Glucodensities: a new representation of glucose profiles using distributional data analysis

TL;DR

This paper introduces glucodensities, a novel functional data representation for glucose profiles from biosensor data, enabling more sensitive and comprehensive analysis of glucose metabolism in diabetes management.

Contribution

The paper presents a new distributional data analysis framework using glucodensities, improving upon existing methods for analyzing continuous glucose monitoring data.

Findings

Glucodensities are highly sensitive to clinical biomarkers.

They provide richer information than traditional biomarkers.

The method generalizes the time in range metric.

Abstract

Biosensor data has the potential ability to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state of the art analysis methods. In particular, our findings demonstrate that i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes, ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information, and iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics, and provides deeper insight into assessing glucose metabolism.