Journal of Neurology

, Volume 254, Issue 3, pp 315–321 | Cite as

Joint effects of risk factors for stroke and transient ischemic attack in a German population

The EPIC Potsdam Study
  • Cornelia Weikert
  • Klaus Berger
  • Christin Heidemann
  • Manuela M. Bergmann
  • Kurt Hoffmann
  • Kerstin Klipstein-Grobusch
  • Heiner Boeing



Single, modifiable risk factors for stroke have extensively been studied. In contrast, differences of their combined effects among stroke and transitoy ischemic attack (TIA) have been rarely investigated. The aim of the present study was to assess single and joint effects of risk factors on the incidence of stroke and TIA and to compare their magnitudes in a large population-based German cohort.


Incident cases of stroke and TIA were identified among 25538 participants (aged 35–65 at baseline) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Relative risks for stroke and TIA related to modifiable risk factors were estimated using Cox proportional hazard models.


During 4.3 years of follow-up 100 stroke cases and 112 TIA cases occurred. Incidences of stroke and TIA were 91.7 and 102.7 per 100 000 person-years, respectively. Relative risks for ischemic stroke (RR 5.12, 95% CI 1.49–17.6, p for trend <0.0001) and for TIA (RR 3.08, 95% CI 1.00–9.44, p for trend <0.024) were highest among participants having 4 or 5 modifiable risk factors. 58.5% of ischemic strokes and 26.2% of TIA cases were attributable to the 5 risk factors hypertension, diabetes mellitus, high alcohol consumption, hyperlipidemia, and smoking.


Our data indicate that classical risk factors may explain almost 60% of ischemic stroke but only one in four TIA cases. Analysing potential differences of known risk factors between ischemic stroke and TIAs and the identification of other determinants of ischemic attacks are important steps to better explain the burden of stroke.


stroke TIA incidence relative risk epidemiology 


Stroke is among the leading causes of morbidity and mortality in developed countries [33] and it is the most important cause of impairment and disability in the elderly. Treatment options of the cause are limited to thrombolysis, but only few patients receive this treatment owing to restrictions in application time and indication [11]. Thus, primary prevention remains the most important general strategy for reducing the impact of stroke [21]. Single risk factors have been extensively studied but their joint effects on stroke occurrence have been less frequently investigated [15]. If evaluated, this was mainly done for the purpose of stroke risk score development [22]. As there are several well-established, so called “classical”, modifiable risk factors such as hypertension, smoking, cardiac disease, and diabetes, stroke appears to be a preventable disease to a large extent [18, 25]. In addition, the aim of many primary prevention programs is a change in lifestyle, thus, in various health related behaviors and not just in a single risk factor. Lifestyle changes are likely to influence risk factor prevalences, which in turn may modify the risk of stroke [15].

The transitory ischemic attack (TIA) constitutes an established risk factor for ischemic stroke [19]. Thus, the primary and secondary prevention of TIA is also an important goal for efficiently decreasing stroke incidence. Diagnosis of TIA is based on clinical symptoms and differs from ischemic stroke in its duration. By definition the symptoms of TIA are lasting less then 24hours [27]. This definition goes back to the era before brain imaging was widely available and is now a matter of controversy [1, 14, 27]. Varying criteria used by clinicians to define and identify TIA cases might be a reason why few prior studies have evaluated risk factors for TIA [24].

Our study aimed at assessing the incidence of stroke and TIA in the “European Prospective Investigation into Cancer and Nutrition (EPIC) – Potsdam Study” and to compare the single and combined effects of so called classical risk factors on the incidence of stroke and TIA.


Study population

The European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study is part of the large-scale European-wide prospective cohort study EPIC and includes 27,548 individuals aged 35 to 65 years who were recruited between 1994 and 1998 from the general population [8]. All participants gave written informed consent and the Ethics Committee of the Federal State Brandenburg gave approval for all study procedures. Information about changes in lifestyle and incident diseases is biennially assessed by a self-administered questionnaire [7].

In the follow-up questionnaire a validated, brief screening instrument for stroke and TIA was included [3]. It consisted of one general question asking for physician-diagnosed stroke in the past and three questions on common stroke or TIA symptoms. The latter included weakness or paralysis of a limb, slurred speech or problems in speaking and visual symptoms. To improve the understanding of visual symptoms photographs demonstrating hemianopsia, central scotoma, diplopia, monocular loss of vision, and loss of vision at the top of the form were used [3].

Subsequent validation of stroke and TIA was based on medical records and followed an established protocol [2, 4] that included a standardized form filled in by the treating physician or the study physician. Information was collected about subtype of stroke with ICD-10 code, hospitalization, symptoms, duration, diagnostic procedures and outcome. A stroke was defined as a focal neurological deficit of sudden onset and vascular mechanism lasting longer than 24 hours, and a TIA less than 24 hours, respectively. For definition of TIA we did not consider pathological findings in brain imaging. On the basis of all information strokes were classified into ischemic (ICD-10 I63.0–I63.9 [30]), intracerebral (ICD-10 I61.0–I61.9 [30]) and subarachnoidal hemorrhage (ICD-10 I60.0–I60.9 [30]), undetermined strokes (ICD-10 I64.0–I64.9 [30]) and TIA (ICD-10 G45.0–G45.9 [30]) by two physicians in the study center.

Participants with missing follow-up status (n = 723) as well as subjects with self reported prevalent stroke or TIA at baseline (n = 1287) were excluded, leaving 25538 participants for the analyses of incident events. Among these, we identified 100 cases of stroke and 112 cases of TIA occurring between baseline and the second follow-up. For risk estimations we further excluded 2 ischemic stroke cases and 234 non-cases with missing covariate variables as well as those 2 participants with an undefined stroke.

Assessment of risk factors and covariates

Baseline assessment of lifestyle factors, medical history, and socio-demographic characteristics was done by computer-assisted person-to-person interviews or self-administered questionnaires [16]. Anthropometric data and blood pressure were measured by trained and quality-monitored personnel [16]. All participants filled in a food frequency questionnaire that included questions on frequency and portion size of 148 food items eaten during the previous year [17].

Educational attainment was dichotomized into less than high school education versus high school education or university degree. Blood pressure was measured three times following a standard protocol. Hypertension was defined as a systolic blood pressure (140 mmHg or a diastolic blood pressure of (90 mmHg or taking antihypertensive medication or self report of a diagnosis of hypertension. A history of diabetes was based on self reports of a diagnosis or taking antidiabetic medication. A history of hyperlipidemia was based on self reports of a diagnosis or of taking cholesterol-lowering medication. If participants reported a previous diagnosis of coronary heart disease or myocardial infarction at baseline, a prevalent ischemic heart disease has been assumed. Physical activity was dichotomized into the categories less than two hours sporting activity, and two or more hours of physical activity per week. Smoking status was defined as nonsmoker and former smoker, and current smoker. For the purpose of calculation of the combined risks the three categories of smoking were collapsed into nonsmoker plus former smoker versus current smoker. Body mass index (BMI) was calculated as body weight divided by body height squared. Obesity was defined as a body mass index of ≥30 kg/m2. High alcohol consumption was defined as drinking more than 15 g of alcohol per day in women and drinking more than 30 g alcohol per day in men.

Statistical analysis

All statistical analyses were performed using SAS software, version 9.13 (SAS Institute, Cary, NC, USA). Age standardized rates of stroke and TIA were calculated for four age categories using the age distribution of all participants. Baseline characteristics of participants are expressed as mean ± standard deviation (SD) or percentages.

We used Cox’s proportional hazard model for analyses of the association between five modifiable classical risk factors (hypertension, current smoking, diabetes, hyperlipidemia and high alcohol consumption) and incident stroke or TIA. Age was used as the underlying time variable in the counting process with entry time defined as the subject’s age at recruitment and exit time defined as the subject’s age at stroke or TIA diagnosis or censoring. Relative risks were estimated sex adjusted and in further analyses additionally adjusted for educational attainment, obesity, physical activity, prevalent coronary heart disease and the respective above-mentioned other modifiable classical risk factors.

Furthermore the association between the number of present risk factors (from hypertension, current smoking, diabetes, hyperlipidemia and high alcohol consumption) and ischemic stroke or TIA was estimated. For this purpose, we defined indicator variables for the presence of one, two, three, and more than three risk factors. Trends in ischemic stroke and TIA risk were assessed in Cox’s proportional hazard models using the number of present risk factors as an independent variable. Population attributable risks were estimated for each of the five selected risk factors separately and combined. Here, we applied the formula of Bruzzi et al. [10] using the prevalence of risk factors within cases and estimates of adjusted relative risks [10].


During an average follow-up time of 4.3 years, 100 strokes and 112 TIA occurred. Among all stroke cases, 83 ischemic strokes, 15 hemorrhagic strokes (4 subarachnoid bleedings, 11 intracerebral hemorrhage) and two of undeterminable subtype were observed. The overall incidence per 100,000 person-years was 102.7 for TIA and 91.7 for stroke (Table 1). The incidence of ischemic stroke, intracerebral and subarachnoid hemorrhage was 76.2, 10.1 and 3.7 per 100.000 person-years, respectively. As expected, the incidence of stroke and TIA showed a strong age dependency.
Table 1

Incidence rates for TIA, ischemic, hemorrhagic and total stroke per 100,000 person years during 4.3 years of follow-up in the EPIC-Potsdam Study



Ischemic stroke

Hemorrhagic strokea

Total stroke

Cases (n)


Cases (n)


Cases (n)


Cases (n)





























65 + y




































a These cases are 4 subarachnoid bleedings and 11 intracerebral hemorrhage. y = years

Almost all participants who had experienced a hemorrhagic stroke had been treated in a hospital (Table 2). In contrast only 39 (about one third) of TIA cases were hospitalized (Table 2). About one third of TIA cases had no brain imaging. However, for 28 TIA cases pathological findings in brain imaging were reported in the respective medical records.
Table 2

Clinical aspects of stroke and TIA in the EPIC-Potsdam Study


TIA (n = 112)

Ischemic stroke (n = 83)

Hemorrhagic stroke (n = 15)

Total Stroke (n = 100)














Brain imaging

Pathological findings









Not done







a Diagnosed by autopsy.

Baseline characteristics for cases and non-cases are shown in Table 3. Stroke and TIA cases were older than non-cases and had a higher prevalence in hypertension at baseline. The prevalence of hyperlipidemia was higher in ischemic stroke and TIA, but not in hemorrhagic stroke cases. Obesity prevalence was also higher in cases than in non-cases. Incident stroke cases had a higher baseline alcohol intake and were less physically active. In contrast, the baseline alcohol consumption in incident TIA cases was lower than in non-cases.
Table 3

Baseline characteristicsa for cases and non-cases according to stroke or TIA status in the EPIC-Potsdam Study


TIA (n = 112)

Ischemic strokes (n = 81)

Hemorrhagic stroke (n = 15)

Non-cases (n = 25092)

Sociodemographic factors






Age (years)

56.6 ± 7.1

56.4 ± 7.6

53.6 ± 9.2

49.7 ± 9.0

High educationb

















Diabetes mellitus





Coronary heart disease





Lifestyle factors


26.8 ± 3.6

27.2 ± 4.3

27.7 ± 5.4

26.2 ± 4.3






Current cigarette smoker










High alcohol consumptione





Physical activity (≥2 h/week)





a Values are means ± SD or percentages.

b High education is defined as high school education or university degree.

c Hypertension is defined by a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg or taking antihypertensive medication or self reporting a hypertension diagnosis.

d Obesity is defined by a body mass index ≥30 kg/m2.

e High alcohol consumption is based on alcohol consumption >15 g/d in women and >30 g/d in men.

The relative risks (RR) of five selected risk factors for stroke and TIA are shown in Table 4. Only modifiable risk factors with either significant or borderline significant (p ≤ 0.1) associations with either endpoint were selected. Nonetheless multivariate models were adjusted for higher education, obesity, physical activity, and prevalent coronary heart disease. Hypertension as the most important risk factor was associated with a 2.3 fold increase in the risk of ischemic stroke in the multivariate-adjusted model. There was no significant association between hypertension and TIA risk. The association of hypertension and risk of hemorrhagic stroke in the multivariate-adjusted model was of borderline significance (RR: 3.37, p = 0.08) most likely due to the small case numbers. After adjustment for potential confounders the RR for ischemic stroke associated with blood pressure categories <140 and < 90, 140–159 or 90–99, and ≥ 160 or ≥ 100 mmHg were 1.00 (reference), 1.85 (95% confidence interval (95%CI), 1.05–3.27) and 3.80 (95%CI 2.11–6.86). The corresponding risks for hemorrhagic stroke were 1.00, 1.85 (95%CI 0.43–6.70), 4.90 (95%CI 1.26–19.00), and for TIA 1.0, 1.28 (95%CI 0.81–2.03), 1.80 (95%CI 1.04–3.12), respectively.
Table 4

Relative risks (95% CI) of five selected classical modifiable risk factors for TIA and ischemic stroke in the EPIC-Potsdam Study

Risk factor

TIA (n = 112)

Ischemic stroke (n = 81)

Model 1a

Model 2b

Model 1a

Model 2b


1.20 (0.81–1.78)

1.14 (0.76–1.71)

2.44 (1.44–4.14)

2.32 (1.35–3.99)

Diabetes mellitus

1.44 (0.77–2.70)

1.31 (0.69–2.51)

2.33 (1.27–4.28)

2.02 (1.08–3.76)


1.55 (1.06–2.26)

1.46 (0.99–2.15)

1.32 (0.84–2.06)

1.14 (0.72–1.81)


1.11 (0.68–1.80)

1.13 (0.69–1.84)

1.51 (0.90–2.52)

1.49 (0.89–2.50)

High alcohol consumption

1.19 (0.75–1.89)

1.17 (0.74–1.87)

1.61 (0.98–2.64)

1.57 (0.95–2.59)

a Model 1: Relative risks are hazard rates adjusted for sex. Age is the dependent time variable.

b Model2: Relative risks are hazard rates adjusted for sex, education, physical activity, obesity, prevalent coronary heart disease, and the other risk factors mentioned in this table. Age is the dependent time variable.

Hyperlipidemia was significantly associated with the risk for TIA in the sex-adjusted model, but no significant association was observed for ischemic or hemorrhagic stroke. Sex- and multivariate-adjusted RR of high alcohol consumption, diabetes mellitus and current smoking for hemorrhagic stroke were not significant.

Further, we estimated joint effects of selected risk factors for ischemic stroke and TIA (Table 5). Participants having four or five risk factors represented 1.5% of the non-cases, but almost 5% among ischemic strokes. They had a more than 5 fold increased risk for ischemic stroke than participants without one of these risk factors (p for trend <0.0001). The population attributable risks for those five risk factors regarding ischemic stroke were 42.6% for hypertension, 8.1% for diabetes, 5.2% for hyperlipidemia, 9.9% for high alcohol consumption and 8.1% for smoking. All together 58.5% of ischemic stroke cases were attributable to these five risk factors. As shown in Table 5 we did not observe such a strong association between the number of risk factors and TIA, but nevertheless the risk increased significantly with a higher number of risk factors (p for trend 0.024). All together 26.2% of TIA cases were attributable to these 5 risk factors in our study population (7.6% for hypertension, 2.3% for diabetes, 14.4% for hyperlipidemia, 3.0% for high alcohol consumption and 2.1% for smoking).
Table 5

Prevalence and relative risks a for the increasing number of selected modifiable classical risk factors (hypertension, smoking, diabetes, hyperlipidemia and high alcohol consumption) in the EPIC-Potsdam Study

Number of dichotomised risk factors

Non-cases (n = 25092)

TIA (n = 112)

Ischemic stroke (n = 81)

Prevalence (%)

Prevalence (%)

RR (95% CI)

Prevalence (%)

RR (95% CI)










1.37 (0.76–2.46)


1.43 (0.63–3.24)




1.70 (0.93–3.09)


2.62 (1.18–5.82)




1.83 (0.87–3.84)


4.36 (1.84–10.3)

4 or 5



3.08 (1.00–9.44)


5.12 (1.49–17.6)

P for trend:




< 0.0001

a Relative risks (RR) are hazard rates adjusted for sex, education, physical activity, obesity, prevalent coronary heart disease, and the other risk factors mentioned in this table. Age is the dependent time variable.


In this population-based prospective cohort study with more than 25000 participants a lower incidence for stroke than for TIA and a strong age dependency for both outcomes were observed. The stroke incidence observed in this population-based study is comparable with the data reported in other studies of the general population in Germany [5]. Data on the incidence of TIA in population studies are scarce and only reported in one prior study in Germany [3]. Among the few available data two studies evaluating a relationship between TIA and stroke incidence observed a higher incidence for stroke than TIA [9, 20]. However, TIA is still an event difficult to identify in epidemiologial studies, because of varying definitions applied by clinicians [24]. In a retrospective study by Lemesle et al. [19] a TIA was diagnosed, when the clinical symptoms disappeared within 24 hours and no sequelae were noted on CT. In our study TIA was solely based on clinical symptoms. Brain imaging was not taken into consideration because it was not done in many TIA cases, although it showed pathological findings in about one fourth of our TIA cases. Thus, differences in the definition of TIA may explain, at least in part, differences in incidences.

Hypertension was confirmed as the single most important risk factor for both ischemic and hemorrhagic stroke. The prevalence of two risk factors was associated with a more than 2-fold risk and the prevalence of 4 or 5 factors with a more than 5-fold increased risk for ischemic stroke. The combined risk factors explained as much as 60% of the ischemic stroke incidence in this population. In contrast the combined risk effects were considerably lower in magnitude among TIA cases. A prevalence of 4 or 5 risk factors yielded a 3-fold increased risk and explained only one fourth of incident TIA cases in the EPIC-Potsdam cohort.

Interestingly, associations between most classical modifiable risk factors and incident TIAs were weaker than those observed for stroke. There are only very few studies investigating risk factors for TIA and their results have been inconsistent [26, 28, 29].

In contrast to our data, Whisnant et al. [29] observed similar odds ratios for stroke and TIA related to the classical risk factors hypertension and diabetes conducted in the United States. Only for smoking did the study detect a higher odds ratio for stroke than for TIA.

Self reported hyperlipidemia is an important risk factor for TIA in our study population. This is biologically plausible, since hyperlipidemia is a key factor in the development of arteriosclerosis. It must be kept in mind that subjects aware of this condition may also be relatively health conscious and more sensitive to mild TIA symptoms, which may in part be responsible for the observed associations.

Our study is one of the very few studies investigating joint effects of classical risk factors on stroke and TIA. In contrast to the few other studies [22] our aim was not to develop a new stroke risk prediction model but to compare risk factor magnitudes between stroke and TIA. The most commonly used risk score for stroke, developed by the Framingham Study [32], was based on the inclusion of both TIA and stroke cases. Our results suggest that pooling both types of events would underestimate predicted stroke risks due to the different magnitude of combined risks for both outcomes.

However, it has to be pointed out that previous studies investigating joint effects of classical modifiable risk factors were conducted solely in North America [22, 34], where the impact of modifiable risk factors and their combinations may differ significantly from their impact in a European population. For instance the prevalence of obesity is much higher in the United States than in Germany [6, 12]. Thus our data may be specific for German populations.

The prospective study design, the large sample size and the use of a validated stroke symptom questionnaire that also allowed the classification of TIA [3] are among the strengths of our investigation. Furthermore, all cases were verified by medical records. Our study also provides information on aspects of diagnostic management and treatment of TIA, which varies among different physicians. Not all patients with a TIA were admitted to a hospital as an emergency, although this is generally recommended.

Owing to difficulties in accurately diagnosing TIA misclassification may be a relevant threat to the validity of studies investigating TIA risk factors, since it may result in substantial attenuation of relative risk estimates and could be responsible for differences in risk factor profiles between TIA and stroke. In particular, this issue may apply to the non-hospitalized TIA cases included in our study. But our findings are in line with findings from a clinical register, which clearly showed differences in the prevalence of classical risk factors between TIA and ischemic stroke cases even though only hospitalized cases were included [28]. As observed in our study, patients with a TIA were less likely to be smokers and to have diabetes mellitus or hypertension, but more likely to have a history of hyperlipidemia.

Our study population is limited to participants aged 35–65 at baseline. It is well-known that stroke is a disease of older ages and the risk of stroke doubles for each successive decade after age 55 [5, 13]. The impact of risk factors may change with subject’s age, as has been shown for hypertension [13]. Thus, the findings of our study may be specific for a middle-aged German population and applicability may be limited for older populations.

The mean follow-up of 4.3 years is rather short, restricting the number of incident TIA and stroke cases. This limits the statistical power to detect significant associations between risk factors and outcomes, especially for hemorrhagic stroke. The assessment of risk factor status was mainly based on self-reports (without measurements of e.g. cholesterol or fasting glucose at baseline), which is a potential source of bias. However, the relatively high socioeconomic status of our study population may be associated with a sufficient quality of self-reports [23]. Another issue may be introduced by dichotomising the risk factor variables. Thereby the information on a specific factor will be simplified and the chosen cut points for selected risk factors such as hypertension and alcohol may not reflect true threshold levels of the dose-response relationship. Finally, we were unable to assess the prevalence of atrial fibrillation in the study population, which is an important risk factor for ischemic stroke in older ages [31].

In conclusion, almost 60% of incident ischemic stroke in the EPIC-Potsdam cohort could be explained by classical modifiable risk factors, describing the potential of lifestyle changes for stroke risk reduction. Hypertension is confirmed as the most important single risk factor for stroke, but may be less important for TIA. Our findings demonstrate that only 25% of TIA cases can be attributed to the classical risk factors for stroke. TIA is an important condition for the subsequent development of stroke. Thus, a better characterization of the risk factor profile of TIA is of high importance for refining strategies of primary stroke prevention.



The authors thank all study participants for their cooperation. We especially thank our study physician Wolfgang Fleischhauer and our data manager Kay Behling and Ellen Kohlsdorf.


  1. 1.
    Albers GW, Caplan LR, Easton JD, Fayad PB, Mohr JP, Saver JL, Sherman DG (2002) Transient ischemic attack–proposal for a new definition. N Engl J Med 347:1713–1716PubMedCrossRefGoogle Scholar
  2. 2.
    Atiya M, Kurth T, Berger K, Buring JE, Kase CS (2003) Interobserver agreement in the classification of stroke in the Women’s Health Study. Stroke 34:565–567PubMedCrossRefGoogle Scholar
  3. 3.
    Berger K, Hense HW, Rothdach A, Weltermann B, Keil U (2000) A single question about prior stroke versus a stroke questionnaire to assess stroke prevalence in populations. Neuroepidemiology 19:245–257PubMedCrossRefGoogle Scholar
  4. 4.
    Berger K, Kase CS, Buring JE (1996) Interobserver agreement in the classification of stroke in the physicians’ health study. Stroke 27:238–242PubMedGoogle Scholar
  5. 5.
    Berger K, Kolominsky-Rabas P, Heuschmann P, Keil U (2000) [Frequency of stroke in Germany: prevalence, incidence and sources of these data]. Dtsch Med Wochenschr 125:21–25PubMedGoogle Scholar
  6. 6.
    Bergmann KE, Mensink GB (1999) [Anthropometric data and obesity]. Gesundheitswesen 61 Spec No:S115–120PubMedGoogle Scholar
  7. 7.
    Bergmann MM, Bussas U, Boeing H (1999) Follow-up procedures in EPIC-Germany–data quality aspects. European Prospective Investigation into Cancer and Nutrition. Ann Nutr Metab 43:225–234PubMedCrossRefGoogle Scholar
  8. 8.
    Boeing H, Korfmann A, Bergmann MM (1999) Recruitment procedures of EPIC-Germany. European Investigation into Cancer and Nutrition. Ann Nutr Metab 43:205–215PubMedCrossRefGoogle Scholar
  9. 9.
    Brown RD Jr., Petty GW, O’Fallon WM, Wiebers DO, Whisnant JP (1998) Incidence of transient ischemic attack in Rochester, Minnesota, 1985–1989. Stroke 29:2109–2113PubMedGoogle Scholar
  10. 10.
    Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C (1985) Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol 122:904–914PubMedGoogle Scholar
  11. 11.
    Davalos A (2005) Thrombolysis in acute ischemic stroke: successes, failures, and new hopes. Cerebrovasc Dis 20(Suppl 2):135–139PubMedGoogle Scholar
  12. 12.
    Flegal KM, Carroll MD, Ogden CL, Johnson CL (2002) Prevalence and trends in obesity among US adults, 1999–2000. Jama 288:1723–1727PubMedCrossRefGoogle Scholar
  13. 13.
    Goldstein LB, Adams R, Alberts MJ, Appel LJ, Brass LM, Bushnell CD, Culebras A, Degraba TJ, Gorelick PB, Guyton JR, Hart RG, Howard G, Kelly-Hayes M, Nixon JV, Sacco RL (2006) Primary prevention of ischemic stroke: a guideline from the American Heart Association/American Stroke Association Stroke Council: cosponsored by the Atherosclerotic Peripheral Vascular Disease Interdisciplinary Working Group; Cardiovascular Nursing Council; Clinical Cardiology Council; Nutrition, Physical Activity, and Metabolism Council; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: the American Academy of Neurology affirms the value of this guideline. Stroke 37:1583–1633PubMedGoogle Scholar
  14. 14.
    Hennessy MJ, Britton TC (2000) Transient ischaemic attacks: evaluation and management. Int J Clin Pract 54:432–436PubMedGoogle Scholar
  15. 15.
    Hu G, Sarti C, Jousilahti P, Peltonen M, Qiao Q, Antikainen R, Tuomilehto J (2005) The Impact of History of Hypertension and Type 2 Diabetes at Baseline on the Incidence of Stroke and Stroke Mortality. StrokeGoogle Scholar
  16. 16.
    Kroke A, Bergmann MM, Lotze G, Jeckel A, Klipstein-Grobusch K, Boeing H (1999) Measures of quality control in the German component of the EPIC study. European Prospective Investigation into Cancer and Nutrition. Ann Nutr Metab 43:216–224PubMedCrossRefGoogle Scholar
  17. 17.
    Kroke A, Klipstein-Grobusch K, Voss S, Moseneder J, Thielecke F, Noack R, Boeing H (1999) Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. Am J Clin Nutr 70:439–447PubMedGoogle Scholar
  18. 18.
    Kuller LH (2000) Epidemiology and prevention of stroke, now and in the future. Epidemiol Rev 22:14–17PubMedGoogle Scholar
  19. 19.
    Lemesle M, Madinier G, Menassa M, Billiar T, Becker F, Giroud M (1998) Incidence of transient ischemic attacks in Dijon, France. A 5-year community-based study. Neuroepidemiology 17:74–79PubMedCrossRefGoogle Scholar
  20. 20.
    Lemesle M, Milan C, Faivre J, Moreau T, Giroud M, Dumas R (1999) Incidence trends of ischemic stroke and transient ischemic attacks in a well-defined French population from 1985 through 1994. Stroke 30:371–377PubMedGoogle Scholar
  21. 21.
    Leys D, Deplanque D, Mounier-Vehier C, Mackowiak-Cordoliani MA, Lucas C, Bordet R (2002) Stroke prevention: management of modifiable vascular risk factors. J Neurol 249:507–517PubMedCrossRefGoogle Scholar
  22. 22.
    Lumley T, Kronmal RA, Cushman M, Manolio TA, Goldstein S (2002) A stroke prediction score in the elderly: validation and Web-based application. J Clin Epidemiol 55:129–136PubMedCrossRefGoogle Scholar
  23. 23.
    Mackenbach JP, Looman CW, van der Meer JB (1996) Differences in the misreporting of chronic conditions, by level of education: the effect on inequalities in prevalence rates. Am J Public Health 86:706–711PubMedCrossRefGoogle Scholar
  24. 24.
    Sacco RL (2004) Risk factors for TIA and TIA as a risk factor for stroke. Neurology 62:S7–11PubMedGoogle Scholar
  25. 25.
    Sacco RL, Benjamin EJ, Broderick JP, Dyken M, Easton JD, Feinberg WM, Goldstein LB, Gorelick PB, Howard G, Kittner SJ, Manolio TA, Whisnant JP, Wolf PA (1997) American Heart Association Prevention Conference. IV. Prevention and Rehabilitation of Stroke. Risk factors. Stroke 28:1507–1517PubMedGoogle Scholar
  26. 26.
    Sempere AP, Duarte J, Cabezas C, Claveria LE (1998) Etiopathogenesis of transient ischemic attacks and minor ischemic strokes: a community-based study in Segovia, Spain. Stroke 29:40–45PubMedGoogle Scholar
  27. 27.
    Warlow C, Sudlow C, Dennis M, Wardlaw J, Sandercock P (2003) Stroke. Lancet 362:1211–1224PubMedCrossRefGoogle Scholar
  28. 28.
    Weimar C, Kraywinkel K, Rodl J, Hippe A, Harms L, Kloth A, Diener HC (2002) Etiology, duration, and prognosis of transient ischemic attacks: an analysis from the German Stroke Data Bank. Arch Neurol 59:1584–1588PubMedCrossRefGoogle Scholar
  29. 29.
    Whisnant JP, Brown RD, Petty GW, O’Fallon WM, Sicks JD, Wiebers DO (1999) Comparison of population-based models of risk factors for TIA and ischemic stroke. Neurology 53:532–536PubMedGoogle Scholar
  30. 30.
    WHO (1992) International Statistical Classification of Diseases and Related Health Problems. World Health Organisation, GenevaGoogle Scholar
  31. 31.
    Wolf PA, Abbott RD, Kannel WB (1991) Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke 22:983–988PubMedGoogle Scholar
  32. 32.
    Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB (1991) Probability of stroke: a risk profile from the Framingham Study. Stroke 22:312–318PubMedGoogle Scholar
  33. 33.
    Wolfe CD (2000) The impact of stroke. Br Med Bull 56:275–286PubMedCrossRefGoogle Scholar
  34. 34.
    Yusuf HR, Giles WH, Croft JB, Anda RF, Casper ML (1998) Impact of multiple risk factor profiles on determining cardiovascular disease risk. Prev Med 27:1–9PubMedCrossRefGoogle Scholar

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© Steinkopff Verlag Darmstadt 2007

Authors and Affiliations

  • Cornelia Weikert
    • 1
    • 2
  • Klaus Berger
    • 3
  • Christin Heidemann
    • 2
  • Manuela M. Bergmann
    • 2
  • Kurt Hoffmann
    • 2
  • Kerstin Klipstein-Grobusch
    • 2
    • 4
  • Heiner Boeing
    • 2
  1. 1.Dept. of EpidemiologyGerman Institute of Human Nutrition, Potsdam-RehbrueckeNuthetalGermany
  2. 2.Dept. of EpidemiologyGerman Institute of Human Nutrition, Potsdam-RehbrueckeGermany
  3. 3.Institute of Epidemiology and Social MedicineUniversity of MuensterGermany
  4. 4.School of Public Health, Faculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa

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