A deep learning-derived digital biomarker of dysglycemia and its association with genetic risk of type 2 diabetes.

Abstract

Type 2 diabetes is a global health burden driven by genetic and environmental factors. Continuous glucose monitoring (CGM) can effectively guide lifestyle interventions in non-diabetic. However, predefined CGM metrics fail to fully capture the dysglycemic information contained in the high-dimensional time-series CGM data. This study employed deep learning to learn dysglycemia features from CGM data associated with diabetes and derived a digital biomarker of dysglycemia, validated against traditional dysglycemic biomarkers and diabetes polygenic risk score (PRS). Output of the deep learning model, called the deep learning-score, was significantly associated with multiple existing dysglycemic biomarkers and PRS of diabetes (P = 0.007). Moreover, existing CGM metrics were not associated with prevalent diabetes after adjusting for the deep learning-score, while the deep learning-score remained significantly associated with prevalent diabetes (P < 0.001) in a regression analysis. This digital biomarker demonstrated potential for providing dynamic feedback on dysglycemia and improving long-term intervention adherence.