- Email: [email protected]
Impact of the Metabolic Syndrome on the Clinical Outcomes of Non-Clinically Diagnosed Diabetic Patients With Acute Coronary Syndrome
Impact of the Metabolic Syndrome on the Clinical Outcomes of Non-Clinically Diagnosed Diabetic Patients With Acute Coronary Syndrome Micha S. Feinberg, MDa,*, Roseline Schwartz, MScb, David Tanne, MDc, Enrique Z. Fisman, MDd, Hanoch Hod, MDa, Doron Zahger, MDe, Ehud Schwammethal, MDd, Michael Eldar, MDa, Solomon Behar, MDb, and Alexander Tenenbaum, MDd The aim of this study is to explore the impact of metabolic syndrome (MS) on the outcome of patients with non-clinically diagnosed diabetes with acute coronary syndrome (ACS) based on a comprehensive nationwide registry during a 1-year follow-up. In the ACS Israeli Survey, 1,060 consecutive patients with non-clinically diagnosed diabetes were admitted due to ACS; 359 patients with MS features on admission were compared with 701 subjects without MS. A modified National Cholesterol Education Program Adult Treatment Panel III definition of MS was used in patients who presented with >3 of the 5 components: (1) hyperglycemia, defined as occasional blood glucose on admission >140 mg/dl; (2) preexisting hypertension; (3) body mass index >28 kg/m2; (4) high-density lipoprotein cholesterol <40 mg/dl (men) or <50 mg/dl (women); and (5) triglycerides >150 mg/dl. Patients with MS were more frequently women (27% vs 12%, p ⴝ 0.001), were in Killip >II on admission (19% vs 14%, p ⴝ 0.03), and had higher 30-day (5.0% vs 1.7%, p ⴝ 0.002) and 1-year (8.9% vs 4.6%, p ⴝ 0.005) crude mortality rates. Patients with hyperglycemia (glucose >140 mg/dl) and MS had higher 30-day mortality rates compared with patients with hyperglycemia without MS (8.3% vs 2.5%, p <0.05). Multivariate analysis identified MS as a strong independent predictor of 30-day and 1-year mortality with hazard ratios of 2.54 (95% confidence interval 1.22 to 5.31) and 1.96 (95% confidence interval 1.18 to 3.24), respectively. In conclusion, MS defined early at admission is a strong independent predictor of mortality and morbidity in patients with non-clinically diagnosed diabetes with ACS. © 2007 Elsevier Inc. All rights reserved. (Am J Cardiol 2007;99:667– 672)
Only limited information is available regarding the association of metabolic syndrome (MS) with outcomes in patients without diabetes after recent myocardial infarction.1–3 Moreover, the impact of MS defined for the first time at admission on the outcome of patients with non-clinically diagnosed diabetes with acute coronary syndrome (ACS) is unknown. We addressed this question by analyzing the impact of MS on the clinical outcome of patients included in a comprehensive national registry during a 1-year follow-up. Methods Patients were selected from the ACS Israeli Survey (ACSIS 2004), a prospective nationwide survey conducted during February and March 2004 in all 25 coronary care units and cardiology wards operating in Israel. Demographic, historical, and clinical data were recorded by study physicians on a Heart Institute, bNeufeld Cardiac Research Institute, cDepartment of Neurology and Neuro-Vascular Laboratory, and dCardiac Rehabilitation Institute, Sheba Medical Center, Tel Hashomer, Sackler Faculty of Medicine, Tel Aviv University, and eSoroka Medical Center, Beer Sheba, Israel. Manuscript received August 26, 2006; revised manuscript received and accepted October 9, 2006. *Corresponding author: Tel: 972-3-530-2433; fax: 972-3-530-2407. E-mail address: [email protected] (M. Feinberg).
0002-9149/07/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.amjcard.2006.10.023
prespecified forms for all patients admitted with a diagnosis of ACS. Admission and discharge diagnoses were recorded by attending physicians based on clinical, electrocardiographic, and enzymatic criteria and local policy. Patient management was left to the discretion of each center. Onsite coronary catheterization with percutaneous intervention was available in 22 centers and with coronary bypass graft facilities in 12 centers. Mortality rates at 7 and 30 days, 6 months, and 1 year were determined for all participants from hospital charts and by matching patient identification numbers with the Israeli National Population Register. Patients who denied a history of diabetes mellitus and were not treated with insulin or oral hypoglycemic medications were defined as nondiabetic. The diagnosis of hypertension was based on the patient’s history (preexisting diagnosed hypertension). Of 2,094 patients with ACS enrolled in ACSIS 2004, the presence or absence of MS could not be determined in 346 (16.5%) because of missing data. Of the remaining 1,748 patients, 688 had known diabetes mellitus and were excluded from further analysis. Therefore, the final population for our study included 1,060 patients with ACS, of whom 359 (34%) had MS. Definition of metabolic syndrome on admission: For purposes of our study, MS is defined de novo in the clinical context of ACS in accordance with clinical data obtained early at admission. MS was defined in patients who prewww.AJConline.org
668
The American Journal of Cardiology (www.AJConline.org)
sented with ⱖ3 of the 5 diagnostic components proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III).4 Their cut-off points were modified slightly for the setting of admission as (1) impaired glucose metabolism defined as occasional (instead of fasting) blood glucose level ⬎140 mg/dl at admission, (2) increased triglycerides ⱖ150 mg/dl, (3) high-density lipoprotein (HDL) cholesterol ⬍40 mg/dl in men or ⬍50 mg/dl in women, (4) self-reported preexisting hypertension diagnosed by primary physician, and (5) overweight, central obesity. Because waist circumference data were not available, we used body mass index (BMI) ⬎28.0 kg/m2 as a criterion for inclusion for purposes of this analysis. Statistical analysis: Patients were classified into 2 groups: the MS group and the non-MS group. Baseline demographic characteristics of the 2 groups were compared. Continuous variables are expressed as mean ⫾ SD. Discrete data are given as frequency and percentage. Proportions were compared using chi-square tests, and means, using Student’s t tests. To examine the relation between MS and mortality, several models were built. One-year event-free survival in the 2 groups is presented as Kaplan-Meier curves and compared using the log-rank test. Multivariate Cox proportional hazards analysis was applied to assess the effect of MS on event-free survival. To control for confounding in the Cox model, potential prognostic variables (identified in previous published studies as risk factors for mortality or clinical variables associated with 1-year mortality) evaluated using univariate analysis and selected based on clinical and statistical significance were entered as covariates in the Cox model. For 1-year mortality, we included in the model: age (as a continuous covariate); gender; history of myocardial infarction, cerebrovascular accident, or congestive heart failure; chronic renal failure; and Killip class ⱖII on admission as categorical variables. Because of the low number of events within 30-day follow-up, we included only age, gender, and Killip class ⱖII as covariates in the Cox model for 30-day mortality. Adjusted survival curves were constructed using variables entered into the Cox model set to their mean values in the total population. Results of the Cox logistic regression model were presented as hazard ratio (HR) and 95% confidence interval (CI). Adjusted relative risks (RRs) for age and gender, calculated using a Mantel-Haenszel direct adjustment, were computed to determine the significance of each MS component for predicted 1-year mortality. To assess the impact of MS on 1-year mortality in patients with hyperglycemia, hypertension, increased BMI and triglycerides, and decreased HDL cholesterol, the RR of MS and 1-year mortality rates were determined accordingly in subjects with and without MS. A 2-sided p value ⬍0.05 was considered statistically significant. All analyses were performed using SAS software (version 8.2, SAS Institute Inc., Cary, North Carolina). Results Distribution of metabolic syndrome components: Clinical conditions used as MS criteria were distributed in patients with MS (n ⫽ 359) as (1) hyperglycemia on admission (glucose ⬎140 mg/dl), 59%; (2) history of hyper-
Table 1 Baseline patient characteristics Variable
MS (n ⫽ 359)
Others* (n ⫽ 701)
p Value
Age (yrs) 63 ⫾ 13 Women 98 (27%) 29.4 ⫾ 4.2 BMI (kg/m2) Hypertension 275 (77%) Hyperlipidemia 197 (55%) Smoker (current) 145 (40%) Previous myocardial infarction 71 (20%) Previous angina pectoris 106 (30%) Previous coronary artery 33 (9%) bypass grafting Previous percutaneous 63 (18%) coronary intervention Previous heart failure 13 (4%) Chronic renal failure 24 (7%) Previous stroke 30 (8%) Previous peripheral vascular 19 (5%) disease Malignancy 13 (4%) Killip II 43 (12%) Killip II 22 (6%) Killip III 2 (1%) Killip on admission ⱖII 67 (19%) Admission systolic blood 146 ⫾ 31 pressure (mm Hg) Admission diastolic blood 84 ⫾ 17 pressure (mm Hg) Glucose on admission (mg/dl) 156 ⫾ 62 Triglycerides (mg/dl) 202 ⫾ 113 HDL (mg/dl) 36 ⫾ 9 Left ventricular ejection 47 ⫾ 12 fraction (%)† ACS Q-wave myocardial 168 (47%) infarction Non–Q-wave myocardial 114 (32%) infarction Unstable angina pectoris 77 (21%) Myocardial infarction location Anterior 123 (34%) Inferior 114 (32%)
62 ⫾ 13 130 (19%) 25.3 ⫾ 3.3 216 (31%) 279 (40%) 313 (45%) 147 (21%) 182 (26%) 50 (7%)
0.12 0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.19 0.65 0.22 0.24
122 (17%)
0.95
24 (3%) 33 (5%) 36 (5%) 28 (4%)
0.87 0.18 0.04 0.33
28 (4%) 70 (10%) 23 (3%) 5 (1%) 98 (14%) 138 ⫾ 29
0.77
0.03 ⬍0.001
81 ⫾ 17
0.003
121 ⫾ 35 127 ⫾ 92 46 ⫾ 13 47 ⫾ 11
⬍0.001 ⬍0.001 ⬍0.001 0.56
338 (48%) 199 (28%) 164 (23%)
0.49
245 (35%) 205 (30%)
0.82 0.52
* Patients without MS. Available in 72% of patients.
†
tension, 77%; (3) hypertriglyceridemia, 74%; (4) low HDL cholesterol, 83%; and (5) increased BMI, 68%. In those without MS (n ⫽ 701), these conditions were distributed as (1) hyperglycemia on admission, 17%; (2) history of hypertension, 31%; (3) hypertriglyceridemia, 19%; (4) decreased HDL cholesterol, 35%; and (5) increased BMI, 14%. Baseline characteristics: There were more women in the MS group compared with the group without MS (27% vs 19%, p ⫽ 0.001). Patients with MS more frequently presented with Killip class ⱖII on admission (19% vs 14%, p ⫽ 0.03), more often had a history of hypertension and hyperlipidemia (77% vs 31%, p ⬍0.001; 55% vs 40%, p ⬍0.001, respectively), and had more previous strokes (8% vs 5%, p ⫽ 0.04). Age and prevalence of previous myocardial infarction, anginal syndrome, coronary artery bypass grafting, percutaneous coronary intervention, congestive
Coronary Artery Disease/MS in Nondiabetic Patients With ACS
669
Table 2 Hospital management and outcome Variable
MS (n ⫽ 359)
Others* (n ⫽ 701)
Primary reperfusion Coronary angiography Any percutaneous coronary intervention Coronary stent implantation Coronary artery bypass grafting Mechanical ventilation Intra-aortic balloon pump Discharge medications Aspirin Anticoagulants Nitrates Clopidogrel Angiotensin-converting enzyme inhibitors/ angiotensin receptor blockers Aldosterone  blockers Diuretics Statins Insulin Oral hypoglycemic medications Congestive heart failure Paroxysmal atrial fibrillation Ventricular tachyarrhythmia‡ Stroke Recurrent myocardial infarction Renal failure 7-D mortality 30-D mortality 6-Mo mortality 1-Yr mortality
130 (36%) 283 (79%) 216 (76%)†
290 (41%) 584 (83%) 465 (80%)†
0.10 0.07 0.27
189 (88%) 25 (7%)
401 (86%) 35 (5%)
0.66 0.19
16 (5%) 14 (4%)
13 (2%) 21 (3%)
0.01 0.44
333 (95%) 45 (13%) 53 (15%) 237 (68%) 263 (75%)
670 (96%) 73 (11%) 110 (16%) 504 (73%) 468 (67%)
0.44 0.25 0.79 0.12 0.008
17 (5%) 297 (85%) 75 (22%) 299 (86%) 3 (0.9%) 6 (1.7%)
29 (4%) 559 (80%) 96 (14%) 580 (84%) 0 (0%) 4 (0.6%)
p Value
0.60 0.064 0.002 0.35 0.01 0.07
50 (14%) 18 (5%) 28 (8%) 5 (1%) 4 (1%)
66 (9%) 22 (3%) 61 (9%) 1 (⬍0.5%) 6 (1%)
0.03 0.13 0.62 0.01 0.68
19 (5%) 8 (2.2%) 18 (5.0%) 29 (8.1%) 32 (8.9%)
23 (3%) 7 (1.0%) 12 (1.7%) 25 (3.6%) 32 (4.6%)
0.11 0.11 0.002 0.002 0.005
* Patients without MS. Percent total coronary angiography. ‡ Ventricular fibrillation/tachycardia. †
heart failure, renal failure, and peripheral vascular disease were similar in the 2 groups (Table 1). Ischemic electrocardiographic characteristics on admission (ST-segment shifts, elevation, and depression) did not differ significantly between patients with versus without MS (52% vs 58% and 22% vs 16%, respectively). As expected, blood glucose levels at admission (156 ⫾ 62 vs 121 ⫾ 35 mg/dl, p ⬍0.001), BMI (29.4 ⫾ 4.2 vs 25.3 ⫾ 3.3 kg/m2, p ⬍0.001), triglyceride levels (202 ⫾ 113 vs 127 ⫾ 92 mg/dl, p ⬍0.001), and systolic and diastolic blood pressure at admission (146 ⫾ 31 vs 138 ⫾ 29 mm Hg, p ⬍0.001; 84 ⫾ 17 vs 81 ⫾ 17 mm Hg, p ⫽ 0.003, respectively) were significantly higher in the MS group, whereas HDL cholesterol level (36 ⫾ 9 vs 46 ⫾ 13 mg/dl, p ⬍0.001) was significantly lower (Table 1). Hospital course: Proportions of patients with Q-wave and non–Q-wave myocardial infarction, unstable angina, and anterior and inferior myocardial infarction were similar
Figure 1. Kaplan-Meier 1-year survival curve.
in patients with MS versus without MS (Table 1), as were frequencies of primary reperfusion, coronary angiography, any percutaneous coronary intervention, coronary stent implantation, and coronary artery bypass grafting (Table 2). Mechanical ventilation was used more commonly in patients with MS (5% vs 2%, p ⫽ 0.01; Table 2), indicating the association of MS with severe congestive heart failure. At discharge, there were no significant differences in use of aspirin,  blockers, and statins between the 2 groups. However, angiotensin-converting enzyme inhibitors or angiotensin II receptor antagonists and diuretics were prescribed more commonly for patients with MS (75% vs 67%, p ⫽ 0.008; 22% vs 14%, p ⫽ 0.002, respectively; Table 2). Congestive heat failure and stroke during hospitalization occurred more often in patients with MS (14% vs 9%, p ⫽ 0.03; 1% vs ⬍0.5%, p ⫽ 0.03, respectively; Table 2). Crude mortality rates: Mortality rates were higher in patients with MS at 30 days (5.0% vs 1.7%, p ⫽ 0.002) and remained so after 1 year (8.9% vs 4.6%, p ⫽ 0.005; Table 2). Kaplan-Meier 1-year survival curves of patients with and without MS are shown in Figure 1. Individual effects of metabolic syndrome components on mortality: Age- and gender-adjusted RRs and 95% CIs of MS components for 1-year mortality are listed in Table 3. Glucose level at admission of ⬎140 mg/dl was associated strongly and significantly with 1-year mortality. After adjustment for hyperglycemia at admission (in addition to age and gender), increased triglycerides were associated significantly with 1-year mortality as well (Table 3). Preexisting hypertension was associated with a trend for 1-year mortality with a crude RR of 1.53 (95% CI 0.98 to 2.58). However, adjustments for age and gender decreased this association (RR 0.87, 95% CI 0.63 to 1.43; Table 3). Impact of metabolic syndrome on 30-day and 1-year mortality in patients with hyperglycemia: Most patients who died by 30 days (74%) had glucose levels ⬎140 mg/dl, and most (85%) had MS. The impact of MS on 30-day and 1-year mortality in patients with and without hyperglycemia on admission is shown in Figure 2. Patients with hyperglycemia and MS at admission had significantly higher 30-day mortality
670
The American Journal of Cardiology (www.AJConline.org)
Table 3 Metabolic syndrome criteria, relative risk ratios (RRs), and 95% confidence intervals (CIs) for 1-year mortality likelihood Criteria Glucose ⬎140 (mg/dl) HDL cholesterol (mg/dl) (men ⱕ40, women ⱕ50) Triglycerides ⱖ150 (mg/dl) History of hypertension BMI ⬎28 (kg/m2)
Crude RR
RR Adjusted for Age, Gender
RR Adjusted for Age, Gender, Glucose
4.75 (2.80–8.01) 1.51 (0.85–2.65) 1.01 (0.62–1.65) 1.53 (0.98–2.58) 0.89 (0.52–1.53)
3.37 (1.97–5.75) 1.42 (0.80–2.52) 1.81 (1.11–2.96) 0.87 (0.63–1.43) 1.18 (0.59–2.02)
1.63 (0.92–2.92) 1.72 (1.05–2.83) 0.86 (0.52–1.42) 1.08 (0.63–1.86)
Figure 3. RR (95% CI) of 1-year mortality associated with MS (MetS) in patients with different clinical conditions used as criteria for MS. TG ⫽ triglycerides. Table 4 Cox adjusted hazard ratio (HR) and 95% confidence intervals (CIs) of 1-year mortality for metabolic syndrome and admission risk factors Parameter Killip ⬎I MS Age (10-yr increment) Renal failure Previous stroke Previous myocardial infarction Previous congestive heart failure Gender (male)
HR (95% CI) 4.78 (2.76–8.29) 1.96 (1.18–3.24) 1.76 (1.38–2.25) 1.54 (0.80–2.97) 1.53 (0.75–3.12) 1.29 (0.72–2.29) 0.90 (0.40–2.06) 0.82 (0.47–1.45)
associated with higher mortality rates in patients with MS; however, statistically significant increased mortality was shown only in patients with hypertriglyceridemia. Figure 2. Thirty-day and 1-year mortality rates in patients with and without hyperglycemia (glucose ⬎140 mg/dl) on admission in patients with and without MS.
rates compared with patients with hyperglycemia but without MS (8.3% vs 2.5%, p ⬍0.05). The 1-year mortality rate of patients with hyperglycemia at admission (glucose ⬎140 mg/ dl) was higher in those with and without MS (Figure 2). Long-term impact of metabolic syndrome on mortality in different clinical conditions: To assess the long-term impact of MS per se on outcome in patients with hyperglycemia, hypertension, increased BMI and triglycerides, and decreased HDL cholesterol, 1-year mortality rates were determined accordingly in subjects with and without MS (Figure 3). All studied clinical conditions (also used as MS criteria) were
Multivariate analysis: Multivariate analysis that included all admission characteristics that differed significantly between patients with and without MS (gender, past stroke, and Killip class on admission) and other known risk factors for 1-year survival (age, chronic renal failure, past myocardial infarction, and past congestive heart failure) suggest that MS is a strong independent predictor associated with increased 1-year mortality in patients without diabetes admitted due to ACS with a HR of 1.96 (95% CI 1.18 to 3.24; Table 4 and Figure 4). Adjustment for age, gender, and Killip class ⬎I on admission suggests that MS is also a strong independent predictor of 30-day morality (HR 2.54, 95% CI 1.22 to 5.31). Discussion This study describes the impact of MS on hospital course and 1-year mortality of patients with non-clinically diag-
Coronary Artery Disease/MS in Nondiabetic Patients With ACS
Figure 4. One-year survival curves of patients with MS and others after controlling for age, gender, Killip at admission, renal failure, past stroke, myocardial infarction, and congestive heart failure in a Cox regression model. MS remained associated with increased mortality rate in patients without diabetes admitted with ACS (HR 1.96, 95% CI 1.18 to 3.24, p ⬍0.01).
nosed diabetes with ACS from a large consecutive nationwide cohort. MS in the context of ACS conferred an increased risk of congestive heart failure and stroke during hospitalization and was a strong independent predictor of 30-day and 1-year mortality. The standard NCEP ATP III definition of MS is based on detection of ⱖ3 of 5 components in stable fasting condition of (1) impaired glucose metabolism, (2) hypertension, (3) obesity, (4) decreased HDL cholesterol, and (5) increased triglycerides.14 However, the standard fasting cut-off levels for glucose and blood pressure cannot be applied at admission in patients with ACS. It should be noted that in the absence of diabetes, nonfasting blood glucose level generally is not ⬎140 mg/dl.5 We therefore used blood glucose level ⬎140 mg/dl (increased occasional glucose) as a criterion for impaired glucose metabolism.5 In our study, increased blood pressure at admission (systolic ⬎130 mm Hg or diastolic ⬎85 mm Hg) was associated with a decreased mortality rate. Because hypotension is a well-characterized marker of high-risk patients admitted with ACS, reflecting pump failure, higher blood pressure levels may be identified as protective in such a population. To avoid this pitfall, we used a preexisting diagnosis of hypertension as the criterion for MS. Patients with preexisting hypertension had a 53% higher crude mortality rate, but adjusting for age and gender eliminated this association. Serum lipids (HDL cholesterol and triglycerides) are expected to change less early at admission,6 and those criteria were not modified. Increased BMI (⬎28 kg/m2) can be used as a criterion for obesity when waist circumference is not available.7 Zeller et al1 found a significant association between MS, mortality, and congestive heart failure in patients with acute myocardial infarction. However, the association in patients without diabetes was not reported. The definition of MS was performed using relatively stable predischarge parameters. Blood pressure was evaluated 1 day before discharge and not collected from those who died from cardiogenic shock. Fasting glucose was used to determine impaired glucose metabolism. Levantesi et al3 described the impact of MS in a large multicenter study of patients with recent myocardial
671
infarction (ⱕ3 months, median 16 days). This study showed that patients with MS without a history of diabetes mellitus had increased 3.5-year mortality compared with patients without diabetes without MS. In the Myocardial Ischemia Reduction with Aggressive Cholesterol Lowering (MIRACLE) trial, patients with ACS and MS (including patients with diabetes) had increased all-cause mortality and an increased primary end point (death, nonfatal myocardial infarction, cardiac arrest, or recurrent unstable myocardial ischemia) during a 16-week follow-up.2 An independent graded association between increasing levels of admission glucose and adverse clinical outcomes was reported.8 Hyperglycemia in patients without diabetes was frequently associated with congestive heart failure.8 –10 We found that patients with hyperglycemia and MS had more than threefold higher 30-day mortality compared with patients with hyperglycemia but without MS (Figure 2). The exact means by which hyperglycemia confers an increase in cardiovascular risk in the setting of ACS is not completely understood. Several potential mechanisms were proposed to explain the association between stress hyperglycemia on admission for ACS and increased early and late mortality.11–16 Undiagnosed diabetes is common in patients admitted because of ACS.17 Although the stressful situation of ACS leads to a nonspecific increase in plasma glucose, the real prevalence of undiagnosed diabetes and impaired glucose tolerance was reported to be high in patients with ACS.18 Furthermore, it was shown that newly detected abnormal glucose tolerance in patients with ACS is a strong predictor of future cardiovascular events.19 Our study shows that hyperglycemia, hypertension, increased BMI and triglycerides, and decreased HDL cholesterol at admission of patients without diabetes were associated with higher mortality in patients with MS (Figure 3). However, this association reached statistical significance only in patients with hypertriglyceridemia. Patients with MS without diabetes mellitus have an increased incidence of congestive heart failure and stroke during hospitalization and an augmented mortality rate that persists for ⱖ1 year. We show that early de novo identification of MS on admission using modified NCEP ATP III criteria is feasible even in the nonfasting and stressful situation generally associated with ACS. These patients might benefit from early aggressive risk reduction. Based on these findings, we believe that de novo identification of MS on admission has the potential to improve risk stratification and management of patients with ACS. MS is defined by NCEP ATP III criteria in a stable condition. At admission, patients with ACS usually were not fasting, and the general stressful situation into which they were introduced influenced criteria for MS. We adjusted the criteria to define MS accordingly and showed that this high-risk prevalent group of patients can be identified. However, caution should be used in interpreting our finding because it was identified retrospectively in a national survey. Prospectively designed studies for this purpose are warranted. 1. Zeller M, Steg PG, Ravisy J, Laurent Y, Manificat LJ, L’Huillier I, Beer JC, Oudot A, Rioufol G, Makki H, et al, for the Observatoire des Infarctus de Cote-d’Or Survey Working Group. Prevalence and impact
672
2.
3.
4.
5. 6. 7. 8.
9.
10.
The American Journal of Cardiology (www.AJConline.org) of MS on hospital outcomes in acute myocardial infarction. Arch Intern Med 2005;165:1192–1198. Schwartz GG, Szarek M, Olsson AG, Saseila WJ. Relation of characteristics of MS to short-term prognosis and effects of intensive statin therapy after acute coronary syndrome. Diabetes Care 2005;28:2508 – 2513. Levantesi G, Macchia A, Marfisi RM, Franzosi MG, Maggioni AP, Nicolosi GL, Schweiger C, Tavazzi L, Tognoni G, Valagussa F, Marchioli R. Metabolic syndrome and risk of cardiovascular events after myocardial infarction. J Am Coll Cardiol 2005;46:277–283. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C, American Heart Association, National Heart, Lung, and Blood Institute. Definition of MS: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004;109:433– 438. American Diabetes Association Consensus Statement Postprandial blood glucose. Diabetes Care 2001:24;775–778. Henkin Y, Crystal E, Goldberg Y, Friger M, Lorber J, Zuili I, Shany S. Usefulness of lipoprotein changes during acute coronary syndromes for predicting post-discharge lipoprotein levels. Am J Cardiol 2002;89:7–11. Tenenbaum A, Motro M, Fisman EZ, Tanne D, Boyko V, Behar S. Bezafibrate for the secondary prevention of myocardial infarction in patients with MS. Arch Intern Med 2005;165:1154 –1160. Svensson AM, McGuire DK, Abrahamsson P, Dellborg M. Association between hyper- and hypoglycaemia and 2 year all-cause mortality risk in diabetic patients with acute coronary events. Eur Heart J 2005;26:1255–1261. Stranders I, Diamant M, van Gelder RE, Spruijt HJ, Twisk JWR, Heine RJ, Visser FC. Admission blood glucose level as risk indicator of death after myocardial infarction in patients with and without diabetes mellitus. Arch Intern Med 2004;164:982–988. Grundy SM. Metabolic syndrome: connecting and reconciling cardiovascular and diabetes worlds. J Am Coll Cardiol 2006;47:1093–1100.
11. Tansey MJ, Opie LH. Relation between plasma free fatty acids and arrhythmias within the first twelve hours of acute myocardial infarction. Lancet 1983;2:419 – 422. 12. Oliver MF, Opie LH. Effects of glucose and fatty acids on myocardial ischaemia and arrhythmias. Lancet 1994;343:155–158. 13. Tansey MJ, Opie LH. Plasma glucose on admission to hospital as a metabolic index of the severity of acute myocardial infarction. Can J Cardiol 1986;2:326 –331. 14. Davi G, Catalano I, Averna M, Notarbartolo A, Strano A, Ciabattoni G, Patrono C. Thromboxane biosynthesis and platelet function in type II diabetes mellitus. N Engl J Med 1990;322:1769 –1774. 15. Jain SK, Nagi DK, Slavin BM, Lumb PJ, Yudkin JS. Insulin therapy in type 2 diabetic subjects suppresses plasminogen activator inhibitor (PAI-1) activity and proinsulin-like molecules independently of glycaemic control. Diabet Med 1993;10:27–32. 16. Williams SB, Goldfine AB, Timimi FK, Ting HH, Roddy MA, Simonson DC, Creager MA. Acute hyperglycemia attenuates endothelium-dependent vasodilation in humans in vivo. Circulation 1998;97: 1695–1701. 17. Norhammar A, Tenerz A, Nilsson G, Hamsten A, Efendic S, Ryden L, Malmberg K. Glucose metabolism in patients with acute myocardial infarction and no previous diagnosis of diabetes mellitus: a prospective study. Lancet 2002;359:2140 –2144. 18. Bartnik M, Ryden L, Ferrari R, Malmberg K, Pyorala K, Simoons M, Standl E, Soler-Soler J, Ohrvik J; Euro Heart Survey Investigators. The prevalence of abnormal glucose regulation in patients with coronary artery disease across Europe: The Euro Heart Survey on Diabetes and the Heart. Eur Heart J 2004;25:1880 –1890. 19. Bartnik M, Malmberg K, Norhammar A, Tenerz A, Ohrvik J, Ryden L. Newly detected abnormal glucose tolerance: an important predictor of long-term outcome after myocardial infarction. Eur Heart J 2004;25: 1990 –1997.
Only limited information is available regarding the association of metabolic syndrome (MS) with outcomes in patients without diabetes after recent myocardial infarction.1–3 Moreover, the impact of MS defined for the first time at admission on the outcome of patients with non-clinically diagnosed diabetes with acute coronary syndrome (ACS) is unknown. We addressed this question by analyzing the impact of MS on the clinical outcome of patients included in a comprehensive national registry during a 1-year follow-up. Methods Patients were selected from the ACS Israeli Survey (ACSIS 2004), a prospective nationwide survey conducted during February and March 2004 in all 25 coronary care units and cardiology wards operating in Israel. Demographic, historical, and clinical data were recorded by study physicians on a Heart Institute, bNeufeld Cardiac Research Institute, cDepartment of Neurology and Neuro-Vascular Laboratory, and dCardiac Rehabilitation Institute, Sheba Medical Center, Tel Hashomer, Sackler Faculty of Medicine, Tel Aviv University, and eSoroka Medical Center, Beer Sheba, Israel. Manuscript received August 26, 2006; revised manuscript received and accepted October 9, 2006. *Corresponding author: Tel: 972-3-530-2433; fax: 972-3-530-2407. E-mail address: [email protected] (M. Feinberg).
0002-9149/07/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.amjcard.2006.10.023
prespecified forms for all patients admitted with a diagnosis of ACS. Admission and discharge diagnoses were recorded by attending physicians based on clinical, electrocardiographic, and enzymatic criteria and local policy. Patient management was left to the discretion of each center. Onsite coronary catheterization with percutaneous intervention was available in 22 centers and with coronary bypass graft facilities in 12 centers. Mortality rates at 7 and 30 days, 6 months, and 1 year were determined for all participants from hospital charts and by matching patient identification numbers with the Israeli National Population Register. Patients who denied a history of diabetes mellitus and were not treated with insulin or oral hypoglycemic medications were defined as nondiabetic. The diagnosis of hypertension was based on the patient’s history (preexisting diagnosed hypertension). Of 2,094 patients with ACS enrolled in ACSIS 2004, the presence or absence of MS could not be determined in 346 (16.5%) because of missing data. Of the remaining 1,748 patients, 688 had known diabetes mellitus and were excluded from further analysis. Therefore, the final population for our study included 1,060 patients with ACS, of whom 359 (34%) had MS. Definition of metabolic syndrome on admission: For purposes of our study, MS is defined de novo in the clinical context of ACS in accordance with clinical data obtained early at admission. MS was defined in patients who prewww.AJConline.org
668
The American Journal of Cardiology (www.AJConline.org)
sented with ⱖ3 of the 5 diagnostic components proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III).4 Their cut-off points were modified slightly for the setting of admission as (1) impaired glucose metabolism defined as occasional (instead of fasting) blood glucose level ⬎140 mg/dl at admission, (2) increased triglycerides ⱖ150 mg/dl, (3) high-density lipoprotein (HDL) cholesterol ⬍40 mg/dl in men or ⬍50 mg/dl in women, (4) self-reported preexisting hypertension diagnosed by primary physician, and (5) overweight, central obesity. Because waist circumference data were not available, we used body mass index (BMI) ⬎28.0 kg/m2 as a criterion for inclusion for purposes of this analysis. Statistical analysis: Patients were classified into 2 groups: the MS group and the non-MS group. Baseline demographic characteristics of the 2 groups were compared. Continuous variables are expressed as mean ⫾ SD. Discrete data are given as frequency and percentage. Proportions were compared using chi-square tests, and means, using Student’s t tests. To examine the relation between MS and mortality, several models were built. One-year event-free survival in the 2 groups is presented as Kaplan-Meier curves and compared using the log-rank test. Multivariate Cox proportional hazards analysis was applied to assess the effect of MS on event-free survival. To control for confounding in the Cox model, potential prognostic variables (identified in previous published studies as risk factors for mortality or clinical variables associated with 1-year mortality) evaluated using univariate analysis and selected based on clinical and statistical significance were entered as covariates in the Cox model. For 1-year mortality, we included in the model: age (as a continuous covariate); gender; history of myocardial infarction, cerebrovascular accident, or congestive heart failure; chronic renal failure; and Killip class ⱖII on admission as categorical variables. Because of the low number of events within 30-day follow-up, we included only age, gender, and Killip class ⱖII as covariates in the Cox model for 30-day mortality. Adjusted survival curves were constructed using variables entered into the Cox model set to their mean values in the total population. Results of the Cox logistic regression model were presented as hazard ratio (HR) and 95% confidence interval (CI). Adjusted relative risks (RRs) for age and gender, calculated using a Mantel-Haenszel direct adjustment, were computed to determine the significance of each MS component for predicted 1-year mortality. To assess the impact of MS on 1-year mortality in patients with hyperglycemia, hypertension, increased BMI and triglycerides, and decreased HDL cholesterol, the RR of MS and 1-year mortality rates were determined accordingly in subjects with and without MS. A 2-sided p value ⬍0.05 was considered statistically significant. All analyses were performed using SAS software (version 8.2, SAS Institute Inc., Cary, North Carolina). Results Distribution of metabolic syndrome components: Clinical conditions used as MS criteria were distributed in patients with MS (n ⫽ 359) as (1) hyperglycemia on admission (glucose ⬎140 mg/dl), 59%; (2) history of hyper-
Table 1 Baseline patient characteristics Variable
MS (n ⫽ 359)
Others* (n ⫽ 701)
p Value
Age (yrs) 63 ⫾ 13 Women 98 (27%) 29.4 ⫾ 4.2 BMI (kg/m2) Hypertension 275 (77%) Hyperlipidemia 197 (55%) Smoker (current) 145 (40%) Previous myocardial infarction 71 (20%) Previous angina pectoris 106 (30%) Previous coronary artery 33 (9%) bypass grafting Previous percutaneous 63 (18%) coronary intervention Previous heart failure 13 (4%) Chronic renal failure 24 (7%) Previous stroke 30 (8%) Previous peripheral vascular 19 (5%) disease Malignancy 13 (4%) Killip II 43 (12%) Killip II 22 (6%) Killip III 2 (1%) Killip on admission ⱖII 67 (19%) Admission systolic blood 146 ⫾ 31 pressure (mm Hg) Admission diastolic blood 84 ⫾ 17 pressure (mm Hg) Glucose on admission (mg/dl) 156 ⫾ 62 Triglycerides (mg/dl) 202 ⫾ 113 HDL (mg/dl) 36 ⫾ 9 Left ventricular ejection 47 ⫾ 12 fraction (%)† ACS Q-wave myocardial 168 (47%) infarction Non–Q-wave myocardial 114 (32%) infarction Unstable angina pectoris 77 (21%) Myocardial infarction location Anterior 123 (34%) Inferior 114 (32%)
62 ⫾ 13 130 (19%) 25.3 ⫾ 3.3 216 (31%) 279 (40%) 313 (45%) 147 (21%) 182 (26%) 50 (7%)
0.12 0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.19 0.65 0.22 0.24
122 (17%)
0.95
24 (3%) 33 (5%) 36 (5%) 28 (4%)
0.87 0.18 0.04 0.33
28 (4%) 70 (10%) 23 (3%) 5 (1%) 98 (14%) 138 ⫾ 29
0.77
0.03 ⬍0.001
81 ⫾ 17
0.003
121 ⫾ 35 127 ⫾ 92 46 ⫾ 13 47 ⫾ 11
⬍0.001 ⬍0.001 ⬍0.001 0.56
338 (48%) 199 (28%) 164 (23%)
0.49
245 (35%) 205 (30%)
0.82 0.52
* Patients without MS. Available in 72% of patients.
†
tension, 77%; (3) hypertriglyceridemia, 74%; (4) low HDL cholesterol, 83%; and (5) increased BMI, 68%. In those without MS (n ⫽ 701), these conditions were distributed as (1) hyperglycemia on admission, 17%; (2) history of hypertension, 31%; (3) hypertriglyceridemia, 19%; (4) decreased HDL cholesterol, 35%; and (5) increased BMI, 14%. Baseline characteristics: There were more women in the MS group compared with the group without MS (27% vs 19%, p ⫽ 0.001). Patients with MS more frequently presented with Killip class ⱖII on admission (19% vs 14%, p ⫽ 0.03), more often had a history of hypertension and hyperlipidemia (77% vs 31%, p ⬍0.001; 55% vs 40%, p ⬍0.001, respectively), and had more previous strokes (8% vs 5%, p ⫽ 0.04). Age and prevalence of previous myocardial infarction, anginal syndrome, coronary artery bypass grafting, percutaneous coronary intervention, congestive
Coronary Artery Disease/MS in Nondiabetic Patients With ACS
669
Table 2 Hospital management and outcome Variable
MS (n ⫽ 359)
Others* (n ⫽ 701)
Primary reperfusion Coronary angiography Any percutaneous coronary intervention Coronary stent implantation Coronary artery bypass grafting Mechanical ventilation Intra-aortic balloon pump Discharge medications Aspirin Anticoagulants Nitrates Clopidogrel Angiotensin-converting enzyme inhibitors/ angiotensin receptor blockers Aldosterone  blockers Diuretics Statins Insulin Oral hypoglycemic medications Congestive heart failure Paroxysmal atrial fibrillation Ventricular tachyarrhythmia‡ Stroke Recurrent myocardial infarction Renal failure 7-D mortality 30-D mortality 6-Mo mortality 1-Yr mortality
130 (36%) 283 (79%) 216 (76%)†
290 (41%) 584 (83%) 465 (80%)†
0.10 0.07 0.27
189 (88%) 25 (7%)
401 (86%) 35 (5%)
0.66 0.19
16 (5%) 14 (4%)
13 (2%) 21 (3%)
0.01 0.44
333 (95%) 45 (13%) 53 (15%) 237 (68%) 263 (75%)
670 (96%) 73 (11%) 110 (16%) 504 (73%) 468 (67%)
0.44 0.25 0.79 0.12 0.008
17 (5%) 297 (85%) 75 (22%) 299 (86%) 3 (0.9%) 6 (1.7%)
29 (4%) 559 (80%) 96 (14%) 580 (84%) 0 (0%) 4 (0.6%)
p Value
0.60 0.064 0.002 0.35 0.01 0.07
50 (14%) 18 (5%) 28 (8%) 5 (1%) 4 (1%)
66 (9%) 22 (3%) 61 (9%) 1 (⬍0.5%) 6 (1%)
0.03 0.13 0.62 0.01 0.68
19 (5%) 8 (2.2%) 18 (5.0%) 29 (8.1%) 32 (8.9%)
23 (3%) 7 (1.0%) 12 (1.7%) 25 (3.6%) 32 (4.6%)
0.11 0.11 0.002 0.002 0.005
* Patients without MS. Percent total coronary angiography. ‡ Ventricular fibrillation/tachycardia. †
heart failure, renal failure, and peripheral vascular disease were similar in the 2 groups (Table 1). Ischemic electrocardiographic characteristics on admission (ST-segment shifts, elevation, and depression) did not differ significantly between patients with versus without MS (52% vs 58% and 22% vs 16%, respectively). As expected, blood glucose levels at admission (156 ⫾ 62 vs 121 ⫾ 35 mg/dl, p ⬍0.001), BMI (29.4 ⫾ 4.2 vs 25.3 ⫾ 3.3 kg/m2, p ⬍0.001), triglyceride levels (202 ⫾ 113 vs 127 ⫾ 92 mg/dl, p ⬍0.001), and systolic and diastolic blood pressure at admission (146 ⫾ 31 vs 138 ⫾ 29 mm Hg, p ⬍0.001; 84 ⫾ 17 vs 81 ⫾ 17 mm Hg, p ⫽ 0.003, respectively) were significantly higher in the MS group, whereas HDL cholesterol level (36 ⫾ 9 vs 46 ⫾ 13 mg/dl, p ⬍0.001) was significantly lower (Table 1). Hospital course: Proportions of patients with Q-wave and non–Q-wave myocardial infarction, unstable angina, and anterior and inferior myocardial infarction were similar
Figure 1. Kaplan-Meier 1-year survival curve.
in patients with MS versus without MS (Table 1), as were frequencies of primary reperfusion, coronary angiography, any percutaneous coronary intervention, coronary stent implantation, and coronary artery bypass grafting (Table 2). Mechanical ventilation was used more commonly in patients with MS (5% vs 2%, p ⫽ 0.01; Table 2), indicating the association of MS with severe congestive heart failure. At discharge, there were no significant differences in use of aspirin,  blockers, and statins between the 2 groups. However, angiotensin-converting enzyme inhibitors or angiotensin II receptor antagonists and diuretics were prescribed more commonly for patients with MS (75% vs 67%, p ⫽ 0.008; 22% vs 14%, p ⫽ 0.002, respectively; Table 2). Congestive heat failure and stroke during hospitalization occurred more often in patients with MS (14% vs 9%, p ⫽ 0.03; 1% vs ⬍0.5%, p ⫽ 0.03, respectively; Table 2). Crude mortality rates: Mortality rates were higher in patients with MS at 30 days (5.0% vs 1.7%, p ⫽ 0.002) and remained so after 1 year (8.9% vs 4.6%, p ⫽ 0.005; Table 2). Kaplan-Meier 1-year survival curves of patients with and without MS are shown in Figure 1. Individual effects of metabolic syndrome components on mortality: Age- and gender-adjusted RRs and 95% CIs of MS components for 1-year mortality are listed in Table 3. Glucose level at admission of ⬎140 mg/dl was associated strongly and significantly with 1-year mortality. After adjustment for hyperglycemia at admission (in addition to age and gender), increased triglycerides were associated significantly with 1-year mortality as well (Table 3). Preexisting hypertension was associated with a trend for 1-year mortality with a crude RR of 1.53 (95% CI 0.98 to 2.58). However, adjustments for age and gender decreased this association (RR 0.87, 95% CI 0.63 to 1.43; Table 3). Impact of metabolic syndrome on 30-day and 1-year mortality in patients with hyperglycemia: Most patients who died by 30 days (74%) had glucose levels ⬎140 mg/dl, and most (85%) had MS. The impact of MS on 30-day and 1-year mortality in patients with and without hyperglycemia on admission is shown in Figure 2. Patients with hyperglycemia and MS at admission had significantly higher 30-day mortality
670
The American Journal of Cardiology (www.AJConline.org)
Table 3 Metabolic syndrome criteria, relative risk ratios (RRs), and 95% confidence intervals (CIs) for 1-year mortality likelihood Criteria Glucose ⬎140 (mg/dl) HDL cholesterol (mg/dl) (men ⱕ40, women ⱕ50) Triglycerides ⱖ150 (mg/dl) History of hypertension BMI ⬎28 (kg/m2)
Crude RR
RR Adjusted for Age, Gender
RR Adjusted for Age, Gender, Glucose
4.75 (2.80–8.01) 1.51 (0.85–2.65) 1.01 (0.62–1.65) 1.53 (0.98–2.58) 0.89 (0.52–1.53)
3.37 (1.97–5.75) 1.42 (0.80–2.52) 1.81 (1.11–2.96) 0.87 (0.63–1.43) 1.18 (0.59–2.02)
1.63 (0.92–2.92) 1.72 (1.05–2.83) 0.86 (0.52–1.42) 1.08 (0.63–1.86)
Figure 3. RR (95% CI) of 1-year mortality associated with MS (MetS) in patients with different clinical conditions used as criteria for MS. TG ⫽ triglycerides. Table 4 Cox adjusted hazard ratio (HR) and 95% confidence intervals (CIs) of 1-year mortality for metabolic syndrome and admission risk factors Parameter Killip ⬎I MS Age (10-yr increment) Renal failure Previous stroke Previous myocardial infarction Previous congestive heart failure Gender (male)
HR (95% CI) 4.78 (2.76–8.29) 1.96 (1.18–3.24) 1.76 (1.38–2.25) 1.54 (0.80–2.97) 1.53 (0.75–3.12) 1.29 (0.72–2.29) 0.90 (0.40–2.06) 0.82 (0.47–1.45)
associated with higher mortality rates in patients with MS; however, statistically significant increased mortality was shown only in patients with hypertriglyceridemia. Figure 2. Thirty-day and 1-year mortality rates in patients with and without hyperglycemia (glucose ⬎140 mg/dl) on admission in patients with and without MS.
rates compared with patients with hyperglycemia but without MS (8.3% vs 2.5%, p ⬍0.05). The 1-year mortality rate of patients with hyperglycemia at admission (glucose ⬎140 mg/ dl) was higher in those with and without MS (Figure 2). Long-term impact of metabolic syndrome on mortality in different clinical conditions: To assess the long-term impact of MS per se on outcome in patients with hyperglycemia, hypertension, increased BMI and triglycerides, and decreased HDL cholesterol, 1-year mortality rates were determined accordingly in subjects with and without MS (Figure 3). All studied clinical conditions (also used as MS criteria) were
Multivariate analysis: Multivariate analysis that included all admission characteristics that differed significantly between patients with and without MS (gender, past stroke, and Killip class on admission) and other known risk factors for 1-year survival (age, chronic renal failure, past myocardial infarction, and past congestive heart failure) suggest that MS is a strong independent predictor associated with increased 1-year mortality in patients without diabetes admitted due to ACS with a HR of 1.96 (95% CI 1.18 to 3.24; Table 4 and Figure 4). Adjustment for age, gender, and Killip class ⬎I on admission suggests that MS is also a strong independent predictor of 30-day morality (HR 2.54, 95% CI 1.22 to 5.31). Discussion This study describes the impact of MS on hospital course and 1-year mortality of patients with non-clinically diag-
Coronary Artery Disease/MS in Nondiabetic Patients With ACS
Figure 4. One-year survival curves of patients with MS and others after controlling for age, gender, Killip at admission, renal failure, past stroke, myocardial infarction, and congestive heart failure in a Cox regression model. MS remained associated with increased mortality rate in patients without diabetes admitted with ACS (HR 1.96, 95% CI 1.18 to 3.24, p ⬍0.01).
nosed diabetes with ACS from a large consecutive nationwide cohort. MS in the context of ACS conferred an increased risk of congestive heart failure and stroke during hospitalization and was a strong independent predictor of 30-day and 1-year mortality. The standard NCEP ATP III definition of MS is based on detection of ⱖ3 of 5 components in stable fasting condition of (1) impaired glucose metabolism, (2) hypertension, (3) obesity, (4) decreased HDL cholesterol, and (5) increased triglycerides.14 However, the standard fasting cut-off levels for glucose and blood pressure cannot be applied at admission in patients with ACS. It should be noted that in the absence of diabetes, nonfasting blood glucose level generally is not ⬎140 mg/dl.5 We therefore used blood glucose level ⬎140 mg/dl (increased occasional glucose) as a criterion for impaired glucose metabolism.5 In our study, increased blood pressure at admission (systolic ⬎130 mm Hg or diastolic ⬎85 mm Hg) was associated with a decreased mortality rate. Because hypotension is a well-characterized marker of high-risk patients admitted with ACS, reflecting pump failure, higher blood pressure levels may be identified as protective in such a population. To avoid this pitfall, we used a preexisting diagnosis of hypertension as the criterion for MS. Patients with preexisting hypertension had a 53% higher crude mortality rate, but adjusting for age and gender eliminated this association. Serum lipids (HDL cholesterol and triglycerides) are expected to change less early at admission,6 and those criteria were not modified. Increased BMI (⬎28 kg/m2) can be used as a criterion for obesity when waist circumference is not available.7 Zeller et al1 found a significant association between MS, mortality, and congestive heart failure in patients with acute myocardial infarction. However, the association in patients without diabetes was not reported. The definition of MS was performed using relatively stable predischarge parameters. Blood pressure was evaluated 1 day before discharge and not collected from those who died from cardiogenic shock. Fasting glucose was used to determine impaired glucose metabolism. Levantesi et al3 described the impact of MS in a large multicenter study of patients with recent myocardial
671
infarction (ⱕ3 months, median 16 days). This study showed that patients with MS without a history of diabetes mellitus had increased 3.5-year mortality compared with patients without diabetes without MS. In the Myocardial Ischemia Reduction with Aggressive Cholesterol Lowering (MIRACLE) trial, patients with ACS and MS (including patients with diabetes) had increased all-cause mortality and an increased primary end point (death, nonfatal myocardial infarction, cardiac arrest, or recurrent unstable myocardial ischemia) during a 16-week follow-up.2 An independent graded association between increasing levels of admission glucose and adverse clinical outcomes was reported.8 Hyperglycemia in patients without diabetes was frequently associated with congestive heart failure.8 –10 We found that patients with hyperglycemia and MS had more than threefold higher 30-day mortality compared with patients with hyperglycemia but without MS (Figure 2). The exact means by which hyperglycemia confers an increase in cardiovascular risk in the setting of ACS is not completely understood. Several potential mechanisms were proposed to explain the association between stress hyperglycemia on admission for ACS and increased early and late mortality.11–16 Undiagnosed diabetes is common in patients admitted because of ACS.17 Although the stressful situation of ACS leads to a nonspecific increase in plasma glucose, the real prevalence of undiagnosed diabetes and impaired glucose tolerance was reported to be high in patients with ACS.18 Furthermore, it was shown that newly detected abnormal glucose tolerance in patients with ACS is a strong predictor of future cardiovascular events.19 Our study shows that hyperglycemia, hypertension, increased BMI and triglycerides, and decreased HDL cholesterol at admission of patients without diabetes were associated with higher mortality in patients with MS (Figure 3). However, this association reached statistical significance only in patients with hypertriglyceridemia. Patients with MS without diabetes mellitus have an increased incidence of congestive heart failure and stroke during hospitalization and an augmented mortality rate that persists for ⱖ1 year. We show that early de novo identification of MS on admission using modified NCEP ATP III criteria is feasible even in the nonfasting and stressful situation generally associated with ACS. These patients might benefit from early aggressive risk reduction. Based on these findings, we believe that de novo identification of MS on admission has the potential to improve risk stratification and management of patients with ACS. MS is defined by NCEP ATP III criteria in a stable condition. At admission, patients with ACS usually were not fasting, and the general stressful situation into which they were introduced influenced criteria for MS. We adjusted the criteria to define MS accordingly and showed that this high-risk prevalent group of patients can be identified. However, caution should be used in interpreting our finding because it was identified retrospectively in a national survey. Prospectively designed studies for this purpose are warranted. 1. Zeller M, Steg PG, Ravisy J, Laurent Y, Manificat LJ, L’Huillier I, Beer JC, Oudot A, Rioufol G, Makki H, et al, for the Observatoire des Infarctus de Cote-d’Or Survey Working Group. Prevalence and impact
672
2.
3.
4.
5. 6. 7. 8.
9.
10.
The American Journal of Cardiology (www.AJConline.org) of MS on hospital outcomes in acute myocardial infarction. Arch Intern Med 2005;165:1192–1198. Schwartz GG, Szarek M, Olsson AG, Saseila WJ. Relation of characteristics of MS to short-term prognosis and effects of intensive statin therapy after acute coronary syndrome. Diabetes Care 2005;28:2508 – 2513. Levantesi G, Macchia A, Marfisi RM, Franzosi MG, Maggioni AP, Nicolosi GL, Schweiger C, Tavazzi L, Tognoni G, Valagussa F, Marchioli R. Metabolic syndrome and risk of cardiovascular events after myocardial infarction. J Am Coll Cardiol 2005;46:277–283. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C, American Heart Association, National Heart, Lung, and Blood Institute. Definition of MS: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004;109:433– 438. American Diabetes Association Consensus Statement Postprandial blood glucose. Diabetes Care 2001:24;775–778. Henkin Y, Crystal E, Goldberg Y, Friger M, Lorber J, Zuili I, Shany S. Usefulness of lipoprotein changes during acute coronary syndromes for predicting post-discharge lipoprotein levels. Am J Cardiol 2002;89:7–11. Tenenbaum A, Motro M, Fisman EZ, Tanne D, Boyko V, Behar S. Bezafibrate for the secondary prevention of myocardial infarction in patients with MS. Arch Intern Med 2005;165:1154 –1160. Svensson AM, McGuire DK, Abrahamsson P, Dellborg M. Association between hyper- and hypoglycaemia and 2 year all-cause mortality risk in diabetic patients with acute coronary events. Eur Heart J 2005;26:1255–1261. Stranders I, Diamant M, van Gelder RE, Spruijt HJ, Twisk JWR, Heine RJ, Visser FC. Admission blood glucose level as risk indicator of death after myocardial infarction in patients with and without diabetes mellitus. Arch Intern Med 2004;164:982–988. Grundy SM. Metabolic syndrome: connecting and reconciling cardiovascular and diabetes worlds. J Am Coll Cardiol 2006;47:1093–1100.
11. Tansey MJ, Opie LH. Relation between plasma free fatty acids and arrhythmias within the first twelve hours of acute myocardial infarction. Lancet 1983;2:419 – 422. 12. Oliver MF, Opie LH. Effects of glucose and fatty acids on myocardial ischaemia and arrhythmias. Lancet 1994;343:155–158. 13. Tansey MJ, Opie LH. Plasma glucose on admission to hospital as a metabolic index of the severity of acute myocardial infarction. Can J Cardiol 1986;2:326 –331. 14. Davi G, Catalano I, Averna M, Notarbartolo A, Strano A, Ciabattoni G, Patrono C. Thromboxane biosynthesis and platelet function in type II diabetes mellitus. N Engl J Med 1990;322:1769 –1774. 15. Jain SK, Nagi DK, Slavin BM, Lumb PJ, Yudkin JS. Insulin therapy in type 2 diabetic subjects suppresses plasminogen activator inhibitor (PAI-1) activity and proinsulin-like molecules independently of glycaemic control. Diabet Med 1993;10:27–32. 16. Williams SB, Goldfine AB, Timimi FK, Ting HH, Roddy MA, Simonson DC, Creager MA. Acute hyperglycemia attenuates endothelium-dependent vasodilation in humans in vivo. Circulation 1998;97: 1695–1701. 17. Norhammar A, Tenerz A, Nilsson G, Hamsten A, Efendic S, Ryden L, Malmberg K. Glucose metabolism in patients with acute myocardial infarction and no previous diagnosis of diabetes mellitus: a prospective study. Lancet 2002;359:2140 –2144. 18. Bartnik M, Ryden L, Ferrari R, Malmberg K, Pyorala K, Simoons M, Standl E, Soler-Soler J, Ohrvik J; Euro Heart Survey Investigators. The prevalence of abnormal glucose regulation in patients with coronary artery disease across Europe: The Euro Heart Survey on Diabetes and the Heart. Eur Heart J 2004;25:1880 –1890. 19. Bartnik M, Malmberg K, Norhammar A, Tenerz A, Ohrvik J, Ryden L. Newly detected abnormal glucose tolerance: an important predictor of long-term outcome after myocardial infarction. Eur Heart J 2004;25: 1990 –1997.