Sparse Principal Component Analysis for the parsimonious description of glucose variability in diabetes

Abnormal glucose variability (GV) is considered to be a risk factor for the development of diabetes complications. For its quantification from continuous glucose monitoring (CGM) data, tens of different indices have been proposed in the literature, but the information carried by them is highly redundant. In the present work, the Sparse Principal Component Analysis (SPCA) technique is used to select, from a wide pool of GV metrics, a smaller subset of indices that preserves the majority of the total original variance, providing a parsimonious but still comprehensive description of GV. In detail, SPCA is applied to a set of 25 literature GV indices evaluated on CGM time-series collected in 17 type 1 (T1D) and 13 type 2 (T2D) diabetic subjects. Results show that the 10 GV indices selected by SPCA preserve more than the 75% of the variance of the original set of 25 indices, both in T1D and T2D. Moreover, 6 indices of the parsimonious set are shared by T1D and T2D.