Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

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http://hdl.handle.net/10138/302391

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Longato , E , Acciaroli , G , Facchinetti , A , Hakaste , L , Tuomi , T , Maran , A & Sparacino , G 2018 , ' Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data ' , Computers in Biology and Medicine , vol. 96 , pp. 141-146 . https://doi.org/10.1016/j.compbiomed.2018.03.007

Title: Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data
Author: Longato, Enrico; Acciaroli, Giada; Facchinetti, Andrea; Hakaste, Liisa; Tuomi, Tiinamaija; Maran, Alberto; Sparacino, Giovanni
Contributor: University of Helsinki, Endokrinologian yksikkö
University of Helsinki, Centre of Excellence in Complex Disease Genetics
Date: 2018-05-01
Language: eng
Number of pages: 6
Belongs to series: Computers in Biology and Medicine
ISSN: 0010-4825
URI: http://hdl.handle.net/10138/302391
Abstract: Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters—age, sex, BMI, and waist circumference—with an accuracy of 87.1%.
Subject: 3111 Biomedicine
3121 General medicine, internal medicine and other clinical medicine
Classification
Continuous glucose monitoring
Diabetes
Glycaemic variability
INDEX
MANAGEMENT
MARKERS
RISK
SYSTEM
Support vector machine
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