Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Clinical Diabetes and Healthcare |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcdhc.2023.1244613/full |
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| author | Elvis Han Cui Allison B. Goldfine Michelle Quinlan David A. James Oleksandr Sverdlov |
| author_facet | Elvis Han Cui Allison B. Goldfine Michelle Quinlan David A. James Oleksandr Sverdlov |
| author_sort | Elvis Han Cui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T01:36:50Z |
| format | Article |
| id | doaj.art-0b9d0c6469524d3d96058b394533bb20 |
| institution | Directory Open Access Journal |
| issn | 2673-6616 |
| language | English |
| last_indexed | 2024-03-12T01:36:50Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Clinical Diabetes and Healthcare |
| spelling | doaj.art-0b9d0c6469524d3d96058b394533bb202023-09-11T05:56:43ZengFrontiers Media S.A.Frontiers in Clinical Diabetes and Healthcare2673-66162023-09-01410.3389/fcdhc.2023.12446131244613Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysisElvis Han Cui0Allison B. Goldfine1Michelle Quinlan2David A. James3Oleksandr Sverdlov4Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United StatesDivision of Translational Medicine, Cardiometabolic Disease, Novartis Institutes for Biomedical Research, Cambridge, MA, United StatesEarly Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, NJ, United StatesMethodology and Data Science, Novartis Pharmaceuticals Corporation, East Hanover, NJ, United StatesEarly Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, NJ, United StatesIntroductionContinuous 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.https://www.frontiersin.org/articles/10.3389/fcdhc.2023.1244613/fullCGMfunctional data analysisglucodensitypharmacodynamicsvisualization |
| spellingShingle | Elvis Han Cui Allison B. Goldfine Michelle Quinlan David A. James Oleksandr Sverdlov Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis Frontiers in Clinical Diabetes and Healthcare CGM functional data analysis glucodensity pharmacodynamics visualization |
| title | Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis |
| title_full | Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis |
| title_fullStr | Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis |
| title_full_unstemmed | Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis |
| title_short | Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis |
| title_sort | investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes an exploratory analysis |
| topic | CGM functional data analysis glucodensity pharmacodynamics visualization |
| url | https://www.frontiersin.org/articles/10.3389/fcdhc.2023.1244613/full |
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