Metabolomics from a Controlled Feeding Study and Large Cohorts to Identify Diet-Associated Biomarkers, Augmented by Genetics to Infer Causality for Cardiometabolic Outcomes

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Diet has a strong relationship with health and illness. Many studies have correlated variation in dietary composition with cardiometabolic outcomes such as obesity, type 2 diabetes (T2D) or cardiovascular disease (CVD. However, correlation does not prove causation. A key challenge is to determine which (if any) of the effects of diet are causally linked to long-term health outcomes. Classically, randomized controlled trials (RCTs) are used to prove causality. However, such trials are essentially infeasible for assessing mechanism and causal effects of diet on long-term health outcomes. Dietary modifications alter many variables, with myriad effects on physiology. Furthermore, compliance typically wanes much more quickly than outcomes accrue, and robust biomarkers are needed that indicate that a participant has complied with a diet and that the diet has had the desired effect. Fortunately, comprehensive metabolite profiling, including in the setting of a large, well-controlled dietary RCT and population-based cohorts, augmented by human genetics, offers a potential route forward to identify robust biomarkers and to assess the predictive and causal relationships between dietary patterns, biomarkers, and long term cardiometabolic outcomes. We propose to take advantage of new metabolomics and genetic methods to identify robust biomarkers of a key dietary pattern, carbohydrate-to-fat ratio, and to use these biomarkers to examine the relationship between this dietary pattern and cardiometabolic outcomes (obesity, T2D, and CVD). We will achieve three specific aims. In SA#1, we will build on exciting preliminary results and generate comprehensive untargeted metabolite profiling data from a large carefully controlled dietary RCT, to identify metabolites (known and unknown) that are associated with dietary carbohydrate-to-fat ratio in weight-stable subjects. In SA#2, we will leverage large existing population-based cohorts with dietary and metabolomic data to validate these metabolites as robust biomarkers. In SA#3, we will use longitudinal data to test whether these biomarkers predict cardiometabolic outcomes, and will employ an approach called Mendelian randomization (the genetic equivalent of a RCT) to assess whether the diet-associated metabolites are causal for obesity, T2D or CVD. We will deploy multiple innovative approaches, including metabolite profiling in a dietary RCT, large genetic studies of untargeted metabolite profiling data, methods to avoid known difficulties with Mendelian randomization, and also methods to harmonize unidentified metabolites from untargeted profiling across studies. Successful completion of these aims will develop robust biomarkers of a key dietary pattern (carbohydrate-to- fat ratio), test whether these biomarkers are useful predictors of long term health outcomes, and test causality of the physiological responses to this dietary pattern for cardiometabolic outcomes. This approach will provide a paradigm for improving nutritional interventional studies, for better understanding the relationship between diet and health, and ultimately enhance dietary treatments to prevent and treat chronic diseases.