then, metabolites will be linked to type 2 diabetes risk. Next, for diabetes-related metabolites, it will be investigated whether they are influenced by dietary exposures that are known to be related to type 2 diabetes risk. The metabolomics networks will be used to differentiate between direct effects on type 2 diabetes risk and indirect effects that merely reflect the correlation with other metabolites. The repeated measurements and the long follow-up time imply the possibility to model change, e.g. relating change in dietary habits to subsequent change in metabolomics profiles. Finally, hypotheses will be generated based on the results of the first three work packages and will then be further investigated with modern causal inference tools. Genetic variants will be used to validate the links between metabolites and diabetes risk in Mendelian randomization studies, and to identify subgroup-specific effects of diet on metabolism in gen-diet interaction analyses. Effects of hypothetical dietary interventions on metabolism and type 2 diabetes risk will be predicted based on the repeated measurements.The expected results will support dietary interventions to prevent type 2 diabetes on the population level and will inform personalized dietary recommendations.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Frank B. Hu, Ph.D.