Deep-learning-based characterization of glucose biomarkers to identify type 2 diabetes, prediabetes, and healthy individuals

Sina Akbarian, Qayam Jetha, Jouhyun Jeon

Keywords: Continuous Glucose Monitoring, Type 2 Diabetes, Prediabetes, Blood Glucose time series data, Machine Learning, Signal Processing

TL;DR: A short-time series of continuous glucose measurements provide information to identify T2D patients, prediabetic patients, and healthy individuals.

Abstract: Type 2 Diabetes (T2D) is a common chronic disease that can lead to serious comorbidities. Prediabetes is a state of increased health risk that is defined by abnormal glucose homeostasis and is strongly associated with the development of T2D and diabetic complications. Novel diagnostic or screening tools are required to identify T2D and prediabetic patients. In this study, we developed a predictive model that uses continuous glucose monitoring (CGM) signals to classify individuals as T2D, prediabetic, or healthy. We tested different durations of CGM signals to determine the minimum length of time required to achieve a reliable prediction of diabetic outcomes. We found that 12 hours of CGM signals were sufficient to achieve a classifier with a high degree of accuracy. The performance of the 12-hour model was equivalent to the performance of a model using the full period of CGM signals. The 12-hour model achieved AUCs of 0.83, 0.69, and 0.77 to identify T2D, prediabetes, and healthy individuals, respectively. The overall AUC of the 12-hour ensemble model was 0.86. Our findings propose a new application of currently available CGM systems to identify T2D and prediabetes based on only a short-time series of glucose profiles.