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Stroke. 2008;39:1036-1038
Published online before print January 31, 2008, doi: 10.1161/STROKEAHA.107.498311
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(Stroke. 2008;39:1036.)
© 2008 American Heart Association, Inc.


Research Letters

Metabolic Syndrome and Its Components as Predictors of Ischemic Stroke in Type 2 Diabetic Patients

Ioannis Protopsaltis, MD; Panagiotis Korantzopoulos, MD; Haralampos J. Milionis, MD; Anastasios Koutsovasilis, MD; Georgios K. Nikolopoulos, PhD; Eftihia Dimou, MD; Stelios Kokkoris, MD; Paris Brestas, MD; Moses S. Elisaf, MD Andreas Melidonis, MD

From the Diabetes Center (I.P., E.D., S.K., P.B., A.M.), ‘Tzanio’ General Hospital of Piraeus, Greece; Department of Cardiology (P.K.), School of Medicine, University of Ioannina, Greece; Department of Internal Medicine (H.J.M., M.S.E.), School of Medicine, University of Ioannina, Greece; Hellenic Centre for Diseases Control and Prevention (G.K.N.), Athens, Greece.

Correspondence to Haralampos Milionis, MD, Assistant Professor of Internal Medicine, School of Medicine, University of Ioannina, 451 10 Ioannina, Greece. E-mail hmilioni{at}cc.uoi.gr


*    Abstract
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Background and Purpose— The available data regarding the association between metabolic syndrome (MS) or MS components and ischemic stroke in type 2 diabetics are limited and inconsistent. This study aimed to investigate these associations.

Methods— Five hundred ninety-nine consecutive type 2 diabetic patients (mean age 60.4±9.6 years, 54% men) were followed-up for 10.1 years (median period). Baseline clinical and laboratory characteristics and the occurrence of a first-ever ischemic stroke during follow-up were recorded.

Results— Seventy-eight patients developed a first-ever ischemic stroke. According to Cox proportional hazard model, waist circumference (hazard ratio, HR:1.006, 95% CI:1.002 to 1.010, P=0.003) and age (HR:1.061, 95% CI:1.002 to 1.125, P=0.04) were significant predictors. After incorporating various combinations of MS components in multivariate models, only age and waist circumference remained significant.

Conclusions— MS per se at baseline or combinations of its components do not predict the development of ischemic stroke in type 2 diabetic patients. Waist circumference represents an independent prognostic factor and could be used as a clinical tool for stroke prevention in this population.


Key Words: ischemic stroke • metabolic syndrome • risk factors • type 2 diabetes


*    Introduction
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*Introduction
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Dyslipidemia, obesity, and hypertension are common comorbidities that contribute to the increased stroke risk in patients with type 2 diabetes.1 These risk factors, along with abdominal obesity, represent components of the metabolic syndrome (MS).2 Of note, the evidence regarding the association between MS, its components, and stroke risk is limited and inconsistent, especially in the diabetic population.3,4 In this study, we examined whether the individual components of MS or their specific combinations have a different predictive value for ischemic stroke in type 2 diabetic patients than the presence of MS alone.


*    Subjects and Methods
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A total of 599 consecutive type 2 diabetics without known cardiovascular disease were prospectively recruited over a period of 5 years and were followed-up for a median period of 10.1 years (8.2 to 13.4 years). The following parameters were determined at baseline: presence of MS, number of MS components, age, sex, blood pressure, total cholesterol, LDL-cholesterol, triglycerides, HDL-cholesterol, glycosylated hemoglobin (HbA1C), smoking status, and diabetes duration. MS was diagnosed according to the National Cholesterol Education Program/Adult Treatment Panel III criteria.2 Individuals receiving antihypertensive medications were considered hypertensive regardless of blood pressure measurements. Each patient was tested for proteinuria and retinopathy.

All participants gave written informed consent and the Institutional Ethics Committee approved the study protocol.

Stroke incidents were obtained from departmental outpatient database, local hospitals’ discharge diagnoses, and self-reported disease history. The diagnosis of a first-ever ischemic nonembolic stroke in each case was validated by a consultant neurologist. The study protocol included a brain CT scan at the acute phase to detect intracerebral or subarachnoid hemorrhage, a carotid Doppler ultrasound to exclude a potential embolic cause and a new brain CT scan to confirm ischemic stroke, as indicated. Electrocardiographic and transthoracic echocardiographic studies were performed to exclude potential embolic sources. Subjects with a history of stroke, atrial fibrillation, valvular heart disease, endocarditis, pulmonary vein thrombosis, atrial myxoma, stable angina, recent acute coronary syndrome, peripheral artery disease, infections, or diagnosed as having transient ischemic attack were excluded.

Statistical Analysis
Baseline characteristics were analyzed using Student t test and {chi}2 test as appropriate. Survival analysis methodology was used to evaluate the time until the occurrence of stroke (end point). To determine the impact of MS, its constituents and the cumulative number of MS components on stroke risk, a multivariate Cox proportional hazards regression was used that included as explanatory variables: MS (categorical variable), its constituents (continuous variables), and the number of MS criteria (ordinal variable receiving values from 1 to 5). Additional models were run that incorporated various potential stroke risk factors as covariates: gender, age, smoking, body mass index, HbA1C, lipids, and diabetes duration. Significance levels were set at P<0.05. All statistical analyses were performed with SPSS 13.0 (SPSS Inc).


*    Results
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up arrowAbstract
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*Results
down arrowDiscussion
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Baseline characteristics of the study population and the corresponding prevalence of MS components are presented in Table 1. Seventy-eight subjects developed an ischemic stroke during follow-up. In univariate analysis, stroke incident correlated with systolic blood pressure, waist circumference, and low HDL-cholesterol levels, but not with MS as an entity (Table 2). After performing a multivariate Cox model (stepwise-analysis), only waist circumference was significantly associated with stroke risk (Table 2).


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Table 1. Baseline Characteristics of the Study Population


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Table 2. Results of Cox Hazards Regression Analysis Associating MS and Its Components With Stroke as End Point

In a multivariate Cox regression model incorporating various stroke risk factors, with the exception of both MS and its constituents, age was the single factor to correlate with stroke (Table 3).


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Table 3. Results of Multivariate Cox Regression Analysis for Potential Prognostic Factors of Stroke in Type 2 Diabetics

With regard to the effect of MS component combinations on stroke risk, subjects fulfilling the triad consisting of diabetes, hypertension and high triglyceride levels had a 63% (hazard ratio, HR=1.63, 95% CI: 1.178 to 2.849, P=0.007) higher risk of developing stroke. Nevertheless, when age, waist circumference and the combination of diabetes-hypertension-elevated triglycerides were modeled together, only age (HR=1.058, 95% CI: 1.001 to 1.124, P=0.028) and waist circumference (HR=1.029, 95% CI: 1.012 to 1.068, P=0.004) remained significant predictors. After controlling for age, a 10-cm increase in waist circumference was associated with a 5.8% higher risk of ischemic stroke. Incorporation of available data regarding new onset of medications, including statins, and antiplatelet agents, in the previous models did not influence the results of the analyses.


*    Discussion
up arrowTop
up arrowAbstract
up arrowIntroduction
up arrowSubjects and Methods
up arrowResults
*Discussion
down arrowReferences
 
The present study demonstrates that waist circumference and age represent significant predictors of first-ever ischemic stroke in type 2 diabetics independent of body mass index and other traditional risk factors. No association was found between stroke and the presence of MS per se at baseline, or the cumulative number of MS components. The relatively low prevalence of MS and hypertension observed in our diabetic population compared to other studies5 could be attributed to the relatively low mean age and the adoption of a Mediterranean diet6 by Greek patients.

Our results are consistent with earlier large-scale epidemiological studies, which showed no association between stroke and conventional cardiovascular risk factors, including dyslipidemia, in diabetic patients,7 thus supporting the notion that the atherosclerotic process in the intracranial vascular bed may be different than that occurring in coronary arteries.

In accordance with earlier reports, waist circumference, as a measure of abdominal obesity, was a strong predictor of ischemic stroke.8 The effect of abdominal adiposity on stroke risk is possibly mediated, in part, by the intense endocrine activity of intra-abdominal adipocytes via secretion of adipokines (leptin, TNF-{alpha}, interleukin-6, resistin, and adiponectin), and indirectly through insulin resistance.3,9 Other relevant mechanisms include dyslipidemia, hypertension, prothrombotic and proinflammatory states, which commonly coexist in obese subjects and strongly predict the cardiovascular outcomes.9–11

In summary, waist circumference could be a useful clinical tool in identifying type 2 diabetic patients at high-risk of ischemic stroke independent of other established cardiovascular risk factors. Whether intra-abdominal adiposity should be included in strategies aiming at preventing stroke incidents in type 2 diabetes needs to be further tested.


*    Acknowledgments
 
Disclosures

None.

Received July 4, 2007; accepted August 14, 2007.


*    References
up arrowTop
up arrowAbstract
up arrowIntroduction
up arrowSubjects and Methods
up arrowResults
up arrowDiscussion
*References
 
1. Lehto S, Ronnemaa T, Pyorala K, Laakso M. Predictors of stroke in middle-aged patients with non-insulin-dependent diabetes. Stroke. 1996; 27: 63–68.[Abstract/Free Full Text]

2. Grundy SM. Metabolic syndrome: connecting and reconciling cardiovascular and diabetes world. J Am Coll Cardiol. 2006; 47: 1093–1100.[Abstract/Free Full Text]

3. Najarian RM, Sullivan LM, Kannel WB, Wilson PWF, D’Agostino RB, Wolf PA. Metabolic syndrome compared with type 2 diabetes mellitus as a risk factor for stroke: The Framingham offspring study. Arch Intern Med. 2006; 166: 106–111.[Abstract/Free Full Text]

4. Guzder RN, Gatling W, Mullee MA, Byrne CD. Impact of metabolic syndrome criteria on cardiovascular disease risk in people with newly diagnosed type 2 diabetes. Diabetologia. 2006; 49: 49–55.[CrossRef][Medline] [Order article via Infotrieve]

5. Hanefeld M, Koehler C, Gallo S, Benke I, Ott P. Impact of the individual components of the metabolic syndrome and their different combinations on the prevalence of atherosclerotic vascular disease in type 2 diabetes: the Diabetes in Germany (DIG) study. Cardiovasc Diabetol. 2007; 6: 13.[CrossRef][Medline] [Order article via Infotrieve]

6. Panagiotakos D, Pitsavos C, Chrysohoou C, Skoumas J, Tousoulis D, Toutouza M, Toutouzas P, Stefanadis C. Impact of lifestyle habits on the prevalence of the metabolic syndrome among Greek adults from the ATTICA Study. Am Heart J. 2004; 147: 106–112.[CrossRef][Medline] [Order article via Infotrieve]

7. Shahar E, Chambless LE, Rosamond WD, Boland LL, Ballantyne CM, McGovern PG, Sharrett AR. Plasma lipid profile and incident ischemic stroke. The ARIC study. Stroke. 2003; 34: 623–631.[Abstract/Free Full Text]

8. Haffner SM, Despres JP, Balkau B, Deanfield JE, Barter P, Bassand J-P, Fox K, Van Gaal L, Wittchen HU, Tan CE, Smith SC. Waist circumference and body mass index are both independently associated with cardiovascular disease: the International Day for the Evaluation of Abdominal Obesity (IDEA) survey. J Am Coll Cardiol. 2006; 4 suppl: 358A.

9. Poirier P, Lemieux I, Mauriege P, Dewailly E, Blanchet C, Bergeron J, Despres JP. Impact of waist circumference on the relationship between blood pressure and insulin: the Quebec Health Survey. Hypertension. 2005; 45: 363–367.[Abstract/Free Full Text]

10. Cigolini M, Targher G, Bergamo Andreis IA, Tonoli M, Agostino G, De Sandre G. Visceral fat accumulation and its relation to plasma hemostatic factors in healthy men. Arterioscler Thromb Vasc Biol. 1996; 16: 368–374.[Abstract/Free Full Text]

11. Rost NS, Wolf PA, Kase CS, Kelly-Hayes M, Silbershatz H, Massaro JM, D’Agostino RB, Franzblau C, Wilson PW. Plasma concentration of C-reactive protein and risk of ischemic stroke and transient ischemic attack: the Framingham study. Stroke. 2001; 32: 2575–2579.[Abstract/Free Full Text]





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