Journal of Obesity & Metabolic Syndrome

Search

Article

J Obes Metab Syndr 2024; 33(3): 240-250

Published online September 30, 2024 https://doi.org/10.7570/jomes24001

Copyright © Korean Society for the Study of Obesity.

Associations between Global Diet Quality Score and Risk of Metabolic Syndrome and Its Components: Tehran Lipid and Glucose Study

Firoozeh Hosseini-Esfahani1, Shahrzad Daei1, Azam Ildarabadi1, Glareh Koochakpoor2, Parvin Mirmiran1,* , Fereidoun Azizi3

1Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran; 2Maragheh University of Medical Sciences, Maragheh; 3Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Correspondence to:
Parvin Mirmiran
https://orcid.org/0000-0003-2391-4924
Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, No. 24 Areabi Street, Yemen Avenue, Chamran Highway, Tehran, Iran
Tel: +98-21-22432500
Fax: +98-21-22402463
E-mail: Parvin.mirmiran@gmail.com

The first two authors contributed equally to this study.

Received: January 9, 2024; Reviewed : February 3, 2024; Accepted: April 26, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Various food quality indicators have been proposed as tools for predicting metabolic syndrome (MetS). This study investigated the association between global diet quality score (GDQS) and the risks of developing MetS and its components.
Methods: In this secondary analysis, we included elective adult participants (n=4,548) from the Tehran Lipid and Glucose Study. Dietary data were collected by a valid and reliable semi-quantitative food frequency questionnaire. MetS was defined according to the Iranian modified National Cholesterol Education Program. Multivariable Cox proportional hazard regression models were used to estimate the incidence of MetS in association with GDQS.
Results: This study involved 1,762 men and 2,786 women with a mean±standard deviation age of 38.6±14.3 and 35.9±11.8 years, respectively. A total of 1,279 subjects developed MetS during the mean follow-up of 6.23 years. Incidence of MetS was associated with GDQS (hazard ratio [HR], 1.00; 0.90 [95% confidence interval, CI, 0.82 to 0.98]; 0.84 [95% CI, 0.76 to 0.91]; 0.80 [95% CI, 0.73 to 0.89]; P for trend <0.001) after adjusting for confounding variables. The healthy food group component of GDQS was related to MetS incidence. GDQS in the range of 12%–17% in the fourth quartile was associated with a decrease in incidence of MetS components. Both healthy and unhealthy food group components of the GDQS decreased the incidence of high triglycerides, high blood pressure, and high fasting blood glucose.
Conclusion: Higher GDQS was associated with a lower risk of the incidence of MetS or its components among Tehranian adults. Higher intake of healthy food group components and lower consumption of unhealthy food group components of the GDQS predicted lower MetS incidence and risk factors.

Keywords: Metabolic syndrome, Global diet quality score, Triglycerides, Blood pressure, Waist circumference, HDL cholesterol, Fasting blood glucose

Metabolic syndrome (MetS) is a group of metabolic disorders including insulin resistance, dyslipidemia, abdominal obesity, and high blood pressure (BP), which together increase the risk of coronary heart disease, diabetes, stroke, and other serious health problems.1 Nutritional recommendations to manage MetS have been proposed over the years, but the increasing prevalence of this syndrome in the past few decades has prompted health experts to reevaluate existing nutritional strategies.2 In the past, nutritional recommendations were based on the consumption of one or more nutrients, while current recommendations are mostly based on consumption of whole foods due to food–nutrient interactions. In this regard, food quality indicators have been proposed as tools for predicting MetS.

The diet quality index is an indicator of the diversity of key food groups consumed based on national recommended guidelines.3 The evolution of methods to measure diet quality in recent years has led to the emergence of nearly 30 food quality indicators.4 Higher scores on these food quality indicators have been associated with a lower prevalence of MetS in previous studies. In the study of Saraf-Bank et al.5, the risk of developing MetS was 28% lower in women who were in the upper quintile of healthy eating indices (HEI). In other studies, higher Dietary Approach to Stop Hypertension (DASH) scores were associated with a lower prevalence of MetS.6,7 However, these indicators have limitations that cannot be ignored; for example, many of these indicators need a food composition database which may not be available in countries with limited resources.8 Furthermore, use of many of these indicators is difficult and time-consuming, and finally, many of these indicators are based on data from developed countries; it is not clear to what extent they accurately predict nutritional adequacy in developing countries.9,10 All these limitations have led to the development of an overall metric of diet quality called the global diet quality score (GDQS). This index uses a combination of healthy and unhealthy food groups and not only addresses all the above-mentioned limitations but also allows comparison of food quality between communities.10 Furthermore, GDQS can be used to independently compare the relationships between healthy and unhealthy food groups with MetS.9

The effectiveness of the GDQS in tracking and predicting non-communicable diseases (NCDs) related to nutrition has been investigated in only a handful of studies. Fung et al.11 reported that a higher of GDQS was inversely related to the risk of diabetes in women; inverse associations between GDQS and cardiovascular disease or gestational diabetes have also been observed in other studies.12,13 However, no prior study has investigated the effectiveness of GDQS in predicting MetS or its components. Therefore, our aim in this study was to investigate the relationship between the GDQS with MetS and its components in a group of Tehranian adults.

Materials and methods

The Tehran Lipid and Glucose Study (TLGS) was performed prospectively on district no. 13 Tehran residents to determine risk factors for NCDs.14,15 Subjects of this cohort study were selected from among TLGS participants using a multistage stratified cluster random sampling technique. The first observation study was performed from 1999 to 2001 on 15,005 subjects aged ≥3 years, and follow-up observations were performed every 3 years; 2002–2005 (surveillance 2), 2005–2008 (surveillance 3), 2008–2011 (surveillance 4), 2012–2015 (surveillance 5), and 2015–2018 (surveillance 6) to identify recently developed diseases or risk factors. For our secondary analysis, among subjects participating in surveys 3 (n=3,665) and 4 (n=7,847; baseline), 8,091 subjects aged ≥18 years were randomly selected to complete a dietary assessment. Subjects with MetS diagnosis at baseline16 and pregnant or lactating women were excluded. Furthermore, individuals with over- or under-reporting of energy intake (≥4,200 or <800 kcal/day; n=780) were omitted.17 A total of 4,954 adult women and men with accessible biochemical, anthropometric, and dietary data were selected as the baseline population and they were tracked until survey 6. Subjects who did not provide follow-up data were removed from the analysis; as a result, 4,548 subjects remained and were included in the analysis (Fig. 1). Other independent lines of exclusion were done for the MetS components of abdominal obesity, high BP, low high-density lipoprotein cholesterol (HDL-C), high fasting blood glucose (FBG), and high triglycerides (TG) (Fig. 1).

All participants signed a written informed consent form before taking part in this study. The study was performed according to the tenants of the Declaration of Helsinki. The study proposal was approved by the ethics committee of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (IR.SBMU.ENDOCRINE.REC.1401.122) (Grant no. 43003733).

Dietary assessment

Skilled nutritionists gathered dietary data by applying a valid and reliable semi-quantitative food frequency questionnaire through face-to-face personal interviews. Usual dietary intake was determined based on household portion sizes during the last year.18-20

The portion amounts of foods consumed were converted from household measurements to grams per day. Due to the incompleteness of and limited data on the nutrient content of cooked food items in the Iranian Food Composition Table (FCT), United States Department of Agriculture (USDA) FCT data were used to determine the nutrient composition of food items (e.g., breads, legumes, nuts, and white or red meat). The Iranian FCT was applied to national foods not present in the USDA FCT.

Globally, the GDQS is a validated index that reflects nutritional adequacy and predicts major NCDs.10 Scores are based on 3 or 4 categories of eaten amounts (g/day) of specific food groups. It comprises 16 healthy, seven unhealthy, and two unhealthy food groups when consumed in excessive amounts (Supplementary Table 1). The achievable range score is 0–49 points. In the healthy food group, higher scores are given for higher intake, while in the unhealthy group, higher scores are given for lower intakes. In the unhealthy-when-consumed-in-excessive-amounts group, higher scores are given until a certain amount has been eaten, after which no additional score is added. Nutrient contents of food items and food groups were estimated cumulatively during follow-up visits from the first survey to the time of diagnosis of MetS or its components or the last follow-up survey.

Physical activity measurements

A Persian-translated modifiable activity questionnaire was applied to estimate physical activity levels by an expert interviewer.21 A previous study reported moderate validity and high reliability of this questionnaire.21 Based on routine daily activities, the time, frequency, and intensity of light, middle, high, and challenging activities were recorded during the last year. Activity data were transformed into metabolic equivalent/minutes/week.

Blood pressure and anthropometric measurements

Weight (kg) and height (cm) were determined using a digital scale (accuracy 100 g; Seca 707) with light clothes and no shoes in the standing position and a non-flexible tape measure (accuracy 0.5 cm), respectively. We measured waist circumference (WC) after exhaling without pressure on the surface of the body with light clothing. Our measurements were accurate to 0.1 cm. A standardized mercury sphygmomanometer was applied to determine systolic and diastolic BP (SBP and DBP; mmHg) according to accepted protocols described previously.

Biochemical analysis

Blood samples were collected between 7:00 AM and 9:00 AM after 12–14 hours of overnight fasting. FBG and 2 hours postprandial glucose levels were measured. Blood samples were analyzed on the day of blood collection using an Selectra 2 auto-analyzer at the TLGS research laboratory. FBG concentration was determined using an enzymatic colorimetric method as well as the glucose oxidase technique (Vital Scientific).

At follow-up appointments, all participants who did not take glucose-lowering medications were given 82.5 g glucose monohydrate solution (equivalent to 75 g anhydrous glucose) orally. TG and total cholesterol (TC) concentrations were estimated by an enzymatic colorimetric method using glycerol phosphate oxidase and cholesterol esterase and cholesterol oxidase, respectively (Pars Azmoon Inc.). HDL-C concentration was evaluated after precipitation of apolipoprotein B-containing lipoproteins with phosphotungstic acid. Inter- and intra-assay coefficients of variations of glucose were 2.2%. Inter- and intra-assay coefficients of variations of TG were 1.6% and 0.6%, respectively.

Outcome definition

Subjects with three or more of the following criteria were diagnosed with the MetS phenotype according to the Iranian modified National Cholesterol Education Program/Adult1,16: (1) abdominal obesity (WC ≥95 cm in men and women); (2) BP ≥130/85 mmHg or antihypertensive drug treatment; (3) HDL-C <1.30 mmol/L (<50 mg/dL) in women and <1.04 mmol/L (<40 mg/dL) in men or receiving drug treatment; (4) FBG ≥6.11 mmol/L (≥110 mg/dL) or drug treatment for hyperglycemia; and (5) TG ≥1.70 mmol/L (≥150 mg/dL) or drug treatment.

Statistical analysis

IBM SPSS software version 26 (IBM Co.), and STATA version 12 (StataCorp.) were used to analyze data. A two-sided P-value <0.05 was recognized as statistically significant. To compare the means and frequencies of participants’ baseline characteristics across quartiles of the GDQS, χ2 tests and one-way analysis of variance were used for categorical and continuous variables, respectively. P-values for trends across the GDQS subgroup categories were calculated by designating continuous variables in a linear regression model. Multivariable Cox proportional hazard regression analyses were used to determine hazard ratios (HRs) and 95% confidence intervals (CIs) for MetS and its components’ incidences. There were no interactions between GDQS and age or sex concerning MetS incidence. The first quartile was considered the reference quartile. Confounders were chosen based on the literature15,22,23 and were age, sex, education level (elementary, diploma, and higher diploma), total energy intake (kcal/day), saturated fat intake (percentage of energy), fiber (g/1,000 kcal), smoking (never smoked, past smoker, or current smoker), physical activity, and body mass index (BMI) at baseline. Moreover, each confounder was entered in the univariable Cox regression model; a two-tailed P<0.20 was applied to determine inclusion in the model.

In models to estimate the HRs of high BP, high TG, high FBG, and low HDL-C, the continuous amount of each risk factor at the baseline of study corresponding to each model was added to the adjustment factors. Furthermore, we applied each confounder in the univariable Cox regression model with a two-tailed P<0.2. The quantitative score of the GDQS was used as a continuous variable to estimate the P for trends in the Cox proportional hazard regression models. The definition of time to event was based on the mid-time between baseline and the event date (for incidence cases) or the time between baseline and the last follow-up (for censored subjects), whichever occurred first.

The proportional hazard assumption was confirmed by the Schoenfeld residuals test and plot of log [–log (survival)] versus log (time) to see if they were parallel.

Four thousand five hundred forty-eight subjects were included in the analysis of the relationship between GDQS and MetS (1,762 men and 2,786 women). Mean ages of men and women were 38.6±14.3 and 35.9±11.8 years, respectively. Among these subjects, there were 1,279 incident cases of MetS (incidence rate, 47.7; 95% CI, 45.1 to 50.4) with a median follow-up of 6.23 years. There were 1,002 (incidence rate, 66.0; 95% CI, 62.0 to 70.2), 1,391 (incidence rate, 44.8; 95% CI, 42.5 to 47.3), 1,373 (incidence rate, 57.5; 95% CI, 54.6 to 60.6), 1,009 (incidence rate, 45.8; 95% CI, 43.1 to 48.6), and 1,385 (incidence rate, 47.4; 95% CI, 44.9 to 49.9) incident cases of low HDL-C, high FBG, high TG, high WC, and high BP, respectively. The mean±standard deviation (SD) of the GDQS for the total population was 29.0±18.5.

In Table 1, we show the baseline attributes of subjects according to quartiles of GDQS. Subjects in the higher quartiles of the GDQS were older than subjects in the lower quartiles. There was a statistically significant association between sex and GDQS quartile. Percentage of current smokers was lower in the upper quartiles of the GDQS than the lower quartiles. In addition, individuals with a higher level of education and physical activity, BMI, and WC were in the higher quartiles of GDQS than the lower quartiles, but there were associations between TG, HDL-C, SBP, DBP, TC, and FBG with GDQS quartiles.

Participants in the higher quartiles of the GDQS had higher energy intake, carbohydrates, total protein, and fiber intake than lower quartiles (Table 2). Moreover, participants in the higher quartiles of the GDQS had lower total fat, poly-unsaturated fatty acid, and mono-unsaturated fatty acid intakes than those in lower quartiles. Individuals with higher total GDQS, healthy, unhealthy, and unhealthy-in-excessive-amount food group scores were in the upper quartiles of the GDQS (in the unhealthy and unhealthy-in-excessive amounts groups, scores are reversed, so a higher score indicates a lower intake of these food groups).

HRs (95% CI) of MetS and its components across quartiles of the GDQS and its subgroups are shown in Table 3. The incidence of MetS was inversely associated with GDQS quartiles (HR, 1.00; 0.90 [95% CI, 0.82 to 0.98]; 0.84 [95% CI, 0.76 to 0.91]; 0.80 [95% CI, 0.73 to 0.89]; P for trend <0.001) after adjusting for confounding variables. After adjusting for confounding variables, the healthy food component subgroup of the GDQS was conversely related to MetS incidence (HR, 1.00; 0.91 [95% CI, 0.83 to 0.99]; 0.87 [95% CI, 0.79 to 0.95]; 0.81 [95% CI, 0.72 to 0.89]; P for trend <0.001). The unhealthy food component of the GDQS showed no relationship with MetS incidence after adjusting for confounding factors.

GDQS was related to high FBG incidence after adjusting for confounding variables (HR, 1.00; 0.91 [95% CI, 0.84 to 0.98]; 0.86 [95% CI, 0.80 to 0.94]; 0.83 [95% CI, 0.76 to 0.92]; P for trend <0.001). Healthy and unhealthy components of the GDQS decreased the incidence of high FBG by 13% and 10%, respectively, in the fourth quartile of the adjusted models.

GDQS was related to low HDL-C incidence after adjusting for confounding variables (HR, 1.00; 0.90 [95% CI, 0.80 to 1.01]; 0.87 [95% CI, 0.77 to 0.97]; 0.88 [95% CI, 0.78 to 1.0]; P for trend=0.02). The unhealthy component of the GDQS was related to the incidence of low HDL-C in the adjusted model.

GDQS was related to abdominal obesity incidence after adjusting for confounding variables (HR, 1.00; 0.96 [95% CI, 0.87 to 1.05]; 0.90 [95% CI, 0.81 to 0.99]; 0.88 [95% CI, 0.79 to 0.98]; P trend <0.001). Healthy and unhealthy components of the GDQS decreased the incidence of high WC by 6% and 15%, respectively, in the fourth quartile of the adjusted models.

After controlling for confounders, a significant reverse association was observed between GDQS and the HR of high BP across quartiles of the GDQS (HR in fourth quartile, 0.83; 95% CI, 0.75 to 0.91; P trend <0.001). This significant finding was also found for the healthy and unhealthy components of the GDQS, indicating that healthy and unhealthy components of the GDQS decreased the incidence of high BP by 15% and 10%, respectively, in the fourth quartile of the adjusted models.

A significant reverse association was observed between GDQS and the HR of high TG across quartiles of the GDQS (HR in the fourth quartile, 0.84; 95% CI, 0.76 to 0.94; P for trend <0.001) after controlling for confounders. Healthy and unhealthy components of the GDQS decreased the incidence of high TG by 9% and 6%, respectively, in the fourth quartile of adjusted models.

Participants in the fourth quartile received the highest scores for consumption of healthy food group foods such as whole grains, other fruits, other vegetables, and citrus fruits while participants in the unhealthy food group received the highest scores from consumption of sugar-sweetened beverages, high fat dairy, and red meat (Supplementary Table 2).

We found an inverse relationship between GDQS and the risk of MetS incidence among Tehranian adults based on 6.23 years of follow-up. This association appears to be due intake of healthy food group items and limiting unhealthy foods. We also found inverse associations between the GDQS and all five MetS components in adjusted models and a decrease in the incidences of MetS components in the GDQS range of 12% to 17%. However, among the MetS components, significant reverse relationships between the healthy food group component of the GDQS and risk of high FBG, central obesity, high BP, and hypertriglyceridemia were found after adjusting for multiple confounding factors. The unhealthy food group component of the GDQS was inversely related to the incidence of all five MetS components.

GDQS is a food group-based score of diet quality that has found to be associated with health outcomes and nutrient quality in diverse regions of the world.10 The mean GDQS score in our study was higher than that reported in the 2010–2012 China National Nutrition and Health Survey of 19.8 (standard error, 0.03).24 Average scores of other studies are not available; however, a study of non-pregnant, non-lactating women of reproductive age in 10 African countries, as well as China, India, Mexico, and the United States reported that a GDQS ≥23 was associated with a low risk of both NCD risk and nutrient adequacy, while scores ≥15 and <23 were associated with moderate NCD risk and scores <15 were associated with a risk of NCD.10

GDQS significantly predicted MetS in urban China (odds ratio, 0.58; 95% CI, 0.45 to 0.75) and decreased odds of high WC, hypertension, and low HDL-C in rural China in cross-sectional studies.10,24 Unlike our study, GDQS was not predictive of MetS in rural or urban Mexico in cross-sectional studies9,10; however, GDQS was inversely related to BMI, WC, and low-density lipoprotein cholesterol in this population.9 In Mexican teachers’ and Nurses’ Health studies, a 1-SD increase in GDQS was related to less weight and WC gains.25,26 Fung et al.11 reported that a higher GDQS was associated with lower type 2 diabetes mellitus risk, mostly due to a lower dietary intake of unhealthy foods, in the United States women. Several studies that used different indices of diet quality including the DASH diet and dietary guidelines for adherence index (DGA-2005) reduced the risk of MetS in our population27; however, they all consumed more whole grains, fruits and vegetables, legumes, lean protein, and less sugar, red and processed meats, and refined grains in general.27 Like other diet quality indices, the GDQS index takes into account intake of healthy and unhealthy food groups, but in the GDQS, food groups are classified in a more detailed manner to form 25 groups, and nutrients are more extensively examined, so GDQS better predicts cardiovascular risk factors than other diet quality indices.10 Furthermore, calculating the GDQS is easier than calculating other diet quality metrics because GDQS relies only on food groups, not FCTs.10

The unhealthy food group component of the GDQS, driven by lower intake of unhealthy food groups and unhealthy-in-excessive amount food groups, contributed significantly to the association between GDQS score and MetS risk factors. Moderate consumption of red meat and full-fat dairy is scored in the GDQS to highlight the value of these foods as sources of protein, iron, and calcium in low-income countries.9,10,25

The unhealthy food group score displayed minimal variation across quartile groups, whereas the healthy food group score exhibited much larger variation across quartile groups. Additionally, it is worth noting that the effect size of HRs was also larger in the healthy component of the GDQS than in the unhealthy component of the GDQS.

Unlike in our study, the ATTICA study (a population-based health and nutrition survey in Greece) found that healthy food group intake was more important than unhealthy food group intake concerning protection against cardio-metabolic outcomes; this study also reported that lower consumption of unhealthy food groups was not associated with cardio-metabolic outcomes. However, this study was conducted in a population with moderate to high adherence to the Mediterranean diet.28

We found that lower intake of unhealthy food groups was strongly related to a lower risk of MetS components. Previous studies reported that high intake of fast foods, red and processed meats, refined grains (white rice), energy-dense nutrient-poor snacks (both salty and sweet), and sugar-sweetened beverages was directly associated with MetS or its components.15,27 The unhealthy food group component of the GDQS was inversely associated with MetS, low HDL-C, high WC, and high TG in China, and this component was related to a lower odds of high WC in India.10,24 The unhealthy food group component of the GDQS was not related to MetS in Mexico in a cross-sectional study.9 We found that a higher unhealthy food group component score was associated with a 17% reduced incidence of MetS in the third quartile of this score; however, this association disappeared in the fourth quartile. Overall unhealthy dietary pattern extracted from principal component analysis was associated with MetS in a previous study.18

In our study, the healthy food group component of the GDQS was inversely associated with MetS, high FBG, high WC, high BP, and high TG. Healthy dietary patterns with consumption of healthy components of the GDQS have been found in previous studies to be inversely associated with MetS and other dietary indices like HEI or DASH diet have been successful at predicting MetS or its components.5,7

HEI was established based on key recommendations of the 2015–2020 dietary guidelines for Americans, including 13 (nine healthy and four unhealthy) dietary components. DASH score includes eight (five healthy and three unhealthy) dietary components; however, the GDQS includes an extensive series of local food groups which are categorized into healthy and unhealthy food groups. This categorization allows further insight into the association between healthy and unhealthy food groups and outcomes of interest.

Fung et al.11 did not observe an association between the healthy food group components of the GDQS with diabetes, whereas in our study, the healthy food group component of the GDQS reduced the incidence of high FBG. This may be due to the higher score of healthy components in our population than in Fung et al.’ study.11

Strengths of this study include its large sample size study and cohort design in a Middle Eastern country. Lifestyle and dietary information were not self-reported. Multiple confounders were adjusted for in the models; however, other possible confounders like tea or coffee consumption were not adjusted for. We applied the GDQS, which has been validated and used in different regions of the world, so the result of our study are comparable with those conducted in different regions around the world with a wide range of nutritional threats and economic resources.

Previous analyses of the associations of the GDQS with cardiovascular risk factors have been performed in Western populations.24-26 Regional differences in food consumption and the effect of this on the high incidence of cardiovascular risk factors in developing countries warrants further exploration.15

In conclusion, we found that higher GDQS scores were associated with a lower risk of the incidence of MetS or its components among Tehranian adults. Our results showed that higher intake of healthy food group components and lower consumption of unhealthy food group components of the GDQS predicted a lower risk of MetS and its component factors. This score can be used to improve dietary strategies to reduce the increasing trend of MetS risk factors in the Iranian population.

The authors would like to thank all participants and Tehran Lipid and Glucose Study (TLGS) personnel for their participation.

Study concept and design: FHE and SD; acquisition of data: FHE, SD, and GK; analysis and interpretation of data: FHE, SD, and AI; drafting of the manuscript: FHE, SD, AI, and GK; critical revision of the manuscript: FHE, PM, and FA; statistical analysis: FHE, SD, and AI; administrative, technical, or material support: FHE, SD, and GK; and study supervision: PM and FA.

Fig. 1. Outline of the study participant selection process. TLGS, Tehran Lipid and Glucose Study; MetS, metabolic syndrome; HDL-C, high-density lipoprotein cholesterol.

Baseline characteristics of adult participants of the Tehran Lipid and Glucose Study according to GDQS

Variable Quartiles of the GDQS P
Q1 (≤26.2) Q2 (26.3–29.0) Q3 (29.1–31.7) Q4 (≥31.8)
Baseline age (yr) 34.7 ± 12.7 36.2 ± 12.7 37.7 ± 13.1 39.4 ± 12.6 < 0.001
Sex (% women) 58.1 62.3 61.0 63.7 0.044
Smoking (%) < 0.001
Current smoker 14.5 9.8 10.1 5.4
Education (%) 0.003
Higher diploma 25.2 27.9 30.9 31.5
Physical activity (MET/min/wk) 485.1 ± 674.3 520.5 ± 703.4 577.4 ± 850.3 630.2 ± 985.7 0.023
BMI (kg/m2) 25.3 ± 4.4 25.7 ± 4.3 26.0 ± 4.3 26.4 ± 4.3 < 0.001
WC (cm) 85.6 ± 12.1 86.5 ± 11.4 87.4 ± 11.4 87.6 ± 11.0 < 0.001
SBP (mmHg) 107.2 ± 13.5 107.4 ± 13.9 108.3 ± 13.4 108.3 ± 13.4 0.011
DBP (mmHg) 71.6 ± 9.6 71.3 ± 10.0 72.4 ± 9.3 72.5 ± 9.1 0.004
Total cholesterol (mg/dL) 177.1 ± 36.2 179.6 ± 36.1 181.7 ± 36.2 183.5 ± 36.8 < 0.001
HDL-C (mg/dL) 47.6 ± 10.6 48.1 ± 11.6 48.1 ± 10.7 48.9 ± 11.2 0.036
FBG (mg/dL) 89.4 ± 14.6 88.6 ± 10.0 89.2 ± 11.0 91.0 ± 17.6 < 0.001
TG (mg/dL) 97.5 (73.7–121.4) 97.5 (73.7–134.3) 98.5 (73.7–134.5) 102 (81.4–134.2) 0.147

Values are presented as mean±standard deviation or median (interquartile range). P-values were derived from analysis of variance and chi-square tests for continuous and dichotomous variables, respectively.

GDQS, global diet quality score; MET, metabolic equivalent; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; TG, triglyceride.

Dietary macronutrient intake and healthy and unhealthy food group scores of participants across quartiles of the GDQS

Dietary variable Quartiles of the GDQS P
Q1 (≤26.2) Q2 (26.3–29.0) Q3 (29.1–31.7) Q4 (≥31.8)
Energy intake, kcal/day 2,014 ± 591 2,307 ± 835 2,385 ± 594 2,652 ± 635 < 0.001
Carbohydrate (% of energy) 58.4 ± 6.2 58.7 ± 6.1 58.8 ± 5.2 59.0 ± 4.8 < 0.001
Protein (% of energy) 13.9 ± 2.8 14.7 ± 3.5 14.9 ± 2.2 15.4 ± 2.2 < 0.001
Total fat (% of energy) 30.6 ± 6.1 30.4 ± 7.5 29.9 ± 4.8 29.7 ± 4.5 < 0.001
SFA (% of energy) 10.2 ± 4.0 10.1 ± 6.1 9.7 ± 2.2 9.4 ± 1.8 0.135
PUFA (% of energy) 6.3 ± 2.0 6.2 ± 5.9 6.0 ± 1.5 6.1 ± 1.4 < 0.001
MUFA (% of energy) 10.5 ± 2.5 10.4 ± 6.0 10.0 ± 2.0 9.9 ± 2.1 < 0.001
Fiber (% of energy) 8.5 ± 2.5 10.0 ± 2.8 10.9 ± 3.1 12.5 ± 4.9 < 0.001
GDQS 23.6 ± 2.2 27.8 ± 0.8 30.5 ± 0.8 34.2 ± 1.7 < 0.001
Unhealthy component of the GDQS 7.9 ± 1.9 7.8 ± 1.8 8.1 ± 1.7 8.2 ± 1.7 < 0.001
Unhealthy-in-excessive amounts component of the GDQS 2.2 ± 0.8 2.5 ± 0.6 2.6 ± 0.6 2.7 ± 0.5 < 0.001
Healthy component of the GDQS 13.4 ± 2.8 17.4 ± 1.9 19.8 ± 1.9 23.3 ± 2.1 < 0.001

Values are presented as mean±standard deviation. P-values were derived from analysis of variance.

GDQS, global diet quality score; SFA, saturated fatty acid; PUFA, poly-unsaturated fatty acid; MUFA, mono-unsaturated fatty acid.

Hazard ratios (95% CI) of incidence of metabolic syndrome and its risk factors across quartiles of the GDQS and its categories* in adult participants of the Tehran Lipid and Glucose Study

Variable Quartiles P trend
Q1 (≤26.2) Q2 (26.3–29.0) Q3 (29.1–31.7) Q4 (≥31.8)
MetS
Cases/events 1,137/306 1,127/301 1,190/328 1,094/344
Person time (yr) 6,721.22 6,702.01 7,057.11 6,322.73
Incidence rate (95% CI) 45.55 (40.71–50.90) 44.90 (40.11–50.23) 46.47 (41.71–51.73) 54.43 (48.91–60.40)
GDQS Ref. 0.90 (0.82–0.98) 0.84 (0.76–0.91) 0.80 (0.73–0.89) < 0.001
Healthy Ref. 0.91 (0.83–0.99) 0.87 (0.79–0.95) 0.81 (0.72–0.89) < 0.001
Unhealthy Ref. 0.86 (0.79–0.94) 0.83 (0.76–0.90) 0.92 (0.84–1.01) 0.065
High FBG
Cases/events 1,291/323 1,295/334 1,376/368 1,241/366
Person time (yr) 7,652.03 7,749.06 8,230.81 7,359.28
Incidence rate (95% CI) 42.20 (37.84–47.02) 43.11 (38.70–47.95) 44.78 (40.38–49.57) 49.74 (44.83–55.07)
GDQS Ref. 0.91 (0.84–0.98) 0.86 (0.80–0.94) 0.83 (0.76–0.92) < 0.001
Healthy Ref. 0.92 (0.85–1.00) 0.88 (0.80–0.95) 0.87 (0.79–0.96) < 0.001
Unhealthy Ref. 0.91 (0.84–0.99) 0.90 (0.83–0.98) 0.90 (0.83–0.97) 0.003
Low HDL-C
Cases/events 662/230 703/243 695/242 718/287
Person time (yr) 3,581.33 3,899.04 3,855.06 3,841.39
Incidence rate (95% CI) 64.27 (56.44–73.08) 62.36 (54.90–70.61) 62.77 (55.36–71.22) 74.70 (66.55–83.81)
GDQS Ref. 0.90 (0.80–1.01) 0.87 (0.77–0.97) 0.88 (0.78–1.00) 0.023
Healthy Ref. 0.92 (0.83–1.03) 0.90 (0.80–1.01) 0.93 (0.82–1.06) 0.161
Unhealthy Ref. 0.95 (0.85–1.07) 0.90 (0.80–1.00) 0.92 (0.83–1.03) 0.042
High WC
Cases/events 966/240 965/249 1,024/279 939/277
Person time (yr) 5,705.80 5,684.82 5,995.43 5,429.36
Incidence rate (95% CI) 42.07 (37.08–47.71) 43.80 (38.66–49.51) 46.50 (41.33–52.32) 51.00 (45.35–57.39)
GDQS Ref. 0.96 (0.87–1.05) 0.90 (0.81–0.99) 0.88 (0.79–0.98) < 0.001
Healthy Ref. 1.00 (0.91–1.09) 0.93 (0.85–1.03) 0.94 (0.84–1.05) 0.033
Unhealthy Ref. 0.84 (0.76–0.92) 0.84 (0.77–0.92) 0.85 (0.77–0.93) < 0.001
High BP
Case/events 1,242/332 1,233/326 1,323/392 1,188/335
Person time (yr) 7,327.95 7,262.91 7,600.23 7,026.12
Incidence rate (95% CI) 45.36 (40.63–50.48) 44.84 (40.34–50.07) 51.55 (46.70–56.91) 47.66 (42.85–53.02)
GDQS Ref. 0.96 (0.88–1.04) 0.93 (0.86–1.01) 0.83 (0.75–0.91) < 0.001
Healthy Ref. 0.94 (0.86–1.02) 0.94 (0.86–1.03) 0.85 (0.77–0.94) < 0.001
Unhealthy Ref. 0.88 (0.81–0.96) 0.84 (0.77–0.91) 0.90 (0.83–0.97) 0.002
High TG
Cases/events 1,063/341 1,024/312 1,105/351 1,015/369
Person time (yr) 5,955.04 5,912.53 6,297.61 5,684.90
Incidence rate (95% CI) 57.21 (51.44–63.65) 52.70 (47.23–58.90) 55.71 (50.11–61.84) 64.90 (58.62–71.85)
GDQS Ref. 0.92 (0.84–1.00) 0.91 (0.83–1.00) 0.84 (0.76–0.94) 0.001
Healthy Ref. 0.95 (0.87–1.04) 0.96 (0.87–1.05) 0.91 (0.81–1.01) 0.036
Unhealthy Ref. 0.94 (0.86–1.03) 0.90 (0.82–0.99) 0.94 (0.86–1.03) 0.027

The model was adjusted for age and sex, physical activity, education, total energy intake, saturated fat intake, fiber, smoking, and body mass index at baseline. In models to estimate the hazard ratio of high BP, high TG, high FBG, and low HDL-C respectively, the continuous amount of each risk factor at the baseline of study corresponding to each model was added to the adjustment factors. Test for P of trend was performed based on GDQS or its categories as continuous variable in the adjusted Cox proportional hazard model.

*GDQS score categories: Healthy food group and the unhealthy food group scores. Food groups of the healthy component of the GDQS are citrus fruits, deep orange fruits, other fruits, dark green leafy vegetables, cruciferous vegetables, deep orange vegetables, other vegetables, legumes, deep orange tubers, nuts and seeds, whole grains, liquid oils, fish and shellfish, poultry and game meat, low fat dairy, eggs (this component was categorized based on quartiles of this score); Unhealthy components of the GDQS score are processed meat, refined grains and baked goods, sweets and ice cream, sugar sweetened beverages, juice, white roots and tubers, purchase deep fried foods, high fat dairy, and red meat. This component was categorized based on quartiles of this score.

CI, confidence interval; GDQS, global diet quality score; MetS, metabolic syndrome; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; WC, waist circumference; BP, blood pressure; TG, triglyceride.

  1. Grundy SM, Hansen B, Smith SC Jr, Cleeman JI, Kahn RA, et al; American Heart Association. Clinical management of metabolic syndrome: report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association conference on scientific issues related to management. Arterioscler Thromb Vasc Biol 2004;24:e19-24.
    Pubmed CrossRef
  2. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis 2017;11:215-25.
    Pubmed KoreaMed CrossRef
  3. Kirkpatrick SI, Reedy J, Krebs-Smith SM, Pannucci TE, Subar AF, Wilson MM, et al. Applications of the healthy eating index for surveillance, epidemiology, and intervention research: considerations and caveats. J Acad Nutr Diet 2018;118:1603-21.
    Pubmed KoreaMed CrossRef
  4. Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. BMJ 2018;361:k2396.
    Pubmed KoreaMed CrossRef
  5. Saraf-Bank S, Haghighatdoost F, Esmaillzadeh A, Larijani B, Azadbakht L. Adherence to Healthy Eating Index-2010 is inversely associated with metabolic syndrome and its features among Iranian adult women. Eur J Clin Nutr 2017;71:425-30.
    Pubmed CrossRef
  6. Kang SH, Cho KH, Do JY. Association between the modified dietary approaches to stop hypertension and metabolic syndrome in postmenopausal women without diabetes. Metab Syndr Relat Disord 2018;16:282-9.
    Pubmed CrossRef
  7. Saneei P, Fallahi E, Barak F, Ghasemifard N, Keshteli AH, Yazdannik AR, et al. Adherence to the DASH diet and prevalence of the metabolic syndrome among Iranian women. Eur J Nutr 2015;54:421-8.
    Pubmed CrossRef
  8. Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, et al. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 2012;142:1009-18.
    Pubmed KoreaMed CrossRef
  9. Castellanos-Gutiérrez A, Rodríguez-Ramírez S, Bromage S, Fung TT, Li Y, Bhupathiraju SN, et al. Performance of the Global Diet Quality Score with nutrition and health outcomes in Mexico with 24-h recall and FFQ data. J Nutr 2021;151(12 Suppl 2):143S-51S.
    Pubmed KoreaMed CrossRef
  10. Bromage S, Batis C, Bhupathiraju SN, Fawzi WW, Fung TT, Li Y, et al. Development and validation of a novel food-based Global Diet Quality Score (GDQS). J Nutr 2021;151(12 Suppl 2):75S-92S.
    Pubmed KoreaMed CrossRef
  11. Fung TT, Li Y, Bhupathiraju SN, Bromage S, Batis C, Holmes MD, et al. Higher Global Diet Quality Score is inversely associated with risk of type 2 diabetes in US women. J Nutr 2021;151(12 Suppl 2):168S-75S.
    Pubmed KoreaMed CrossRef
  12. Fung TT, Isanaka S, Hu FB, Willett WC. International food group-based diet quality and risk of coronary heart disease in men and women. Am J Clin Nutr 2018;107:120-9.
    Pubmed KoreaMed CrossRef
  13. Gicevic S, Gaskins AJ, Fung TT, Rosner B, Tobias DK, Isanaka S, et al. Evaluating pre-pregnancy dietary diversity vs. dietary quality scores as predictors of gestational diabetes and hypertensive disorders of pregnancy. PLoS One 2018;13:e0195103.
    Pubmed KoreaMed CrossRef
  14. Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials 2009;10:5.
    Pubmed KoreaMed CrossRef
  15. Hosseini-Esfahani F, Bahadoran Z, Moslehi N, Asghari G, Yuzbashian E, Hosseinpour-Niazi S, et al. Metabolic syndrome: findings from 20 years of the Tehran Lipid and Glucose Study. Int J Endocrinol Metab 2018;16(4 Suppl):e84771.
    Pubmed KoreaMed CrossRef
  16. Azizi F, Hadaegh F, Khalili D, Esteghamati A, Hosseinpanah F, Delavari A, et al. Appropriate definition of metabolic syndrome among Iranian adults: report of the Iranian National Committee of Obesity. Arch Iran Med 2010;13:426-8.
    Pubmed
  17. Willett WC. Issues in analysis and presentation of dietary data. In: Willett WC, editor. Nutritional epidemiology. 2nd ed. Oxford University Press; 1998. p. 321-46.
    CrossRef
  18. Hosseini-Esfahani F, Jessri M, Mirmiran P, Bastan S, Azizi F. Adherence to dietary recommendations and risk of metabolic syndrome: Tehran Lipid and Glucose Study. Metabolism 2010;59:1833-42.
    Pubmed CrossRef
  19. Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr 2010;13:654-62.
    Pubmed CrossRef
  20. Esfahani FH, Asghari G, Mirmiran P, Azizi F. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J Epidemiol 2010;20:150-8.
    Pubmed KoreaMed CrossRef
  21. Momenan AA, Delshad M, Sarbazi N, Rezaei Ghaleh N, Ghanbarian A, Azizi F. Reliability and validity of the Modifiable Activity Questionnaire (MAQ) in an Iranian urban adult population. Arch Iran Med 2012;15:279-82.
    Pubmed
  22. Hosseini-Esfahani F, Alafchi B, Cheraghi Z, Doosti-Irani A, Mirmiran P, Khalili D, et al. Using machine learning techniques to predict factors contributing to the incidence of metabolic syndrome in Tehran: cohort study. JMIR Public Health Surveill 2021;7:e27304.
    Pubmed KoreaMed CrossRef
  23. Esfandiar Z, Hosseini-Esfahani F, Mirmiran P, Habibi-Moeini AS, Azizi F. Red meat and dietary iron intakes are associated with some components of metabolic syndrome: Tehran Lipid and Glucose Study. J Transl Med 2019;17:313.
    Pubmed KoreaMed CrossRef
  24. He Y, Fang Y, Bromage S, Fung TT, Bhupathiraju SN, Batis C, et al. Application of the Global Diet Quality Score in Chinese adults to evaluate the double burden of nutrient inadequacy and metabolic syndrome. J Nutr 2021;151(12 Suppl 2):93S-100S.
    Pubmed KoreaMed CrossRef
  25. Angulo E, Stern D, Castellanos-Gutiérrez A, Monge A, Lajous M, Bromage S, et al. Changes in the Global Diet Quality Score, weight, and waist circumference in Mexican women. J Nutr 2021;151(12 Suppl 2):152S-61S.
    Pubmed KoreaMed CrossRef
  26. Fung TT, Li Y, Bromage S, Bhupathiraju SN, Batis C, Fawzi W, et al. Higher Global Diet Quality Score is associated with less 4-year weight gain in US women. J Nutr 2021;151(12 Suppl 2):162S-7S.
    Pubmed KoreaMed CrossRef
  27. Hosseini-Esfahani F, Hosseinpour-Niazi S, Asghari G, Bahadoran Z, Moslehi N, Golzarand M, et al. Nutrition and cardio-metabolic risk factors: findings from 20 years of the Tehran Lipid and Glucose Study. Int J Endocrinol Metab 2018;16(4 Suppl):e84772.
    Pubmed KoreaMed CrossRef
  28. Damigou E, Kouvari M, Chrysohoou C, Barkas F, Kravvariti E, Dalmyras D, et al. Diet quality and consumption of healthy and unhealthy foods measured via the Global Diet Quality Score in relation to cardiometabolic outcomes in apparently healthy adults from the Mediterranean region: the ATTICA Epidemiological Cohort Study (2002-2022). Nutrients 2023;15:4428.
    Pubmed KoreaMed CrossRef