Principal Investigator: Dr Daniel Robertson
Department: Indiana Biosciences Research InstituteTags: 61168, cardiovascular disease, Chronic kidney disease, diabetes, disease-progression, liver-disease
Summary: Type 2 Diabetes (T2D) is projected to become the biggest epidemic disease in the world. People with T2D often progress to complex co-morbidity like cardiovascular disease, kidney disease and liver disease. These associated diseases are driving increased healthcare costs. T2D itself is driving by a complex set of biological, socio-economic, and behavior factors. Effective prevention or management of T2D improves long-term outcomes and reduced healthcare costs.
Aims: This study aims to analyze progression of T2D patients to different co-morbidities is universal or is dependent on the population i.e. social determinants, environment, diet, drugs, genotype and compare these across two geographically diverse populations.
The aim(s) of this study is to:
(1) Profile T2D patients from the UK Biobank with respect to the following co-morbidities: cardiovascular diseases, chronic kidney diseases, and liver diseases
(2) Identify potential markers that track with these comorbidity disease progression
(3) Compare/contrast these results from UK Biobank with patient population from the State of Indiana
(4) Identify specific interesting subsets of the broader patient population for further study.
Scientific Rationale: As progression of T2D patients to any of the complex co-morbidities results in the decline of patient’s quality of life and increases mortality. A similar research project is underway using data from the State of Indiana. Being able to compare and contrast the results across these diverse geographic patient populations, should be able to better identify key factors associated with socio-economic/environment from biological/behavioral.
Project Duration: It is expected this project will be completed in less than two years, depending on availability of appropriate data.
Public Health Impact: This study by cross-comparing with a non-overlapping dataset, should allow key factors to be better identified that can then be used for policies, new interventions, and/or therapeutic targets to slow the current trends and/or reduce the impact of the impacts of diabetes. Identification of these transferable or invariant factors and subsequent validation, should allow a reduction of currently predicted epidemic rates and associated healthcare costs.