BACKGROUND: Current methods used to assess glycemic control use averaged measures and provide little information on the glycemic pathology of the patients. In this article we propose visual tools and their related mathematical formulas that allow for improved characterization of the glycemic behavior and achieve better glycemic control. METHODS: We present a reanalysis of published data, based on SMBG measurements from clinical trials of both men and women older than 18 years who were either healthy volunteers, prediabetes, or type 1 or type 2 diabetes. New graphic visualizations of glycemia as well as mathematical formulas that describe the glycemic behavior are presented and described, as well as suggested methods for their use to improve glycemic control. RESULTS: Patients with different problems in their glycemic control had different histogram shapes. In addition, patients who had the same HbA1c level at the time of the trial revealed significantly different glucose histograms with different shapes, variability and glycemic burden. The derived graphic visualizations provided information about the temporal evolution of the glycemic control. CONCLUSIONS: A paradigm change of the existing model of diabetes control is proposed, shifting from standardized treatment algorithms based on HbA1c follow-up to a new controlling approach that is based on the personal glucose density histogram. The histogram is an informative, detailed tool for the current patient glycemic behavior, and a future histogram can be targeted for a successful treatment. In addition, the glucose burden and the glucose severity index are proposed as informative markers for successful treatment. This is applicable to any glycemic data, by means of invasive and noninvasive glucometers.
BACKGROUND: Current methods used to assess glycemic control use averaged measures and provide little information on the glycemic pathology of the patients. In this article we propose visual tools and their related mathematical formulas that allow for improved characterization of the glycemic behavior and achieve better glycemic control. METHODS: We present a reanalysis of published data, based on SMBG measurements from clinical trials of both men and women older than 18 years who were either healthy volunteers, prediabetes, or type 1 or type 2 diabetes. New graphic visualizations of glycemia as well as mathematical formulas that describe the glycemic behavior are presented and described, as well as suggested methods for their use to improve glycemic control. RESULTS:Patients with different problems in their glycemic control had different histogram shapes. In addition, patients who had the same HbA1c level at the time of the trial revealed significantly different glucose histograms with different shapes, variability and glycemic burden. The derived graphic visualizations provided information about the temporal evolution of the glycemic control. CONCLUSIONS: A paradigm change of the existing model of diabetes control is proposed, shifting from standardized treatment algorithms based on HbA1c follow-up to a new controlling approach that is based on the personal glucose density histogram. The histogram is an informative, detailed tool for the current patient glycemic behavior, and a future histogram can be targeted for a successful treatment. In addition, the glucose burden and the glucose severity index are proposed as informative markers for successful treatment. This is applicable to any glycemic data, by means of invasive and noninvasive glucometers.
Authors: Hsin-Chieh Yeh; Todd T Brown; Nisa Maruthur; Padmini Ranasinghe; Zackary Berger; Yong D Suh; Lisa M Wilson; Elisabeth B Haberl; Jessica Brick; Eric B Bass; Sherita Hill Golden Journal: Ann Intern Med Date: 2012-09-04 Impact factor: 25.391
Authors: Darrell M Wilson; Dongyuan Xing; Jing Cheng; Roy W Beck; Irl Hirsch; Craig Kollman; Lori Laffel; Jean M Lawrence; Nelly Mauras; Katrina J Ruedy; Eva Tsalikian; Howard Wolpert Journal: Diabetes Care Date: 2011-04-19 Impact factor: 19.112