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

• Published in: Statistical methods in medical research Vol. 30; no. 6; pp. 1445 - 1464
• Main Authors: Matabuena, Marcos, Petersen, Alexander, Vidal, Juan C, Gude, Francisco
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-06-2021; Sage Publications Ltd
• Subjects: Analysis; Biological markers; Biomarkers; Biosensors; Blood Glucose; Blood Glucose Self-Monitoring; Clinical medicine; Data Analysis; Diabetes; Diabetes Mellitus; Diabetes Mellitus, Type 1; Disease control; Glucose; Humans; Living conditions; Metabolism; Representations; distributional data analysis; CGM technology; diabetes; biosensor data
• ISSN: 0962-2802; 1477-0334
• DOI: 10.1177/0962280221998064

Biosensor data have the potential 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 more in-depth insight into assessing glucose metabolism.