The ambulatory glucose profile and its interpretation

Lin, Rose S.Y.; Brown, Fran; Ekinci, Elif I

doi:10.5694/mja2.51666

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…CGM data collection is increasingly common in diabetes research ( 6 , 21 , 31 – 33 ). Recently, many statistical software packages have been developed to facilitate CGM data visualization and analysis.…”

Section: Discussionmentioning

confidence: 99%

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

Cui,

Goldfine,

Quinlan

et al. 2023

Front. Clin. Diabetes Healthc.

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IntroductionContinuous 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.MethodsIn 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.ResultsDistinct 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.DiscussionOur 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.

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