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. 2023 Sep 11:4:1244613.
doi: 10.3389/fcdhc.2023.1244613. eCollection 2023.

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

Elvis Han Cui  1 , Allison B Goldfine  2 , Michelle Quinlan  3 , David A James  4 , Oleksandr Sverdlov  3
Affiliations

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

Elvis Han Cui et al. Front Clin Diabetes Healthc. .

Abstract

Introduction: Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show 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.

Methods: In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range.

Results: Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range.

Discussion: Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling 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.

Keywords: CGM; functional data analysis; glucodensity; pharmacodynamics; visualization.

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Conflict of interest statement

The analyzed data is from a study sponsored by Novartis Institutes for Biomedical Research. Authors AG, MQ, DJ, and OS are employed by the company Novartis. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Lasagna plot of 30 subjects in the study. For any given subject, at any given time point in the 0- to 24-h range, the average glucose level over study days corresponding to that time point for that subject is displayed.
Figure 2
Figure 2
Ambulatory glucose profile (AGP) plots of 30 subjects in the study. Within each subject panel, the blue curves correspond to the raw CGM data acquired from the subject on different study days, and a red curve represents the mean AGP. The black dashed lines represent the range of 70 mg/dL to 180 mg/dL.
Figure 3
Figure 3
Dynamic time-in-range plots of 30 subjects in the study. Within each subject panel, the blue curve represents the individual time-in-range value on a given study day, the red curve is a modeled mean profile, and the gray area represents the uncertainty around the mean. The x-axis range is different across the subjects, as it reflects the different number of days of CGM data per subject. The y-axis range is displayed in a subject-specific manner because there was a substantial variation in the time-in-range values across the subjects.
Figure 4
Figure 4
Clustering based on estimated glucodensities.
Figure 5
Figure 5
Boxplots of individual values and the results of a non-parametric analysis of variance (ANOVA) comparing three clusters with respect to the baseline C-peptide level (top left plot), time in range (top right plot), time above range (bottom left plot), and time below range (bottom right plot).
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