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Association of Dietary Patterns with Sociodemographic and Health-related Factors among Coronary Artery Disease (CAD) Patients a
a
a
Haleh Esmaili , Rokiah Mohd Yusof , Hazizi Abu Saad , Ali Ghaemian a
& Nasrin Darani Zad a
Department of Nutrition and Dietetics, Universiti Putra Malaysia, Selangor, Malaysia Published online: 27 Oct 2014.
Click for updates To cite this article: Haleh Esmaili, Rokiah Mohd Yusof, Hazizi Abu Saad, Ali Ghaemian & Nasrin Darani Zad (2015) Association of Dietary Patterns with Sociodemographic and Health-related Factors among Coronary Artery Disease (CAD) Patients, Ecology of Food and Nutrition, 54:1, 4-19, DOI: 10.1080/03670244.2014.930031 To link to this article: http://dx.doi.org/10.1080/03670244.2014.930031
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Ecology of Food and Nutrition, 54:4–19, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0367-0244 print/1543-5237 online DOI: 10.1080/03670244.2014.930031
Association of Dietary Patterns with Sociodemographic and Health-related Factors among Coronary Artery Disease (CAD) Patients HALEH ESMAILI, ROKIAH MOHD YUSOF, HAZIZI ABU SAAD, ALI GHAEMIAN, and NASRIN DARANI ZAD Downloaded by [Lakehead University] at 15:18 13 March 2015
Department of Nutrition and Dietetics, Universiti Putra Malaysia, Selangor, Malaysia
This study aimed to identify the association of dietary patterns with sociodemographic and health-related characteristics among coronary artery disease patients. In this cross-sectional study, the participants were 250 patients coronary artery disease aged ≥ 40 years old. Data collection was done using questionnaires related to sociodemographics, health-related factors, and food-frequency intake information. Three dietary patterns (traditional, western, and healthy) were obtained using principal component analysis. The result showed that dietary patterns were associated with sociodemographic and health-related factors. According to the result, all the factors were taken very seriously when planning a promotional program for healthy lifestyle in prevention of CAD. KEYWORDS coronary artery disease, dietary patterns, healthrelated factors, sociodemographics
Cardiovascular diseases (CVDs) are heart and blood vessel disorders, characterized by endothelial dysfunction and include coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolism (WHO 2007). The most common type of CVD is coronary artery disease (CAD), a disorder caused by the obstruction of coronary arteries as the narrow and become harden (Escott-Stump 2008; Lotfi, Kannan, and Sundaram 2008). According to Lotfi and colleagues (2008), plaques formed Address correspondence to Haleh Esmaili, MSc, Department of Nutrition and Dietetics, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. E-mail: haleh. [email protected] 4
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by cholesterol in the wall of coronary arteries cause CAD (Escott-Stump 2008; Lotfi, Kannan, and Sundaram 2008). These days, CAD is regarded as the single most important disease in the world in terms of mortality, morbidity, disability and economic loss (Fuster et al. 2006). The most important causes of heart disease, according to a report by WHO published in 2011, are behavioral factors including unhealthy diet, sedentary lifestyle and using tobacco. As the report highlighted, 80% of coronary heart disease and cerebrovascular diseases are related to these factors (WHO 2011). In Iran in addition to malnutrition and poor dietary quality that challenge the people’s lives, obesity and chronic diseases such as cardiovascular disease have rapidly emerged as factors that pose fatal threats, particularly in urban areas due to nutrition transition. As the literature highlights cardiovascular and cerebrovascular diseases are the first and major causes of mortality in Iran (Azizi et al. 2002; Ghassemi, Harrison, and Mohammad 2002). Nearly 50% of all deaths per year have been caused by CAD, which reconfirms the gravity of CAD as a leading cause of mortality and disability in the Iranian population (Hatmi et al. 2007). Most of the earlier studies that examined nutrition, diet and chronic diseases have focused on single nutrients, linking them with the risk of disease. Examination of individual food components, however, exhibited some notable limitations, as they neglected many unknown nutrients and nonnutrient compounds in foods. People consume a combination of foods that contain multiple nutrients, where the nutrients of a particular food may interact with the nutrients of others (Esmaillzadeh and Azadbakht 2008a; McCann et al. 2001). Food patterns as an important factor, in addition, may be affected by culture, food availability, economic status and many other factors, especially environmental factors (Jacques and Tucker 2001). The dietary pattern, in this respect, emerges as a beneficial method to examine diet–disease relationships within the given population, examining the effects of overall diet and eating patterns on a particular sample (Hu et al. 1999; Hu et al. 2000). Unfortunately, most data in this area and related factors have been drawn from studies carried out in the Western countries (Europe and North America) and the diets of western nations cannot be generalized and extrapolated to developing nations. In this respect, due to insufficient information regarding dietary patterns and their associations with demographic and lifestyle factors in developing countries the gap still exists (Cho et al. 2011; Rezazadeh, Rashidkhani, and Omidvar 2010; Safdar et al. 2013). The imbalance of dietary intake, being poor in healthy items, and the relation between diet quality and lifestyle in adults is a matter of concern (Mirmiran et al. 2011). Moreover, it is important to identify the relation between dietary patterns and environmental factors, as environmental factors play a significant role in the dietary pattern choices. Dietary patterns show specific associations with sociodemographic, lifestyle and other health factors that can help identify subgroups for nutrition guidelines in public health
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(Kesse-Guyot et al. 2009). This study aimed to identify dietary patterns of CAD patients in Sari, Mazandarn province, Iran, and the association of each pattern with demographic (i.e., age, gender, location, monthly income, and educational level) and health-related factors (i.e., physical activity, smoking status, and supplements intake) in this sample group as predictors of dietary pattern choices.
MATERIALS AND METHODS
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Participants and Study Design This cross-sectional study took place in a government specialized hospital in Sari, the capital of Mazandaran province in the north of Iran. It is the only government specialized hospital in Mazandaran, serving all CAD patients in the province. Mazandaran is located in the north of Iran between the Caspian Sea and the Alborz mountains, with a humid climate and food diversity different from other parts of Iran. The province consists of urban and rural population, with the rural part neighboring the cities. Mazandaran is divided into 15 counties, with the census of 2006 marking the population around 2,922,432 people, 53.18% of whom were registered as urban dwellers, 46.82% as villagers, and the remainder as non-residents (Statistical Center of Iran 2010). Based on the sample size formula for systematic random sampling and cross-sectional study (Daniel 2010) and sample size for factor analysis, it was recommended that there should be 5–10 cases for each item to be factor analyzed, requiring 250 patients for examination (Coakes 2005; Pallanat 2007). Participants in this study were 130 male and 120 female CAD patients, aged 40 and over, selected through systematic random sampling, corresponding with hospital lists of registered patients. After angiography, patients who were candidates for percutaneous trans-luminal coronary angioplasty (PTCA) or coronary artery bypass graft (CABG) were registered in the hospital. Around 12 to 15 patients were hospitalized each morning (from Saturday until Wednesday). The candidates for PTCA or CABG in the hospital were chosen for the study. Only four or five patients were recruited each day. Based on systematic random sampling, a sampling interval was calculated by dividing the total number of patients (n = 15) of each day by the number of samples (n = 5) that were interviewed every day; 15/5 = 3. Thus, the first participant was randomly picked from the list of patients. Then, the sampling countdown started, corresponding with the number in the list, and continued based on the sampling interval. Participants with a former history of myocardial infarction (MI) and cancer, CVDs, diabetes, or stroke were excluded, as these diseases can lead to a change in diet- and health-related factors; and the sampling continued with the next patient on the list. This study was approved by the Medical Research Ethic Committee of Faculty of Medicine and Health Sciences in Universiti Putra Malaysia (UPM). A pilot
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study was performed among 25 patients (10% of the required sample size) in the hospital before starting the actual data collection, with the sole objective to reduce errors and determine the reliability and understandability of the questionnaires. These patients were not included in the main study.
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Assessment of Sociodemographic and Health-related Factors Status Self-reported sociodemographic and personal information was collected using questionnaires, for which respondents provided their age, gender, residence information (habitat), educational status, monthly household income, and occupation. The health-related factors of the questionnaire included physical activity, smoking, and supplement intake. Physical-activity information was obtained using the International Physical Activity Questionnaire (IPAQ 2005) and participants were asked to record all activities. IPAQ was developed for use in adults aged 15–69 years, although some findings have indicated that caution is warranted in administering IPAQ to adults aged ≥ 65 years (Craig et al. 2003; Heesch et al. 2010; Kolbe-Alexander et al. 2006; Rzewnicki, Auweele, and De Bourdeaudhuij 2003). Additional information regarding current cigarette, shisha, and pipe-smoking habits (patients who smoked every day at least, in the recent five years, were considered smokers) and current intake of dietary supplements (vitamins and minerals), was obtained via the questionnaires. (A shisha is an oriental tobacco pipe with a long flexible tube connected to a container where the smoke is cooled by passing through water.) Current smoking status was dichotomous (current or non- or ex-) within the last five years. Supplement intake was dichotomous (yes or no) within the last year, and intake was at least two times weekly.
Assessment of Dietary Intake A validated semi-quantitative food frequency questionnaire (FFQ) consisting of 127 food items commonly eaten by Iranians, assessed the usual dietary intake over the previous year based on, daily, weekly, or monthly consumption. The questionnaire is represented in Persian languages, including a common list of Iranian food items, validated using Comparative validity. Comparative validity was determined by comparison with the estimated intake from the average of 12 24-hour dietary recalls (Esmaillzadeh et al. 2008; Esmaillzadeh and Azadbakht 2008; Esmaillzadeh, Mirmiran, and Azizi 2005). The questionnaires were modified based on food diversity in different parts of Iran. Participants were asked to mention their frequency of consumption and serving size of each food items during the previous year. Due to the fact that subjects had a limited knowledge of food portions and lacked conceptualization skills, the questionnaire was completed by the interviewer. When patients could not answer these questions properly and completely, other family members who either knew about the patient’s daily food intake or prepared food for them would accompany the patient or be interviewed
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in turn (Thompson and Subar 2008). Some portions (e.g., rice, fruit juice, or milk) were shown in pictures for simplicity and better understanding. These measuring instruments were validated based on set of common household scale, cooking method and food intake questionnaire (Ghaffarpour, HoshyarRad, and Kianfar 1999). The reported frequency for each food items was converted to a daily intake. The serving size was determined according to the household measurement tools of Iranian food and then converted to grams (Ghaffarpour et al. 2000). The amount of foods was measured based on g/day, and used to assess dietary patterns. The input value of food groups was the number of servings per day. Hence, foods from 127 items of the FFQ were classified into 25 food groups based on nutrient profile and culinary styles (Block 2004; CDC 2000; Esmaillzadeh et al. 2007) (table 1). Foods that did not fit into any of the groups were left as individual categories and considered as a separate food TABLE 1 The Food Groupings Initially Used in Dietary Pattern Analysis Food groups Fast food Red meat Organ meat Fish Poultry Eggs Butter Low fat dairy products High fat dairy products Tea Legumes Garlic Nuts French fries Whole grains Refined grains Mayonnaise Olive Sweets and desserts Soft drinks Pickles Hydrogenated oil Vegetable oils Fruits
Vegetables
Foods in each group Hot dog, cold cuts, pizza Meat, minced meat, hamburger Kale pache, heart, kidney Fishes, tuna Chicken, duck Egg Animal butter Low fat milk, yogurt, dugh, cheese High fat milk, fatty yogurt, creamy cheese, pizza cheese, kashk, ice cream, traditional ice cream, cream Tea Lentil, beans, peas, split peas, broad beans, soya beans Garlic Peanut, pistachio, walnut, hazelnut, sunflower seed, sesame, halva shekari French fries, chips Barbari, sangak, bread with bran Lavash, taftoon, baguette, rice, spaghetti, reshteh Mayonnaise Green olive, olive oil Chocolate, dry and creamed pastries, cake, candies, sugar, biscuits, halva shekari Sandis, Coca Cola Torshi, shoor, pickle Plant oil (solid), animal oil, margarine Liquid plant oil Apple, watermelon, banana, cantaloupe, melon, sweet lemon, lime, pear, apricot, strawberry, plum, grape, cherry, sour cherry, peach, fig, kiwi, citrus fruit, persimmon, pomegranate, dates, natural fruit juice, melon juice, lemon juice Lettuce, tomato, cucumber, vegetables, vegetable for Ash, pumpkin, marrow, eggplant, celery, peas, green beans, carrot, carrot juice, mushroom, onion, fried onion, cabbage, capsicum (bell pepper), spinach, turnip
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groups. For instance, tea and mayonnaise were entered as separate groups. Coffee and dried fruits were omitted from the food groups and the analysis, as a large percentage (95%) of respondents did not consume them at least once a month.
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Statistical Method Preliminary assumption was tested on the normality of variables. Checking normality was based on skewness > ± 2, the histogram diagram and Kolmogorov-Smirnov. Variables that were not normally distributed were transformed, based on the type of variable. Factor analysis (principal component analysis) with orthogonal rotation was used to identify dietary pattern based on 25 food items that was entered into the factor analysis in SPSS in order to categorize the food items into factors (dietary patterns) (Esmaillzadeh et al., 2007; Franco et al. 2009; Heideman et al. 2011). The factors were orthogonally transformed using Varimax rotation. To determine the number of factors, eigenvalues > 1.5 (Denova-Gutiérrez et al. 2010; Mullie et al. 2009; Mullie et al. 2010; Sofianou, Fung, and Tucker 2011) of the criterion were used. Results of principal components factor analysis include a factor loading matrix, which comprises the dietary patterns for the entire sample, and a factor score for each individual, which is derived by summarizing the individual intakes of the food items weighted by standardized scoring coefficients for each dietary pattern factor. Therefore, each individual has a score for each factor or dietary pattern (Esmaillzadeh and Azadbakht 2008a).The factors of dietary patterns were named according to the food groups that remained in each pattern, based on the highest loading. Food groups have positive and negative loadings on the factors showing contributions directly and inversely in dietary pattern. The high positive loading indicates a strong association between the given food groups and patterns, whereas negative loadings indicate negative associations with the pattern. A score was calculated as the sum of the intake in each food group weighted by the corresponding loading. A higher score indicated a higher adherence to representative dietary pattern (Gullar-Catillón et al. 2010). General linear model (GLM) univaraiate was used to indicate the association of sociodemographic and health-related factors as predictors which play a role in dietary choices with dietary patterns.
RESULTS Characteristics of Patients Characteristics of 250 patients who were included in this study are presented in (table 2). 52.0% (n = 130) were male and 48.0% (n = 120) were female. The mean age of patients was 59.68 ± 10.2 with minimum age of 40 years
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H. Esmaili et al. TABLE 2 Descriptive Characteristics of Participants
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Characteristics Gender Male Female Age (y) Mean ± SD 40−49 50−59 60−69 ≥ 70 Habitat Urban Rural Income < 500 USD 500−1,000 USD > 1,000 USD Educational levels Uneducated Primary school Secondary school and diploma University degree Occupations Farmer Housewife Retired Business Clerical Unemployed Others Physical activity Low (MET-min/w) < 600 Moderate (MET-min/w) 600−1,500 High (MET-min/w) > 1,500 Current smokers Supplement intake
Total n (%) 130 (52.0) 120 (48.0) 59.68 ± 10.2 47 (18.8) 75 (30.0) 82 (32.8) 46 (18.4) 130 (52.0) 120 (48.0) 59 (23.6) 184 (73.6) 7 (2.8) 74 107 62 7
(29.6) (42.8) (24.8) (2.8)
50 107 48 21 7 1 16
(20.0) (42.8) (19.2) (8.4) (2.8) (0.4) (6.4)
129 55 66 68 27
(51.6) (22.0) (26.4) (27.2) (10.8)
Note. MET = Metabolic equivalent task (one MET = energy expenditure of sitting quietly or approximately 1 kcal/kg of body weight per min), 1 USD = 10,000 Iranian Rials (IRR).
and maximum age being 82 years old. The largest age group (32.8%) represented in this study was in the 60–69 year-old range, while those above 70 formed the minority, with 14.8% of that totality; of patients, only 2.0% were older than 75 years old. In term of residence, 52.0% of the patients lived in urban areas and 48.0% lived in rural areas. Most patients (73.6%) earned 500–1,000 USD/month, and the smallest proportion (2.8%) earned > 1000 USD/month. Among three levels of education, most patients had primary school education (42.8%), while 27.6% of the patients had secondary school education and above. Most patients were housewives (42.8%), followed by farmers (20.0%), and retirees (19.2%).
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Regarding physical activity, only 26.4% of penitents were highly active (MET- min/wk > 1,500) and most of them (51.6%) had low physical activity (MET-min/wk < 600). There was a significant difference in physical activity between men and women, χ 2 (df = 2) = 16.07, p < .01). Among men, 42.3% had low physical activity, 20.8% had moderate physical activity, and 36.9% had high physical activity. Among women, low, moderate, and high levels of physical activity were 61.7%, 23.3%, and 15%, respectively. Of patients, only 27.2% were current smokers (50.8% men vs. 1.7% women); most of them had stopped smoking or had never smoked. About 10.8% of patients were taking minerals and vitamins supplements; and most of these patients were women (18.3% vs. 3.8% men).
Dietary Patterns The Kaiser-Meyer-Oklin (KMO) value was 0.663. High values of KMO (more than 0.5) indicate that a factor analysis is useful with the data. The Bartlett’s Test of Sphericty reached significance (p < .01), supporting the factorability of the correlation matrix. Principal components analysis revealed the presence of three factors or dietary patterns with eigenvalues exceeding 1.5, explaining 12.1%, 12.0%, and 9.0% of the variance, respectively; and totally 33.2% of the variance of original dietary intake. In scree plot, the eigenvalues of the factors dropped substantially after the second factor and remained more similar to each other after the third factor. The factor loading matrices for the three dietary patterns are shown in (table 3). For simplicity, food items with rotated factor loadings lower than 0.2 were excluded and in each food item the highest loading score was considered, if we had more than one loading score for each food item which had the high correlation to the factor (Esmaillzadeh et al. 2007; Osier et al. 2002; Rezazadeh et al. 2010). The patterns were labeled subjectively based on the nature of the food groups with high loadings in each dietary pattern. Patterns were named according to their interpretation, and food items were included. The first pattern was labeled as the “traditional dietary pattern” (based on Mazandaran traditional food habit), characterized by higher consumption of red meat, organ meat, poultry, butter, high fat dairy products, tea, legumes, garlic, refined grains, nuts, olive, hydrogenated oils, pickles, fruits and vegetables. The second pattern was the “western pattern,” characterized by a high intake of fast foods, eggs, mayonnaise, sweets and desserts, soft drinks, French fries, and chips. The third pattern was named “healthy pattern,” with a high consumption of fish, low-fat dairy products, whole grains, vegetable oils, and a low consumption of hydrogenated oils and high-fat dairy products. According to general linear model univariate between dietary patterns and demographic and health-related factors (table 4), in the traditional pattern the R 2 of 0.225 implied that the four predictor variables explain 22.5% of the variation in traditional dietary pattern. In addition, educational level
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TABLE 3 Rotated Factor Loading for Dietary Patterns of Patients Factor loading
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Food groups Fast food Red meat Organ meat Fish Poultry Eggs Butter Low-fat dairy product High-fat dairy product Tea Legumes Garlic French fries Mayonnaise Olive Sweets and deserts Soft drinks Hydrogenated oils Vegetable oils Pickles Fruits Vegetables Whole grains Refined grains Nuts
Traditional pattern
Western pattern
Healthy pattern
0.610 0.503 0.347 0.485 0.436 0.141 0.440 0.493 −0.362
0.415 0.430 0.566 0.580 0.381 0.586 0.311 0.819 0.813 0.232
−0.721 0.653
0.468 0.466 0.525 0.213 0.318 0.373
Note. Values < 0.2 were excluded for simplicity. The first factor explained 12.1% of the total variance, the second and third factor explained 12.0 % and 9.0% of the total variance respectively (using factor analysis).
(uneducated: t = 2.70, p = .007, primary school: t = 2.89, p = .004), income (t = –2.21, p = .028), occupation (t = –2.80, p = .005) and physical activity (t = –2.43, p = .016) were found to be significant predictors of traditional dietary pattern. In the other words, patients who had low education (uneducated and primary school), had moderate to high physical activity and a monthly income > 500 USD were more likely to consume the traditional pattern. This pattern (traditional) was associated with low education (uneducated and primary school), moderate to high physical activity and a monthly income > 500 USD. In addition, this pattern was negatively associated with housewives. In the western dietary pattern the R 2 showed that three predictor variables explained 18.6% of the variation in this pattern. The western pattern was positively related to smoking (t = 2.06, p = .040) and negatively related to occupation (farmers: t = –3.31, p = .001, housewives: t = –3.27, p = .001) and age (t = –4.12, p = .000), while other factors were not significant. The western pattern was negatively associated with farmers and housewives; in other words, patients those who consume the western
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Health-related Factors in Coronary Artery Disease in Iran TABLE 4 Factors Associated with Adherence to Three Dietary Patterns among Participants
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Socioeconomic and lifestyle factors Traditional pattern Uneducated Primary school Income < 500 USD Housewives Low physical activity Western pattern Age Farmers Housewives Smokers Healthy pattern Male Farmers Income < 500 USD Urban
B
R2
CI (95%)
p
0.13, 0.83 0.13, 0.73 −1.58, −0.09 −1.00, −0.01 −0.65, −0.07
.007 .004 .028 .005 .016
−0.03, −0.01 −1.26, −0.32 −1.16, −0.29 0.01, 0.63
.000 .001 .001 .040
−1.12, −0.001 −1.37, −0.12 −1.66, −0.10 0.06, 0.61
.049 .020 .027 .015
0.22 0.480 0.436 −0.836 −0.590 −0.363 −0.025 −0.796 −0.728 0.327 −0.563 −0.749 −0.882 0.343
0.18
0.14
Note. B = Regression coefficient (a positive coefficient implies greater adherence to the pattern); R 2 = Coefficient of determination; CI = Confidence interval; Data were adjusted for all variables listed in the table (sociodemographics, lifestyle). P values reflect the overall relationship between factors and three dietary patterns using GLM univariate; p < .05 means are significantly related.
pattern were younger and more likely to smoke. Farmers and housewives were less likely to consume the western pattern. The third pattern was the healthy pattern, with an R 2 value equal to 0.146. Four predictors for the healthy pattern explained 14.6% of variation. The healthy pattern score was inversely related to gender (male) (t = –1.97, p = .049), occupation (t = –2.34, p = .020), and income (t = –2.22, p = .027); and positively related to habitat (t = 2.45, p = .015). More female patients, and patients who had a moderate and high monthly income (> 500 USD), and had a residency in urban areas were more likely to intake this pattern; whereas farmers were less likely to consume according to the healthy pattern. A moderate and high monthly income (> 500 USD), more female, and subjects who resided in urban areas were positively related to the healthy pattern; whereas being a farmer was negatively related to the healthy pattern.
DISCUSSION Three major dietary patterns were identified in this study and were named as “traditional,” “western,” and “healthy” dietary patterns. The traditional pattern in this study is somewhat similar to the Mediterranean diet, and this similarity includes vegetables, olive oil, fruits, nuts, and legumes (Guigliano and Esposito 2007; Gullar-Castillón et al. 2010). However, the high intake of red meat, poultry, refined grains, butter, and high dairy products as researched
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in this study was different from the Mediterranean pattern; the latter of which includes a considerable amount of whole grains. The traditional pattern examined in this study was similar to the Iranian dietary pattern, especially considering the scale of refined grains, potatoes, tea, hydrogenated fats, and legumes (Esmaillzadeh and Azadbakht 2008b). The prudent pattern that was characterized for white and black men and women with metabolic syndrome in the United States, by the consumption of vegetables, fruits, fish, and poultry, shared common elements with the traditional pattern in the current study, except for the consumption of fish (Lutsey, Steffen, and Stevens 2008). The traditional pattern of this study emerges as a combination of the Mediterranean, Iranian, and prudent patterns. The second pattern in this study was the western pattern, which was somewhat similar to the convinced pattern with similar components in sweets and desserts, ready-to-eat products (fast foods), and beverages (Kesse-Guyot et al. 2009); or an unhealthy diet that was identified by Rezazadeh and colleagues (2010), being similar in that it included mayonnaise, soft drinks, sweets, French fries, and eggs. The western pattern described in a study by Esmaillzadeh and Azadbakht (2008a) was similar in that it included sweets and desserts, pizza, eggs, and soft drinks. The western pattern in the current study was similar to the Lebanese western pattern, which is high in French fries, sweets, soft drinks, pizza, and fast foods (Naja et al. 2011). The third pattern in this study was the healthy pattern, characterized by a high consumption of fish, whole grains, low-fat dairy products, and vegetable oil—reflecting foods that are commonly considered to be healthy. A similar result was identified with similar food items included in fish and low fat products that consisted of low-fat products such as fish, vegetables, legumes, cereals, and fruits (Aghajani-Delavar et al. 2009). In this study, low education (uneducated and primary school), moderate and high physical activity, and a monthly income > 500 USD were associated with the traditional dietary pattern. Similarly, low education and high physical activity in other studies were more likely to be associated with a high consumption of vegetables, fruits, poultry, and red organ meats; in addition, a consumption of more fruits, vegetables, poultry, legumes, olives, garlic, and chicken were associated with a higher level of physical activity (Lopez-Garcia et al. 2004; van Dam et al. 2003). In contrast, a study by KesseGuyot and colleagues (2009) indicated that a high consumption of meat and poultry is positively associated with urban residence and smoking, and negatively associated with educational level and physical activity. The current study did not show any significant association with smoking, nor with place of residence (p > .05). The current study showed that the western consumption pattern was related to smoking and younger age. Based on occupation, farmers and housewives had the least association with the western pattern. The study
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revealed that dietary changes to the western pattern were associated with younger age and urbanized occupations. Results in other studies indicated that a high consumption of sweets and desserts had a negative association with age, but had a positive association with education level (Kesse-Guyot et al. 2009; van Dam et al. 2003). Nevertheless, in the study by Kesse-Guyot and colleagues, there was no significant association found between education level and the western pattern (Kesse-Guyot et al. 2009). Based on other studies, those who were more likely to smoke and less likely to be physically active correlated to consuming more sweets and desserts, and more processed meat and French fries (Lopez-Garcia et al. 2004; van Dam et al. 2003). Unlike the current study, the western pattern (i.e., high in sweets, butter, eggs, and mayonnaise) in another study in Iran was related to older age (45.2 ± 6.00 vs. 42.3 ± 6.1 y, p < .001), and being female (91.0% vs. 79.0%, p < .05) (Amini et al. 2010). In this study, women with an income > 500 USD, and patients who lived in urban areas, were associated with eating a higher quantity of fish, low-fat dairy products, vegetable oils, and whole grains. The healthy consumption pattern was negatively associated with men and with farmers, likely due to the higher level of information received by urban residents compared to those in rural areas. Although in this study, no association was found between educational level; in the healthy consumption pattern, in the other study, a high consumption of vegetable oils and fish was positively associated with educational level and with age, but negatively associated with smoking (Kesse-Guyot et al. 2009). Another study highlighted an association between a dietary pattern that included nuts, vegetables, fruits, and tea; with high educational level and high income (Mullie et al. 2010). Mullie and colleagues (2010) showed a significant association between the high consumption of whole grains, low-fat dairy products, and fish with high levels of education and income. In the present study, a wide age range (Hamer and Mishra 2010; Hoffmann et al. 2004; Nettleton et al. 2008) was used to identify possible links between age and diet. The results, however, illustrated a significant association between the western pattern and age, as other food patterns did not show similar results. In the other words, in the current study age was not a significant factor and it was not related to the traditional and healthy patterns. Based on our knowledge, a few studies have assessed these factors in Middle Eastern and developing countries, and studies in this area can provide unique opportunities to recognize associations between diet and socioeconomic, demographic, and health-related factors. Because of economic limitations in these countries, dietary intake directly relates to socioeconomic and demographic factors (Rezazadeh et al. 2010). This study had some limitations. Firstly, because it was cross-sectional design, it cannot determine causality. Secondly, like other measurements, FFQ food assessment has its own errors. Thirdly, we cannot generalize the
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dietary patterns of Mazandaran to the other parts of the country because food intake in this province is somewhat different from other provinces in this country. In conclusion, the present study provides a better understanding of the nutritional intake of the CAD patients who live in northern Iran. This study identified three dietary patterns in a sample of men and women and showed significant association between sociodemographic and health-related characteristics and dietary patterns. It can help identify subgroups for public health nutrition programs and guidelines. Based on our study, these programs are necessary for all groups, especially those with low social status (low income and low education) in order to decrease coronary artery disease. We suggest further studies to define relationships between socioeconomic, demographic, and health-related factors, with dietary patterns that are related to coronary artery disease.
REFERENCES Aghajani-Delavar, M., L. Munn-Sann, K. Geok Lin, S. T. Syed Hassan, and P. Hanachi. 2009. Dietary patterns and the metabolic syndrome in middle aged women, Babol, Iran. Asia Pacific Journal of Clinical Nutrition 18:285–292. Amini, M., A. Esmaillzadeh, S. Shafaeizadeh, J. Behrooz, and M. Zare. 2010. Relationship between major dietary patterns and metabolic syndrome among individuals with impaired glucose tolerance. Nutrition 26:986–992. Azizi, F., M. Rahmani, H. Emami, P. Mirmiran, R. Hajipour, M. Madjid, J. Ghanbili, A. Ghanbarian, J. Mehrabi, and N. Saadat. 2002. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Social and Preventive Medicine 47:408–426. Block, G. 2004. Foods contributing to energy intake in the U.S.: Data from NHANES III and NHANES 1999–2000. Journal of Food Composition and Analysis 17:439–447. CDC. 2000. NHANES III, Third National Health and Nutrition Examination Survey: Healthy Eating Index Data File; NHANES III Series 11, No. 6A. Atlanta, GA: Centers for Disease Control and Prevention. Cho, E. R., A. Shin, S. Y. Lim, and J. Kim. 2011. Dietary patterns and their associations with health behaviors in Korea. Public Health Nutrition 14:356–364. Coakes, S. J. 2005. Factor analysis. In SPSS version 12 for Windows: Analysis without anguish, 154–167. Queensland, Australia: John Wiley Australia, Ltd. Craig, C. L., A. L. Marshall, M. Sjostrom, A. E. Bauman, M. L. Booth, B. Ainsworth, M. Pratt, et al. 2003. International Physical Activity Questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise 195:3508–1381. Daniel, W. W. 2010. Biostatistics: Basic concepts and methodology for the health sciences. New York, NY: John Wiley & Sons. Denova-Gutiérrez, E., S. Castañón, J. O. Talavera, K. Gallegos-Carrillo, M. Flores, D. Dosamantes-Carrasco, W. C. Willett, and J. Salmerón. 2010. Dietary patterns are associated with metabolic syndrome in an urban Mexican population. Journal of Nutrition 140:1855–1863.
Downloaded by [Lakehead University] at 15:18 13 March 2015
Health-related Factors in Coronary Artery Disease in Iran
17
Escott-Stump, S. 2008. Atherosclorosis, coronary aretery disease, dislipidemia. In Nutrition and diagnosis related care. 6th ed., ed. D. B. Troy, 323–330. Baltimore, MD: Wolters Kluwer/Lippincott Williams and Wilkins. Esmaillzadeh, A., and L. Azadbakht. 2008a. Food intake patterns may explain the high prevalence of cardiovascular risk factors among Iranian women. Journal of Nutrition 138:1469–1475. Esmaillzadeh, A., and L. Azadbakht. 2008b. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. Journal of Nutrition 138:358–363. Esmaillzadeh, A., M. Entezari, Z. Paknahad, M. Safavi, M. Jalali, R. Ghiasvand, N. Bahreini, M. Nourian, and L. Azadbakht. 2008. Identification of diet–disease relations through dietary pattern approach: A review. Journal of Research in Medical Science 13:337–348. Esmaillzadeh, A., M. Kimiagar, Y. Mehrabi, L. Azadbakht, F. B. Hu, and W. C. Willett. 2007. Dietary patterns and markers of systemic inflammation among Iranian women. Journal of Nutrition 137:992–998. Esmaillzadeh, A., P. Mirmiran, and F. Azizi. 2005. Whole-grain intake and the prevalence of hypertriglyceridemic waist phenotype in Tehranian adults. The American Journal of Clinical Nutrition 81:55–63. Fuster, V., L. E. Rydén, D. S. Cannom, H. J. Crijns, A. B. Curtis, K. A. Ellenbogen, J. L. Halperin, et al. 2006. ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation—Executive summary. Journal of the American College of Cardiology 48:854. Franco, M., A. V. Diez-Roux, J. A. Nettleton, M. Lazo, F. Brancati, B. Caballero, T. Glass, and L. V. Moore. 2009. Availability of healthy foods and dietary patterns: The multi-ethnic study of atherosclerosis. American Journal of Clinical Nutrition 89:897–904. Ghaffarpour, M., A. Houshyar-Rad, and H. Kianfar. 1999. The manual for household measures, cooking yields factors and edible portion of foods. [In Persian.] Tehran, Iran: Keshavarzi Press. Ghassemi, H., G. Harrison, and K. Mohammad. 2002. An accelerated nutrition transition in Iran. Public Health Nutrition 5:149–155. Guallar-Castillón, P., F. Rodríguez-Artalejo, M. Tormo, M. Sánchez, L. Rodríguez, J. Quirós, C. Navarro, et al. 2010. Major dietary patterns and risk of coronary heart disease in middle-aged persons from a Mediterranean country: The EPICSpain cohort study. Nutrition Metabolism and Cardiovascular Diseases 22 (3): 192–199. Guigliano, D., and K. Esposito. 2007. Mediterranean diet and metabolic diseases. Current Opinion in Epidemiology 19:63–68. Hamer, M., and G. Mishra. 2010. Dietary patterns and cardiovascular risk markers in the UK Low Income Diet and Nutrition Survey. Nutrition, Metabolism and Cardiovascular Diseases 20:491–497. Hatmi, Z., S. Tahvildari, A. G. Motlag, and A. S. Kashani. 2007. Prevalence of coronary artery disease risk factors in Iran: A population based survey. BMC Cardiovascular Disorders 7:32. Heesch, K. C., J. G. Z. van Uffelen, R. L. Hill, and W. J. Brown. 2010. What do IPAQ questions mean to older adults? Lessons from cognitive interviews. International Journal of Behavioral Nutrition Physical Activity 7:35.
Downloaded by [Lakehead University] at 15:18 13 March 2015
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H. Esmaili et al.
Heidemann, C., C. Scheidt-Nave, A. Richter, and G. B. M. Mensink. 2011. Dietary patterns are associated with cardiometabolic risk factors in a representative study population of German adults. British Journal of Nutrition 106:1253–1262. Hoffmann, K., B. C. Zyriax, H. Boeing, and E. Windler. 2004. A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease. American Journal of Clinical Nutrition 80 (3): 633–640. Hu, F. B., E. Rimm, S. A. Smith-Warner, D. Feskanich, M. J. Stampfer, A. Ascherio, L. Sampson, and W. C. Willett. 1999. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. American Journal of Clinical Nutrition 69:243–249. Hu, F. B., E. B. Rimm, M. J. Stampfer, A. Ascherio, D. Spiegelman, and W. C. Willett. 2000. Prospective study of major dietary patterns and risk of coronary heart disease in men. American Journal of Clinical Nutrition 72:912–921. IPAQ. 2005. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ). Stockholm, Sweden: Karolinska Institutet, IPAC. Jacques, P. F., and K. L. Tucker. 2004. Are dietary patterns useful for understanding the role of diet in chronic disease? American Journal of Clinical Nutrition 73:1–2. Kesse-Guyot, E., S. Bertrais, S. Peneau, C. Estaquio, L. Dauchet, A. Vergnaud, S. Czernichow, P. Galan, S. Hercberg, and F. Bellisle. 2009. Dietary patterns and their sociodemographic and behavioral correlates in French middle-aged adults from the SU. VI. MAX cohort. European Journal of Clinical Nutrition 63:521–528. Kolbe-Alexander, T. L., E. V. Lambert, J. B. Harkins, and U. Ekelund. 2006. Comparison of two methods of measuring physical activity in South African older adults. Journal of Aging and Physical Activity 14:98–114. Lopez-Garcia, E., M. B. Schulze, T. T. Fung, J. B. Meigs, N. Rifai, J. A. E. Manson, and F. B. Hu. 2004. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. American Journal of Clinical Nutrition 80:1029–1035. Lotfi, M., A. Kannan, and S. D. K. R. Sundaram. 2008. The role of adverse lifestyle changes in the causation of coronary artery disease. Acta Medica Iranica 46. Lutsey, P. L., L. M. Steffen, and J. Stevens. 2008. Dietary intake and the development of the metabolic syndrome. Circulation 117:754–761. McCann, S. E., J. R. Marshall, J. R. Brasure, S. Graham, and J. L. Freudenheim. 2001. Analysis of patterns of food intake in nutritional epidemiology: Food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer. Public Health Nutrition 4:989–997. Mirmiran, P., F. Hosseini-Esfahani, M. Jessri, L. K. Mahan, N. Shiva, and F. Azizi. 2011. Does dietary intake by Tehranian adults align with the 2005 dietary guidelines for Americans? Observations from the Tehran lipid and glucose study. Journal of Health, Population and Nutrition 29:39. Mullie, P., I. Guelinckx, P. Clarys, E. Degrave, M. Hulens, and G. Vansant. 2009. Cultural, socioeconomic and nutritional determinants of functional food consumption patterns. European Journal of Clinical Nutrition 63:1290–1296 Mullie, P., P. Clarys, M. Hulens, and G. Vansant. 2010. Dietary patterns and socioeconomic position. European Journal of Clinical Nutrition 64:231–238.
Downloaded by [Lakehead University] at 15:18 13 March 2015
Health-related Factors in Coronary Artery Disease in Iran
19
Naja, F., L. Nasreddine, L. Itani, M. C. Chamieh, N. Adra, A. M. Sibai, and N. Hwalla. 2011. Dietary patterns and their association with obesity and sociodemographic factors in a national sample of Lebanese adults. Public Health Nutrition 1:1–9. Nettleton, J. A., M. B. Schulze, R. Jiang, N. S. Jenny, G. L. Burke, and D. R. Jacobs, Jr. 2008. A priori–defined dietary patterns and markers of cardiovascular disease risk in the Multi-Ethnic Study of Atherosclerosis (MESA). The American Journal of Clinical Nutrition 88:185–194. Osier, M., A. H. Andreasen, B. Heitmann, S. Høidrup, U. Gerdes, L. Jørgensen, and M. Schroll. 2002. Food intake patterns and risk of coronary heart disease: A prospective cohort study examining the use of traditional scoring techniques. European Journal of Clinical Nutrition 56:568–574. Pallanat, J. 2007. SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. 3rd ed., 179–200. Berkshire, UK: McGraw Hill Open University Press. Rezazadeh, A., B. Rashidkhani, and N. Omidvar. 2010. Association of major dietary patterns with socioeconomic and lifestyle factors of adult women living in Tehran, Iran. Nutrition 26:337–341. Rzewnicki, R., Y. V. Auweele, and I. De Bourdeaudhuij. 2003. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutrition 6:299–306. Safdar, N. F., E. Bertone-Johnson, L. Cordeiro, T. H. Jafar, and N. L. Cohen. 2013. Dietary patterns of Pakistani adults and their associations with sociodemographic, anthropometric and life-style factors. Journal of Nutritional Science 2:1–10. Sofianou, A., T. T. Fung, and K. L. Tucker. 2011. Differences in diet pattern adherence by nativity and duration of US residence in the Mexican-American population. Journal of the American Dietetic Association 111:1563–1569. Statistical Center of Iran. 2010. Annual statistic of Mazandaran province. http://www. amar.org.ir/Default.aspx?tabid=1700&agentType=View&PropertyID=2556. Thompson, F. E., and A. F. Subar. 2008. Nutrition in the prevention and treatment of disease. In Dietary assessment methodology, 2nd ed., 5–46. San Diego, CA: Academic Press. van Dam, R. M., L. Grievink, M. C. Ocké, and E. J. M. Feskens. 2003. Patterns of food consumption and risk factors for cardiovascular disease in the general Dutch population. American Journal of Clinical Nutrition 77:1156–1163. WHO. 2007. Cardiovascular disease. http://www.who.int/mediacentre/factsheets/ fs317/en. WHO. 2011. Cardiovascular diseases (CVDs). WHO Media Center Fact Sheet No. 317. http://www.who.int/mediacentre/factsheets/fs317/en/index. html (accessed October 2011).
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Association of Dietary Patterns with Sociodemographic and Health-related Factors among Coronary Artery Disease (CAD) Patients a
a
a
Haleh Esmaili , Rokiah Mohd Yusof , Hazizi Abu Saad , Ali Ghaemian a
& Nasrin Darani Zad a
Department of Nutrition and Dietetics, Universiti Putra Malaysia, Selangor, Malaysia Published online: 27 Oct 2014.
Click for updates To cite this article: Haleh Esmaili, Rokiah Mohd Yusof, Hazizi Abu Saad, Ali Ghaemian & Nasrin Darani Zad (2015) Association of Dietary Patterns with Sociodemographic and Health-related Factors among Coronary Artery Disease (CAD) Patients, Ecology of Food and Nutrition, 54:1, 4-19, DOI: 10.1080/03670244.2014.930031 To link to this article: http://dx.doi.org/10.1080/03670244.2014.930031
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Ecology of Food and Nutrition, 54:4–19, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0367-0244 print/1543-5237 online DOI: 10.1080/03670244.2014.930031
Association of Dietary Patterns with Sociodemographic and Health-related Factors among Coronary Artery Disease (CAD) Patients HALEH ESMAILI, ROKIAH MOHD YUSOF, HAZIZI ABU SAAD, ALI GHAEMIAN, and NASRIN DARANI ZAD Downloaded by [Lakehead University] at 15:18 13 March 2015
Department of Nutrition and Dietetics, Universiti Putra Malaysia, Selangor, Malaysia
This study aimed to identify the association of dietary patterns with sociodemographic and health-related characteristics among coronary artery disease patients. In this cross-sectional study, the participants were 250 patients coronary artery disease aged ≥ 40 years old. Data collection was done using questionnaires related to sociodemographics, health-related factors, and food-frequency intake information. Three dietary patterns (traditional, western, and healthy) were obtained using principal component analysis. The result showed that dietary patterns were associated with sociodemographic and health-related factors. According to the result, all the factors were taken very seriously when planning a promotional program for healthy lifestyle in prevention of CAD. KEYWORDS coronary artery disease, dietary patterns, healthrelated factors, sociodemographics
Cardiovascular diseases (CVDs) are heart and blood vessel disorders, characterized by endothelial dysfunction and include coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolism (WHO 2007). The most common type of CVD is coronary artery disease (CAD), a disorder caused by the obstruction of coronary arteries as the narrow and become harden (Escott-Stump 2008; Lotfi, Kannan, and Sundaram 2008). According to Lotfi and colleagues (2008), plaques formed Address correspondence to Haleh Esmaili, MSc, Department of Nutrition and Dietetics, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. E-mail: haleh. [email protected] 4
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by cholesterol in the wall of coronary arteries cause CAD (Escott-Stump 2008; Lotfi, Kannan, and Sundaram 2008). These days, CAD is regarded as the single most important disease in the world in terms of mortality, morbidity, disability and economic loss (Fuster et al. 2006). The most important causes of heart disease, according to a report by WHO published in 2011, are behavioral factors including unhealthy diet, sedentary lifestyle and using tobacco. As the report highlighted, 80% of coronary heart disease and cerebrovascular diseases are related to these factors (WHO 2011). In Iran in addition to malnutrition and poor dietary quality that challenge the people’s lives, obesity and chronic diseases such as cardiovascular disease have rapidly emerged as factors that pose fatal threats, particularly in urban areas due to nutrition transition. As the literature highlights cardiovascular and cerebrovascular diseases are the first and major causes of mortality in Iran (Azizi et al. 2002; Ghassemi, Harrison, and Mohammad 2002). Nearly 50% of all deaths per year have been caused by CAD, which reconfirms the gravity of CAD as a leading cause of mortality and disability in the Iranian population (Hatmi et al. 2007). Most of the earlier studies that examined nutrition, diet and chronic diseases have focused on single nutrients, linking them with the risk of disease. Examination of individual food components, however, exhibited some notable limitations, as they neglected many unknown nutrients and nonnutrient compounds in foods. People consume a combination of foods that contain multiple nutrients, where the nutrients of a particular food may interact with the nutrients of others (Esmaillzadeh and Azadbakht 2008a; McCann et al. 2001). Food patterns as an important factor, in addition, may be affected by culture, food availability, economic status and many other factors, especially environmental factors (Jacques and Tucker 2001). The dietary pattern, in this respect, emerges as a beneficial method to examine diet–disease relationships within the given population, examining the effects of overall diet and eating patterns on a particular sample (Hu et al. 1999; Hu et al. 2000). Unfortunately, most data in this area and related factors have been drawn from studies carried out in the Western countries (Europe and North America) and the diets of western nations cannot be generalized and extrapolated to developing nations. In this respect, due to insufficient information regarding dietary patterns and their associations with demographic and lifestyle factors in developing countries the gap still exists (Cho et al. 2011; Rezazadeh, Rashidkhani, and Omidvar 2010; Safdar et al. 2013). The imbalance of dietary intake, being poor in healthy items, and the relation between diet quality and lifestyle in adults is a matter of concern (Mirmiran et al. 2011). Moreover, it is important to identify the relation between dietary patterns and environmental factors, as environmental factors play a significant role in the dietary pattern choices. Dietary patterns show specific associations with sociodemographic, lifestyle and other health factors that can help identify subgroups for nutrition guidelines in public health
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(Kesse-Guyot et al. 2009). This study aimed to identify dietary patterns of CAD patients in Sari, Mazandarn province, Iran, and the association of each pattern with demographic (i.e., age, gender, location, monthly income, and educational level) and health-related factors (i.e., physical activity, smoking status, and supplements intake) in this sample group as predictors of dietary pattern choices.
MATERIALS AND METHODS
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Participants and Study Design This cross-sectional study took place in a government specialized hospital in Sari, the capital of Mazandaran province in the north of Iran. It is the only government specialized hospital in Mazandaran, serving all CAD patients in the province. Mazandaran is located in the north of Iran between the Caspian Sea and the Alborz mountains, with a humid climate and food diversity different from other parts of Iran. The province consists of urban and rural population, with the rural part neighboring the cities. Mazandaran is divided into 15 counties, with the census of 2006 marking the population around 2,922,432 people, 53.18% of whom were registered as urban dwellers, 46.82% as villagers, and the remainder as non-residents (Statistical Center of Iran 2010). Based on the sample size formula for systematic random sampling and cross-sectional study (Daniel 2010) and sample size for factor analysis, it was recommended that there should be 5–10 cases for each item to be factor analyzed, requiring 250 patients for examination (Coakes 2005; Pallanat 2007). Participants in this study were 130 male and 120 female CAD patients, aged 40 and over, selected through systematic random sampling, corresponding with hospital lists of registered patients. After angiography, patients who were candidates for percutaneous trans-luminal coronary angioplasty (PTCA) or coronary artery bypass graft (CABG) were registered in the hospital. Around 12 to 15 patients were hospitalized each morning (from Saturday until Wednesday). The candidates for PTCA or CABG in the hospital were chosen for the study. Only four or five patients were recruited each day. Based on systematic random sampling, a sampling interval was calculated by dividing the total number of patients (n = 15) of each day by the number of samples (n = 5) that were interviewed every day; 15/5 = 3. Thus, the first participant was randomly picked from the list of patients. Then, the sampling countdown started, corresponding with the number in the list, and continued based on the sampling interval. Participants with a former history of myocardial infarction (MI) and cancer, CVDs, diabetes, or stroke were excluded, as these diseases can lead to a change in diet- and health-related factors; and the sampling continued with the next patient on the list. This study was approved by the Medical Research Ethic Committee of Faculty of Medicine and Health Sciences in Universiti Putra Malaysia (UPM). A pilot
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study was performed among 25 patients (10% of the required sample size) in the hospital before starting the actual data collection, with the sole objective to reduce errors and determine the reliability and understandability of the questionnaires. These patients were not included in the main study.
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Assessment of Sociodemographic and Health-related Factors Status Self-reported sociodemographic and personal information was collected using questionnaires, for which respondents provided their age, gender, residence information (habitat), educational status, monthly household income, and occupation. The health-related factors of the questionnaire included physical activity, smoking, and supplement intake. Physical-activity information was obtained using the International Physical Activity Questionnaire (IPAQ 2005) and participants were asked to record all activities. IPAQ was developed for use in adults aged 15–69 years, although some findings have indicated that caution is warranted in administering IPAQ to adults aged ≥ 65 years (Craig et al. 2003; Heesch et al. 2010; Kolbe-Alexander et al. 2006; Rzewnicki, Auweele, and De Bourdeaudhuij 2003). Additional information regarding current cigarette, shisha, and pipe-smoking habits (patients who smoked every day at least, in the recent five years, were considered smokers) and current intake of dietary supplements (vitamins and minerals), was obtained via the questionnaires. (A shisha is an oriental tobacco pipe with a long flexible tube connected to a container where the smoke is cooled by passing through water.) Current smoking status was dichotomous (current or non- or ex-) within the last five years. Supplement intake was dichotomous (yes or no) within the last year, and intake was at least two times weekly.
Assessment of Dietary Intake A validated semi-quantitative food frequency questionnaire (FFQ) consisting of 127 food items commonly eaten by Iranians, assessed the usual dietary intake over the previous year based on, daily, weekly, or monthly consumption. The questionnaire is represented in Persian languages, including a common list of Iranian food items, validated using Comparative validity. Comparative validity was determined by comparison with the estimated intake from the average of 12 24-hour dietary recalls (Esmaillzadeh et al. 2008; Esmaillzadeh and Azadbakht 2008; Esmaillzadeh, Mirmiran, and Azizi 2005). The questionnaires were modified based on food diversity in different parts of Iran. Participants were asked to mention their frequency of consumption and serving size of each food items during the previous year. Due to the fact that subjects had a limited knowledge of food portions and lacked conceptualization skills, the questionnaire was completed by the interviewer. When patients could not answer these questions properly and completely, other family members who either knew about the patient’s daily food intake or prepared food for them would accompany the patient or be interviewed
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in turn (Thompson and Subar 2008). Some portions (e.g., rice, fruit juice, or milk) were shown in pictures for simplicity and better understanding. These measuring instruments were validated based on set of common household scale, cooking method and food intake questionnaire (Ghaffarpour, HoshyarRad, and Kianfar 1999). The reported frequency for each food items was converted to a daily intake. The serving size was determined according to the household measurement tools of Iranian food and then converted to grams (Ghaffarpour et al. 2000). The amount of foods was measured based on g/day, and used to assess dietary patterns. The input value of food groups was the number of servings per day. Hence, foods from 127 items of the FFQ were classified into 25 food groups based on nutrient profile and culinary styles (Block 2004; CDC 2000; Esmaillzadeh et al. 2007) (table 1). Foods that did not fit into any of the groups were left as individual categories and considered as a separate food TABLE 1 The Food Groupings Initially Used in Dietary Pattern Analysis Food groups Fast food Red meat Organ meat Fish Poultry Eggs Butter Low fat dairy products High fat dairy products Tea Legumes Garlic Nuts French fries Whole grains Refined grains Mayonnaise Olive Sweets and desserts Soft drinks Pickles Hydrogenated oil Vegetable oils Fruits
Vegetables
Foods in each group Hot dog, cold cuts, pizza Meat, minced meat, hamburger Kale pache, heart, kidney Fishes, tuna Chicken, duck Egg Animal butter Low fat milk, yogurt, dugh, cheese High fat milk, fatty yogurt, creamy cheese, pizza cheese, kashk, ice cream, traditional ice cream, cream Tea Lentil, beans, peas, split peas, broad beans, soya beans Garlic Peanut, pistachio, walnut, hazelnut, sunflower seed, sesame, halva shekari French fries, chips Barbari, sangak, bread with bran Lavash, taftoon, baguette, rice, spaghetti, reshteh Mayonnaise Green olive, olive oil Chocolate, dry and creamed pastries, cake, candies, sugar, biscuits, halva shekari Sandis, Coca Cola Torshi, shoor, pickle Plant oil (solid), animal oil, margarine Liquid plant oil Apple, watermelon, banana, cantaloupe, melon, sweet lemon, lime, pear, apricot, strawberry, plum, grape, cherry, sour cherry, peach, fig, kiwi, citrus fruit, persimmon, pomegranate, dates, natural fruit juice, melon juice, lemon juice Lettuce, tomato, cucumber, vegetables, vegetable for Ash, pumpkin, marrow, eggplant, celery, peas, green beans, carrot, carrot juice, mushroom, onion, fried onion, cabbage, capsicum (bell pepper), spinach, turnip
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groups. For instance, tea and mayonnaise were entered as separate groups. Coffee and dried fruits were omitted from the food groups and the analysis, as a large percentage (95%) of respondents did not consume them at least once a month.
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Statistical Method Preliminary assumption was tested on the normality of variables. Checking normality was based on skewness > ± 2, the histogram diagram and Kolmogorov-Smirnov. Variables that were not normally distributed were transformed, based on the type of variable. Factor analysis (principal component analysis) with orthogonal rotation was used to identify dietary pattern based on 25 food items that was entered into the factor analysis in SPSS in order to categorize the food items into factors (dietary patterns) (Esmaillzadeh et al., 2007; Franco et al. 2009; Heideman et al. 2011). The factors were orthogonally transformed using Varimax rotation. To determine the number of factors, eigenvalues > 1.5 (Denova-Gutiérrez et al. 2010; Mullie et al. 2009; Mullie et al. 2010; Sofianou, Fung, and Tucker 2011) of the criterion were used. Results of principal components factor analysis include a factor loading matrix, which comprises the dietary patterns for the entire sample, and a factor score for each individual, which is derived by summarizing the individual intakes of the food items weighted by standardized scoring coefficients for each dietary pattern factor. Therefore, each individual has a score for each factor or dietary pattern (Esmaillzadeh and Azadbakht 2008a).The factors of dietary patterns were named according to the food groups that remained in each pattern, based on the highest loading. Food groups have positive and negative loadings on the factors showing contributions directly and inversely in dietary pattern. The high positive loading indicates a strong association between the given food groups and patterns, whereas negative loadings indicate negative associations with the pattern. A score was calculated as the sum of the intake in each food group weighted by the corresponding loading. A higher score indicated a higher adherence to representative dietary pattern (Gullar-Catillón et al. 2010). General linear model (GLM) univaraiate was used to indicate the association of sociodemographic and health-related factors as predictors which play a role in dietary choices with dietary patterns.
RESULTS Characteristics of Patients Characteristics of 250 patients who were included in this study are presented in (table 2). 52.0% (n = 130) were male and 48.0% (n = 120) were female. The mean age of patients was 59.68 ± 10.2 with minimum age of 40 years
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H. Esmaili et al. TABLE 2 Descriptive Characteristics of Participants
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Characteristics Gender Male Female Age (y) Mean ± SD 40−49 50−59 60−69 ≥ 70 Habitat Urban Rural Income < 500 USD 500−1,000 USD > 1,000 USD Educational levels Uneducated Primary school Secondary school and diploma University degree Occupations Farmer Housewife Retired Business Clerical Unemployed Others Physical activity Low (MET-min/w) < 600 Moderate (MET-min/w) 600−1,500 High (MET-min/w) > 1,500 Current smokers Supplement intake
Total n (%) 130 (52.0) 120 (48.0) 59.68 ± 10.2 47 (18.8) 75 (30.0) 82 (32.8) 46 (18.4) 130 (52.0) 120 (48.0) 59 (23.6) 184 (73.6) 7 (2.8) 74 107 62 7
(29.6) (42.8) (24.8) (2.8)
50 107 48 21 7 1 16
(20.0) (42.8) (19.2) (8.4) (2.8) (0.4) (6.4)
129 55 66 68 27
(51.6) (22.0) (26.4) (27.2) (10.8)
Note. MET = Metabolic equivalent task (one MET = energy expenditure of sitting quietly or approximately 1 kcal/kg of body weight per min), 1 USD = 10,000 Iranian Rials (IRR).
and maximum age being 82 years old. The largest age group (32.8%) represented in this study was in the 60–69 year-old range, while those above 70 formed the minority, with 14.8% of that totality; of patients, only 2.0% were older than 75 years old. In term of residence, 52.0% of the patients lived in urban areas and 48.0% lived in rural areas. Most patients (73.6%) earned 500–1,000 USD/month, and the smallest proportion (2.8%) earned > 1000 USD/month. Among three levels of education, most patients had primary school education (42.8%), while 27.6% of the patients had secondary school education and above. Most patients were housewives (42.8%), followed by farmers (20.0%), and retirees (19.2%).
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Regarding physical activity, only 26.4% of penitents were highly active (MET- min/wk > 1,500) and most of them (51.6%) had low physical activity (MET-min/wk < 600). There was a significant difference in physical activity between men and women, χ 2 (df = 2) = 16.07, p < .01). Among men, 42.3% had low physical activity, 20.8% had moderate physical activity, and 36.9% had high physical activity. Among women, low, moderate, and high levels of physical activity were 61.7%, 23.3%, and 15%, respectively. Of patients, only 27.2% were current smokers (50.8% men vs. 1.7% women); most of them had stopped smoking or had never smoked. About 10.8% of patients were taking minerals and vitamins supplements; and most of these patients were women (18.3% vs. 3.8% men).
Dietary Patterns The Kaiser-Meyer-Oklin (KMO) value was 0.663. High values of KMO (more than 0.5) indicate that a factor analysis is useful with the data. The Bartlett’s Test of Sphericty reached significance (p < .01), supporting the factorability of the correlation matrix. Principal components analysis revealed the presence of three factors or dietary patterns with eigenvalues exceeding 1.5, explaining 12.1%, 12.0%, and 9.0% of the variance, respectively; and totally 33.2% of the variance of original dietary intake. In scree plot, the eigenvalues of the factors dropped substantially after the second factor and remained more similar to each other after the third factor. The factor loading matrices for the three dietary patterns are shown in (table 3). For simplicity, food items with rotated factor loadings lower than 0.2 were excluded and in each food item the highest loading score was considered, if we had more than one loading score for each food item which had the high correlation to the factor (Esmaillzadeh et al. 2007; Osier et al. 2002; Rezazadeh et al. 2010). The patterns were labeled subjectively based on the nature of the food groups with high loadings in each dietary pattern. Patterns were named according to their interpretation, and food items were included. The first pattern was labeled as the “traditional dietary pattern” (based on Mazandaran traditional food habit), characterized by higher consumption of red meat, organ meat, poultry, butter, high fat dairy products, tea, legumes, garlic, refined grains, nuts, olive, hydrogenated oils, pickles, fruits and vegetables. The second pattern was the “western pattern,” characterized by a high intake of fast foods, eggs, mayonnaise, sweets and desserts, soft drinks, French fries, and chips. The third pattern was named “healthy pattern,” with a high consumption of fish, low-fat dairy products, whole grains, vegetable oils, and a low consumption of hydrogenated oils and high-fat dairy products. According to general linear model univariate between dietary patterns and demographic and health-related factors (table 4), in the traditional pattern the R 2 of 0.225 implied that the four predictor variables explain 22.5% of the variation in traditional dietary pattern. In addition, educational level
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TABLE 3 Rotated Factor Loading for Dietary Patterns of Patients Factor loading
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Food groups Fast food Red meat Organ meat Fish Poultry Eggs Butter Low-fat dairy product High-fat dairy product Tea Legumes Garlic French fries Mayonnaise Olive Sweets and deserts Soft drinks Hydrogenated oils Vegetable oils Pickles Fruits Vegetables Whole grains Refined grains Nuts
Traditional pattern
Western pattern
Healthy pattern
0.610 0.503 0.347 0.485 0.436 0.141 0.440 0.493 −0.362
0.415 0.430 0.566 0.580 0.381 0.586 0.311 0.819 0.813 0.232
−0.721 0.653
0.468 0.466 0.525 0.213 0.318 0.373
Note. Values < 0.2 were excluded for simplicity. The first factor explained 12.1% of the total variance, the second and third factor explained 12.0 % and 9.0% of the total variance respectively (using factor analysis).
(uneducated: t = 2.70, p = .007, primary school: t = 2.89, p = .004), income (t = –2.21, p = .028), occupation (t = –2.80, p = .005) and physical activity (t = –2.43, p = .016) were found to be significant predictors of traditional dietary pattern. In the other words, patients who had low education (uneducated and primary school), had moderate to high physical activity and a monthly income > 500 USD were more likely to consume the traditional pattern. This pattern (traditional) was associated with low education (uneducated and primary school), moderate to high physical activity and a monthly income > 500 USD. In addition, this pattern was negatively associated with housewives. In the western dietary pattern the R 2 showed that three predictor variables explained 18.6% of the variation in this pattern. The western pattern was positively related to smoking (t = 2.06, p = .040) and negatively related to occupation (farmers: t = –3.31, p = .001, housewives: t = –3.27, p = .001) and age (t = –4.12, p = .000), while other factors were not significant. The western pattern was negatively associated with farmers and housewives; in other words, patients those who consume the western
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Health-related Factors in Coronary Artery Disease in Iran TABLE 4 Factors Associated with Adherence to Three Dietary Patterns among Participants
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Socioeconomic and lifestyle factors Traditional pattern Uneducated Primary school Income < 500 USD Housewives Low physical activity Western pattern Age Farmers Housewives Smokers Healthy pattern Male Farmers Income < 500 USD Urban
B
R2
CI (95%)
p
0.13, 0.83 0.13, 0.73 −1.58, −0.09 −1.00, −0.01 −0.65, −0.07
.007 .004 .028 .005 .016
−0.03, −0.01 −1.26, −0.32 −1.16, −0.29 0.01, 0.63
.000 .001 .001 .040
−1.12, −0.001 −1.37, −0.12 −1.66, −0.10 0.06, 0.61
.049 .020 .027 .015
0.22 0.480 0.436 −0.836 −0.590 −0.363 −0.025 −0.796 −0.728 0.327 −0.563 −0.749 −0.882 0.343
0.18
0.14
Note. B = Regression coefficient (a positive coefficient implies greater adherence to the pattern); R 2 = Coefficient of determination; CI = Confidence interval; Data were adjusted for all variables listed in the table (sociodemographics, lifestyle). P values reflect the overall relationship between factors and three dietary patterns using GLM univariate; p < .05 means are significantly related.
pattern were younger and more likely to smoke. Farmers and housewives were less likely to consume the western pattern. The third pattern was the healthy pattern, with an R 2 value equal to 0.146. Four predictors for the healthy pattern explained 14.6% of variation. The healthy pattern score was inversely related to gender (male) (t = –1.97, p = .049), occupation (t = –2.34, p = .020), and income (t = –2.22, p = .027); and positively related to habitat (t = 2.45, p = .015). More female patients, and patients who had a moderate and high monthly income (> 500 USD), and had a residency in urban areas were more likely to intake this pattern; whereas farmers were less likely to consume according to the healthy pattern. A moderate and high monthly income (> 500 USD), more female, and subjects who resided in urban areas were positively related to the healthy pattern; whereas being a farmer was negatively related to the healthy pattern.
DISCUSSION Three major dietary patterns were identified in this study and were named as “traditional,” “western,” and “healthy” dietary patterns. The traditional pattern in this study is somewhat similar to the Mediterranean diet, and this similarity includes vegetables, olive oil, fruits, nuts, and legumes (Guigliano and Esposito 2007; Gullar-Castillón et al. 2010). However, the high intake of red meat, poultry, refined grains, butter, and high dairy products as researched
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in this study was different from the Mediterranean pattern; the latter of which includes a considerable amount of whole grains. The traditional pattern examined in this study was similar to the Iranian dietary pattern, especially considering the scale of refined grains, potatoes, tea, hydrogenated fats, and legumes (Esmaillzadeh and Azadbakht 2008b). The prudent pattern that was characterized for white and black men and women with metabolic syndrome in the United States, by the consumption of vegetables, fruits, fish, and poultry, shared common elements with the traditional pattern in the current study, except for the consumption of fish (Lutsey, Steffen, and Stevens 2008). The traditional pattern of this study emerges as a combination of the Mediterranean, Iranian, and prudent patterns. The second pattern in this study was the western pattern, which was somewhat similar to the convinced pattern with similar components in sweets and desserts, ready-to-eat products (fast foods), and beverages (Kesse-Guyot et al. 2009); or an unhealthy diet that was identified by Rezazadeh and colleagues (2010), being similar in that it included mayonnaise, soft drinks, sweets, French fries, and eggs. The western pattern described in a study by Esmaillzadeh and Azadbakht (2008a) was similar in that it included sweets and desserts, pizza, eggs, and soft drinks. The western pattern in the current study was similar to the Lebanese western pattern, which is high in French fries, sweets, soft drinks, pizza, and fast foods (Naja et al. 2011). The third pattern in this study was the healthy pattern, characterized by a high consumption of fish, whole grains, low-fat dairy products, and vegetable oil—reflecting foods that are commonly considered to be healthy. A similar result was identified with similar food items included in fish and low fat products that consisted of low-fat products such as fish, vegetables, legumes, cereals, and fruits (Aghajani-Delavar et al. 2009). In this study, low education (uneducated and primary school), moderate and high physical activity, and a monthly income > 500 USD were associated with the traditional dietary pattern. Similarly, low education and high physical activity in other studies were more likely to be associated with a high consumption of vegetables, fruits, poultry, and red organ meats; in addition, a consumption of more fruits, vegetables, poultry, legumes, olives, garlic, and chicken were associated with a higher level of physical activity (Lopez-Garcia et al. 2004; van Dam et al. 2003). In contrast, a study by KesseGuyot and colleagues (2009) indicated that a high consumption of meat and poultry is positively associated with urban residence and smoking, and negatively associated with educational level and physical activity. The current study did not show any significant association with smoking, nor with place of residence (p > .05). The current study showed that the western consumption pattern was related to smoking and younger age. Based on occupation, farmers and housewives had the least association with the western pattern. The study
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revealed that dietary changes to the western pattern were associated with younger age and urbanized occupations. Results in other studies indicated that a high consumption of sweets and desserts had a negative association with age, but had a positive association with education level (Kesse-Guyot et al. 2009; van Dam et al. 2003). Nevertheless, in the study by Kesse-Guyot and colleagues, there was no significant association found between education level and the western pattern (Kesse-Guyot et al. 2009). Based on other studies, those who were more likely to smoke and less likely to be physically active correlated to consuming more sweets and desserts, and more processed meat and French fries (Lopez-Garcia et al. 2004; van Dam et al. 2003). Unlike the current study, the western pattern (i.e., high in sweets, butter, eggs, and mayonnaise) in another study in Iran was related to older age (45.2 ± 6.00 vs. 42.3 ± 6.1 y, p < .001), and being female (91.0% vs. 79.0%, p < .05) (Amini et al. 2010). In this study, women with an income > 500 USD, and patients who lived in urban areas, were associated with eating a higher quantity of fish, low-fat dairy products, vegetable oils, and whole grains. The healthy consumption pattern was negatively associated with men and with farmers, likely due to the higher level of information received by urban residents compared to those in rural areas. Although in this study, no association was found between educational level; in the healthy consumption pattern, in the other study, a high consumption of vegetable oils and fish was positively associated with educational level and with age, but negatively associated with smoking (Kesse-Guyot et al. 2009). Another study highlighted an association between a dietary pattern that included nuts, vegetables, fruits, and tea; with high educational level and high income (Mullie et al. 2010). Mullie and colleagues (2010) showed a significant association between the high consumption of whole grains, low-fat dairy products, and fish with high levels of education and income. In the present study, a wide age range (Hamer and Mishra 2010; Hoffmann et al. 2004; Nettleton et al. 2008) was used to identify possible links between age and diet. The results, however, illustrated a significant association between the western pattern and age, as other food patterns did not show similar results. In the other words, in the current study age was not a significant factor and it was not related to the traditional and healthy patterns. Based on our knowledge, a few studies have assessed these factors in Middle Eastern and developing countries, and studies in this area can provide unique opportunities to recognize associations between diet and socioeconomic, demographic, and health-related factors. Because of economic limitations in these countries, dietary intake directly relates to socioeconomic and demographic factors (Rezazadeh et al. 2010). This study had some limitations. Firstly, because it was cross-sectional design, it cannot determine causality. Secondly, like other measurements, FFQ food assessment has its own errors. Thirdly, we cannot generalize the
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dietary patterns of Mazandaran to the other parts of the country because food intake in this province is somewhat different from other provinces in this country. In conclusion, the present study provides a better understanding of the nutritional intake of the CAD patients who live in northern Iran. This study identified three dietary patterns in a sample of men and women and showed significant association between sociodemographic and health-related characteristics and dietary patterns. It can help identify subgroups for public health nutrition programs and guidelines. Based on our study, these programs are necessary for all groups, especially those with low social status (low income and low education) in order to decrease coronary artery disease. We suggest further studies to define relationships between socioeconomic, demographic, and health-related factors, with dietary patterns that are related to coronary artery disease.
REFERENCES Aghajani-Delavar, M., L. Munn-Sann, K. Geok Lin, S. T. Syed Hassan, and P. Hanachi. 2009. Dietary patterns and the metabolic syndrome in middle aged women, Babol, Iran. Asia Pacific Journal of Clinical Nutrition 18:285–292. Amini, M., A. Esmaillzadeh, S. Shafaeizadeh, J. Behrooz, and M. Zare. 2010. Relationship between major dietary patterns and metabolic syndrome among individuals with impaired glucose tolerance. Nutrition 26:986–992. Azizi, F., M. Rahmani, H. Emami, P. Mirmiran, R. Hajipour, M. Madjid, J. Ghanbili, A. Ghanbarian, J. Mehrabi, and N. Saadat. 2002. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Social and Preventive Medicine 47:408–426. Block, G. 2004. Foods contributing to energy intake in the U.S.: Data from NHANES III and NHANES 1999–2000. Journal of Food Composition and Analysis 17:439–447. CDC. 2000. NHANES III, Third National Health and Nutrition Examination Survey: Healthy Eating Index Data File; NHANES III Series 11, No. 6A. Atlanta, GA: Centers for Disease Control and Prevention. Cho, E. R., A. Shin, S. Y. Lim, and J. Kim. 2011. Dietary patterns and their associations with health behaviors in Korea. Public Health Nutrition 14:356–364. Coakes, S. J. 2005. Factor analysis. In SPSS version 12 for Windows: Analysis without anguish, 154–167. Queensland, Australia: John Wiley Australia, Ltd. Craig, C. L., A. L. Marshall, M. Sjostrom, A. E. Bauman, M. L. Booth, B. Ainsworth, M. Pratt, et al. 2003. International Physical Activity Questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise 195:3508–1381. Daniel, W. W. 2010. Biostatistics: Basic concepts and methodology for the health sciences. New York, NY: John Wiley & Sons. Denova-Gutiérrez, E., S. Castañón, J. O. Talavera, K. Gallegos-Carrillo, M. Flores, D. Dosamantes-Carrasco, W. C. Willett, and J. Salmerón. 2010. Dietary patterns are associated with metabolic syndrome in an urban Mexican population. Journal of Nutrition 140:1855–1863.
Downloaded by [Lakehead University] at 15:18 13 March 2015
Health-related Factors in Coronary Artery Disease in Iran
17
Escott-Stump, S. 2008. Atherosclorosis, coronary aretery disease, dislipidemia. In Nutrition and diagnosis related care. 6th ed., ed. D. B. Troy, 323–330. Baltimore, MD: Wolters Kluwer/Lippincott Williams and Wilkins. Esmaillzadeh, A., and L. Azadbakht. 2008a. Food intake patterns may explain the high prevalence of cardiovascular risk factors among Iranian women. Journal of Nutrition 138:1469–1475. Esmaillzadeh, A., and L. Azadbakht. 2008b. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. Journal of Nutrition 138:358–363. Esmaillzadeh, A., M. Entezari, Z. Paknahad, M. Safavi, M. Jalali, R. Ghiasvand, N. Bahreini, M. Nourian, and L. Azadbakht. 2008. Identification of diet–disease relations through dietary pattern approach: A review. Journal of Research in Medical Science 13:337–348. Esmaillzadeh, A., M. Kimiagar, Y. Mehrabi, L. Azadbakht, F. B. Hu, and W. C. Willett. 2007. Dietary patterns and markers of systemic inflammation among Iranian women. Journal of Nutrition 137:992–998. Esmaillzadeh, A., P. Mirmiran, and F. Azizi. 2005. Whole-grain intake and the prevalence of hypertriglyceridemic waist phenotype in Tehranian adults. The American Journal of Clinical Nutrition 81:55–63. Fuster, V., L. E. Rydén, D. S. Cannom, H. J. Crijns, A. B. Curtis, K. A. Ellenbogen, J. L. Halperin, et al. 2006. ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation—Executive summary. Journal of the American College of Cardiology 48:854. Franco, M., A. V. Diez-Roux, J. A. Nettleton, M. Lazo, F. Brancati, B. Caballero, T. Glass, and L. V. Moore. 2009. Availability of healthy foods and dietary patterns: The multi-ethnic study of atherosclerosis. American Journal of Clinical Nutrition 89:897–904. Ghaffarpour, M., A. Houshyar-Rad, and H. Kianfar. 1999. The manual for household measures, cooking yields factors and edible portion of foods. [In Persian.] Tehran, Iran: Keshavarzi Press. Ghassemi, H., G. Harrison, and K. Mohammad. 2002. An accelerated nutrition transition in Iran. Public Health Nutrition 5:149–155. Guallar-Castillón, P., F. Rodríguez-Artalejo, M. Tormo, M. Sánchez, L. Rodríguez, J. Quirós, C. Navarro, et al. 2010. Major dietary patterns and risk of coronary heart disease in middle-aged persons from a Mediterranean country: The EPICSpain cohort study. Nutrition Metabolism and Cardiovascular Diseases 22 (3): 192–199. Guigliano, D., and K. Esposito. 2007. Mediterranean diet and metabolic diseases. Current Opinion in Epidemiology 19:63–68. Hamer, M., and G. Mishra. 2010. Dietary patterns and cardiovascular risk markers in the UK Low Income Diet and Nutrition Survey. Nutrition, Metabolism and Cardiovascular Diseases 20:491–497. Hatmi, Z., S. Tahvildari, A. G. Motlag, and A. S. Kashani. 2007. Prevalence of coronary artery disease risk factors in Iran: A population based survey. BMC Cardiovascular Disorders 7:32. Heesch, K. C., J. G. Z. van Uffelen, R. L. Hill, and W. J. Brown. 2010. What do IPAQ questions mean to older adults? Lessons from cognitive interviews. International Journal of Behavioral Nutrition Physical Activity 7:35.
Downloaded by [Lakehead University] at 15:18 13 March 2015
18
H. Esmaili et al.
Heidemann, C., C. Scheidt-Nave, A. Richter, and G. B. M. Mensink. 2011. Dietary patterns are associated with cardiometabolic risk factors in a representative study population of German adults. British Journal of Nutrition 106:1253–1262. Hoffmann, K., B. C. Zyriax, H. Boeing, and E. Windler. 2004. A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease. American Journal of Clinical Nutrition 80 (3): 633–640. Hu, F. B., E. Rimm, S. A. Smith-Warner, D. Feskanich, M. J. Stampfer, A. Ascherio, L. Sampson, and W. C. Willett. 1999. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. American Journal of Clinical Nutrition 69:243–249. Hu, F. B., E. B. Rimm, M. J. Stampfer, A. Ascherio, D. Spiegelman, and W. C. Willett. 2000. Prospective study of major dietary patterns and risk of coronary heart disease in men. American Journal of Clinical Nutrition 72:912–921. IPAQ. 2005. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ). Stockholm, Sweden: Karolinska Institutet, IPAC. Jacques, P. F., and K. L. Tucker. 2004. Are dietary patterns useful for understanding the role of diet in chronic disease? American Journal of Clinical Nutrition 73:1–2. Kesse-Guyot, E., S. Bertrais, S. Peneau, C. Estaquio, L. Dauchet, A. Vergnaud, S. Czernichow, P. Galan, S. Hercberg, and F. Bellisle. 2009. Dietary patterns and their sociodemographic and behavioral correlates in French middle-aged adults from the SU. VI. MAX cohort. European Journal of Clinical Nutrition 63:521–528. Kolbe-Alexander, T. L., E. V. Lambert, J. B. Harkins, and U. Ekelund. 2006. Comparison of two methods of measuring physical activity in South African older adults. Journal of Aging and Physical Activity 14:98–114. Lopez-Garcia, E., M. B. Schulze, T. T. Fung, J. B. Meigs, N. Rifai, J. A. E. Manson, and F. B. Hu. 2004. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. American Journal of Clinical Nutrition 80:1029–1035. Lotfi, M., A. Kannan, and S. D. K. R. Sundaram. 2008. The role of adverse lifestyle changes in the causation of coronary artery disease. Acta Medica Iranica 46. Lutsey, P. L., L. M. Steffen, and J. Stevens. 2008. Dietary intake and the development of the metabolic syndrome. Circulation 117:754–761. McCann, S. E., J. R. Marshall, J. R. Brasure, S. Graham, and J. L. Freudenheim. 2001. Analysis of patterns of food intake in nutritional epidemiology: Food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer. Public Health Nutrition 4:989–997. Mirmiran, P., F. Hosseini-Esfahani, M. Jessri, L. K. Mahan, N. Shiva, and F. Azizi. 2011. Does dietary intake by Tehranian adults align with the 2005 dietary guidelines for Americans? Observations from the Tehran lipid and glucose study. Journal of Health, Population and Nutrition 29:39. Mullie, P., I. Guelinckx, P. Clarys, E. Degrave, M. Hulens, and G. Vansant. 2009. Cultural, socioeconomic and nutritional determinants of functional food consumption patterns. European Journal of Clinical Nutrition 63:1290–1296 Mullie, P., P. Clarys, M. Hulens, and G. Vansant. 2010. Dietary patterns and socioeconomic position. European Journal of Clinical Nutrition 64:231–238.
Downloaded by [Lakehead University] at 15:18 13 March 2015
Health-related Factors in Coronary Artery Disease in Iran
19
Naja, F., L. Nasreddine, L. Itani, M. C. Chamieh, N. Adra, A. M. Sibai, and N. Hwalla. 2011. Dietary patterns and their association with obesity and sociodemographic factors in a national sample of Lebanese adults. Public Health Nutrition 1:1–9. Nettleton, J. A., M. B. Schulze, R. Jiang, N. S. Jenny, G. L. Burke, and D. R. Jacobs, Jr. 2008. A priori–defined dietary patterns and markers of cardiovascular disease risk in the Multi-Ethnic Study of Atherosclerosis (MESA). The American Journal of Clinical Nutrition 88:185–194. Osier, M., A. H. Andreasen, B. Heitmann, S. Høidrup, U. Gerdes, L. Jørgensen, and M. Schroll. 2002. Food intake patterns and risk of coronary heart disease: A prospective cohort study examining the use of traditional scoring techniques. European Journal of Clinical Nutrition 56:568–574. Pallanat, J. 2007. SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. 3rd ed., 179–200. Berkshire, UK: McGraw Hill Open University Press. Rezazadeh, A., B. Rashidkhani, and N. Omidvar. 2010. Association of major dietary patterns with socioeconomic and lifestyle factors of adult women living in Tehran, Iran. Nutrition 26:337–341. Rzewnicki, R., Y. V. Auweele, and I. De Bourdeaudhuij. 2003. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutrition 6:299–306. Safdar, N. F., E. Bertone-Johnson, L. Cordeiro, T. H. Jafar, and N. L. Cohen. 2013. Dietary patterns of Pakistani adults and their associations with sociodemographic, anthropometric and life-style factors. Journal of Nutritional Science 2:1–10. Sofianou, A., T. T. Fung, and K. L. Tucker. 2011. Differences in diet pattern adherence by nativity and duration of US residence in the Mexican-American population. Journal of the American Dietetic Association 111:1563–1569. Statistical Center of Iran. 2010. Annual statistic of Mazandaran province. http://www. amar.org.ir/Default.aspx?tabid=1700&agentType=View&PropertyID=2556. Thompson, F. E., and A. F. Subar. 2008. Nutrition in the prevention and treatment of disease. In Dietary assessment methodology, 2nd ed., 5–46. San Diego, CA: Academic Press. van Dam, R. M., L. Grievink, M. C. Ocké, and E. J. M. Feskens. 2003. Patterns of food consumption and risk factors for cardiovascular disease in the general Dutch population. American Journal of Clinical Nutrition 77:1156–1163. WHO. 2007. Cardiovascular disease. http://www.who.int/mediacentre/factsheets/ fs317/en. WHO. 2011. Cardiovascular diseases (CVDs). WHO Media Center Fact Sheet No. 317. http://www.who.int/mediacentre/factsheets/fs317/en/index. html (accessed October 2011).
