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J Diabetes Sci Technol. 2012 May 01;6(3):572-80. doi: 10.1177/193229681200600312.

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.

Journal of diabetes science and technology

Andreas Thomas, Lutz Heinemann

Affiliations

  1. Medtronic GmbH, Meerbusch, Germany. [email protected]

PMID: 22768888 PMCID: PMC3440049 DOI: 10.1177/193229681200600312

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.

© 2012 Diabetes Technology Society.

References

  1. Diabetes Care. 2006 Nov;29(11):2433-8 - PubMed
  2. J Thromb Haemost. 2004 Aug;2(8):1453-9 - PubMed
  3. Diabetes Care. 2000 Apr;23 Suppl 2:B21-9 - PubMed
  4. N Engl J Med. 2008 Oct 2;359(14):1464-76 - PubMed
  5. Lancet. 1998 Sep 12;352(9131):837-53 - PubMed
  6. Diabetes Care. 2003 Mar;26(3):688-96 - PubMed
  7. JAMA. 2002 Nov 27;288(20):2579-88 - PubMed
  8. Diabet Med. 2007 Jul;24(7):753-8 - PubMed
  9. Diabetes. 1995 Aug;44(8):968-83 - PubMed
  10. Diabetes. 2005 Jan;54(1):1-7 - PubMed
  11. Endocrinol Metab Clin North Am. 1996 Jun;25(2):243-54 - PubMed
  12. Diabetes Res Clin Pract. 1995 May;28(2):103-17 - PubMed
  13. Diabetes Technol Ther. 2009 Jun;11(6):399-409 - PubMed
  14. Diabetologia. 2007 Nov;50(11):2239-44 - PubMed
  15. Diabetologia. 1996 Dec;39(12):1577-83 - PubMed
  16. Diabetes Care. 2008 Aug;31(8):1473-8 - PubMed
  17. N Engl J Med. 1993 Sep 30;329(14):977-86 - PubMed
  18. Diabetes. 2004 Apr;53(4):955-62 - PubMed
  19. Lancet. 1999 Aug 21;354(9179):617-21 - PubMed
  20. Am J Physiol Endocrinol Metab. 2001 Nov;281(5):E924-30 - PubMed
  21. Diabetes Technol Ther. 2010 Jul;12(7):507-15 - PubMed
  22. Diabetes Technol Ther. 2005 Apr;7(2):253-63 - PubMed
  23. JAMA. 2006 Apr 12;295(14):1681-7 - PubMed

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