Participants
This cross-sectional was performed among a sample of 203 Iranian adolescents (102 girls and 101 boys), aged 12–18 years. The sample size of the study was determined based on data from previous published studies (26, 27), estimating that 40–60% of the Iranian overweight and obese adolescents have been MUO. Thus, considering a power of 80% and type I error of 0.05, desired confidence interval of 0.95, and precision (d) of 7%, the minimum required sample size was estimated to be 188 subjects. Participants were chosen by multistage cluster random sampling method. Students were selected randomly from five major regions of Isfahan, Iran. Based on age- and gender standardized BMI z-scores (28), overweight and obese children were selected to participate in the current investigation from sixteen schools. Using this approach, adolescents from a wide range of social and economic status were involved in this study. Participants who were on a weight-loss diet, using vitamin and mineral supplements, or taking drugs that might affect their body weight, blood glucose, lipid profile or blood pressure were excluded. Furthermore, we did not include students with genetic or endocrine abnormalities (such as type 1 diabetes mellitus, hypothyroidism and Cushing syndrome) in this investigation. Therefore, we enrolled 203 overweight/obese adolescents in the current analysis. Written informed agreement was obtained from each participant. In addition, informed consent were obtained from their parents as few minors were involved in the study. The Isfahan University of Medical Sciences local ethics committee was responsible for the study protocol.
Dietary assessment and calculation of DTAC
A validated 147-item food frequency questionnaire (FFQ) was applied to collect data on the dietary intakes of the individuals (29). Former investigations showed that this FFQ could be an accurate indicator of dietary intakes in relation to various diseases among Iranian adolescent (30, 31). Consumption for each food item was questioned based on a daily, weekly, or monthly frequency. Afterward, using household measurements, portion sizes of consumed food items were converted to grams per day (32). Then, gram/day values were input into Nutritionist IV software to calculate nutrients intake. The Nutritionist IV software used the USDA food composition database, with some modifications based on Iranian foods. Ferric Reducing-Antioxidant Power (FRAP) values were obtained from previously published report to estimate DTAC (33). The FRAP assay measures dietary antioxidants ability to convert ferric ions to ferrous, and is indicated as mmol/100 g of food (34). Whenever FRAP data was not available for a specific food item, the value of the closest equivalent item was allocated. Finally, the frequency of consumption of each food item was multiplied by their related FRAP values. FRAP values were then summed up to compute the DTAC for each participant.
Assessment of anthropometric indices and cardio metabolic risk factors
Two trained nutritionists used a stadiometer to measure standing height without shoes (to the nearest 0.1 cm). Weight was also measured with a calibrated electronic scale to the nearest 0.1 kg in minimal clothing and without shoes. Then, participants were classified as normal, overweight or obese adolescents based on the age-and sex-standardized BMI z-scores defined by World Health Organization (WHO) for adolescents (28). After normal respiration and without putting any pressure on the body surface, waist circumference (WC) value was measured with a flexible tape (to the nearest 0.1 cm). A mercury sphygmomanometer with a suitable cuff size was used to measure systolic blood pressure (SBP) and diastolic blood pressure (DBP) on the right arm, twice after a 15-minute recovery time. The average of the two measurements was calculated and used in this study. The concentrations of triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), fasting blood glucose (FBG), and insulin were measured. The Homeostasis Model Insulin Resistance (HOMA-IR) was determined using the following formula to measure Insulin resistance (IR) (35) HOMA-IR=[Fasting glucose (mg/dl) × fasting insulin (µu/ml)]/405.
Assessment of metabolic status
Two strategies were applied to classify participants as MHO or MUO individuals. The first approach had the same components as metabolic syndrome (MetS) based on the children International Diabetes Federation criteria (36). MUO children were defined as having two or more MetS components such as increased triglycerides (≥ 150 mg/dL), decreased HDL-c (< 40 mg/dL for the age of < 16 y, and < 50 mg/dL in girls/ < 40 mg/dL in boys for the age of ≥ 16 y), increased fasting blood glucose (≥ 100 mg/dL) and elevated blood pressure (≥ 130/85 mmHg). Other than, they were defined as MHO subjects. In the second method, HOMA-IR was considered along with IDF criteria that were used in the first classification method. Individuals with HOMA-IR score ≥ 3.16 who had at least two other metabolic risk factors were classified as MUO; and those with HOMA-IR < 3.16 were considered as MHO adolescents (9, 37).
Assessment of other variables: The Physical Activity Questionnaire for Adolescents (PAQ-A) was applied to assess the participants’ physical activity levels. This questionnaire consists of nine questions on different activities to evaluate physical activity in the last week (38). Items 1 to 8 of the questionnaire were scored from 1–5; a score of 1 indicated the lowest and 5 showed the highest level of physical activity. The ninth question evaluated the unusual activity of adolescents during the previous 7 days. Scores were summed up and adolescent were categorized into very active (score ≥ 4), moderate (4 < score ≤ 3), less active (3 < score ≤ 2), and sedentary or not having an orderly week activity (score < 2). A validated demographic questionnaire was used by trained researchers to evaluate the socioeconomic status (SES) of the students (39) based on the following variables: parental job, the number of family members, parental education level, having cars in the family, having computers/laptops, having personal room and taking trips in the year. Afterward, the SES was computed to have a total score. A questionnaire was used to record the participants’ age, gender, medical history, medication and supplement use.
Statistical analysis: FRAP values were adjusted for total energy intake using residual method to have an independent exposure from the energy intake. Then, we classified individuals according to tertiles of FRAP (T1: <6.42; T2: 6.42–8.33; T3: >8.39 mmol/d). General characteristics of study participants across tertiles of FRAP were reported as means ± SDs for continuous and percentages for categorical variables. To examine the differences across tertiles of FRAP, we used Analysis of Variance (ANOVA) for continuous and Chi-square test for categorical variables. The age, sex, and energy-adjusted dietary intakes of study participants across tertiles of FRAP were evaluated using Analysis of Covariance (ANCOVA). To identify the association between tertiles of FRAP and MUO, multivariable logistic regression was applied. The odds ratios (OR) and 95% confidence intervals (CI) for MUO were calculated in crude and adjusted models. Energy intake could affect the outcome of interest based on previous investigations (40–42); so, it was considered as a confounder and adjustment was performed to obtain an independent relation. In the first model, we adjusted for age, gender and energy intake. In the second model, further adjustments were done for physical activity levels, and SES. Prior studies have shown that omega-3 could also be associated with metabolic risk factors; so, we considered intake of omega-3 fatty acids as a confounder (43). In the last model, omega-3 fatty acids intake and BMI were adjusted in addition to the previous confounders. In all models, the first tertile of FRAP was considered as the reference. To evaluate the trend of ORs across tertiles of FRAP, we considered FRAP tertiles as an ordinal variable in the analysis. P-values < 0.05 (two-sided) were considered as the statistically significance level. SPSS software version 26 (IBM, Chicago, IL) was used for all analyses.