Integration of dietary factors and multi-omic signatures of genetic subtypes to understand diabetes heterogeneity

PROJECT SUMMARY/ABSTRACT Type 2 diabetes (T2D) is a heterogeneous disease that is the result of the interaction between environmental and genetic factors. T2D heterogeneity has been described with a set of reproducible T2D genetic subtypes representing the contribution of specific pathways such as insulin resistance and insulin secretion, for T2D risk. However, the molecular basis of these differences and their potential clinical applicability has not been studied. Omic technologies allow us to measure metabolic products (metabolomics) and host environment (gut microbiome) signatures as sources of interindividual variation that can potentially mediate the differences in T2D risk and may be modified by diet. The overall objective of this project is to identify the metabolomic and gut microbiome signatures by which these genetic subtypes contribute to heterogeneity in T2D risk and how they may be affected by diet. I propose to examine 3 Specific Aims: In Aim 1 [K99]: I will define the metabolomic signatures that correlate and mediate the association between T2D genetic subgroups and T2D risk. For this, I will leverage data from five multi-ancestry TOPMed cohorts (N=14,306). In Aim 2 [R00], I will define the gut microbiome signatures that correlate and mediate the association between T2D genetic subgroups and T2D risk in three multi-ancestry cohorts (N=5,016) from the CHARGE consortium. In Aim 3 [R00], I will determine the effect modification of diet on omics signatures (metabolomic and gut microbiome) of T2D genetic subtypes in two settings: observational and after an intervention. For this, I will conduct an interaction analysis to test how omics signatures of T2D genetic subtypes change by dietary patterns in five multi-ancestry cohorts (N=14,306) and how these changes correlate with T2D risk. I also will conduct an interaction analysis to identify the differences between omic signatures of T2D genetic subtypes in response to different diets: nutrient-rich minimally processed diet, typical American diet, and high-fat low-carb diet. Here, I will leverage data from the Nutrition for Precision Health trial module 2 (N=1,500), a multi-ancestry, crossover, randomized study. The successful completion of these aims will yield an integrative model that combines genetic, individual metabolic variation, and environmental exposures and will identify modifiable targets for tailored dietary interventions in multi-ancestry populations. The proposed project is within the scope of the NIDDK’s Strategic Plan for Research Goal 1 (Understanding biological and environmental disease contributors). Together, my expertise, research plan, synergistic proposed training under the mentorship of interdisciplinary key leaders in mediation analysis (Dr. Manning) and gut microbiome (Dr. Khalili, Dr. Qi) and metabolism physiology (Dr. Udler/Dr.Shah) uniquely position me for the completion of these aims and a successful independent career.