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Letters to the Editor
Glycemic variability predicts cardiovascular complications in acute myocardial infarction patients with type 2 diabetes mellitus☆ Xiangfei Wang a,1, Xiaolong Zhao b,1, Tashi Dorje a, Hongmei Yan c, Juying Qian a,⁎, Junbo Ge a,⁎ a b c
Shanghai Institute of Cardiovascular Diseases, Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China Department of Endocrinology & Metabolism, Huashan Hospital, Fudan University, Shanghai, China Department of Endocrinology & Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
a r t i c l e
i n f o
Article history: Received 2 November 2013 Received in revised form 1 January 2014 Accepted 7 January 2014 Available online 21 January 2014 Keywords: Glycemic variability Acute myocardial infarction (AMI) Major adverse cardiac event
In diabetes mellitus (DM) patients, glycosylated hemoglobin is closely related to the number of diabetic complications. It has recently been found that glycemic variability is also an important parameter that may be associated with the occurrence of diabetic complications [1] and the prognosis of critically ill patients [2]. Evidence of the predictive value of glycemic variability for poor outcomes in patients with type 2 diabetes mellitus (T2DM) is less sufficient than that in type 1 diabetes mellitus (T1DM) patients. A variety of glucose parameters are associated with poor prognosis in patients with acute myocardial infarction (AMI) [3], but unlike other critically ill patients, the relevance of glycemic variability and the prognosis of AMI patients are uncertain [4–6]. Furthermore, previous studies used fingertip blood glucose sampling and the definitions of glycemic variability were inconsistent. Recent studies showed that a continuous glucose monitoring system (CGMS) can better identify the abnormity of blood glucose in patients with ACS [7,8]. Su et al. found that the mean amplitude of glycemic excursion (MAGE) derived from CGMS independently predicts one-year major adverse cardiac events (MACE) in patients with AMI [9], despite only around half of the patients having preexisting diabetes. This is a single-center, prospective observational clinical study. All eligible patients with AMI admitted to the cardiac care unit of our institute were consecutively recruited from October 2011 to May 2012. The diagnosis of myocardial infarction was made according to contemporary guidelines [8]. Inclusion criteria for this study were: (1) meeting the diagnostic criteria for AMI with a time of symptom onset within 24 h regardless of whether revascularization was performed; (2) having been diagnosed with DM before admission or with an HbA1c greater than 6.5% [10]; (3) age N 18 years; (4) eGFR ≥ 45 ml/min/ 1.73 m2; and (5) Killip classification greater than stage two. In hospital mortality risk was assessed by using GRACE score for ACS [11]. eGFR was calculated by the CKD-EPI formula [12]. This research was in accordance with the declaration of Helsinki and approved by the local research ethics committee, and written informed consent was required before any research procedure. The authors of this manuscript have certified that they comply with the principles of ethical publishing in the International Journal of Cardiology.
☆ The authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. ⁎ Corresponding authors. Tel.: +86 21 6404 1990x2745; fax: + 86 21 6443 7078. E-mail addresses: [email protected] (J. Qian), [email protected] (J. Ge). 1 These authors contributed equally to this work.
Clinical records of all patients were reviewed. During the hospital stay, all patients were placed on continuous blood glucose monitoring with CGMS, and routine fingertip blood glucose was measured. To control blood glucose within 10 mmol/l, insulin therapy was given once fingertip blood glucose greater than 10 mmol/l was detected. Laboratory data of the first day and third day after admission were collected, function of beta-cell and insulin resistance were calculated by two-hour postprandial blood glucose, insulin and C-peptide [13]. Cardiac troponin T (cTnT) was measured every 6 h in the first day and repeated at 48 and 72 h. Transthoracic echocardiography was preformed within 72 h of admission. All the examinations and treatments were based on current clinical guideline recommendations. All patients were followed-up by a hospital visit or telephone interview for clinical evaluation and MACE was defined as a composite of all-cause death, recurrent AMI, and re-hospitalization for acute decompensated heart failure (ADHF). Categorical data were presented as numbers and percentages, and compared with Fisher's exact test. Continuous variables were expressed as mean ± standard deviation (SD) and compared with Student's t test. Logarithmic transformation was used for skewed data including cTnT, NT-proBNP, hs-CRP, insulin, and C-peptide. To assess the relationship between the occurrence of MACE and clinical parameters, binary logistic regression analysis was performed. Receiver operating characteristic (ROC) curve was used to determine the optimum cut-off levels of MAGE to predict MACE. All tests were 2sided and a probability (p) value b0.05 was considered statistically significant. Statistical analysis was performed by using SPSS 11.0 (IBM, Armonk, NY, USA). A total of 34 patients were recruited in this study, among them 28 patients had previously been diagnosed with diabetes, and 6 patients with HbA1c N 6.5%. No MACE occurred during hospitalization. All subjects completed the follow-up (17.03 ± 1.66 months), during the follow-up 4 patients died, 1 re-infarcted, and 1 was hospitalized for ADHF, making the total number of MACE 6. Patients were then divided into two groups based on whether or not they had MACE. Baseline characteristics are presented in Table 1. There was no significant difference between the two groups regarding cardiovascular risk factors, laboratory results and echocardiographic parameters. There were also no differences in myocardial infarction type, proportion of patients who received revascularization therapy, GRACE score, number of diseased vessels and patients with implanted drug-eluting stents. All patients received the following therapy: aspirin, clopidogrel, carvedilol, ACEI/ARB, and lipid-lowering agents. Meanwhile, some received spironolactone (p N 0.05). Regarding glucose metabolism parameters, there were no differences in random blood glucose after admission, fasting blood glucose, 2 h of postprandial plasma glucose, HbA1c, GA, fasting and postprandial insulin, and fasting and postprandial C-peptide between the two groups. Insulin resistance according to the HOMA-IR and the QUICKI formula showed no significant differences between the two groups. However, data from CGMS analysis showed that the MACE group had significantly higher MAGE (8.84 ± 2.29 mmol/l vs. 6.19 ± 2.34 mmol/l; p = 0.017) and greater SD (3.25 ± 0.70 vs. 2.40 ± 0.82; p = 0.026). No significant differences in other parameters were found between the two groups.
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Table 1 Basic demographics and clinical characteristics of study subjects. Characteristic Risk factors Age, years Sex (male), n (%) BMI, kg/m2 Hypertension, n (%) Dyslipidemia, n (%) Smoking, n (%) Myocardial infarction history, n (%) Heart rate, BPM Mean artery pressure, mm Hg STEMI/NSTEMI, n GRACE score Vessel disease (single/multiple, n) Revascularization, n (%) Lab tests Hemoglobin, g/l White blood cell, ∗109/l eGFR, ml/min/1.73 m2 Triglyceride, mmol/l Total cholesterol, mmol/l HDL-c, mmol/l LDL-c, mmol/l Maximum cTnT, ng/ml Maximum NT-proBNP, pg/ml hs-CRP, mg/l Echocardiography LV end-diastolic diameter, mm LV end-systolic diameter, mm LV ejection fraction, % Glucose parameters Randomized blood glucose, mmol/l First FBG, mmol/l Fasting insulin, μU/l Fasting C-peptide, nmol/l First PBG, mmol/l Postprandial insulin, μU/l Postprandial C-peptide, nmol/l Secondary FBG, mmol/l Secondary PBG, mmol/l HbA1c, % Glycated albumin, % MAGE, mmol/l Mean sensor glucose, mmol/l Standard deviation Duration of hyperglycemia (h/day) Duration of hypoglycemia (h/day) Mean AUCCGM (mmol/l/24 h) Low area Fasting C-peptide/glucose ratio Delta C-peptide/delta glucose ratio HOMA-IR QUICKI
MACE group (n = 6)
Non-MACE group (n = 28)
p value
65.35 ± 14.78 5 (83.3%) 24.55 ± 2.72 4 (66.7%) 0 (0%) 3 (50.0%) 1 (16.7%) 90.67 ± 35.99 85.94 ± 11.97 2/4 221.17 ± 16.41 0/6 5 (83.3%)
62.27 ± 10.82 24 (85.7%) 23.97 ± 2.96 17 (60.7%) 8 (28.6%) 13 (46.4%) 3 (10.7%) 80.57 ± 14.86 92.95 ± 14.73 14/14 208.87 ± 18.03 10/18 25 (89.3%)
0.556 1.000 0.645 1.000 0.297 1.000 0.559 0.528 0.285 0.660 0.134 0.197 0.559
131.50 ± 11.73 9.43 ± 1.20 77.82 ± 21.00 2.32 ± 2.13 4.46 ± 0.90 0.93 ± 0.19 2.68 ± 0.70 2.22 (0.919–3.468) 1730 (593–5817) 25.45 (3.13–83.98)
135.39 ± 14.47 10.04 ± 3.06 95.18 ± 21.70 1.99 ± 1.07 4.45 ± 0.86 1.03 ± 0.23 2.57 ± 0.79 1.43 (0.825–4.075) 910 (425–1366) 6.80 (3.75–14.35)
0.543 0.638 0.083 0.574 0.975 0.344 0.753 0.982 0.360 0.275
46.83 ± 6.05 30.33 ± 4.59 52.83 ± 7.96 11.90 ± 3.83 9.93 ± 2.33 7.65 (5.68–17.55) 2.57 (1.69–3.41) 16.15 ± 3.39 46.50 (18.93–94.90) 4.82 (3.50–7.79) 9.48 ± 1.92 14.10 ± 3.82 9.35 ± 1.53 24.62 ± 3.45 8.84 ± 2.29 11.00 ± 2.08 3.25 ± 0.70 13.58 ± 4.58 0.42 ± 1.02 2.07 ± 1.56 0.02 ± 0.04 0.31 (0.14–0.41) 0.45 (0.27–1.08) 5.83 ± 7.64 0.32 ± 0.04
48.96 ± 6.10 33.57 ± 6.82 54.43 ± 8.69 9.66 ± 3.71 8.24 ± 2.29 8.35 (5.53–14.43) 2.58 (1.62–3.57) 13.957 ± 3.0244 50.50 (25.68–77.55) 6.16 (3.41–8.38) 8.00 ± 2.08 14.69 ± 2.68 8.35 ± 1.63 20.88 ± 5.42 6.19 ± 2.34 9.69 ± 1.73 2.40 ± 0.82 10.34 ± 5.60 0.26 ± 0.79 1.17 ± 1.05 0.01 ± 0.04 0.31 (0.20–0.51) 0.61 (0.28–1.17) 7.08 ± 18.48 0.33 ± 0.04
0.443 0.278 0.682 0.192 0.111 0.991 0.886 0.124 0.479 0.066 0.120 0.657 0.178 0.054 0.017 0.113 0.026 0.199 0.688 0.097 0.793 0.388 0.775 0.873 0.725
Data are means ± SD, medians (25th–75th percentile), or n (%). STEMI = ST-segment elevated myocardial infarction; NSTEMI = non-ST-segment elevated myocardial infarction; eGFR = estimated glomerular filtration rate (ml/min/1.73 m2); HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; cTnT = cardiac troponin T; NT-proBNP = N-terminal pro-brain natriuretic peptide; hs-CRP, high-sensitive C-reactive protein; LV = left ventricular; HbA1c = glycated hemoglobin; MAGE = mean amplitude of glycemic excursions; HOMA-IR = homeostasis model assessment of insulin resistance; and QUICKI = quantitative insulin sensitivity check index.
In univariate logistic regression analysis, eGFR (odds ratio = 0.963; 95% CI, 0.922–1.006; p = 0.094), MAGE (odds ratio = 1.592; 95% CI, 1.034–2.451; p = 0.035) and SD (odds ratio = 3.151; 95% CI, 1.036– 9.583; p = 0.043) were the only independent risk factors for MACE (p b 0.10). Furthermore, multivariate logistic regression analysis in forward selection showed that only MAGE could predict MACE. Additionally, MAGE N 6.05 mmol/l was identified as the most useful cut-off level for the prediction of MACE by ROC curve analysis, with a sensitivity and specificity of 100% and 53.6% respectively, and the area under the curve was 0.798 (p = 0.024). When patients were divided into the high MAGE group (n = 19) and low MAGE group (n = 15), all MACE happened in the high MAGE group, whereas no MACE occurred in the low MAGE group (p = 0.024). Kaplan–Meier survival curve analysis
showed that high MAGE group had a significantly lower event-free survival rate (p = 0.023) (Fig. 1). Our study showed that MAGE is an independent predictor of MACE in diabetic patients with AMI which supports the finding of Su et al. Recently, the predictive value of glycemic variability in patients with ACS has been investigated in many studies, but the conclusions are inconsistent. The main reason may be that those studies use different inclusion criteria and glucose variability indicators. It was proven that GV derived from CGMS is more reliable than other methods, because the accuracy of variability is closely related to the number of detection data. CGMS is widely used in diabetic patients for glycemic monitoring and studies showed that it can also be used for glycemic monitoring
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Events-free survival, %
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ill patients and their prognosis. It showed that the composite mortality, after correction, was related to SD and MAGE, but had no association with mean absolute glucose change per hour (MAG) and the glycemic lability index (GLI). In surgical patients, the composite mortality was related to SD, MAGE and MAG; in critically ill patients, the composite mortality was only related to SD [18]. However, as to which is more valuable, the MAGE or SD derived from CGMS, remains to be studied. Our results suggested that MAGE might be better.
100 80 60 Log Rank P = 0.023
40
MAGE 6.05 mmol/L
0 0
5
10
15
20
Months Fig. 1. Kaplan–Meier event-free survival curves for freedom from MACE in two patient groups by MAGE levels. The event-free survival rate was significantly lower in the high MAGE level patients.
in critically ill patients. However, there is little research about the applications of CGMS in patients with ACS. Radermecker et al. found that it can better identify hyperglycemia in non-diabetic patients [7]. Sampaio et al. found that CGMS was able to recognize more hypoglycemia in patients with AMI receiving insulin therapy [8]. However, the association between adverse events and glycemic variability was not reported in their studies. Our study as well as a study conducted by Su et al. showed that MAGE obtained from CGMS predicted the prognosis of patient at about one-year follow-up, rather than MACE for the duration of hospital stay, probably because the influence of blood glucose is mainly long-term rather than shortterm. Despite increasingly more data revealing that an increase in glycemic variability is associated with poor outcomes, it is unclear whether it is beneficial to correct excessive glycemic variability, as a primary aim of study, and so far, there is no clinical research about this. Intensive insulin therapy had no effect on SD, aside from increasing the MAGE during the day [14]. Moreover, intensive insulin therapy based on real-time CGMS has not been able to lower the glycemic variability (SD, GLI, MPG) [15]. Studies showed that exenatide can better lower the glycemic variability than glargine, and less extreme values appear [16]. Although there is no significant difference in HbA1c, detemir could better reduce glycemic variability than neutral protamine Hagedorn (NPH) insulin, and with less weight gain during the therapy [17]. Further clarification regarding which drug and what kind of therapy can be effective in achieving this purpose is required before initiating clinical trials about ameliorating glycemic variability. One of the problems of research is that there is no acknowledged gold standard for numerous indicators reflecting glycemic variability. In a meta-analysis from Eslami et al. [2], the glycemic variabilities of critically ill patients were associated with a poor prognosis, but different clinical trials have various indicators of glycemic variability and it is difficult to make direct comparisons. In the largest prospective study (20,375 patients), Meynaar et al. observed the relationship between glycemic variability of critically
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References [1] Nalysnyk L, Hernandez-Medina M, Krishnarajah G. Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes Metab 2010;12:288–98. [2] Eslami S, Taherzadeh Z, Schultz MJ, et al. Glucose variability measures and their effect on mortality: a systematic review. Intensive Care Med 2011;37:583–93. [3] Deedwania P, Kosiborod M, Barrett E, et al. Hyperglycemia and acute coronary syndrome: a scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 2008;117:1610–9. [4] Siegelaar SE, Kerr L, Jacober SJ, et al. 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;34:855–7. [5] Lipska KJ, Venkitachalam L, Gosch K, et al. Glucose variability and mortality in patients hospitalized with acute myocardial infarction. Circ Cardiovasc Qual Outcomes 2012;5:550–7. [6] Mellbin LG, Malmberg K, Ryden L, et al. The relationship between glycaemic variability and cardiovascular complications in patients with acute myocardial infarction and type 2 diabetes: a report from the DIGAMI 2 Trial. Eur Heart J 2013;34:374–9. [7] Radermecker RP, Sultan A, Piot C, et al. Continuous glucose monitoring as a tool to identify hyperglycaemia in non-diabetic patients with acute coronary syndromes. Diabet Med 2009;26:167–70. [8] Sampaio CR, Franco DR, Goldberg DJ, et al. Glucose control in acute myocardial infarction: a pilot randomized study controlled by continuous glucose monitoring system comparing the use of insulin glargine with standard of care. Diabetes Technol Ther 2012;14:117–24. [9] Su G, Mi SH, Tao H, et al. Impact of admission glycemic variability, glucose, and glycosylated hemoglobin on major adverse cardiac events after acute myocardial infarction. Diabetes Care 2013;36:1026–32. [10] Standards of Medical Care in Diabetes—2012. Diabetes Care 2012;35(Suppl 1): S11–63. [11] Eagle KA, Lim MJ, Dabbous OH, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA 2004;291:2727–33. [12] Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12. [13] Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care 2004;27:1487–95. [14] Meyfroidt G, Keenan DM, Wang X, et al. Dynamic characteristics of blood glucose time series during the course of critical illness: effects of intensive insulin therapy and relative association with mortality. Crit Care Med 2010;38:1021–9. [15] Brunner R, Adelsmayr G, Herkner H, et al. Glycemic variability and glucose complexity in critically ill patients: a retrospective analysis of continuous glucose monitoring data. Critical Care 2012;16:R175. [16] Heine RJ, Van Gaal LF, Johns D, et al. Exenatide versus insulin glargine in patients with suboptimally controlled type 2 diabetes: a randomized trial. Ann Intern Med 2005;143:559–69. [17] Haak T, Tiengo A, Draeger E, et al. Lower within-subject variability of fasting blood glucose and reduced weight gain with insulin detemir compared to NPH insulin in patients with type 2 diabetes. Diabetes Obes Metab 2005;7:56–64. [18] Meynaar IA, Eslami S, Abu-Hanna A, et al. Blood glucose amplitude variability as predictor for mortality in surgical and medical intensive care unit patients: a multicenter cohort study. J Crit Care 2012;27:119–24.
Letters to the Editor
Glycemic variability predicts cardiovascular complications in acute myocardial infarction patients with type 2 diabetes mellitus☆ Xiangfei Wang a,1, Xiaolong Zhao b,1, Tashi Dorje a, Hongmei Yan c, Juying Qian a,⁎, Junbo Ge a,⁎ a b c
Shanghai Institute of Cardiovascular Diseases, Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China Department of Endocrinology & Metabolism, Huashan Hospital, Fudan University, Shanghai, China Department of Endocrinology & Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
a r t i c l e
i n f o
Article history: Received 2 November 2013 Received in revised form 1 January 2014 Accepted 7 January 2014 Available online 21 January 2014 Keywords: Glycemic variability Acute myocardial infarction (AMI) Major adverse cardiac event
In diabetes mellitus (DM) patients, glycosylated hemoglobin is closely related to the number of diabetic complications. It has recently been found that glycemic variability is also an important parameter that may be associated with the occurrence of diabetic complications [1] and the prognosis of critically ill patients [2]. Evidence of the predictive value of glycemic variability for poor outcomes in patients with type 2 diabetes mellitus (T2DM) is less sufficient than that in type 1 diabetes mellitus (T1DM) patients. A variety of glucose parameters are associated with poor prognosis in patients with acute myocardial infarction (AMI) [3], but unlike other critically ill patients, the relevance of glycemic variability and the prognosis of AMI patients are uncertain [4–6]. Furthermore, previous studies used fingertip blood glucose sampling and the definitions of glycemic variability were inconsistent. Recent studies showed that a continuous glucose monitoring system (CGMS) can better identify the abnormity of blood glucose in patients with ACS [7,8]. Su et al. found that the mean amplitude of glycemic excursion (MAGE) derived from CGMS independently predicts one-year major adverse cardiac events (MACE) in patients with AMI [9], despite only around half of the patients having preexisting diabetes. This is a single-center, prospective observational clinical study. All eligible patients with AMI admitted to the cardiac care unit of our institute were consecutively recruited from October 2011 to May 2012. The diagnosis of myocardial infarction was made according to contemporary guidelines [8]. Inclusion criteria for this study were: (1) meeting the diagnostic criteria for AMI with a time of symptom onset within 24 h regardless of whether revascularization was performed; (2) having been diagnosed with DM before admission or with an HbA1c greater than 6.5% [10]; (3) age N 18 years; (4) eGFR ≥ 45 ml/min/ 1.73 m2; and (5) Killip classification greater than stage two. In hospital mortality risk was assessed by using GRACE score for ACS [11]. eGFR was calculated by the CKD-EPI formula [12]. This research was in accordance with the declaration of Helsinki and approved by the local research ethics committee, and written informed consent was required before any research procedure. The authors of this manuscript have certified that they comply with the principles of ethical publishing in the International Journal of Cardiology.
☆ The authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. ⁎ Corresponding authors. Tel.: +86 21 6404 1990x2745; fax: + 86 21 6443 7078. E-mail addresses: [email protected] (J. Qian), [email protected] (J. Ge). 1 These authors contributed equally to this work.
Clinical records of all patients were reviewed. During the hospital stay, all patients were placed on continuous blood glucose monitoring with CGMS, and routine fingertip blood glucose was measured. To control blood glucose within 10 mmol/l, insulin therapy was given once fingertip blood glucose greater than 10 mmol/l was detected. Laboratory data of the first day and third day after admission were collected, function of beta-cell and insulin resistance were calculated by two-hour postprandial blood glucose, insulin and C-peptide [13]. Cardiac troponin T (cTnT) was measured every 6 h in the first day and repeated at 48 and 72 h. Transthoracic echocardiography was preformed within 72 h of admission. All the examinations and treatments were based on current clinical guideline recommendations. All patients were followed-up by a hospital visit or telephone interview for clinical evaluation and MACE was defined as a composite of all-cause death, recurrent AMI, and re-hospitalization for acute decompensated heart failure (ADHF). Categorical data were presented as numbers and percentages, and compared with Fisher's exact test. Continuous variables were expressed as mean ± standard deviation (SD) and compared with Student's t test. Logarithmic transformation was used for skewed data including cTnT, NT-proBNP, hs-CRP, insulin, and C-peptide. To assess the relationship between the occurrence of MACE and clinical parameters, binary logistic regression analysis was performed. Receiver operating characteristic (ROC) curve was used to determine the optimum cut-off levels of MAGE to predict MACE. All tests were 2sided and a probability (p) value b0.05 was considered statistically significant. Statistical analysis was performed by using SPSS 11.0 (IBM, Armonk, NY, USA). A total of 34 patients were recruited in this study, among them 28 patients had previously been diagnosed with diabetes, and 6 patients with HbA1c N 6.5%. No MACE occurred during hospitalization. All subjects completed the follow-up (17.03 ± 1.66 months), during the follow-up 4 patients died, 1 re-infarcted, and 1 was hospitalized for ADHF, making the total number of MACE 6. Patients were then divided into two groups based on whether or not they had MACE. Baseline characteristics are presented in Table 1. There was no significant difference between the two groups regarding cardiovascular risk factors, laboratory results and echocardiographic parameters. There were also no differences in myocardial infarction type, proportion of patients who received revascularization therapy, GRACE score, number of diseased vessels and patients with implanted drug-eluting stents. All patients received the following therapy: aspirin, clopidogrel, carvedilol, ACEI/ARB, and lipid-lowering agents. Meanwhile, some received spironolactone (p N 0.05). Regarding glucose metabolism parameters, there were no differences in random blood glucose after admission, fasting blood glucose, 2 h of postprandial plasma glucose, HbA1c, GA, fasting and postprandial insulin, and fasting and postprandial C-peptide between the two groups. Insulin resistance according to the HOMA-IR and the QUICKI formula showed no significant differences between the two groups. However, data from CGMS analysis showed that the MACE group had significantly higher MAGE (8.84 ± 2.29 mmol/l vs. 6.19 ± 2.34 mmol/l; p = 0.017) and greater SD (3.25 ± 0.70 vs. 2.40 ± 0.82; p = 0.026). No significant differences in other parameters were found between the two groups.
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Table 1 Basic demographics and clinical characteristics of study subjects. Characteristic Risk factors Age, years Sex (male), n (%) BMI, kg/m2 Hypertension, n (%) Dyslipidemia, n (%) Smoking, n (%) Myocardial infarction history, n (%) Heart rate, BPM Mean artery pressure, mm Hg STEMI/NSTEMI, n GRACE score Vessel disease (single/multiple, n) Revascularization, n (%) Lab tests Hemoglobin, g/l White blood cell, ∗109/l eGFR, ml/min/1.73 m2 Triglyceride, mmol/l Total cholesterol, mmol/l HDL-c, mmol/l LDL-c, mmol/l Maximum cTnT, ng/ml Maximum NT-proBNP, pg/ml hs-CRP, mg/l Echocardiography LV end-diastolic diameter, mm LV end-systolic diameter, mm LV ejection fraction, % Glucose parameters Randomized blood glucose, mmol/l First FBG, mmol/l Fasting insulin, μU/l Fasting C-peptide, nmol/l First PBG, mmol/l Postprandial insulin, μU/l Postprandial C-peptide, nmol/l Secondary FBG, mmol/l Secondary PBG, mmol/l HbA1c, % Glycated albumin, % MAGE, mmol/l Mean sensor glucose, mmol/l Standard deviation Duration of hyperglycemia (h/day) Duration of hypoglycemia (h/day) Mean AUCCGM (mmol/l/24 h) Low area Fasting C-peptide/glucose ratio Delta C-peptide/delta glucose ratio HOMA-IR QUICKI
MACE group (n = 6)
Non-MACE group (n = 28)
p value
65.35 ± 14.78 5 (83.3%) 24.55 ± 2.72 4 (66.7%) 0 (0%) 3 (50.0%) 1 (16.7%) 90.67 ± 35.99 85.94 ± 11.97 2/4 221.17 ± 16.41 0/6 5 (83.3%)
62.27 ± 10.82 24 (85.7%) 23.97 ± 2.96 17 (60.7%) 8 (28.6%) 13 (46.4%) 3 (10.7%) 80.57 ± 14.86 92.95 ± 14.73 14/14 208.87 ± 18.03 10/18 25 (89.3%)
0.556 1.000 0.645 1.000 0.297 1.000 0.559 0.528 0.285 0.660 0.134 0.197 0.559
131.50 ± 11.73 9.43 ± 1.20 77.82 ± 21.00 2.32 ± 2.13 4.46 ± 0.90 0.93 ± 0.19 2.68 ± 0.70 2.22 (0.919–3.468) 1730 (593–5817) 25.45 (3.13–83.98)
135.39 ± 14.47 10.04 ± 3.06 95.18 ± 21.70 1.99 ± 1.07 4.45 ± 0.86 1.03 ± 0.23 2.57 ± 0.79 1.43 (0.825–4.075) 910 (425–1366) 6.80 (3.75–14.35)
0.543 0.638 0.083 0.574 0.975 0.344 0.753 0.982 0.360 0.275
46.83 ± 6.05 30.33 ± 4.59 52.83 ± 7.96 11.90 ± 3.83 9.93 ± 2.33 7.65 (5.68–17.55) 2.57 (1.69–3.41) 16.15 ± 3.39 46.50 (18.93–94.90) 4.82 (3.50–7.79) 9.48 ± 1.92 14.10 ± 3.82 9.35 ± 1.53 24.62 ± 3.45 8.84 ± 2.29 11.00 ± 2.08 3.25 ± 0.70 13.58 ± 4.58 0.42 ± 1.02 2.07 ± 1.56 0.02 ± 0.04 0.31 (0.14–0.41) 0.45 (0.27–1.08) 5.83 ± 7.64 0.32 ± 0.04
48.96 ± 6.10 33.57 ± 6.82 54.43 ± 8.69 9.66 ± 3.71 8.24 ± 2.29 8.35 (5.53–14.43) 2.58 (1.62–3.57) 13.957 ± 3.0244 50.50 (25.68–77.55) 6.16 (3.41–8.38) 8.00 ± 2.08 14.69 ± 2.68 8.35 ± 1.63 20.88 ± 5.42 6.19 ± 2.34 9.69 ± 1.73 2.40 ± 0.82 10.34 ± 5.60 0.26 ± 0.79 1.17 ± 1.05 0.01 ± 0.04 0.31 (0.20–0.51) 0.61 (0.28–1.17) 7.08 ± 18.48 0.33 ± 0.04
0.443 0.278 0.682 0.192 0.111 0.991 0.886 0.124 0.479 0.066 0.120 0.657 0.178 0.054 0.017 0.113 0.026 0.199 0.688 0.097 0.793 0.388 0.775 0.873 0.725
Data are means ± SD, medians (25th–75th percentile), or n (%). STEMI = ST-segment elevated myocardial infarction; NSTEMI = non-ST-segment elevated myocardial infarction; eGFR = estimated glomerular filtration rate (ml/min/1.73 m2); HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; cTnT = cardiac troponin T; NT-proBNP = N-terminal pro-brain natriuretic peptide; hs-CRP, high-sensitive C-reactive protein; LV = left ventricular; HbA1c = glycated hemoglobin; MAGE = mean amplitude of glycemic excursions; HOMA-IR = homeostasis model assessment of insulin resistance; and QUICKI = quantitative insulin sensitivity check index.
In univariate logistic regression analysis, eGFR (odds ratio = 0.963; 95% CI, 0.922–1.006; p = 0.094), MAGE (odds ratio = 1.592; 95% CI, 1.034–2.451; p = 0.035) and SD (odds ratio = 3.151; 95% CI, 1.036– 9.583; p = 0.043) were the only independent risk factors for MACE (p b 0.10). Furthermore, multivariate logistic regression analysis in forward selection showed that only MAGE could predict MACE. Additionally, MAGE N 6.05 mmol/l was identified as the most useful cut-off level for the prediction of MACE by ROC curve analysis, with a sensitivity and specificity of 100% and 53.6% respectively, and the area under the curve was 0.798 (p = 0.024). When patients were divided into the high MAGE group (n = 19) and low MAGE group (n = 15), all MACE happened in the high MAGE group, whereas no MACE occurred in the low MAGE group (p = 0.024). Kaplan–Meier survival curve analysis
showed that high MAGE group had a significantly lower event-free survival rate (p = 0.023) (Fig. 1). Our study showed that MAGE is an independent predictor of MACE in diabetic patients with AMI which supports the finding of Su et al. Recently, the predictive value of glycemic variability in patients with ACS has been investigated in many studies, but the conclusions are inconsistent. The main reason may be that those studies use different inclusion criteria and glucose variability indicators. It was proven that GV derived from CGMS is more reliable than other methods, because the accuracy of variability is closely related to the number of detection data. CGMS is widely used in diabetic patients for glycemic monitoring and studies showed that it can also be used for glycemic monitoring
Letters to the Editor
Events-free survival, %
500
ill patients and their prognosis. It showed that the composite mortality, after correction, was related to SD and MAGE, but had no association with mean absolute glucose change per hour (MAG) and the glycemic lability index (GLI). In surgical patients, the composite mortality was related to SD, MAGE and MAG; in critically ill patients, the composite mortality was only related to SD [18]. However, as to which is more valuable, the MAGE or SD derived from CGMS, remains to be studied. Our results suggested that MAGE might be better.
100 80 60 Log Rank P = 0.023
40
MAGE 6.05 mmol/L
0 0
5
10
15
20
Months Fig. 1. Kaplan–Meier event-free survival curves for freedom from MACE in two patient groups by MAGE levels. The event-free survival rate was significantly lower in the high MAGE level patients.
in critically ill patients. However, there is little research about the applications of CGMS in patients with ACS. Radermecker et al. found that it can better identify hyperglycemia in non-diabetic patients [7]. Sampaio et al. found that CGMS was able to recognize more hypoglycemia in patients with AMI receiving insulin therapy [8]. However, the association between adverse events and glycemic variability was not reported in their studies. Our study as well as a study conducted by Su et al. showed that MAGE obtained from CGMS predicted the prognosis of patient at about one-year follow-up, rather than MACE for the duration of hospital stay, probably because the influence of blood glucose is mainly long-term rather than shortterm. Despite increasingly more data revealing that an increase in glycemic variability is associated with poor outcomes, it is unclear whether it is beneficial to correct excessive glycemic variability, as a primary aim of study, and so far, there is no clinical research about this. Intensive insulin therapy had no effect on SD, aside from increasing the MAGE during the day [14]. Moreover, intensive insulin therapy based on real-time CGMS has not been able to lower the glycemic variability (SD, GLI, MPG) [15]. Studies showed that exenatide can better lower the glycemic variability than glargine, and less extreme values appear [16]. Although there is no significant difference in HbA1c, detemir could better reduce glycemic variability than neutral protamine Hagedorn (NPH) insulin, and with less weight gain during the therapy [17]. Further clarification regarding which drug and what kind of therapy can be effective in achieving this purpose is required before initiating clinical trials about ameliorating glycemic variability. One of the problems of research is that there is no acknowledged gold standard for numerous indicators reflecting glycemic variability. In a meta-analysis from Eslami et al. [2], the glycemic variabilities of critically ill patients were associated with a poor prognosis, but different clinical trials have various indicators of glycemic variability and it is difficult to make direct comparisons. In the largest prospective study (20,375 patients), Meynaar et al. observed the relationship between glycemic variability of critically
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