Food Patterns and Framingham Risk Score in Iranian Adults: Tehran Lipid and Glucose Study: 2005

Abstract

Background:

Healthy dietary habits can prevent cardiovascular diseases (CVD). The aim of this study was to investigate the association between dietary patterns and predicted risk of CVD development using Framingham Risk Score (FRS) in Iranian population over 6 years of follow-up.

Methods:

In this prospective study, 2333 individuals aged 30–74 years with a 10-year predicted risk <10% and without a CVD diagnosis at baseline were recruited from participants in the third survey of Tehran Lipid and Glucose Study. Usual dietary intake was assessed using a validated semiquantitative food frequency questionnaire. Dietary patterns were identified using factor analysis. The risk of developing CVD was evaluated using FRS questionnaire. The association between dietary patterns and risk of development of CVD was evaluated using binary logistic regression models.

Results:

Three major dietary patterns were identified, which explained 32% of total variance in dietary intake of participants. The healthy dietary pattern was characterized by high intake of fruits, fruit juices, vegetables, liquid oils, and nuts and lower intake of refined grains. The western dietary pattern was characterized by high intake of fast foods, soft drinks, sweets and sugars, and red meat, organ meat, and egg. The traditional dietary pattern was characterized by high intake of legumes and vegetables and low intake of high-fat dairy products. After adjustment for all confounding factors, participants in the highest tertile of the healthy dietary pattern had a lower risk of CVD development (odds ratio [OR]: 0.67; 95% CI: 0.51–0.87, P-trend: 0.003).

Conclusion:

Our findings in a large cohort of Iranian population confirm the current recommendation regarding consuming healthy dietary pattern for primary prevention of CVDs.

Introduction

Globally, noncommunicable diseases, including cardiovascular disease (CVD), have become the main cause of morbidity and mortality in recent years and annually, 16 million deaths, which occur before the age of 70, are related to these diseases.¹ In Iran, it is estimated that the burden of CVD will be doubled in 2050 compared to 2005,² which is related to rapid nutrition transition and undesirable changes in dietary habits.³ Considering economic burden of morbidity and mortality in a population, primary prevention of CVD through modification of CVD-related risk factors has become a priority.

Several modifiable risk factors, including hyperlipidemia, hyperglycemia, unhealthy diet, obesity, inactivity, and smoking, have been suggested to increase the risk of CVD.² Among these factors, dietary intakes exert a great influence on CVD risk and several investigations have shown that dietary modifications significantly and rapidly reduce CVD mortality in populations.¹ Many previous studies have focused on effects of single food groups or nutrients such as fruit and vegetables, unsaturated fatty acids, nuts, processed red meat, and dietary cholesterol or fiber on CVD risk.^4

–8 Foods are not consumed separately in a diet and any health effect of food items may be due to interactions with other nutrients and foods consumed simultaneously. Thus, studying overall diet quality and dietary patterns leads to a more comprehensive understanding of the relationship between food intakes and cardiometabolic health. The association of dietary patterns and CVD risk has been mentioned in different populations around the world. Prospective studies generally indicated that a dietary pattern characterized by high intake of fruits, vegetables, nuts, beans, whole grains, and fish is associated with lower risk of CVD, while high intake of red meat and processed meat, refined grains, fried foods, and sweets are associated with a higher risk of CVD.^9

–12 However, dietary habits vary among populations and further studies are needed to clarify the long-term association between dietary patterns and the CVD risk in different countries.

To predict the long-term CVD risk accurately, a valid risk prediction tool is required. A valid and frequently used tool is Framingham risk score (FRS) tool, which uses data from the “Framingham heart study” to determine the 10-year chance of an individual for developing CVD.¹³ Previous studies have used the FRS to investigate the association of dietary patterns with the CVD risk.^14

–17 Validity and reliability of this tool to predict CVD risk in an Iranian population have been reported previously.¹⁸ According to rapid nutrition transition in Iran,¹⁹ like many other developing countries, recognition of dietary habits leading to chronic diseases may reduce the burden of noncommunicable diseases in the country. In this study, we aimed to analyze the association between dietary patterns and the 10-year CVD risk using FRS in an Iranian population.

Materials and Methods

This prospective cohort study was undertaken to assess the relationship of dietary pattern on CVD Framingham over 6.2 years in the Tehran Lipid and Glucose Study (TLGS). The TLGS study details are described elsewhere.²⁰ In brief, the TLGS is a large prospective study that aims to determine the risk factors of noncommunicable diseases among the urban population of Tehran aged 3 years and above. TLGS started in March 1999 and follow-up data are collected every 3 years to update and identify newly developed diseases. This study was conducted on individuals who participated in the third phase (2005–2008) of TLGS and were followed to the fifth phase (2012–2015) of the TLGS (median follow-up: 6.2 years). A total of 12,523 participants enrolled in the third phase of the TLGS, of which 5764 participants who were 30–74 years were selected. Participants who were lost to follow-up by the end of fifth phase; were pregnant or lactating (female participants) during follow-up phases; were taking steroids or hormonal medications, as well as those who had a history of serious illnesses such as cancer or cardiovascular events; had uncompleted biochemical or dietary data; had underreported or overreported energy intakes (less than 700 kcal/day or more than 6000 kcal/day, respectively); and had baseline FRS ≥10% were excluded. Finally, data of 2333 participants were included for statistical analysis (Fig. 1). All participants completed the informed written consent and the study protocol was approved by the ethics committee of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences.

FIG. 1.

Flow chart of study population.

Sociodemographic information, including age and educational level, as well as information on past and family medical history, medication use, and smoking habits was collected by trained interviewers using a pretested questionnaire.²¹ Education level was categorized as illiterate, primary school (<6 years of education), high school (6–12 years of education), university degree (12–16 years of education), and higher university degree (>16 years of education). Participants were considered current smokers if they smoke daily or occasionally, or if they had quit smoking for less than a year at the time of interview. Physical activity (PA) during the past year of examination was recorded as metabolic equivalent task (MET) based on hours per week (MET/hr/week) using the modifiable activity questionnaire.²² Validity and reliability of this questionnaire for Iranian population have been reported previously.²³ According to the questionnaire, the PA level was categorized as light (MET <3), moderate (3≤ MET <6), and vigorous (MET ≥6) intensity.²⁴

To assess usual dietary intake, a semiquantitative food frequency questionnaire (FFQ) consisting of 168 food items was used. The validity and reliability of the FFQ for Iranian population were reported before.²⁵ The usual consumption frequency of each food item in a day, week, or month was asked and then converted to daily intake. Portion size of each food item was asked in common household measures and then converted to grams. To have a more accurate estimation of usual dietary intake during the study period, the average of dietary intakes of participants in the phases 3 and 4 was calculated. Based on the similarity in nutrient composition and culinary usage, 21 food groups were categorized. To derive dietary patterns, dietary intake values of food groups were truncated and then log-transformed to normalize their distribution. Food group intakes were adjusted for total energy intake using residual method.²⁶ The food groups were applied in the principal component analysis to determine dietary patterns. Varimax rotation was used to keep the components uncorrelated and improve the interpretation. Dietary patterns were selected based on eigenvalues, scree plot test, and interpretability of components. According to previous studies, a significant contribution of each food item was defined as having an absolute factor loading ≥0.20.

Weight was measured using a digital scale, with participants wearing minimal clothes and no shoes. Weight was recorded to the nearest 0.1 kg. Height was measured using a stadiometer while participants were standing without shoes and had shoulders in a normal position. Height was recorded to the nearest 0.5 cm. Body mass index (BMI) was calculated as a ratio of weight (kg) divided by height (m²).

Blood pressure was measured on the right hand, using a standard mercury sphygmomanometer and after a 15-min rest in a sitting position. The average of two measurements with at least a 30-sec interval was recorded. For biochemical measurements, blood samples were taken after a 12–14 hr overnight fasting at baseline and at the end of the follow-up. Biochemical parameters, including fasting blood sugar, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and triacylglycerol were measured using a Selectra-2 autoanalyzer (Vital Scientific) as described in details elsewhere.²⁷

To identify participants at high risk of CVD, a sex-specific risk prediction model proposed by D'Agostino et al. was used.²⁸ Participants were categorized into those at high risk of CVD (FRS ≥10%) and those at low risk of CVD (FRS <10%) according to multiple risk factors, including age, total and HDL-C levels, systolic blood pressure, use of antihypertension medication, smoking, and diabetes. The defined outcome of our study was the development of FRS ≥10% in the 6.2 years between baseline and follow-up data collection.

Statistical analysis was done using SPSS software version 16.0. Student's t-test and chi-square test were used to compare quantitative and qualitative variables, respectively. Histograms were used to visually check the normal distribution of quantitative variables and not normally distributed variables were log transformed. One-way ANOVA test was used for comparison of dietary variables between tertiles of each dietary pattern. Binary logistic regression models were used to assess the association between dietary patterns and predicted risk of CVD. Education level (illiterate, primary school, high school, university degree, and higher university degree), family history of CVD (yes or no), total energy intake, total PA (low, moderate, and vigorous), and BMI were considered confounders. Confounders were determined according to previous studies.^15,17,29 To test trends across tertiles of dietary patterns, median values of each tertile were assigned to the respective categories and then entered as a continuous variable in the binary logistic models. A P value <0.05 was considered significant.

Results

A total of 2333 participants (930 men and 1403 women) were included in this study. The median length of follow-up was 6.2 years. Baseline characteristics of the participants according to sex groups are summarized in Table 1. In this study, 62% of the participants were women. After 6.2 years of follow-up, high risk of developing CVD (FRS ≥10%) was detected in 18.5% of the participants.

Table 1.

Baseline Characteristics of the Participants According to Sex Groups: Tehran Lipid and Glucose Study, 2005–2011

Variables	Men (n = 930)	Female (n = 1403)	Total	P
Age (years)	40.5 ± 7.0^a	43.3 ± 8.7	42.2 ± 8.2	<0.001
Low physical activity (%)	74	73	73	<0.001
Current smoking (%)	19.6	3.1	9.7	<0.001
BMI (kg/m²)	27.2 ± 4.1	28.4 ± 4.6	27.9 ± 4.5	<0.001
Waist circumference (cm)	95.5 ± 10.5	88.1 ± 11.6	91.1 ± 11.8	<0.001
SBP (mmHg)	112 ± 11.4	107 ± 13.0	109 ± 12.6	<0.001
Diastolic blood pressure (mmHg)	75.5 ± 9.3	71.5 ± 9.2	73.1 ± 9.4	<0.001
Total cholesterol (mg/dL)	188 ± 34.3	190 ± 36.5	189 ± 35.7	0.09
HDL-C (mg/dL)	37.6 ± 8.5	44.4 ± 10.4	41.7 ± 10.2	<0.001
Low density lipoprotein cholesterol (mg/dL)	117 ± 31.0	119 ± 32.2	118 ± 31.8	0.10
Triglycerides (mg/dL)	174 ± 101	137 ± 75.6	152 ± 88.9	<0.001
Fasting blood sugar (mg/dL)	91.9 ± 17.7^a	90.0 ± 19.0	91.3 ± 19.1	0.23
Incident predicted CVD risk^b,%	27	13	18.5	<0.001

Student's t-test was used for comparisons between quantitative variables, and chi-square test was used for comparisons between qualitative variables.

Mean ± SD (all such value).

Ten-year risk of developing CVD based on Framingham CVD risk score ≥10%, calculated based on risk factors, age, total and HDL-C levels, SBP, use of antihypertension medication, smoking, and diabetes.

CVD, cardiovascular disease; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure.

Factor loading values of the three dietary patterns are summarized in Table 2. The “healthy dietary pattern” (HDP) was loaded with high intake of fruits, fruit juices, green vegetables, yellow-red vegetables, other vegetables, nuts, and liquid oils and low intake of refined grains. The “western dietary pattern” (WDP) was loaded with high intake of fast foods, soft drinks, sweets and sugars, and red meat, organ meat, and egg. The “traditional dietary pattern” (TDP) was loaded with high intake of legumes, starchy vegetables, green vegetables, yellow-red vegetables, and other vegetables. The mentioned dietary patterns explained 32% of total variance in dietary intake of participants.

Table 2.

Factor Loadings for the Three Identified Dietary Patterns in the Participants: Tehran Lipid and Glucose Study, 2005–2011

	Dietary patterns^a
Food groups	Healthy	Western	Traditional
Fruits	0.67^b	—	—
Green vegetables	0.52	—	0.36
Yellow and red vegetables	0.58	—	0.39
Other vegetables	0.58	—	0.49
Low-fat dairy	0.22	—	0.25
Nuts	0.48	—	—
Liquid oil	0.44	—	—
Refined grains	−0.62	—	—
Fruit juice	0.35	0.37	—
Fish and poultry	0.22	0.30	—
Solid oil	−0.28	0.26	—
Salty snacks	—	0.38	—
Soft drinks	—	0.66	—
Fast foods	—	0.67	—
Sweets and sugar	—	0.59	—
Red meat, organ meat and egg	—	0.57	—
Tea and coffee	—	0.27	—
High-fat dairy	—	0.25	−0.31
Starchy vegetables	—	0.28	0.65
Whole grains	—	—	0.29
Legumes	—	—	0.70
Variance (%)	12.2	11.3	8.6

Kaiser-Meyer-Olkin (KMO) index: 0.74.

Values are factor loadings of dietary patterns measured by factor analysis. Values below 0.2 are not shown in the table for simplicity.

At baseline, participants in the highest tertile of the HDP had a higher intake of protein and fiber than did those in the lowest tertile. Participants in the highest tertile of WDP had a lower intake of carbohydrate, protein, and fiber, as well as higher intake of total fat, saturated fat, MUFAs, and PUFAs than those in the lowest tertile. Finally, participants in the TDP had a higher intake of carbohydrate, protein, and fiber, as well as a lower intake of total fat, saturated fat, MUFAs, and PUFAs (Table 3).

Table 3.

Baseline Dietary Intake of Participants According to Quartiles of the Three Dietary Patterns: Tehran Lipid and Glucose Study, 2005–2011

	Food patterns
	Healthy				Western				Traditional
Dietary intake	Q1 (n = 777)	Q2 (n = 778)	Q3 (n = 778)	P	Q1 (n = 777)	Q2 (n = 778)	Q3 (n = 778)	P	Q1 (n = 777)	Q2 (n = 778)	Q3 (n = 778)	P
Energy (Kcal/day)	2407 ± 813	2393 ± 738	2428 ± 727	0.7	2400 ± 899	2402 ± 720	2425 ± 639	0.8	2427 ± 883	2371 ± 709	2430 ± 672	0.2
Carbohydrate (% TE)	59.4 ± 8.6	58.3 ± 6.5	59.1 ± 6.1	0.009	60.6 ± 9.2	59.2 ± 5.6	57.0 ± 5.7	<0.001	58.3 ± 7.1	58.7 ± 6.3	59.8 ± 7.9	<0.001
Protein (%TE)	14.4 ± 7.0	14.7 ± 2.3	15.1 ± 2.7	0.004	15.1 ± 7.2	14.6 ± 7.2	14.5 ± 2.2	0.01	13.9 ± 2.6	14.6 ± 2.3	15.7 ± 6.9	<0.001
Total fat (%TE)	29.9 ± 16.2	30.3 ± 5.6	30.3 ± 5.3	0.7	29.3 ± 16.2	29.7 ± 5.4	31.5 ± 5.2	<0.001	31.1 ± 6.8	30.1 ± 5.4	29.3 ± 15.6	0.003
Saturated fat (%TE)	9.6 ± 2.9	9.9 ± 2.9	9.6 ± 2.2	0.01	9.3 ± 2.9	9.6 ± 2.3	10.2 ± 2.1	<0.001	10.1 ± 2.5	9.8 ± 2.3	9.1 ± 2.5	<0.001
MUFAs (%TE)	9.9 ± 2.7	10.2 ± 2.4	10.1 ± 2.3	0.2	9.6 ± 2.8	9.9 ± 2.2	10.6 ± 2.2	<0.001	10.3 ± 2.6	10.1 ± 2.4	9.7 ± 2.3	<0.001
PUFAs (%TE)	6.3 ± 3.3	6.1 ± 1.9	6.0 ± 1.8	0.2	6.0 ± 3.5	6.0 ± 1.6	6.4 ± 1.7	0.002	6.4 ± 2.8	6.1 ± 1.6	6.0 ± 2.7	<0.001
Fiber (g/1000 kcal)	19.6 ± 7.4	18.9 ± 5.2	20.1 ± 5.4	0.001	21.3 ± 6.9	19.9 ± 5.8	17.5 ± 4.8	<0.001	17.6 ± 5.8	19.1 ± 5.6	21.9 ± 6.0	<0.001

One-way ANOVA test was used for comparison between tertiles of each dietary pattern.

TE, total energy; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids.

Binary logistic regression analyses showed a significant association between all three dietary patterns and predicted risk of developing CVD (Model 1; Table 4). Adjustment for confounding factors, including education level and family history of CVD diseases, attenuated the association between WDP and TDP with the predicted risk of developing CVD (Model 2). Additional adjustment for total energy intake, total PA, and BMI (model 3) did not change the results and finally, the association between HDP and the predicted risk of developing CVD remained significant. Participants in the highest tertile of the HDP had 33% lower predicted risk of developing CVD in 10 years.

Table 4.

Odds Ratios and 95% Confidence Intervals of Developing a Framingham Cardiovascular Disease Risk Score ≥10% by Tertiles of Dietary Patterns or Food Group Intakes: Tehran Lipid and Glucose Study, 2005–2011

Dietary patterns	Q1	Q2	Q3	P_trend
Healthy
Model 1	1.0 (ref.)	0.91 (0.70–1.16)	0.76 (0.59–0.99)	0.04
Model 2	1.0 (ref.)	0.90 (0.70–1.16)	0.74 (0.57–0.96)	0.03
Model 3	1.0 (ref.)	0.86 (0.67–1.11)	0.67(0.51–0.87)	0.003
Western
Model 1	1.0 (ref.)	0.76 (0.59–0.97)	0.65 (0.50–0.84)	0.001
Model 2	1.0 (ref.)	0.84 (0.65–1.08)	0.77 (0.59–1.00)	0.05
Model 3	1.0 (ref.)	0.90 (0.69–1.16)	0.81 (0.62–1.06)	0.13
Traditional
Model 1	1.0 (ref.)	0.99 (0.77–1.30)	1.30 (1.01–1.68)	0.04
Model 2	1.0 (ref.)	0.97 (0.75–1.25)	1.18(0.91–1.53)	0.22
Model 3	1.0 (ref.)	0.98 (0.75–1.28)	1.20 (0.92–1.56)	0.18
Green vegetables
Model 1	1.0 (ref.)	0.88 (0.68–1.13)	0.75 (0.58–0.97)	0.03
Model 2	1.0 (ref.)	0.86 (0.66–1.10)	0.67 (0.52–0.88)	0.004
Model 3	1.0 (ref.)	0.82 (0.63–1.07)	0.63 (0.49–0.83)	0.001
Liquid oil
Model 1	1.0 (ref.)	0.75 (0.59–0.96)	0.58 (0.44–0.75)	<0.001
Model 2	1.0 (ref.)	0.79 (0.61–1.02)	0.62 (0.48–0.81)	<0.001
Model 3	1.0 (ref.)	0.79 (0.61–1.02)	0.60 (0.46–0.79)	<0.001

Model 1: crude values. Model 2: values are adjusted for education level (illiterate, primary school, high school, university degree, and higher university degree) and family history of CVD (yes, no). Model 3: additionally adjusted for total energy intake, total physical activity (low, moderate, and vigorous), and BMI.

Among different food groups in dietary patterns, green vegetables and liquid oils had a significant effect on the association between HDP and the predicted risk of developing CVD. Participants in the highest tertile of green vegetables' intake and liquid oils' intake had significantly 37% and 40% lower risk of developing CVD, respectively.

Discussion

In this study, three identified dietary patterns, including HDP, WDP, and TDP, were associated with the risk of CVD. Adjustment for the confounding factors attenuated the association between WDP and TDP and only the negative association between HDP and the FRS remained significant. Among different food groups in the HDP, higher intakes of green vegetables and liquid oils significantly lowered the FRS and risk of developing CVD.

Previous studies have reported the association between dietary patterns and risk of CVD in western and non-western populations. Some of these investigations have studied the link between dietary patterns and individual risk factors for CVD,^{21,22,30

–34} while some others have mentioned the overall risk of CVD.^{14,15,17,35

–39} CVD is multifactorial; thus, combining individual risk factors into a multivariate assessment tool to predict the risk of developing the disease over years leads to a more logical evaluation of candidates for CVD.²³

In our study, the HDP was inversely associated with the predicted risk of CVD in 10 years. Among different food items in HDP, green vegetables and liquid oils had a significant effect on the association between HDP and the risk of developing CVD. Results from prior studies confirm our data regarding benefits of consuming healthy diets. In a study on urban Mexican adult population, participants at the highest quintile of the prudent dietary pattern (mainly characterized by high intakes of fruits, vegetables, and whole grains and low intake of refined grains and soft drinks) had 60% lower risk of developing CVD in 10 years.¹⁵ In another study on Greek population, higher intake of fruits, vegetables, cereals, small fish, hardtack, and olive oil was associated with a lower 5-year incidence of CVD.³⁹ Whole grains and fruit dietary pattern was linked to a 5-year lower risk of CVD in a multiethnic population after adjustment for demographic and lifestyle confounders.³⁸ In Canadians, higher predicted 10-year risk of CVD was associated with lower consumption of breakfast cereal, fruits, and vegetables, along with higher consumption of potato.¹⁷ The HDP identified in this study (mainly loaded by fruits and fruit juices, vegetables, nuts, and liquid oils) is similar to HDP identified in other Iranian studies,^40,21 but intakes of some healthy food groups, including fish, low-fat dairy, whole grains, and legumes, are not as high as that of the prudent diet previously proposed in other populations.^41,35,36 However, a common characteristic in healthy or prudent dietary patterns is high consumption of plant source foods, especially fruits and vegetables. Studies have confirmed the link between high consumption of fruits and vegetables and cardiovascular health.^42,43 These food groups are high in antioxidants and polyphenols, which enhance the antioxidant defense of the body and thus inhibit lipids oxidations and improve endothelial function.⁴⁴

In this study, we could not show the association between WDP and 10-year predicted risk of developing CVD after adjustment for confounding factors. Previous studies have supported the link between WDP and the risk of CVD. High intake of red meat, processed meat, refined grains, sweets and desserts, French fries, and high-fat dairy products has been linked to higher risk of CHD in both men³⁶ and women.³⁵ High intake of refined grains, corn tortillas, soft drinks, and alcohol was positively associated with higher risk of developing CVD in 10 years in the urban Mexican population.¹⁵ In the ATTICA study on Greek population, higher consumption of red meat, salty nuts, hard cheese, margarine, sweets, and alcoholic beverages was associated with an increased risk of developing CVD events in 5 years.³⁹ In REGARDS study, a dietary pattern characterized by added fats, fried food, eggs, organ and processed meats, and sugar-sweetened beverages increased the risk of acute CHD by 37% in 4 years.⁴⁵ Similarly, in a prospective cohort study in Iran, a dietary pattern loaded by higher intakes of fast foods, confectioneries, salty snacks, mayonnaise, and soft drinks was linked to the development of CVD-related events.⁴⁶

The WDP identified in our study is positively loaded with high consumption of soft drinks, fast foods, sweets and sugars, as well as red meat, organ meat, and egg, which resemble WDP described in previous reports in Iran and other population. However, along with the mentioned foods, moderate consumption of fish, poultry, tea, and coffee was loaded in our WDP. Beneficial effects of above-mentioned foods as well as different processing and preparation of foods may have attenuated the link between WDP and risk of CVD in this study.^47

–50

Traditional dietary pattern identified in this study was not associated with the 10-year predicted risk of CVD. Prior studies in Iran have reported negative,^21,51,52 positive,^53,54 and null^46,55,56 association of the traditional Iranian diet with diseases. Iranian traditional diet is loaded with both healthy (such as vegetables) and unhealthy (red meat) food items, which may somewhat explain controversies in research findings.

This study had some limitations, which have to be pointed out. The main limitation was using factor analysis method for determining dietary patterns. This method is highly dependent on subjective decisions such as grouping of foods, the number of retained components, and the method of rotation.⁵⁷ Although we have used a valid and reliable FFQ, measurement errors in evaluating dietary intakes are unavoidable. These errors may have resulted in a null association between dietary patterns and the predicted risk of CVD. However, the prospective design reduces the possibility of recall bias in this study. Meanwhile, exclusion of individuals with known CVD at baseline reduced any potential for recall bias or bias from dietary change due to a known disease.

In conclusion, the findings of this study further support the knowledge regarding benefits of a healthy dietary pattern mainly consisting of plant-based foods in prevention of CVDs. We were not able to show the association of western dietary pattern and traditional dietary pattern with the risk of CVD development in the Iranian population.

Footnotes

Acknowledgments

The authors acknowledge the participants in the TLGS for their enthusiastic support and the staff of the Research Institute for Endocrine Sciences, TLGS Unit, for their valuable help. This study was supported by the Research Institute for Endocrine Sciences, SBMU, Tehran, Iran. The authors' contributions are as follows: S.E. and F.H.-E. designed the study, analyzed the data, and wrote the article; M.F. participated in collecting data; and F.A. and P.M. supervised the research and critically revised the article for important intellectual content.

Funding Source

Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences.

Author Disclosure Statement

No conflicting financial interests exist.

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Food Patterns and Framingham Risk Score in Iranian Adults: Tehran Lipid and Glucose Study: 2005–2011

Abstract

Background:

Methods:

Results:

Conclusion:

Introduction

Materials and Methods

Results

Discussion

Footnotes

Acknowledgments

Funding Source

Author Disclosure Statement

References