Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics

TL;DR

This paper introduces glucodensity metrics derived from CGM data, which better predict long-term glucose outcomes than traditional metrics, highlighting the importance of capturing complex glucose dynamics.

Contribution

It presents a novel glucodensity approach that utilizes the speed and acceleration of CGM profiles as biomarkers, outperforming existing glucose metrics in predicting long-term outcomes.

Findings

Glucodensity metrics improve prediction of HbA1c and FPG by over 20% in adjusted R^2.

The approach captures critical aspects of glucose dynamics overlooked by traditional metrics.

Results demonstrate the clinical value of complex functional CGM metrics.

Abstract

Continuous glucose monitoring (CGM) data has revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as hypoglycemia. Recently, there has been growing interest in utilizing CGM devices in clinical studies involving healthy and diabetes populations. However, efficiently exploiting the high temporal resolution of CGM profiles remains a significant challenge. Numerous indices -- such as time-in-range metrics and glucose variability measures -- have been proposed, but evidence suggests these metrics overlook critical aspects of glucose dynamic homeostasis. As an alternative method, this paper explores the clinical value of glucodensity metrics in capturing glucose dynamics -- specifically the speed and acceleration of CGM time series -- as new biomarkers for predicting long-term glucose outcomes. Our results demonstrate significant information gains, exceeding 20\% in terms of adjusted $R^2$, in forecasting glycosylated hemoglobin (HbA1c) and fasting plasma glucose (FPG) at five and eight years from baseline AEGIS data, compared to traditional non-CGM and CGM glucose biomarkers. These findings underscore the importance of incorporating more complex CGM functional metrics, such as the glucodensity approach, to fully capture continuous glucose fluctuations across different time-scale resolutions.