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

Frontiers in clinical diabetes and healthcare Pub Date : 2023-09-11 eCollection Date: 2023-01-01 DOI:10.3389/fcdhc.2023.1244613
Elvis Han Cui, Allison B Goldfine, Michelle Quinlan, David A James, Oleksandr Sverdlov
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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.

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探讨1型糖尿病患者连续血糖监测数据的糖密度分析的价值:一项探索性分析。
简介:连续血糖监测(CGM)设备捕获间质血糖水平的纵向数据,并越来越多地用于显示糖尿病代谢的动力学。鉴于CGM数据的复杂性,通过高效的可视化和统计分析技术提取隐藏在这些数据中的重要模式至关重要。方法:在本文中,我们采用了糖密度的概念,并使用一项正在进行的针对新发1型糖尿病儿童和年轻人的临床试验的数据子集,对糖密度进行了聚类分析。我们使用方差分析(ANOVA)评估了已确定聚类之间的差异,方差分析涉及残余胰腺β细胞功能和一些标准CGM衍生的参数,如范围内时间、范围以上时间和范围以下时间。结果:使用基于葡萄糖密度的聚类分析确定了不同的CGM数据模式。在胰腺β细胞功能替代物(C肽)的基线水平、范围内的时间和范围以上的时间方面,聚类之间显示出统计学上的显著差异。讨论:我们的研究结果为葡萄糖密度在CGM数据分析中的价值提供了支持性证据。CGM数据建模中的一些挑战包括不平衡的数据结构、缺失的观察结果以及许多已知和未知的混杂因素,这说明了在分析这些数据时采用综合临床、统计和数据科学专业知识的方法的重要性,并为其提供了机会。
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