Association of glycemic variability and the presence and severity of coronary artery disease in patients with type 2 diabetes

Gong Su, Shuhua Mi, Hong Tao, Zhao Li, Hongxia Yang, Hong Zheng, Yun Zhou, Changsheng Ma, Gong Su, Shuhua Mi, Hong Tao, Zhao Li, Hongxia Yang, Hong Zheng, Yun Zhou, Changsheng Ma

Abstract

Background: Glucose variability is one of components of the dysglycemia in diabetes and may play an important role in development of diabetic vascular complications. The objective of this study was to assess the relationship between glycemic variability determined by a continuous glucose monitoring (CGM) system and the presence and severity of coronary artery disease (CAD) in patients with type 2 diabetes mellitus (T2DM).

Methods: In 344 T2DM patients with chest pain, coronary angiography revealed CAD (coronary stenosis ≥ 50% luminal diameter narrowing) in 252 patients and 92 patients without CAD. Gensini score was used to assess the severity of CAD. All participants' CGM parameters and biochemical characteristics were measured at baseline.

Results: Diabetic patients with CAD were older, and more were male and cigarette smokers compared with the controls. Levels of the mean amplitude of glycemic excursions (MAGE) (3.7 ± 1.4 mmol/L vs. 3.2 ± 1.2 mmol/L, p < 0.001), postprandial glucose excursion (PPGE) (3.9 ± 1.6 mmol/L vs. 3.6 ± 1.4 mmol/L, p = 0.036), serum high-sensitive C-reactive protein (hs-CRP) (10.7 ± 12.4 mg/L vs. 5.8 ± 6.7 mg/L, p < 0.001) and creatinine (Cr) (87 ± 23 mmol/L vs. 77 ± 14 mmol/L, p < 0.001) were significantly higher in patients with CAD than in patients without CAD. Gensini score closely correlated with age, MAGE, PPGE, hemoglobin A1c (HbA1c), hs-CRP and total cholesterol (TC). Multivariate analysis indicated that age (p < 0.001), MAGE (p < 0.001), serum levels of HbA1c (p = 0.022) and hs-CRP (p = 0.005) were independent determinants for Gensini score. Logistic regression analysis revealed that MAGE ≥ 3.4 mmol/L was an independent predictor for CAD. The area under the receiver-operating characteristic curve for MAGE (0.618, p = 0.001) was superior to that for HbA1c (0.554, p = 0.129).

Conclusions: The intraday glycemic variability is associated with the presence and severity of CAD in patients with T2DM. Effects of glycemic excursions on vascular complications should not be neglected in diabetes.

Figures

Figure 1
Figure 1
Distribution of Gensini score among participants.
Figure 2
Figure 2
Simple linear correlation of Gensini score and age, MAGE, PPGE and hemoglobin A1c in patients with type 2 diabetes.
Figure 3
Figure 3
Multivariate analysis for independent determinants of coronary artery disease (CAD). Smoking, male, older age, MAGE and hs-CRP were independent risk factors for CAD.
Figure 4
Figure 4
Receiver-operating characteristic (ROC) curve for MAGE and hemoglobin A1c (HbA1c) in predicting coronary artery disease (CAD) in patients with type 2 diabetes (T2DM). Area under the receiver-operating characteristic curve: MAGE 0.618 (95% CI 0.555, 0.680), p = 0.001; HbA1c 0.554 (95% CI 0.487, 0.620), p = 0.129. MAGE, but not HbA1c, displayed significant value in predicting CAD in patients with T2DM.

References

    1. Buse JB, Ginsberg HN, Bakris GL, Clark NG, Costa F, Eckel R, Fonseca V, Gerstein HC, Grundy S, Nesto RW, Pignone MP, Plutzky J, Porte D, Redberg R, Stitzel KF, Stone NJ. American Heart Association; American Diabetes Association. Primary prevention of cardiovascular diseases in people with diabetes mellitus: a scientific statement from the American Heart Association and the American Diabetes Association. Circulation. 2007;115(1):114–126. doi: 10.1161/CIRCULATIONAHA.106.179294.
    1. Stolar MW, Chilton RJ. Type 2 diabetes, cardiovascular risk, and the link to insulin resistance. Clin Ther. 2003;25(Suppl B):B4–31. doi: 10.1016/S0149-2918(03)80240-0.
    1. Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–986. doi: 10.1056/NEJM199309303291401.
    1. Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321(7258):405–412. doi: 10.1136/bmj.321.7258.405.
    1. Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem. 2002;48(3):436–472.
    1. Gorus F, Mathieu C, Gerlo E. How should HbA1c measurements be reported? Diabetologia. 2006;49(1):7–10. doi: 10.1007/s00125-005-0073-7.
    1. United Kingdom Prospective Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet. 1998;352(9131):837–853. doi: 10.1016/S0140-6736(98)07019-6.
    1. Hanefeld M, Fischer S, Julius U, Schulze J, Schwanebeck U, Schmechel H, Ziegelasch HJ, Lindner J. Risk factors for myocardial infarction and death in newly detected NIDDM: the Diabetes Interventional Study, 11-year follow-up. Diabetologia. 1996;39(12):1577–1583. doi: 10.1007/s001250050617.
    1. Monnier L, Colette C. Glycemic variability: should we and can we prevent it? Diabetes Care. 2008;31(Suppl 2):S150–154. doi: 10.2337/dc08-s241.
    1. 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(5):1349–1354. doi: 10.2337/db08-0063.
    1. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. Activation of oxidative stress by acute glucose fuctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006;295(14):1681–1687. doi: 10.1001/jama.295.14.1681.
    1. Hu Y, Liu W, Huang R, Zhang X. Postchallenge plasma glucose excursions, carotid intima-media thickness, and risk factors for atherosclerosis in Chinese population with type 2 diabetes. Atherosclerosis. 2010;210(1):302–306. doi: 10.1016/j.atherosclerosis.2009.11.015.
    1. Temelkova-Kurktschiev TS, Koehler C, Henkel E, Leonhardt W, Fuecker K, Hanefeld M. Postchallenge plasma glucose and glycemic spikes are more strongly associated with atherosclerosis than fasting glucose or HbA1c level. Diabetes Care. 2000;23(12):1830–1834. doi: 10.2337/diacare.23.12.1830.
    1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2004;27(Suppl 1):S5–S10.
    1. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130(6):461–470.
    1. Monnier L, Colette C, Owens DR. Glycemic variability: The third component of the dysglycemia in diabetes. Is it important? How to measure it? J Diabetes Sci Technol. 2008;2(6):1094–1100.
    1. Khaw KT, Wareham N, Bingham S, Luben R, Welch A, Day N. Association of haemoglobin A1c with cardiovascular disease and mortality in adults: The European Prospective Investigation into Cancer in Norfolk. Ann Intern Med. 2004;141(6):413–420.
    1. Kilpatrick ES, Rigby AS, Atkin SL. For debate. Glucose variability and diabetes complication risk: we need to know the answer. Diabet Med. 2010;27(8):868–871. doi: 10.1111/j.1464-5491.2010.02929.x.
    1. Siegelaar SE, Holleman F, Hoekstra JB, DeVries JH. Glucose variability; does it matter? Endocr Rev. 2010;31(2):171–182. doi: 10.1210/er.2009-0021.
    1. Brownlee M, Hirsch IB. Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic complications. JAMA. 2006;295(14):1707–1708. doi: 10.1001/jama.295.14.1707.
    1. 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(2):78–81. doi: 10.1016/S1056-8727(02)00197-6.
    1. Kilpatrick ES, Rigby AS, Atkin S. The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care. 2006;29(7):1486–1490. doi: 10.2337/dc06-0293.
    1. 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(4):995–1001. doi: 10.2337/db07-1618.
    1. Zaccardi F, Stefano PD, Busetto E, Federici MO, Manto A, Infusino F, Lanza GA, Pitocco D, Ghirlanda G. Group of signs: A new method to evaluate glycemic variability. J Diabetes Sci Technol. 2008;2(6):1061–1065.
    1. Muggeo M, Zoppini G, Bonora E, Brun E, Bonadonna RC, Moghetti P, Verlato G. Fasting plasma glucose variability predicts 10-year survival of type 2 diabetic patient. Diabetes Care. 2000;23(1):45–50. doi: 10.2337/diacare.23.1.45.
    1. Dossett LA, Cao H, Mowery NT, Dortch MJ, Morris JM Jr, May AK. Blood glucose variability is associated with mortality in the surgical intensive care unit. Am Surg. 2008;74(8):679–685.
    1. Krinsley JS. Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit Care Med. 2008;36(11):3008–3013. doi: 10.1097/CCM.0b013e31818b38d2.
    1. Hirshberg E, Larsen G, Van Duker H. Alterations in glucose homeostasis in the pediatric intensive care unit: Hyperglycemia and glucose variability are associated with increased mortality and morbidity. Pediatr Crit Care Med. 2008;9(4):361–366. doi: 10.1097/PCC.0b013e318172d401.
    1. Hou ZQ, Li HL, Gao L, Pan L, Zhao JJ, Li GW. Involvement of chronic stresses in rat islet and INS-1 cell glucotoxicity induced by intermittent high glucose. Mol Cell Endocrinol. 2008;291(1-2):71–78. doi: 10.1016/j.mce.2008.03.004.
    1. Quagliaro L, Piconi L, Assaloni R, Martinelli L, Motz E, Ceriello A. Intermittent high glucose enhances apoptosis related to oxidative stress in human umbilical vein endothelial cells: the role of protein kinase C and NAD(P)H-oxidase activation. Diabetes. 2003;52(11):2795–2804. doi: 10.2337/diabetes.52.11.2795.
    1. Kim MK, Jung HS, Yoon CS, Ko JH, Jun HJ, Kim TK, Kwon MJ, Lee SH, Ko KS, Rhee BD, Park JH. The effect of glucose fluctuation on apoptosis and function of INS-1 pancreatic beta cells. Korean diabetes J. 2010;34(1):47–54. doi: 10.4093/kdj.2010.34.1.47.
    1. Siegelaar SE, Kulik W, van Lenthe H, Mukherjee R, Hoekstra JB, Devries JH. A randomized clinical trial comparing the effect of basal insulin and inhaled mealtime insulin on glucose variability and oxidative stress. Diabetes Obes Metab. 2009;11(7):709–714. doi: 10.1111/j.1463-1326.2009.01037.x.
    1. Rodríguez-Colón SM, Li X, Shaffer ML, He F, Bixler EO, Vgontzas AN, Cai J, Liao D. Insulin resistance and circadian rhythm of cardiac autonomic modulation. Cardiovasc Diabetol. 2010;9:85.
    1. Gupta AK, Cornelissen G, Greenway FL, Dhoopati V, Halberg F, Johnson WD. Abnormalities in circadian blood pressure variability and endothelial function: pragmatic markers for adverse cardiometabolic profiles in asymptomatic obese adults. Cardiovasc Diabetol. 2010;9:58. doi: 10.1186/1475-2840-9-58.
    1. Takei Y, Tomiyama H, Tanaka N, Yamashina A. Close relationship between sympathetic activation and coronary microvascular dysfunction during acute hyperglycemia in subjects with atherosclerotic risk factors. Circ J. 2007;71(2):202–206. doi: 10.1253/circj.71.202.

Source: PubMed

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