Dietary Patterns Associated with Metabolic Syndrome:
A Study Conducted in Khorramabad
 
Ebrahim Falahi; PhD^1,2, Khatere Anbari; PhD³, Farzad Ebrahimzadeh; MSc⁴ & Sajjad Roosta; BSc^*5
 
1 Nutrition Health Research Center, School of Public Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran.
² Department of Nutrition, School of Public Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran.
³ Department of Social Medicine, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran.
⁴ Department of Public Health, School of Public Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran.
⁵ School of Public Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran.
 

ARTICLE INFO		ABSTRACT
ORIGINAL ARTICLE		Background: Metabolic syndrome (MetS) is a complex disorder considered as a worldwide epidemic. The aim of this study was to characterize the dietary patterns of Iranian adults and examine its association with metabolic syndrome. Methods: In this cross-sectional study, 973 persons were selected using multi-stage cluster, random sampling method in Khorramabad city. Dietary intake was assessed using a validated, 168 food-item, self-administrated, and semi-quantitative food-frequency questionnaire (FFQ). To identify the dietary patterns, factor analysis was used for principal components. Results: Three major dietary patterns were identified: the western dietary pattern (WDP), the healthy dietary pattern (HDP), and the traditional dietary pattern (TDP). Participants in the highest quintile of HDP had lower odds of MetS (OR: 0.45; 95% CI: 0.27-0.77) than those in the lowest quintile, whereas those in the highest quintile of the WDP score had greater odds of the MetS (OR: 3.44; 95% CI: 2.08-5.70) than participants of the lowest quintile. Multi linear regression showed that the WDP score was associated negatively with serum high density lipoprotein-cholesterol and positively with other components of MetS. Even after body mass index adjustment, the association remained significant, except for fasting plasma insulin. Conclusions: a HDP is associated with reduced risk of MetS. In contrast, a WDP is associated with a greater risk of the MetS. Keywords: Metabolic syndrome; Obesity; Dietary pattern; Body mass index
Article history: Received: 2 Apr 2016 Revised: 4 Nov 2016 Accepted: 5 Dec 2016
*Corresponding author: sajjad.roosta@yahoo.com School of Public Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran.   Postal code: 6816163879 Tel: +986633409974

 

Introduction

M

etabolic syndrome (MetS) is a complex disorder with high socioeconomic cost that is considered as a worldwide epidemic. MetS is defined by a cluster of interconnected factors
that directly increase the risk of coronary heart disease (CHD), other forms of cardiovascular atherosclerotic diseases (CVDs), and diabetes mellitus type 2 (Kassi et al., 2011). Its main components are central obesity, hyper-insulinemia, insulin resistance and hyperglycemia, hypertension, as well as elevated blood lipids (Noel et al., 2009, Panagiotakos et al., 2007, Wirfält et al., 2001)  .
The prevalence of MetS increases with age and varies by ethnicity (Noel et al., 2009). CVDs are one of the main causes of mortality in Iran and Lorestan province (Azadbakht et al., 2005a, Falahi et al., 2012). People with the MetS are also at greater risk of CVDs (Azadbakht et al., 2005a). A recent study in Tehran showed an estimated MetS prevalence of higher than 30% in adults (Azizi et al., 2003).
The cause of this syndrome is largely unknown and variations worldwide have prompted researchers to present hypotheses for the natural development of the syndrome (Azadbakht et al., 2005b). As indicated in several studies, among factors influencing risk of developing metabolic syndrome, dietary pattern seems to play an important role (Panagiotakos et al., 2007). At this point, it should be mentioned that majority of studies followed the approach of assessing single nutrients or food items, instead of assessing dietary patterns; however, people do not eat isolated nutrients. They consume meals consisting of a variety of foods with complex combinations of nutrients. Thus, using a holistic dietary approach is recommended to prevent diseases (Azadbakht et al., 2005b, Panagiotakos et al., 2007) .
Despite the rising prevalence of MetS, few recent studies have examined the role of dietary patterns and their relationship with MetS (Deshmukh-Taskar et al., 2009). Regarding high prevalence of MetS in Iran, variety of food patterns among various people of the world, cultural differences, and accessibility of foods, this study was carried out to characterize dietary patterns of Iranian adults and to examine its associations with metabolic syndrome.
Materials and Methods
Study population: A descriptive, cross-sectional, and correlational design was used to conduct this research. In the study, 1980 people aged  18 years and higher were selected using multi-stage cluster random sampling method in Khorramabad city, located in west of Iran from July 2011 to February 2012. Exclusion criteria consisted of having history of CVDs, diabetes, stroke, hyper or hypothyroidism, being on diet, having medication, pregnancy or lactation, daily energy intake out of 800-4200 kcal/d range. Therefore, 1009 people (273 men and 736 women) aged 18-75 years participated in the study; 36 participants did not continue the study because of several reasons (e.g., lack of time or unexpected reasons that forced them to cancel the study). Finally, 973 participants were analyzed who had complete data.
Dietary assessment: Dietary intake was assessed with the use of a validated, 168 food- item, self-administrated, and semi-quantitative food-frequency questionnaire (FFQ) (Esmaillzadeh et al., 2007). All the completed questionnaires were evaluated by trained dietitians. The FFQ consisted of a list with a usual portion size. Participants were asked to report their consumption frequency of each food item during the past year. Each food/beverage item provided 9 possible responses, ranging from "never or less than once a month" to "six or more times per day". The selected frequency choice indicated by the participants for each food/beverage was converted to daily intake. Because of their high variety and frequency, food items were categorized into one of the 40 defined food groups (Table 1).
Socio-demographic and other lifestyle variables: A questionnaire on age, gender, marital status (single or married), smoking status (non-smoker, current smoker, ex-smoker), alcohol intake (yeas or no), monthly income (less than or equal to 300 $ and higher than or equal to 900 $), and education level (i.e., illiterate, <=12 years, and >12 years) was later completed by the participants. Physical activity was assessed by the international physical activity questionnaire (IPAQ). The short version of IPAQ (seven items) was applied which provided information on weekly time spent for walking, vigorous,  moderate, intense, and sedentary activity (Oyeyemi et al., 2011). Finally, metabolic equivalent minutes per week (MET-min/wk.) were calculated and sum of them were determined as physical activity.
Anthropometric, clinical, and biochemical assessments: Weight and height were measured while participants were with light clothes and no shoes. Body mass index (BMI) was calculated as the weight in kilograms divided by square of height in meter. Waist circumference (WC) was measured at the narrowest level between the lowest rib and the iliac crest by an upstretched tape measure without any pressure to body surface. Measurements were recorded to the nearest 0.1 cm.
Blood pressure was measured twice after participants' rest for 15 minutes (Azizi et al., 2002). All blood samples were obtained in the morning following a 12 h fast. Fasting plasma glucose (FPG) concentrations were measured by the glucose oxidase method using a commercial kit (Pars Azmon Co., Tehran, Iran); a commercial radioimmunoassay kit measured plasma immune-reactive insulin concentration (Immunotech co., France). Indices of insulin sensitivity were calculated according to the quantitative insulin sensitivity check index (QUICKI) formula (1/(log FPG (mg/dL) + log fasting plasma insulin (µU/mL); higher QUICKI values indicated greater insulin sensitivity and the homeostasis model assessment of insulin resistance (HOMA-IR) formula (FPG (mg/dL) × fasting plasma insulin (µU/mL))/ 405; higher HOMA-IR values indicated greater insulin resistance. Serum concentrations of total cholesterol (TC), high density lipoprotein-cholesterol (HDl-c), low density lipoprotein-cholesterol (LDL-c), and triacylglycerol (TG) were measured using commercially available enzymatic reagents (Pars Azmoon, Tehran, Iran). Blind duplicates were used for quality control of all analyses.
Definition of MetS: It was defined as presence of at least three of the following risk factors as recommended by ATP Ш (Panel, 2002): 1) abdominal obesity (WC >= 102 cm in men
and >= 88 cm in women); 2) elevated blood pressure (>= 130/85 mmHg) 3) high serum TG concentrations (>= 150 mg/dL); 4) low serum HDL-c (<40 mg/dL in men or <50 mg/dL in women 5) high FBG (>= 100 mg/dL).
Data analysis: SPSS software (version 19; SPSS Inc., Chicago IL) was used to conduct data analyses. To identify the dietary patterns based on 40 food groups (Table 1), principal components factor analysis was used (Table 2). Participants were categorized by dietary pattern scores' quintiles. Linear regression was used to determine association between dietary patterns and MetS. First, age and gender were adjusted, then, adjustment was conducted for smoking, physical activity, drug using, history of diabetes, history of heart disease, and finally for BMI.
Analysis of covariance through Tukey post-hoc test was used to examine differences in mean servings of foods from the dietary pattern; demographic and lifestyle characteristics. Statistical significance was set at P-value <= 0.05.
Ethical considerations: Written informed consents were collected from each participant. The protocol of study was approved by the ethic committee of Lorestan University of Medical Sciences.
Results
Three major dietary patterns were identified: the western dietary pattern (WDP), rich in red meat, processed meat, organ meats, margarine, coffee, sweets and desserts, soft drinks, condiments, and dried fruits, the healthy dietary pattern (HDP), rich in poultry, low- fat dairy products, high- fat dairy products, fish, fruits, yellow vegetables, cruciferous vegetables, green leafy vegetables, other vegetables, legumes, whole grains, and olives,  and the traditional dietary pattern (TDP), rich in refined grains, tea, nuts, fruit juices, eggs, pickles, hydrogenated oils, vegetables oils, sugars, salt. These dietary patterns' factor-loading matrixes are shown in Table 2.

 

Table 1. Components of food groups used in the dietary pattern analyses
Food groups	Food Items
Fruits	Apples, cherries, bananas, pears, apricots, peaches, nectarine, cantaloupe, watermelon, oranges, tangerine, lemons, pineapples, pomegranates, mulberry, plums, grapefruit, kiwi, grapes, strawberries, persimmons, fresh figs , black cherries
Fruit Juices	Orange juice, apple juice, other fruit juices
Cruciferous vegetables	Cauliflower, cabbage, Brussels sprouts, kale
Green leafy vegetables	Spinach, lettuce
Yellow vegetables	Carrots, Pumpkins
Tomatoes	Tomatoes, tomato sauce, tomato pasta
Other vegetables	Cucumber, celery, green pea, mixed vegetables, eggplant, green beans, green paper, turnip, squash, mushrooms, onions, radish, spring onions
Whole grains	Wheat germ, cornflakes, popcorn, barley bread, dark breads (Sangak, tafton,  barbari), bulgur
Refined grains	White breads (lavash, baguettes), noodles, pasta, rice, toasted bread,  white flour, starch, biscuits, milled barley
Potatoes	Potatoes
Legumes	Lentils, beans, peas, lima beans, broad beans, soy
Low fat dairy products	Skim or low-fat milk, low–fat yogurt
High fat dairy products	High –fat milk, whole milk, chocolate milk, , fruits milk, cream , high-fat yogurt, cream yogurt, cream cheese, other cheeses, ice cream
Poultry	Chicken with or without skin
Fish	Canned tuna fish, other fish
Red meats	Beef, lamb, hamburger
Processed meats	Sausages, Kalbas
Organ meats	Beef liver, kidney, and heart, lamb liver, kidney, and heart
Eggs	Eggs
Tea	Tea
Coffee	Coffee
Hydrogenated oils	Hydrogenated fats, animal fats
Vegetable oils	Vegetable oils ( except for olive oil)
Butter	Butter
Margarine	Margarine
Sweets and desserts	Chocolates, cookies, cakes, confections
Pizza	Pizza
Soft drinks	Soft drinks
Snacks	Corn puffs, Potato chips
French fries	French fries
Mayonnaise	Mayonnaise
Broth	Broth
Nuts	Pistachios, almonds, peanuts, hazelnuts, roasted seeds, walnuts
Olives	Olives, olive oil
Sugars	Sugars, candies, gaz ( an Iranian confectionery)
Condiments	Jam, jelly, honey
Dried fruits	Dried figs, dried dates, dried mulberries, other dried fruits
Salt	Salt
Spices	Peppers, turmeric, curry powder
Pickles	Pickles

 

Table 2. Factor-loadings  matrix for dietary patterns a
Food groups	Dietary patterns
	Western	Healthy	Traditional
Fruits	0.22	0.37	0.27
Fruit Juices	0.31	0.26	0.31
Cruciferous vegetables	0.28	0.53	-
Green leafy vegetables	-	0.63	-
Yellow vegetables	-	0.57	-
Tomatoes	-	-	-
Other vegetables	-	0.49	0.30
Whole grains	-	0.24	-
Refined grains	-	-	0.26
Potatoes	0.57	-	-
Legumes	-	0.61	-
Low fat dairy products	-	0.52	-
High fat dairy products	0.27	0.41	0.27
Poultry	-	0.36	-
Fish	-	0.61	-
Red meats	0.75	-	-0.47
Processed meats	0.75	-	-0.47
Organ meats	0.23	-	-
Eggs	-	-	-
Tea	-	-	0.26
Coffee	0.70	-	-
Hydrogenated oils	-	-	0.35
Vegetable oils	-	-	-
Butter	0.38	-	0.27
Margarine	-	-	-
Sweets and desserts	0.62	-	0.36
Pizza	-	-	-
Soft drinks	0.44	-	0.39
Snacks	0.52	-	0.38
French fries	0.26	-	-
Mayonnaise	-	-	-
Broth	-	-	-
Nuts	0.39	0.31	0.42
Olives	-	0.38	-
Sugars	0.43	-	0.49
Condiments	0.34	-	-
Dried fruits	0.61	0.46	-
Salt	-	-	-
Spices	-	-	0.23
Pickles	-	0.34	0.35
Variability explained	12.64%	10.59%	6.65%

^a: values <0.20 were exclude
 

Characteristics of participants across quintile categories of dietary pattern scores are represented in Table 3. Compared with participants in the lowest quintile; those in the highest quintile of the TDP and WDP had significantly higher BMI and MetS prevalence; they were also more significantly obese. Conversely, in comparison with people in the lowest quintile, those in the highest quintile of the healthy dietary pattern had significantly lower prevalence of the MetS. Participants in the highest quintile of the TDP were significantly older. No significant differences were found in the distribution of current smokers, drug users, diabetic, and heart diseases history across quintile categories of dietary patterns.
Table 4 shows odds ratios (ORs) for the MetS across quintile categories of dietary pattern scores. After adjusting for age, participants in the highest quintile of the HDP had lower MetS odds (OR: 0.45; 95% CI: 0.27-0.77) than did those in the lowest quintile. However, those in the highest quintile of the WDP score had greater odds of the MetS (OR: 3.44; 95% CI: 2.08-5.70) than participants in the lowest quintile. In spite of adjustment for other confounding variables, such as current smoking, physical activity, drug using, and history of diabetic and heart disease, the significant difference remained. However, after adjustment for BMI, the positive association of the TDP with MetS disappeared.
Participants in the highest quintile of the HDP score had lower odds for elevated blood pressure (OR: 0.56, 95%; CI: 0.34-0.93). This association remained even after adjustment for BMI (Table 5). In contrast, those in the highest quintile of the WDP score had significantly higher odds for some MetS components (2.28, 1.70, and 2.11 for abdominal obesity, elevated TG, and elevated blood pressure, respectively). This association disappeared after adjustment for BMI, except for elevated blood pressure. However, with or without adjustment for BMI, the association was not significant for elevated blood glucose and low HDL-c. Participants in the highest quintile of the TDP score had higher odds only for abdominal obesity (OR:  2.05, 95% CI: 1.28-3.27). After adjustment for BMI, this significant association disappeared.
Multi linear regression showed that the WDP score was associated negatively with serum HDL-c and positively with other components of MetS. Even after adjustment for BMI, the association remained significant, except for fasting plasma insulin (Table 6). The HDP score had independent inverse relation with various metabolic risk factors except serum HDL-c. The TDP score was significantly associated only with HDL-c after adjusting for BMI (Table 6).
Discussion
In this study three major dietary patterns were identified among adult Iranian population (west of Iran): the WDP, the HDP, and the TDP. Consumption of the HDP was associated with lower risk of MetS, whereas WDP and TDP were associated with higher risks of this syndrome.
Esmaillzadeh (Esmaillzadeh et al., 2007) identified three major dietary patterns among Tehranian women. They reported a negative association between healthy dietary pattern and a positive association between the Western dietary pattern and the metabolic syndrome. However, no association was noted for occurrence of MetS and WDP in a study by  Deshmukh-Taskar et al. (Deshmukh-Taskar et al., 2009). In a longitudinal study, participants who were in the highest quintile of the WDP scores (comprising of refined grains, processed meat, fried foods, and red meat) had an 18% greater risk of MetS than those in the lowest quintile for the WDP scores (Deshmukh-Taskar et al., 2009). Noel et al. showed that meat and French fries pattern was not associated with the overall prevalence of MetS, while prevalence of this syndrome was higher for consuming a traditional diet (comprising of rice, beans, and oils) (Noel et al., 2009). Other studies suggested that increased consumption of healthier foods, including fruits and vegetables, whole grains/ cereals, dairy products, and other low fat foods may prevent chronic diseases. Conversely, meat and alcohol intake were associated with increased levels of clinical and biological markers linked to MetS (Azadbakht et al., 2005a, Bazzano et al., 2002, Deshmukh-Taskar et al., 2009, Johnston et al., 2004, Panagiotakos et al., 2007, Qi et al., 2006, Rolls et al., 2004, Ruidavets et al., 2007, Wirfält et al., 2001, Zemel et al., 2005).

Table 3. Characteristics of participants by quintile (Q) categories of dietary pattern scores		WDP score	P-value		0.06		0.04		0.07		0.82		0.36		0.63		0.49		0.78		0.80		0.02		0.001	Met: metabolic equivalent; HDP: healthy dietary pattern; TDP: traditional dietary pattern; WDP: western dietary pattern, a: One way ANOVA for age, body mass index, waist circumference, and physical activity and chi-square test for other variables, b:mean±SD, c : Obesity defines as the BMI >= 30 kg/m2, d: defines as the presence of  >= 3 of the following components: 1) waist circumference  > 88 cm for women and >102 for men; 2) serum triacylglycerol >= 150 mg/dL; 3) High density lipoprotein-cholesterol (50 mg/ dL for women and < 40 for men); 4) fasting blood glucose >= 100 mg/dL; 5) blood pressure >= 130/85 mmHg)
			Q5		32.7±10.9		27.3±5.6		90.9±14.0		3186±4059		70.6		3.6		3.1		3.6		21.1		29.9		39.7
			Q3		34.5±11.0		26.8±5.3		89.2±12.9		3274±4803		79.0		2.1		2.1		2.6		18.5		22.6		28.2
			Q1		35.2±10.4		26.0±4.8		87.9±12.3		3481±5256		76.3		2.6		4.1		2.6		19.6		18.0		22.7
		TDP score	P-value		0.001		<0.001		<0.001		0.57		0.38		0.14		0.17		0.60		0.87		0.003		0.002
			Q5		33.6±10.8		27.3±5.4		89.8±13.2		3086±4062		80.4		3.1		3.6		3.1		17.5		28.9		34
			Q3		35.5±10.4		27.1±4.7		91.1±11.5		3334±4025		74.4		3.1		5.6		2.6		19.5		22.1		31.8
			Q1		31.7±10.3		25.4±4.4		86.0±11.8		3553±4875		74.2		0.5		2.1		1.5		19.1		14.4		19.1
		HDP score	P-value a		0.12		0.65		0.63		0.39		0.79		0.65		0.30		0.77		0.58		0.38		0.001
			Q5		32.1±11.1		26.2±4.8		87.9±11.9		3696±4792		78.9		2.1		4.6		2.6		19.6		19.1		16.0
			Q3	Age (y)	33.6±10.9	Body mass index (kg/m2)	26.6±4.9	Waist circumference (cm)	89.0±12.5	Physical activity (Met.min/wk)	3022±3405		76.3	Family history of diabetes (%)	3.6	History of heart disease (%)	2.6	Current daily smoker (%)	1.5		21.1		24.7	Metabolic syndrome (%)d	29.4
			Q1		34.3±10.5b		26.5±5.4		89.1±13.6		3382±5972	Female (%)	75.3		3.1		2.1		2.1	Drug using (%)	17	Obesity (%) c	23.2		30.4
		Variables

 

Table 4. Multivariate adjusted odds ratios (95% CIs) for metabolic syndromea across quintile (Q) categories of dietary pattern scores		WDP score	P-vaule	<0.001	0.001	0.002	HDP: healthy dietary pattern; TDP: traditional dietary pattern; WDP: western dietary pattern , a: defines as the presence of  >= 3 of the following components: 1) waist circumference  > 88 cm for women and >102 for men; 2) serum triacylglycerol >= 150 mg/dL; 3) High density lipoprotein-cholesterol (50 mg/ dL for women and < 40 for men); 4) fasting blood glucose >= 100 mg/dL; 5) blood pressure >= 130/85 mmHg),  b: OPPM, overall prediction power of model, c : Adjusted for age and gender, d : further adjusted for smoking, physical activity, drug using, history of diabetes, and history of heart disease, e : additionally adjusted for BMI
			OPPM	73.9	75	79.9
			Q5	3.44 (2.08-5.70)	3.49 (2.09-5.84)	2.70 (1.52-4.79)
			Q3	1.51 (0.91-2.50)	1.51 (0.90-2.53)	1.29 (0.72-2.28)
			Q1	1	1	1
		TDP score	OPPM	75.6	77.7	79.4
			P-value	0.020	0.023	0.480
			Q5	2.06 (1.24-3.40)	2.05 (1.23-3.42)	1.43 (0.80-2.54)
			Q3	1.56 (0.94-2.58)	1.53 (0.91-2.56)	1.24 (0.70-2.18)
			Q1	1	1	1
		HDP score	P-value	0.005	0.011	0.004
			OPPMb	77.8	78.7	79.7
			Q5	0.45 (0.27-0.77)	0.47 (0.27-0.81)	0.39 (0.21-0.71)
			Q3	0.99 (0.62-1.60)	0.98 (0.61-1.59)	0.92 (0.54-1.57)
			Q1	1	1	1
				Model 1c	Model 2d	Model 3e

The inverse association between the HDP and MetS could be attributed to that pattern’s healthy constituents including vegetables, fruits, legumes, dairy products, whole grains, and fish (Azadbakht et al., 2005a, Panagiotakos et al., 2007, Ruidavets et al., 2007, Ventura et al., 2008). This association may be explained by their vitamin/mineral, phytochemical, fiber content, and antioxidant contents. Reduced insulin demand may be another protective mechanism associated with higher intake of such foods. Furthermore, most foods in HDP have a low glycemic load and are rich in fiber, so their consumption may increase insulin sensitivity and plasma levels of anti-inflammatory cytokines and reduce serum markers of systemic inflammation (McKeown et al., 2004, Qi et al., 2006). The identified HDP in this study is somewhat similar to the ‘prudent dietary pattern’ in other studies (Deshmukh-Taskar et al., 2009, Lopez-Garcia et al., 2004), DASH eating plan recommended for decreasing blood pressure (Ramond and Couch, 2016), and improvement of MetS features (Azadbakht et al., 2005a). A positive association was observed between TDP and MetS.  Some studies showed no association between TDP and MetS that could be attributed to complex nature of these patterns (Esmaillzadeh et al., 2007, Naja et al., 2013) . These dietary patterns were loaded with both healthy (whole grain, legume, fruits and vegetables) and unhealthy (refined grains, potatoes, and hydrogenated fats) foods. Whereas, other studies on TDP confirmed results of the current study (Murakami et al., 2006, Noel et al., 2009). Song and Joung reported that traditional Korean dietary pattern have beneficial effects on some metabolic abnormality. However, high prevalence of low HDL-c, attributable to a high carbohydrate diet should be considered (Song and Joung, 2012). These differences between various studies may be related to difference in the contents of various traditional dietary patterns from one country to another.

 

In this study, TDP contents refined grains, hydrogenated fats, vegetable oils, and sugar which may be responsible for the positive relation to MetS. Of course the positive association between TDP and MetS in the present study disappeared after adjustment for BMI that can explain this association. The positive relation between the WDP and the MetS could be attributed to lower amounts of beneficial foods and nutrients of this pattern. Higher intake of processed meat, meat, sweets and desserts, soft drink, snacks, and condiments (Deshmukh-Taskar et al., 2009, Esmaillzadeh et al., 2007, Panagiotakos et al., 2007, Song and Joung, 2012) in this food patterns also explain part of this relation.
Some of the associations remained even after BMI adjustment. This shows that general obesity cannot explain all relations between diet and chronic diseases and that other factors, such as abdominal adiposity, may be responsible.
A positive relation between the HDP and HDL-c and a significantly inverse association between the HDP and other components of the MetS was observed. This relation remained even after adjustment for BMI. In a cross-sectional study a healthy dietary pattern (comprising of raw vegetables, fruits, fish, pasta, and rice) was inversely associated with central obesity, FPG, and TG; it was also positively correlated with HDL-c (Williams et al., 2000). Multi linear regression did not show any significant relationship between the TDP and the MetS components. This food pattern contains both healthy (eggs, nuts, fruit juices, and tea) and unhealthy (refined grains, hydrogenated fats, sugar, salt, and condiments) foods which may explain the findings. A positive association was found between components of MetS with the WDP. However, a significant invers relation was found between HDL-c and the WDP. These associations were remained even after controlling for BMI. Deshmukh-Tasker et al. (Deshmukh-Taskar et al., 2009), reported the same results similar to our findings. Conversely, Other studies are not in agreement with our result (Pelkman et al., 2004, Samaha, 2005). In general, diets high in saturated fatty acids (the same as WDP in our study) tend to increase the cardio-protective HDL-c levels along with increasing other chronic heart disease-causing lipid (e.g., TC) (Samaha, 2005). Conversely, low-fat high carbohydrate diets tend not only to decrease HDL-c, but also decrease TC and LDL-c (Pelkman et al., 2004).
The use of factor analysis to identify dietary patterns is the major strength of the present study. Although using factor analysis for dietary data reduction has been criticized for its subjectivity in nature and difficulty of results' replication in other populations (Martínez et al., 1998). However, similarity of dietary patterns and MetS' results achieved from the present study to those reported in longitudinal (Lutsey et al., 2008) and cross-sectional studies (Bazzano et al., 2002, Deshmukh-Taskar et al., 2009, Esmaillzadeh et al., 2007, Panagiotakos et al., 2007) strengthens the current results.
This study has some limitations. First, the cross-sectional design and therefore its findings can mainly be used to test association rather than assessing causal relationships. Second, the FFQs have some limitation (such as semi quantitative nature) that may affect dietary information collection. Third, food items could not be analyzed to their nutrient contents. Better explanation could be provided if this has been done.
Conclusions
The present results indicate that a dietary pattern characterized by high consumption of poultry, dairy products, vegetables, fruits, legumes, whole grains, fish, and olives are associated with reduced metabolic syndrome risk in both male and female. In contrast, a dietary pattern with high amounts of red meats, organs meat, processed meats, soft drinks, snacks, sweets and deserts, condiments, margarine, and coffee is associated with a greater risk for metabolic syndrome.
Acknowledgements
We are grateful to the deputy of Research and Technology of Lorestan University of Medical Sciences for financial support of this project.
Authors’ contributions
Falahi E, Anbari K, Ebrahimzadeh F and Roosta S designed research; Falahi E and Roosta S conducted research; Anbari K and Ebrahimzadeh F analyzed the data; and Falahi E and Roosta S wrote



the paper. Roosta S had primary responsibility for final content. All authors read and approved the final manuscript.
Conflicts of interest
The authors declare no conflict of interest.

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