Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data.
Giada Acciaroli, Giovanni Sparacino, Liisa Hakaste, Andrea Facchinetti, Giorgio Maria Di Nunzio, Alessandro Palombit, Tiinamaija Tuomi, Rafael Gabriel, Jaime Aranda, Saturio Vega, Claudio Cobelli
Author Information
Giada Acciaroli: 1 Department of Information Engineering, University of Padova, Padova, Italy.
Giovanni Sparacino: 1 Department of Information Engineering, University of Padova, Padova, Italy.
Liisa Hakaste: 2 Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Andrea Facchinetti: 1 Department of Information Engineering, University of Padova, Padova, Italy.
Giorgio Maria Di Nunzio: 1 Department of Information Engineering, University of Padova, Padova, Italy.
Alessandro Palombit: 1 Department of Information Engineering, University of Padova, Padova, Italy.
Tiinamaija Tuomi: 2 Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Rafael Gabriel: 5 Escuela Nacional de Sanidad, Instituto de Salud Carlos III, Madrid, Spain.
Jaime Aranda: 6 Servicio de Endocrinologia Hospital General de Cuenca, Cuenca, Spain.
Saturio Vega: 7 Centro de Salud de Arevalo, Avila, Spain.
Claudio Cobelli: 1 Department of Information Engineering, University of Padova, Padova, Italy.
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.
