AUTHOR=Cui Elvis Han , Goldfine Allison B. , Quinlan Michelle , James David A. , Sverdlov Oleksandr 

TITLE=Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis

JOURNAL=Frontiers in Clinical Diabetes and Healthcare

VOLUME=Volume 4 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/clinical-diabetes-and-healthcare/articles/10.3389/fcdhc.2023.1244613

DOI=10.3389/fcdhc.2023.1244613

ISSN=2673-6616

ABSTRACT=Continuous glucose monitoring (CGM) devices capture extensive longitudinal data on interstitial glucose levels and are increasingly used to describe the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques. Unique opportunities related to CGM data analysis arise in the context of randomized controlled trials of novel therapies for diabetes. In this paper, we adopt the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric and young adults with new onset type 1 diabetes, we demonstrate that distinct CGM data patterns can be identified by cluster analysis of glucodensities. We found evidence of statistically significant differences among the identified clusters with respect to residual pancreatic beta cell function and with respect to some standard CGM measures such as time-in-range and time above range. Overall, our findings provide supportive evidence for the value of the glucodensity in the analysis of CGM data. Some challenges in the modelling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of-and provides opportunities for-taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.