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. 2018 Jan;12(1):105-113.
doi: 10.1177/1932296817710478. Epub 2017 Jun 1.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

Giada Acciaroli  1 , Giovanni Sparacino  1 , Liisa Hakaste  2   3 , Andrea Facchinetti  1 , Giorgio Maria Di Nunzio  1 , Alessandro Palombit  1 , Tiinamaija Tuomi  2   3   4 , Rafael Gabriel  5 , Jaime Aranda  6 , Saturio Vega  7 , Claudio Cobelli  1
Affiliations

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

Giada Acciaroli et al. J Diabetes Sci Technol. 2018 Jan.

Abstract

Background: Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

Methods: The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

Results: Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

Conclusions: Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Keywords: classification; continuous glucose monitoring; glycemic variability; impaired glucose tolerance; type 2 diabetes.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Representative CGM traces from representative healthy (first panel), IGT (second panel), and T2D (third panel) subjects.
Figure 2.
Figure 2.
Simplified representation of sigmoid function with n = 2 dimensions. The sigmoid surface divides the probabilistic space into two areas, corresponding to the two classes.
Figure 3.
Figure 3.
Scheme of confusion matrix for the evaluation of classification performance.
See this image and copyright information in PMC

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