Abstract

The aim of the present study was to identify dietary patterns and associations with socioeconomic characteristics, lifestyle, nutritional status, lipid profile and inflammatory profile in adolescents. A cross-sectional study was conducted with a probabilistic sample using baseline data (2014) from the Longitudinal Study on Sedentary Behavior, Physical Activity, Eating Habits and Adolescent Health. A total of 1,438 adolescents (10 to 14 years old) from public schools in the city of João Pessoa, Brazil, participated in the study. Data were collected on socioeconomic characteristics, nutritional status, lifestyle and the results of biochemical tests. Dietary data were obtained using the 24-hour recall method and dietary patterns were identified by exploratory factor analysis. Associations of interest were estimated using multiple logistic regression. Three dietary patterns were found: “Traditional”, “Snacks” and “Western”. These patterns were associated with age, socioeconomic status, parental education and lifestyle. The “Traditional” pattern was associated lower adiposity and a better lipid profile. However, with the increase in age, greater frequencies of the “Snacks” and “Western” patterns were found. The present findings underscore the need for strategies that encourage healthy behaviors.

Key words:
Dietary patterns; Adolescents; Socioeconomic status; Lifestyle; Nutritional status

Introduction

Epidemiological studies on nutrition in Brazil have traditionally focused on food and nutrient intake in an isolated manner. However, investigations of eating patterns may be more advantageous, as foods are analyzed simultaneously in this approach, considering complex combinations of nutrients¹1 Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002; 13(1):3-9.

2 Kant AK. Dietary patterns health outcomes. J Am Diet Assoc 2004; 104(4):615-635.

3 Newby PK, Muller D, Hallfrisch J, Qiao N, Andres R, Tucker KL. Dietary patterns and changes in body mass index and waist circumference in adults. Am J Clin Nutr 2003; 77(6):1417-1425.^-44 Moeller SM, Reedy J, Millen AE, Dixon LB, Newby PK, Tucker KL. Dietary patterns: challenges and opportunities in dietary patterns research an Experimental Biology workshop. J Am Diet Assoc 2007; 107(7):1233-1239., and facilitating the description of the effects of diet on health and disease outcomes⁵5 Kristiansen AL, Lande B, Sexton JA, Andersen LF. Dietary patterns among Norwegian 2-year-olds in 1999 and in 2007 and associations with child and parent characteristics. Br J Nutr 2013; 110(1):135-144.^,66 Azevedo ECC, Diniz AS, Monteiro JS, Cabral PC. Padrão alimentar de risco para as doenças crônicas não transmissíveis e sua associação com a gordura corporal - uma revisão sistemática. Cien Saude Colet 2014; 19(5):1447-1458.. Moreover, dietary patterns are influenced by socioeconomic, demographic, cultural, psychological and lifestyle issues, with healthier patterns found in the female sex, older individuals and those with more schooling⁷7 Willet WC, editor. Nutritional Epidemiology. 2ª ed. New York: Oxford University Press; 1998.

8 Cutler GJ, Flood A, Hannan P, Neumark-Sztainer D. Multiple sociodemographic and socioenvironmental characteristics are correlated with major patterns of dietary intake in adolescents. J Am Diet Assoc 2011; 111(2):230-240.

9 Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, Golçalves-Silva RMV. Factors associated with dietary patterns in adolescents: a school-based study in Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15(3):662-674.

10 Bertin M, Touvier M, Dubuisson C, Dufour A, Havard S, Lafay L, Volatier J-L, Lioret S. Dietary patterns of French adults: associations with demographic, socio-economic and behavioural factors. J Hum Nutr Diet 2016; 29(2): 241-54.^-1111 Arruda SP, Silva AA, Kac G, Goldani MZ, Bettiol H, Barbieri MA. Socioeconomic and demographic factors are associated with dietary patterns in a cohort of young Brazilian adults. BMC Public Health 2014; 14:654..

Adolescence is a phase marked by physical and emotional changes as well as the development of greater autonomy and is therefore considered decisive to the establishment of habits and behaviors that exert an influence on health¹²12 Currie C, Zanotti C, Morgan A, Currie D, Looze M, Roberts C, Samdal O, Smith ORF, Barnekow V. Social determinants of health and well-being among young people. Health Behaviour in School-aged Children (HBSC) study: international report from the 2009/2010 survey. Copenhagen: WHO; 2012.. The eating habits Brazilian adolescents are characterized by a high intake of energy-dense foods rich in fats and sugar and poor in fiber¹³13 Levy RB, Castro IRR, Cardoso LO, Tavares LF, Sardinha LMV, Gomes FS, Costa AWN. Food consumption and eating behavior among Brazilian adolescents: National Adolescent School-based Health Survey (PeNSE), 2009. Cien Amp Saude Colet 2010; 15:3085-3097.

14 Prochnik CCE, Costa RS, Sichieri R, Pereira RA, Veiga GV. Meal consumption patterns and anthropometric measurements in adolescents from a low socioeconomic neighborhood in the metropolitan area of Rio de Janeiro, Brazil. Appetite 2009; 52(3):735-739.

15 Souza AM, Barufaldi LA, Abreu GA, Giannini DT, Oliveira CL, Santos MM, Leal VS, Vasconcelos FAG. ERICA: intake of macro and micronutrients of Brazilian adolescents. Rev Saude Publica 2016; 50(Supl. 1):5s.

16 Barufaldi LA, Abreu GA, Oliveira JS, Santos DF, Fujimori E, Vasconcelos SML, Vasconcelos FAG, Tavares BM. ERICA: prevalence of healthy eating habits among Brazilian adolescents. Rev Saude Publica 2016; 50(Supl. 1):6s.^-1717 Alves MA, Souza AM, Barufaldi LA, Tavares BM, Bloch KV, Vasconcelos FAG. Dietary patterns of Brazilian adolescents according to geographic region: an analysis of the Study of Cardiovascular Risk in Adolescents (ERICA). Cad Saude Publica 2019; 35(6):e00153818..

This type of behavior can lead to excess weight and the development of chronic non-communicable diseases (NCDs). Studies have shown that eating habits established in adolescence are related to the development of health problems that can persist into adulthood¹⁸18 Joung H, Hong S, Song Y, Ahn BC, Park MJ. Dietary patterns and metabolic syndrome risk factors among adolescents. Korean J Pediatr 2012; 55(4):128-135.^,1919 Malik VS, Fung TT, van Dam RM, Rimm EB, Rosner B, Hu FB. Dietary patterns during adolescence and risk of type 2 diabetes in middle-aged women. Diabetes Care 2011; 35(1):12-18., affecting morbidity and mortality rates²⁰20 Frankel S, Gunnell DJ, Peters TJ, Maynard M, Davey Smith G. Childhood energy intake and adult mortality from cancer: the Boyd Orr Cohort Study. BMJ 1998; 316(7130):499-504.

21 Organização Pan-Americana da Saúde (OPAS). Doenças crônico-degenerativas e obesidade: estratégia mundial sobre alimentação saudável, atividade física e saúde. Brasília: OPAS; 2003.^-2222 World Health Organization (WHO). World Health Statistics 2012. Genebra: WHO; 2012.. Therefore, the identification of eating patterns among adolescents is of considerable importance to public health, enabling the development of strategies aimed at changing dietary habits in this group to reduce the risk of obesity and the occurrence of NCDs²³23 Pinho L, Silveira MF, Botelho ACC, Caldeira AP. Identification of dietary patterns of adolescents attending public schools. J Pediatr 2014; 90(3):267-272.^,2424 Nicklas TA, Baranowski T, Cullen KW, Berenson G. Eating patterns, dietary quality and obesity. J Am Coll Nutr 2001; 20(6):599-608.. However, few Brazilian studies have identified the dietary patterns of adolescents and associations with different factors, such as markers of cardiometabolic risk²⁵25 Silva DFO, Lyra CO, Lima SCVC. Padrões alimentares de adolescentes e associação com fatores de risco cardiovascular: uma revisão sistemática. Cien Saude Colet 2016; 21(4):1181-1196..

The aim of the present study was to identify dietary patterns and associations with socioeconomic characteristics, lifestyle, nutritional status, the lipid profile and inflammatory profile in adolescents in the city of João Pessoa, Northeastern Brazil.

Methods

Sample

In this study, we used data from the baseline (2014) of the Estudo Longitudinal sobre Comportamento Sedentário, Atividade Física, Alimentação e Saúde dos Adolescentes (LONCAAFS [Longitudinal Study on Sedentary Behavior, Physical Activity, Eating Habits and Adolescent Health]) conducted with a representative sample of adolescents in the 6^th year of public schools in the city of João Pessoa. The aim of the LONCAAFS study is to analyze interrelations between sedentary behavior, physical activity, diet and health among adolescents.

The sample size was calculated considering a reference population of 9520 6^th grade students enrolled at public schools in the city in the year 2011, a 50% prevalence rate of the outcome, 4% rate of error, a 95% confidence interval and a design effect of 2. The minimum sample size was established to be 1,130 adolescents, but the sample was increased by 40% to compensate for possible refusals and dropouts, resulting in a total of 1,582 adolescents.

Twenty-eight schools (14 municipal schools and 14 state-run schools) were systematically selected to compose the sample. The schools were distributed proportionally to number of students enrolled in the 6^th grade and geographic region (North, South, East and West). All 6^th grade students at the selected schools were invited to participate in the study.

Among the 28 schools selected to compose the sample, 17 were randomly chosen based on the same selection criteria to compose a subsample for the biochemical exams. This decision was made for reasons of logistics. The subsample had the same representativeness of the population regarding the distribution of schools by type and region of the city.

The exclusion criteria for the LONCAAFS study were adolescents outside the age range of interest (<10 and >14 years of age), any physical or intellectual limitation that would impede or hinder answering the questionnaire and currently being pregnant. For the present study, adolescents who did not undergo anthropometric measurements or did not do the 24-hour recall (24hR) were also excluded.

Data collection

Data collection was performed between February and December 2014 during school hours by a trained team composed of university students as well as professionals in the fields of nutrition and physical education. Sociodemographic variables, sedentary behavior and the practice of physical activity were obtained using a questionnaire administered in interview format. Average application time was 50 minutes.

Information on food intake was collected using the 24hR method administered by nutritionists and students of the nutrition course. The adolescents provided information on all foods and beverages consumed in the previous 24 hours, how meals were prepared, the commercial brands of processed foods, weight and portion size. The 24hR data were tabulated using the Virtual Nutri Plus software program, which was chosen based on the need to use information on Brazilian foods and the ease of entering foods, meals and nutritional information. A second 24hR was applied to 30% of the total sample to estimate intrapersonal variability and increase the precision of the estimate of dietary intake²⁶26 Verly-Jr E, Castro MA, Fisberg RM, Marchioni DM. Precision of Usual Food Intake Estimates According to the Percentage of Individuals with a Second Dietary Measurement. J Acad Nutr Diet 2012; 112(7):1015-1020..

The foods were first grouped into 29 categories and then regrouped into 14 categories based on correlations and/or similarities regarding nutritional composition. Items on the 24hR consumed by less than 5% of the population were excluded from the analysis.

The following sociodemographic variables were collected: sex (male and female), age in completed years (difference between the collection data and birthdate), skin color (white and non-white), mother’s and father’s schooling (incomplete primary school, incomplete high school and complete high school or more) and economic class (based on the criteria of the Brazilian Association of Research Firms²⁷27 Associação Brasileira de Empresas de Pesquisa (ABEP). Critério de Classificação Econômica Brasil [Internet]. [acessado 2018 nov 10]. Disponível em: http://www.abep.org/criterio-brasil.
http://www.abep.org/criterio-brasil... , which consider the presence of material goods, a live-in housekeeper and schooling of the parents, grouping individuals in the following classes: A, B, C, D and E).

Sedentary behavior (“screen time”) was determined based on the average time spent watching television, using a computer or tablet and playing videogames on weekdays and weekends (considered separately). For the purposes of analysis, the arithmetic mean was calculated by multiplying the average time on weekdays by five and the average time on weekends by two, adding these figures and then dividing by seven to obtain the mean number of hours per day dedicated to sedentary behavior. A cutoff point of more than two hours a day was used to define excessive screen time (sedentary behavior)²⁸28 American Academy of Pediatrics. Children, adolescents, and television. Pediatrics 2001; 107(2):423-426..

The level of physical activity was measured using the Physical Activity Questionnaire for Adolescents²⁹29 Farias Júnior JC, Lopes AS, Mota J, Santos MP, Ribeiro JC, Hallal PC. Validity and reproducibility of a physical activity questionnaire for adolescents: adapting the Self-Administered Physical Activity Checklist. Rev Bras Epidemiol 2012; 15(1):198-210.. Based on a checklist of 19 physical activities of moderate to vigorous intensity plus active movement from place to place, the adolescents reported practicing or not practicing each activity for at least 10 minutes in the previous week. They also reported the frequency and duration of each activity practiced. The physical activity score was estimated in minutes per week based on the sum of the time spent per week practicing all activities. The adolescents were classified as physically active when practicing 300 minutes or more of physical activity per week³⁰30 United States Department of Health and Human Services. Physical Activity Guidelines Advisory Committee Report, 2008. Washington: United States Department of Health and Human Services; 2008..

Body mass and height were measured in triplicate always by the same examiner and the mean was considered in the analysis. Body mass was determined on a digital scale (Techline) with a precision of 100 grams. Height was determined using a portable stadiometer (Sanny). The body mass index (BMI) was classified using the guidelines of the World Health Organization considering sex and age. BMI was then categorized as without excess body weight and with excess body weight³¹31 World Health Organization (WHO). Growth reference data for 5-19 years [Internet]. 2007 [acessado 2018 nov 10]. Disponível em: http://www.who.int/growthref/en/.
http://www.who.int/growthref/en... . The waist-to-height ratio was calculated using waist circumference (cm) measured at the midpoint between the last rib and the iliac crest divided by height (cm). The median of the population was used as the cutoff point for the classification of the adolescents³²32 Brannsether B, Roelants M, Bjerknes R, Júlíusson PB. Waist circumference and waist-to-height ratio in Norwegian children 4-18 years of age: reference values and cut-off levels. Acta Paediatr 2011; 100(12):1576-1582..

For the evaluation of the lipid and inflammatory profiles, blood was collected after 12 hours of fasting. Blood was collected at the schools after taking patient histories to identify factors that may interfere with the biochemical analyses (use of medications, allergies, having failed to fast for 10 to 12 hours and the practice of vigorous physical activity). The collections were performed by nursing technicians with experience in collecting blood from children and adolescents.

Concentrations of triglycerides, total cholesterol and high-density lipoprotein (HDL) cholesterol were determined by turbidimetry using the Labmax 240 Premium automated biochemical analyzer (Labtest, Lagoa Santa, Brazil). Low-density lipoprotein (LDL) cholesterol was determined using the Friedwald equation considering the concentrations of HDL, VLDL and total cholesterol ([total cholesterol - HDL cholesterol] - [triglycerides/5]). The classification of lipid profile markers as normal or altered was based on the reference cutoff points for children and adolescents stipulated in the updated Brazilian Guidelines of Dyslipidemia and the Prevention of Atherosclerosis³³33 Faludi AA, Izar MCO, Saraiva JFK, Chacra APM, Bianco HT, Afiune Neto A, Bertolami A, Pereira AC, Lottenberg AM, Sposito AC, Chagas ACP, Casella-Filho A, Simão AF, Alencar Filho AC, Caramelli B, Magalhães CC, Magnoni D, Negrão CE, Ferreira CES, Scherr C, Feio CMA, Kovacs C, Araújo DB, Calderaro D, Gualandro DM, Mello Junior EP, Alexandre ERG, Sato IE, Moriguchi EH, Rached FH, Santos FC, Cesena FHY, Fonseca FAH, Fonseca HAR, Xavier HT, Pimentel IC, Giuliano ICB, Issa JS, Diament J, Pesquero JB, Santos JE, Faria Neto JR, Melo Filho JX, Kato JT, Torres KP, Bertolami MC, Assad MHV, Miname MH, Scartezini M, Forti NA, Coelho OR, Maranhão RC, Santos Filho RD, Alves RJ, Cassani RL, Betti RTB, Carvalho T, Martinez TLR, Giraldez VZR, Salgado Filho W. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose. Arq Bras Cardiol 2017; 109(2 Supl. 1):1-92.. Triglycerides were considered altered when higher than 90 mg/dL. Total cholesterol was considered altered when higher than 170 mg/dL. LDL was considered altered when higher than 170 mg/dL and HDL was considered altered when lower than 45 mg/dL.

The inflammation profile was evaluated considering the serum concentration of C-reactive protein (CRP), which was determined using ultrasensitive ELISA. Serum CRP is considered a predictor of cardiovascular events and is frequently used in studies involving adolescents³⁴34 Khayyatzadeh SS, Bagherniya M, Fazeli M, Khorasanchi Z, Bidokhti MS, Ahmadinejad M, Khoshmohabbat S, Arabpour M, Afkhamizadeh M, Ferns GA, Masoudifar M, Ghayour-Mobarhan M. A Western dietary pattern is associated with elevated level of high sensitive C-reactive protein among adolescent girls. Eur J Clin Invest 2018; 48(4):e12897.

35 Perng W, Fernandez C, Peterson KE, Zhang Z, Cantoral A, Sanchez BN, Solano-González M, Téllez-Rojo MM, Baylin A. Dietary Patterns Exhibit Sex-Specific Associations with Adiposity and Metabolic Risk in a Cross-Sectional Study in Urban Mexican Adolescents. J Nutr 2017; 147(10):1977-1985.^-3636 Sureda A, Bibiloni MDM, Julibert A, Bouzas C, Argelich E, Llompart I, Pons A, Tur JÁ. Adherence to the Mediterranean Diet and Inflammatory Markers. Nutrients 2018; 10(1):62.. CRP was classified using the values stipulated in the updated Brazilian Guidelines of Dyslipidemia and the Prevention of Atherosclerosis³³33 Faludi AA, Izar MCO, Saraiva JFK, Chacra APM, Bianco HT, Afiune Neto A, Bertolami A, Pereira AC, Lottenberg AM, Sposito AC, Chagas ACP, Casella-Filho A, Simão AF, Alencar Filho AC, Caramelli B, Magalhães CC, Magnoni D, Negrão CE, Ferreira CES, Scherr C, Feio CMA, Kovacs C, Araújo DB, Calderaro D, Gualandro DM, Mello Junior EP, Alexandre ERG, Sato IE, Moriguchi EH, Rached FH, Santos FC, Cesena FHY, Fonseca FAH, Fonseca HAR, Xavier HT, Pimentel IC, Giuliano ICB, Issa JS, Diament J, Pesquero JB, Santos JE, Faria Neto JR, Melo Filho JX, Kato JT, Torres KP, Bertolami MC, Assad MHV, Miname MH, Scartezini M, Forti NA, Coelho OR, Maranhão RC, Santos Filho RD, Alves RJ, Cassani RL, Betti RTB, Carvalho T, Martinez TLR, Giraldez VZR, Salgado Filho W. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose. Arq Bras Cardiol 2017; 109(2 Supl. 1):1-92.: CRP≤1 mg/L=low risk; CRP>1 mg/L and ≤2 mg/L=medium risk; and CRP>2 mg/L=high risk.

Statistical analyses

The data were tabulated using the EpiData 3.1 program (Epidata Assoc., Odense, Denmark), with double-entry and the automatic checking of consistency and the response range of the variables.

To identify dietary patterns, principal component factor analysis was performed considering the 14 food categories. The habitual consumption of the food categories was estimated using the multiple source method (MSM), which estimate dietary data based on intrapersonal variability. The adequacy of the data from the factor analysis was checked using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity³⁷37 Hair JR, Anderson RE, Tatham RL, Black WC. Análise multivariada de dados. Porto Alegre: Editora Bookman; 2009.. To identify the number of patterns to be retained, we used eigenvalues (value higher than 1.0), a scree plot and the interpretability of the patterns³⁷37 Hair JR, Anderson RE, Tatham RL, Black WC. Análise multivariada de dados. Porto Alegre: Editora Bookman; 2009.. Varimax orthogonal rotation was used to facilitate the interpretation of the findings. Factor loadings higher than 0.30 were considered for naming the patterns encountered³⁷37 Hair JR, Anderson RE, Tatham RL, Black WC. Análise multivariada de dados. Porto Alegre: Editora Bookman; 2009.. The naming of the patterns was based on the interpretability and characteristics of the items retained in each pattern.

The Shapiro-Wilk test was used to determine the normality of the data. The Student’s t-test and ANOVA were used to compare the mean score of the patterns according sociodemographic characteristics (sex, age group, time of classes [morning or afternoon], type of school [municipal or state-run], region of the city, skin color, economic class and parents’ schooling). The chi-square test was used to compare the proportion of adolescents in different strata of nutritional status, level of physical activity, sedentary behavior, lipid profile and inflammatory profile according to dietary patterns.

To analyze associations between dietary patterns and the characteristics of the adolescents, the tertiles of the pattern scores were determined and dichotomized; the third tertile indicated greater adherence to a given pattern and the other tertiles were considered the reference category. Logistic regression analysis was performed to evaluate the associations of interest. In the adjusted model, variables were selected using the stepwise method. The goodness-of-fit of the model was checked using the Hosmer-Lemeshow test. The level of significance was set to 5% (p<0.05) for all tests. All analyses were performed using the Stata 13.0 program (StataCorp LP, College Station, USA).

Ethical aspects

The LONCAAFS study received approval from the Human Research Ethics Committee of the Center for Health Sciences of the Federal University of Paraíba. The volunteers and their legal guardians received clarifications regarding the objectives of the study and signed a statement of informed consent. The present study received approval from the Human Research Ethics Committee of the School of Public Health of the University of São Paulo.

Results

The final sample was composed of 1,438 adolescents, 53% of whom were girls and 44% were 11 years of age. Most declared themselves non-white (81%) and belonged to economic class C (58%). Approximately 32% were classified with excess weight. One-third (33%) was classified as physically inactive and 77% were classified as having sedentary behavior.

Three eating patterns were found. The first was denominated “Traditional”, which was characterized by high positive factor loadings for the group of base foods (rice, corn-based couscous and tubers), beans and meat as well as a negative factor loading for soup. The second group was denominated “Snacks”, which was characterized by processed meats, butter, margarine, bread, crackers, cheese, coffee and tea. The third group was denominated “Western”, which was characterized by sweets, pastries and chips/crisps, sweetened beverages and cheese (Table 1).

The “Traditional” pattern was more frequent among boys and students at schools located in the southern and western portions of the city of João Pessoa. The “Snacks” pattern was more frequent among boys, individuals between 12 and 14 years of age, students at schools in the southern and western portions of the city, those who studied in the afternoon, those whose mother’s had a lower level of schooling, those in economic classes C and D and physically active individuals. The “Western” pattern was more frequent among white adolescents, those whose fathers had a higher level of schooling, those in the highest economic classes (A e B), those classified as physically active and those with more than two hours of sedentary behavior per day (Table 2).

The majority of adolescents with adiposity (waist/height ratio) below the median was in the third tertile of the “Traditional” pattern, demonstrating greater adherence to this pattern (p=0.05). Likewise, the majority of adolescents with LDL in the normal range was in the third tertile of this same pattern (p=0.03) (Table 3).

The “Traditional” pattern was positively associated with age between 12 and 14 years (OR=1.44; 95%CI: 1.07 to 1.94) and inversely associated with the female sex (OR=0.65; 95%CI: 0.48 to 0.87), adiposity above the median for the population (OR=0.61; 95%CI: 0.45 to 0.82) and altered LDL (OR=0.71; 95%CI: 0.51 to 0.98) (Table 4).

The “Snacks” pattern was positively associated with age between 12 and 14 years (OR=1.64; 95%CI: 1.29 to 2.08), economic class C (OR=1.39; 95%CI: 1.08 to 1.80) and economic class D/E (OR=2.62; 95%CI: 1.52 to 4.53). This pattern was also inversely associated with the female sex (OR=0.60; 95%CI: 0.47 to 0.77) (Table 4).

The “Western” pattern was positively associated with age between 12 and 14 years (OR=1.53; 95%CI: 1.15 to 2.03) and adolescents whose fathers had a complete high school education or higher (OR=1.89; 95%CI: 1.37 to 2.60). Moreover, this pattern was inversely associated with adolescents with excess weight (OR=0.76; 95%CI: 0.41 to 0.99) and those classified as physically inactive (OR=0.67; 95%CI: 0.49 to 0.92) (Table 4).

Discussion

In the present study, we identified three dietary patterns among adolescent students in the city of João Pessoa, denominated “Traditional”, “Snacks” and “Western”. The “Traditional” pattern was composed of foods from the traditional diet of northeastern Brazil, such as rice, beans, cassava and corn-based couscous. The “Snacks” pattern was characterized by the consumption of bread, butter, margarine, cheese, processed meats and coffee and the “Western” pattern was characterized by the consumption of energy-dense, nutrient-poor foods.

In agreement with both national¹³13 Levy RB, Castro IRR, Cardoso LO, Tavares LF, Sardinha LMV, Gomes FS, Costa AWN. Food consumption and eating behavior among Brazilian adolescents: National Adolescent School-based Health Survey (PeNSE), 2009. Cien Amp Saude Colet 2010; 15:3085-3097.^,3838 Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de Orçamentos Familiares, 2008-2009. Análise do Consumo Alimentar Pessoal no Brasil. Rio de Janeiro: IBGE; 2011. and international³⁹39 World Health Organization (WHO). Salud para los adolescentes del mundo. Una segunda oportunidad en la segunda década. Genebra: WHO; 2014.

40 Duffey KJ, Popkin BM. Causes of increased energy intake among children in the U.S., 1977-2010. Am J Prev Med 2013; 44(2):1-8.

41 Moreno LA, González-Gross M, Kersting M, Molnár D, Henauw S, Beghin L, Sjöström M, Hagströmer M, Manios Y, Gilbert CC, Ortega FB, Dallongeville J, Arcella D, Wärnberg J, Hallberg M, Fredriksson H, Maes L, Widhalm K, Kafatos AG, Marcos A, HELENA Study Group. Assessing, understanding and modifying nutritional status, eating habits and physical activity in European adolescents: the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Study. Public Health Nutr 2008; 11(3):288-299.^-4242 Slining MM, Popkin BM. Trends in intakes and sources of solid fats and added sugars among U.S. children and adolescents: 1994-2010. Pediatr Obes 2013; 8(4):307-324. studies, the present investigation found that the diet of adolescents is increasingly composed of foods with a high energy value rich in sugar, fat and salt and with a low nutritional value, such as sweets, desserts, ice creams, cookies, crackers, cakes, torts, sweetened beverages, snacks in general and chips/crisps. These findings are worrisome, as this dietary profile combined with low levels of physical activity and considerable time spent on sedentary activities contribute directly to the development of NCDs, which can emerge in adolescence and persist in adulthood⁴³43 Daw J, Margolis R, Wright L. Emerging Adulthood, Emergent Health Lifestyles: Sociodemographic Determinants of Trajectories of Smoking, Binge Drinking, Obesity, and Sedentary Behavior. J Health Soc Behav 2017; 58(2):181-197.^,4444 World Health Organization (WHO). Obesity and overweight [Internet]. [acessado 2018 nov 10]. Disponível em: http://www.who.int/mediacentre/factsheets/fs311/en/.
http://www.who.int/mediacentre/factsheet... .

The proportion of adolescents with a sedentary behavior was high and this behavior was positively associated with the “Western” dietary pattern. This result is in agreement with data from previous studies that report an association between this behavior and the consumption of foods belonging to the “Western” pattern⁴⁵45 Cuenca-García M, Huybrechts I, Ruiz JR, Ortega FB, Ottevaere C, González-Gross M, Moreno LA, Vicente-Rodríguez G, Molnár D, Polito A, Manios Y, Plada M, Vanhelst J, Widhalm K, Sjöström M, Kersting M, Castillo MJ, HELENA study group. Clustering of multiple lifestyle behaviors and health-related fitness in European adolescentes. J Nutr Educ Behav 2013; 45(6):549-557.

46 Matias TS, Silva KS, Silva JA, Mello GT, Salmon J. Clustering of diet, physical activity and sedentary behavior among Brazilian adolescents in the national school - based health survey (PeNSE 2015). BMC Public Health 2018; 18:1283.

47 Van Den Bulck J, Van Mierlo J. Energy intake associated with television viewing in adolescents, a cross sectional study. Appetite 2004; 43(2):181-184.

48 Utter J, Neumark-Sztainer D, Jeffery R, Story M. Couch potatoes or French fries: Are sedentary behaviors associated with body mass index, physical activity, and dietary behaviors among adolescents? J Am Diet Assoc 2003; 103(10):1298-1305.

49 Stroebele N, Castro JM. Television viewing is associated with an increase in meal frequency in humans. Appetite 2004; 42(1):111-113.^-5050 Deshmukh-Taskar PR, O'Neil CE, Nicklas TA, Yang SJ, Liu Y, Gustat J, Berenson GS. Dietary patterns associated with metabolic syndrome, sociodemographic and lifestyle factors in young adults: The Bogalusa Heart Study. Public Health Nutr 2009; 12(12):2493-2503.. One explanation for this result may be the fact that adolescents are exposed to an increasing number of advertisements that stimulate the consumption of foods rich in fat, sugar and salt⁵¹51 Coon KA, Goldberg J, Rogers BL, Tucker KL. Relationships between use of television during meals and children's food consumption patterns. Pediatrics 2001; 107(1):E7.^,5252 Hastings G, Stead M, McDermott L, Forsyth A, MacKintosh AM, Rayner M, Godfrey C, Caraher M, Angus K. Review of Research on the Effects of Food Promotion to Children. London: Food Standards Agency; 2003., leading to the consumption of these products during “Screen” activities, such as watching television⁴⁷47 Van Den Bulck J, Van Mierlo J. Energy intake associated with television viewing in adolescents, a cross sectional study. Appetite 2004; 43(2):181-184.

48 Utter J, Neumark-Sztainer D, Jeffery R, Story M. Couch potatoes or French fries: Are sedentary behaviors associated with body mass index, physical activity, and dietary behaviors among adolescents? J Am Diet Assoc 2003; 103(10):1298-1305.^-4949 Stroebele N, Castro JM. Television viewing is associated with an increase in meal frequency in humans. Appetite 2004; 42(1):111-113..

Although the prevalence of excess weight was high in the population of the present study (32%), nutritional status evaluated based on the BMI was not significantly associated with any of the dietary patterns, which is in line with data reported in a systematic review study⁵³53 Cunha CM, Costa PRF, Oliveira LPM, Queiroz VAO, Pitangueira JCD, Oliveira AM. Dietary patterns and cardiometabolic risk factors among adolescents: systematic review and meta-analysis. Br J Nutr 2018; 119(8):859-879.. Moreover, adiposity above the median was inversely associated with both the “Traditional” and “Western” patterns, which is in disagreement with data reported in previous studies⁵⁴54 Ambrosini GL, Emmett PM, Northstone K, Howe LD, Tilling K, Jebb SA. Identification of a dietary pattern prospectively associated with increased adiposity during childhood and adolescence. Int J Obes 2012; 36(10):1299-1305.^,5555 Morais CMM, Pinheiro LGB, Lima SCVC, Lyra CO, Evangelista KCMS, Lima KC, Pedrosa LFC. Dietary patterns of young adolescents in urban areas of northeast Brazil. Nutr Hosp 2013; 28(6):1977-1984.. These results may be explained by reverse causality, which is common among cross-sectional studies⁵⁶56 Enes CC, Pegolo GE, Silva MV. Influência do consumo alimentar e do padrão de atividade física sobre o estado nutricional de adolescentes de Piedade, São Paulo. Rev Paul Pediatr 2009; 27(3):265-271.^,5757 Perozzo G, Olinto MTA, Dias-da-Costa JS, Henn RL, Sarriera J, Pattussi MP. Associação dos padrões alimentares com obesidade geral e abdominal em mulheres residentes no Sul do Brasil. Cad Saude Publica 2008; 24(10):2427-2439., as well as possible under-reporting on the part of the adolescents with excess weight, who may have stated consuming smaller amounts than they actually consumed. This is one of the main limitations of the dietary assessment method employed⁵⁸58 Black AE, Bingham SA, Johansson G, Coward WA. Validation of dietary intakes of protein and energy against 24-hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements. Eur J Clin Nutr 1997; 51(6):405-413.^,5959 Avelino GF, Previdelli AN, Castro MA, Marchioni DML, Fisberg RM. Underreporting of energy intake and associated factors in a population-based study. Cad Saude Publica 2014; 30(3):663-668..

Although several studies have found a positive association between a dietary pattern similar to the “Western” pattern and serum levels of LDL in adolescents⁶⁰60 Dishchekenian VR, Escrivão MA, Palma D, Ancona-Lopez F, Araújo EA, Taddei JA. Padrões alimentares de adolescentes obesos e diferentes repercussões metabólicas. Rev Nutr 2011; 24(1):17-29.^,6161 Ochoa-Avilés A, Verstraeten R, Lachat C, Andrade S, Van Camp J, Donoso S, Kolsteren P. Dietary intake practices associated with cardiovascular risk in urban and rural Ecuadorian adolescents: a cross-sectional study. BMC Public Health 2014; 14:939., no such association was found in the present investigation. However, an altered LDL level was inversely associated with the “Traditional” pattern. A probable hypothesis for this finding is related to the high fiber content in some of the foods that compose the “traditional” pattern, such as beans, corn-based couscous, roots and tubers⁶²62 Kim S, Joung H, Shi S. Dietary pattern, dietary total antioxidant capacity, and dyslipidemia in Korean adults. Nutr J 2019; 18(1):37..

With the increase in age, adolescents become more autonomous and independent with regards to their food choices. However, this greater autonomy is generally associated with inadequate dietary behavior⁶³63 Neumark-Sztainer D, Wall M, Larson NI, Eisenberg ME, Loth K. Dieting and disordered eating behaviors from adolescence to young adulthood: Findings from a 10-year longitudinal study. J Am Diet Assoc 2011; 111(7):1004-1011.^,6464 Pirouznia M. The association between nutrition knowledge and eating behavior in male and female adolescents in the US. Int J Food Sci Nutr 2001; 52(2):127-132.. The present results confirm this, as adolescents between 12 and 14 years of age had a 52% and 49% greater chance of adhering to the “Snacks” and “Western” patterns, respectively.

According to the United Nations, health disparities among adolescents are related to socioeconomic inequalities in different countries of the world, including Brazil⁶⁵65 United Nations Development Programme. Human development report 2003: millennium development goals: a compact among nations to end poverty. New York: Oxford University Press; 2003.. The schooling and, consequently, income levels of parents are factors that exert an influence on the dietary pattern to which adolescents adhere⁸8 Cutler GJ, Flood A, Hannan P, Neumark-Sztainer D. Multiple sociodemographic and socioenvironmental characteristics are correlated with major patterns of dietary intake in adolescents. J Am Diet Assoc 2011; 111(2):230-240.^,6666 Northstone K, Smith AD, Cribb VL, Emmett PM. Dietary patterns in UK adolescents obtained from a dual-source FFQ and their associations with socio- economic position, nutrient intake and modes of eating. Public Health Nutr 2014; 17(7):1476-1485.. In the present study, economic level was positively associated with the “Western” pattern, which was more frequent among 12-to-14-year-old adolescents whose fathers were in the highest stratum for schooling. This result is similar to data described in Brazilian studies⁹9 Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, Golçalves-Silva RMV. Factors associated with dietary patterns in adolescents: a school-based study in Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15(3):662-674.^,2323 Pinho L, Silveira MF, Botelho ACC, Caldeira AP. Identification of dietary patterns of adolescents attending public schools. J Pediatr 2014; 90(3):267-272., which report greater consumption of junk food (similar to the “Western” pattern) among adolescents from families with a higher socioeconomic status.

Regarding economic level, the lower economic classes were positively associated with the “Snacks” pattern. A school-based study conducted in the city of Cuiabá, Brazil⁹9 Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, Golçalves-Silva RMV. Factors associated with dietary patterns in adolescents: a school-based study in Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15(3):662-674., also found an association between a lower socioeconomic status and greater adherence to a dietary pattern with the same foods as those pertaining to the “Snacks” pattern in the present study. This association may be due to a less varied diet, indicating less availability of and access to a variety of foods⁶⁷67 Aranceta J, Perez-Rodrigo C, Ribas L, Serra-Majem LI. Sociodemographic and lifestyle determinants of food patterns in Spanish children and adolescents: the enKid study. Eur J Clin Nutr 2003; 57(Supl. 1):40-44..

The fact that students at schools in the southern and western portions of the city adhered more to the “Traditional” and “Snacks” patterns may be explained by the fact that the Northern and Eastern regions are higher income areas with a higher human development index when the heads of families have higher schooling, whereas lower mean values are found for these indicators in the southern and western portions. These results are in disagreement with findings from a Brazilian study that used data from the 2009 National Student Health Survey, which reported a negative correlation between the human development index and a dietary pattern composed of sweetened carbonated beverages, cakes and cookies/crackers⁶⁸68 Tavares LF, Castro IRR, Levy RB, Cardoso LO, Claro RM. Dietary patterns of Brazilian adolescents: results of the Brazilian National School- Based Health Survey (PeNSE). Cad Saude Publica 2014; 30(12):2679-2690..

Adolescents who studied in the afternoon adhered more to the “Snacks” pattern compared to those who studied in the morning and those who studied full time. This association may be due to the fact that adolescents who study in the afternoon (generally between 1 and 6 pm) tend to have breakfast later, which may affect their appetite at lunchtime, preferring to consume foods outside the home, which favors the choice of convenience foods, such as those that compose the “Snacks” and “Western” patterns⁹9 Rodrigues PRM, Pereira RA, Cunha DB, Sichieri R, Ferreira MG, Vilela AAF, Golçalves-Silva RMV. Factors associated with dietary patterns in adolescents: a school-based study in Cuiabá, Mato Grosso. Rev Bras Epidemiol 2012; 15(3):662-674.^,6969 Bezerra IN, Sichieri R. Eating out of home and obesity: a Brazilian nationwide survey. Public Health Nutr 2009; 12(11):2037-2043..

The results of the present study reflect the nutritional transition that has been occurring in recent decades in Brazil. This process involves a set of changes in nutritional patterns resulting from changes in the structure of the diet, which are associated with social, economic, demographic and health-related changes⁷⁰70 Batista Filho M, Rissin A. A transição nutricional no Brasil: tendências regionais e temporais. Cad Saude Publica 2003; 19(Supl. 1):S181-S191.^,7171 Kac G, Velásquez-Meléndez G. A transição nutricional e a epidemiologia da obesidade na América Latina. Cad Saude Publica 2003; 19(Supl. 1):S4-S5.. Thus, diet in adolescence merits attention, as inadequate eating habits established in this phase of life can have immediate harmful effects, such as physical and psychosocial alterations, as well as long-term effects, such as the development of NCDs in adulthood.

The present study has limitations that should be considered. Although the LONCAAFS study has a longitudinal design, we only analyzed baseline data, exploring associations between variables in a cross-sectional analysis. It was therefore not possible to determine causal relations. Despite being the most widely used technique for deriving dietary patterns, factor analysis involves arbitrary decision making, even though it is the basis of scientific knowledge on the diet of the population studied. Thus, the patterns identified are specific to the population studied and comparisons to different populations are limited. The food intake assessment method based on self-reports is also subject to recall bias and under-reporting.

Conclusion

The analysis of dietary patterns in the present study enabled a global assessment of diet, contributing knowledge on the dietary patterns of the adolescents. The results indicate that the dietary patterns of adolescents are associated with several factors, especially age, socioeconomic status, parents’ schooling and lifestyle. The “Traditional” pattern seems to have a protective effect regarding adiposity and LDL levels. However, the increase in age was associated with greater adherence to the “Snacks” and “Western” patterns. It is therefore important for all adolescents to be the focus of strategies that encourage healthy behaviors by broadening knowledge on nutrition as well as a healthy diet and lifestyle in the school and family settings.

References

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