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The association of dietary approaches to stop hypertension measured by the food frequency questionnaire with metabolic syndrome and some inflammatory biomarkers in adolescents with obesity: a case-control study

Journal of Health, Population and Nutrition volume 44, Article number: 12 (2025) Cite this article

Abstract

Background

Globally, obesity trends are a serious public health concern. Adolescent obesity is associated with cardiometabolic risk and metabolic disorders in adolescence and may persist into adulthood. The current study was designed to explore the Dietary Approaches to Stop Hypertension (DASH) in adolescents and its relationship with obesity, insulin resistance, metabolic syndrome (MetS), and some inflammatory biomarkers.

Methods

A total of 90 adolescents with obesity and 90 adolescents with normal weight, participated in the study. Data from a validated 168-item semi-quantitative food frequency questionnaire were used to calculate the DASH score. The association of DASH score with cardiometabolic risk factors was estimated using multivariable logistic regression models. To assess the correlation between the DASH score and dietary factor, the Pearson correlation coefficient (r) was used.

Results

Adolescents with a high DASH score had significantly higher intakes of potassium, magnesium, vitamin C, and vitamin K and lower intakes of sodium compared with those with a low DASH score (P < 0.05 ). There were no significant differences in the DASH score and its components between adolescents with and without metabolic syndrome. Adolescents with metabolic syndrome had significantly higher concentrations of triglycerides, low HDL-C, and high blood pressure compared with those without metabolic syndrome (P < 0.05). There were no significant associations between DASH score and MetS and other cardiometabolic risk factors in crude and multivariate-adjusted models. In addition, the DASH score was positively associated with potassium, magnesium, sodium, vitamins D and C, docosahexaenoic acid, and soluble fiber (P < 0.05).

Conclusion

In the current study, there was no significant association between adherence to the DASH diet and odds of metabolic syndrome, and other cardiometabolic risk factors in adolescent. Further prospective studies are needed to confirm these findings.

Trial registration

Ethics approval was obtained from the research ethics committee of Tabriz University of Medical Sciences (IR.TBZMED.REC.1397.179.).

Introduction

Obesity in children and adolescents is a multifactorial and chronic condition that is characterized by a wide range of causes and has serious consequences for health [1]. By 2025, it is estimated that there will be 70 million overweight or obese children and adolescents worldwide [2]. The cluster of chronic diseases, previously seen mainly in adults, is becoming increasingly common in children and adolescents [3]. Adolescent obesity is associated with cardiometabolic risk throughout adolescence and later with cardiovascular disease and metabolic disorders including, obesity, insulin resistance, impaired glucose tolerance, hypertension, and dyslipidemia through adulthood [4]. The most important modifiable cardiometabolic risk factors are the accumulation of excess weight, a sedentary lifestyle, and adherence to unhealthy dietary patterns [5]. However, there is still no consistent evidence of the impact of different dietary patterns in children and adolescents on overall cardiometabolic health.

Dietary patterns analysis is useful for understanding the complex association between combining different foods and nutrients and how they interact to affect metabolic status [6]. Previous studies have shown that unhealthy or Western diets high in refined grains, red meat, processed foods, and saturated fats are positively associated with several metabolic risk factors, including dyslipidemia, obesity, insulin resistance, and low-grade systemic inflammation [7]. Conversely, better metabolic health is associated with a healthy diet high in plant-based foods, seafood, and healthy oils [7]. It is believed that the Dietary Approaches to Stop Hypertension (DASH), as a healthy dietary pattern, could be a strategy for preventing and managing metabolic risk factors and disease-related obesity [8]. DASH is a dietary intervention that encourages the consumption of whole grains, fruits, vegetables, low-fat dairy products, poultry, fish, nuts, and seeds and recommends less intake of red and processed meat, sugary drinks, and sodium [8]. Studies have reported that this type of dietary intervention is effective in reducing blood pressure and body weight because the DASH diet leads to higher intakes of magnesium, calcium, potassium, and fiber [9]. Research has also shown that the DASH dietary pattern improves lipid profile and glycemic control, resulting in a lower risk of cardiovascular disease in adults [10]. In addition, the DASH diet may also suppress the inflammatory process because of its high anti-inflammatory components, such as whole grains, fruits, vegetables, fiber, legumes, and magnesium, which have been suggested to improve low-grade systemic inflammation [11]. However, epidemiological research investigating the effects of the DASH diet on metabolic risk factors in adolescents is limited and has yielded conflicting results. In a cross-sectional study, no association was found between the DASH diet and blood pressure, waist circumference, serum levels of glucose, HDL cholesterol, or triglycerides in 11- to 30-year-olds [12]. In contrast, three cohort studies reported a negative association between a high level of adherence to the DASH diet and body weight and blood pressure in adolescents [13,14,15]. A recent cohort study found that a higher DASH score was a relationship with a reduction in insulin resistance, but not with an improvement in other metabolic risk factors in children and young adults [16].

Therefore, this case-control study aimed to evaluate the association between the DASH diet score and cardiometabolic risk factors including metabolic syndrome (MetS) and its components, insulin resistance and some inflammatory biomarkers, in adolescents aged 12–18 years.

Materials and methods

Study design and population

The participants in this study were 90 adolescents with obesity and 90 normal-weight adolescents between the ages of 12 and 18 years who were recruited from urban health centers and volunteers who agreed to take part in the study via a public call. The control (normal-weight) and case (obese) groups were matched based on age and gender.

The current study is a sub-analysis of our previous study [17]. For sample size calculation, based on the study by Asghari et al. [18] we used data from the association between DASH diet score and waist circumference using PS software, and the sample size was calculated at 180 participants (OR: 0.35(0.14–0.39); P0: 0.3; Power: 80% ; N = 180). As all 180 children and adolescents were not willing to give a blood sample, we calculated another sample size to measure the biochemical parameters. Therefore, the second sample size for biochemical testing were estimated based on the mean and standard deviation of the spexin factor and using PS software according to the study by Kumar et al. (81 participants and with 11% attrition, 90 participants), [Δ = 0.13; σ = 0.21; m = 1; Power = 80%; N = 90] [19] .

The inclusion criteria for the case group were adolescents with a body mass index (BMI) at or above the 95th percentile of the Centers for Disease Control and Prevention (CDC) growth charts; and for the control group, adolescents with a BMI between the 5th and 85th percentile. Exclusion criteria included a history of endocrine, neurological, cardiovascular, gastrointestinal, or respiratory disease, following a special diet such as a weight-loss diet or vegetarian diet, suspected obesity-related syndromes (e.g. Prader-Willi syndrome), or cancer. The participants and their parents were informed of the details of the study and were asked to sign an informed consent form.

Biochemical, and blood pressure measurements

Of the 180 participants (90 obese and 90 normal weight) whose completed FFQ and anthropometric data and body composition measurements were available, 90 were willing to take blood. After 12 h of overnight fasting, blood samples were taken from 90 adolescents (45 obese and 45 normal weight). The control (normal weight) and case (obese) groups were matched for age and sex. Serum concentrations of IL-1β and IL-10 were assessed by enzyme-linked immunosorbent assay (ELISA) method (Bioassay Technology Laboratory (BT Lab); Shanghai Korean Biotech Co Ltd) according to the manufacturer’s instructions. Serum levels of high-sensitivity C-reactive protein (hs-CRP) were measured using the turbidimetric method. Serum fasting blood glucose (FBG), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-c) were measured enzymatically using a colorimetric technique using commercial kits (Pars-Azmoon Co., Tehran, Iran). The Friedewald equation was used to calculate low-density lipoprotein cholesterol (LDL-c). The concentration of insulin was measured using the ELISA method (Monobind, Lake Forest, CA, USA). The HOMA-IR was calculated using the following formula [20]:

HOMA-IR = [fasting insulin (µIU/ml) × fasting glucose (mg/dl)]/405.

Systolic and diastolic blood pressures (SBP and DBP, respectively) were measured using a mercury sphygmomanometer. The cuff size was appropriate for each adolescent who was placed in a supine position. Blood pressure readings were taken after a 15-minute rest period.

Definition of metabolic syndrome

Based on the criteria of Cook et al. [21], adolescents were diagnosed with MetS if they had at least three of the five criteria, as follows: WC ≥ 90th percentile for age and sex [based on national reference curves [22]]; HDL-C < 40 mg/dL; TGs ≥ 110 mg/dL; FBG ≥ 110 mg/dL [according to the American Diabetes Association [23]]; SBP and DBP ≥ 90th percentile for sex, age, and height [according to the National Heart, Lung, and Blood Institute’s recommended cut-off points [24]].

Assessment of insulin resistance

The fasting insulin level was used to diagnose hyperinsulinism. The pre-pubertal, pubertal, and post-pubertal cut-off values for fasting insulin were 15 µU/mL, 30 µU/mL, and 20 µU/mL, respectively [25, 26]. Furthermore, cut-off values for HOMA-IR were determined according to age and sex. Insulin resistance was diagnosed when the HOMA-IR was 2.67/ 2.22 in boys/girls in the pre-pubertal period and 5.22/ 3.82 in boys/girls in the pubertal period [26].

Physical activity and Anthropometric Assessment

The Modifiable Activity Questionnaire (MAQ) was used to assess the level of physical activity in adolescents. The reliability and validity of the Persian version of the MAQ in young people were 97% and 49%, respectively [27]. Participants were requested to report on the physical activities in which they had been involved in the past 12 months including the frequency and duration of each activity identified. Each activity was weighted according to its relative intensity, referred to as the metabolic equivalent of the task (MET). For all levels of activity, the MET obtained has been multiplied by the time spent on each level of activity. MET time from each level was added to a total of 24 h MET time, representing the average daily physical activity.

Adolescents’ height was measured by a stadiometer with a precision of 0.5 centimeters while standing and barefoot. The weight of the adolescents was measured using a SECA digital weighing scale (Seca 707; Seca Corporation, Hanover, Maryland; range: 0.1–150 kg) with an accuracy of 0.1 kg while they were wearing light clothing. Weight (kg) divided by the square of height (m) was also used to calculate BMI. Waist circumference (WC) was measured at the site of the umbilicus using an outstretched tape measure and without applying pressure to any surfaces, and was recorded to the nearest 0.1 cm. Hip circumference (HC) was measured with the participants standing at the point where the maximum circumference over the buttocks was obtained using a tape measure accurate to 0.1 cm. A Tanita MC-780 S MA (Amsterdam, The Netherlands) was used to measure body composition, including fat mass (FM) and fat-free mass (FFM).

Dietary assessment and DASH calculation

A trained interviewer recorded the adolescents’ dietary intake over the previous year using a validated 168-item semi-quantitative food frequency questionnaire (FFQ). The reliability and validity of the FFQ have been tested in young people [28]. Food frequency (daily, weekly, monthly, and yearly) was recorded for all participants based on standard portion sizes for each food item. Using standard Iranian household measures, all intakes were converted to grams per day. The parents were asked to help their child remember the type of food and the amount of food consumed in the FFQ items. Values for energy, food group items, and nutrient intakes were determined from the FFQ information using the revised Nutritionist IV software (version 3.5.2). The dietary data from the FFQs were used for the calculation of the DASH scores for all participants. The reproducibility and validity of the FFQ for calculating the DASH score have been previously investigated [29].

According to the Fung et al. study, adherence to the DASH diet was evaluated based on intake of eight food groups, including fruits, vegetables, whole grains, legumes and nuts, low-fat dairy products, red or processed meats, sodium, and sweetened beverages [30]. Daily intakes of eight food groups were calculated in grams per 1000 kcal to adjust for the effect of daily energy intake. The intakes of the eight food groups in grams/1000 kcal were then divided into quintiles to calculate the DASH score. Participants in the lowest quintile of intake of fruits, vegetables, nuts and legumes, low-fat dairy products, and whole cereals received a score of 1, and those in the highest quintile received a score of 5. The scoring method was reversed for red and processed meat, sugar-sweetened beverages, and sodium. Hence, participants in the lowest intake quintile were given a score of 5, and those in the highest intake quintile were given a score of 1. Then, to calculate the participant’s score for adherence to the DASH diet, the scores for all eight components were added together for each participant. The overall DASH score ranged from 5 (minimum adherence) to 40 (maximum adherence), with 40 representing greater consistency between the self-reported diet and the DASH diet score.

Statistical analysis

The normality of the distribution of the data was analyzed using the skewness kurtosis test. Mean [standard deviation (SD)] and median [interquartile range (IQR)] were reported for normal and abnormal quantitative variables, respectively. Independent samples t-test (normal variables) or Mann-Whitney U-test (non-normal variables) were used for the determination of differences in quantitative variables. Frequency and percent were used for categorical variables. The chi-squared test was used to assess differences between categories for qualitative variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression models; model 1: crude (unadjusted), multivariable-adjusted model 2: adjusted for sex, age, and maturity, multivariable-adjusted model 3: adjustment for total dietary calorie intake, birth weight, parental obesity plus model 2. To estimate the correlation between the DASH score and dietary factor, the Pearson correlation coefficient (r) was used. The studied adolescents were divided into two groups including “adolescents with a high DASH score” and “adolescents with a low DASH score” based on the DASH score median (median = 24).

Moreover, the studied population were divided in to three and four groups, based on DASH score (tertiles and quartiles).

SPSS version 23.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. A P-value of less than 0.05 or a 95% CI for the OR that excludes 1.0 is considered to be statistically significant. We used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement checklist for reporting our data.

Results

As shown in Fig. 1, a total of 90 obese adolescents (45 girls, 45 boys) and 90 normal-weight adolescents (45 girls, 45 boys) between the ages of 12 and 18 years were selected for the study. Among the 90 adolescents who had biochemical data, 14 subjects (one person from the control group and 13 subjects from the obese group) met the criteria for metabolic syndrome and 76 subjects did not have metabolic syndrome.

Fig. 1
figure 1

Flowchart of study

Full size image

General characteristics and cardiometabolic risk factors of study adolescents with a high DASH score and adolescents with a low DASH score were compared in Table 1. No significant associations were observed between two groups regarding general characteristics and cardiometabolic risk factors. The results of analyses with tertile and quartile divisions were also non-significant (data not shown). However, potassium, magnesium, vitamin C, and vitamin K intakes were significantly higher in adolescents with a high DASH score, and sodium intake was significantly higher in those with a low DASH score.

Table 1 General characteristics and cardiometabolic risk factors comparison of the adolescents with a high and low DASH score
Full size table

Table 2 compares DASH score, dietary intakes, anthropometric measurements, and biochemical characteristics of adolescents with and without metabolic syndrome. No significant associations were observed between adolescents with and without metabolic syndrome regarding DASH score and its components. Participants with metabolic syndrome had significantly lower HDL-C concentrations and higher TG, SBP, DBP, and FM (P < 0.05) compared with those without metabolic syndrome. However, there was no difference in any other parameter between adolescents with and without the metabolic syndrome.

Table 2 DASH score, dietary intakes, anthropometric measurements, and biochemical characteristics of adolescents with and without metabolic syndrome
Full size table

Table 3 shows the ORs and 95% CIs for the association of the DASH score with MetS and it’s components including obesity, TG, WC, HDL, insulin resistance and SBP. In crude and multivariate-adjusted models, there were no significant associations between the DASH score, and MetS and other cardiometabolic risk factors.

Table 3 The association of adherence to DASH diet with odds of metabolic syndrome and it’s components in the studied adolescents1
Full size table

Table 4 shows the Pearson correlation coefficient (r) between the DASH score and some dietary factors. The DASH score was positively associated with potassium, magnesium, sodium, vitamins D and C, docosahexaenoic acid, and soluble fiber(P < 0.05).DASH score, dietary intakes, anthropometric measurements, and biochemical characteristics of adolescents with and without obesity were compared in Supplementary Table e1. Adolescents with obesity were found to have low adherence to the DASH diet, although there were no significant differences from normal-weight adolescents. Moreover, supplementary Table 2 reports the comparison of the adolescents with a high and low DASH score regarding dietary intakes.

Table 4 Associations of DASH score with some dietary factors (N = 180)
Full size table

Discussion

In this case-control study, we examined the associations of adherence to DASH diet with metabolic syndrome, and other cardiometabolic risk factors in Iranian adolescents aged 12 to 18 years. Our results showed no association between the DASH diet score and cardiometabolic risk factors in the crude and adjusted final analysis models. Adolescents with obesity were found to have low adherence to the DASH diet, although there were no significant differences from normal-weight adolescents.

Although several studies have shown an association between the DASH score and a range of diseases including obesity [31], diabetes mellitus [32], metabolic syndrome [33], neurological diseases [34], and cancer [35] in adults, the association between this healthy dietary score and chronic diseases in children has been less well studied. Consistent with our findings, Bricarello et al. [36] showed no association between the DASH diet score and overweight/obesity in Brazilian adolescents. However, Hajna et al. [37] study reported that a higher DASH score was negatively associated with body composition measures and BMI. The relationship between the DASH diet and MetS in children/adolescents has been analyzed in only a few previous studies. In a cross-sectional study by Heidari et al. [38] greater adherence to the DASH diet was associated with a significantly lower risk of metabolic disorders such as hyperglycemia, insulin resistance, hypertriglyceridemia, and low HDL-c, especially in overweight subjects and girls. Following the DASH diet was inversely associated with the incidence of MetS and some of its components, including abdominal obesity, hyperglycemia, and hypertension, in the Tehran Lipid and Glucose Study, a prospective study of 425 healthy adolescents with a mean age of 13.6 years, followed for 3.6 years [18]. However, in line with our findings in another epidemiological study of 628 young people aged 10–18 years, higher DASH scores were not associated with the risk of dyslipidemia, although DASH was inversely associated with the risk of general and central obesity in adolescents [39]. Furthermore, Park et al. [40] found no association between the DASH diet and cardiometabolic risk factors in young people. In another observational study of overweight and obese Iranian children aged 6 to 13 years, greater adherence to the DASH diet was associated with a reduced likelihood of insulin resistance, but there was no notable association with other cardio-metabolic risk factors [41]. The observed inconsistencies among the results of the aforementioned studies may be explained by several factors. First, differences in the study population age range could lead to varying levels of cardiometabolic risk. Second, the Study features like differences in sample size, retrospective or prospective design, which could impact the ability to detect associations. Third, the criteria used to define cardiometabolic risk factors, such as hypertension, dyslipidemia, or insulin resistance, were not consistent across studies, leading to potential differences in categorization. Finally, the inclusion and control of potential confounders, such as lifestyle factors, medication use, or underlying health conditions, differed between studies, potentially influencing the observed associations. However, we tried to adjust the effect of confounding variables as much as possible, but certainly due to the limitation of the study, we were not able to measure some confounding variables, and the failure to discover the relationship may be because of this fact. The lack of association of the DASH score with cardiometabolic risk factors in this study may be related to the following two reasons. Firstly, although the percentage of metabolic disorders in the obese group was higher than in the control group, 55% of the obese adolescents had a metabolically healthy phenotype, with no evidence of insulin resistance or metabolic syndrome. Second, healthy obese children had higher intakes of protein, fruit, and vegetables, and this may have been a protective factor against changes in their metabolic phenotype to an unhealthy state [42].

In our study, we found no differences in inflammatory biomarkers such as IL-10, IL-1 beta, and Hs-CRP in adolescents with high and low adherence to the DASH diet. To the best of our knowledge, there has been no study that has examined the association between DASH and biomarkers of inflammation in young people. Recently, a study by Zhang et al. [43] showed that a high pro-inflammatory diet was associated with higher levels of inflammatory cytokines such as CRP, especially in overweight and obese children and adolescents aged 6–19 years. The DASH diet has a low inflammatory score and may modify systemic inflammation and inhibit the development and progression of inflammatory conditions by reducing serum concentrations of CRP and IL-6 and improving endothelial function parameters [44].

The exact mechanisms by which the DASH diet score might be associated with metabolic conditions are not yet clear. However, several possibilities have been suggested. The favorable effects of the DASH diet on metabolic health may be explained by the lower amounts of saturated fat, refined sugar, and salt in the DASH diet [45]. A DASH diet is rich in whole grains, fruits, vegetables, legumes, and nuts; consequently, the high levels of magnesium, potassium, fiber, and antioxidants, may have benefits for inflammation and cardiometabolic risk factors [46]. In addition, the production of short-chain fatty acids (SCFAs) by the gut microbiota could be increased by high fiber levels [45]. SCFAs have been shown to improve glucose and lipid metabolism in most tissues and to reduce biomarkers of inflammation [47]. In addition, whole-grain foods are associated with a lower incidence of insulin resistance due to their low glycemic index and slow rate of absorption [48]. The key role of low-grade systemic inflammation and oxidative stress in the development of cardiometabolic disorders has been well-documented [49]. Therefore, the antioxidant content of healthy foods in DASH scores could reduce oxidative stress through the scavenging of free radicals [9]. In addition, results from previous studies have shown inverse associations between calcium intake and glycemic status, lipid profiles, and hypertension [50].

Some of the strengths and limitations of this survey need to be highlighted. First, to our knowledge, few studies have examined the association between the DASH diet and cardiometabolic risk factors in overweight and obese Iranian adolescents. Second, in the current study, several factors including inflammatory biomarkers, HOMA-IR, MetS, and its components have been assessed concerning cardiometabolic risk factors in adolescents. However, the design of this study was case-control and did not allow us to draw any conclusions about causality. It is, therefore, necessary to carry out further prospective studies. In addition, because of the use of FFQ to assess dietary intake, misclassification of individuals was inevitable. Finally, residual confounders, such as birth weight, paternal obesity, sleep disturbances, and dietary habits, could alter the results despite the control of various covariates.

Conclusion

In conclusion, this case-control study showed that the DASH diet, as assessed by a food frequency questionnaire, was not associated with cardiometabolic risk factors, including MetS, and its components, adiposity and insulin resistance in adolescents with obesity. To confirm these findings, further prospective studies on a larger scale and from different countries are needed.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

DASH:

Dietary approaches to stop hypertension

BMI:

Body mass index

CVD:

Cardiovascular disease

FFQ:

Food frequency questionnaire

HDL-C:

High-density lipoprotein cholesterol

hs-CRP:

High-sensitivity C-reactive protein

IL:

Interleukin

LDL-C:

Low-density lipoproteins cholesterol

MetS:

Metabolic syndrome

IR:

Insulin resistance

FBG:

Fasting blood glucose

HOMA-IR:

Homeostatic model assessment of insulin resistance

TG:

Triglyceride

TNF-α:

Tumor necrosis factor-α

WC:

Waist circumference

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

MAQ:

Modifiable activity questionnaire

CDC:

Charts of centers for disease control and prevention

OR:

Odds ratio

CI:

Confidence interval

ELISA:

Enzyme-linked immunosorbent assay

SCFAs:

Short-chain fatty acids

FFM:

Fat-free mass

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Acknowledgements

The authors would like to thank all the subjects who eagerly participated in the current study. We would like to thank the Clinical Research Development Unit of Zahra Mardani Azari Children’s Educational and Treatment Center, Tabriz University of Medical Sciences, Tabriz, Iran. For their assistance in this research. We would like to thank Dr. Farnush Bakhshimoghaddam for reviewing the article and providing language help.

Funding

The current study was performed in the nutrition research center, at Tabriz University of Medical Science, Iran from April to October 2019. The study was supported financially by the Research Vice-Chancellor, and Nutrition Research Center of Tabriz University of Medical Sciences, as a thesis proposal for the Ph.D. degree of the First author.

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Authors and Affiliations

  1. Nutrition Research Center, Department of Clinical Nutrition, School of Nutrition & Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

    Maryam Behrooz & Alireza Ostadrahimi

  2. Pediatric Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Maryam Behrooz, Afshin Ghalegolab Behbahani & Siamak Shiva

  3. Student of Nutrition Sciences, Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Samaneh Hajjarzadeh

  4. Pediatric Department Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

    Mirhadi Mousavi

Authors
  1. Maryam Behrooz
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  2. Alireza Ostadrahimi
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  3. Samaneh Hajjarzadeh
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  4. Mirhadi Mousavi
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  5. Afshin Ghalegolab Behbahani
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  6. Siamak Shiva
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Contributions

This study was conceptualized by SS, AG, and MB. Data collection was performed by MB, SS, and SH. Formal analysis was conducted by MB and AG, and funding acquisition was by MB and SS. The investigation was carried out by MB, SH, MM, and AO, the methodology was set by MB and SH. The study was supervised by SS and MB. Writing of the original draft was performed by MB, SH, and SS. Reviewing and editing were carried out by AO, MB, SH, MM, and SS.

Corresponding authors

Correspondence to Maryam Behrooz or Siamak Shiva.

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Ethics approval and consent to participate

Ethics approval was obtained from the research ethics committee of Tabriz University of Medical Sciences (IR.TBZMED.REC.1397.179.). The ethical standards of the institutional and/or national research committee and the Helsinki Declaration of 1964 and its subsequent amendments or comparable ethical standards were followed for all procedures in studies involving human participants. Informed consent was obtained from all subjects or their parents on behalf of the participants. The purpose of the study was explained to the participants.

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Not applicable.

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The authors declare no competing interests.

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Behrooz, M., Ostadrahimi, A., Hajjarzadeh, S. et al. The association of dietary approaches to stop hypertension measured by the food frequency questionnaire with metabolic syndrome and some inflammatory biomarkers in adolescents with obesity: a case-control study. J Health Popul Nutr 44, 12 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00744-2

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Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

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