Prognostic value of early in-hospital glycemic excursion in elderly patients with acute myocardial infarction

Study population

Consecutive patients admitted to the cardiology department of Beijing An Zhen Hospital of Capital Medical University for AMI between July 2010 and October 2011 were selected. The inclusion criteria were: (i) age ≥ 60 years old; (ii) confirmed admission diagnosis of AMI including ST segment elevated myocardial infarction (STEMI) and non-ST segment elevated myocardial infarction (NSTEMI); (iii) admission glucose < 16.7 mmol/L, and without diabetic ketosis or nonketotic hyperosmolar coma. AMI was defined as acute if the time elapsed between the first symptom and admission was 72 h or less. To enable long-term follow-up and repeated visits to our outpatient clinic, only patients under the age of 80 and living within the hospital’s catchment area were eligible. The exclusion criteria were severe non-cardiac disease with expected survival of less than one year and unwillingness to participate. A patient could only be included once. Data, including information on previous clinical history, cardiovascular risk factors and medication, were collected in hospital. Type 2 diabetes mellitus (T2DM) was diagnosed according to the American Diabetes Association criteria or the use of insulin or glucose-lowering medication. The estimated glomerular filtration rate (eGFR) value was calculated by MDRD equation [9]. The Global Registry of Acute Coronary Events (GRACE) risk score were calculated as admission [10], which is recommended by the National Institute for Health and Clinical Excellence (NICE) to assess risk in patients with ACS. Patients were categorized according to tertiles of the mean amplitude of glycemic excursions (MAGE) level (<2.55 mmol/L, 2.55-3.94 mmol/L and >3.94 mmol/L), and according to HbA_1c (<6.5% and ≥6.5%) [11]. The study protocol was approved beforehand by the Medical Ethics Committee of Beijing An Zhen Hospital of Capital Medical University and the procedures followed were in accordance with the institutional guidelines. The study complied with the declaration of Helsinki and informed consent was obtained from all patients.

Continuous glucose monitoring

All patients were equipped with CGMS (Medtronic MiniMed, USA), and were monitored for 72 consecutive hours after admission. A CGMS sensor was inserted into the subcutaneous abdominal fat tissue, calibrated according to the standard Medtronic MiniMed operating guidelines. During CGMS monitoring, patients checked their blood glucose level with a self-monitoring of blood glucose (SMBG) device (Medisafe Mini, Terumo, Japan) at least 4 times per day. Then, they entered the SMBG data and time of each meal into the CGMS. After monitoring for 72 hours, the recorded data were downloaded into a personal computer for analysis of the glucose profile and glycemic excursion parameters with MiniMed Solutions software. After downloading the recorded data, the intermediate 48 hours of recording was analyzed to avoid bias due to insertion and removal of the CGMS or insufficient stability of the monitoring system, and MAGE was caculated from the first 24 hours of them. Since measurable range of glucose by CGMS was mechanically limited from 2.2 to 22.2 mmol/L, the case showing the data out of this range was excluded from the study. The MAGE was calculated by measuring the arithmetic mean of the differences between consecutive peaks and nadirs, provided that the differences are greater than one standard deviation (SD) of the mean glucose value [12]. Patients would maintain anti-hyperglycemic therapy as usual and be avoided glucose infusion during CGMS monitoring period. Otherwise, the patient would be excluded from the study.

Biochemical investigations

Blood samples were collected after overnight fasting and stored at −70°C prior to analysis. Serum creatinine, total cholesterol (TC) and triglyceride (TG) levels were measured by automatic biochemical analyzer (Hitachi 747, Tokyo, Japan). Serum concentration of hemoglobin A_1c (HbA_1c) was determined by high-performance liquid chromatographic method using automatic HbA_1c analyzer (Tosoh HLC-723 G7, Japan).

Follow-up

Patients were followed up prospectively for about 1 year. During follow-up period, incidences of major adverse cardiac event (MACE) were registered, including new-onset myocardial infarction, acute heart failure and cardiac death. All MACE data were adjudicated by an experienced cardiovascular physician blinded to clinical details and outcomes.

Statistical analysis

All statistical analyses were performed by using SPSS for Windows 13.0 (SPSS Inc, Chicago, IL, USA). Data are presented as frequencies and percentages for categorical variables and mean ± SD for continuous variables, unless otherwise indicated. Differences between two groups were assessed by using the Chi-square and unpaired t-tests. Correlation between continuous variables was determined by Spearman correlation coefficients. MAGE was included as a continuous and as a categorized (<2.55 mmol/L, 2.55-3.94 mmol/L and >3.94 mmol/L) variable. HbA_1c level was also included as a continuous and categorized (<6.5% and ≥6.5%) variable. Kaplan-Meier survival curve analysis was used to represent the proportional risk of MACE for the MAGE and HbA_1c values, and the log-rank test was performed to assess differences between high MAGE level and low MAGE level, and high HbA_1c level and low HbA_1c level. Cox proportional-hazards regression models were used to estimate hazard ratios of clinical variables with regard to MACE. A value of p < 0.05 was considered statistically significant.

Baseline characteristics

Incidences of MACE

At the end of 1-year follow-up, 10 patients had died (5.4%) for cardiac causes, 13 patients had new-onset myocardial infarction (7.0%), and 10 patients had acute heart failure (5.4%). As expected, elderly AMI patients with MAGE level >3.94 mmol/L had significantly higher incidence of MACE compared with elderly AMI patients with MAGE level from 2.55 mmol/L to 3.94 mmol/L, or < 2.55 mmol/L (30.2% vs. 14.8% vs. 8.1%, p = 0.004). No significant rates of adverse cardiovascular events were observed between patients with HbA_1c level ≥6.5% and patients with HbA_1c level < 6.5% (22.7% vs. 14.4%, p = 0.148). Elderly AMI patients with a higher MAGE level had a significantly higher cardiac mortality compared with elderly AMI patients with lower MAGE levels (11.1% vs. 1.6% vs. 3.2%, p = 0.043) (Figure 1). No significant differences in rates of adverse cardiovascular events were observed between elderly AMI patients with high HbA_1c level and patients with low HbA_1c level (Figure 2). Kaplan-Meier survival curves for patient groups by MAGE are shown in Figure 3; those for HbA_1c in Figure 4.

Multivariable analysis

To investigate the associations between MAGE, HbA_1c level and incidences of MACE with respect to baseline characteristics, we used multivariable analysis. Include variables were: age, gender, and all variables that were significantly different between MAGE or HbA_1c categories [current smoking, diabetes, previous coronary artery disease (CAD), heart rate, eGFR, anti-hyperglycemic agents, fasting blood glucose, the STEMI presentation, left ventricular ejection fraction (LVEF, categorized into <50% = 1 and ≥50% = 0) and diuretics]. The independent predictors of MACE were: age (HR 1.624, 95% CI 1.036-2.548, p = 0.035), previous CAD (HR 2.907, 95% CI 1.227-6.896, p =0.015), LVEF (HR 2.611, 95% CI 1.107-6.135, p = 0.028) and MAGE (HR 3.131, 95% CI 1.422-6.710, p = 0.014). HbA_1c level was not significantly associated with MACE (HR 1.522, 95% CI 0.841-2.757, p = 0.165). After adjustment for the GRACE score, MAGE was found to be also associated with incidences of MACE (HR 3.107, 95% CI 1.190-8.117, p = 0.021), but HbA_1c was not (HR 1.438, 95% CI 0.619-5.464, p = 0.273).

Study limitations

The sample size was relatively small, so that comparisons of some subgroups might lack power to detect significant differences for selected variables. For lack of microvascular complications data, we didn’t include those risk factors in analysis. Although we had maintained the patients’ anti-hyperglycemic therapy as usual and avoided glucose infusion during CGMS monitoring period, some factors, such as different diets, physical and emotional stress etc., which may affect glucose fluctuations couldn’t be all prevented. In addition, tests to detect diabetes were not routinely done, so some cases of diabetes may have been missed. However, if the observed relation between glycemic excursion and MACE was due to undiagnosed diabetes, one would have expected a more distinct association between HbA_1c and outcomes.