"Glucose-at-a-Glance": New Method to Visualize the Dynamics of Continuous Glucose Monitoring Data

Teresa Henriques; Medha N Munshi; Alissa R Segal; Madalena D Costa; Ary L Goldberger

doi:10.1177/1932296814524095

"Glucose-at-a-Glance": New Method to Visualize the Dynamics of Continuous Glucose Monitoring Data

J Diabetes Sci Technol. 2014 Mar;8(2):299-306. doi: 10.1177/1932296814524095. Epub 2014 Mar 2.

Authors

Teresa Henriques¹, Medha N Munshi², Alissa R Segal³, Madalena D Costa⁴, Ary L Goldberger⁵

Affiliations

¹ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Center for Research in Health Technologies and Information Systems, Porto, Portugal Instituto de Telecomunicações, Porto, Portugal.
² Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA Joslin Diabetes Center, Boston, MA, USA Harvard Medical School, Boston, MA, USA.
³ Joslin Diabetes Center, Boston, MA, USA MCPHS University, Boston, MA, USA.
⁴ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁵ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA agoldber@bidmc.harvard.edu.

Abstract

The standard continuous glucose monitoring (CGM) output provides multiple graphical and numerical summaries. A useful adjunct would be a visualization tool that facilitates immediate assessment of both long- and short-term variability. We developed an algorithm based on the mathematical method of delay maps to display CGM signals in which the glucose value at time t_i is plotted against its value at time t_i+1. The data points are then color-coded based on their frequency of occurrence (density). Examples of this new visualization tool, along with the accompanying time series, are presented for selected patients with type 2 diabetes and non-diabetic controls over the age of 70 years. The method reveals differences in the structure of the glucose variability between subjects with a similar range of glucose values. We also observe that patients with comparable hemoglobin A1c (HbA1c) values may have very different delay maps, consistent with marked differences in the dynamics of glucose control. These differences are not accounted by the amplitude of the fluctuations. Furthermore, the delay maps allow for rapid recognition of hypo- and hyperglycemic periods over the full duration of monitoring or any subinterval. The glucose-at-a-glance visualization tool, based on colorized delay maps, provides a way to quickly assess the complex data acquired by CGM systems. This method yields dynamical information not contained in single summary statistics, such as HbA1c values, and may also serve as the basis for developing novel metrics of glycemic control.

Keywords: Poincaré map; continuous glucose monitor; density delay maps; glucose; hemoglobin A1c.

Abstract

Grants and funding