Dietary diversity score is favorably associated with the metabolic syndrome in Tehranian adults



Assessing overall diet instead of the effects of a single nutrient on diet–disease relations may be more informative. This study was conducted to evaluate the relationship between dietary diversity score (DDS) and metabolic syndrome in Tehranian adults.


Cross-sectional study.


A representative sample of 581 healthy subjects aged over 18 y selected randomly from among participants of the Tehran Lipid and Glucose Study.


Usual dietary intake was assessed using a validated semi quantitative food frequency questionnaire. DDS was calculated based on scoring to the five-food group. The DDS range was 0–10. Weight and height were measured according to standard protocols and body mass index (BMI) was calculated. Fasting blood samples were taken for biochemical measurements and blood pressure was assessed according to standard methods. Metabolic syndrome was defined according to ATPIII. Subjects were categorized based on quartile cut-points of DDS.


Means (±s.d.) of age and BMI were 37±12 y and 25.7±4.3 kg/m2, respectively. Mean (±s.d.) of DDS was 6.15±1.02. The probability of having metabolic syndrome decreased with quartiles of DDS (odds ratios among quartiles: 1.00, 0.82, 0.76, 0.70, P<0.05, and odds ratios among quartiles after further adjustment for BMI: 1.00, 0.88, 0.80, 0.77, P<0.05). After controlling for confounders, a significantly decreasing trend was observed for the risk of having high blood pressure, impaired glucose homeostasis and high triglyceride levels.


DDS had inverse association with metabolic syndrome and some of its features in this cross-sectional study. A higher dietary diversity, therefore, might be associated with lower possibility of having some metabolic disorders.


The metabolic syndrome is a pattern of metabolic disturbances including central obesity, insulin resistance and abnormal glucose homeostasis, dyslipidemia and high blood pressure. 1 The risk of cardiovascular disease, the prevalence of which continues to rise in Iran,2 increases with the occurrence of metabolic syndrome.1 A recent study showed that over 30% of Tehranian adults have metabolic syndrome,3 which is higher than that reported from USA.4 Although this syndrome has an unknown heterogeneous etiology, environmental factors, including diet, play a major role in its development.5

Until now, several studies have investigated the role of nutrients in chronic diseases,6, 7 but comparatively little emphasis has been laid on the specific contribution of overall diet characteristics. Assessing overall diet instead of the effects of a single nutrient on diet–disease relations may be more informative. Dietary diversity score (DDS), which is an indicator of overall diet, is associated with nutrient adequacy ratio of some nutrients and quality of diet.8, 9, 10 According to previous studies, higher DDS is associated with greater intake of fiber,8, 9 as well as vitamin C9 and calcium.8 These nutrients have a negative association with cardiovascular disease, hypertension and obesity.11, 12, 13 On the other hand, it is reported that increased variety in the food supply may contribute to the development and maintenance of obesity14, 15, 16 Diverse diets have been shown to protect against chronic diseases such as cancer,17 as well as being associated with prolonged longevity18 and improved health status.19 Wahlqvist et al20 showed a significant correlation between total food variety and arterial wall index. Miller et al21 showed a significant association between a poorly diversified diet and hypertension.

A few observations studied the role of diet in the etiology of metabolic syndrome,22, 23 but there is no report about the association between dietary diversity and metabolic syndrome. Therefore, evaluating the association between DDS and central adiposity as a component of metabolic syndrome, which has not been addressed before, would be interesting.

The Tehran Lipid and Glucose Study (TLGS),24 has provided an opportunity to assess the diet–disease relationship from an epidemiologic perspective. Our previous study showed that DDS is a good indicator of nutritional adequacy of diet.8 The present study has been undertaken to assess the relation between DDS and metabolic syndrome in an urban adult population of Tehran.

Subjects and methods


This study was conducted within the framework of the TLGS, a prospective study performed on residents of district 13 of Tehran, with the aim of determining the prevalence of noncommunicable disease-risk factors and developing a healthy lifestyle to improve these risk factors.24 In the TLGS, 15 005 people aged 3 y and over were selected by a multistage cluster random sampling method. A representative sample of 1476 people, aged 3 y and over, including 861 subjects aged 18–74 y was randomly selected for dietary assessment. Subjects with a prior history of cardiovascular disease, diabetes and stroke were excluded because of possible changes in diet. We also excluded subjects whose reported daily energy intakes were not between 800 kcal/day (3347 kJ/day) and 4200 kcal/day (17537 kJ/day).25 Users of antihypertensive drugs were excluded because blood pressure was one of the determining factors for metabolic syndrome. This left 581 subjects (295 men and 286 women) aged 18–74 y, having all the relevant data, for the present analysis. The proposal of this study was approved by the research council of Endocrine Research Center of Shaheed Beheshti University of Medical Sciences and informed written consent was obtained from each subject.

Assessment of dietary intake

Usual dietary intake was assessed by using a 168-item semiquantitative food frequency questionnaire (FFQ). The questionnaires were administered by trained dietitians, who had ≤5 y of experience in the Nationwide Food Consumption Survey project.26 The FFQ consisted of a list of foods and a standard serving size for each (Willett format; Rimm et al27). In this form, subjects were asked about frequency of consumption of the given serving.27 Participants were asked to report their frequency of consumption of a given serving of each food item during the previous year on a daily (eg bread), weekly (eg rice, meat) or monthly (eg fish) basis. Portion sizes of consumed foods were converted to grams using household measures.28 Each food and beverage was then coded according to the prescribed protocol and analyzed for the content of energy and the other nutrients using Nutritionist III software (Version 7.0; N Squared Computing, Salem, OR, USA), which was designed for evaluation of Iranian foods.

The reliability of the FFQ in this cohort was evaluated in a randomly chosen subgroup of 132 subjects by comparing nutrient consumption determined by responses to the FFQ on two occasions. The correlation coefficients for the repeatability of grain, vegetable, fruit, dairy and meat were 0.85, 0.79, 0.71, 0.74 and 0.70, respectively. The FFQ also had high reliability for nutrients. For example, the correlation coefficients were 0.81 for dietary fiber, 0.75 for calcium and 0.71 for vitamin C. Comparative validity was determined by comparison with intake estimated from the average of twelve 24-h dietary recalls (one for each month of the year). Preliminary analysis of the validation study showed that nutrients were moderately correlated between these two methods after controlling for total energy intake. These correlation coefficients were 0.69 for dietary fiber, 0.64 for vitamin C and 0.68 for calcium intake. Overall, these data indicate that the FFQ provides reasonably valid measures of the average long-term dietary intake.

Dietary diversity score

We used a procedure developed by Kant et al18, 29, 30 based on five groups of bread-grains, vegetables, fruits, meats and their substitutions and dairy for scoring dietary diversity. The main groups mentioned were divided into 23 subgroups. These subgroups show the dietary diversity across the groups of the Food Guide Pyramid. We expanded the number of bread-grain group into seven subgroups (refined bread, biscuits, macaroni, whole bread, corn flakes, rice, refined flour) to reflect the diversity and importance of plant-based foods and to better reflect the number of servings of grain products recommended in the Food Guide Pyramid. Fruit was divided into two subgroups (fruit and fruit juice, berries and citrus), vegetable was divided into seven subgroups (vegetables, potato, tomato, starchy vegetables, legumes, yellow vegetables, green vegetables). The subgroups of meat were four (red meat, poultry, fish, eggs), and for dairies, we considered three subgroups (milk, yogurt, cheese). To be counted as a ‘consumer’ for any of the food group categories, a respondent needed to consume at least one-half serving on 1 day as defined by the Food Pyramid quantity criteria. Each of the five broad food categories receive a maximum diversity score of 2 out of the 10 possible score points. The maximum and minimum scores of diversity were between 0 and 10. Within each of the food groups, the score reflects the percentage of the possible maximum score.31 Total score was the sum of the scores of the five main groups.16

Assessment of other variables

Weight was measured, while the subjects were minimally clothed without shoes using digital scales and recorded to the nearest 100 g. Height was measured in a standing position, without shoes, using a tape meter, while the shoulders were in a normal position. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Waist circumference (WC) was measured at the narrowest level and that of the hip at the maximum level over light clothing, using an unstretched tape meter, without any pressure to body surface; measurements were recorded to the nearest 0.1 cm. Waist-to-hip ratio was calculated as WC divided by hip circumference. To avoid subjective error, all measurements were taken by the same person.8 A blood sample was drawn between 0700 and 0900 into vacutainer tubes from all study participants after 12–14 h overnight fasting. Blood samples were taken in a sitting position according to the standard protocol and centrifuged within 30–45 min of collection. All blood lipid and glucose analyses were carried out at the TLGS research laboratory on the day of blood collection.28 Blood glucose was measured by enzymatic colorimetric method using glucose oxidase. Serum total cholesterol and triglyceride concentration were measured by commercially available enzymatic reagents (Pars Azmoon, Iran) adapted to Selectra autoanalyzer. HDL-cholesterol was measured after precipitation of the apolipoprotein B containing lipoproteins with phosphotungistic acid. LDL-cholesterol was calculated according to the Friedwald method.32 It was not calculated when serum triglyceride concentration was greater than 400 mg/dl. Assay performance was monitored once every 20 tests interval using the lipid control serum, Percinorm (normal range) and Percipath (Pathologic range), wherever applicable (Boehringer Mannheim, Germany; cat. no. 1446070 for Percinorm and 171778 for Percipath). Lipid standard (C.f.a.s., Boehringer Mannheim, Germany; Cat. no. 759350) was used to calibrate the selectra 2 auto-analyzer for each day of laboratory analyses. All samples were analyzed when internal quality control met the acceptable criteria. Inter- and intra-assay coefficients of variation were 1.6 and 0.6% for TGs.33 Blood pressure was measured twice after the participants sat for 15 min.2 Additional covariate information regarding age, smoking habits,33 physical activity,34 medical history and current use of medications3 was obtained using validated questionnaires, as reported earlier.

Definition of terms

Metabolic syndrome was defined as the presence of three or more of the following components as recommended by ATP III:35 (1) abdominal adiposity (WC>102 cm in men and >88 cm in women); (2) low serum HDL-cholesterol (<40 mg/dl for men and <50 mg/dl for women); (3) high serum triglyceride levels (150 mg/dl); (4) elevated blood pressure (130/85 mmHg); and (5) abnormal glucose homeostasis (fasting plasma glucose level ≥110 mg/dl).

Statistical methods

Statistical Package for Social Science (SPSS Inc., Chicago IL, USA; Version 9.05) was used for all statistical analyses. In separate models, first-order interactions between sex and DDS were entered to determine whether associations were similar between men and women. There were no significant interactions by sex on the association of DDS and metabolic risk factors. Cut-points for quartiles of DDS were calculated and subjects were categorized based on quartile cut-points: 1st, <3.5; 2nd, 3.5–<6; 3rd, 6–<8; and 4th, ≥8. The number of subjects in the quartiles are different because we used quartile cut-points for categorizing subjects, not the distribution of subjects based on quartiles. Significant differences in general characteristics across quartile categories of DDS were searched using one-way analysis of variance. If there was a significant main effect, Bonferroni was used to detect pairwise differences. χ2 test was used to detect any significant differences in the distribution of subjects across quartile categories of DDS with regard to qualitative variables. We determined multivariate- (age, sex, physical activity, smoking, BMI, WHR, total energy intake, percent of energy from fat and estrogen replacement therapy) adjusted means for components of metabolic syndrome and age-, sex- and energy-adjusted means for dietary variables across quartiles of DDS by using GLM. Analysis of covariance with the correction of Bonferroni was used to compare these means. All correlation coefficients reported were calculated as Pearson's correlation coefficients. To determine the association of DDS with components of metabolic syndrome, we used multivariable logistic regression in two models. The first model was controlled for age (y), energy intake (kcal/day), percent of energy from fat, use of blood pressure medication (yes or no), cigarette smoking (categorical), physical activity level (light, moderate, severe) and current estrogen replacement therapy among women (yes or no), and the second model was controlled for the items mentioned as well as for BMI. In all multivariate models, first quartile of DDS was considered as a reference. The Mantel–Haenszel extension χ2 test was performed to assess the overall trend of an increasing quartile of DDS associated with an increasing or decreasing likelihood of being classified as high risk.


Mean and s.d. of DDS was 6.15±1.02. The maximum and minimum scores of diversity were related to the fruit (1.48±0.60) and bread-grain (0.85±0.24) groups, respectively. Mean and s.d. of age and anthropometric measures as well as the distribution of subjects with regard to obesity, smoking and physical activity status across quartile categories of DDS are shown in Table 1. Compared with participants in the upper category, those in the lower category of DDS were younger and had lower values of anthropometric measures. There was no significant difference regarding waist across quartiles of DDS. Most subjects had light physical activity in all quartiles group of DDS. There was no regular trend across the quartiles of DDS to be daily smokers. The prevalence of obesity was higher among those in the upper category of DDS compared to lower one. Age-, sex- and energy-adjusted means for dietary variables across quartile categories of dietary diversity score are presented in Table 2. A higher DDS was associated with a healthier diet, with those in the upper category also consuming less cholesterol and meat and more dietary fiber, fruit, vegetables and vegetable oil. The higher DDS was positively associated with total intakes of dietary fiber (r=0.43), calcium (r=0.51) and vitamin C (r=0.48).

Table 1 Characteristics of the participants of the TLGS by quartiles of DDS
Table 2 Dietary intakes of participants of the TLGS by DDS quartile categoriesa

Multivariate-adjusted means for metabolic risk factors as well as the distribution of subjects suffering from these risk factors across quartile categories of DDS are shown in Table 3. A higher DDS was associated with lower level of systolic and diastolic blood pressure, fasting blood glucose and WC. There were no significant differences across quartile categories of DDS for serum HDL-cholesterol and triglyceride. Table 4 shows the odds ratio and 95% confidence intervals for having metabolic syndrome and its features across quartile categories of DDS in two models. The probability of having metabolic syndrome and some metabolic risk factors like diabetes, high blood pressure and high triglyceride level decreased with quartiles of DDS in two models. Odds ratio of having metabolic syndrome, diabetes, high blood pressure and high triglyceride level became weaker after adjusting for BMI.

Table 3 Metabolic risk factors by quartile categories of DDS
Table 4 Multivariate-adjusted odds ratio and 95% confidence intervals for having metabolic syndrome and its features


The present study, conducted in a representative sample of Tehranian adults, showed an inverse association of DDS with the metabolic syndrome as well as elevated blood pressure, high triglyceride level and abnormal glucose homeostasis. According to our knowledge, this is the first study reporting the inverse association of DDS with metabolic syndrome.

An inverse association between DDS and metabolic risks may be attributed to higher consumption of the healthier food groups associated with higher DDS. Subjects who had higher DDS consumed more fiber, fruit, vegetable, and vegetable oil and less meat and cholesterol in this study. However, the apparently protective effect of DDS persisted in multivariate models accounting for known coronary risk factors. Second, some intermediary events, including dyslipidemia or high blood pressure, could have led to changes in diet and may therefore confound the association between DDS and metabolic risks. However, any confounding effects from these indications would tend to attenuate the protective effect of DDS because the tendency would be for subjects to increase their intake of healthy food, which could be associated with higher DDS, if they perceived themselves to be at elevated risk for chronic diseases.

Although people in the higher categories of DDS had higher energy intake and were more obese, the higher amount of energy could be attributed to further consumption of vegetable, fruit, vegetable oil and dairy, that is, healthy nutrition patterns. Therefore, it is suggested that increasing the diversity score of fruit and vegetable may be associated with reducing the probability of metabolic risks and increasing the whole dietary diversity, which include the diversity of fat and sugar may be associated with an elevated risk of obesity. In the present study, the probability of having abdominal obesity was lower in the fourth quartile of DDS, but neither the trend of odds ratios nor the odds ratios for having metabolic syndrome was significant across the quartiles of DDS. Therefore, DDS might not be associated with abdominal obesity, and as DDS is calculated based on the Food Guide Pyramid, this result is not very surprising because Food Guide Pyramid does not control calory intake. Furthermore, after adjusting the effect of physical activity, there was no significant trend in the odds ratios of having abdominal obesity.

In the present study, higher DDS was associated with lower risk of high triglyceride level, high blood pressure and abnormal glucose homeostasis. Therefore, increased dietary diversity was related to reduced risk of metabolic risks. These results may be attributed to higher consumption of vegetables and fruits in subjects with higher DDS. Furthermore, people in higher quartiles of DDS consumed higher amounts of dairy products. Previous studies showed that dairy consumption is adversely correlated with high blood pressure.11, 12, 13, 36 Osborn et al37 found that calcium had an important role in blood pressure regulation and that adequate intake of calcium might reduce the risk of hypertension. Some mechanisms responsible for this effect of calcium may be the natriuretic effect, regulation of the sympathetic nervous system, and prevention of vessel constriction.38 In the study by Azizi et al,12 an inverse correlation between blood pressure and calcium intake was seen. On the other hand, calcium could bind to fatty acids and inhibit the absorption of fats.13

After adjusting for BMI, the relationship between DDS and metabolic risks became weaker. Therefore, the relationship between DDS and metabolic risks could to some extent be mediated by BMI. The previous study conducted in this area of Tehran showed that DDS is correlated with nutrient adequacy ratio of vitamin C and calcium.22 As mentioned in previous studies, these two nutrients have a negative association with cardiovascular disease, hypertension and obesity.11, 12, 13 Previous studies showed that DDS is a good indicator of diet quality.9, 22, 39This study has shown that it is also correlated with metabolic syndrome. As previous researches in the field of the relationship between DDS and chronic diseases focused mostly on cancer;40, 41 the present study provided the opportunity for investigating this association with other chronic diseases, via, metabolic syndrome. Unfortunately, there is no study regarding the association between the metabolic syndrome and DDS for us to compare our results with.

There are several limitations that should be considered when examining the results of this study. In this study, we used cross-sectional data to identify the association of DDS with the metabolic syndrome. Future studies using longitudinal data will provide stronger evidence on this association. High DDS appear to reflect an overall healthier lifestyle that may not have been accurately captured and controlled in our analysis, resulting in residual confounding. Subjects with known CAD, diabetes and stroke were excluded from the study. These exclusions may have reduced the likelihood of finding significant trends in odds of having metabolic risks across quartile categories of DDS. Also, chronic diseases, for example, the metabolic syndrome are heterogeneous and besides dietary pattern, other factors such as hereditary factors may need to be considered. Additionally, most of the risk factors are inter-related and this could confound the relationship between DDS and metabolic risk factors. As we used the Kant et al11, 29 method, we were unable to consider vegetable oil in the calculation of DDS.

This study has several strengths: using a population representative sample of Tehran, using logistic regression models and simultaneous adjustment of confounding variables in the association of DDS to metabolic syndrome and finding a cross-sectional relationship between DDS with metabolic syndrome and some of its features like high blood pressure, high triglyceride level and abnormal glucose homeostasis. In conclusion, DDS was inversely associated with metabolic syndrome. Lower dietary diversity was associated with higher probability of having metabolic risks such as elevated blood pressure, high serum triglyceride level and abnormal glucose homeostasis. Therefore, efforts should be made to increase the diversity scores of diets, this and following the recommendations of dietary guidelines, potentially will be associated with lower probability of having the metabolic syndrome.


  1. 1

    Reaven GM . Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988; 37: 1595–1607.

  2. 2

    Azizi F, Ghanbarian A, Madjid M, Rahmani M . Distribution of blood pressure and prevalence of hypertension in Tehran adult population: Tehran Lipid and Glucose Study (TLGS), 1999–2000. J Hum Hypertens 2002; 16: 305–312.

  3. 3

    Azizi F, Emami H, Salehi P, Ghanbarian A, Mirmiran P, Mirbolooki M, Azizi T . Cardiovascular risk factors in the elderly: the Tehran Lipid and Glucose Study. J Cardiovasc Risk 2003; 10: 65–73.

  4. 4

    Ford ES, Giles WH, Dietz WH . Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA 2002; 16: 356–359.

  5. 5

    Hollenberg NK . Genetic versus environmental etiology of the metabolic syndrome among male and female twins. Curr Hypertens Rep 2002; 4: 178.

  6. 6

    Rimm EB, Willett WC, Hu FB, Sampson L, Colditz GA, Manson JE, Hennekens C, Stampfer MJ . Folate and vitamin B6 from diet and supplements in relation to risk of coronary heart disease among women. JAMA 1998; 279: 359–364.

  7. 7

    Wolk A, Manson JE, Stampfer MJ, Colditz GA, Hu FB, Speizer FE, Hennekens CH, Willett WC . Long-term intake of dietary fiber and decreased risk of coronary heart disease among women. JAMA 1999; 281: 1998–2004.

  8. 8

    Mirmiran P, Azadbakht L, Esmaillzadeh A, Azizi F . Dietary diversity score in adolescents — a good indicator of the nutritional adequacy of diets: Tehran lipid and glucose study. Asia Pacific J Clin Nutr 2004; 13: 56–60.

  9. 9

    Hatloy A, Torheim LE, Oshaug A . Food variety — a good indicator of nutritional adequacy of the diet? A case study from an urban area in Mali, West Africa. Eur J Clin Nutr 1998; 52: 891–898.

  10. 10

    Drewnowski A, Ahlstrom Henderson S, Driscoll A, Rolls BJ . The dietary variety score: assessing diet quality in healthy young & older adults. J Am Diet Assoc 1997; 97: 266–271.

  11. 11

    Jorde R, Bonaa KH . Calcium from dairy products, vitamin D intake, and blood pressure: the Tromso Study. Am J Clin Nutr 2000; 71: 1530–1535.

  12. 12

    Azizi F, Mirmiran P, Azadbakht L . Predictors of cardiovascular risk factors in Tehranian adolescents: Tehran Lipid & Glucose Study. Int J Vitam Nutr Res 2004; 74: 307–312.

  13. 13

    Weaver CM, Heaney RP . Calcium. In: Shils M et al (eds) Modern Nutrition in Health and Disease 9th edn. Lippincott, Williams and Wilkins: Philadelphia; 1998. p 148.

  14. 14

    Raynor HA, Epstein LH . Dietary variety, energy regulation, and obesity. Psychol Bull 2001; 127: 325–341.

  15. 15

    Kennedy E . Dietary diversity, diet quality, and body weight regulation. Nutr Rev 2004; 62: S78–S81.

  16. 16

    Raynor HA, Jeffery RW, Tate DF, Wing RR . Relationship between changes in food group variety, dietary intake, and weight during obesity treatment. Int J Obes Relat Metab Disord 2004; 28: 813–820.

  17. 17

    McCollough ML, Feskanich D, Stampfer MJ, Fiovannucci EL, Rimm EB, Hu FB, Spiegelman D, Hunter DJ, Colditz GA, Willett WC . Diet quality and major chronic disease risk in men and women: moving toward improved dietary guidance. Eur J Clin Nutr 2002; 57: 930–939.

  18. 18

    Kant AK, Schatzkin A, Ziegler RG . Dietary diversity and subsequent cause of specific mortality in the NHANESI epidemiologic follow up study. J Am Coll Nutr 1995; 14: 233–238.

  19. 19

    Hodgson JM, Hsu-Hage BH, Wahlqvist ML . Food variety as a quantitative descriptor of food intake. Ecol Food Nutr 1994; 32: 137–148.

  20. 20

    Wahlqvist ML, Lo CS, Myers KA . Food variety is associated with less macrovascular disease in those with type II diabetes and their healthy controls. J Am Coll Nutr 1989; 8: 515–523.

  21. 21

    Miller WL, Crabtree BF, Evans DK . Exploratory study of the relationship between hypertension and diet diversity among Saba Islanders. Public Health Rep 1992; 107: 426–432.

  22. 22

    Pereira MA, Jacobs Jr DR, Van Horn L, Slattery ML, Kartashov AI, Ludwig DS . Dairy consumption, obesity, and the insulin resistance syndrome in young adults: the CARDIA Study. JAMA 2002; 287: 2081–2089.

  23. 23

    Mennen L, Lafay L, Feskens EJ, Novak M, Lepinary P, Balkau B . Possible protective effect of bread and dairy products on the risk of the metabolic syndrome. Nutr Res 2000; 20: 335–347.

  24. 24

    Azizi F, Rahmani M, Emami H, Madjid M . Tehran Lipid and Glucose Study: rational and design. CVD Prev 2000; 3: 242–247.

  25. 25

    Fung TT, Rimm EB, Spiegelman D, Rifai N, Tofler GH, Willett WC, Hu FB . Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr 2001; 73: 61–67.

  26. 26

    Kimiagar SM, Ghaffarpour M, Houshiar-Rad A, Hormozdyari H, Zellipour L . Food consumption pattern in the Islamic Republic of Iran and its relation to coronary heart disease. East Mediterr Health J 1998; 4: 539–547.

  27. 27

    Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC . Reproducibility and validity of an expanded self administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol 1992; 135: 1114–1126.

  28. 28

    Ghaffarpour M, Houshiar-Rad A, Kianfar H . The Manual for Household Measures, Cooking Yields Factors and Edible Portion of Food. Keshavarzi Press: Tehran; 1999. (in Farsi).

  29. 29

    Kant AK, Schatzkin A, Ziegler RG, Nestle M . Dietary diversity in the US population, NHANES II, 1976–1980. J Am Diet Assoc 1991; 15: 1526–1531.

  30. 30

    US Department of Agriculture. USDA'S Food Guide Pyramid Booklet. US Department of Agriculture: Washington, DC; 1996.

  31. 31

    Haines PS, Siega-Riz AM, Popkin BM . The Diet Quality Index revised: a measurement instrument for populations. J Am Diet Assoc 1999; 99: 697–704.

  32. 32

    Friedewald WT, Levy RI, Fredrickson DS . Estimation of the concentration of low- density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972; 8: 499–502.

  33. 33

    Azizi F, Rahmani M, Ghanbarian A, Emami H, Salehi P, Mirmiran P, Sarbazi N . Serum lipid levels in an Iranian adults population: Tehran Lipid and Glucose Study. Eur J Epidemiol 2003; 18: 311–319.

  34. 34

    Mirmiran P, Mohammadi F, Allahverdian S, Azizi F . Estimation of energy requirements for adults: Tehran lipid and glucose study. Int J Vitam Nutr Res 2003; 73: 193–200.

  35. 35

    National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002; 106: 3143–3421.

  36. 36

    Mirmiran P, Esmaillzadeh A, Azizi F . Dairy consumption and body mass index: an inverse relationship. Int J Obese Relat Metab Disord 2005; 29: 115–121.

  37. 37

    Ascherio A, Rimm EB, Giovannucci EL, Colditz GA, Rosner B, Willett WC, Sacks F, Stampfer MJ . A prospective study of nutritional factors and hypertension among US men. Circulation 1992; 86: 1475–1484.

  38. 38

    Kotchen TA, Kotchen JM . Nutrition, diet and blood pressure. In: Shils M, et al (eds) Modern Nutrition in Health & Disease, 9th edn. Lippincott, Williams and Wilkins: Philadelphia; 1999. pp 1217–1228.

  39. 39

    Torheim LE, Ouattara F, Diarra MM, Thiam FD, Barikmo I, Hatloy A, Oshaug A . Nutrient adequacy and dietary diversity in rural Mali: association and determinants. Eur J Clin Nutr 2004; 58: 594–604.

  40. 40

    Fernandez E, Negri E, La Vecchia C, Franceschi S . Diet diversity and colorectal cancer. Prev Med 2000; 31: 11–14.

  41. 41

    La Vecchia C, Munoz SE, Braga C, Ferandez E, Decarli A . Diet diversity and the risk of colorectal cancer in northern Italy. Cancer Epidemiol Biomarkers Prev 1996; 5: 433–436.

Download references


We express our appreciation to the participants of the Tehran Lipid and Glucose Study for their enthusiastic support and the staff of the Endocrine Research Center, Tehran Lipid and Glucose Study unit for their valuable help in the conducting of this study. We acknowledge greatly Dr LE Torheim, from Akershus University College, Lillestrom, Norway, for sharing with us his valuable PhD thesis and papers in the field of dietary diversity score.

Author information



Corresponding author

Correspondence to F Azizi.

Additional information

LA and PM designed the study, collected and analyzed the data and wrote the manuscript. FA supervised the research.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Azadbakht, L., Mirmiran, P. & Azizi, F. Dietary diversity score is favorably associated with the metabolic syndrome in Tehranian adults. Int J Obes 29, 1361–1367 (2005).

Download citation


  • dietary diversity score
  • metabolic syndrome
  • high blood pressure
  • impaired glucose homeostasis

Further reading