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Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis
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 efficie...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518413/ https://www.ncbi.nlm.nih.gov/pubmed/37753312 http://dx.doi.org/10.3389/fcdhc.2023.1244613 |
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author | Cui, Elvis Han Goldfine, Allison B. Quinlan, Michelle James, David A. Sverdlov, Oleksandr |
author_facet | Cui, Elvis Han Goldfine, Allison B. Quinlan, Michelle James, David A. Sverdlov, Oleksandr |
author_sort | Cui, Elvis Han |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10518413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105184132023-09-26 Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis Cui, Elvis Han Goldfine, Allison B. Quinlan, Michelle James, David A. Sverdlov, Oleksandr Front Clin Diabetes Healthc Clinical Diabetes and Healthcare 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. Frontiers Media S.A. 2023-09-11 /pmc/articles/PMC10518413/ /pubmed/37753312 http://dx.doi.org/10.3389/fcdhc.2023.1244613 Text en Copyright © 2023 Cui, Goldfine, Quinlan, James and Sverdlov https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Clinical Diabetes and Healthcare Cui, Elvis Han Goldfine, Allison B. Quinlan, Michelle James, David A. Sverdlov, Oleksandr Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis |
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 | Clinical Diabetes and Healthcare |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518413/ https://www.ncbi.nlm.nih.gov/pubmed/37753312 http://dx.doi.org/10.3389/fcdhc.2023.1244613 |
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