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Comparative Analysis of Cardiovascular Disease Risk Factors Influencing Nonfatal Acute Coronary Syndrome and Ischemic Stroke
Comparative Analysis of Cardiovascular Disease Risk Factors Influencing Nonfatal Acute Coronary Syndrome and Ischemic Stroke Christina-Maria Kastorini, PhDa,b, Ekavi Georgousopoulou, MScb, Konstantinos N. Vemmos, MDc, Vassilios Nikolaou, MDd, Dimitrios Kantas, MDa, Haralampos J. Milionis, MD, PhDa, John A. Goudevenos, MD, PhDa, and Demosthenes B. Panagiotakos, PhDb,* The aim of the present work was to compare the influence of classic cardiovascular disease (CVD) risk factors on the development of acute coronary syndrome (ACS) and ischemic stroke. During 2009e2010, 1,000 participants were enrolled: 250 were consecutive patients with a first ACS, 250 were consecutive patients with a first ischemic stroke, and 500 were population-based, control subjects, 1-for-1 matched to the patients by age and gender. The following CVD risk factors were evaluated: smoking/passive smoking, family history of CVD, physical inactivity, hypertension, hypercholesterolemia, diabetes mellitus, presence of overweight and obesity, trait anxiety (assessed with the Spielberger State-Trait Anxiety Inventory form Y-2), and adherence to the Mediterranean diet (assessed by the MedDietScore). Furthermore, participants graded the perceived significance of the aforementioned factors, using a scale from 1 (not important) to 9 (very important). The risk factors with the highest effect size for ACS, as determined by the Wald criterion, were smoking and hypercholesterolemia; regarding stroke, they were anxiety and family history of CVD (all p <0.01). When the odds ratios of each factor for ACS and stroke were compared, insignificant differences were observed, except for smoking. On the basis of the participants’ health beliefs, smoking and stress emerged as the most important risk factors, whereas all subjects graded passive smoking as a least important factor. In conclusion, similarities of the risk factors regarding ACS and ischemic stroke facilitate simultaneous primary prevention measures. Ó 2013 Elsevier Inc. All rights reserved. (Am J Cardiol 2013;112:349e354) The aims of the present study were to compare the effect of individual cardiovascular disease (CVD) risk factors on the occurrence of acute coronary syndrome (ACS) versus ischemic stroke and to evaluate the perceived importance of CVD risk factors in a sample of 1,000 CVD patients and healthy subjects. Methods This was a multicenter, case-control study with individual (1-for-1) matching by age (within 3 years) and gender.1 From October 2009 to December 2010, 500 of the 615 consecutive patients with a first ACS event (n ¼ 250, 209 acute myocardial infarction, 41 unstable angina) or ischemic stroke (n ¼ 250) and without any suspicion of previous CVD who presented to the cardiology or pathology clinics or emergency units of 3 major general hospitals in Greece agreed to participate (participation rate 81%). For the a School of Medicine, University of Ioannina, Ioannina, Greece; Department of Nutrition and Dietetics, Harokopio University, Athens, Greece; cAcute Stroke Unit, Department of Clinical Therapeutics, Alexandra Hospital, Athens, Greece; and dCardiology Clinic, “Hellenic Red Cross” Hospital, Athens, Greece. Manuscript received January 26, 2013; revised manuscript received and accepted March 16, 2013. The study was supported by the Hellenic Cardiological Society (2012e2013). Dr. Kastorini has received scholarships for her PhD thesis from the National Scholarships Foundation and the Hellenic Atherosclerosis Society. See page 353 for disclosure information. *Corresponding author: Tel: 30210-9603116; fax: 30210-9600719. E-mail address: [email protected] (D.B. Panagiotakos). b
0002-9149/13/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.amjcard.2013.03.039
stroke patients who were unable to communicate (speech disorders, aphasia, memory problems), the information was obtained by a valid surrogate respondent (first-degree relative living in the same home as the patient and aware of the participant’s dietary habits and medical history). Patients with chronic neoplasmatic disease or chronic inflammatory disease, as well as individuals with recent changes in their dietary habits, were not enrolled in the study. Five hundred control subjects (250 matched 1-for-1 with ACS patients and another 250 matched 1-for-1 with stroke patients) were selected concurrently with the patients on a volunteer, population basis and from the same region as the patients. Controls were without any clinical symptoms or suspicions of CVD in their medical history, as assessed by a physician. On the basis of a priori statistical power analysis, a sample size of 500 patients (250 ACS, 250 stroke) and 500 age- and gender-matched healthy subjects, was adequate to evaluate 2-sided odds ratios (ORs) equal to 1.20, achieving statistical power >0.80 at 0.05 probability level (p value). The study was approved by the Ethics Committee of the University Hospital of Ioannina and was carried out in accordance with the Declaration of Helsinki (1989) of the World Medical Association. Before collection of any information, participants (or valid surrogate respondents) were informed about the aims and procedures of the study and provided their signed consent. Regarding the ACS patients, clinical symptoms were evaluated at hospital entry and a 12-lead electrocardiogram was performed. Evidence of myocardial cell death was assessed with blood tests and measurement of the levels of www.ajconline.org
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Table 1 Sociodemographic, lifestyle, and clinical characteristics of the study participants Variable Age (yrs) Men Smoking habits No smoker/no passive smoker No smoker/passive smoker Ever smoker/no passive smoker Ever smoker/passive smoker Physical inactivity Family history of CVD Hypertension Hypercholesterolemia Diabetes mellitus Body mass index (kg/m2) Normal weight (18.5e24.9) Overweight (25e29.9) Obese (>30) MedDietScore (range 0e55) First tertile (0e29) Second tertile (30e33) Third tertile (34e55) STAI Y-2 (range 20e80) 20e39: low anxiety 40e59: moderate anxiety 60e80: severe anxiety
ACS Patients (n ¼ 250)
ACS Controls (n ¼ 250)
Stroke Patients (n ¼ 250)
Stroke Controls (n ¼ 250)
60 12 208 (83.2%)
60 12 208 (83.2%)
77 9 139 (55.6%)
73 9 139 (55.6%)
20 (8.4%)* 32 (13.4%) 37 (15.5%) 150 (62.5%) 84 (35.9%)* 81 (36.2%)* 148 (62.2%)* 165 (71.4%)* 58 (26.1%)* 27.82 4.29 57 (24.9%) 116 (50.7%) 56 (24.5%) 30.67 5.02* 86 (41.1%)* 66 (31.6%) 57 (27.3%) 40.52 10.05* 109 (48.7%)* 105 (46.9%) 10 (4.5%)
62 (26.4%) 37 (15.7%) 45 (19.1%) 91 (38.7%) 43 (17.5%) 39 (16.7%) 90 (37.7%) 100 (45.5%) 29 (12.4%) 27.23 3.50 63 (26.3%) 132 (55%) 45 (18.8%) 32.50 4.41 50 (21.9%) 79 (34.6%) 99 (43.4%) 36.55 9.26 158 (64.5%) 84 (34.3%) 3 (1.2%)
59 (33.7%) 38 (21.7%) 24 (13.7%) 54 (30.9%) 111 (52.9%)* 51 (31.3%)* 206 (84.4%)* 159 (68.5%)† 71 (32.9%)† 26.72 3.57 79 (33.1%) 124 (51.9%) 36 (15.0%) 29.99 3.79* 94 (49.5%)* 64 (33.7%) 32 (16.8%) 45.66 7.17* 37 (17.9%)* 167 (80.7%) 3 (1.4%)
77 (33.2%) 51 (22.0%) 27 (11.6%) 77 (33.2%) 61 (25.2%) 38 (16.7%) 137 (56.8%) 119 (54.1%) 50 (21.5%) 27.35 4.24 73 (30%) 120 (49.4%) 50 (20.6%) 32.03 4.08 60 (26.8%) 82 (36.6%) 82 (36.6%) 38.65 9.86 135 (54.9%) 106 (43.1%) 5 (2%)
Data are expressed as mean SD or frequencies (n, %). p Values derived from Student’s t test or the chi-square test. Patients whose average blood pressure levels were 140/90 mm Hg or were under antihypertensive medication were classified as having hypertension. Hypercholesterolemia was defined as total serum cholesterol levels >200 mg/dL or the use of lipid-lowering agents. * p <0.001 compared with the ACS or stroke control group, respectively. † p <0.05 compared with the ACS or stroke control group, respectively.
Table 2 Results from logistic regression analysis developed to evaluate the likelihood of having acute coronary syndrome (ACS) or ischemic stroke (outcome), according to exposure to potential cardiovascular disease risk factors Independent Variables
MedDietScore (per 1/55 unit) Physical inactivity (yes/no) Smoking habits No smoker/no passive smoker (reference) No smoker/passive smoker Ever smoker/no passive smoker Ever smoker/passive smoker Family history of CVD (yes/no) Hypertension (yes/no) Hypercholesterolemia (yes/no) Diabetes mellitus (yes/no) Overweight/obese (yes/no) STAI-Y2 (per 1/80 unit)
ACS
Stroke
p
OR (95% CI)
Wald
OR (95% CI)
Wald
0.93 (0.88e0.99) 2.94 (1.47e5.88)
5.98 9.22
0.91 (0.84e1.00) 1.97 (0.91e4.26)
4.12 2.96
4.33 5.15 8.62 2.40 2.81 3.80 1.91 0.56 1.04
1.00 (1.52e12.38) (1.82e14.53) (3.52e21.14) (1.23e4.69) (1.52e5.21) (2.15e6.68) (0.88e4.15) (0.29e1.08) (1.01e1.07)
— 7.49 9.57 22.16 6.63 10.75 21.31 2.68 2.99 5.48
1.32 1.69 0.75 2.35 1.60 1.86 1.31 1.00 1.06
1.00 (0.55e3.18) (0.43e6.69) (0.29e1.94) (1.07e5.17) (0.74e3.44) (0.89e3.87) (0.60e2.86) (0.44e2.24) (1.02e1.10)
— 0.37 0.55 0.35 4.51 1.45 2.75 0.45 <0.001 8.92
0.710 0.414 — 0.054 0.127 <0.001 0.960 0.223 0.090 0.483 0.236 0.257
All groups (ACS cases, ACS control participants, stroke cases, stroke control participants), n ¼ 250. Results are presented as OR (95% CI), Wald test, obtained from multiple conditional logistic regression. p Values derived from cross-model postestimation tests regarding the comparison between the ORs of each CVD risk factor.
troponin I and the MB fraction of total creatinine phosphokinase (according to the Universal Definition of Myocardial Infarction, Joint European Society of Cardiology/American College of Cardiology Foundation/American Heart Association/World Heart Federation Task Force)2; unstable angina was defined by the occurrence of 1 angina episode(s), at
rest, within the preceding 48 hours, corresponding to class III of the Braunwald classification.3 Ischemic strokes were defined through symptoms of neurologic dysfunction of acute onset of any severity, consistent with focal brain ischemia and imaging/laboratory confirmation of an acute vascular ischemic pathology.4
Coronary Artery Disease/Comparison of Cardiovascular Disease Risk Factors
Dietary habits of the past year were assessed through a 90-item, validated semiquantitative food frequency questionnaire that has been previously described.1,5 Level of adherence to the Mediterranean diet was evaluated using an 11-item large-scale composite index, the MedDietScore. The theoretical range of the MedDietScore was between 0 and 55. Higher values of this diet score indicate greater adherence to the Mediterranean diet. The validation properties of the MedDietScore have been presented elsewhere in the literature.6 Sociodemographic variables recorded were age and gender (for the matching procedure). Current smokers were defined as those who smoked at least 1 cigarette per day, former smokers as those who had stopped smoking more than 1 year previously, and the rest of the participants were defined as noncurrent smokers. Passive smokers were defined as those who were exposed to the smoke of others (colleagues, partner, parents, children, roommates) for >30 minutes per day. A new variable was then developed, including the following categories: no smoker/no passive smoker, no smoker/passive smoker, ever smoker (i.e., current and former smoker)/no passive smoker, ever smoker/passive smoker. Physical activity was assessed using the International Physical Activity Questionnaire index,7 which has been validated for the Greek population.8 According to their physical activity levels, participants were classified as inactive or physically active (moderate or vigorously active). Body mass index (BMI) was calculated as weight (in kilograms) divided by standing height (in meters squared); overweight and obesity were defined as BMI 25.0 to 29.9 kg/m2 and >29.9 kg/m2, respectively. Detailed medical history was recorded for all participants, including family history of CVD and personal and family history of hypertension, hypercholesterolemia, hypertriglyceridemia, and diabetes. Patients whose average blood pressure levels were 140/90 mm Hg or were under antihypertensive medication were classified as having hypertension. Hypercholesterolemia was defined as total serum cholesterol levels >200 mg/dl or the use of lipid-lowering agents, and diabetes mellitus was defined as fasting blood glucose 126 mg/dl or the use of antidiabetic medication. A previously translated and validated version of the Spielberger Trait Anxiety Inventory (STAI form Y-2, range 20e80) was used for the assessment of trait anxiety.9,10 Participants were asked to grade in a 9-unit scale (i.e., 1 ¼ not important to 9 ¼ extremely important) the significance of 8 traditional CVD risk factors: active smoking, exposure to passive smoking, physical inactivity, stress, unhealthy dietary habits, and presence of overweight/ obesity, hypertension, hypercholesterolemia, diabetes mellitus, and family history of CVD.1 Normally distributed continuous variables (age, BMI, MedDietScore, STAI Y-2, participants’ beliefs) are presented as mean values SD and categorical variables (gender, smoking habits, physical activity, medical history, BMI categories, MedDietScore categories, and STAI Y-2 categories) as frequencies. Associations between categorical variables were tested by the calculation of the chi-square test. Comparisons of mean values of normally distributed continuous variables by clinical outcome were performed using Student’s t test. Correlations between continuous
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Figure 1. Estimated attributable risks (%) of traditional CVD risk factors regarding ACS and ischemic stroke. Data are presented clockwise, beginning from the top right, from “smoking (ever smoker).”
variables were evaluated using the Pearson’s r or Spearman rho coefficients. Normality of the variables was tested using P-P plots. Estimations of the relative odds of having ACS or stroke according to the exposure measurements were performed through conditional logistic regression analysis; results are presented as ORs and the corresponding 95% confidence intervals (CIs). The Hosmer-Lemeshow statistic was calculated to evaluate model’s goodness-of-fit. Comparisons between the effect size measures (i.e., ORs) of the 2 logistic models (1 for ACS and 1 for stroke) were based on the Wald test (i.e., log[OR]/Var[log OR]; the higher, the better), which incorporates the estimated effect parameter in relation to its variance and thus standardizes the effect. The likelihood-ratio test was also used to confirm the previous results. The comparison regarding the ORs of each CVD risk factor between the 2 models (i.e., ACS and stroke) was performed using cross-model postestimation tests. All reported p values were based on 2-sided hypotheses. SPSS 18.0 software (SPSS Inc., Chicago, Illinois) was used for all the statistical calculations. Results Demographic, clinical, psychological, and nutritional characteristics of the participants are presented in Table 1.
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Table 3 Health beliefs of study participants (n ¼ 1,000) ACS Patients (n ¼ 250) Health belief: Smoking Ever smoker No smoker Health belief: passive smoking Passive smoker No passive smoker Health belief: physical inactivity Physically active Sedentary Health belief: stress Low stress Moderate/severe stress Health belief: unhealthy dietary habits MedDietScore 32 MedDietScore < 32 Health belief: overweight/obesity BMI < 25 kg/m2 BMI 25 kg/m2 Health belief: diabetes mellitus/ hypercholesterolemia/hypertension Presence Absence Health belief: family history of CVD Yes No
ACS Controls (n ¼ 250)
Stroke Patients (n ¼ 250)
Stroke Controls (n ¼ 250)
7.03 7.24 6.29 5.94 6.02 5.67 6.07 6.10 5.96 7.76 7.68 7.92 6.88 7.42 6.59 6.63 6.31 6.75 6.56
2.53*,† 2.37*,†,z 2.95*,† 2.54†† 2.56 2.61† 2.32**,†† 2.33* 2.22*,† 1.72 1.78 1.64* 2.19* 1.70z 2.42*,† 2.36*,† 2.78* 2.14*,†† 2.46*,††
7.62 7.69 7.53 6.23 6.25 6.25 6.83 6.83 6.76 7.54 7.71 7.25 7.25 7.28 7.50 7.39 7.53 7.39 7.24
1.81 1.59 2.07 2.04 2.13 2.02 1.69 1.72 1.56 1.58 1.47z 1.71 1.61 1.61 1.37 1.65 1.51 1.58 1.78
7.68 7.86 7.57 6.88 6.66 6.72 6.88 6.55 6.92 7.60 6.69 7.79 7.20 6.49 7.28 7.40 6.89 7.72 7.69
2.32 2.03 2.48 2.24 3.35 2.44 2.08 2.24 2.01 1.68 2.73z 1.38* 2.10 2.93 1.98 2.29* 2.67z 1.98 2.08*
7.61 7.31 7.89 6.49 6.51 6.63 6.71 6.68 6.83 7.48 7.73 7.18 7.28 7.25 7.48 7.29 7.25 7.34 7.18
1.96 2.05z 1.77 2.12 2.17 1.99 1.89 1.99 1.56 1.63 1.55z 1.67 1.65 1.63 1.57 1.70 1.76 1.63 1.82
6.63 6.52 5.96 6.82 5.53
2.48*,†† 2.27 2.54*,†† 2.19z 2.61*,†
7.27 7.29 6.47 6.97 6.34
1.66 1.95 2.16 2.07 2.22
7.78 8.50 6.87 6.71 6.72
1.95* 1.00 2.25 2.61 2.40
7.22 7.30 6.48 7.41 6.31
1.79 1.76 2.20 1.86z 2.26
Data are expressed as mean SD. Risk factors were graded by the participants on a scale from 1 (not important) to 9 (very important). p Values derived from Student’s t-test. * p <0.05, ** p <0.001 compared with the ACS or stroke control group, respectively. † p <0.05, †† p <0.001 for the comparison between the ACS and stroke patients. z p <0.05 for the comparison between the subcategories examined (i.e., regarding the belief about the significance of smoking between smokers and nonsmokers, etc.).
Patients (with both ACS and stroke) tended to be less physically active than the control participants, with higher prevalence of almost all traditional cardiovascular risk factors: hypercholesterolemia, hypertension, diabetes mellitus, anxiety, unhealthy dietary patterns, and family history of CVD. Results from the multivariable models regarding ACS and stroke, including traditional CVD risk factors, are presented in Table 2. Of the factors associated with the odds of having an ACS, the most significant (in terms of effect size measure) was smoking—in particular, being both a smoker and a passive smoker. Hypercholesterolemia was the second most significant factor, followed by hypertension. Regarding stroke, anxiety emerged as the most significant factor, followed by family history of CVD and adherence to the Mediterranean diet. To examine whether the risk factors exerted a similar or different impact on the 2 CVD manifestations (i.e., ACS or ischemic stroke), the ORs between the 2 models were compared. Insignificant differences were observed for the majority of the risk factors included in the models, except for smoking habits. Furthermore, an estimation of the attributable risk for ACS or stroke, for each risk factor was calculated (continuous variables were transformed into categorical ones). Results are presented in Figure 1. In Table 3, the health beliefs of the participants concerning the common CVD risk factors are presented. All
grades were >5 (on a 9-unit scale), reflecting that all participants recognized the detrimental influence of these factors on their health status. The ACS patients reported that the most important factor influencing CVD development was stress, followed by smoking and unhealthy dietary habits, whereas the ACS controls concluded that smoking was the most detrimental factor, along with stress and overweight or obesity. The stroke patients recognized the presence of hypertension, hypercholesterolemia, and diabetes as most important, followed by smoking, and then stress. For the stroke control participants, smoking was the most important factor, followed by stress and being overweight or obese. However, even though smoking was considered as 1 of the most important CVD risk factors, passive smoking was graded as 1 of the least important ones by all patient and control groups. Furthermore, the ACS patients tended to underestimate the role of the risk factors, compared with their control participants, but this was not observed between the stroke patients and control participants. Finally, stroke patients had higher scores for the majority of the factors evaluated compared with the ACS patients. Discussion Results of the present work highlight some differences in the ranking of traditional CVD risk factors, yet important
Coronary Artery Disease/Comparison of Cardiovascular Disease Risk Factors
overall similarities regarding ACS and ischemic stroke. Furthermore, this is one of the few studies that has examined the perception of the magnitude of CVD risk factors by patients and healthy subjects. Despite the limitations of the study’s observational design, interpretation of the findings could be used to disseminate important public health messages, which could contribute to better CVD prevention strategies. Of major importance was the finding that modifiable risk factors, such as smoking and unhealthy dietary habits, emerged as crucial factors for CVD development. This may be because, apart from their independent detrimental actions on cardiovascular health, they could modify other risk factors such as hypercholesterolemia, hypertension, and diabetes mellitus. Results from other studies in general verify our findings. The Physicians’ Health Study showed that hypertension, hypercholesterolemia, diabetes mellitus, smoking, and physical inactivity were associated with higher risk of coronary heart disease and stroke, with comparable influences.11 Furthermore, the Dubbo Study of Australian Elderly observed substantial similarities for the risk factors of male gender, current smoking, diabetes mellitus, low-density lipoprotein cholesterol, reduced peak expiratory flow, and physical disability.12 According to the Health Survey for England, even though significant similarities were observed regarding the influence of age, smoking, systolic blood pressure, diabetes mellitus, and physical activity on ischemic heart disease and stroke development, important differences were also noticed regarding the factors high-density lipoprotein cholesterol, Creactive protein, fibrinogen, BMI, and total cholesterol.13 Results of the INTERHEART study indicated that ApoB to apoA1, current smoking, and psychosocial factors had the higher population attributable risk regarding myocardial infarction.14 Finally, according to the INTERSTROKE study, a self-reported history of hypertension was the most significant risk factor for stroke.15 Regarding the second aim of this work, it is known that the perception of the importance of the risk factors for CVD development varies and is based on the matching of personal risk factors with convictions about the nature of the disease.16 In fact, perceptions and beliefs regarding disease predisposition hold a significant place in health behavior models, such as the Health Belief Model,17 the Theory of Reasoned Action,18 the Subjective Expected Utility Theory,19 and the Protection Motivation Theory.20 In the present work, most participants graded all traditional CVD risk factors with a value >5 out of 9, recognizing the detrimental role of these factors on cardiovascular health. Risk factors that could be significantly improved through lifestyle changes or lifestyle changes combined with drug treatment emerged as most important. Finally, according to a recent study, factors that were positively associated with perceived risk of CVD were increased cholesterol levels, family history of CVD, unhealthy dietary patterns, and lack of physical activity.21 Despite the aforementioned individual risk factors approach, the multifactorial nature of CVD ought not to be disregarded. In particular, taking into consideration the influence and combined interactions of major cardiovascular risk factors (i.e., estimating the global CVD risk) is of
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crucial importance and should be encouraged. Toward this direction, the use of risk charts should be promoted for the identification of individuals at high CVD risk among apparently healthy persons because they are easy to use and enable objective risk assessment.22,23 Some limitations due to the retrospective, observational design of the study, such as selection and the recall bias, and the lack of causal interpretations should be reported. To minimize selection bias, only cases with a first event were enrolled, and to minimize recall bias, accurate and detailed data from all participants during the first 3 days of hospitalisation were obtained. For dietary evaluation, a food frequency questionnaire was administered; although these tools may carry measurement error and be less accurate (especially in energy and nutrient assessment) than a diary, an effort was made to reduce these errors and inaccuracies of dietary reporting with its application by trained dieticians through face-to-face interviews. Over- and underestimation in reporting may also exist, especially in the measurement of diet, smoking habits, the onset of CVD risk factors, and the grading of the CVD risk factors. However, an effort was given to retrieve accurate information from participants’ medical records, as well as their relatives. For the stroke patients, self-reported information was obtained from 76% of the sample; 60 patients (24%) were unable to answer the interviewer because of their condition. Thus, data were collected from a valid surrogate respondent. Moreover, the coronary and stroke patients who died at hospital entry or the following day were not included in the study (survivor bias); thus, the results should be generalized only to CVD survivors. Finally, the inclusion of patients and control participants from only 2 regions may limit the generalization of the findings to the whole country; nevertheless, it should be noted that Athens metropolitan area and Ioannina city in western Greece represent a vast majority of the Greek urban and rural population. Acknowledgment: We thank the field investigators of the study: Kallirroi Kalantzi, Aggeliki Ionnidi, Eva Ntziou, Markella Symeopoulou, Zoe Konidari, Eirini Trichia, Stavroula Bitsi, Vissarion Euthimiou, Eftychia Bika, Michael Kostapanos, Vaia Salma, Antonis Kramvis, Glykeria Papagiannopoulou, Alexandra Litsardopoulou, Alexia Katsarou, Fani Lioliou, Labros Papadimitriou, Konstantina Siganou, Ioanna Kousoula, Eleni Koroboki, Anastasia Vemmou, Paraskevi Savvari, and Vassiliki Vlachaki. Disclosures The authors have no conflicts of interest to disclose. 1. Kastorini CM, Milionis HJ, Goudevenos JA, Panagiotakos DB. Modelling the role of dietary habits and eating behaviours on the development of acute coronary syndrome or stroke: aims, design, and validation properties of a case-control study. Cardiol Res Pract 2010. pii: 313948. Available at: http://www.hindawi.com/journals/crp/2011/ 313948. Accessed April 11, 2013. 2. Thygesen K, Alpert JS, White HD; Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. J Am Coll Cardiol 2007;50:2173e2195. 3. Braunwald E. Heart Disease. 5th ed. London: W.B. Saunders Company, 1997.
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4. Kidwell CS, Warach S. Acute ischemic cerebrovascular syndrome: diagnostic criteria. Stroke 2003;34:2995e2998. 5. Bountziouka V, Bathrellou E, Giotopoulou A, Katsagoni C, Bonou M, Vallianou N, Barbetseas J, Avgerinos PC, Panagiotakos DB. Development, repeatability and validity regarding energy and macronutrient intake of a semi-quantitative food frequency questionnaire: methodological considerations. Nutr Metab Cardiovasc Dis 2012;22:659e667. 6. Panagiotakos DB, Pitsavos C, Stefanadis C. Dietary patterns: a Mediterranean diet score and its relation to clinical and biological markers of cardiovascular disease risk. Nutr Metab Cardiovasc Dis 2006;16: 559e568. 7. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35:1381e1395. 8. Papathanasiou G, Georgoudis G, Papandreou M, Spyropoulos P, Georgakopoulos D, Kalfakakou V, Evangelou A. Reliability measures of the short International Physical Activity Questionnaire (IPAQ) in Greek young adults. Hellenic J Cardiol 2009;50:283e294. 9. Spielberger CD, Gorsuch RL, Luchene RE. Manual for the State-Trait Anxiety Inventory (Self-Evaluation Questionnaire). Palo Alto, CA: Consulting Psychologists Press, 1970. 10. Fountoulakis KN, Papadopoulou M, Kleanthous S, Papadopoulou A, Bizeli V, Nimatoudis I, Iacovides A, Kaprinis GS. Reliability and psychometric properties of the Greek translation of the State-Trait Anxiety Inventory form Y: preliminary data. Ann Gen Psychiatry 2006;5:2. 11. Glynn RJ, Rosner B. Comparison of risk factors for the competing risks of coronary heart disease, stroke, and venous thromboembolism. Am J Epidemiol 2005;162:975e982. 12. Simons LA, Simons J, Friedlander Y, McCallum J. A comparison of risk factors for coronary heart disease and ischaemic stroke: the Dubbo study of Australian elderly. Heart Lung Circ 2009;18:330e333. 13. Hamer M, Batty GD, Stamatakis E, Kivimaki M. Comparison of risk factors for fatal stroke and ischemic heart disease: a prospective follow up of the health survey for England. Atherosclerosis 2011;219: 807e810. 14. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004;364:937e952.
15. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, Rangarajan S, Islam S, Pais P, McQueen MJ, Mondo C, Damasceno A, Lopez-Jaramillo P, Hankey GJ, Dans AL, Yusoff K, Truelsen T, Diener HC, Sacco RL, Ryglewicz D, Czlonkowska A, Weimar C, Wang X, Yusuf S. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a casecontrol study. Lancet 2010;376:112e123. 16. Weinstein ND. What does it mean to understand a risk? Evaluating risk comprehension. J Natl Cancer Inst Monogr 1999;25:15e20. 17. Janz NK, Becker MH. The Health Belief Model: a decade later. Health Educ Q 1984;11:1e47. 18. Ajzen I, Fishbein M. Understanding Attitudes and Predicting Behavior. Englewood Cliffs, NJ: Prentice Hall, 1980. 19. Edwards W. The theory of decision making. Psychol Bull 1954;51: 380e417. 20. Rogers RW. A protection motivation theory of fear appeals and attitude change. J Psychol 1975;91:93e114. 21. Claassen L, Henneman L, Nijpels G, Dekker J, Marteau T, Timmermans D. Causal beliefs and perceptions of risk for diabetes and cardiovascular disease, The Netherlands, 2007. Prev Chronic Dis 2011;8:A130. 22. Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Soliman EZ, Sorlie PD, Sotoodehnia N, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation 2012;125:e2ee220. 23. Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, Verschuren M, Albus C, Benlian P, Boysen G, Cifkova R, Deaton C, Ebrahim S, Fisher M, Germano G, Hobbs R, Hoes A, Karadeniz S, Mezzani A, Prescott E, Ryden L, Scherer M, Syvänne M, Scholte op Reimer WJ, Vrints C, Wood D, Zamorano JL, Zannad F. European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J 2012;33:1635e1701.
0002-9149/13/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.amjcard.2013.03.039
stroke patients who were unable to communicate (speech disorders, aphasia, memory problems), the information was obtained by a valid surrogate respondent (first-degree relative living in the same home as the patient and aware of the participant’s dietary habits and medical history). Patients with chronic neoplasmatic disease or chronic inflammatory disease, as well as individuals with recent changes in their dietary habits, were not enrolled in the study. Five hundred control subjects (250 matched 1-for-1 with ACS patients and another 250 matched 1-for-1 with stroke patients) were selected concurrently with the patients on a volunteer, population basis and from the same region as the patients. Controls were without any clinical symptoms or suspicions of CVD in their medical history, as assessed by a physician. On the basis of a priori statistical power analysis, a sample size of 500 patients (250 ACS, 250 stroke) and 500 age- and gender-matched healthy subjects, was adequate to evaluate 2-sided odds ratios (ORs) equal to 1.20, achieving statistical power >0.80 at 0.05 probability level (p value). The study was approved by the Ethics Committee of the University Hospital of Ioannina and was carried out in accordance with the Declaration of Helsinki (1989) of the World Medical Association. Before collection of any information, participants (or valid surrogate respondents) were informed about the aims and procedures of the study and provided their signed consent. Regarding the ACS patients, clinical symptoms were evaluated at hospital entry and a 12-lead electrocardiogram was performed. Evidence of myocardial cell death was assessed with blood tests and measurement of the levels of www.ajconline.org
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Table 1 Sociodemographic, lifestyle, and clinical characteristics of the study participants Variable Age (yrs) Men Smoking habits No smoker/no passive smoker No smoker/passive smoker Ever smoker/no passive smoker Ever smoker/passive smoker Physical inactivity Family history of CVD Hypertension Hypercholesterolemia Diabetes mellitus Body mass index (kg/m2) Normal weight (18.5e24.9) Overweight (25e29.9) Obese (>30) MedDietScore (range 0e55) First tertile (0e29) Second tertile (30e33) Third tertile (34e55) STAI Y-2 (range 20e80) 20e39: low anxiety 40e59: moderate anxiety 60e80: severe anxiety
ACS Patients (n ¼ 250)
ACS Controls (n ¼ 250)
Stroke Patients (n ¼ 250)
Stroke Controls (n ¼ 250)
60 12 208 (83.2%)
60 12 208 (83.2%)
77 9 139 (55.6%)
73 9 139 (55.6%)
20 (8.4%)* 32 (13.4%) 37 (15.5%) 150 (62.5%) 84 (35.9%)* 81 (36.2%)* 148 (62.2%)* 165 (71.4%)* 58 (26.1%)* 27.82 4.29 57 (24.9%) 116 (50.7%) 56 (24.5%) 30.67 5.02* 86 (41.1%)* 66 (31.6%) 57 (27.3%) 40.52 10.05* 109 (48.7%)* 105 (46.9%) 10 (4.5%)
62 (26.4%) 37 (15.7%) 45 (19.1%) 91 (38.7%) 43 (17.5%) 39 (16.7%) 90 (37.7%) 100 (45.5%) 29 (12.4%) 27.23 3.50 63 (26.3%) 132 (55%) 45 (18.8%) 32.50 4.41 50 (21.9%) 79 (34.6%) 99 (43.4%) 36.55 9.26 158 (64.5%) 84 (34.3%) 3 (1.2%)
59 (33.7%) 38 (21.7%) 24 (13.7%) 54 (30.9%) 111 (52.9%)* 51 (31.3%)* 206 (84.4%)* 159 (68.5%)† 71 (32.9%)† 26.72 3.57 79 (33.1%) 124 (51.9%) 36 (15.0%) 29.99 3.79* 94 (49.5%)* 64 (33.7%) 32 (16.8%) 45.66 7.17* 37 (17.9%)* 167 (80.7%) 3 (1.4%)
77 (33.2%) 51 (22.0%) 27 (11.6%) 77 (33.2%) 61 (25.2%) 38 (16.7%) 137 (56.8%) 119 (54.1%) 50 (21.5%) 27.35 4.24 73 (30%) 120 (49.4%) 50 (20.6%) 32.03 4.08 60 (26.8%) 82 (36.6%) 82 (36.6%) 38.65 9.86 135 (54.9%) 106 (43.1%) 5 (2%)
Data are expressed as mean SD or frequencies (n, %). p Values derived from Student’s t test or the chi-square test. Patients whose average blood pressure levels were 140/90 mm Hg or were under antihypertensive medication were classified as having hypertension. Hypercholesterolemia was defined as total serum cholesterol levels >200 mg/dL or the use of lipid-lowering agents. * p <0.001 compared with the ACS or stroke control group, respectively. † p <0.05 compared with the ACS or stroke control group, respectively.
Table 2 Results from logistic regression analysis developed to evaluate the likelihood of having acute coronary syndrome (ACS) or ischemic stroke (outcome), according to exposure to potential cardiovascular disease risk factors Independent Variables
MedDietScore (per 1/55 unit) Physical inactivity (yes/no) Smoking habits No smoker/no passive smoker (reference) No smoker/passive smoker Ever smoker/no passive smoker Ever smoker/passive smoker Family history of CVD (yes/no) Hypertension (yes/no) Hypercholesterolemia (yes/no) Diabetes mellitus (yes/no) Overweight/obese (yes/no) STAI-Y2 (per 1/80 unit)
ACS
Stroke
p
OR (95% CI)
Wald
OR (95% CI)
Wald
0.93 (0.88e0.99) 2.94 (1.47e5.88)
5.98 9.22
0.91 (0.84e1.00) 1.97 (0.91e4.26)
4.12 2.96
4.33 5.15 8.62 2.40 2.81 3.80 1.91 0.56 1.04
1.00 (1.52e12.38) (1.82e14.53) (3.52e21.14) (1.23e4.69) (1.52e5.21) (2.15e6.68) (0.88e4.15) (0.29e1.08) (1.01e1.07)
— 7.49 9.57 22.16 6.63 10.75 21.31 2.68 2.99 5.48
1.32 1.69 0.75 2.35 1.60 1.86 1.31 1.00 1.06
1.00 (0.55e3.18) (0.43e6.69) (0.29e1.94) (1.07e5.17) (0.74e3.44) (0.89e3.87) (0.60e2.86) (0.44e2.24) (1.02e1.10)
— 0.37 0.55 0.35 4.51 1.45 2.75 0.45 <0.001 8.92
0.710 0.414 — 0.054 0.127 <0.001 0.960 0.223 0.090 0.483 0.236 0.257
All groups (ACS cases, ACS control participants, stroke cases, stroke control participants), n ¼ 250. Results are presented as OR (95% CI), Wald test, obtained from multiple conditional logistic regression. p Values derived from cross-model postestimation tests regarding the comparison between the ORs of each CVD risk factor.
troponin I and the MB fraction of total creatinine phosphokinase (according to the Universal Definition of Myocardial Infarction, Joint European Society of Cardiology/American College of Cardiology Foundation/American Heart Association/World Heart Federation Task Force)2; unstable angina was defined by the occurrence of 1 angina episode(s), at
rest, within the preceding 48 hours, corresponding to class III of the Braunwald classification.3 Ischemic strokes were defined through symptoms of neurologic dysfunction of acute onset of any severity, consistent with focal brain ischemia and imaging/laboratory confirmation of an acute vascular ischemic pathology.4
Coronary Artery Disease/Comparison of Cardiovascular Disease Risk Factors
Dietary habits of the past year were assessed through a 90-item, validated semiquantitative food frequency questionnaire that has been previously described.1,5 Level of adherence to the Mediterranean diet was evaluated using an 11-item large-scale composite index, the MedDietScore. The theoretical range of the MedDietScore was between 0 and 55. Higher values of this diet score indicate greater adherence to the Mediterranean diet. The validation properties of the MedDietScore have been presented elsewhere in the literature.6 Sociodemographic variables recorded were age and gender (for the matching procedure). Current smokers were defined as those who smoked at least 1 cigarette per day, former smokers as those who had stopped smoking more than 1 year previously, and the rest of the participants were defined as noncurrent smokers. Passive smokers were defined as those who were exposed to the smoke of others (colleagues, partner, parents, children, roommates) for >30 minutes per day. A new variable was then developed, including the following categories: no smoker/no passive smoker, no smoker/passive smoker, ever smoker (i.e., current and former smoker)/no passive smoker, ever smoker/passive smoker. Physical activity was assessed using the International Physical Activity Questionnaire index,7 which has been validated for the Greek population.8 According to their physical activity levels, participants were classified as inactive or physically active (moderate or vigorously active). Body mass index (BMI) was calculated as weight (in kilograms) divided by standing height (in meters squared); overweight and obesity were defined as BMI 25.0 to 29.9 kg/m2 and >29.9 kg/m2, respectively. Detailed medical history was recorded for all participants, including family history of CVD and personal and family history of hypertension, hypercholesterolemia, hypertriglyceridemia, and diabetes. Patients whose average blood pressure levels were 140/90 mm Hg or were under antihypertensive medication were classified as having hypertension. Hypercholesterolemia was defined as total serum cholesterol levels >200 mg/dl or the use of lipid-lowering agents, and diabetes mellitus was defined as fasting blood glucose 126 mg/dl or the use of antidiabetic medication. A previously translated and validated version of the Spielberger Trait Anxiety Inventory (STAI form Y-2, range 20e80) was used for the assessment of trait anxiety.9,10 Participants were asked to grade in a 9-unit scale (i.e., 1 ¼ not important to 9 ¼ extremely important) the significance of 8 traditional CVD risk factors: active smoking, exposure to passive smoking, physical inactivity, stress, unhealthy dietary habits, and presence of overweight/ obesity, hypertension, hypercholesterolemia, diabetes mellitus, and family history of CVD.1 Normally distributed continuous variables (age, BMI, MedDietScore, STAI Y-2, participants’ beliefs) are presented as mean values SD and categorical variables (gender, smoking habits, physical activity, medical history, BMI categories, MedDietScore categories, and STAI Y-2 categories) as frequencies. Associations between categorical variables were tested by the calculation of the chi-square test. Comparisons of mean values of normally distributed continuous variables by clinical outcome were performed using Student’s t test. Correlations between continuous
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Figure 1. Estimated attributable risks (%) of traditional CVD risk factors regarding ACS and ischemic stroke. Data are presented clockwise, beginning from the top right, from “smoking (ever smoker).”
variables were evaluated using the Pearson’s r or Spearman rho coefficients. Normality of the variables was tested using P-P plots. Estimations of the relative odds of having ACS or stroke according to the exposure measurements were performed through conditional logistic regression analysis; results are presented as ORs and the corresponding 95% confidence intervals (CIs). The Hosmer-Lemeshow statistic was calculated to evaluate model’s goodness-of-fit. Comparisons between the effect size measures (i.e., ORs) of the 2 logistic models (1 for ACS and 1 for stroke) were based on the Wald test (i.e., log[OR]/Var[log OR]; the higher, the better), which incorporates the estimated effect parameter in relation to its variance and thus standardizes the effect. The likelihood-ratio test was also used to confirm the previous results. The comparison regarding the ORs of each CVD risk factor between the 2 models (i.e., ACS and stroke) was performed using cross-model postestimation tests. All reported p values were based on 2-sided hypotheses. SPSS 18.0 software (SPSS Inc., Chicago, Illinois) was used for all the statistical calculations. Results Demographic, clinical, psychological, and nutritional characteristics of the participants are presented in Table 1.
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Table 3 Health beliefs of study participants (n ¼ 1,000) ACS Patients (n ¼ 250) Health belief: Smoking Ever smoker No smoker Health belief: passive smoking Passive smoker No passive smoker Health belief: physical inactivity Physically active Sedentary Health belief: stress Low stress Moderate/severe stress Health belief: unhealthy dietary habits MedDietScore 32 MedDietScore < 32 Health belief: overweight/obesity BMI < 25 kg/m2 BMI 25 kg/m2 Health belief: diabetes mellitus/ hypercholesterolemia/hypertension Presence Absence Health belief: family history of CVD Yes No
ACS Controls (n ¼ 250)
Stroke Patients (n ¼ 250)
Stroke Controls (n ¼ 250)
7.03 7.24 6.29 5.94 6.02 5.67 6.07 6.10 5.96 7.76 7.68 7.92 6.88 7.42 6.59 6.63 6.31 6.75 6.56
2.53*,† 2.37*,†,z 2.95*,† 2.54†† 2.56 2.61† 2.32**,†† 2.33* 2.22*,† 1.72 1.78 1.64* 2.19* 1.70z 2.42*,† 2.36*,† 2.78* 2.14*,†† 2.46*,††
7.62 7.69 7.53 6.23 6.25 6.25 6.83 6.83 6.76 7.54 7.71 7.25 7.25 7.28 7.50 7.39 7.53 7.39 7.24
1.81 1.59 2.07 2.04 2.13 2.02 1.69 1.72 1.56 1.58 1.47z 1.71 1.61 1.61 1.37 1.65 1.51 1.58 1.78
7.68 7.86 7.57 6.88 6.66 6.72 6.88 6.55 6.92 7.60 6.69 7.79 7.20 6.49 7.28 7.40 6.89 7.72 7.69
2.32 2.03 2.48 2.24 3.35 2.44 2.08 2.24 2.01 1.68 2.73z 1.38* 2.10 2.93 1.98 2.29* 2.67z 1.98 2.08*
7.61 7.31 7.89 6.49 6.51 6.63 6.71 6.68 6.83 7.48 7.73 7.18 7.28 7.25 7.48 7.29 7.25 7.34 7.18
1.96 2.05z 1.77 2.12 2.17 1.99 1.89 1.99 1.56 1.63 1.55z 1.67 1.65 1.63 1.57 1.70 1.76 1.63 1.82
6.63 6.52 5.96 6.82 5.53
2.48*,†† 2.27 2.54*,†† 2.19z 2.61*,†
7.27 7.29 6.47 6.97 6.34
1.66 1.95 2.16 2.07 2.22
7.78 8.50 6.87 6.71 6.72
1.95* 1.00 2.25 2.61 2.40
7.22 7.30 6.48 7.41 6.31
1.79 1.76 2.20 1.86z 2.26
Data are expressed as mean SD. Risk factors were graded by the participants on a scale from 1 (not important) to 9 (very important). p Values derived from Student’s t-test. * p <0.05, ** p <0.001 compared with the ACS or stroke control group, respectively. † p <0.05, †† p <0.001 for the comparison between the ACS and stroke patients. z p <0.05 for the comparison between the subcategories examined (i.e., regarding the belief about the significance of smoking between smokers and nonsmokers, etc.).
Patients (with both ACS and stroke) tended to be less physically active than the control participants, with higher prevalence of almost all traditional cardiovascular risk factors: hypercholesterolemia, hypertension, diabetes mellitus, anxiety, unhealthy dietary patterns, and family history of CVD. Results from the multivariable models regarding ACS and stroke, including traditional CVD risk factors, are presented in Table 2. Of the factors associated with the odds of having an ACS, the most significant (in terms of effect size measure) was smoking—in particular, being both a smoker and a passive smoker. Hypercholesterolemia was the second most significant factor, followed by hypertension. Regarding stroke, anxiety emerged as the most significant factor, followed by family history of CVD and adherence to the Mediterranean diet. To examine whether the risk factors exerted a similar or different impact on the 2 CVD manifestations (i.e., ACS or ischemic stroke), the ORs between the 2 models were compared. Insignificant differences were observed for the majority of the risk factors included in the models, except for smoking habits. Furthermore, an estimation of the attributable risk for ACS or stroke, for each risk factor was calculated (continuous variables were transformed into categorical ones). Results are presented in Figure 1. In Table 3, the health beliefs of the participants concerning the common CVD risk factors are presented. All
grades were >5 (on a 9-unit scale), reflecting that all participants recognized the detrimental influence of these factors on their health status. The ACS patients reported that the most important factor influencing CVD development was stress, followed by smoking and unhealthy dietary habits, whereas the ACS controls concluded that smoking was the most detrimental factor, along with stress and overweight or obesity. The stroke patients recognized the presence of hypertension, hypercholesterolemia, and diabetes as most important, followed by smoking, and then stress. For the stroke control participants, smoking was the most important factor, followed by stress and being overweight or obese. However, even though smoking was considered as 1 of the most important CVD risk factors, passive smoking was graded as 1 of the least important ones by all patient and control groups. Furthermore, the ACS patients tended to underestimate the role of the risk factors, compared with their control participants, but this was not observed between the stroke patients and control participants. Finally, stroke patients had higher scores for the majority of the factors evaluated compared with the ACS patients. Discussion Results of the present work highlight some differences in the ranking of traditional CVD risk factors, yet important
Coronary Artery Disease/Comparison of Cardiovascular Disease Risk Factors
overall similarities regarding ACS and ischemic stroke. Furthermore, this is one of the few studies that has examined the perception of the magnitude of CVD risk factors by patients and healthy subjects. Despite the limitations of the study’s observational design, interpretation of the findings could be used to disseminate important public health messages, which could contribute to better CVD prevention strategies. Of major importance was the finding that modifiable risk factors, such as smoking and unhealthy dietary habits, emerged as crucial factors for CVD development. This may be because, apart from their independent detrimental actions on cardiovascular health, they could modify other risk factors such as hypercholesterolemia, hypertension, and diabetes mellitus. Results from other studies in general verify our findings. The Physicians’ Health Study showed that hypertension, hypercholesterolemia, diabetes mellitus, smoking, and physical inactivity were associated with higher risk of coronary heart disease and stroke, with comparable influences.11 Furthermore, the Dubbo Study of Australian Elderly observed substantial similarities for the risk factors of male gender, current smoking, diabetes mellitus, low-density lipoprotein cholesterol, reduced peak expiratory flow, and physical disability.12 According to the Health Survey for England, even though significant similarities were observed regarding the influence of age, smoking, systolic blood pressure, diabetes mellitus, and physical activity on ischemic heart disease and stroke development, important differences were also noticed regarding the factors high-density lipoprotein cholesterol, Creactive protein, fibrinogen, BMI, and total cholesterol.13 Results of the INTERHEART study indicated that ApoB to apoA1, current smoking, and psychosocial factors had the higher population attributable risk regarding myocardial infarction.14 Finally, according to the INTERSTROKE study, a self-reported history of hypertension was the most significant risk factor for stroke.15 Regarding the second aim of this work, it is known that the perception of the importance of the risk factors for CVD development varies and is based on the matching of personal risk factors with convictions about the nature of the disease.16 In fact, perceptions and beliefs regarding disease predisposition hold a significant place in health behavior models, such as the Health Belief Model,17 the Theory of Reasoned Action,18 the Subjective Expected Utility Theory,19 and the Protection Motivation Theory.20 In the present work, most participants graded all traditional CVD risk factors with a value >5 out of 9, recognizing the detrimental role of these factors on cardiovascular health. Risk factors that could be significantly improved through lifestyle changes or lifestyle changes combined with drug treatment emerged as most important. Finally, according to a recent study, factors that were positively associated with perceived risk of CVD were increased cholesterol levels, family history of CVD, unhealthy dietary patterns, and lack of physical activity.21 Despite the aforementioned individual risk factors approach, the multifactorial nature of CVD ought not to be disregarded. In particular, taking into consideration the influence and combined interactions of major cardiovascular risk factors (i.e., estimating the global CVD risk) is of
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crucial importance and should be encouraged. Toward this direction, the use of risk charts should be promoted for the identification of individuals at high CVD risk among apparently healthy persons because they are easy to use and enable objective risk assessment.22,23 Some limitations due to the retrospective, observational design of the study, such as selection and the recall bias, and the lack of causal interpretations should be reported. To minimize selection bias, only cases with a first event were enrolled, and to minimize recall bias, accurate and detailed data from all participants during the first 3 days of hospitalisation were obtained. For dietary evaluation, a food frequency questionnaire was administered; although these tools may carry measurement error and be less accurate (especially in energy and nutrient assessment) than a diary, an effort was made to reduce these errors and inaccuracies of dietary reporting with its application by trained dieticians through face-to-face interviews. Over- and underestimation in reporting may also exist, especially in the measurement of diet, smoking habits, the onset of CVD risk factors, and the grading of the CVD risk factors. However, an effort was given to retrieve accurate information from participants’ medical records, as well as their relatives. For the stroke patients, self-reported information was obtained from 76% of the sample; 60 patients (24%) were unable to answer the interviewer because of their condition. Thus, data were collected from a valid surrogate respondent. Moreover, the coronary and stroke patients who died at hospital entry or the following day were not included in the study (survivor bias); thus, the results should be generalized only to CVD survivors. Finally, the inclusion of patients and control participants from only 2 regions may limit the generalization of the findings to the whole country; nevertheless, it should be noted that Athens metropolitan area and Ioannina city in western Greece represent a vast majority of the Greek urban and rural population. Acknowledgment: We thank the field investigators of the study: Kallirroi Kalantzi, Aggeliki Ionnidi, Eva Ntziou, Markella Symeopoulou, Zoe Konidari, Eirini Trichia, Stavroula Bitsi, Vissarion Euthimiou, Eftychia Bika, Michael Kostapanos, Vaia Salma, Antonis Kramvis, Glykeria Papagiannopoulou, Alexandra Litsardopoulou, Alexia Katsarou, Fani Lioliou, Labros Papadimitriou, Konstantina Siganou, Ioanna Kousoula, Eleni Koroboki, Anastasia Vemmou, Paraskevi Savvari, and Vassiliki Vlachaki. Disclosures The authors have no conflicts of interest to disclose. 1. Kastorini CM, Milionis HJ, Goudevenos JA, Panagiotakos DB. Modelling the role of dietary habits and eating behaviours on the development of acute coronary syndrome or stroke: aims, design, and validation properties of a case-control study. Cardiol Res Pract 2010. pii: 313948. Available at: http://www.hindawi.com/journals/crp/2011/ 313948. Accessed April 11, 2013. 2. Thygesen K, Alpert JS, White HD; Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. J Am Coll Cardiol 2007;50:2173e2195. 3. Braunwald E. Heart Disease. 5th ed. London: W.B. Saunders Company, 1997.
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