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J Am Coll Cardiol, 2005; 46:277-283, doi:10.1016/j.jacc.2005.03.062 (Published online 5 July 2005).
© 2005 by the American College of Cardiology Foundation
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CLINICAL RESEARCH: MYOCARDIAL INFARCTION

Metabolic Syndrome and Risk of Cardiovascular Events After Myocardial Infarction

Giacomo Levantesi, MD*, Alejandro Macchia, MD*, RosaMaria Marfisi, MS*, Maria G. Franzosi, MSc{dagger}, Aldo P. Maggioni, MD{ddagger}, Gian L. Nicolosi, MD§, Carlo Schweiger, MD||, Luigi Tavazzi, MD, Gianni Tognoni, MD*, Franco Valagussa, MD#, Roberto Marchioli, MD*,* on behalf of the GISSI-Prevenzione Investigators

* Consorzio Mario Negri Sud, Santa Maria Imbaro, Chieti
{ddagger} Centro Studi ANMCO, Firenze
{dagger} Department of Cardiovascular Disease, Istituto Mario Negri, Milano
|| Ospedale Civile, Presidio di Riabilitazione, Passirana di Rho, Milano
IRCCS Policlinico San Matteo, Pavia
# Ospedale San Gerardo, Monza
§ Ospedale S. Maria degli Angeli, Pordenone, Italy.

Manuscript received November 9, 2004; revised manuscript received March 16, 2005, accepted March 29, 2005.

* Reprint requests and correspondence: Dr. Roberto Marchioli, Laboratory of Clinical Epidemiology of Cardiovascular Disease, Department of Clinical Pharmacology and Epidemiology, Consorzio Mario Negri Sud, Via Nazionale, 66030 Santa Maria Imbaro, Italy. (Email: marchioli{at}negrisud.it).


   Abstract
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 Abstract
Methods
 Results
 Discussion
 References
 
OBJECTIVES: We aimed to assess the prevalence and prognostic role of metabolic syndrome (METS) and diabetes in post-myocardial infarction (MI) patients.

BACKGROUND: Diabetes is a well known risk factor for patients with previous MI, but glycemic dysmetabolism develops over a protracted period of time. Scanty data are available on the role of METS in patients with previous MI.

METHODS: Adjusted Cox’s regression models, having diabetes, death, major cardiovascular events (CVE), and hospitalization for congestive heart failure (CHF) during follow-up as outcome events, were fitted on 11,323 patients with prior MI enrolled in the GISSI-Prevenzione Trial.

RESULTS: At baseline, 21% and 29% of patients had diabetes mellitus and METS, respectively. The METS patients had a significant (93%) increased risk of diabetes during follow-up. As compared with control subjects, the probability of death and CVE were higher in both METS (+29%, p = 0.002; +23%, p = 0.005) and diabetic patients (+68%, p <0.0001; +47%, p <0.0001), although diabetic but not METS patients were more likely to be hospitalized for CHF (+89%, p <0.0003 and +24%, p = 0.241). Moderate (–6% to –10%) and substantial (>–10%) weight reduction were associated with a significant (18% and 41%, respectively) decreased risk of diabetes. Weight gain was significantly associated with increased risk of diabetes. The risk conferred by METS and diabetes tended to be higher among women.

CONCLUSIONS: In patients with MI, METS and diabetes were highly prevalent and are associated with increased risk of death and CVE. Diabetes is also associated with increased risk of hospitalization for CHF. Weight reduction significantly decreased the risk of becoming diabetic in patients with METS.

Abbreviations and Acronyms
  ACE = angiotensin-converting enzyme
  BMI = body mass index
  CHF = congestive heart failure
  CVE = cardiovascular events
  IR = insulin resistance
  METS = metabolic syndrome
  MI = myocardial infarction
  NCEP-ATP III = National Cholesterol Education Program Adult Treatment Panel III
  NYHA = New York Heart Association
  PUFA = polyunsaturated fatty acids

Type 2 diabetes mellitus is a well recognized risk factor for cardiovascular morbidity and mortality (1–5). Glucose metabolism abnormalities, however, develop over a prolonged period of time during which individuals are at high risk of cardiovascular events despite glucose levels that could be considered as normal (6–10). This period is characterized by a progressive resistance to the action of insulin, a process called insulin resistance (IR) that usually clusters with several cardiovascular risk factors (11–15). The diagnosis of IR is complex and cannot be easily performed in clinical practice (16–19); however, the metabolic syndrome (METS) is characterized by the clustering of risk factors related to IR and is considered to be an early indicator of impaired glucose metabolism (20–24). The diagnosis of METS proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) is based on simple clinical criteria (20) and is considered a prognostic indicator of vascular risk in patients with no overt coronary artery disease (20–24).

Because scanty data are available on the prognostic role of METS in patients with previous myocardial infarction (MI) (25), we analyzed the GISSI-Prevenzione Trial database (26) to assess the prevalence of METS and diabetes as well as their association with cardiovascular events in post-MI patients.


   Methods
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 Abstract
 Methods
Results
 Discussion
 References
 
Patients.   A detailed description of the study has been reported previously (26). Briefly, 11,323 patients with recent (≤3 months, median 16 days) MI were enrolled in the GISSI-Prevenzione Trial, a multicenter open-label clinical study, with blinded validation of events and follow-up duration of 3.5 years, on the efficacy of polyunsaturated fatty acids (PUFA) (1 g daily) and vitamin E (300 mg daily). Clinical, laboratory, and instrumental evaluation of the patients recruited in the study were carried out at baseline and follow-up visits that were scheduled at 6, 12, 18, 30, and 42 months.

The outcomes measures for this analysis were: diabetes development during follow-up (defined as fasting glucose ≥126 mg/dl or antidiabetic treatment) in non-diabetic patients at baseline, all-cause mortality, cumulative rate of cardiovascular events (CVE) (cardiovascular death, nonfatal MI, nonfatal stroke), and development of congestive heart failure (CHF) that was assessed according to hospitalization for CHF during follow-up in patients with no heart failure at baseline.

The analysis on overall mortality and CVE was carried out in 10,384 patients (i.e., after excluding 939 patients who had missing data for METS components at baseline). For the analysis of diabetes development during follow-up, we excluded 2,139 patients with diabetes at baseline and 777 cases with no measurement of glucose levels during follow-up. The analysis on CHF hospitalizations was carried out on 8,417 patients who were free from CHF (New York Heart Association [NYHA] functional class II or physician-reported CHF) at baseline and had complete information for CHF assessment (NYHA functional class, medication report) at least at one scheduled follow-up visit.

To assess the effect of weight change on the risk of CVE and diabetes development during follow-up, 7,027 patients who had body weight measurements both at baseline and at the first follow-up visit and who were free of cardiovascular events and diabetes until the first follow-up visit were included in the analysis. We also evaluated the effect of weight change in 4,422 patients with body mass index (BMI) >25 kg/m2. We considered patients who had no or a light decrement of body weight (<–5%) as the reference class. Body weight changes between the baseline and the first follow-up visit were defined as moderate (–6% to –10%) and substantial (>–10%) weight reductions, whereas weight increments were defined as light (≥0% to +5%), moderate (+6% to +10%), and substantial (>+10%).

For the diagnosis of METS at baseline, we modified the NCEP-ATP III criteria for abdominal obesity by using the median value of BMI ≥26 kg/m2 instead of the waist circumference, which was not available. The cutoff for BMI at the median value was indicative of overweight, and being over the upper limit of normality, it could be considered a proxy of visceral adiposity. Accordingly, diagnosis of METS was established when non-diabetic subjects had at least three of the following five criteria: visceral adiposity (BMI ≥26 kg/m2), high triglycerides (≥150 mg/dl), low high-density lipoprotein cholesterol (<40 mg/dl in men and <50 mg/dl in women), hypertension (systolic blood pressure ≥ 130 mm Hg or diastolic blood pressure ≥85 mm Hg or antihypertensive treatment or history of hypertension), and impaired fasting glucose (≥110 mg/dl and <126mg/dl). For diagnosis of METS, we did a sensitivity analysis by using a BMI value of 28 and 29 kg/m2 (corresponding to the upper quartile and upper quintile) with no substantial difference in terms of prognostic capacity of METS (data not shown). Diabetes at baseline was diagnosed if either glucose ≥126 mg/dl, patients were on anti-diabetic treatment, or physician-reported diabetes.

Statistical methods.   One-way analysis of variance and chi-square test were used to test continuous and categorical variables at baseline, respectively.

Cox proportional models were fitted with all-cause mortality, CVE, late onset CHF, and new diagnosis of diabetes during follow-up as outcome measures. The following potential confounders were included in the multivariable models: 1) age and gender; 2) electrical instability (defined as ≥10 premature ventricular beats/h, sustained or repetitive arrhythmias during 24-h Holter monitoring), residual ischemia (angina pectoris, positive exercise testing), ejection fraction; 3) smoking, total cholesterol, peripheral vascular disease; and 4) n-3 PUFA, vitamin E, antiplatelet agents, angiotensin-converting enzyme (ACE) inhibitors, statins, and beta-blockers. The multivariate model that was aimed at assessing the effect of weight reduction on risk of late onset diabetes also included BMI levels measured at baseline. Cox proportional hazards event-free survival curves, adjusted for covariates means, were plotted. All probability values are two-sided. All computations used the SAS statistical package (SAS Institute Inc., Cary, North Carolina).


   Results
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 Abstract
 Methods
 Results
Discussion
 References
 
Baseline descriptive statistics.   Out of a total of 10,384 patients, 2,139 (20.6%) had diabetes mellitus and 3,047 (29.3%) had METS (Table 1). As compared with METS and control subjects, diabetic patients were more likely to be female, older, and with higher prevalence of peripheral artery disease and MI before the index event leading to recruitment into the study. Diabetic patients were also more likely to have an impaired left ventricular function with lower levels of ejection fraction and higher prevalence of NYHA functional class II. As to medication use, diabetic patients were more likely to receive ACE inhibitors and less likely to be given aspirin and beta-blockers (Table 1).


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  		Table 1. Baseline Characteristics of 10,384 Patients of the GISSI-Prevenzione Database
 
Normal and METS patients were much more similar, the latter group having a higher prevalence of women (12.3% vs. 15.4%, p < 0.0001) and of use of beta-blockers, ACE inhibitors, and aspirin (Table 1).

There were some minor differences at baseline between patients who were included in the analysis and those who were not because of missing data. As compared with patients who were included in the analysis, those excluded were younger (58 vs. 60 years), less likely to be in NYHA functional class II (8% vs. 10%), and less often treated with antiplatelet drugs (85% vs. 92%) and beta blockers (36% vs. 45%).

Outcome events.   As compared with control subjects (Figs. 1 and 2, Table 2), the probability of death and CVE were higher in both METS (+29%, p = 0.002; +23%, p = 0.005) and diabetic patients (+68%, p <0.0001; +47%, p < 0.0001). Diabetic but not METS patients (Fig. 3, Table 2) were more likely to be hospitalized for CHF (+89%, p = 0.0003) and METS (+24%, p = 0.241), as compared with control subjects. In addition to a near two-fold increased risk of developing diabetes (+93%, p < 0.0001; Fig. 4), METS patients had a risk of diabetes steeply increasing with the number of diagnostic components for METS. Of the METS patients, 2,120 (70%), 841 (28%), and 86 (3%) had three, four, and five diagnostic components for METS, respectively. As compared with patients with three components for METS, the risk of developing diabetes was 60% (p < 0.0001) and 273% (p < 0.0001) higher in patients with four and five components for METS diagnosis, respectively (Fig. 5).



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  		Figure 1 Survival curves of control subjects (bottom black line), patients with metabolic syndrome (middle black line), and diabetic patients (top black line). Normal patients: relative risk 1.00 (reference category); metabolic syndrome patients: relative risk 1.29, 95% confidence interval (CI) 1.10 to 1.51, p = 0.002; diabetic patients: relative risk 1.68, 95% CI 1.44 to 1.95, p < 0.0001. Cox proportional hazard model adjusted for age, gender, smoking habits, total cholesterol, presence of peripheral vascular disease, electrical instability, residual ischemia, left ventricular ejection fraction, n-3 polyunsaturated fatty acids, vitamin E, antiplatelet agents, angiotensin-converting enzyme inhibitors, statins at six months, and beta-blockers.

 

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  		Table 2. Final Predictive Models of Overall Mortality, CVE, Hospitalization for CHF, and Late-Onset Diabetes for Control Subjects, Patients With METS, and Diabetic Patients Adjusted for Potential Confounders (see Methods): Distribution, RR, 95% CI, p Value
 


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  		Figure 3 Hospitalization for heart failure-free survival curves of control subjects (bottom black line), patients with metabolic syndrome (middle black line), and diabetic patients (top black line). Normal patients: relative risk 1.00 (reference category); metabolic syndrome patients: relative risk 1.24, 95% confidence interval (CI) 0.86 to 1.79, p = 0.241; diabetic patients: relative risk 1.89, 95% CI 1.34 to 2.67, p = 0.0003. CHF = congestive heart failure. Methods as in Figure 1.

 


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  		Figure 4 Diabetes-free survival curves for control subjects (thin black line) and patients with metabolic syndrome (thick black line). Normal patients: relative risk 1.00 (reference category); metabolic syndrome patients: relative risk 1.93, 95% confidence interval 1.69 to 2.19, p < 0.0001. Methods as in Figure 1.

 


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  		Figure 5 Diabetes-free survival curves according to the number of diagnostic components of the metabolic syndrome (METS): three components (bottom black line), four components (middle black line), and five components (top black line). Three METS components: relative risk 1.00 (reference category); four METS components: relative risk 1.60, 95% confidence interval (CI) 1.32 to 1.93, p < 0.0001; five METS components: relative risk 3.73, 95% CI 2.58 to 5.39, p < 0.0001. Methods as in Figure 1.

 
Diabetic patients with fewer than three components of METS had a near significant 40% (p = 0.067) increased risk of death compared with METS with four or five components, and no difference was apparent as to the risk of CVE between these two groups of patients (p = 0.708).

In a subgroup analysis, in patients with METS, women, as compared with men, had higher risk of developing diabetes (169% vs. 86% [p = 0.092]), mortality (40% vs. 29% [p = 0.679]), and CVE (51% vs. 20% [p = 0.298]), although these differences were not statistically significant. Compared with diabetic men, diabetic women had significant higher risks of death (193% vs. 54% [p = 0.006]) and of CVE (146% vs. 32% [p = 0.004]).

In 7,027 patients, weight changes during the first six months of follow-up had a significant impact on the probability of developing diabetes (Fig. 6). As compared with patients who had a body weight decrement <5% (n = 2,066; 29.4%), the risk of developing diabetes was 48%, 57%, and 113% higher in those with a light (n = 2,749; 39.1%, p < 0.0001), moderate (n = 889, 12.6%; p < 0.0001), and substantial (n = 396; 5.6%, p < 0.0001) weight gain, respectively. But patients who achieved a moderate (n = 678; 9.6%) or substantial (n = 249; 3.5%) reduction of weight had an 18% (p = 0.1664) and 41% (p < 0.0275) decreased risk of diabetes development during follow-up, respectively. A subgroup analysis in 4,422 patients with BMI >25 kg/m2 showed that all levels of weight change had slightly, though significantly, increased the risk of developing diabetes; as compared with patients who had none to light decrement of body weight (n = 1,470; 33.2%), the risk was 50%, 63%, and 121% higher in those with light (n = 1,581; 35.8%, p < 0.0001), moderate (n = 454; 10.2%, p < 0.0001), and substantial weight gain (n = 141; 3.2%, p < 0.0001), respectively. Patients who achieved moderate (n = 550; 12.4%) and substantial (n = 226; 5.1%) reduction of weight, however, had a 15% (p = 0.278) and 45% (p < 0.02) decreased risk of diabetes development during follow-up, respectively.



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  		Figure 6 Relative risk (RR) of diabetes according to body weight variation at six months after recruitment. Reference category: weight change <–5% (RR 1.00); weight change from 0% to +5% (RR 1.48, 95% confidence interval (CI) 1.25 to 1.75, p < 0.0001); weight change from +6% to +10% (RR 1.57, 95% CI 1.26 to 1.96, p < 0.0001); weight change from >+10% (RR 2.13, 95% CI 1.60 to 2.83, p < 0.0001); weight change from –6% to –10% (RR 0.82, 95% CI 0.63 to 1.08, p = 0.1664); weight change >–10% (RR 0.59, 95% CI 0.37 to 0.94, p < 0.0275). Cox proportional hazard model adjusted for age, gender, body mass index, smoking habits, total cholesterol, number of metabolic syndrome components, presence of peripheral vascular disease, electrical instability, residual ischemia, left ventricular ejection fraction, n-3 polyunsaturated fatty acids, vitamin E, antiplatelet agents, angiotensin-converting enzyme inhibitors, statins at six months, and beta-blockers.

 
Weight change during the first six months of follow-up did not show any significant effect on the risk of CVE in all 7,027 patients or in 4,422 overweight patients (data not shown).

The effects of n-3 PUFA and vitamin E treatments on outcome measures were consistent with the main results of the trial and did not differ significantly in the various subgroups of patients considered in this analysis (p for heterogeneity = NS).


   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
The main results of this analysis are: 1) in patients who have had an MI within the previous three months, IR is a common finding, because 50% of patients had either type 2 diabetes mellitus (20.6%) or METS (29.3%); 2) both conditions were associated with unfavorable prognosis in terms of all-cause mortality and CVE; 3) diabetic but not METS patients had an increased risk of hospitalization for CHF; 4) METS was a strong predictor of diabetes development, and this association was related to the number of components of the syndrome; and 5) the risk of developing diabetes decreased and increased proportionally according to the magnitude of weight change.

According to our findings, glucose dysmetabolism affects 50% of patients who survive MI. Although results about diabetes are in line with other reports (2–5), the high prevalence of patients with METS as well as their poor prognosis underline the need not to overlook this syndrome in post-MI patients. As in primary prevention (21), metabolic syndrome was a strong predictor of late-onset diabetes in post-MI. We found a strong association between the number of factors constituting METS and subsequent risk of diabetes, which possibly indicates increasing levels of hyperinsulinemia and IR to be associated with a progressive increment of risk. In the Bruneck Study (27), IR correlated with the number of METS abnormalities, and IR was almost always present when several abnormalities clustered together. This fact underlines the necessity to treat these patients in order to avoid late-onset diabetes. Lifestyle habits are paramount in this regard, and we found that unfavorable weight changes during the first six months after the index MI were associated with an increased risk of diabetes mellitus. This finding confirms previous evidence suggesting that diet, increased physical exercise, and weight loss (with or without pharmacologic treatment) are able to reduce the risk of diabetes by more than 50% (28–30).

Diagnosis of METS was associated with a risk of CVE higher than the one of control subjects and lower of that of diabetic patients. The risk of death among METS patients was mainly associated with the transformation in diabetes. In fact, the proportion of deaths in the last 2.5 years of follow-up among subjects who were diabetic at baseline or METS patients who became diabetic during the first year of follow-up was 9.2% and 6.5%, respectively. Conversely, control subjects and METS patients who did not become diabetic during the first year follow-up had similar risks of death in the last 2.5 years of follow-up (4.7% and 5.0%, respectively). These findings suggest that METS is a syndrome somewhere in the middle between normal patients and those with diabetes: about 10% of patients with METS became diabetic after one year of follow-up, and their risk of death increased and became intermediate between that of subjects who were normal or diabetic at baseline.

Insulin resistance is often present several years before the onset of manifest hyperglycemia (6–10), and METS can indicate a state of IR and hyperinsulinemia (20,31–33), but without abnormal glucose levels. Because of the stricter relationship of macrovascular complications with hyperinsulinemia than with hyperglycemia (11,34–36), METS could represent an early indicator of increased vascular risk, whereas diabetes could be considered the last step of a progressive and long-term phenomenon involving increased IR (6–10).

Our findings underline the need to identify precociously and treat more aggressively patients with METS with coronary heart disease who have an absolute cardiovascular risk that is definitely higher than that of primary prevention. These findings confirm the clinical utility of METS and diabetes diagnoses to identify patients at high risk of hard events who may benefit from specific preventive pharmacologic treatments such as targeting lower levels of low-density lipoprotein (LDL) cholesterol (37); these findings also extend previous evidence of the prognostic role of METS in patients without coronary heart disease to those with previous MI (21–24).

We noted that the risk conferred by METS and diabetes diagnosis tended to be higher among female patients than male subjects, despite correction by major confounding. The reasons for this were not clear, but we hypothesize that it could be related to heavier risk factor burden and a longer exposure to conventional and non-conventional risk factors (38).

In our study, patients with diabetes but not those with METS had an increased risk of hospitalization for CHF. The United Kingdom Prospective Diabetes Study (UKPDS) showed that for every absolute 1% reduction in HbA1C there was a 16% reduction of risk of heart failure in diabetic patients (39). We used, however, a hard definition of heart failure (i.e., CHF that requires hospitalization), so the small number of events may have impaired the statistical power; diabetic patients could have more severe forms of CHF requiring hospitalization.

Study limitations.   Our study shares the limitations of post-hoc analysis, and for the definition of obesity we adopted an arbitrary cut point. No agreement exists on BMI level that can be considered equivalent to waist circumference (40–44), and we finally selected a BMI ≥26 kg/m2, because sensitivity analysis by using a BMI value of 28 and 29 kg/m2 (corresponding to the upper quartile and upper quintile) did not show differences in prognostic capacity of METS. Moreover, the area under the receiver-operating curves, using the three different cut points of 26, 28, and 29, did not yield significantly different results.

In conclusion, METS and diabetes—two conditions related to IR—were found in one-half of the patients surviving MI and were associated with progressively worsening prognosis, despite the preventive measures already adopted, suggesting that there is room for a more aggressive therapeutic strategy having lifestyle modifications as cornerstone.



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  		Figure 2 Cardiovascular event-free survival curves of control subjects (bottom black line), patients with metabolic syndrome (middle black line), and diabetic patients (top black line). Normal patients: relative risk 1.00 (reference category); metabolic syndrome patients: relative risk 1.23, 95% confidence interval (CI) 1.06 to 1.42, p = 0.005; diabetic patients: relative risk 1.47, 95% CI 1.27 to 1.70, p < 0.0001. CV = cardiovascular. Methods as in Figure 1.

 


   Footnotes
 
This work was supported by the Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico (GISSI); Associazione Nazionale Medici Cardiologi Ospedalieri (ANMCO); Istituto di Ricerche Farmacologiche Mario Negri – Consorzio Mario Negri Sud, Santa Maria Imbaro, Italy. The names of the GISSI-Prevenzione Investigators are listed in reference 26.


   References
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 Abstract
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 Discussion
 References
 

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