Glucose variability assessment in diabetes mellitus monitoring and control

This dissertation is focused on the assessment of glucose variability (GV) in the treatment of the pathology of diabetes mellitus. GV is a risk factor for the development of diabetes complications, and its assessment combined with the evaluation of glycated hemoglobin levels is believed to be useful to characterize the functioning of glucose metabolism. Given the importance of GV in diabetes, a number of indicators to measure it from the retrospective analysis of sparse self-monitoring of blood glucose (SMBG) or continuous glucose monitoring (CGM) recordings have been proposed in the literature, but several issues are still open. For instance, some GV indicators have been developed specifically from SMBG data, and their use on CGM time-series has not been validated yet. Moreover, the availability of a large number of metrics to quantify GV gives rise to problems in terms of redundant conveyed information, and a compact way to extensively characterize GV would be desirable. Finally, the exploitation of CGM signals and GV to classify the metabolic condition of normal and diabetic subjects is a relatively unexplored problem that could deserve an investigation. These three topics are the object of this dissertation, which is specifically made up of six chapters whose content is briefly outlined below. Chapter 1 will describe the etiology of the different types of diabetes, discuss the development of diabetes complications, and introduce the technologies used to monitor blood glucose levels and the strategies exploited to manage the treatment of type 1 (T1DM) and type 2 (T2DM) diabetes mellitus. Chapter 2 will focus specifically on GV and its quantification, and, after highlighting the existing open issues, will precisely state the aims of the thesis. Chapter 3 will consider the problem of adapting some GV indicators originally developed and validated from SMBG, to the use with CGM signals. In particular, we will specifically look at low blood glucose index (LBGI) and high blood glucose index (HBGI), popular metrics that allow to provide a rapid classification of the quality of glucose control in diabetic subjects, and will provide alternate versions of these indicators adapted to the characteristics of CGMs by modeling the relationship between LBGI/HBGI values obtained from SMBG and CGM recordings. A dataset of 28 T1DM subjects monitored with both SMBG and CGM devices will be used to tune and assess the proposed methodology. Chapter 4 will address the issue of redundant information conveyed by the available GV indices by using the sparse principal component analysis (SPCA) technique as a tool to provide a parsimonious but still comprehensive characterization of GV in both T1DM and T2DM. Specifically, we will consider 25 GV indicators evaluated on CGM profiles acquired from 33 T1DM and 13 T2DM subjects as initial pool of variables. SPCA will be applied to this set of metrics and will be shown to be able to select a small subset of up to 10 indices that can save more than 60% of the original variance in both applications. The subset of metrics provided by SPCA can be used to parsimoniously describe GV in diabetes. Chapter 5 will be devoted to the assessment of the possibility of using the outputs from SPCA to build GV-based classifiers of the metabolic condition of normal and diabetic subjects. In particular, by resorting to a dataset of 55 T1DM subjects, 34 normal subjects at high risk of developing T2DM, 39 impaired glucose tolerance subjects, and 29 subjects with T2DM diagnosed, we will show that support vector machines are able to successfully classify the quality of glycemic control and the metabolic condition of disordered subjects, allowing to achieve an accuracy of classification always greater than 70%. The investigation will be performed using both the whole initial pool of 25 indicators and the parsimonious set selected by SPCA as features to design the classifiers; the fact that similar results were obtained in the two scenarios strengthens the speculation that the compact description of GV provided by SPCA is effectively comprehensive for characterizing the subjects' metabolic condition. Chapter 6 will close this dissertation, with a discussion on possible future developments of the presented investigations.