Characterizing the Heterogeneity of Drug Response in Type 2 Diabetes Through Deep Phenotyping and Data Integration

September 2020 Council

Lead Division/Office

DEM

Point(s) of Contact

Salvatore Sechi, Ph.D.

Executive Summary

Type 2 Diabetes (T2D) is a chronic metabolic disorder usually characterized by insulin resistance and impaired insulin secretion. T2D is diagnosed by measuring glycemic parameters in blood (e.g., fasting blood glucose or HbA1c). It has become apparent that T2D is a heterogenous disease with a complex genetic basis, and several environmental factors, including lifestyle, have been shown to be important in disease development and progression. Although T2D is diagnosed by measuring glycemic parameters in blood, those measurements may not accurately describe the etiology of the disease or the likelihood of responding to a specific treatment. Good predictive markers that can discriminate responders from non-responders are sorely needed. Recent molecular profiling technologies have reached a new level of sensitivity and throughput and can be more readily integrated with genomic data. Furthermore, Electronic Health Records (EHR) are now becoming more accessible and, together with improvements in patient characterization (e.g., Continuous Glucose Monitoring, physical activity monitors, etc.), provide an opportunity to better stratify patient populations for both drug responsiveness and adverse events. This initiative will solicit applications proposing to use cutting-edge molecular profiling technologies along with defined patient phenotyping (from EHR records and other patient data) to develop predictive marker(s) of drug response in T2D patients.