For this cross-sectional study, 378 overweight and obese children (age- and sex-specific body mass index (BMI) Z-score ≥ 1 based on the criteria established by the World Health Organization) aged 6–13 years, were recruited from three main districts of Tehran, the capital of Iran. Individuals were eligible for inclusion if they had no known medical illnesses such as diabetes, liver, or kidney diseases, were not taking any dietary supplements, or using pharmaceutical agents that affect glucose and lipid metabolism. Twenty-three participants who missed anthropometric, dietary, biochemical and ultrasound assessments were excluded. Furthermore, those who over- or under-reported were also excluded (n = 16). To define over- and underreports, the reported energy intake was divided by the estimated energy requirement (EER), calculated according to equations proposed by the Institute of Medicine who were not within the 2SD range . Finally, the statistical analysis was performed on 339 overweight and obesity children and adolescents.
The design of this study was approved by the institutional ethics committee of the Research Institute for Endocrine Sciences, affiliated to the Shahid Beheshti University of Medical Sciences, and written informed consent was obtained from participants’ parents.
Anthropometric measurements were taken by a trained nutritionist according to standard methods. Weight was measured to the nearest 100 g, while participants were minimally clothed without shoes, using the scale function of the GAIA 359 PLUS (Jawon Medical Co. Ltd., Shinsang, Korea). Height was measured to the nearest 0.5 cm, in standing position with shoulders in normal alignment and without shoes, using a standard tape. BMI was calculated as weight divided by the square of the height (kg/m2). Waist circumference was measured to the nearest 0.5 cm using a measuring tape in the standing position after a gentle respiration, at the level of the umbilicus.
Blood pressure of participants was measured after 15 min of resting with a standard mercury sphygmomanometer on the right arm. It was measured twice and the mean of the two measurements was recorded as the participant’s blood pressure. Pubertal status was classified according to the definitions of Tanner stages, determined by a well-trained endocrinologist. The pubertal developmental stage was categorized into 2 groups based on breast and genital stages (pre-pubertal: boys at genital stage I, girls at breast stage I; pubertal: boys at genital stage ≥II, girls at breast stage ≥II).
Physical activity was assessed by using the Modifiable Activity Questionnaire (MAQ), to calculate metabolic equivalent task minutes per week. Reliability and moderate validity have been specified previously for the Persian translated MAQ in adolescents . Low level of physical activity were considered as metabolic equivalent task < 600 min/wk.
Blood samples were drawn between 7:00 and 9:00 AM from all study participants after 12–14 h of overnight fasting. All the blood analyses were done at the Tehran Lipid Glucose Study (TLGS) research laboratory on the day of blood collection. Fasting plasma glucose (FPG) was measured by the enzymatic colorimetric method using glucose oxidase. Serum triglycerides (TGs) were assayed using an enzymatic colorimetric method with glycerol phosphate oxidase. These analyses were performed using commercial kits (Pars Azmoon, Tehran, Iran) and a Selectra 2 autoanalyzer (Vital Scientific, Spankeren, The Netherlands), with intra- and inter-assay coefficients of variation (CVs) of 1.1 and 1.4% for FPG and both less than 2% for TGs, respectively.
Dietary intake was gathered using a reliable and validated, semi-quantitative food frequency questionnaire (FFQ) to assess the regular dietary intakes of participants over the previous year. Trained dieticians, during face-to-face interviews, asked participants and their mothers (when children were unable to recall) to designate their intake frequency for each food item consumed during the past year on a daily, weekly, or monthly basis. For each food item on the FFQ, a portion size was specified using US Department of Agriculture (USDA) serving sizes (eg, bread, 1 slice; apple, 1 medium; dairy, 1 cup) whenever possible; if this was not possible, household measures (eg, beans, 1 tablespoon; chicken meat, 1 leg, breast, or wing; rice, 1 large, medium, or small plate) were chosen and were then converted to grams and servings. Energy and nutrient contents were obtained from USDA food composition tables (FCT) because Iranian FCTs are incomplete and with limited data on nutrient content of raw foods and beverages foods, although, Iranian FCTs were used for traditional food items (Like Kashk) that are not listed in the USDA FCT. To evaluate the reproducibility of the FFQ, 132 participants completed the questionnaire twice, with a 14-month interval. Twelve dietary recalls were also collected (1 per month) to assess the validity of the FFQ. The adjusted correlation coefficients to assess validity of the FFQ for total food groups was 0.44 (nuts: 0.54) in men and 0.37 (nuts: 0.39) in women; intraclass correlation coefficients, which reflect the reproducibility of food groups in the FFQ, was 0.51 in men (nuts: 0.34) and 0.59 in women (nuts: 0.52) .
Snacking patterns are usually classified based on two main criteria; the type of snack and the time when the snack was eaten . In the current study, snacks were divided into low-energy high-nutrient (nuts) and unhealthy energy-dense nutrient-poor solid foods (salty and sweet). Sweet snacks included candies, chocolates, cookies, cakes, biscuits, confectionery, caramels, and traditional Iranian confectioneries, Gaz, Sohan, Noghl, Halva, Yazdi cakes, and salty ones included potato chips and puff (a corn snack or crisp coated with a mixture of cheese or cheese flavored). Total energy dense nutrient poor solid snacks were calculated by the summation of the sweet and salty snacks. Moreover, nuts included all kinds of tree nuts and seeds, including peanuts, almonds, walnuts, pistachios, hazelnuts, and seeds. Sugar-sweetened beverages or high nutritional value snacks such as fruits, vegetables, and dairy products were not included in this study.
Carotid intima-media thickness measurement
Carotid intima-media thickness was measured by a trained radiologist (P.D.). Participants were examined in the supine position with head slightly extended and rotated to the opposite side of examination; carotid arteries were investigated using a high-resolution Samsung ultrasound machine (model UGEO WS80A) with a linear-array transducer operating at a frequency of at least 7 MHz. Depth, gain and focus was adjusted for each participant individually so that the arterial lumen was completely anechoic and in the center of the image. Common cIMT was measured from longitudinal B-mode images of the distal 1 cm of the far wall of each common carotid artery (CCA) between the intimal-luminal and the medial-adventitial interfaces of the carotid artery wall represented as a double-line density on the ultrasound image. Measurements were performed using the automated edge-tracking software (automated IMT calculator) which obviated the need to perform manual measurements .
Statistical analysis was performed using the Statistical Package for Social Sciences (version 15.0; SPSS, Chicago IL). The normality of the distribution of variables was assessed by the Kolmogorov-Smirnov tests. Characteristics of participants according to tertiles of nuts intake were expressed as mean ± SD or median and interquartile range for continuous and percentages for categorical variables. To investigate the trend of variables according to the tertiles, ANOVA and chi-square test were used for continuous and categorical variables, respectively. Plasma TG, systolic and diastolic blood pressure were skewed, so the log transformation was used. A linear regression model was used to assess the relation of cIMT with snack consumptions. Multivariable logistic regression models were used to examine the association of snack consumptions with cIMT. The odds ratio (OR) and 95% confidence intervals (CIs) for the incidence of high cIMT were calculated. Because of the continuous nature of cIMT, we binned it into tertiles and put the two first tertiles as low cIMT and the last one as high cIMT. The first model was adjusted for age, sex, total energy intake, physical activity and pubertal status and the second was further adjusted for BMI.
We estimated the associations of substituting 1 serving of nuts for 1 serving of sweet, salty, and energy-dense nutrient-poor solid snacks with cIMT by including them as continuous variables in the same multivariate model, which also contained non-dietary covariates and total energy intake. The difference in their coefficients and in their own variances and covariance were used to estimate the substitution associations .
In this study, we have conducted the sensitivity analysis to assess the robustness of the results. For this reason, we have added more atherosclerosis risk factor such as systolic (SBP) and diastolic (DBP) blood pressure, and intakes of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids as further potential confounders.
In addition, we used Spline regression to examine a dose-response relation between snacks consumption and risk of high cIMT.