Artigos

Introduction
Micronutrients, such as vitamins A, C and E and the minerals zinc and selenium, are fundamental components for several enzymes involved in inflammatory and metabolic pathways associated with the synthesis, regulation and action of insulin [1–4]. Although the interaction between insulin metabolism and the complex inflammatory process, it has been established that excess body weight, especially in the abdominal region, predisposes individuals to the development of pro-oxidative conditions that reduce sensitivity to insulin and trigger several chronic noncommunicable diseases in different age groups [5,6]. Studies have suggested that the maintenance of adequate serum levels and the regular intake of food sources of antioxidants (zinc, selenium, ß-carotene, vitamin C and vitamin E) can attenuate oxidative stress, which is a promising strategy for the control of cardiometabolic risk factors [4,7,8].
The issue of antioxidant micronutrients in the adolescent population has been addressed little in the literature. However, the specific analysis of isolated nutrients has been reported. In a study conducted by Suarez-Ortégon et al. [9], after statistical adjustments for age, body mass index (BMI), socioeconomic status and the ingestion of fats, proteins and ascorbic acid, the highest quartile of the consumption of zinc-rich foods by male adolescents was associated with a lower frequency of metabolic syndrome and low cardiovascular risk based on waist circumference.
In another research investigating antioxidant vitamin and mineral intake among adolescents, adults and elderly who participated in the Pesquisa de Orçamentos Familiares (POF) conducted in Brazil in 2008-2009, Tureck et al. [10] found lower intake than the recommended Dietary Reference Intake (DRI) values for vitamins A and E and higher percentages of the inadequate intake of vitamin C, vitamin E and manganese independently of sex and nutritional status among the adolescents evaluated.
The literature offers divergent data on the issue of micronutrients with antioxidant properties and the association with insulin resistance and anthropometric variables related to cardiovascular risk, including the isolated and combined effects of nutrients, doses, forms of use and the effects over time. Studies addressing this issue in adolescents could assist in the objective determination of dietary components associated with the prevention of chronic diseases in early phases of life.
Therefore, the aim of the present study was to estimate the dietary intake of antioxidant micronutrients among Brazilian adolescents as well as investigate its relation to anthropometric variables and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), according to sex and age group.

Methods

Participants
A school-based cross-sectional study was conducted with adolescents who participated in the Estudo de Riscos Cardiovasculares em Adolescentes (ERICA [Study of Cardiovascular Risk in Adolescents]), which was a national multicenter study conducted between February 2013 and November 2014 with adolescents 12 to 17 years of age enrolled at public and private schools in municipalities with more than 100 thousand residents in the 26 states of Brazil and the Federal District. The study population was stratified into 32 geographic strata (27 capitals and five strata that correspond to municipalities in each of the country's five macro-regions). In each geographic stratum, schools were selected with a probability proportional to size. In the second stage, three classes were selected in each sampled school with equal probabilities during fieldwork and, finally, in each selected class, all students were invited to participate in the study. The protocol, rationale, design and sample calculation for the ERICA study were previously described by Bloch et al. [11] and Vasconcellos et al. [12].
The inclusion of students in the study was determined based on the following previously defined eligibility criteria [12]: Male and female adolescents between 12 and 17 years of age in the last three years of primary school or one of the three years of high school. Individuals with physical disabilities that impeded the anthropometric evaluation, those with chronic conditions, except obesity, those who regularly took medications with adverse effects on blood pressure, glycemia or lipid metabolism, pregnant girls and individuals with endogenous or secondary obesity were excluded from the sample.
In accordance with the protocol of the ERICA study, the adolescents needed to have fasted for 12 hours prior to the collection of blood for the biochemical exams, which impeded the acquisition of these data from students who studied in the afternoon shift. Around 78,000 eligible adolescents had some information collected in ERICA, however, as the present investigation used biochemical data for descriptive and inferential analyses, only adolescents enrolled in the morning shift were selected, totaling 37,023 Brazilian adolescents included in the present analysis.
All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Universidade Federal do Rio de Janeiro (Federal University of Rio de Janeiro) (Report n.01/2009, Process n.45/2008) and by the Research Ethics Committees in all 26 States and in the Federal District.

General characterization variables
The participants answered a questionnaire designed specifically for the study, which was available on a self-administered electronic data collector: Personal Digital Assistant - PDA (LG GM750Q, LG Electronics, Seoul, South Korea).
The participants reported their sex and age. The Brazilian Economic Classification Criteria were used for the categorization of economic status into upper (subcategories A1 and A2), middle (subcategories B1, B2 and C1) and lower (subcategories C2, D and E).
The adolescents self-reported their stage of sexual maturation. To facilitate this identification, indicative figures standardized by Tanner (1991) in "Growth at adolescence" were presented to the students. From the analysis of these figures, the participants were classified as pre-pubescent (when the respondent reported being in Stage I), pubescent (Stages II, III and IV) and post-pubescent (Stage V). As the sample included adolescents beginning at 12 years of age, the percentage of pre-pubescent individuals was low. Therefore, to enhance the analytical power of the study, this group of individuals was included in the pubescent category to enable the dichotomisation of sexual maturation as pubescent or post-pubescent.
The level of physical activity was determined based on the International Physical Activity Questionnaire (IPAQ) [13]. Adolescents who reported practicing at least 60 minutes of moderate to rigorous physical activity five or more days per week were considered physically active and all other adolescents were considered insufficiently active.

Anthropometric variables
Anthropometric datas were collected by duly trained professionals. The anthropometric data were recorded directly into the PDA and all information was simultaneously transferred to the central server of the ERICA study for the composition of the databank.
Weight, height and waist circumference (WC) were measured. Weight was determined on an electronic scale (Lider® Balanças, Sa?o Paulo, Brazil, Model P150m) with a capacity of 200 kg and a precision of 50 g. Height was measured twice using a portable stadiometer (AlturaExata®, Minas Gerais, Brazil) with a precision of 0.1 cm (allowing a maximum difference of 0.5 cm between readings, followed by the calculation of the mean). For the weight and height measurements, the adolescents wore light clothing and stood barefoot [11]. WC was measured twice with a nonelastic metric tape (Sanny®, São Bernardo do Campo, Brazil) with a capacity of 150 cm and precision of 0.1 cm. For such, the adolescent remained standing with the abdomen relaxed, arms alongside the body, feet together and weight divided equally between both legs. The metric tape was placed horizontally at the midpoint between the lower edge of the last rib and the iliac crest [11].
The classification of nutritional status was performed with the aid of the Anthro (2007) software program, considering body mass index for age (BMI/A) expressed in z-scores. The reference standard for the categorization of weight and height was that recommended by the World Health Organization (WHO) [14]: adolescents with BMI/A ? +1 were classified as without excess weight, those with z-score > +1 and ? +2 were classified with overweight and those with BMI/A > +2 z-score or > +3 z-score were classified with obesity and severe obesity, respectively.
For WC, the cutoff points recommended by Freedman et al. [15] were used, which identify WC ? 90th percentile (P90) as indicative of cardiovascular risk. WC and height were used for the calculation of the waist to height (W/Ht) ratio, establishing ? 0.5 as the cutoff point for abdominal obesity [16].

Biochemical variables
Fasting glycemia and insulinemia were determined through the biochemical analysis. The adolescents and guardians were instructed with regards to the need for 12 hours of fasting prior to the collection of the blood scheduled for the following day. Blood collection was the responsibility of the clinical analysis laboratory and performed by trained lab technicians. Prior to the collection, the adolescents were interviewed to ensure compliance with fasting. Blood was collected at the schools in a standardized manner through venous puncture using disposable material and a 5-mL serum tube in ice [17]. The samples were analyzed at a single laboratory.
Plasma glucose was measured using the GOD-PAP enzymatic method with the aid of the Roche analytical equipment. Plasma insulin was determined using the preferred routine immunometric methods at the clinical laboratory due to the greater sensitivity and specificity. Fasting glycemia and insulinemia were used to calculate the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) using the preestablished formula: HOMA-IR = (fasting insulinemia x fasting glycemia)/22.5 [18] and categorized based on the 75th percentile (P75) of the distribution for each sex and sexual maturation stage, due to the absence of age-specific cut-off points for the outcome studied [19–22].
The results of the biochemical exams were recorded on spreadsheets under the technical care of the clinical analysis laboratory and sent to the central server of ERICA in an informatized system designed especially for the study.

Dietary variables
Trained professionals used a specific software program for the direct input of the dietary variables on netbooks. The device had a list of foods from the food and beverage acquisition database of the 2008-2009 Family Budget Survey [23,24]. Foods not found in the database were included by the interviewers. Food intake was determined using the 24-hour recall method (24hR) with the aid of the ERICA-REC24 software program designed especially for the study [11].
The Multiple Pass Method (MPM) [25] was used to minimize recall bias, which is a common dietary survey tool. Firstly, the adolescents were asked what foods and beverages they had ingested the previous day. They were then asked about items that are generally not informed during interviews, such as gum, sweets, snacks and soft drinks. Next, the adolescents were asked about the time spent for the consumption of each meal reported in the R24h. After that, they were asked to describe in detail the amounts of the foods, revising information on the time and occasion of the intake. In this stage, the interviewers could access the ERICA-REC24 program to present images of household measures to the adolescents. Lastly, the interviewers performed a final review of all items listed, investigating unreported consumed items [25].
Nutrient intake was determined using the Nutritional Composition Table of Food Consumed in Brazil [23] and the Reported Measures Table for Foods Consumed in Brazil [24]. The intake of the following antioxidant nutrients was considered: Vitamin A (expressed as retinol activity equivalents - RAE), vitamin C, vitamin E, zinc and selenium.

Data analysis
The statistical analyses were performed using the STATA program, version 14.0, employing the Survey (svy) command to correct for sampling weights and the complex study design. Categorical data were expressed as absolute and relative frequencies accompanied by respective 95% confidence intervals (95%CI). Continuous data were tested for normality using the Shapiro-Wilk test and expressed as either mean and standard deviation values (variables with normal distribution) or median and interquartile range (IQR) (variables with non-normal distribution).
Antioxidant micronutrient intake was analyzed with the variables in their continuous form. The residual method was used to adjust dietary micronutrient for total energy intake. Energy?adjusted micronutrient intakes were calculated as the residuals from the regression model, with absolute nutrient intake as the dependent variable, and total energy intake as the independent variable [26].
Mann-Whitney test was used to determine differences between medians of crude and energy-adjusted intake of micronutrients and Spearman's correlation coefficients (rho) were used to measure the strength of the correlations between energy-adjusted antioxidant intake and both anthropometric variables and HOMA-IR. Considering the physiological changes inherent to adolescence, the correlation analyses were stratified by sex and age group. A p-value < 0.05 was considered indicative of statistical significance.

Results

Sample description
Median age was 15 years (IQR = 13-16); 50.2% were female and 49.8% were male. Mean HOMA-IR was 2.0 ± 1.87. Table 1 displays the data for the characterization of the sample stratified by sex.
Most boys and girls were in the 15-to-17-year-old age group and the middle economic status category. Adolescents without insulin resistance, excess weight, abdominal obesity and cardiovascular risk (based on WC) were more prevalent. Boys were more physically active and with high cardiovascular risk in comparison to girls. The male sex also had higher frequencies of individuals classified in the stage IV of sexual maturation (Table 1).

Antioxidant micronutrient intake
Regarding antioxidants, medians of crude and energy-adjusted intake varied with the type of nutrient and significant differences were found in the intake of all vitamins and minerals according to sex, sexual maturation and insulin resistance (Tables 2 and 3).
In general, pubescent and post-pubescent boys with insulin resistance had lower medians of crude intake of zinc, selenium, vitamins A, C and E (p < 0.05). In the energy-adjusted analysis, the intake of selenium (p = 0.0121) and vitamins A and C (p < 0.001) was higher in pubescent boys with insulin resistance, and, in the group of post-pubescent, the median intake of vitamin C was higher in boys with insulin resistance (p < 0.001) (Table 2).
Pubescent girls with insulin resistance had lower intake of crude zinc and vitamin A (p < 0.05). In post-pubescent girls a significant difference for all crude micronutrient intake was found between the groups of girls with and without insulin resistance. The energy-adjusted analysis showed a higher median intake of selenium (p = 0.0228) and vitamin C (p < 0.01) in pubescent and post-pubescent girls with insulin resistance, respectively. A significant difference in energy-adjusted intake of vitamin E was also found, where post-pubescent girls with insulin resistance had a lower intake (p < 0.01) (Table 3).

Antioxidant micronutrient intake vs. anthropometric variables
Significant correlations between the anthropometric variables and all micronutrients were found in the group of boys with insulin resistance, where higher nutrient intake correlated with higher anthropometric measures (Table 4).
In the analysis of girls with insulin resistance, zinc, vitamin A and vitamin C were significantly correlated with the anthropometric variables (Table 4).
Among boys and girls without insulin resistance, with the exception of vitamin E, positive correlations were found between anthropometric variables and all vitamins and minerals (Table 4).

Antioxidant micronutrient intake vs. HOMA-IR
The associations between antioxidants and the HOMA-IR index according to nutritional status, age group and sex are displayed in Table 5.
Among boys between 12 and 14 years of age, HOMA-IR was inversely associated with zinc in those classified as overweight. Among boys between 15 and 17 years of age, HOMA-IR was inversely associated with vitamins A, C and E in those classified as overweight (Table 5).
Among girls between 12 and 14 years of age, no significant association was found between HOMA-IR and antioxidant micronutrients. In the group of girls between 15 and 17 years of age, HOMA-IR was inversely associated with zinc in those without excess weight, and vitamin A in those classified as overweight and obesity (Table 5).

Discussion
Studies report that subclinical inflammation is associated with the development of insulin resistance and that healthy, lasting dietary patterns can assist in the modulation of the inflammatory process and the prevention of chronic noncommunicable disease in different stages of life [27,28]. Considering this issue, there continues to be an important gap in the literature regarding the evaluation of antioxidant micronutrient intake in different population groups, especially adolescents.
In our study, some of the medians of micronutrient intake between boys and girls with and without insulin resistance lost its significance when adjusted for total energy intake. In this regard, Motamed et al. [29] found no association between energy-adjusted micronutrients and metabolic syndrome in women.
Previous studies recommend the use of the residual method in epidemiological analyses that aim to understand the diet-disease relationship [30,31]. Adjustment for total energy intake in nutritional epidemiology is appropriate to control for confounding, remove extraneous variation, and simulate the effect of dietary intervention [26,30]. Thus, with this correction in dietary estimation, some associations shown in crude analysis can be changed.
Comparing the Estimated Average Requirements (EAR) [32] with the energy-adjusted values of antioxidant intake evaluated in the present investigation, zinc, selenium and vitamin C intake were similar to or even higher than the recommended values. In contrast, the median intake of vitamin A equivalents (EAR: 445 µg/day [9-13 years] to 630 µg/day [14-18 years] for boys and 420 µg/day [9-13 years] to 485 µg/day [14-18 years] for girls) and, especially, vitamin E (EAR: 9 mg/day [9-13 years] to 12 mg/day [14-18 years] for both sexes), were considerably lower than the recommended values.
It is inferred that current dietary practices are associated with this finding. Data from the 2013-2014 ERICA Study show that Brazilian adolescents have a high consumption of snack foods [33], which can lead to a lower selection of fruits, legumes, nuts, seeds and whole grains (sources rich in vitamins A and E) as part of the dietary routine.
In a cross-sectional study conducted in the city of Pamplona, Spain with 40 healthy university students without excess weight between 18 and 28 years of age, Carraro et al. [34] found that the greater ingestion of fruit (> 293.4 g/day) was associated with better glycemic homeostasis and low HOMA-IR index values (p = 0.014). The authors attributed this effect to the presence of antioxidants and bioactive compounds in these foods. Comparing this median intake (293.4 g/day) to the recommendation of the World Health Organization [35] for the consumption of fruits and vegetables (400 g/day), one may postulate that the daily intake of an antioxidant-rich diet combined with a balanced lifestyle offers important benefits to glycemic control.
In the present study, significant positive correlations were found between the anthropometric variables (BMI, WC and W/Ht ratio) and all antioxidant micronutrients. This finding may be attributed to a reverse causality, which is one of the main issues related to correlational studies [36]. Besides that, it is known that dietary pattern of adolescents is characterized by a high intake of energy-dense ultra-processed food [27], usually fortified foods [37]. This may contribute to micronutrient supply but may increase the risk of excessive micronutrient intake [37] and weight gain in adolescence [27]. However, the present analysis did not test this hypothesis.
According to Yokoyama [38], the increase in WC, which is related to visceral obesity, is one of the main risk factors for insulin resistance, as it is associated with the installation of an inflammatory state that enables the buildup of fat in the central region of the body and a reduction in sensitivity to insulin.
Considering the correlations between HOMA-IR index and antioxidant nutrients according to sex, age group and nutritional status in our study, zinc was inversely correlated with HOMA-IR in male adolescents 12 to 14 years of age with overweight. In those between 15 to 17 years of age, classified as overweight, vitamins A, C and E were inversely correlated with HOMA-IR. Among girls 15 to 17 years of age without excess weight, zinc was inversely correlated with HOMA-IR, and, in those with overweight and obesity, vitamin A was the most correlated nutrient.
A randomized trial performed by Ho et al. [39] with adolescents aged 10–17 years who were overweight or obese and had either pre-diabetes or clinical features of insulin resistance, found a positive correlation between plasma zinc concentration and total dietary zinc intake (r = 0.23, p = 0.045) and energy-adjusted zinc intake (r = 0.24, p = 0.036) and an inverse correlation between plasma zinc concentration and percent body fat (r = ?0.28, p = 0.008), a result consistent with insulin resistance. Several studies suggest that zinc plays a dynamic role as a cellular second messenger in the control of insulin signaling and glucose homeostasis [40,41].
Adolescents are particularly vulnerable to suboptimal zinc status because of increased requirements during the pubertal growth spurt and sexual maturation [42], where a physiological insulin resistance develops to enhance the expression of sex hormone binding globulin, a protein associated with the initiation of puberty [43]. Thereby, a regular consumption of zinc-rich foods is essential for metabolic functions and adequate growth of adolescents [42,44].
A study conducted by Dybkowska et al. [45] with Polish adolescents found that the intake of vitamins C and E did not reach the recommended values for the sex and age group of these individuals. Oliveira et al. [46] found that only vitamin E was below nutritional requirements among sedentary adolescents and runners in Rio de Janeiro, Brazil.
Among the American adolescents who participated in the 2001-2006 National Health and Nutrition Examination Survey (NHANES) [47], boys had greater intake of both vitamin E (p < 0.001) and vitamin C (p = 0.003) compared to girls. Moreover, the authors found lower serum levels of vitamin C and higher concentrations of vitamin E with the increase in age, highlighting inverse associations between vitamin E status and abdominal obesity, hyperglycemia and HOMA-IR in the group of adolescents evaluated. Apparently, concentrations of fat-soluble vitamins, such as vitamin E, can be low in youths with overweight and obesity due to both inadequate intake and the entrapment of the nutrient in adipose tissue [48,49].
Likewise, vitamin A appears to influence several biomarkers of insulin resistance, such as obesity, triglyceride and insulin levels [40]. In a study performed by Harari et al. [50] with Australian adults, serum carotenoids correlated inversely with weight, BMI, WC, total body fat, body fat-free mass, central body fat, fasting insulinemia and HOMA-IR. Our results regarding dietary vitamin A (RAE) intake showed that adolescents aged 15-17 years who were overweight or obese had a lower intake of vitamin A. Few investigations evaluated these aspects in the group of adolescents. Nevertheless, a study carried-out with post-menarcheal schoolgirls with anaemia found a high prevalence (62.3%) of inadequate intake of vitamin A (RAE) [51].
The dietary intake data in the present study should be interpreted considering some important limitations: he use of only one 24hR, which impeded a comparative analysis related to the patterns of inadequate micronutrient intake, enabling only the comparison of medians with the EAR; the use of nutritional composition tables that may be deficient in some micronutrients (e.g., selenium) and may reflect dietary intake values beyond the usual; the non-assessment of fiber intake, which is known to interfere in bioavailability of micronutrients; voluntary omission; and the occurrence of under-reporting for the consumption of certain dietary components, such as oils and fats added to meals, which may affect the evaluation of the actual intake of vitamin E. In addition, the absence of control in the analysis with pro-oxidant activities, such as smoking, alcoholism, sleep quality and physical inactivity should be considered as a limiting factor. Despite these aspects, the analysis of vitamin and mineral intake enabled outlining an antioxidant micronutrient intake profile controlled by characteristics of the adolescents (nutritional status, sex, age group and sexual maturation) and by insulin resistance, which is an event associated with the inflammatory process secondary to the low intake of antioxidant nutrients [27,52,53].
Antioxidant micronutrient intake by Brazilian adolescents varied according to sex and age group, with similar or higher median zinc, selenium and vitamin C intake compared to the Estimated Average Requirements and lower values than these requirements for vitamins A and E.
The vitamins and minerals evaluated were significantly correlated with anthropometric variables independently of the occurrence of insulin resistance. In addition, inverse correlations between HOMA-IR and antioxidant micronutrients was found in boys and girls with and without overweight/obesity.
Furthermore, it was observed that adequate intake of antioxidant micronutrients, according to the EAR, was inversely associated with IR assessed by the HOMA-IR index in the adjusted model, while those with low intake lost effect. However, more studies are needed to define a safe and effective dose-response that can help with inflammatory modulation and, consequently, improve glycemic homeostasis.
The present findings underscore the importance of food and nutrition education practices in households and schools as a means for promoting lasting healthy habits. Studies with other designs more in-depth evaluation methods, including biomarkers of antioxidant micronutrient intake, should be conducted to identify actual intake and the possible determination of scores as recommendations for the content of antioxidant vitamins and minerals as a whole in the dietary practices of adolescents.

Financing source

This work was supported by the Financiadora de Estudos e Projetos (FINEP [Study and Project Funder]) (grant number 01090421) and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq [National Council of Scientific and Technological Development] grant numbers 565037/2010-2, 405.009/2012-7). The funders had no role in the study design, collection, analyses, or interpretation of the data; in the writing of the report; and in the decision to submit the article for publication. None of the authors has a conflict of interest.

References

1. Fontenelle LC, Feitosa MM, Morais JBS, Severo JS, Freitas TEC de, Beserra JB, Henriques GS, Marreiro DN. The Role of Selenium in Insulin Resistance. Braz J Pharm Sci 2018;54(1):1-11.
2. Stenzel A, Carvalho R, Jesus P, Bull A, Pereira S, Saboya C, Ramalho A. Serum Antioxidant Associations with Metabolic Characteristics in Metabolically Healthy and Unhealthy Adolescents with Severe Obesity: An Observational Study. Nutrients 2018;30(10):150-159.
3. Bailey RL, West-Jr. KP, Black RE. The Epidemiology of Global Micronutrient Deficiencies. Ann Nutr Metab 2015; 66(2):22–33.
4. García O, Ronquillo D, Caamaño C.M, Martínez G, Camacho M, López V, Rosado J. Zinc, Iron and Vitamins A, C and E Are Associated with Obesity, Inflammation, Lipid Profile and Insulin Resistance in Mexican School-Aged Children. Nutrients 2013;5(12):5012–5030.
5. Uranga RM, Keller JN. The Complex Interactions Between Obesity, Metabolism and the Brain. Front Neurosci 2019;13(513): 1-21.
6. Hurrle S, Hsu WH. The Etiology of Oxidative Stress in Insulin Resistance. Biomed J 2017;40(5):257–262.
7. Marcadenti A. Dietary Antioxidant and Oxidative Stress: Interaction between Vitamins and Genetics. J Nutr Health Food Sci 2015;3(1):1-7.
8. Goñi I, Hernández-Galiot A. Intake of Nutrient and Non-Nutrient Dietary Antioxidants. Contribution of Macromolecular Antioxidant Polyphenols in an Elderly Mediterranean Population. Nutrients 2019;11(9):2165.
9. Suarez-Ortegón MF, Ordoñez-Betancourth JE, Aguilar-de Plata C. Dietary Zinc Intake Is Inversely Associated to Metabolic Syndrome in Male but Not in Female Urban Adolescents: Zinc and Copper Intakes and Metabolic Syndrome. Am J Hum Biol 2013;25(4):550–554.
10. Tureck C, Locateli G, Corrêa VG, Koehnlein EA. Avaliação da ingestão de nutrientes antioxidantes pela população brasileira e sua relação com o estado nutricional. Rev Bras Epidemiol 2017;20(1):30–42.
11. Bloch KV, Szklo M, Kuschnir MC, Abreu Gde A, Barufaldi LA, Klein CH, de Vasconcelos MT, da Veiga GV, Figueiredo VC, Dias A, Moraes AJ, Souza AL, de Oliveira AM, Schaan BD, Tavares BM, de Oliveira CL, Cunha Cde F, Giannini DT, Belfort DR, Ribas DL, Santos EL, de Leon EB, Fujimori E, Oliveira ER, Magliano Eda S, Vasconcelos Fde A, Azevedo GD, Brunken GS, Dias GM, Filho HR, Monteiro MI, Guimarães IC, Faria Neto JR, Oliveira JS, de Carvalho KM, Gonçalves LG, Santos MM, Muniz PT, Jardim PC, Ferreira PA, Montenegro RM Jr, Gurgel RQ, Vianna RP, Vasconcelos SM, da Matta SS, Martins SM, Goldberg TB, da Silva TL. The Study of Cardiovascular Risk in Adolescents – ERICA: Rationale, Design and Sample Characteristics of a National Survey Examining Cardiovascular Risk Factor Profile in Brazilian Adolescents. BMC Public Health 2015;15(94):1-10.
12. Vasconcellos MTL de, Silva PLN, Szklo M, Kuschnir MCC, Klein CH, Abreu GA, Barufaldi LA, Bloch KV. Sampling Design for the Study of Cardiovascular Risks in Adolescents (ERICA). Cad Saúde Pública 2015;31(5):921–930.
13. Guedes DP, Lopes CC. Reproducibility and Validity of the International Physical Activity Questionnaire in Adolescents. Rev Bras Med Esporte 2005;11(2):151-158.
14. WHO Child Growth Standards. Length/Height-for-Age, Weight-for-Age, Weight-for-Length, Weight-for-Height and Body Mass Index-for-Age: Methods and Development. WHO Press: Geneva; 2006.
15. Freedman DS, Serdula MK, Srinivasan SR, Berenson GS. Relation of Circumferences and Skinfold Thicknesses to Lipid and Insulin Concentrations in Children and Adolescents: The Bogalusa Heart Study. Am J Clin Nutr 1999;69(2):308–317.
16. Ashwell M, Hsieh SD. Six Reasons Why the Waist-to-Height Ratio Is a Rapid and Effective Global Indicator for Health Risks of Obesity and How Its Use Could Simplify the International Public Health Message on Obesity. Int J Food Sci Nutr 2005;56(5):303–307.
17. Cureau FV, Bloch KV, Henz A, Schaan CW, Klein CH, Oliveira CL, Giannini DT, Leon EB, Abreu GA, Telo GH, Dias GM, Carvalho KMB, Barufaldi LA, Kuschnir MCC, Szklo M, Montenegro R Jr, Silva TLND, Ekelund U, Schaan BD. Challenges for Conducting Blood Collection and Biochemical Analysis in a Large Multicenter School-Based Study with Adolescents: Lessons from ERICA in Brazil. Cad Saúde Pública 2017;33(4):1-13.
18. Matthews DR, Hosker JR, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis Model Assessment: Insulin Resistance and Fl-Cell Function from Fasting Plasma Glucose and Insulin Concentrations in Man. Diabetologia 1985;28:412-419.
19. Qi Q, Hua S, Perreira KM, Cai J, Van Horn L, Schneiderman N, Thyagarajan B, Delamater AM, Kaplan RC, Isasi CR. Sex Differences in Associations of Adiposity Measures and Insulin Resistance in US Hispanic/Latino Youth: The Hispanic Community Children’s Health Study/Study of Latino Youth (SOL Youth). J Clin Endocrinol Metab 2017;102(1):185-194.
20. Shashaj B, Luciano R, Contoli B, Morino GS, Spreghini MR, Rustico C, Sforza RW, Dallapiccola B, Manco M. Reference Ranges of HOMA-IR in Normal-Weight and Obese Young Caucasians. Acta Diabetol 2016; 53(2):251–260.
21. da Silva CC, Zambon MP, Vasques AC, Rodrigues AM, Camilo DF, Antonio MÂ, Cassani RS, Geloneze B. Neck Circumference as a New Anthropometric Indicator for Prediction of Insulin Resistance and Components of Metabolic Syndrome in Adolescents: Brazilian Metabolic Syndrome Study. Rev Paul Pediatr 2014;32(2):221–229.
22. Baba R, Koketsu M, Nagashima M, Tamakoshi A, Inasaka H. Role of insulin resistance in non-obese adolescents. Nagoya J Med Sci 2010;72(3):161-166.
23. Household Budget Survey 2008-2009, Instituto Brasileiro de Geografia e Estatística. Nutritional Composition Table of Food Consumed in Brazil. IBGE: Rio de Janeiro; 2010.
24. Household Budget Survey 2008-2009, Instituto Brasileiro de Geografia e Estatística. Reported Measures Table for Foods Consumed in Brazil. IBGE: Rio de Janeiro; 2010.
25. Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ. Effectiveness of the US Department of Agriculture 5-Step Multiple-Pass Method in Assessing Food Intake in Obese and Nonobese Women. Am J Clin Nutr 2003;77(5):1171–1178.
26. Willett WC, Howe GR, Kushi LW. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 1997;65(4):1220-1228.
27. Costa CS, Rauber F, Leffa PS, Sangalli CN, Campagnolo PDB, Vitolo MR. Ultra-Processed Food Consumption and Its Effects on Anthropometric and Glucose Profile: A Longitudinal Study during Childhood. Nutr Metab Cardiovasc Dis 2019;29(2):177–184.
28. Martínez-González MA, García-Arellano A, Toledo E. A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial. PLoS ONE 2012;7(8):1-10.
29. Motamed S, Ebrahimi M, Safarian M, Ghayour-Mobarhan M, Mouhebati M, Azarpazhouh M, Esmailie H, Norouzi A, Ferns GA. Micronutrient intake and the presence of the metabolic syndrome. N Am J Med Sci 2013;5(6):377-385.
30. Rhee JJ, Cho E, Willett WC. Energy adjustment of nutrient intakes is preferable to adjustment using body weight and physical activity in epidemiological analyses. Public Health Nutr 2013;17(5):1054-1060.
31. Mirmiran P, Esmaillzadeh A, Azizi F. Under-reporting of energy intake affects estimates of nutrient intakes. Asia Pac J Clin Nutr 2006;15(4):459-464.
32. Padovani RM, Amaya-Farfán J, Colugnati FAB, Domene SMA. Dietary reference intakes: application of tables in nutritional studies. Rev Nutr 2006;19(6):741–760.
33. Oliveira JS, Barufaldi LA, Abreu Gde A, Leal VS, Brunken GS, Vasconcelos SM, dos Santos MM, Bloch KV. ERICA: Use of Screens and Consumption of Meals and Snacks by Brazilian Adolescents. Rev Saúde Pública 2016;50(1):1-9.
34. Carraro JCC, Hermsdorff HHM, Mansego ML, Zulet MA, Milagro FI, Bressan J, Martínez JA. Fruit Intake Is Related to TNF-? Hypomethylation and Better Glucose Tolerance in Healthy Subjects. J Nutr 2016;9(2):95–105.
35. Joint WHO-FAO Expert Consultation on Diet, Nutrition, and the Prevention of Chronic Diseases. Diet, Nutrition, and the Prevention of Chronic Diseases: Report of a WHO-FAO Expert Consultation. WHO: Geneva; 2003.
36. Ruxton GD, Colegrave N. Experimental design for the life sciences. Oxford University Press, New York; 2011.
37. Sangalli CN, Rauber F, Vitolo MR. Low prevalence of inadequate micronutrient intake in young children in the south of Brazil: a new perspective. Br J Nutr 2016;116(5):890-896.
38. Yokoyama, H. Beneficial Effects of Ethanol Consumption on Insulin Resistance Are Only Applicable to Subjects Without Obesity or Insulin Resistance; Drinking Is Not Necessarily a Remedy for Metabolic Syndrome. Int J Environ Res Public Health 2011;8(7):3019-3031.
39. Ho M, Heath AM, Gow M, Baur LA, Cowell CT, Samman S, Garnett SP. Zinc intake, zinco bioavailability and plasma zinc in obese adolescents with clinical insulin resistance following low energy diets. Ann Nutr Metab 2016;69(2):135-141.
40. Ekpenyong CE. Micronutrient deficiency, a novel nutritional risk factor for insulin resistance and Syndrom X. Arch Foof Nutr Sci 2018;2:16-30.
41. Norouzi S, Adulcikas J, Sohal SS, Myers S. Zinc transporters and insulin resistance: therapeutic implications for type 2 diabetes and metabolic disease. J Biomed Sci 2017;24(87):1-10.
42. Kawade R. Zinc status and its association with the health of adolescents: a review of studies in India. Glob Health Action 2012;5(7353):1-10.
43. Pinkney J, Streeter A, Hosking J, Mostazir M, Jeffery A, Wilkin T. Adiposity, chronic inflammation, and the prepubertal decline of sex hormone binding globulin in children: evidence for associations with the timing of puberty. J Clin Endocrinol Metab 2014; 99(9):3224-3232.
44. Villamor E, Jansen EC. Nutritional determinants of the timing of puberty. Annu Rev Public Health 2016;37:33-46.
45. Dybkowska E, Waszkiewicz-Robak B, Piekot E. Evaluation of vitamins a, c and e content in diets of adolescents living in Warsaw, Poland. Rocz Pa?stw Zak? Hig 2014;65(1):21-25.
46. Oliveira KJF, Koury JC, Donangelo CM. Micronutrientes e capacidade antioxidante em adolescentes sedentários e corredores. Rev Nutr 2007;20(2):171–179.
47. Beydoun MA, Canas JA, Beydoun HA, Chen X, Shroff MR, Zonderman AB. Serum Antioxidant Concentrations and Metabolic Syndrome Are Associated among U.S. Adolescents in Recent National Surveys. J Nutr 2012;142(9):1693–1704.
48. Strauss RS. Comparison of Serum Concentrations of ?-Tocopherol and ?-Carotene in a Cross-Sectional Sample of Obese and Nonobese Children (NHANES III). J Pediatr 1999;134(2):160-165.
49. Hozumi M, Murata T, Morinobu T, Manago M, Kuno T, Tokuda M, Konishi K, Mingci Z, Tamai H. Plasma beta-carotene, retinol, and alpha-tocopherol levels in relation to glycemic control of children with insulin-dependent diabetes mellitus. J Nutr Sci Vitaminol 1998;44(1):1-9.
50. Harari A, Coster ACF, Jenkins A, Xu A, Greenfield JR, Harats D, Shaish A, Samocha-Bonet D. Obesity and insulin resistance are inversely associated with serum and adipose tissue carotenoid concentrations in adults. J Nutr 2020;150(1):38-46.
51. Htet MK, Fahmida U, Thurnham DI, Hlaing LM, Akib A, Utomo B, Houghton LA. Folate and vitamin B12 status and dietary intake of anaemic adolescent schoolgirls in the delta region of Myanmar. Br J Nutr 2016;116(1):36-41.
52. Sarmento RA, Silva FM, Sbruzzi G, Schaan BD, Almeida JC. Antioxidant Micronutrients and Cardiovascular Risk in Patients with Diabetes: A Systematic Review. Arq Bras Cardiol. 2013; 101(3):240-248.
53. Cembranel F, Hallal ALC, González-Chica DA, d’Orsi E. Relationship between dietary comsumption of vitamins and minerals, body mass index, and waist circumference: a population based study of adults in southern Brazil. Cad Saúde Pública 2017;33(12):1-17.