Giada Acciaroli1, Giovanni Sparacino1, Liisa Hakaste2,3, Andrea Facchinetti1, Giorgio Maria Di Nunzio1, Alessandro Palombit1, Tiinamaija Tuomi2,3,4, Rafael Gabriel5, Jaime Aranda6, Saturio Vega7, Claudio Cobelli1. 1. 1 Department of Information Engineering, University of Padova, Padova, Italy. 2. 2 Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 3. 3 Folkhälsan Research Center, and Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland. 4. 4 Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland. 5. 5 Escuela Nacional de Sanidad, Instituto de Salud Carlos III, Madrid, Spain. 6. 6 Servicio de Endocrinologia Hospital General de Cuenca, Cuenca, Spain. 7. 7 Centro de Salud de Arevalo, Avila, Spain.
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.
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.
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