title: Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis.
abstract: BACKGROUND
  Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets.
  
  
  METHODS
  A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them.
  
  
  RESULTS
  SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets.
  
  
  CONCLUSIONS
  The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.
authors: 1 Department of Information Engineering, University of Padova , Padova, Italy .
authors: Chiara
authors: C
authors: Fabris
authors: Andrea
authors: A
authors: Facchinetti
authors: Giovanni
authors: G
authors: Sparacino
authors: Mattia
authors: M
authors: Zanon
authors: Stefania
authors: S
authors: Guerra
authors: Alberto
authors: A
authors: Maran
authors: Claudio
authors: C
authors: Cobelli
mesh_terms: D001786:Blood Glucose / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy*; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000517:prevention & control*; D007003:Hypoglycemia / Q000097:blood / Q000517:prevention & control*; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D007328:Insulin / Q000008:administration & dosage*; D007332:Insulin Infusion Systems; D018670:Monitoring, Ambulatory; D025341:Principal Component Analysis; D012189:Retrospective Studies; D012307:Risk Factors
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human