Design and sampling process

This is a cross-sectional study with data from the Study of Cardiovascular Risks in Adolescents (Portuguese acronym: ERICA). It was a cross-sectional, national, school-based report with data collection carried out between the period of March 2013 and December 2014, with a sample composed of adolescents aged between 12 and 17 years of both sexes, enrolled in the last three years of elementary school and three years of high school in Brazilian public and private schools.

The sampled population framework was scaled into 32 geographic strata: each capital of the 27 units of the federation; and five strata comprising municipalities with more than 100,000 inhabitants in each of the country’s five macro-regions, totaling 273 eligible Brazilian municipalities. After geographic stratification, schools and classes were selected [17, 18].

The schools were selected in each geographic stratum with probability proportional to size (PPS). The size measure of each school was set equal to the ratio between the number of students in its eligible classes and the distance from the State capital. The PPS selection was performed in each geographic stratum after sorting the school records by situation (urban or rural areas) and the school governance (private or public). In the second stage, three classes in each sampled school were selected with equal probabilities during field work. In each selected class, all students were invited to participate. [17, 18]

Detailed information about the sample definition, the sampling process, the research protocol, the participant selection, and the data collection has been published in previous reports [17,18,19].

Dependent variable

To build the CMD variable, the Goldberg General Health Questionnaire (GHQ-12) [20] was used, validated for use in adolescents [21]. The GHQ-12 is a widely used self-completed instrument and is known to be a reliable measure of mental health. The GHQ-12 helps to track psychiatric disorders in the community and non-psychiatric clinical settings from an index generated from the individuals’ responses [22].

For the screening among the adolescents in this report, the binary system with a cutoff point of five was considered, that is, the presence of CMD was considered when at least 5 of the 12 items were answered with one of the last two options of the questionnaire (“a little more than normal” or “much more than normal”). This cutoff point has a sensitivity of 86.7%, specificity of 88.9%, a positive predictive value of 71.2%, and ROC curve area (Receiver Operating Characteristics) of 0.94 [23].

Independent variables

Dietary patterns

For the construction of the dietary pattern, this report has used food frequently consumed by adolescents. The 24-h recall was the method used to collect data about the dietary food intake of the adolescents participating in the study. This method was applied through face-to-face interviews conducted by trained researchers. A multiple-pass method [24] has been chosen for the interview technique. This technique consists of a guided interview divided into five stages. This interview method intends to reduce food intake underreporting, through the instrument REC24h-ERICA [25].

The software Brasil-Nutri [26] was used to record food consumption data. This software contained a list of 1,626 food items from the database regarding the acquisition of food and beverages from the 2002–2003 household budget survey carried out by the Brazilian Institute of Geography and Statistics (“Instituto Brasileiro de Geografia e Estatísticas (IBGE)” in Portuguese), [27] developed by the Ministry of Health in partnership with the Institute of Social Medicine. The database used in the National Dietary Survey was developed by IBGE in 2008–2009 [28].

After converting the weight of the food items into grams, [28] the dataset was linked to a nutritional composition table [28] to calculate the energy consumption of each adolescent. The foods were classified based on the degree of processing, as indicated by the NOVA food classification system [29]. This classification system categorizes all foods into the following 4 groups, according to the nature, extent, and purpose of the industrial processes they undergo: unprocessed and minimally processed food, processed culinary ingredients, processed food, and ultra-processed food. [29] The culinary preparations were disaggregated and their ingredients classified into their respective groups. The food was categorized by 2 independent researchers and discrepancies, if any, were resolved by an expert researcher.

For this study, with the aim of investigating Dietary Patterns among adolescents, was calculated using the Principal Component Analysis (PCA). For the calculation of the Dietary Pattern, only the groups were considered: unprocessed or minimally processed, and ultra-processed foods. This choice was based on the Food Guide for the Brazilian Population (“Guia Alimentar para a População Brasileira” in Portuguese) [29], which recommends that a healthy diet should consist mostly of fresh and minimally processed foods and contain as little ultra-processed food as possible [29]. The ultra-processed foods included in the PCA were: sweetened beverages, packaged snacks, candies, ultra-processed high carbohydrate foods, ultra-processed meat products, cookies (biscuits), milk drinks and dairy products. The unprocessed or minimally processed foods included in the PCA were: vegetables, fruits, cereals, eggs and meat. The foods that were considered in each subgroup are listed in the Supplementary Material.

For this study, beverages that did not specify whether or not they contained added sugar were included in the “Sweetened Beverages” group. Other beverages, as in natura fruit juice, were not considered for this study. This decision was based on studies that showed that eating whole fruit is different from drinking a portion of juice, in nutritional terms [30].

After this process, the kilocalories were calculated. To obtain closer analyzes of the consumption of adolescents, the outliers were removed. Outliers were considered and, consequently, excluded from the present report those adolescents who presented food intake below 500 kcal/day or above 6,000 kcal/day [31].

Eating practices

The following variables that refer to eating practices were considered for this report: the practice of having main meals with the family and breakfast consumption. The variable “practice of having the meals with family” was constructed from the grouping of two variables, namely, “Does your father (or stepfather) or your mother (or stepmother) or guardian have lunch with you?” and “Does your father (or stepfather) or mother (or stepmother) or guardian have dinner with you?”. The categories available for the adolescents’ response in each question were: (1) my parents or guardians never or almost never have lunch/dinner with me; (2) my parents or guardians have lunch/dinner with me sometimes; (3) my parents or guardians have lunch/dinner with me almost every day; (4) my parents or guardians have lunch/dinner with me every day. In this study we have chosen to merge categories 3 and 4, being the categories of analysis: “never or almost never”, “sometimes” and “almost every day or every day”.

The variable “breakfast consumption” had as an answer option in the adolescent’s questionnaire: (1) have no breakfast; (2) have breakfast sometimes; (3) have breakfast almost every day; (4) have breakfast every day. The variable was recategorized with the union of the alternatives 3 e 4, being the categories of analysis: “does not consume”, “sometimes” and “almost every day or every day”.

Adjusted variables

The adjusted variables were identified from a theoric model and selected with the aid of a Directed Acyclic Graph (DAG) built in the Dagitty (http://www.dagitty.net/) [32] (Supplementary Material). The minimal sufficient adjustment sets for estimating the total effect of Eating Practices, Dietary Patterns on CMD were age, lives with parents, physical activities, screen time, sex, sleep time, socioeconomic factors and type of school (administrative dependence).

The age of the adolescents was categorized into three age groups: 12–13 years old, 14–15 years old and 16–17 years old. Type of school could be public administration and private administration. As for gender, the alternatives in the student’s questionnaire were: female and male.

The variable living with parents has the following categories: lives with both parents, live only with mother or only father, and does not live with either parent.

The categorization of the time of weekly physical activity level practice was performed according to the cutoff points proposed by the National Adolescent Health Survey (Portuguese acronym: PeNSE) [33], in which adolescents who accumulated 300 min or more of physical activity per week were considered physically “active”, “insufficiently active 1” those between 1 to 149 min per week, “insufficiently active 2” those who practiced any Physical activity level from 150 to 299 min per week. Students who did not practice any Physical activity level in the week before the interview were considered “inactive”. The questionnaire used by ERICA to assess the practice of physical activity by adolescents was the Physical Activity Questionnaire for Adolescents (QAFA), validated by Farias Junior et al. [34].

The socioeconomic status of adolescents was calculated using the Principal Component Analysis (PCA), to get a Pattern of Socioeconomic Indicators (PSI) (Supplementary Material) with the variables described by Ribeiro et al. [35] and Erwling and Barros [36]. The variables considered were: “number of residents per room”, “employees in the residence”, “number of bathrooms” and “number of refrigerators”. The PSI obtained was characterized by the presence of employees, lower number of residents per room, higher number of bathrooms and higher number of refrigerators (Supplementary Material).

The schools considered in this study were: public and private. To obtain the mean of sleep time, the weighted mean was calculated between the time in hours of sleep usually practiced during weekdays and weekend days, separately. Those individuals who reported a practice of sleeping less than 4 h and more than 14 h were excluded, according to Borges [37]. The daily screen time was classified as greater than three hours a day and less than or equal to three hours a day [38].

Statistical analysis

The descriptive analysis included the calculation of absolute and relative frequencies for categorical variables, in addition to measures of central tendency and dispersion when the variables were numerical. The chi-square test was performed to compare the proportions between the variables. When the variable had more than 2 categories, the Bonferroni correction was applied to avoid type I errors from multiple comparisons.

To identify the adolescents’ dietary patterns, Principal Component Analysis (PCA) was performed, which is a descriptive analytical method that condenses the information contained in the observed variables into a smaller number of variables, with minimal loss of information. For the performance of the PCA, were added all unprocessed or minimally processed, and ultra-processed foods. The Kaiser-Mayer-Olkin (KMO) was estimated as a measure of the adequacy of the PCA, with values between 0.5 and 1.0 considered acceptable for this index. Subsequently, components with Eigen Values > 1.0, defined according to the screen plot graph (Supplementary Material), were extracted from the PCA. The structure of the components was obtained from the indicators that had factor loads greater than > 0.3 or less than -0.3, with a variable being generated in units of points for each consumption pattern. Thus, the following groups were considered: fruits, vegetables, legumes, cereals, meats (unprocessed or minimally processed foods) and sweetened beverages, candies, packaged snacks, cookies (biscuits) (ultra-processed foods). For each pattern obtained in the PCA, a categorical variable was created. The obtained values were distributed in terciles, originating three categories.

To verify the magnitude of the association of dietary patterns, breakfast consumption, and the practice of having meals accompanied by the family with the presence of CMD, using the Odds Ratio (OR) and its 95% confidence intervals (95% CI), the binary logistic regression models was adopted.

The bivariate analysis was performed using simple logistic regression models, with the variable “CMD” as dependent and the variables “dietary patterns”, “breakfast consumption” and “practice of having main meals accompanied by parents or guardians” an explanatory.

All analyses adopted 5% significance level. It is noteworthy, that because the Study of Cardiovascular Risks in Adolescents (ERICA) data come from a complex sample, the survey command “svy:” was applied in all statistical analyzes performed in the statistical program Stata software version 14.0 [39].

Ethical aspects

This report was approved by the Research Ethics Committee of the Institute for Collective Health Studies (“Instituto de Estudos de Saúde Coletiva” in Portuguese) of Federal University of Rio de Janeiro (Portuguese acronym: IESC/UFRJ) which belongs to the report’s central coordination (IESC/UFRJ – Approval nº 45/2008) and of each State. Informed consents were obtained from all subjects, parent and their legal guardian(s). The authors confirm that all methods were performed in accordance with the Declaration of Helsinki.

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