University of California, San Francisco (UCSF)

Integrating the Metabolome with the Transcriptome and miRNAome in Underactive Adults at Risk for Type 2 Diabetes

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The mechanisms underlying risk for Type 2 Diabetes (T2D) are not the same across individuals, and therefore risk-reducing interventions are unlikely to work equally well across all individuals. At the cellular level, various biologic systems interact in the manifestation of disease. Beyond the genetic code, which can predispose individuals toward developing T2D, more dynamic –omic measures can also determine to what extent genes are expressed and their subsequent gene products and metabolites.

These complex biologic systems are interdependent but typically studied separately. An integrated multi-omics approach may provide a better understanding of risk for T2D because it captures dynamic responses to the environment and behaviors. Alterations in these multi-omic mechanisms may precede clinically detectable risk for T2D and may provide greater insights about the underlying mechanisms and potential therapeutic targets.

Our central hypothesis is that multi-omics clusters will classify subgroups of individuals at risk for T2D with differing underlying mechanisms, and then those clusters will further predict responders (>6mg/dL decrease in FBG) versus non-responders to a behavioral intervention that reduced risk for T2D. This pilot study will generate new metabolomics data from existing banked blood samples (n=42 participants; 17% of whom were classified as responders) to accomplish the study aims.

The long-term goal of this program of research is to approximate the complex biologic system in which T2D develops so that we may advance precision health approaches to T2D prevention and treatment. The overall objective is to generate preliminary evidence from multiple biologic systems to identify pathways and metabolites that may be prodromal biomarkers, and to differentiate subgroups of individuals at risk for T2D with discrete underlying mechanisms.