Phenomic research aims to characterize and discover new methods to improve health outcomes by integrating data from our health history, behavior, environment, and biological data (e.g., genetics, metabolites, microbiome). Advances in sampling techniques and statistical modeling have enabled new methods to learn how these systems change in accordance with a particular disease state, producing an identifiable “signature.” Type 2 diabetes is a quality candidate for phenomic research as biological imbalances occur before disease manifestation, which may be measured to predict the disease risk and possibly prevent or reverse disease transition. We aim to use UK Biobank data to correlate health history, behavior, and biological data with the transition from pre-diabetes to type 2 diabetes and diabetic kidney disease. We believe that biological signatures will emerge that correlate with disease state and risk of disease development. We believe that developing these “phenomic signatures” will identify novel mechanisms for drug development and behavioral interventions. We intend to demonstrate the predictive value of phenomic research and its utility in transitioning the healthcare system from a disease treatment focus toward disease prevention and health optimization.
