Glucose Variability Indices in Type 1 Diabetes: Parsimonious Set of Indices Revealed by Sparse Principal Component Analysis
Glucose Variability Indices in Type 1 Diabetes: Parsimonious Set of Indices Revealed by Sparse Principal Component Analysis
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DOI：
10.1089/dia.2013.0252
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发表时间：
2014-10-01
影响因子：
5.4
通讯作者：
Cobelli, Claudio
中科院分区：
文献类型：
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作者：
Fabris, Chiara;Facchinetti, Andrea;Cobelli, Claudio
背景：连续血糖监测(CGM)的时间序列经常被回顾分析，以调查葡萄糖变异性(GV)，这是1型糖尿病(T1D)并发症发生的危险因素。在文献中，已经提出了几十种不同的GV量化指标，但其中许多指标包含的信息非常相似。材料和方法：使用Dexcom(R)(加利福尼亚州圣地亚哥)SEVEN(R)Plus在两个不同的临床研究中心使用Dexcom(R)(加利福尼亚州圣地亚哥)SEVEN(R)Plus对两个CGM时间序列数据集(分别包含17个和16个T1D受试者)评估了25个GV指数池。在索引被居中和缩放之后，使用稀疏主成分分析(SPCA)技术来确定允许保留整个原始集合的高百分比方差的缩减的度量集合。为了评估选定的GV指数子集是否依赖于数据集，对这两个数据集以及通过合并得到的数据集进行了分析。结果：SPCA显示，多达10个不同GV指数的子集足以保留所有25个变量最初解释的60%以上的方差。值得注意的是，在所有考虑的T1D数据集中，选择了四个GV指数(即血糖控制指数、因正常血糖引起的血糖风险评估的血糖风险评估百分比、糖尿病方程评分、变异系数百分比和低血糖指数)。结论：在大量文献GV指数中，SPCA方法似乎是一个合适的候选方法，可以确定允许获得简明但仍然全面的GV描述的子集。
Background: Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets.Materials and Methods: A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom((R)) (San Diego, CA) SEVEN (R) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them.Results: SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets.Conclusions: The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.