Endocrinol Metab.  2015 Jun;30(2):167-174. 10.3803/EnM.2015.30.2.167.

Clinical Implications of Glucose Variability: Chronic Complications of Diabetes

• Jung HS

• KMID: 2282006
• DOI: http://doi.org/10.3803/EnM.2015.30.2.167

Abstract

Glucose variability has been identified as a potential risk factor for diabetic complications; oxidative stress is widely regarded as the mechanism by which glycemic variability induces diabetic complications. However, there remains no generally accepted gold standard for assessing glucose variability. Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE). MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems. Despite a lack of randomized controlled trials, recent clinical data suggest that long-term glycemic variability, as determined by variability in hemoglobin A1c, may contribute to the development of microvascular complications. Intraday glycemic variability is also suggested to accelerate coronary artery disease in high-risk patients.

Keyword

Glucose variability; Microvascular complications; Macrovascular complications

MeSH Terms

Coronary Artery Disease
Diabetes Complications
Glucose*
Humans
Oxidative Stress
Risk Factors
Glucose

Figure

• 

  Fig. 1 Twenty-four-hour glycemic curves of two patients with diabetes (red and blue lines). The two patients exhibit different patterns of glycemic variation; however, standard deviations calculated across all four points, before each meal and at bedtime (arrows), do not reflect this because the glucose measures are similar between the two patients at those points.

• 

  Fig. 2 Continuous glucose monitoring in a patient with type 1 diabetes mellitus. Qualifying excursions are shown as blue arrows (only the inflection components in this case). Each inflection incorporates several excursions smaller than 1 standard deviation (SD) within a given day (44 mg/dL for day 1 and 65 mg/dL for day 2). The averaged excursion (that is, mean amplitude of glycemic excursion [MAGE]) is (A) 85.0 mg/dL for day 1 and (B) 156.5 mg/dL for day 2. MAGE calculated from the entire 48-hour time course (SD, 56.5 mg/dL) was 131.5 mg/dL; this level was similar across each day of the study period (120.7 mg/dL). Similar MAGE values could also be calculated from the descending limbs.

• 

  Fig. 3 Glycemic measures in a randomized controlled trial comparing prandial and basal insulin in patients with cardiovascular disease (HEART2D study). Seven-point mean self-monitoring of blood glucose profiles at baseline (dotted line) and throughout the study (solid line) are indicative of the treatment strategy. Only the change in the mean absolute glucose level, an alleged measure of glucose variability, was significantly different between treatments, with no observable differences in standard deviation or mean amplitude of glycemic excursion. Therefore, accurate interpretation of the relationship between glycemic variability and the endpoint of combined cardiovascular events in this trial is prudent. Adapted from Raz et al. [47], with permission from American Diabetes Association. aP<0.05 between treatment.

Reference

1. Service FJ. Glucose variability. Diabetes. 2013; 62:1398–1404.

2. Facchinetti A, Sparacino G, Cobelli C. Modeling the error of continuous glucose monitoring sensor data: critical aspects discussed through simulation studies. J Diabetes Sci Technol. 2010; 4:4–14.

3. Krinsley JS. Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit Care Med. 2008; 36:3008–3013.

4. Egi M, Bellomo R, Stachowski E, French CJ, Hart G. Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology. 2006; 105:244–252.

5. Siegelaar SE, Holleman F, Hoekstra JB, DeVries JH. Glucose variability; does it matter? Endocr Rev. 2010; 31:171–182.

6. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF. Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 1970; 19:644–655.

7. Baghurst PA. Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: an automated algorithm. Diabetes Technol Ther. 2011; 13:296–302.

8. Schlichtkrull J, Munck O, Jersild M. The M-valve, an index of blood-sugar control in diabetics. Acta Med Scand. 1965; 177:95–102.

9. Moberg E, Kollind M, Lins PE, Adamson U. Estimation of blood-glucose variability in patients with insulin-dependent diabetes mellitus. Scand J Clin Lab Invest. 1993; 53:507–514.

10. McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ. A novel approach to continuous glucose analysis utilizing glycemic variation. Diabetes Technol Ther. 2005; 7:253–263.

11. Kishimoto M, Yamasaki Y, Kubota M, Arai K, Morishima T, Kawamori R, Kamada T. 1,5-Anhydro-D-glucitol evaluates daily glycemic excursions in well-controlled NIDDM. Diabetes Care. 1995; 18:1156–1159.

12. Buse JB, Freeman JL, Edelman SV, Jovanovic L, McGill JB. Serum 1,5-anhydroglucitol (GlycoMark): a short-term glycemic marker. Diabetes Technol Ther. 2003; 5:355–363.

13. Kim MJ, Jung HS, Hwang-Bo Y, Cho SW, Jang HC, Kim SY, Park KS. Evaluation of 1,5-anhydroglucitol as a marker for glycemic variability in patients with type 2 diabetes mellitus. Acta Diabetol. 2013; 50:505–510.

14. Dungan KM, Buse JB, Largay J, Kelly MM, Button EA, Kato S, Wittlin S. 1,5-anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes. Diabetes Care. 2006; 29:1214–1219.

15. Shima K, Tanaka R, Morishita S, Tarui S, Kumahara Y. Studies on the etiology of "brittle diabetes". Relationship between diabetic instability and insulinogenic reserve. Diabetes. 1977; 26:717–725.

16. Molnar GD, Taylor WF, Ho MM. Day-to-day variation of continuously monitored glycaemia: a further measure of diabetic instability. Diabetologia. 1972; 8:342–348.

17. Brownlee M, Hirsch IB. Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic complications. JAMA. 2006; 295:1707–1708.

18. Lachin JM, Genuth S, Nathan DM, Zinman B, Rutledge BN. DCCT/EDIC Research Group. Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial: revisited. Diabetes. 2008; 57:995–1001.

19. Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001; 414:813–820.

20. Ceriello A, Esposito K, Piconi L, Ihnat MA, Thorpe JE, Testa R, Boemi M, Giugliano D. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes. 2008; 57:1349–1354.

21. Monnier L, Colette C, Mas E, Michel F, Cristol JP, Boegner C, Owens DR. Regulation of oxidative stress by glycaemic control: evidence for an independent inhibitory effect of insulin therapy. Diabetologia. 2010; 53:562–571.

22. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006; 295:1681–1687.

23. Siegelaar SE, Barwari T, Kulik W, Hoekstra JB, DeVries JH. No relevant relationship between glucose variability and oxidative stress in well-regulated type 2 diabetes patients. J Diabetes Sci Technol. 2011; 5:86–92.

24. Wentholt IM, Kulik W, Michels RP, Hoekstra JB, DeVries JH. Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes. Diabetologia. 2008; 51:183–190.

25. Kilpatrick ES, Rigby AS, Atkin SL. The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care. 2006; 29:1486–1490.

26. Kilpatrick ES, Rigby AS, Atkin SL. Effect of glucose variability on the long-term risk of microvascular complications in type 1 diabetes. Diabetes Care. 2009; 32:1901–1903.

27. Service FJ, O'Brien PC. The relation of glycaemia to the risk of development and progression of retinopathy in the Diabetic Control and Complications Trial. Diabetologia. 2001; 44:1215–1220.

28. Siegelaar SE, Kilpatrick ES, Rigby AS, Atkin SL, Hoekstra JB, Devries JH. Glucose variability does not contribute to the development of peripheral and autonomic neuropathy in type 1 diabetes: data from the DCCT. Diabetologia. 2009; 52:2229–2232.

29. Bragd J, Adamson U, Backlund LB, Lins PE, Moberg E, Oskarsson P. Can glycaemic variability, as calculated from blood glucose self-monitoring, predict the development of complications in type 1 diabetes over a decade? Diabetes Metab. 2008; 34(6 Pt 1):612–616.

30. Soupal J, Skrha J Jr, Fajmon M, Horova E, Mraz M, Skrha J, Prazny M. Glycemic variability is higher in type 1 diabetes patients with microvascular complications irrespective of glycemic control. Diabetes Technol Ther. 2014; 16:198–203.

31. Sartore G, Chilelli NC, Burlina S, Lapolla A. Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes. Acta Diabetol. 2013; 50:437–442.

32. Kilpatrick ES, Rigby AS, Atkin SL. A1C variability and the risk of microvascular complications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care. 2008; 31:2198–2202.

33. Gimeno-Orna JA, Castro-Alonso FJ, Boned-Juliani B, Lou-Arnal LM. Fasting plasma glucose variability as a risk factor of retinopathy in type 2 diabetic patients. J Diabetes Complications. 2003; 17:78–81.

34. Takao T, Ide T, Yanagisawa H, Kikuchi M, Kawazu S, Matsuyama Y. The effects of fasting plasma glucose variability and time-dependent glycemic control on the long-term risk of retinopathy in type 2 diabetic patients. Diabetes Res Clin Pract. 2011; 91:e40–e42.

35. Zoppini G, Verlato G, Targher G, Casati S, Gusson E, Biasi V, Perrone F, Bonora E, Muggeo M. Is fasting glucose variability a risk factor for retinopathy in people with type 2 diabetes? Nutr Metab Cardiovasc Dis. 2009; 19:334–339.

36. Kilpatrick ES, Rigby AS, Atkin SL. Mean blood glucose compared with HbA1c in the prediction of cardiovascular disease in patients with type 1 diabetes. Diabetologia. 2008; 51:365–371.

37. Muggeo M, Verlato G, Bonora E, Zoppini G, Corbellini M, de Marco R. Long-term instability of fasting plasma glucose, a novel predictor of cardiovascular mortality in elderly patients with non-insulin-dependent diabetes mellitus: the Verona Diabetes Study. Circulation. 1997; 96:1750–1754.

38. Siegelaar SE, Kerr L, Jacober SJ, Devries JH. A decrease in glucose variability does not reduce cardiovascular event rates in type 2 diabetic patients after acute myocardial infarction: a reanalysis of the HEART2D study. Diabetes Care. 2011; 34:855–857.

39. Monnier L, Colette C. Glycemic variability: can we bridge the divide between controversies? Diabetes Care. 2011; 34:1058–1059.

40. Su G, Mi SH, Tao H, Li Z, Yang HX, Zheng H, Zhou Y, Tian L. Impact of admission glycemic variability, glucose, and glycosylated hemoglobin on major adverse cardiac events after acute myocardial infarction. Diabetes Care. 2013; 36:1026–1032.

41. Wang X, Zhao X, Dorje T, Yan H, Qian J, Ge J. Glycemic variability predicts cardiovascular complications in acute myocardial infarction patients with type 2 diabetes mellitus. Int J Cardiol. 2014; 172:498–500.

42. Su G, Mi S, Tao H, Li Z, Yang H, Zheng H, Zhou Y, Ma C. Association of glycemic variability and the presence and severity of coronary artery disease in patients with type 2 diabetes. Cardiovasc Diabetol. 2011; 10:19.

43. Esposito K, Giugliano D, Nappo F, Marfella R. Campanian Postprandial Hyperglycemia Study Group. Regression of carotid atherosclerosis by control of postprandial hyperglycemia in type 2 diabetes mellitus. Circulation. 2004; 110:214–219.

44. Snell-Bergeon JK, Roman R, Rodbard D, Garg S, Maahs DM, Schauer IE, Bergman BC, Kinney GL, Rewers M. Glycaemic variability is associated with coronary artery calcium in men with type 1 diabetes: the Coronary Artery Calcification in Type 1 Diabetes study. Diabet Med. 2010; 27:1436–1442.

45. Barbieri M, Rizzo MR, Marfella R, Boccardi V, Esposito A, Pansini A, Paolisso G. Decreased carotid atherosclerotic process by control of daily acute glucose fluctuations in diabetic patients treated by DPP-IV inhibitors. Atherosclerosis. 2013; 227:349–354.

46. Mo Y, Zhou J, Li M, Wang Y, Bao Y, Ma X, Li D, Lu W, Hu C, Li M, Jia W. Glycemic variability is associated with subclinical atherosclerosis in Chinese type 2 diabetic patients. Cardiovasc Diabetol. 2013; 12:15.

47. Raz I, Wilson PW, Strojek K, Kowalska I, Bozikov V, Gitt AK, Jermendy G, Campaigne BN, Kerr L, Milicevic Z, Jacober SJ. Effects of prandial versus fasting glycemia on cardiovascular outcomes in type 2 diabetes: the HEART2D trial. Diabetes Care. 2009; 32:381–386.