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

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.

References in corpus (6)

• Statistical Aspects of Wasserstein Distances
• Multilevel functional principal component analysis
• Distance covariance in metric spaces
• Functional data analysis for density functions by transformation to a Hilbert space
• Generalized reliability based on distances
• Kernel k-Groups via Hartigan's Method

Cited by in corpus (7)

• Distributional data analysis via quantile functions and its application to modelling digital biomarkers of gait in Alzheimer's Disease
• Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease
• Bayesian Adaptive Selection of Basis Functions for Functional Data Representation
• Beyond Scalar Metrics: Functional Data Analysis of Postprandial Continuous Glucose Monitoring in the AEGIS Study
• fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses
• Neural interval-censored survival regression with feature selection
• Fast variable selection for distributional regression with application to continuous glucose monitoring data