Abstract
Background/Aim
Metabolic syndrome (MetS) increases the risk of type 2 diabetes and cardiovascular diseases, with oxidative stress playing an important role in its development. This study aimed to examine the association between the Composite Dietary Antioxidant Index (CDAI), a measure of total dietary antioxidant intake, and the risk of MetS in Iranian adults.
Methods
This prospective cohort study was conducted within the framework of the Tehran Lipid and Glucose Study (TLGS) and included 4,628 adults (> 18 years) with complete dietary assessment data from the third TLGS examination and newly enrolled participants from the fourth examination. Dietary intake was assessed using a validated 168-item food frequency questionnaire, and the CDAI was calculated based on six components: vitamins A, C, and E, manganese, selenium, and zinc. MetS was defined according to the Joint Interim Statement criteria. Multivariable-adjusted Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for MetS.
Results
The mean age and body mass index (BMI) of participants (40.4% men) were 36.1 ± 12.8 years and 25.5 ± 4.28 kg/m², respectively. The median (25th–75th interquartile range) CDAI score was − 0.63 (− 2.70 to 1.88). During a mean follow-up of 9.3 ± 4.2 years, 1,650 (35.7%) incident cases of MetS were identified. After adjustment for age, sex, smoking status, physical activity, and energy intake, a lower risk of MetS was observed across quartiles of CDAI (HR = 0.74; 95% CI: 0.59–0.93; P for trend = 0.013). Further adjustment for all potential confounders strengthened this association, with a 36% lower risk of MetS across CDAI quartiles (HR = 0.64; 95% CI: 0.51–0.80; P for trend < 0.001).
Conclusions
In this cohort of Iranian adults, higher CDAI scores were associated with a lower risk of MetS, providing the first evidence of this association in the Iranian population.
Keywords: Dietary pattern, Antioxidant, Metabolic syndrome, Adults
Introduction
The metabolic syndrome (MetS) refers to a collection of metabolic disorders that elevate the risk of type 2 diabetes (T2D) and cardiovascular disease [1], which can lead to a higher mortality rate and poorer health factors [2]. The main metabolic risk factors include increased blood pressure (BP), high blood sugar and insulin resistance, increased waist circumference (WC) and weight, lower high-density lipoprotein-cholesterol (HDL-C), and elevated triglycerides (TGs) [1]. Globally, MetS affects an estimated 31.4% of adults [3]. In Iran, an updated meta-analysis in 2025 reported a pooled prevalence of approximately 31% among adults [4]. The high prevalence of MetS in Iran has been linked to changes in dietary habits, physical inactivity, and urbanization, among other lifestyle and environmental factors [4].
Oxidative stress occurs when the balance between the production of reactive oxygen species (ROS) and the cell’s ability to neutralize them is disturbed. ROS can damage proteins, lipids, and DNA, leading to cellular deterioration. Oxidative stress plays a role in the development of chronic diseases, including metabolic syndrome [5] Recent studies have shown that dietary patterns that include Dietary antioxidants are vital in modifying inflammation and oxidative stress, which can lead to a decrease in chronic disease and health maintenance [6]. Recently, a dietary index called The Composite Dietary Antioxidant Index (CDAI) has been calculated by the whole intake of dietary antioxidants, including micronutrients such as vitamins (A, C, and E), carotenoids, selenium, and zinc. Higher CDAI scores contribute to a lower risk of chronic diseases [7].
To the best of our knowledge, while the association between the CDAI score and MetS risk has been assessed in recent studies, the existing literature is predominantly limited to the National Health and Nutrition Examination Survey (NHANES) population [2, 8–10]. According to several reports from NHANES cohort study, individuals with higher CDAI scores have a lower risk of developing MetS [2, 8–10], however, this relationship has not yet been explored within the Middle East and North Africa (MENA) region, such as Iranian adults. Despite the increasing evidence highlighting the protective benefits associated with diets abundant in antioxidants, the function of CDAI within the context of MetS remains insufficiently investigated in Iran. Therefore, in the current cohort study, we aimed to investigate the possible association between CDAI and MetS among Tehranian adults using the TLGS data.
Materials and methods
Study population
This prospective cohort study was conducted within the framework of the Tehran Lipid and Glucose Study (TLGS), an ongoing population-based study designed to identify risk factors for chronic diseases. The TLGS collects prospective data at three-year intervals.
In the present study, 3,568 participants were randomly selected from the third TLGS survey (2006–2008), and 7,930 participants from the fourth survey (2009–2011) with complete dietary data were included; duplicate records were subsequently removed. Participants from the third survey who under- or over-reported energy intake (< 800 kcal/day or > 4,200 kcal/day, respectively; n = 225) were excluded. However, if these individuals reported energy intake within the acceptable range during the fourth survey (n = 98), their dietary data from the fourth survey were considered as baseline nutritional data. After further excluding participants from the fourth survey with implausible energy intake (n = 261), a total of 7,357 adults aged> 18 years with complete dietary data were included.
Participants with a history of myocardial infarction or stroke (n = 70), cancer (n = 16), pregnant or lactating women (n = 185), and those with missing data on metabolic syndrome (MetS; n = 75) or prevalent MetS at baseline (n = 2,246) were excluded. Some individuals met more than one exclusion criterion. Among the 4,848 participants eligible for follow-up, 220 were lost to follow-up, and 4,628 participants were included in the final analysis (follow-up rate: 95.46%).
Measurements
Anthropometric measurements
Anthropometric indices, including height, weight, and waist circumference (WC), were measured according to standard protocols. Body weight was measured to the nearest 100 g using a digital scale, with participants wearing light clothing and no shoes. Height was measured to the nearest 1 cm using a measuring tape, with participants standing barefoot in an upright position. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). WC was measured over light clothing at the level of the umbilicus using a non-elastic tape measure, without applying pressure to the skin, and recorded to the nearest 0.1 cm. All measurements were performed by trained personnel to minimize measurement error.
Dietary intake assessment
Dietary intake was assessed by trained interviewers using a validated 168-item food frequency questionnaire (FFQ) administered during phases 3 and 4 of the TLGS [11]. Participants reported the frequency of consumption of each food item over the previous year using standard household measures, which were converted into daily gram intakes. The antioxidant content of foods was calculated using the United States Department of Agriculture (USDA) food composition database. The Composite Dietary Antioxidant Index (CDAI) was calculated by summing standardized intakes of six dietary antioxidants, including vitamins A, C, and E, manganese, selenium, and zinc, according to the method proposed by Wright et al. [12].
 |
the daily intake of antioxidants i;
The mean of xi over the entire cohort for antioxidant i
the SD for 
Physical activity assessment
Physical activity levels were assessed using the Modified Activity Questionnaire (MAQ) [13]. Participants reported the frequency and duration of various physical activities, which were recorded on a daily, monthly, or yearly basis. Each activity was assigned a metabolic equivalent of task (MET) value based on its intensity. One MET corresponds to an oxygen consumption of 3.5 mL/kg/min at rest. Total physical activity was expressed as MET-minutes per week.
Biochemical measurements
Blood samples were collected between 7:00 and 9:00 AM after 12–14 h of overnight fasting to measure serum levels of triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and fasting plasma glucose (FPG). Samples were centrifuged within 30–45 min at 3,000 rpm for 10 min. Biochemical analyses were performed using Pars Azmoun kits. TC was measured using cholesterol esterase and cholesterol oxidase methods (intra-assay CV: 2.2%). HDL-C was determined by precipitation of apo B–containing lipoproteins using phosphotungstic acid (intra-assay CV: 0.5%; inter-assay CV: 2%). TG concentrations were measured enzymatically using glycerol phosphate oxidase (intra-assay CV: 0.6%; inter-assay CV: 1.6%). FPG was measured using enzymatic glucose kits, with intra- and inter-assay coefficients of variation of 2.2%.
Blood pressure measurement
Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by a trained physician using a standard mercury sphygmomanometer and the Korotkoff sound technique. Participants were seated and rested for at least 15 min prior to measurement. Two readings were taken at intervals of at least 30 s, and the average of the two measurements was recorded as the final blood pressure value.
Assessment of other variables
Information on age, sex, smoking status, educational attainment, marital status, occupational status, and medical history was collected using validated, pre-tested questionnaires administered through face-to-face interviews.
Outcome ascertainment
Metabolic syndrome was defined according to the Joint Interim Statement (JIS) criteria as the presence of three or more of the following components: (1) HDL-C < 40 mg/dL in men or < 50 mg/dL in women; (2) SBP ≥ 130 mmHg or DBP ≥ 85 mmHg, or use of antihypertensive medication; (3) WC ≥ 95 cm for both men and women [14], (4) TGs ≥ 150 mg/dL or use of lipid-lowering medication; and (5) FPG ≥ 100 mg/dL or use of glucose-lowering medication [15].
Statistical analysis
Statistical analyses were performed using SPSS software (version 20). The normality of continuous variables was assessed using the Kolmogorov–Smirnov test and histogram inspection. Participants were categorized into quartiles based on CDAI scores. Continuous and categorical variables are presented as mean ± standard error and percentages, respectively. Differences across CDAI quartiles were assessed using linear regression for continuous variables and the chi-square test for categorical variables.
Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the incidence of MetS across CDAI quartiles. For participants who developed MetS, follow-up time was calculated as the midpoint between the visit at which MetS was first diagnosed and the preceding visit at which the participant was MetS-free. Multivariable models were adjusted for age, sex, smoking status, physical activity, energy intake, educational level, marital status, occupational status, and baseline values of FPG, WC, SBP, TG/HDL-C ratio, dietary fiber intake, and monounsaturated fatty acid (MUFA) intake. Additionally, HRs were calculated per one standard deviation (SD) increase in CDAI score. A two-sided P-value < 0.05 was considered statistically significant.
Results
The mean ± SD age and BMI of participants (40.4% men) were 36.1 ± 12.8 years and 25.5 ± 4.3 kg/m², respectively. The median (25th–75th interquartile range) CDAI score in the study population was − 0.63 (− 2.70 to 1.88). During a mean follow-up of 9.3 ± 4.2 years, 1,650 (35.7%) cases of MetS were identified.
The baseline characteristics, anthropometric measurements, and clinical data of participants by CDAI score quartiles are presented in Table 1. Participants in the highest CDAI quartile were more likely to be men and had higher physical activity levels, WC, and SBP compared to those in the lowest quartile (P < 0.05). No significant differences were observed for the other variables across CDAI quartiles.
Table 1.
Baseline characteristics of participants according to quartiles of composite dietary antioxidant index score
| Variables | Q1(n = 1157) | Q2 (n = 1157) | Q3 (n = 1157) | Q4 (n = 1157) | P-trend |
|---|---|---|---|---|---|
| Composite dietary antioxidant index score | -4.09 ± 0.99 | -1.69 ± 0.58 | 0.48 ± 0.73 | 5.30 ± 3.91 | |
| Age (years) | 36.6 ± 13.2 | 35.7 ± 12.8 | 36.3 ± 12.8 | 36.1 ± 12.6 | 0.628 |
| Men n(%) | 421 (36.4%) | 454 (39.2%) | 483 (41.7%) | 504 (43.6%) | 0.003 |
| Body mass index (kg/m2) | 25.5 ± 4.5 | 25.4 ± 4.1 | 25.4 ± 4.1 | 25.8 ± 4.3 | 0.135 |
| physical activity (MET/hour/week) | 59.9 (20.8–96.7) | 61.2 (23.8–102.4) | 68.0 (24.2–108.0) | 63.0 (23.8–106.5) | 0.001 |
| Smoking n(%) | 122 (10.5%) | 113 (9.8%) | 135 (11.7%) | 116 (10.0%) | 0.454 |
| Waist circumference (cm) | 84.5 ± 12.3 | 84.4 ± 11.6 | 85.1 ± 11.5 | 85.8 ± 11.8 | 0.002 |
| Fasting plasma glucose (mg/dl) | 87.4 ± 13.0 | 87.2 ± 12.6 | 88.1 ± 15.0 | 87.4 ± 11.1 | 0.555 |
| Triglycerides (mg/dl) | 99.0 (74.0–137.0) | 100.0 (73.0–139.0) | 102.0 (75.0–138.0) | 102.0 (75.0–140.0) | 0.332 |
| Total cholesterol (mg/dl) | 179.9 ± 36.2 | 177.5 ± 36.9 | 178.1 ± 36.5 | 178.2 ± 35.0 | 0.343 |
| High density lipoprotein- cholesterol (mg/dl) | 44.8 ± 10.7 | 44.1 ± 10.3 | 44.6 ± 10.0 | 44.5 ± 10.4 | 0.736 |
| Systolic blood pressure (mmHg) | 106.8 ± 14.2 | 106.9 ± 13.5 | 108.0 ± 13.6 | 108.2 ± 13.7 | 0.004 |
| Diastolic blood pressure (mmHg) | 70.8 ± 9.5 | 70.7 ± 9.2 | 70.9 ± 9.5 | 71.0 ± 9.5 | 0.458 |
| Academic educated (yes, %) | 274 (23.7%) | 299 (25.8%) | 295 (25.5%) | 285 (24.6%) | 0.630 |
| Marital status (married, %) | 809 (69.9%) | 789 (68.2%) | 815 (70.4%) | 792 (68.5%) | 0.590 |
| Occupation status (employed, %) | 957 (82.7%) | 931 (80.5%) | 946 (81.8%) | 924 (79.9%) | 0.291 |
Data represented as mean ± SD, or median (IQR 25–75) for continuous variables and percentage for categorical variables
*Linear regression and chi-square test were used to test the differences across quartiles of composite dietary antioxidant index score for continuous and categorical variables, respectively
Table 2 presents the dietary intake of macronutrients, micronutrients, and CDAI score components according to CDAI quartiles. Intakes of total energy, carbohydrate, protein, fiber, sodium, vitamin A, vitamin C, vitamin E, selenium, zinc, and manganese significantly increased across CDAI quartiles (P-trend < 0.05). Conversely, intakes of total fat and MUFA decreased significantly across quartiles (P-trend < 0.05). No significant differences were observed for saturated or polyunsaturated fat intake among participants across CDAI quartiles.
Table 2.
Dietary intakes of participants across quartiles of composite dietary antioxidant index score
| Nutritional variables | Quartiles of CDAI | P-trend | |||
|---|---|---|---|---|---|
| Q1(n = 1157) | Q2 (n = 1157) | Q3 (n = 1157) | Q4 (n = 1157) | ||
| Dietary intakes | |||||
| Energy (Kcal/day) | 1572.0 ± 336.8 | 2126.0 ± 385.9 | 2539.7 ± 449.1 | 3111.4 ± 570.1 | < 0.001 |
| Carbohydrate (% of energy) | 57.4 (52.6–61.6) | 57.6 (53.6–61.7) | 57.8 (53.6–62.2) | 60.0 (55.1–64.7) | < 0.001 |
| Protein (% of energy) | 13.7 (12.3–15.4) | 13.9 (12.5–15.4) | 14.1 (12.5–15.6) | 14.6 (13.0–16.5) | < 0.001 |
| Fat (% of energy) | 30.5 (26.0–35.1) | 30.7 (26.6–34.9) | 30.7 (26.8–35.0) | 30.1 (25.8–35.1) | 0.020 |
| Saturated fats (% of energy) | 10.0 (8.4–12.1) | 9.9 (8.3–12.0) | 9.9 (8.2–11.9) | 9.6 (7.8–11.8) | 0.332 |
| Monounsaturated fats (% of energy) | 10.1 (8.4–12.1) | 10.1 (8.5–11.8) | 10.2 (8.7–11.8) | 10.0 (8.4–12.0) | 0.015 |
| Polyunsaturated fats (% of energy) | 5.8 (4.5–7.6) | 5.9 (4.7–7.4) | 6.0 (4.8–7.3) | 5.8 (4.7–7.2) | 0.095 |
| Dietary fiber (g per 1000 kcal/day) | 14.9 (11.4–19.6) | 16.2 (13.0–20.6) | 17.6 (14.1–21.9) | 19.6 (16.1–24.1) | < 0.001 |
| Sodium (mg/day) | 2516 (1940 − 3340) | 3188 (2605–3849) | 3654 (3014–4545) | 4475 (3606–5562) | < 0.001 |
| CDAI components | |||||
| Vitamin A (mcg/day) | 291.3 (215.8–374.4) | 444.2 (348.7–558.8) | 600.1 (471.5–752.6) | 859.2 (627.2–1140.5) | < 0.001 |
| Vitamin C (mg/day) | 74.6 (51.0–102.6) | 118.2 (86.7–157.5) | 167.3 (124.4–215.6) | 248.2 (174.1–345.8) | < 0.001 |
| Vitamin E (mg/day) | 7.3 (5.6–8.7) | 9.7 (8.3–12.1) | 11.9 (10.1–14.4) | 15.3 (12.7–18.5) | < 0.001 |
| Selenium (mcg/day) | 74.2 (61.1–90.0) | 104.0 (87.4–121.7) | 124.1 (103.3–149.0) | 159.5(126.0–204.2) | < 0.001 |
| Zinc (mg/day) | 7.8 (6.5–9.1) | 10.8 (9.5–12.1) | 13.2 (11.5–14.7) | 17.4 (14.8–20.5) | < 0.001 |
| Manganese (mg/day) | 4.9 (3.9–6.1) | 7.0 (5.7–8.4) | 8.81 (6.8–10.8) | 12.5 (9.3–16.3) | < 0.001 |
Data represented as median (IQR 25–75)
Abbreviations: CDAI composite dietary antioxidant index
*Linear regression was used to test the differences across quartiles of CDAI score
Hazard ratios (HRs) and 95% confidence intervals (CIs) for MetS across CDAI quartiles are presented in Table 3. In Model 2, after adjusting for age, sex, smoking status, physical activity, and daily energy intake, the risk of MetS decreased across CDAI quartiles (HR = 0.74 for the highest vs. lowest quartile; 95% CI: 0.59–0.93; P-trend = 0.013). In the fully adjusted model, which accounted for age, sex, smoking status, physical activity, energy intake, education, marital status, occupation, and baseline values of FPG, WC, BP, TGs/HDL-C ratio, dietary intake of fiber and MUFA, the MetS risk decreased by 36% across CDAI quartiles (HR = 0.64 for the highest vs. lowest quartile; 95% CI: 0.51–0.80; P-trend < 0.001). Additionally, Table 3 reports HRs (95% CIs) for MetS per one SD increase in the CDAI score. The risk of MetS decreased significantly per 1 SD increase in the CDAI score in both Model 2 (HR = 0.96; 95% CI: 0.94–0.99; P-trend = 0.012) and the fully adjusted model (HR = 0.88; 95% CI: 0.81–0.96; P-trend = 0.004).
Table 3.
Hazard ratio (95% CI) of metabolic syndrome across quartiles of composite dietary antioxidant index score
| Quartiles of composite dietary antioxidant index score | |||||||
|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | Per 1 SD | P-value | |
|
Follow up period (year) |
9.09 ± 4.27 | 9.31 ± 4.29 | 9.48 ± 4.11 | 9.45 ± 4.13 | |||
| Case/Total | 425 / 1157 | 417 / 1157 | 401 / 1157 | 407 / 1157 | 1650 / 4628 | ||
| Model 1* | 1.00 (Ref) | 0.94 (0.82–1.07) | 0.90 (0.78–1.03) | 0.91 (0.79–1.05) | 0.202 | 0.98 (0.97–1.00) | 0.202 |
| Model 2† | 1.00 (Ref) | 0.89 (0.76–1.03) | 0.78 (0.66–0.93) | 0.74 (0.59–0.93) | 0.013 | 0.96 (0.94–0.99) | 0.012 |
| Model 3‡ | 1.00 (Ref) | 0.83 (0.72–0.97) | 0.74 (0.61–0.88) | 0.64 (0.51–0.80) | < 0.001 | 0.88 (0.81–0.96) | 0.004 |
*Model 1: Crude Model
† Model 2: adjusted for age, sex, smoking, physical activity, and energy intake
‡ Model 3: adjusted for model 2 and education status, marital status, occupation status, baseline values of FPG, WC, SBP, TG/HDL-C, and dietary intakes of fiber and MUFA
Table 4 presents the results of sensitivity analyses based on the fully adjusted model. The inverse association between CDAI quartiles and the risk of MetS remained consistent after excluding participants with academic education, smokers, obese individuals, single participants, and those who were unemployed. Across all sensitivity analyses, participants in the highest CDAI quartile had a significantly lower risk of MetS compared with those in the lowest quartile, with hazard ratios ranging from 0.64 to 0.68 and P-values < 0.05.
Table 4.
Sensitivity analyses of the association between composite dietary antioxidant index score and metabolic syndrome
| Sensitivity analysis | Q2 vs. Q1 | Q3 vs. Q1 | Q4 vs. Q1 | P-value |
|---|---|---|---|---|
| Participants without academic education (n = 3475) | 0.86 (0.72–1.02) | 0.76 (0.62–0.93) | 0.64 (0.49–0.83) | 0.009 |
| Non-smokers (n = 4142) | 0.81 (0.69–0.95) | 0.71 (0.59–0.86) | 0.66 (0.52–0.84) | 0.004 |
| Non-obese participants (n = 3870) | 0.87 (0.73–1.03) | 0.76 (0.62–0.93) | 0.68 (0.52–0.88) | 0.027 |
| Married participants (n = 3133) | 0.83 (0.70–0.98) | 0.72 (0.59–0.88) | 0.66 (0.51–0.85) | 0.007 |
| Employed participants (n = 3758) | 0.86 (0.73–1.01) | 0.73 (0.60–0.88) | 0.65 (0.51–0.84) | 0.004 |
Data represented as ORs (95% CIs)
Models adjusted for age, sex, smoking status, physical activity, energy intake, education status, marital status, occupation status, baseline FBG, WC, SBP, TG/HDL-C, and dietary intakes of fiber and MUFA
For sensitivity analyses, the variable used to define each exclusion was not included in the corresponding model
Discussion
In this prospective study among the Iranian population within the framework of the TLGS cohort, with a mean follow-up of 9.3 ± 4.2 years, higher CDAI scores were associated with a reduced risk of MetS after adjusting for potential confounders.
While recent studies have examined the relationship between CDAI scores and MetS risk, most were limited to the NHANES population [2, 8–10], and no research to date has examined this association in the MENA region, including Iran. Our findings are consistent with previous research showing that higher CDAI scores are inversely associated with MetS risk. For example, Zhou et al. reported a negative association between high CDAI scores and MetS [8], and Liao et al., using NHANES data, observed similar inverse associations [9]. Another NHANES-based cohort study also found a protective effect of higher CDAI scores on MetS risk [10], and Liu et al. reported a downward trend in MetS risk with increasing CDAI scores [2]. Overall, these results align with our findings, suggesting that higher dietary antioxidant intake, as reflected by greater CDAI scores, is associated with a reduced risk of MetS.
Previous research has investigated the potential protective effect of CDAI score components on MetS risk [16–19]. A meta-analysis showed that dietary selenium intake is inversely associated with MetS risk, likely due to its role as a cofactor for thioredoxin reductases and glutathione peroxidases, contributing to antioxidant defense [16, 20]. Dietary zinc intake has also been inversely related to MetS risk, potentially through reductions in glucose concentrations, oxidative stress, and inflammatory cytokine production, as well as improvements in glycemic indices and lipid profiles [17, 21, 22]. While manganese, as a cofactor of superoxide dismutase (Mn-SOD), may help prevent inflammatory diseases by neutralizing excess superoxide and regulating inflammatory cytokines [23], a meta-analysis by Wong et al. found no significant association between dietary or serum manganese and MetS risk [24]. Vitamins C and E have been shown to reduce MetS risk through their antioxidant and anti-inflammatory activities [18, 19, 25, 26], whereas vitamin A (retinol) appears not to be significantly associated with MetS [27]. Vitamin C supplementation may further improve endothelial function and BP by enhancing nitric oxide production and acting as a cofactor for hydroxylase enzymes [28, 29]. The CDAI score, which reflects the combined and synergistic effects of these nutrients, may therefore represent an indicator of MetS risk.
The protective effect of CDAI against MetS may involve two biological mechanisms. First, oxidative stress plays a significant role in MetS development, and dietary antioxidants can counteract mitochondrial dysfunction, cell apoptosis, lipid peroxidation, and protein and DNA damage, thereby maintaining cellular integrity and reducing oxidative stress [9, 26, 30–33]. Second, inflammation contributes to insulin resistance and the deterioration of MetS components, such as obesity, hyperglycemia, and hypertriglyceridemia [34]. Nutrients with anti-inflammatory properties, such as vitamin E, may improve these components by inhibiting pro-inflammatory cytokine responses through pathways like NF-κB [25]. These findings highlight the potential benefit of including antioxidant- and anti-inflammatory-rich foods in dietary strategies aimed at reducing MetS risk.
This study has several strengths. First, it is the first prospective study to investigate the association between the CDAI score and MetS risk in the MENA region. Second, the prospective design of this study is considered a strength because it allows for the examination of the association between the CDAI score and the subsequent risk of MetS. Third, the study benefits from an adequate follow-up period, a large sample size, and the use of validated questionnaires for dietary intake and physical activity tailored to the Iranian population. Some limitations should be acknowledged. Despite using validated questionnaires, measurement error and recall bias in dietary and physical activity data cannot be ruled out. In addition, although participants with specific diets and chronic conditions associated with a high likelihood of medication or supplement use (e.g. individuals with a history of myocardial infarction, stroke, and cancer) were excluded, unmeasured medications and supplement intake in our included population may still have influenced the results. Alcohol consumption, a potential confounder, could not be assessed due to cultural and legal restrictions in Iran. Nutrient and energy intakes were estimated using the USDA FCT because of incomplete data in the Iranian FCT. Finally, although many potential confounders were adjusted for, unmeasured or unknown variables may still influence the observed associations.
Conclusions
Higher adherence to a diet rich in antioxidants, as indicated by a higher CDAI score, may be associated with a lower risk of MetS. While similar studies have been conducted in US populations, additional large-scale prospective studies with long follow-up periods in diverse populations are needed to confirm these findings.
Acknowledgements
We sincerely thank all the participants for their enthusiastic cooperation, as well as the staff of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran, for their valuable support and assistance.
Clinical trial number
Not applicable.
Abbreviations
- BMI
Body mass index
- BP
Blood pressure
- CDAI
Composite Dietary Antioxidant Index
- CI
Confidence intervals
- DBP
Diastolic blood pressure
- FFQ
Food frequency questionnaire
- FPG
Fasting plasma glucose
- HDL-C
High-density lipoprotein-cholesterol
- HR
Hazard Ratio
- JIS
Joint Interim Statement
- MAQ
Modified Activity Questionnaire
- MENA
Middle East and North Africa
- MET
Metabolic equivalent
- MetS
Metabolic syndrome
- NHANES
National Health and Nutrition Examination Survey
- ROS
Reactive oxygen species
- SBP
Systolic blood pressure
- SD
Standard deviation
- TC
Total cholesterol
- T2D
Type 2 diabetes
- TGs
Triglycerides
- TLGS
Tehran Lipid and Glucose Study
- USDA
United States Department of Agriculture
- WC
Waist circumference
Authors' contributions
H.F., H.A., and F.T. contributed to the conception, hypothesis, and design of the study. H.F., MK.J., H.A., Z.D., N.S., and F.T. contributed to the research, statistical analyses, and manuscript preparation. P.M. and F.A. supervised the study. All authors contributed to the manuscript review and critical intellectual content. All authors approved the final version of the manuscript. H.F. and H.A. equally contributed to this work (equally first author).
Funding
This work was supported by the Research Institute for Endocrine Sciences, Shahid Beheshti University Medical Sciences, Tehran, Iran.
Data availability
The datasets analyzed in the current study are available from the corresponding author on reasonable request.
Declaration
Ethics approval and consent to participate
Informed written consent was obtained from all participants. All procedures performed in studies involving human participants adhered to the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study protocol was approved by the research council of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hossein Farhadnejad and Hamid Ahmadirad contributed equally to this work.
Contributor Information
Farshad Teymoori, Email: teymoori.f68@gmail.com.
Parvin Mirmiran, Email: parvin.mirmiran@gmail.com.
References
- 1.Peixoto TDN, Lima L, Lima JG, Sena-Evangelista KCM, Carnauba RA, Lajolo FM, et al. Dietary total antioxidant capacity and polyphenol intake benefit the inflammatory profile and HDL-C concentrations in individuals with metabolic syndrome. Nutr Res. 2025;139:100–12. [DOI] [PubMed] [Google Scholar]
- 2.Liu W, Xu Y, Xiao L, Li K, Liu Q. Composite dietary antioxidant index is associated with the prevalence of metabolic syndrome in females: results from NHANES 2011–2016. Front Nutr. 2025;12:1529332. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wilar G, Suhandi C, Fukunaga K, Shigeno M, Kawahata I, Abdulah R, et al. Effects of nanocurcumin supplementation on metabolic syndrome: A systematic review and meta-analysis of randomized controlled trials. Pharmacol Res. 2025;213:107641. [DOI] [PubMed] [Google Scholar]
- 4.Moradkhani A, Mohammadzadeh P, Assadi S, Saed L, Baradaran HR, Moradi Y. Prevalence of metabolic syndrome and its components in Iran: an updated meta-analysis. BMC Endocr Disord. 2025;25(1):8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Jin S, Kang PM. A Systematic Review on Advances in Management of Oxidative Stress-Associated Cardiovascular Diseases. Antioxid (Basel). 2024;13(8):923. [DOI] [PMC free article] [PubMed]
- 6.Laganà M, Piticchio T, Alibrandi A, Le Moli R, Pallotti F, Campennì A, et al. Effects of Dietary Habits on Markers of Oxidative Stress in Subjects with Hashimoto’s Thyroiditis: Comparison Between the Mediterranean Diet and a Gluten-Free Diet. Nutrients. 2025;17(2):363. [DOI] [PMC free article] [PubMed]
- 7.He Q, Hu F, Wei W, Li J, Yu Y, Zhang H. Association between the composite dietary antioxidant index and cardiovascular-kidney-metabolic syndrome among U.S. adults: evidence from NHANES 2007–2018. Front Nutr. 2025;12:1600651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Zhou Q, Zhou L, Chen X, Chen Q, Hao L. Composite dietary antioxidant index is associated with reduced prevalence of metabolic syndrome but not mortality in metabolic syndrome: Results from NHANES 2001–2018. Prev Med Rep. 2024;41:102704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Liao ZY, Xiao MH, She Q, Xiong BQ. Association between the composite dietary antioxidant index and metabolic syndrome: evidence from NHANES 2003–2018. Eur Rev Med Pharmacol Sci. 2024;28(4):1513–23. [DOI] [PubMed] [Google Scholar]
- 10.Zhu Y, Bing J, Zheng L, Hao P, Teng X, Wan L. The association between composite dietary antioxidant index and the metabolic syndrome: NHANES 2007–2018. J Funct Foods. 2024;116:106200. [Google Scholar]
- 11.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(5):654–62. [DOI] [PubMed] [Google Scholar]
- 12.Wright ME, Mayne ST, Stolzenberg-Solomon RZ, Li Z, Pietinen P, Taylor PR, et al. Development of a comprehensive dietary antioxidant index and application to lung cancer risk in a cohort of male smokers. Am J Epidemiol. 2004;160(1):68–76. [DOI] [PubMed] [Google Scholar]
- 13.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(5):279–82. [PubMed] [Google Scholar]
- 14.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(5):426–8. [PubMed] [Google Scholar]
- 15.Zabetian A, Hadaegh F, Azizi F. Prevalence of metabolic syndrome in Iranian adult population, concordance between the IDF with the ATPIII and the WHO definitions. Diabetes Res Clin Pract. 2007;77(2):251–7. [DOI] [PubMed] [Google Scholar]
- 16.Ding J, Liu Q, Liu Z, Guo H, Liang J, Zhang Y. Associations of the Dietary Iron, Copper, and Selenium Level With Metabolic Syndrome: A Meta-Analysis of Observational Studies. Front Nutr. 2021;8:810494. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ding J, Liu Q, Liu Z, Guo H, Liang J, Zhang Y. Association Between Dietary Zinc Intake and Metabolic Syndrome. A Meta-Analysis of Observational Studies. Front Nutr. 2022;9:825913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Guo H, Ding J, Liu Q, Li Y, Liang J, Zhang Y. Vitamin C and Metabolic Syndrome: A Meta-Analysis of Observational Studies. Front Nutr. 2021;8:728880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Zhang Y, Ding J, Guo H, Liu Z, Liu Q, Li Y, et al. Associations of Dietary and Circulating Vitamin E Level With Metabolic Syndrome. A Meta-Analysis of Observational Studies. Front Nutr. 2021;8:783990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Roman M, Jitaru P, Barbante C. Selenium biochemistry and its role for human health. Metallomics. 2014;6(1):25–54. [DOI] [PubMed] [Google Scholar]
- 21.Khazdouz M, Djalalinia S, Sarrafi Zadeh S, Hasani M, Shidfar F, Ataie-Jafari A, et al. Effects of Zinc Supplementation on Cardiometabolic Risk Factors: a Systematic Review and Meta-analysis of Randomized Controlled Trials. Biol Trace Elem Res. 2020;195(2):373–98. [DOI] [PubMed] [Google Scholar]
- 22.Roberts CK, Sindhu KK. Oxidative stress and metabolic syndrome. Life Sci. 2009;84(21–22):705–12. [DOI] [PubMed] [Google Scholar]
- 23.Liu M, Sun X, Chen B, Dai R, Xi Z, Xu H. Insights into Manganese Superoxide Dismutase and Human Diseases. Int J Mol Sci. 2022;23(24):15893. [DOI] [PMC free article] [PubMed]
- 24.Wong MMH, Chan KY, Lo K. Manganese Exposure and Metabolic Syndrome: A Systematic Review and Meta-Analysis. Nutrients. 2022;14(4):825. [DOI] [PMC free article] [PubMed]
- 25.Wallert M, Börmel L, Lorkowski S. Inflammatory Diseases and Vitamin E-What Do We Know and Where Do We Go? Mol Nutr Food Res. 2021;65(1):e2000097. [DOI] [PubMed] [Google Scholar]
- 26.Ingles DP, Cruz Rodriguez JB, Garcia H. Supplemental Vitamins and Minerals for Cardiovascular Disease Prevention and Treatment. Curr Cardiol Rep. 2020;22(4):22. [DOI] [PubMed] [Google Scholar]
- 27.Beydoun MA, Chen X, Jha K, Beydoun HA, Zonderman AB, Canas JA. Carotenoids, vitamin A, and their association with the metabolic syndrome: a systematic review and meta-analysis. Nutr Rev. 2019;77(1):32–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Juraschek SP, Guallar E, Appel LJ, Miller ER III. Effects of vitamin C supplementation on blood pressure: a meta-analysis of randomized controlled trials. Am J Clin Nutr. 2012;95(5):1079–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Dennis JM, Witting PK. Protective Role for Antioxidants in Acute Kidney Disease. Nutrients. 2017;9(7):718. [DOI] [PMC free article] [PubMed]
- 30.Wei J, Zeng C, Gong Q-y, Li X-x. Lei G-h, Yang T-b. Associations between dietary antioxidant intake and metabolic syndrome. PLoS ONE. 2015;10(6):e0130876. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Shahavandi M, Shahinfar H, Payande N, Sheikhhossein F, Djafarian K, Shab-Bidar S. The association between dietary antioxidant quality score with metabolic syndrome and its components in Iranian adults: A cross‐sectional study. Food Sci Nutr. 2021;9(2):994–1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ahn S, Jun S, Kang M, Shin S, Wie G-A, Baik HW, et al. Association between intake of antioxidant vitamins and metabolic syndrome risk among Korean adults. J Nutr Health. 2017;50(4):313–24. [Google Scholar]
- 33.Masenga SK, Kabwe LS, Chakulya M, Kirabo A. Mechanisms of oxidative stress in metabolic syndrome. Int J Mol Sci. 2023;24(9):7898. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Dandona P, Aljada A, Chaudhuri A, Mohanty P, Garg R. Metabolic syndrome: a comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation. 2005;111(11):1448–54. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed in the current study are available from the corresponding author on reasonable request.