1482-P: Genome-Scale Metabolic Modeling of Type 2 Diabetes Subtypes Elucidates Unique Metabolic Reactions and Flux Activity

We previously used a soft clustering approach to group type 2 diabetes (T2D)-associated single nucleotide variants (SNVs) based on their associations with phenotypes. In previous work, we demonstrated associations between the resulting five clusters and various clinical phenotypes, outcomes, and tissues. To further understand the mechanistic pathways tied to these potential T2D subtypes, here we incorporate genome-scale metabolic models (GEMs).

Cluster-specific partitioned polygenic risk scores (pPS) were generated for subjects in the GTEx database. Regression analyses of the pPS and gene expression levels identified up- and down-regulated genes for each cluster. These genes were used as constraints on a generic human GEM, thereby creating cluster-specific models. We also created individualized models for GTEx subjects, followed by statistical testing of fluxes between the top and bottom pPS decile groups of each cluster.

Over 120 reactions were significantly altered in each cluster (Wilcoxon, p<0.05), with none significant in more than two clusters. In all clusters, the majority of hits were transport reactions. However, some subsystems were more heavily altered in specific clusters, including cholesterol biosynthesis and omega fatty acid metabolism in the beta-cell and lipodystrophy clusters, respectively. Of the 145 subsystems cataloged in our GEM, 39 contained a reaction that was associated with only one cluster, such as ubiquinone synthesis for the liver-lipid cluster (p=6e-4).

By creating metabolic reconstructions of our T2D clusters, we have gained a systems-level understanding of the clusters’ unique characteristics. Our results pinpoint cluster-specific metabolic reactions with differential flux activity and may therefore potentially be useful for future precision medicine efforts.