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(Stroke. 2003;34:2187.)
© 2003 American Heart Association, Inc.

Original Contributions

QTc Interval Prolongation Is a Predictor of Future Strokes in Patients With Type 2 Diabetes Mellitus

From the Department of Internal Medicine, Clementino Fraga Filho University Hospital, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.

Correspondence to Claudia R.L. Cardoso, Rua Cróton 72, Jacarepagua, CEP 22750–240, Rio de Janeiro, Brasil. E-mail claudiacardoso{at}hucff.ufrj.br

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Background and Purpose— QTc interval prolongation is a predictor of cardiovascular morbidity and mortality in general populations and in patients with diabetes. The aim of this study was to investigate the predictors of stroke in patients with type 2 diabetes, with particular emphasis on the independent role of QT interval parameters.

Methods— We carried out a long-term follow-up study with 471 type 2 diabetics. Several clinical, laboratory, ECG, and echocardiographic variables were recorded at baseline. Predictive factors for stroke were evaluated by Kaplan-Meier estimation of survival curves and by univariate and multivariate Cox survival analyses.

Results— After a median follow-up of 57 months (range, 2 to 84 months), 40 incident strokes were observed. QTc interval prolongation (470 ms^1/2) was an independent predictor of stroke, with adjusted hazard ratios ranging from 2.2 to 2.9 (95% confidence intervals, 1.1 to 6.0). Other independent factors associated with stroke were older age; the presence of cerebrovascular disease at baseline; increased 24-hour proteinuria, serum triglycerides, and left ventricular mass; and decreased high-density lipoprotein cholesterol. Excluding patients with previous cerebrovascular disease from the analysis did not change the results significantly.

Conclusions— QTc interval prolongation is a predictor of future stroke in patients with type 2 diabetes. Intervention studies are needed to assess whether this factor could be modified.

Key Words: diabetes mellitus • electrocardiography • risk factors • stroke

	Introduction

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Cardiovascular diseases are the leading causes of morbidity and mortality around the world, and stroke ranks as the third-leading cause of death in the United States¹ and as the leading cause in Brazil.² The best approach to reducing the burden of stroke remains prevention; hence, knowing its associated risk factors is fundamental. Age, male sex, hypertension, and atrial fibrillation are well-known risk factors for stroke, whereas smoking and dyslipidemia are more debatable ones.^3–6

Diabetes mellitus is also a recognized risk factor for stroke, with reported increased relative risks ranging from 2- to 6-fold compared with nondiabetics.^7,8 Furthermore, diabetics have greater mortality and recurrence rate after a first stroke.^8,9 Few studies, however, have investigated predictive factors for stroke in diabetic patients, derived mainly from a population-based study in Finland,¹⁰ from the United Kingdom Prospective Diabetes Study,¹¹ and from the World Health Organization (WHO) Multinational Study of Vascular Disease in Diabetes.¹² None of them, however, evaluated specific ECG abnormalities such as left ventricular hypertrophy (LVH) or prolonged QT interval duration, which appears to be more frequent in diabetic patients than in nondiabetics.¹³ Heart rate–corrected QT interval prolongation, in particular, has been investigated as a cardiovascular mortality risk predictor in both population-based studies^14,15 and diabetic patients.^16–18 Thus, the primary aim of this long-term follow-up prospective study was to assess the predictive factors for fatal or nonfatal stroke in a large group of type 2 diabetic outpatients, with special emphasis on abnormalities involving the QT interval, which is a simple variable obtained from standard ECGs.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Subjects and Baseline Procedures
Study subjects have been described elsewhere.^18,19 In summary, all adult diabetic outpatients diagnosed according to the 1985 WHO criteria²⁰ who had standard ECGs recorded from July 1994 to June 1996 were consecutively enrolled in this follow-up study. After the clinical and ECG exclusion criteria detailed previously^18,19 were applied, a total of 471 type 2 diabetics made up the cohort. Patients with abnormal serum levels of potassium and calcium were excluded from the study. This protocol was approved by the local ethics committee, and all patients gave written, informed consent.

The baseline procedures and criteria for diagnosing clinical variables have also been described elsewhere.^18,19 All subjects were submitted to a thorough clinical examination, with special attention to signs and symptoms of cardiovascular diseases and to diabetic degenerative complications. Mean values of all office systolic (SBP) and diastolic (DBP) blood pressure measurements performed during the first year of follow-up were obtained.

Laboratory evaluation included fasting plasma glucose, serum fructosamine, creatinine, triglycerides, total and high-density lipoprotein (HDL) cholesterol, and 24-hour proteinuria, all obtained by automated methods. Mean values of all laboratory examinations performed in the first year of follow-up were recorded.

Standard resting 12-lead ECGs were recorded with the same equipment and response frequencies at 25-mm/s paper speed and 10-mm/mV amplitude. ECG abnormalities were registered according to the Minnesota code except LVH, which was ascertained by voltage criteria, either Sokolow-Lyon (SV1+RV5 or V63.5 mV) or Cornell sex-specific (SV3+RaVL 2.0 mV in women or 2.8 mV in men). QT intervals were manually measured in every lead possible as previously described¹⁹ by a single experienced observer unaware of other patient data and corrected for heart rate (QTc) by Bazzet’s formula (QTc=QTxRR^-1/2). Both maximum QTc interval duration (QTcmax) and QT interval dispersion (QTd, the difference between the longest and shortest noncorrected QT intervals) were obtained. Forty-five randomly chosen ECGs were measured again at least 6 months after the first measurement to assess intraobserver reproducibility. Mean relative errors were 1.1% for QTcmax and 12% for QTd. The mean intraobserver absolute difference of QTcmax measurement was 24.9 ms^1/2 (SD, 78.9 ms^1/2). This signifies that 95% of the intraobserver variability of QTcmax measurement was within -133 and 183 ms^1/2.

Good-quality comprehensive 2-dimensional echocardiography (Apogee, Interspec, with 3.5-MHz transducer), performed within 1 month of the admission ECG by the same experienced observer, was available in 187 patients (40%). Measurements of septal and posterior wall thicknesses and left ventricular end-diastolic diameter were performed according to the Penn convention, and left ventricular mass (LVM) was calculated by the anatomically validated cube formula of Devereux and Reichek.²¹ Cutoff values for the presence of echocardiographic LVH were those derived from the Framingham study²² (>198 g for women and >294 g for men).

Follow-Up and Ascertainment of Stroke Events
Patients were evaluated regularly at least 2 times a year until June 2001. Those who failed to present to the hospital were contacted annually to determine health status. Forty-three patients (9.1%) were lost to follow-up and were considered censored observations at the date of their last hospital visit. The primary end point was total fatal or nonfatal stroke. Stroke events were defined according to the WHO criteria²³ and ascertained from medical records (including cerebral imaging studies), death certificates, and interviews with attending physicians, patients, and families by use of a standard questionnaire reviewed by an independent observer. Strokes were classified as ischemic, hemorrhagic, or undetermined on the basis of cerebral imaging findings (available in 87.5% of stroke patients). Isolated subarachnoidal bleeding was not included. Stroke cases with negative or nonspecific findings (no sign of hemorrhage) on cerebral imaging performed within the first 48 hours were classified as ischemic. Fatal stroke was defined by death of any cause within 28 days of the index event.

Statistical Analysis
All statistics analyses were performed with the STATA statistical package, version 7.0. Continuous data were described as medians and 5th and 95th percentile values. QT interval variables were evaluated as continuous ones and dichotomized at the upper quintile values (470 ms^1/2 and 70 ms for QTcmax and QTd, respectively). Stroke-free survival analysis was performed by Kaplan-Meier estimation of survival curves (compared by log-rank tests) and by univariate and multivariate proportional-hazards Cox models. Serum creatinine was log₁₀ transformed before inclusion in the models because of its positive skewed distribution. Missing data were frequent for 24-hour proteinuria (25%), HDL cholesterol (32%), and especially echocardiographic LVM (60%). It has been shown that deleting subjects with a missing value on 1 predictor variable included in the multivariate models (the so-called complete cases analysis) commonly leads to biased results and surely to loss of power.^6,24 So, to decrease bias and increase statistical efficiency, we imputed missing data using the expectation maximization method, which is based on the correlations between each variable with missing values and all other variables as estimated from the complete cases. Hence, 3 different Cox multivariate models were fitted in backward stepwise procedures. All variables that showed in univariate analysis a value of P<0.20 (except HDL cholesterol and LVM) entered into the first model. Sex, heart rate, and presence of ECG conduction disturbances were also forced into this model. The second model added HDL cholesterol to the first model, and the third model added echocardiographic LVM to the second model. In addition, multivariate analyses were repeated after exclusion of patients with cerebrovascular disease present at baseline. Assumptions of the proportional-hazards models and interactions were also tested,²⁵ and no violation was observed. Results were presented as hazard ratios with 95% confidence intervals. A 2-tailed value of P<0.05 was considered statistically significant.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Baseline Characteristics and Follow-Up Strokes
During a median follow-up of 57 months (range, 2 to 84 months), there were 40 incident strokes, 9 fatal strokes (22.5%), 32 ischemic strokes (80%), 3 hemorrhagic strokes (7.5%), and 5 strokes of undetermined origin (12.5%). Table 1 shows the baseline characteristics of all 471 diabetic patients and of those with and without stroke. Patients with stroke were older, had diabetes of longer duration, and had increased prevalences of arterial hypertension, heart failure, cerebrovascular disease, use of diuretics and calcium channel blockers, and ECG evidence of LVH than patients without stroke did. Also, stroke patients had increased serum triglycerides and 24-hour proteinuria, decreased serum HDL cholesterol, and increased echocardiographic LVM, and a greater proportion of patients had prolonged QTc interval. Patients lost to follow-up had baseline characteristics identical to those with complete data.

Univariate Survival Analysis
Table 2 shows the results of univariate Cox analyses. Age, presence of heart failure and cerebrovascular disease at baseline, use of diuretics, serum HDL cholesterol, 24-hour proteinuria, ECG LVH, echocardiographic LVM, and prolonged QTc interval were the variables with strongest univariate associations with stroke-free survival. Kaplan-Meier survival curve analyses for patients grouped according to the presence of QTc interval prolongation >470 ms^1/2 and increased QT dispersion >70 ms (Figure 1), to the presence of clinical nephropathy (24-hour proteinuria >0.5 g/24 h) and serum HDL cholesterol levels <0.9 mmol/L (Figure 2), and to the presence of either echocardiographic and ECG LVH (Figure 3) demonstrated that all these parameters except increased QTd were capable of distinguishing subgroups of patients with significantly different stroke-free survivals.

View larger version (17K): [in this window] [in a new window]   Figure 1. Kaplan-Meier estimation of stroke-free survival curves in patients grouped according to presence of QTc interval prolongation (QTcmax 470 ms1/2; top) and increased QT interval dispersion (QTd 70 ms; bottom).

View larger version (17K): [in this window] [in a new window]   Figure 2. Kaplan-Meier stroke-free survival curves in patients grouped according to presence of clinical nephropathy (24-hour proteinuria >0.5 g/24 h; top) and low serum HDL cholesterol levels (0.9 mmol/L; bottom).

View larger version (17K): [in this window] [in a new window]   Figure 3. Kaplan-Meier stroke-free survival curves of patients grouped according to presence of echocardiographic (top) and ECG (bottom) LVH.

Multivariate Survival Analysis
Results of multivariate Cox analyses are shown in Table 3. QTc interval prolongation (470 ms^1/2) was an independent predictor of stroke; its presence increased 3-fold the risk of stroke after adjustment for other potential risk factors, regardless of the presence of cerebrovascular disease at baseline. Increased QT interval dispersion, on the other hand, was not selected as an independent predictor of stroke when it was substituted for QTcmax in any of the multivariate models. Other independent predictive factors for stroke were older age, the presence of previous cerebrovascular disease (which increased stroke risk by 3-fold), decreased serum HDL cholesterol, and increased 24-hour proteinuria, serum triglycerides, and LVM. The presence of heart failure was also selected in the subgroup of patients without previous cerebrovascular disease.

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The main finding of this prospective study with up to 7 years’ follow-up is that QTc interval prolongation constitutes an independent predictive factor for future strokes in patients with type 2 diabetes. Two potential confounders, heart rate and the presence of conduction disturbances on ECG, were specifically forced into the multivariate analysis, although neither showed associations with stroke-free survival in univariate analysis. Heart rate was included because the rate correction of the QT interval duration could have introduced some prognostic information derived from the heart rate itself. The presence of ECG conduction disturbances was included because it is known that QRS prolongation is associated with QTc prolongation.²⁶ Thus, it might be that the prognostic value of QTc prolongation was solely or partly explained by the prognostic information contained in QRS prolongation resulting from the presence of conduction disturbances. Introduction of these variables into the multivariate models ascertained that the demonstrated importance of QTc interval prolongation on stroke incidence, beyond being independent of other known cardiovascular risk factors, was also independent of heart rate and QRS prolongation.

Although a demonstrated predictor of future strokes, QTc interval prolongation did not present enough predictive performance (sensitivity, 30.0%; specificity, 82.1% for QTcmax 470 ms^1/2) to be recommended in isolation as a screening method for detecting stroke-prone diabetics. In fact, none of the identified risk factors showed such a predictive performance.

In general populations, a prolonged QTc interval has been associated with aging, female sex, arterial hypertension (mainly systolic blood pressure), underlying coronary artery disease, and ECG-estimated LVM.^26,27 In addition, diabetes and impaired glucose tolerance have been related to QTc prolongation,^13,28 probably as part of the insulin resistance syndrome resulting from insulin-mediated increased sympathetic activity or impaired glucose use at myocardial cells or both.²⁸ Specifically in diabetic patients, a prolonged QTc interval has been demonstrated to be associated with the presence of cardiac dysautonomia²⁹ and macrovascular complications,³⁰ particularly ischemic heart disease.³¹ Most important, QTc interval prolongation has been shown to be a predictive factor for cardiovascular morbidity and mortality in both population-based studies^14,15,32 and diabetic patients.^16–18,33 Given these relevant relationships, it is not unexpected to find that QTc interval prolongation convincingly does have prognostic value for future atherosclerotic vascular events such as strokes in patients with diabetes mellitus. Prolonged QTc interval may be a marker of silent, undetected atherosclerotic vascular disease, reflecting in conjunction a number of factors associated with the atherosclerotic process such as aging, insulin resistance, increased systolic pressure, and LVH. In this regard, it has been demonstrated that significant associations exist between QTc interval duration and ultrasonographically derived carotid intima-media thickness in nondiabetic subjects³⁴ and between QTc prolongation and activated factor XII levels (a marker of atherosclerotic vascular disease) in type 2 diabetic patients.³⁵ In both studies, the associations remained significant after adjustment for other cardiovascular risk factors, reinforcing the hypothesis that QTc prolongation may be a surrogate indicator of subclinical atherosclerosis.

On the other hand, unlike some studies,^33,36 we were unable to demonstrate any prognostic value for stroke of increased QT interval dispersion. We have previously reported¹⁸ that QT dispersion was a better risk stratifier than maximum QTc duration for fatal or nonfatal cardiac events in this cohort. It is possible that QT interval dispersion reflects best the global ventricular repolarization abnormalities that could be more important in determining future cardiac morbidity and mortality and that QTc interval prolongation reflects best underlying atherosclerosis, which is a determinant of future cerebrovascular events.

The other predictive factors for stroke demonstrated here—older age, increased urinary protein excretion, increased serum triglycerides, decreased serum HDL cholesterol (the strongest one), increased LVM, and presence of cerebrovascular disease at baseline—are all supported by additional studies.^{1,3–6,8,10–12,37} Of note is the absence of any variable related to arterial hypertension, recognizably a major risk factor for stroke.^{1,3–6,8,10–12,37} Some possible explanations should be raised. First, the prevalence of arterial hypertension was very high in this diabetic cohort (60%); thus, being hypertensive became a "common" characteristic, decreasing its prognostic importance. Second, there was the antihypertensive treatment effect on blood pressure levels because nearly all hypertensives were on drug treatment and blood pressure levels were mean values obtained during the first year of follow-up. Finally, some selected prognostic variables clearly incorporated some prognostic information given by arterial hypertension and blood pressure levels such as increased LVM and QTc prolongation itself. (Correlations between QTc duration and systolic blood pressure have been demonstrated.^26,27)

This study has some limitations. Smoking status and body mass, 2 potentially important predictors of stroke, were not available. Smoking, in particular, is a well-known risk factor for stroke,^1,38 although in diabetics some studies failed to show its prognostic value.^10,11 Additionally, no study had demonstrated any relationship between smoking status and QTc interval duration. Body mass is a more debatable risk factor for stroke, with several studies showing no relation between obesity and stroke risk.^8,10,37 Thus, we think that the absence of adjustment for smoking and body mass probably did not influence the strength of the association found between QTc prolongation and stroke risk. Atrial fibrillation, another important risk factor for stroke, also could not be evaluated in this study because it was an ECG exclusion criterion for entry.

In conclusion, this prospective study with follow-up for up to 7 years gives evidence that QTc interval prolongation is a predictor of future strokes in patients with type 2 diabetes independent of other known cardiovascular risk factors. Furthermore, previous cerebrovascular disease, older age, increased 24-hour proteinuria, increased serum triglycerides, decreased serum HDL cholesterol, and increased LVM are also important predictors of stroke in type 2 diabetics. Intervention studies are necessary to evaluate whether this new predictive factor could be modified.

	Acknowledgments

 
We thank Dr Ronir Raggio for assistance in performing the statistical analysis.

Received January 13, 2003; revision received April 14, 2003; accepted May 1, 2003.

	References

Top Abstract Introduction Subjects and Methods Results Discussion References

 
1. Goldstein LB, Adams R, Becker K, Furberg CD, Gorelick PB, Hademenos G, Hill M, Howard G, Howard VJ, Jacobs B, et al. Primary prevention of ischemic stroke: a statement for healthcare professionals from the Stroke Council of the American Heart Association. Stroke. 2001; 32: 280–299.[Free Full Text]

2. Mansur AP, Favarato D, Souza MFM, Avakian SD, Aldrighi JM, César LAM, Ramires JAF. Trends in death from circulatory diseases in Brazil between 1979 and 1996. Arq Bras Cardiol. 2001; 76: 504–510.

3. Manolio TA, Kronmal RA, Burke GL, O’Leary DH, Price TR. Short-term predictors of incident stroke in older adults: the Cardiovascular Health Study. Stroke. 1996; 27: 1479–1486.[Abstract/Free Full Text]

4. Tanne D, Yaari S, Goldbourt U. Risk profile and prediction of long-term ischemic stroke mortality: a 21-year follow-up in the Israeli Ischemic Heart Disease (IIHD) project. Circulation. 1998; 98: 1365–1371.[Abstract/Free Full Text]

5. Simons LA, McCallum J, Friedlander Y, Simons J. Risk factors for ischemic stroke: Dubbo Study of the Elderly. Stroke. 1998; 29: 1341–1346.[Abstract/Free Full Text]

6. Moons KGM, Bots ML, Salonen JT, Elwood PC, Freire de Gonçalves A, Nikitin Y, Sivenius J, Inzitari D, Benetou V, Tuomilehto J, et al. Prediction of stroke in the general population in Europe (EUROSTROKE): is there a role for fibrinogen and electrocardiography? J Epidemiol Community Health. 2002; 56 (suppl I): I30–I36.[Medline] [Order article via Infotrieve]

7. Manson JAE, Colditz GA, Stampfer MJ, Willett WC, Krolewski AS, Rosner B, Arky RA, Speizer FE, Hennekens CH. A prospective study of maturity-onset diabetes mellitus and risk of coronary heart disease and stroke in women. Arch Intern Med. 1991; 151: 1141–1147.[Abstract/Free Full Text]

8. Tuomilehto J, Rastenyte D, Jousilahti P, Sarti C, Vartiainen E. Diabetes mellitus as a risk factor for death from stroke: prospective study of the middle-aged Finnish population. Stroke. 1996; 27: 210–215.[Abstract/Free Full Text]

9. Elneihoum AM, Goransson M, Falke P, Janzon L. Three-year survival and recurrence after stroke in Malmo, Sweden: an analysis of stroke registry data. Stroke. 1998; 29: 2114–2117.[Abstract/Free Full Text]

10. 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]

11. Davis TM, Millns H, Stratton IM, Holman RR, Turner RC. Risk factors for stroke in type 2 diabetes mellitus: United Kingdom Prospective Diabetes Study (UKPDS) 29. Arch Intern Med. 1999; 159: 1097–1103.[Abstract/Free Full Text]

12. Fuller JH, Stevens LK, Wang S-L, for the WHO Multinational Study Group. Risk factors for cardiovascular mortality and morbidity: the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia. 2001; 44 (suppl 2): S54–S64.[CrossRef][Medline] [Order article via Infotrieve]

13. Brown DW, Giles WH, Greenlund KJ, Valdez R, Croft JB. Impaired fasting glucose, diabetes mellitus and cardiovascular disease risk factors are associated with prolonged QTc duration: results from the Third National Health and Nutrition Examination Survey. J Cardiovasc Risk. 2001; 8: 227–233.[CrossRef][Medline] [Order article via Infotrieve]

14. Elming H, Holm E, Jun L, Torp-Pedersen C, Køber L, Kircshoff M, Malik M, Camm J. The prognostic value of the QT interval and QT interval dispersion in all-cause and cardiac mortality and morbidity in a population of Danish citizens. Eur Heart J. 1998; 19: 1391–1400.[Abstract/Free Full Text]

15. de Bruyne MC, Hoes AW, Kors JA, Hofman A, van Bemmel JH, Grobbee DE. Prolonged QT interval predicts cardiac and all-cause mortality in the elderly: the Rotterdam Study. Eur Heart J. 1999; 20: 278–284.[Abstract/Free Full Text]

16. Naas AAO, Davidson NC, Thompson C, Cummings F, Ogston AS, Jung RT, Newton RW, Struthers AD. QT and QTc dispersion are accurate predictors of cardiac death in newly diagnosed non-insulin-dependent diabetes: cohort study. BMJ. 1998; 316: 745–746.[Free Full Text]

17. Christensen PK, Gall MA, Major-Pedersen A, Sato A, Rossing P, Breum L, Pietersen A, Kastrup J, Parving HH. QTc interval length and QT dispersion as predictors of mortality in patients with non-insulin-dependent diabetes. Scand J Clin Lab Invest. 2000; 60: 323–332.[CrossRef][Medline] [Order article via Infotrieve]

18. Cardoso C, Salles G, Deccache W. Prognostic value of QT interval parameters in type 2 diabetes mellitus: results of a long-term follow-up prospective study. J Diabetes Complications. 2003; 17: 169–178.[CrossRef][Medline] [Order article via Infotrieve]

19. Cardoso C, Salles G, Bloch K, Deccache W, Siqueira-Filho AG. Clinical determinants of increased QT dispersion in patients with diabetes mellitus. Int J Cardiol. 2001; 79: 253–262.[CrossRef][Medline] [Order article via Infotrieve]

20. Diabetes Mellitus. Geneva, Switzerland: World Health Organization; 1985. Technical Report Series.

21. Devereux RB, Reichek N. Echocardiographic determination of left ventricular mass in man: anatomic validation of the method. Circulation. 1977; 55: 613–618.[Abstract/Free Full Text]

22. Levy D, Savage DD, Garrison J, Anderson KM, Kannel WB, Castelli WP. Echocardiographic criteria for left ventricular hypertrophy: the Framingham Heart Study. Am J Cardiol. 1987; 59: 956–960.[CrossRef][Medline] [Order article via Infotrieve]

23. Report of the WHO Task Force on Stroke and Other Cerebrovascular Disorders. Stroke-1989: recommendations on stroke prevention, diagnosis and therapy. Stroke. 1989; 20: 1407–1431.[Free Full Text]

24. Greenland S, Finkle WD. A critical look at methods for handling missing covariates in epidemiologic regression analysis. Am J Epidemiol. 1995; 142: 1255–1264.[Abstract/Free Full Text]

25. Concato J, Feinstein AR, Holford TR. The risk of determining risk in multivariate models. Ann Intern Med. 1993; 118: 201–210.[Abstract/Free Full Text]

26. Rautaharju PM, Manolio TA, Psaty BM, Borhani NO, Furberg CD, for the Cardiovascular Health Study Collaborative Research Group. Correlates of QT prolongation in older adults (the Cardiovascular Health Study). Am J Cardiol. 1994; 73: 999–1002.[CrossRef][Medline] [Order article via Infotrieve]

27. Festa A, D’Agostino R Jr, Rautaharju P, Mykkänen L, Haffner SM. Relation of systemic blood pressure, left ventricular mass, insulin sensitivity, and coronary artery disease to QT interval duration in nondiabetic and type 2 diabetic subjects. Am J Cardiol. 2000; 86: 1117–1122.[CrossRef][Medline] [Order article via Infotrieve]

28. Dekker JM, Feskens EJ, Schouten EG, Klootwijk P, Pool J, Kromhout D. QTc duration is associated with levels of insulin and glucose tolerance: the Zutphen Elderly Study. Diabetes. 1996; 45: 376–380.[Abstract]

29. Whitsel EA, Boyko EJ, Siscovick DS. Reassessing the role of QTc in the diagnosis of autonomic failure among patients with diabetes. Diabetes Care. 2000; 23: 241–247.[Abstract]

30. Ko GT, Chan JC, Critchley JA, Cockram CS. Cardiovascular disease in Chinese type 2 diabetic women is associated with a prolonged QTc interval. Int J Cardiol. 2000; 76: 75–80.[CrossRef][Medline] [Order article via Infotrieve]

31. Veglio M, Bruno G, Borra M, Macchia G, Bargero G, D’Errico N, Pagano GF, Cavallo-Perin P. Prevalence of increased QT interval duration and dispersion in type 2 diabetic patients and its relationship with coronary heart disease: a population-based cohort. J Intern Med. 2002; 251: 317–324.[CrossRef][Medline] [Order article via Infotrieve]

32. Okin PM, Devereux RB, Howard BV, Fabsitz RR, Lee ET, Welty TK. Assessment of QT interval and QT dispersion for prediction of all-cause and cardiovascular mortality in American Indians: the Strong Heart Study. Circulation. 2000; 101: 61–66.[Abstract/Free Full Text]

33. Sawicki PT, Kiwitt S, Bender R, Berger M. The value of QT interval dispersion for identification of total mortality risk in non-insulin-dependent diabetes mellitus. J Intern Med. 1998; 243: 49–56.[CrossRef][Medline] [Order article via Infotrieve]

34. Festa A, D’Agostino R, Rautaharju P, O’Leary DH, Rewers M, Mykkänen L, Haffner SM. Is QT interval a marker of subclinical atherosclerosis in nondiabetic subjects? The Insulin Resistance Atherosclerosis Study (IRAS). Stroke. 1999; 30: 1566–1571.[Abstract/Free Full Text]

35. Rutter MK, Viswanath S, McComb JM, Kesteven P, Marshall SM. QT prolongation in patients with type 2 diabetes and microalbuminuria. Clin Auton Res. 2002; 12: 366–372.[CrossRef][Medline] [Order article via Infotrieve]

36. Perkiomaki JS, Sourander LB, Levomaki L, Raiha IJ, Puukka P, Huikuri HV. QT dispersion and mortality in the elderly. Ann Noninvasive Electrocardiol. 2001; 6: 183–192.[CrossRef][Medline] [Order article via Infotrieve]

37. Guerrero-Romero F, Rodriguez-Moran M. Proteinuria is an independent risk factor for ischemic stroke in non-insulin-dependent diabetes mellitus. Stroke. 1999; 30: 1787–1791.[Abstract/Free Full Text]

38. Shinton R, Beevers G. Meta-analysis of relation between cigarette smoking and stroke. BMJ. 1989; 298: 789–794.[Abstract/Free Full Text]

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