Cardiovascular Health Status and Metabolic Syndrome in Adults Living in a Transition European Country: Findings from a Population-Based Study

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Cardiovascular Health Status and Metabolic Syndrome in Adults Living in a Transition European Country: Findings from a Population-Based Study Dragana Stojisavljevic´, PhD,*,† Janko Jankovic´, PhD,‡ Miloš Eric´, Jelena Marinkovic´, PhD,‖ and Slavenka Jankovic´, PhD¶

PhD,§

Background and Purpose: There are only a few published studies on the relationship between cardiovascular health (CVH) status as proposed by the American Heart Association and the metabolic syndrome (MetS) in persons with cardiovascular disease (CVD). The aim of this study was to assess the prevalence of CVH and MetS and their correlation in the adult population of the Republic of Srpska, Bosnia and Herzegovina, in order to evaluate which set of cardiovascular risk factors (low or medium CVH status and MetS), or the combination of both, is a better predictor for the occurrence of CVD. Methods: We included 3601 adults (aged ≥25 years) from the Republic of Srpska National Health Survey 2010. CVH status was evaluated according to the American Heart Association criteria, whereas MetS was defined using the criteria of the National Cholesterol Education Program’s Adult Treatment Panel III. Results: The prevalence of low or medium CVH status and MetS is significantly higher in participants who had experienced CVD than in those free of CVD. Our study showed that predictors for CVD occurrence were presence of MetS (odds ratio 3.61, 95% confidence intervals 2.14-6.07) and presence of both sets of cardiovascular risk factors in the same person (odds ratio 4.23, 95% confidence intervals 1.50-11.93). Conclusion: Our results suggest that presence of both sets of cardiovascular risk factors (low or medium CVH status and MetS) is the strongest predictor of CVD. Identification of individuals with cardiovascular risk factors may provide opportunities to intervene earlier and can help reduce the risk of developing CVD. Key Words: Cardiovascular disease—cardiovascular disease risk factors—cardiovascular health status—epidemiology—metabolic syndrome. © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

From the *Institute of Public Health, Banja Luka, Republic of Srpska, Bosnia and Herzegovina; †Medical Faculty, University of Banja Luka, Banja Luka, Republic of Srpska, Bosnia and Herzegovina; ‡Institute of Social Medicine, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; §Faculty of Economics, Finance and Administration, Metropolitan University, Belgrade, Serbia; ‖Institute of Medical Statistics and Informatics; and ¶Institute of Epidemiology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia. Received February 15, 2017; accepted September 24, 2017. The study was performed at the Institute of Public Health, Banja Luka, Republic of Srpska, Bosnia and Herzegovina. This work is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 175025). Address correspondence to Slavenka Jankovic´, PhD, Institute of Epidemiology, Faculty of Medicine, University of Belgrade, Višegradska 26, 11000 Belgrade, Serbia. E-mail: [email protected]. 1052-3057/$ - see front matter © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.09.046

Journal of Stroke and Cerebrovascular Diseases, Vol. ■■, No. ■■ (■■), 2017: pp ■■–■■

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Introduction Cardiovascular disease (CVD) is the largest single contributor to global mortality. According to the World Health Organization (WHO), 17.5 million people die each year from CVD, representing 31% of all deaths worldwide.1,2 About 80% of all CVD deaths are due to heart attacks and strokes.1 Despite decrease in CVD mortality rates in many developed countries in the last few decades,3 CVD remains the most important cause of death in developing countries and over three quarters of all CVD deaths occurs there.3,4 CVD also causes disability. If no action is taken to improve cardiovascular health (CVH) and current trends continue, WHO estimates that 25% more healthy life years (disabilityadjusted life year) will be lost to CVD globally by 2020.1 CVD is the leading cause of death and disability in the Republic of Srpska (RS),5 1 of 2 constitutional entities of Bosnia and Herzegovina (BH), a middle income Southeastern European country. The most up-to-date data on CVD in Europe show that BH with age-adjusted CVD mortality rates of 474.7 per 100,000 males and 385.4 per 100,000 females is among countries with middle rates.3 It is well known that CVD is related to the prevalence of classical cardiovascular risk factors, especially to lifestyle factors such as the use of tobacco, unhealthy diet, physical inactivity, and stress,6 and that having more than 1 risk factor tends to increase the risk of CVD disease.7-9 Exact assessment of specific cardiovascular risk factors in each country is required to reduce the burden of CVD. There are at least 2 well-defined sets of cardiovascular risk factors. The first set, the metabolic syndrome (MetS), is the cluster of abdominal obesity, increased glucose level, abnormal lipids, and elevated blood pressure (BP). The presence of MetS is associated with an increased risk of CVD and diabetes mellitus type 2.8-10 The second set of cardiovascular risk factors which includes a number of health behaviors (smoking, body mass index [BMI], physical activity, and diet) and health factors (BP, total cholesterol [TC], and fasting blood glucose [FBG]) was coined by the American Heart Association.11 A recently performed meta-analysis suggests that ideal CVH metrics are inversely associated with risk of cardiovascular mortality, coronary heart disease, and stroke.12 The aim of this study was to assess the prevalence of both abovementioned sets of cardiovascular risk factors and their correlation in the adult population of the RS with and without CVD (ischemic heart disease and stroke) to determine which set of risk factors, or the combination of both, is associated with the occurrence of CVD.

Methods Study Design and Participants This cross-sectional study utilized data collected in the 2010 National Health Survey in RS, BH. The methods have already been described in detail elsewhere.13

In brief, 4673 adults aged 18 years or older have been identified in the randomly selected households, out of which 4170 were interviewed yielding a response rate of 89.2%. For the purpose of the present study we used the sample of participants aged 25 years or older (n = 3601). All participants were interviewed and underwent physical examinations (anthropometric and BP measurements, as well as blood tests) at home by a trained staff. Information on demographic (age, sex, marital status and type of settlement), socioeconomic (education and employment status), and lifestyle characteristics (smoking, physical activity, and diet) was collected using standardized questionnaire. Self-perceived health status was measured by a single question on an individual’s perception of his or her own health (poor, average, and good), whereas self-reported diagnosis of CVD (stroke or ischemic heart disease) was recorded if the respondent had ever been told by a doctor of a diagnosis of CVD. All participants gave written informed consent prior to inclusion in the survey. The study was approved by the Ethics Committee of the Public Health Institute of RS.

Assessment of Cardiovascular Health Status CVH status of all participants was evaluated according to the American Heart Association criteria.11 Seven CVH metrics were classified into “ideal,” “intermediate,” or “poor” as the following: (1) smoking: ideal (never or quit >1 year), intermediate (former, quit ≤1 year) and poor (current); (2) BMI: ideal (<25 kg/m2), intermediate (25-29.9 kg/m2), and poor (≥30 kg/m2); (3) physical activity: ideal (active), intermediate (moderately active), and poor (inactive); (4) healthy diet score (HDS): ideal (≥26 points), intermediate (HDS 21-25 points), and poor (HDS <21 points); (5) TC: ideal (<200 mg/dL, untreated), intermediate (200-239 mg/ dL or treated to goal), and poor (≥240 mg/dL); (6) BP: ideal (SBP <120 mm Hg and DBP <80 mm Hg, untreated), intermediate (SBP 120-139 mm Hg or DBP 80-89 mm Hg, or treated to goal), and poor (SBP ≥140 mm Hg or DBP ≥90 mm Hg); and (7) FBG: ideal (<100 mg/dL, untreated), intermediate (100-125 mg/dL or treated to goal), and poor (≥126 mg/dL). To examine the overall effects of these health metrics, we created CVH score (CVHS). Each of the 7 CVH metric was given a point score of 0, 1, or 2 to represent poor, intermediate, or ideal CVH, respectively. Based on the sum of all 7 CVH metrics an overall CVHS was calculated ranging from 0 (all CVH metrics at poor levels) to 14 (all CVH metrics at ideal levels), and then categorized into low (0-4), medium (5-9), or high (10-14) CVH.

Assessment of Metabolic Syndrome MetS was defined using the criteria of the National Cholesterol Education Program’s Adult Treatment Panel III.14 Participants with 3 or more of the following criteria were defined as having the MetS: (1) abdominal obesity given as

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waist circumference (WC >102 cm in men or >88 cm in women); (2) TG 1.7 mmol/L or greater (150 mg/dL) or drug treatment; (3) high BP (≥130/85 mm Hg) or treated hypertension; and (4) FBG 5.6 mmol/L (100 mg/dL) or greater or presence of diabetes mellitus type 2. Because data on high-density lipoprotein cholesterol (HDL-C) were not available, HDL-C was not included in the definition of MetS used in this study.

Measurements The BMI was calculated as weight in kilograms divided by squared height in meters (kg/m2). WC was measured with persons on the supine position at the midpoint between the last rib and the iliac crest. SBP (mm Hg) and DBP (mm Hg) were measured using mercury sphygmomanometer—diplomat-presameter (Riester, CE 0124, Jungingen, Germany) with appropriately sized cuffs. Sitting BP was measured 3 times after a 5-minute rest and the mean of the last 2 measurements was used for analysis. As recommended for developing countries, FBG, TG, and TC were measured from early-morning capillary blood samples after an overnight fast.15,16 The samples were obtained and analyzed using a calibrated Accutrend Plus GCTL analyzer (Roche Diagnostics, Mannheim, Germany).

Statistical Analysis Categorical variables were presented by counts and percentages, whereas continuous variables were described as means with standard deviations. To assess the differences between groups the chi-square test, t-test, and MannWhitney test were used where appropriate. After the preliminary analysis of the data, the multivariate logistic regression was used to perform the analysis to determine whether the likelihood of CVD could be predicted from 2 sets of cardiovascular risk factors. Dependent variable was the absence or presence of CVD, whereas independent variables were age, sex, education, employment status, self-perceived health, CVH status, MetS, and combination of CVH and MetS. We included sociodemographic factors and self-perceived health as possible confounders because these factors are related to CVD events, CVH metrics, and to the components of MetS. Because data on HDL-C were not available, we excluded those with 2 MetS components from the multivariate analysis. All statistical analyses of data were performed using SPSS version 20.0 (SPSS Inc., Chicago, IL). Statistical significance was set at P < .05.

Results Our sample included 3601 participants, 1614 men (44.8%) and 1987 women (55.2%), which represents the adult population (≥25 years) of the RS. Most participants belonged to the middle age group (42.3%). As shown in Table 1,

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44.4% of all participants had low, 47.3% medium, and only 8.2% high education. More than half of the participants (59.3%) lived in a rural area, with almost two thirds (70.6%) being married or living with a partner. Almost a quarter of all participants (22.6%) were unemployed, and nearly half of them (46.9%) were economically inactive. Approximately half of the participants evaluated their health as average (37.0%) or poor (10.2%). Average values for BMI, SBP, DBP, and TG were above the recommended values, the TC was borderline high, while only FBG level was in the normal range. Participants with CVD (157) in comparison with participants without CVD (3444) were significantly older, more frequently men, with low education, and economically inactive (Table 1). In addition, they self-rated their health as poor and average more frequently than participants without CVD. Average values of BMI, SBP, DBP, TG, and FBG were significantly higher in participants with CVD in comparison with those without CVD. Low and medium CVH status (CVHS ≤9) had more than four fifths of all participants (81.7%) (Table 2). The most prevalent CVH metrics were poor diet (59.6%) and physical inactivity (40.0%), and the least prevalent was FBG (6.9%). Poor CVH metrics like physical inactivity, increased BP, TC, and FBG were significantly more frequent in participants with CVD, whereas current smoking, BMI 30 kg/m2 or greater, and poor diet were more prevalent in participants without CVD (Table 2). The most frequent components of the MetS were TG (59.8%) and BP (52.0%), and the least prevalent was FBG (25.9%). The highest percentage of participants (42.3%) had 0-1 MetS components, less than a third of participants (30.6%) had 3-4 MetS components, and 27.1% of participants had 2 MetS components. Participants with CVD significantly more often had increased values of MetS components (WC, BP, TG, FBG) and greater number of MetS components (Table 2). The presence of both sets of cardiovascular risk factors (low or medium CVH status and MetS) was registered in 40.6% of 2625 participants. Only 1 set of risk factors was seen in another 40.2% of participants, and absence of both sets in 19.2%. The presence of both sets was more frequently found in participants with CVD (79.2% versus 39.0%), whereas the presence of only 1 of 2 sets and absence of both sets were more frequent in participants without CVD (Table 2). The results of the multivariate logistic regression analyses showed that predictors for CVD occurrence were the presence of MetS (odds ratio [OR] 3.61, 95% confidence interval [CI] 2.14-6.07) and presence of both sets of cardiovascular risk factors (low or medium CVH status and MetS) in the same person (OR 4.23, 95% CI 1.50-11.93) (Table 3).

Discussion This population-based study suggests that the prevalence of low or medium CVH status and CVH metrics

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Table 1. Characteristics of participants with and without cardiovascular disease Characteristics

Total sample n = 3601

With CVD n = 157

Without CVD n = 3444

P*

Age, mean (SD) Age, n (%) 25-39 40-64 65+ Sex, n (%) Males Females Settlement, n (%) Urban Rural Education, n (%) Low Middle High Marital status, n (%) Married/living with partner Living without partner† Employment status, n (%) Employed Economically inactive Unemployed Self-perceived health, n (%) Poor Average Good BMI, kg/m2, mean ± SD SBP, mmHg, mean ± SD DBP, mmHg, mean ± SD TG, mmol/L mean ± SD TC, mmol/L, mean ± SD FBG, mmol/L, mean ± SD

52.9 ± 15.9 3601 1167 (32.4) 1524 (42.3) 910 (25.3) 3601 1614 (44.8) 1987 (55.2) 3601 1467 (40.7) 2134 (59.3) 3601 1600 (44.4) 1704 (47.3) 297 (8.2) 3444 2541 (70.6) 1060 (29.4) 3601 1097 (30.5) 1689 (46.9) 815 (22.6) 3601 368 (10.2) 1332 (37.0) 1901 (52.8) 26.9 ± 4.9 137.6 ± 21.4 85.2 ± 11.3 2.1 ± 1.5 5.2 ± 1.3 5.1 ± 1.7

66.4 ± 10.9 157 5 (3.2) 63 (40.1) 89 (56.7) 157 91 (58.0) 66 (42.0) 157 60 (38.2) 97 (61.8) 157 91 (58.0) 51 (32.5) 15 (9.6) 157 110 (70.1) 47 (29.9) 157 16 (10.2) 125 (79.6) 16 (10.2) 157 26 (16.6) 76 (48.4) 55 (35.0) 27.8 ± 4.8 148.5 ± 21.0 89.2 ± 11.5 2.4 ± 1.6 5.2 ± 1.5 5.6 ± 2.1

52.3 ± 15.5 3444 1162 (33.7) 1461 (42.4) 821 (23.8) 3444 1523 (44.2) 1921 (55.8) 3444 1407 (40.9) 2037 (59.1) 344 1509 (43.8) 1653 (48.0) 282 (8.2) 344 2431 (70.6) 1013 (29.4) 3444 1081 (31.4) 1564 (45.4) 799 (23.2) 3444 342 (9.9) 1256 (36.5) 1846 (53.6) 26.9 ± 4.9 137.1 ± 21.3 85.0 ± 11.3 2.1 ± 1.5 5.2 ± 1.3 5.0 ± 1.7

.000 .000

.001

.511

.001

.888

.000

.000

.024 .000 .000 .012 .967 .000

Abbreviations: BMI, body mass index; CVD, cardiovascular disease (stroke and ischemic heart disease); DBP, diastolic blood pressure; FBG, fasting blood glucose; SBP, systolic blood pressure; SD, standarddeviation; TC, total cholesterol; TG, triglycerides. *According to chi-square test, t-test, or Mann-Whitney test where appropriate. †Unmarried, divorced, or widowed.

in the poor range is significantly higher in residents of the RS aged 25 years or more who had experienced CVD (a stoke or ischemic heart disease) than in those free of CVD. The same is true for the relationship between the prevalence of MetS components and the presence of CVD. Poorer levels of CVH have shown to be associated with CVD and mortality from all causes and diseases of the circulatory system.17-19 A recently performed meta-analysis of 9 prospective cohort studies including 12,878 participants provides evidence that individuals with the greatest number of ideal CVH metrics at baseline have a significantly lower risk of cardiovascular mortality (relative risk [RR] .25, 95% CI .10-0.63), CVD (RR .20, 95% CI .11-0.37), and stroke (RR .31, 95% CI .25-0.38) in comparison with those with the least number of ideal CVH metrics.12

In the population-based longitudinal study from Norway, a graded association was shown between the CVH metric score and incident myocardial infarction (MI). This indicates that almost 15% of MI could be prevented by improvements in the health metrics.20 There are results from a recent prospective study involving over 160,000 aging postmenopausal women,21 which suggest that women with the lowest CVH scores compared with those with the highest CVH scores had almost 7 times the risk of incident CVD (RR 6.83, 95% CI 5.83-8.00). Ideal CVH metrics were also associated with markers for subclinical CVD. Chang et al22 found an inversely gradient relationship between the number of ideal CVH metrics and lower prevalence of high atherogenic index of plasma, a strong marker for atherosclerosis, while Kulshreshtha et al23 confirmed that ideal CVH had a strong

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Table 2. Prevalence (%) of poor CVH metrics and components of metabolic syndrome and their correlation in participants with and without cardiovascular disease

CVH metrics in the poor range Smoking status, current Body mass index ≥30 kg/m2 No physical activity Poor diet Total cholesterol ≥240 mg/dL Blood pressure ≥140/90 mm Hg Fasting blood glucose ≥126 mg/dL CVH status (low/medium)† Components of MetS Waist circumference ≥102 cm in men and ≥88 cm in women Triglycerides ≥150 mg/dL Blood pressure ≥130/85 mm Hg Fasting glucose ≥100 mg/dL No of MetS components 3-4 MetS components 2 MetS components 0-1 MetS components CVH status (low/medium)† and MetS Both sets of risk factors Only 1 set of risk factors Not 1 set of risk factors

Total sample (n = 3601)

Persons with CVD (n = 157)

Persons without CVD (n = 3444)

P*

1135 (31.5) 1372 (38.1) 1440 (40.0) 2145 (59.6) 819 (22.8) 1138 (31.6) 248 (6.9) 2943 (81.7)

31 (19.7) 49 (31.2) 108 (68.6) 60 (38.2) 42 (26.8) 79 (50.3) 29 (18.5) 148 (94.2)

1104 (32.1) 1323 (38.4) 1332 (38.7) 2085 (60.6) 777 (22.6) 1059 (30.8) 219 (6.4) 2795 (81.1)

.000 .029 .000 .000 .003 .000 .000 .000

1569 (43.6) 2152 (59.8) 1871 (52.0) 934 (25.9)

86 (54.8) 126 (80.3) 127 (80.9) 66 (42.0)

1483 (43.1) 2026 (58.9) 1744 (50.6) 868 (25.2)

.004 .000 .000 .000

1100 (30.6) 976 (27.1) 1524 (42.3) (n = 2625) 1066 (40.6) 1055 (40.2) 504 (19.2)

86 (54.8) 51 (32.5) 20 (12.7) (n = 106) 84 (79.2) 18 (17.0) 4 (3.8)

1014 (29.4) 925 (26.9) 1504 (43.7) (n = 2519) 982 (39.0) 1037 (41.2) 500 (19.8)

.000

.000

Abbreviations: CVD, cardiovascular disease (stroke or ischemic heart disease); CVH, cardiovascular health; MetS, metabolic syndrome. *According to chi-square test. †Cardiovascular health score: ≤9.

Table 3. Results of multivariate logistic regression analysis* (n = 2625) Variable CVH status Low/medium (CVHS ≤9) High (CVHS >9) Metabolic syndrome Yes No Interaction: low/medium CVH status† × MetS Combination of low/medium CVH status and MetS Both sets of risk factors Only 1 set of risk factors Not 1 set of risk factors

OR

95% CI

P

2.23 1.00

.95-5.22

.063

3.61 1.00 3.38

2.14-6.07

.000

2.04-5.59

.000

4.23 1.32 1.00

1.50-11.93 .43-4.00

.006 .621

Abbreviations: CI, confidence intervals; CVH, cardiovascular health; CVHS, cardiovascular health score; MetS, metabolic syndrome; OR, odds ratio. Values in bold are significant. *Adjusted on demographic and socioeconomic variables. †Cardiovascular health score: ≤9.

inverse correlation with carotid intima-media thickness, another preclinical marker for atherosclerosis. If the individual components of CVH obtained in our study are considered, poor CVH metrics as increased BP, TC, FBG, and physical inactivity were significantly more frequent in participants with CVD, whereas current smoking, BMI 30 kg/m2 or more, and poor diet were more prevalent in participants without CVD. The explanation may be that respondents with CVD embraced the importance of lifestyle modification only after receiving their diagnosis and changed their behavior regarding smoking and healthy diet. The presence of physical inactivity in CVD patients could be explained by fear and limitation in performing physical activities, primarily as a consequence of the stroke. Higher prevalence of poor CVH factors (BP, TC, FBG) in people with CVD indicate that changes in eating habits and consequently in body weight, without participation in at least moderate physical activity, are not sufficient for CVH improvement. In the current study we also found that participants with CVD in comparison with those without CVD significantly more often had MetS (OR = 3.61, 95% CI = 2.14-6.07) and increased values of all its components (FBG, BP, TG, and WC).

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The best available evidence suggests that people with MetS are at increased risk of cardiovascular events (coronary heart disease or stroke) worldwide.24-29 In the Atherosclerosis Risk in Communities (ARIC) populationbased cohort study of 15,792 US residents, crude incidence rates per 10,000 person-years for coronary heart disease and ischemic stroke were significantly greater among individuals with the MetS than among comparison group.25 In another large study (INTERHEART) of 26,903 participants from 52 countries, Mente et al28 have demonstrated that the presence of MetS is associated with 2.5-fold increase in the risk of acute MI, and that this risk does not appear to be greater than the risk conferred by MetS constituent components. Several other studies also have shown that individual components and MetS are associated with a similar degree of coronary heart disease risk.30,31 Some authors have suggested that the number of components of MetS can be more useful than MetS itself in the prediction of CVD.32 Our finding that participants with CVD significantly more often had a greater number of MetS components compared with those without CVD is in accordance with other studies in which the risk of CVD increases with the increase in the number of MetS components.8,9 Recently, it has been shown that not only the number of MetS components, but also their different combinations are important in the prediction of cardiovascular risk. There is evidence that the joint effect of diabetes and hypertension on MI is similar to that of having all MetS component factors.28 In 2 recent studies, the most frequent combination observed in patients with ischemic heart disease was high FBG, hypertension, and low HDL-C.29,33 The final aim of this study was to determine which set of cardiovascular risk factors (MetS, low or medium CVH status, or the combination of both) is associated with CVD. There is substantial evidence presenting that both poor CVH status and the MetS can be used as reliable predictors of cardiovascular events in the long-term follow-up.17,27 Results from the present study showed that most CVH metrics in the poor range, low or medium CVH status, MetS, and all MetS components were more prevalent in persons who had experienced a stroke or coronary heart disease than in CVD-free RS residents. Also the combination of both sets of risk factors in the same person (low or medium CVH status and MetS) was associated with the occurrence of CVD. However, having only 1 set of cardiovascular risk factors (either low or medium CVH status or MetS) was not associated with CVD events. In a recently published case-control study, Del Bruto et al34 showed similar CVH status and prevalence of MetS in Atahualpa residents (Ecuador) with and without stroke and ischemic heart disease. A relatively small number of cases could be one of the explanations for such a finding. The first study reporting on the prevalence of ideal CVH

in Japanese male workers indicated a strong inverse relationship between the number of ideal CVH metrics and prevalence of MetS.35

Study Limitations The results of this study should be considered in light of several limitations. First, the cross-sectional study design prevents any conclusions regarding causality to be made. Second, information on several CVH components and the presence or absence of CVD has been obtained through a self-administered questionnaire, which may be subject to recall bias or selective reporting. Third, some criteria for ideal, intermediate, and poor CVH in this study were adjusted such as the criteria for physical activity, and diet score, which may make it difficult to compare with other studies. Fourth, because data on HDL-C were not available, HDL-C was not included in the definition of MetS used in this study. In the light of the most novel finding that low HDL-C in isolation is considerably less predictive of CVD risk in the presence of high TG (≥100 mg/dL),36 the absence of HDL-C is unlikely to alter the key findings of the present study.

Conclusion Notwithstanding all previously mentioned limitations, our study indicated that presence of both sets of cardiovascular risk factors (low or medium CVH status and MetS) is the strongest predictor of CVD. Identification of individuals with cardiovascular risk factors, especially when both sets of risk factors are present, and early intervention programs can provide opportunities for CVD prevention. Acknowledgments: The 2010 National Health Survey was supported by the World Bank and Ministry of Health and Social Welfare of the Republic of Srpska. Funding for this analysis was obtained from the Ministry of Education, Science and Technological Development of the Republic of Serbia (grant No. 175025).

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