title: Prediction of the risk to develop diabetes-related late complications by means of the glucose pentagon model: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring study.
abstract: BACKGROUND
  By taking parameters into account that describe the variability of continuously monitored glucose and long-term metabolic control [hemoglobin A1c (HbA1c)], the glucose pentagon model (GPM) allows characterization of the glucose profile of individual patients with diabetes in a graphical format. A glycemic risk parameter (GRP) derived from this model might allow a better prognosis of the risk to develop diabetes-related complications than the HbA1c.
  
  
  METHODS
  To evaluate this hypothesis, we analyzed a subset of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring (CGM) study. The values of the different parameters that are integrated in the GPM were extracted automatically from CGM profiles registered before and after 6 months by means of the Medtronic CGM system in 108 patients.
  
  
  RESULTS
  In these patients, the significant reduction in HbA1c from 7.4% to 7.0% was accompanied by a reduction in glycemia from 164 to 156 mg/dl, standard deviation from 61 to 57 mg/dl, area under the curve >160 mg/dl 29.2 to 23.1, and time per day >160 mg/dl 634 to 576 min. This led to a subsequent reduction in GRP from 3.3 to 2.7; this decrease by 18.2% was significantly larger than that in HbA1c by 8.6% (p < .001). Changes in individual GPMs/GRPs support this observation. They also show the impact of high glycemic variability on GPM/GRP.
  
  
  CONCLUSIONS
  Our analysis of data of a study with a considerable sample size and study duration showed that the GPM is not only helpful for rapid assessment of individual glycemic profiles and how therapeutic interventions influence these, but also appears to provide a better prognosis of the risk to develop late complications than the HbA1c per se. However, it is also clear that a true validation of such a model requires performance of a long-term study in a large number of patients with diabetes.
authors: Medtronic GmbH, Meerbusch, Germany. andreas.thomas@medtronic.com
authors: Andreas
authors: A
authors: Thomas
authors: Lutz
authors: L
authors: Heinemann
mesh_terms: D001331:Automation; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring; D003661:Decision Support Techniques; D048909:Diabetes Complications / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000628:therapy; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000628:therapy; D018450:Disease Progression; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human