Abstract

The aim of the current research is to analyze the coexistence of modifiable risk behaviors for cardiovascular disease (CVD) in 12-to-17-year-old adolescents living in Brazil and their influence on overweight. National, cross-sectional, school-based epidemiological study focused on estimating the prevalence of cardiovascular risk factors and metabolic syndrome in 12 to 17 year old adolescents enrolled in public and private schools in Brazilian counties accounting for more than 100 thousand inhabitants. The grade of membership method was used to identify the coexistence of risk factors among adolescents. The analytical sample comprised 71,552 adolescents. According to the two herein generated profiles, adolescents classified under Profile 2 have shown behaviors such as smoking, alcohol consumption and diet rich in Ultra-processed food intake ≥ 80% of the percentage of total caloric value. In addition, adolescents presenting CVD risk profile have shown increased likelihood of being overweight. The study has found coexistence of risk factors for CVD in Brazilian adolescents, with emphasis on tobacco smoking and alcoholic beverage intake. In addition, it heads towards the analysis of the association between CVD risk factors and health outcomes, such as overweight.

Palavras-chave:
Adolescentes; Doença cardiovascular; Álcool; Tabagismo; Atividade física; Alimentos ultraprocessados

Resumo

O objetivo desta pesquisa é analisar a coexistência de comportamentos de risco modificáveis para doenças cardiovasculares (DCV) em adolescentes de 12 a 17 anos residentes no Brasil e sua influência no excesso de peso. Estudo epidemiológico nacional, transversal, de base escolar, com foco em estimar a prevalência de fatores de risco cardiovascular e síndrome metabólica em adolescentes de 12 a 17 anos matriculados em escolas públicas e privadas de municípios brasileiros que somam mais de 100 mil habitantes. O grade of membership foi utilizado para identificar a coexistência de fatores de risco entre os adolescentes. A amostra analítica foi composta por 71.552 adolescentes. De acordo com os dois perfis gerados, os adolescentes classificados no Perfil 2 mostraram comportamentos como fumar, consumo de álcool e dieta rica em alimentos ultraprocessados ≥ 80% da porcentagem do valor calórico total. Além disso, adolescentes com perfil de risco para DCV mostraram maior probabilidade de apresentar excesso de peso. O estudo encontrou coexistência de fatores de risco para DCV em adolescentes brasileiros, com destaque para tabagismo e consumo de bebidas alcoólicas. Além disso, demonstra associação entre fatores de risco para DCV e desfechos de saúde, como o excesso de peso.

Palavras-chave:
Adolescentes; Doença cardiovascular; Álcool; Tabagismo; Atividade física; Alimentos ultraprocessados

Introduction

Obesity and overweight rates in adolescents have been increasing worldwide¹1 NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet 2017; 390(10113):2627-2642.^,22 Di Cesare M, Soric M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA, Laxmaiah A, Kengne AP, Bentham J. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med; 17(1):212.; therefore these conditions became a severe public health issue³3 Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev 2016; 17(2):95-107.. Overweight prevalence in adolescents has significantly increased in recent years, mainly in developing countries¹1 NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet 2017; 390(10113):2627-2642.^,44 Rivera JA, Cossío TG, Pedraza LS, Aburto TC, Sánchez TG, Martorell R. Childhood and adolescent overweight and obesity in Latin America: a systematic review. Lancet Diabetes Endocrinol 2014; 2(4):321-332. such as Brazil⁴4 Rivera JA, Cossío TG, Pedraza LS, Aburto TC, Sánchez TG, Martorell R. Childhood and adolescent overweight and obesity in Latin America: a systematic review. Lancet Diabetes Endocrinol 2014; 2(4):321-332..

It is known that overweight during adolescence has important short- and long-term consequences, such as increased risk and early onset of Chronic Non-Transmissible Diseases⁵5 Abdullah A, Wolfe R, Stoelwinder JU, de Courten M, Stevenson C, Walls HL, Peeters A. The number of years lived with obesity and the risk of all-cause and cause-specific mortality. Int J Epidemiol 2011; 40(4):985-996.^,66 Park MH, Falconer C, Viner RM, Kinra S. The impact of childhood obesity on morbidity and mortality in adulthood: a systematic review. Obes Rev 2012; 13(11):985-1000., such as some types of cancer and cardiovascular diseases⁷7 Boyer BP, Nelson JA, Holub SC. Childhood body mass index trajectories predicting cardiovascular risk in adolescence. J Adolesc Health 2015; 56(6):599-605.^,88 Baker JL, Olsen LW, Sørensen TIA. Childhood Body-Mass Index and the Risk of Coronary Heart Disease in Adulthood. N Engl J Med 2015; 357(23):687-696. and premature death⁹9 Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med; 362(6):485-493.. Added to this are problems in the psychological realm, due to its potential risk for social and emotional changes experienced by adolescents, causing diseases such as depression, anxiety, low self-esteem, in addition to emotional and behavioral disorders - due to the social stigma associated with overweight¹⁰10 Quek Y-H, Tam WWS, Zhang MWB, Ho RCM. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev 2017; 18(7):742-754..

The NCD Risk Factor Collaboration (NCD-RisC) study demonstrated that the age-standardized overall prevalence of obesity in children and adolescents increased from 0.7% in 1975 to 5.6% in 2016 in girls, and from 0.9% in 1975 to 7.8% in 2016 in boys¹¹11 NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet 2017; 390(10113):2627-2642.. In the 42 years of analyses, the increase was eightfold in the global estimate, and was fastest in children aged 5 to 19 years²2 Di Cesare M, Soric M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA, Laxmaiah A, Kengne AP, Bentham J. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med; 17(1):212..

In Brazil, a study from the Study of Cardiovascular Risks in Adolescents (ERICA) showed that the prevalence of overweight was 17.1% and obesity was 8.4%, with obesity being higher in males (9.2%) than in females (7.6%)¹²12 Bloch KV, Klein CH, Szklo M, Kuschnir MCC, Abreu GA, Barufaldi LA, Veiga GV, Schaan B, da Silva TL, de Vasconcellos MT, Moraes AJ, Borges AL, Oliveira AM, Tavares BM, Oliveira CL, Cunha CF, Giannini DT, Belfort DR, Santos EL, Leon EB, Fujimori E, Oliveira ER, Magliano ES, Vasconcelos FA, Azevedo GD, Brunken GS, Guimarães IC, Faria Neto JR, Oliveira JS, Carvalho KM, Gonçalves LG, Monteiro MI, Santos MM, Jardim PC, Ferreira PA, Montenegro Jr RM, Gurgel RQ, Vianna RP, Vasconcelos SM, Goldberg TB. ERICA: prevalences of hypertension and obesity in Brazilian adolescents. Rev Saude Publica 2016; 50(Supl. 1):9s.. The prevalence of overweight and obesity in a study with data from the National School Health Survey (PeNSE) showed that 23.7% of adolescents were overweight and 7.8% were obese¹³13 Rocha LL, Gratão LHA, Carmo AS, Costa ABP, Cunha CF, Oliveira TRPR, Mendes LL. School type, eating habits, and screen time are associated with ultra-processed food consumption among Brazilian adolescents. J Acad Nutr Diet 2021; 121(6): 1136-1142..

Increased risk of early cardiovascular disease (CVD) development stands out¹⁴14 Weihrauch-Blüher S, Wiegand S. Risk factors and implications of childhood obesity. Curr Obes Rep 2018; 7(4):254-259. among several issues associated with overweight during adolescence; overweight is a key factor for increased CVD-related morbidity and mortality rates^15.

CVDs account for approximately 50% of deaths caused by noncommunicable diseases (NCDs); most CVD-associated deaths are observed in low- and middle-income countries¹⁶16 NCD Countdown 2030 collaborators. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet 2018; 392(10152):1072-1088.. According to estimates, 17.8 million people have died from CVD in 2017¹⁷17 Jagannathan R, Patel SA, Ali MK, Narayan KMV, Ali MK. Global updates on cardiovascular disease mortality trends and attribution of traditional risk factors. Curr Diab Rep 2019;19(7):44.^,1818 Joseph P, Leong D, Mckee M, Anand SS, Schwalm J, Teo K, et al. Reducing the global burden of cardiovascular disease, part 1. Circ Res 2017; 121(6):677-694.. Although the most severe manifestations, such as acute myocardial infarction and stroke, have higher prevalence in adult individuals, CVD risk factors have been often observed in children and adolescents¹⁹19 Coutinho ESF, França-Santos D, Silva Magliano E, Bloch KV, Barufaldi LA, Cunha CF, Vasconcellos MTL, Szklo M. ERICA: patterns of alcohol consumption in Brazilian adolescents. Rev Saude Publica 2016; 50(Suppl. 1):8s.

20 Figueiredo VC, Szklo AS, Costa LC, Kuschnir MCC, Silva TLN, Bloch KV, Szklo M. ERICA: smoking prevalence in Brazilian adolescents. Rev Saude Publica 2016; 50(Suppl. 1):12s.

21 Cureau FV, Silva TLN, Bloch KV, Fujimori E, Belfort DR, Carvalho KMB, Leon EB, Vasconcellos MT, Ekelund U, Schaan BD. ERICA: leisure-time physical inactivity in Brazilian adolescents. Rev Saude Publica 2016; 50(Suppl. 1):4s.^-2222 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(Suppl. 1):5s..

Epidemiological studies have shown that risk factors acquired in adolescence tend to persist in adulthood²³23 Fórnias L, Rezende M, Lee DH, Keum N, Nimptsch K, Song M, Lee IM, Eluf-Neto J, Ogino S, Fuchs C, Meyerhardt J, Chan AT, Willett W, Giovannucci E, Wu K. Physical activity during adolescence and risk of colorectal adenoma later in life: results from the Nurses' Health Study II. Br J Cancer 2019; 121(1):86-94.

24 Wiium N, Breivik K, Wold B. Growth trajectories of health behaviors from adolescence through young adulthood. Int J Environ Res Public Health 2015; 12(11):13711-13729.^-2525 Collaborators RF. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational , and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2017; 388(10053):1990-2015.. In addition, the incidence of two, or more, risk factors during adolescence is enough to predict cardiovascular events within the next ten years, since the combination of factors increases the extent and severity of vascular lesions that prevail in adulthood²⁶26 Gastaldelli A, Basta G. Ectopic fat and cardiovascular disease: what is the link? Nutr Metab Cardiovasc Dis 2010; 20(7):481-490. https://doi.org/10.1016/j.numecd.2010.05.005.
https://doi.org/10.1016/j.numecd.2010.05... .

Since the combination of two, or more, risk factors can be associated with increased risk of CVD development²⁷27 McAloney K, Graham H, Law C, Platt L. A scoping review of statistical approaches to the analysis of multiple health-related behaviours. Prev Med 2013; 56(6):365-371., studies focused on investigating simultaneous risk factors play key role in measuring the dimension of the current epidemiological issue, as well as provide better guidance for public health interventions. The idea that risk and protection factors for a given outcome coexist in groups (clusters) is highly beneficial²⁷27 McAloney K, Graham H, Law C, Platt L. A scoping review of statistical approaches to the analysis of multiple health-related behaviours. Prev Med 2013; 56(6):365-371., mainly at the time to develop public policies.

In addition, identifying risk behaviors for CVD and their influence on overweight may help substantiating more effective prevention strategies - based on multiple components - aimed at reducing risk factors for overweight among adolescents and, consequently, at avoiding the most severe CVD-associated outcomes. In light of the foregoing, the aim of the current study was to analyze the coexistence of modifiable risk behaviors for CVDs in 12 to 17 year old adolescents living in Brazil, as well as their association with overweight.

Ethics approval and consent to participate

The study was approved by the Research Ethics Committees of the institution coordinating the study (IESC / UFRJ) and of each Brazilian state. Adolescents who agreed to participate in the study have signed the written informed consents form; parents or legal guardians provided written informed consents form for all participants younger than 18. Participants’ identification remained confidential.

Methods

Study population

The current research is part of the Study of Cardiovascular Risks in Adolescents (ERICA), which is a national, cross-sectional, school-based epidemiological study aimed at estimating the prevalence of CVD risk factors and metabolic syndrome in 12 to 17 year old adolescents enrolled in public and private schools in Brazilian counties that account for more than 100 thousand inhabitants²⁸28 Vasconcellos MTL, 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 Saude Publica 2015; 31(5):921-930..

The sample was stratified into 32 strata comprising 27 state capitals and five sets of counties with more than 100 thousand inhabitants, in each of the five geographical regions in the country. Schools were selected in each geographic stratum based on probability proportional to school size and inversely proportional to the distance from the capital. Three classes per school were selected, based on different combinations of shift (morning and afternoon) and grade (seventh, eighth, and ninth grades of elementary school and first, second, and third years of high school). All students in the selected classes were invited to participate in the study²⁸28 Vasconcellos MTL, 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 Saude Publica 2015; 31(5):921-930..

The herein adopted sample was representative of mid- and large-sized counties (≥ 100 thousand inhabitants) at national, regional and metropolitan level. Adolescents who were not in the age group of 12 to 17 years, who had some disability level capable of impairing the anthropometric assessment and questionnaire completion were excluded from the study, as well as pregnant women²⁸28 Vasconcellos MTL, 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 Saude Publica 2015; 31(5):921-930.. Data collection took place between February 2013 and November 2014. Further details on the sampling process can be found in Block et al.²⁹29 Bloch KV, Szklo M, Kuschnir MCC, Abreu GA, Barufaldi LA, Klein CH, Vasconcelos MT, da Veiga GV, Figueiredo VC, Dias A, Moraes AJ, Souza AL, Oliveira AM, Schaan BD, Tavares BM, Oliveira CL, Cunha CF, Giannini DT, Belfort DR, Ribas DL, Santos EL, Leon EB, Fujimori E, Oliveira ER, Magliano ES, Vasconcelos FA, Azevedo GD, Brunken GS, Dias GM, Filho HR, Monteiro MI, Guimarães IC, Faria Neto JR, Oliveira JS, Carvalho KM, Gonçalves LG, Santos MM, Muniz PT, Jardim PC, Ferreira PA, Montenegro Jr RM, Gurgel RQ, Vianna RP, Vasconcelos SM, Matta SS, Martins SM, Goldberg TB, 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. and Vasconcellos et al.²⁸28 Vasconcellos MTL, 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 Saude Publica 2015; 31(5):921-930.

Teenagers participating in ERICA have completed the self-administered questionnaire about different topics associated with health and lifestyle by using Personal Digital Assistants (PDA)²⁸28 Vasconcellos MTL, 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 Saude Publica 2015; 31(5):921-930.^,2929 Bloch KV, Szklo M, Kuschnir MCC, Abreu GA, Barufaldi LA, Klein CH, Vasconcelos MT, da Veiga GV, Figueiredo VC, Dias A, Moraes AJ, Souza AL, Oliveira AM, Schaan BD, Tavares BM, Oliveira CL, Cunha CF, Giannini DT, Belfort DR, Ribas DL, Santos EL, Leon EB, Fujimori E, Oliveira ER, Magliano ES, Vasconcelos FA, Azevedo GD, Brunken GS, Dias GM, Filho HR, Monteiro MI, Guimarães IC, Faria Neto JR, Oliveira JS, Carvalho KM, Gonçalves LG, Santos MM, Muniz PT, Jardim PC, Ferreira PA, Montenegro Jr RM, Gurgel RQ, Vianna RP, Vasconcelos SM, Matta SS, Martins SM, Goldberg TB, 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.. ERICA was approved by the Research Ethics Committees of the Institute of Studies in Collective Health of Federal University of Rio de Janeiro and of each participant state and Federal District unit. All participants have signed the informed consent form and a copy of it was properly filed by the research team in charge of the current study.

Dependent variable

Overweight incidence in adolescents was adopted as dependent variable. Participants’ body mass index (BMI) was calculated based on their weight and height; reference curves set by the World Health Organization (WHO) for adolescents³⁰30 Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 2007; 85(9):660-667. were also adopted. The cutoff points adopted for overweight corresponded to Z-score > +1³⁰30 Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 2007; 85(9):660-667..

Anthropometric measurements of all participants were taken by trained researchers. Participants’ weight was measured with the aid of Leader electronic scale (capacity = 200 kg and variation = 50 g). Portable Alturexata stadiometer (1-mm resolution and field of use of up to 213 cm) was used to measure adolescents’ height¹²12 Bloch KV, Klein CH, Szklo M, Kuschnir MCC, Abreu GA, Barufaldi LA, Veiga GV, Schaan B, da Silva TL, de Vasconcellos MT, Moraes AJ, Borges AL, Oliveira AM, Tavares BM, Oliveira CL, Cunha CF, Giannini DT, Belfort DR, Santos EL, Leon EB, Fujimori E, Oliveira ER, Magliano ES, Vasconcelos FA, Azevedo GD, Brunken GS, Guimarães IC, Faria Neto JR, Oliveira JS, Carvalho KM, Gonçalves LG, Monteiro MI, Santos MM, Jardim PC, Ferreira PA, Montenegro Jr RM, Gurgel RQ, Vianna RP, Vasconcelos SM, Goldberg TB. ERICA: prevalences of hypertension and obesity in Brazilian adolescents. Rev Saude Publica 2016; 50(Supl. 1):9s..

Cardiovascular disease-risk variables

ERICA questionnaire covered specific questions distributed in 11 thematic blocks (sociodemographic features, work and employment, physical activity, eating habits, smoking, alcohol intake, reproductive health, oral health, referred morbidity, sleep duration and common mental health disorder). This study focused on the analysis of thematic blocks referring to alcohol intake, eating habits, smoking and physical activity.

Alcohol intake

This questionnaire block comprised information about the age at which participants drank at least one alcoholic drink for the first time, as well as about drinking days, number of drinks and drink types. The classification “alcohol intake” was defined based on these variables. The following classifications were used: 0 “never drank alcohol”; 1 = “only once”, which corresponded to “do not drink”; 2 = “1 or 2 days”, 3 = “3 to 5 days”, 4 = “6 to 9 days”, 5 = “10 to 19 days”, 6 = “20 to 29 days”, or 7 = “>29 days”, which corresponded to “every day”¹⁹19 Coutinho ESF, França-Santos D, Silva Magliano E, Bloch KV, Barufaldi LA, Cunha CF, Vasconcellos MTL, Szklo M. ERICA: patterns of alcohol consumption in Brazilian adolescents. Rev Saude Publica 2016; 50(Suppl. 1):8s..

Tobacco smoking

Current cigarette smokers comprised individuals who had smoked cigarettes for at least 1 day in the previous 30 days. Both variables have followed definitions adopted by WHO and by the Center for Disease Control and Prevention in the United States (CDC) in the Global Youth Tobacco Surveillance (GYTS)³¹31 Warren CW, Jones NR, Peruga A, Chauvin J, Baptiste J-P, Silva VC, el Awa F, Tsouros A, Rahman K, Fishburn B, Bettcher DW, Asma S; Centers for Disease Control and Prevention (CDC). Global youth tobacco surveillance, 2000-2007. MMWR Surveill Summ 2008; 57(1):1-28.. The following information was used as indicator of frequent tobacco smoking: having smoked cigarettes for at least seven consecutive days²⁰20 Figueiredo VC, Szklo AS, Costa LC, Kuschnir MCC, Silva TLN, Bloch KV, Szklo M. ERICA: smoking prevalence in Brazilian adolescents. Rev Saude Publica 2016; 50(Suppl. 1):12s..

Ultra-processed food (UPF) intake

Food intake was assessed based on a 24-hour dietary recall (R24h) during face-to-face interview performed by trained interviewers. Participants were interviewed by using Brasil Nutri software (http://nebin.com.br/novosite/conteudo.php?id=4), which was specifically designed for food intake data, which were directly recorded in netbooks.

The adopted interview technique lied on the multiple pass method³²32 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., which consists in a five-stage guided interview capable of reducing errors in food intake reports. The software used in this procedure has a list of 1,626 food items deriving from the food and beverage database of the 2002-2003 Household Budget Survey (POF, acronym in Portuguese), which was carried out by the Brazilian Institute of Geography and Statistics (IBGE)³³33 Instituto Brasileiro de Geográfia e Estatistica (IBGE). Orçamentos familiares 2002-2003: análise da disponibilidade domiciliar de alimentos e do estado nutrional no Brasil. Rio de Janeiro: IBGE; 2004., and developed by the Ministry of Health in partnership with the Institute of Social Medicine (Universidade do Estado do Rio de Janeiro). Database used in the National Dietary Survey (INA, acronym in Portuguese) was developed by the Brazilian Institute of Geography and Statistics in 2008-2009³⁴34 Instituto Brasileiro de Geográfia e Estatistica (IBGE). Pesquisa de orçamentos familiares 2008-2009: tabela de composição nutricional dos alimentos consumidos no Brasil. Rio de Janeiro: IBGE; 2011.^,3535 Instituto Brasileiro de Geografia e Estatistica (IBGE). Pesquisa de orçamentos familiares 2008-2009: tabela de medidas referidas para os alimentos consumidos no Brasil. Rio de Janeiro: IBGE; 2011..

Food and beverage intake data were transformed into weight (in grams) and volume (mL) units; then, they were associated with the respective information on nutritional composition, based on the methodology proposed by IBGE to process food intake data extracted from the Family Budget Survey (2008/2009)³⁴34 Instituto Brasileiro de Geográfia e Estatistica (IBGE). Pesquisa de orçamentos familiares 2008-2009: tabela de composição nutricional dos alimentos consumidos no Brasil. Rio de Janeiro: IBGE; 2011.^,3535 Instituto Brasileiro de Geografia e Estatistica (IBGE). Pesquisa de orçamentos familiares 2008-2009: tabela de medidas referidas para os alimentos consumidos no Brasil. Rio de Janeiro: IBGE; 2011..

Caloric and food intake data were analyzed and classified as fresh or minimally processed, processed and ultra-processed food, based on NOVA classification system³⁵35 Instituto Brasileiro de Geografia e Estatistica (IBGE). Pesquisa de orçamentos familiares 2008-2009: tabela de medidas referidas para os alimentos consumidos no Brasil. Rio de Janeiro: IBGE; 2011., by taking into consideration the extent and purpose of food processing. Information about the contribution rate of the UPF intake group to total daily energy (% of total caloric value - TCV) was taken into consideration in the present study. UPF ingredients often include different substances and additives, such as sugar, oil, fat, salt, antioxidants, stabilizers and preservatives³⁶36 Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Martins AP, Canella D, Louzada M, Parra D. NOVA. The star shines bright. World Nutr 2016; 7(1-3):28-38.. Excessive UPF intake was classified as UPF intake higher than, or equal, to the 80^th percentile of UPF intake distribution (45.60% of TCV). Large quintile of UPF intake distribution (P80) had been associated with poor food intake profile and with high risk of obesity in previous studies³⁷37 Monteiro CA, Cannon G, Lawrence M, Louzada MLC, Machado PP. Ultra-processed foods, diet quality, and health using the NOVA classification system. Rome: FAO; 2019..

Physical activity

The total physical activity time was calculated by summing up the time of each activity, including the low-intensity ones, such as walking dogs and taking care of children, commuting and walking to school, home or work. Adolescents who did not accumulate at least 300 min/week of physical activity were classified as inactive at leisure²¹21 Cureau FV, Silva TLN, Bloch KV, Fujimori E, Belfort DR, Carvalho KMB, Leon EB, Vasconcellos MT, Ekelund U, Schaan BD. ERICA: leisure-time physical inactivity in Brazilian adolescents. Rev Saude Publica 2016; 50(Suppl. 1):4s.^,3838 World Health Organization (WHO). Global recommendations on physical activity for health. Geneva: WHO; 2010..

Main effect

The Grade of membership (GoM)³⁹39 Woodbury MA, Clive J, Garson A. Mathematical technique typology: a grade of membership for obtaining disease definition. Comput Biomed Res 1978; 11(3):277-298.^,4040 Sawyer DO, Leite I C, Alexandrino R. Perfis de utilização de serviços de saúde no Brasil. Cien Saude Colet 2002; 7(4):757-776. method was used to identify the coexistence of risk and protective factors among adolescents. This method allows fuzzy membership, i.e., individuals do not need to organize themselves in well-defined sets, as in traditional crisp cluster methods, although they may partly belong to more than one group⁴⁰40 Sawyer DO, Leite I C, Alexandrino R. Perfis de utilização de serviços de saúde no Brasil. Cien Saude Colet 2002; 7(4):757-776..

The aforementioned method estimates the pertinence degree score of each individual based on different, crisp, well-defined sets. It is applied to a dataset composed of i individuals (i = 1, 2, ..., I), with j categorical variables (j = 1, 2, ..., J). There are L_j response levels for each j-th variable. Discrete response variable X_ijl is predicted by two sets of estimated coefficients, namely: λ_kjl and g_ik. The λ_kjl coefficient corresponds to the likely incidence of the L _j -th response level in the k-th profile among i-th individuals who are pure types of these profiles; it can assume any value between 0 and 1. The model estimates the pertinence degree score (g_ik) of each individual; this score represents the degree to which element i belongs to the extreme profile k, and it ranges from 0 to 1. Model identification process imposes two restrictions: (1) the sum of λ_kjl over L for the same k equals 1, and (2) the sum of g_ik over k for the same i equals 1³⁹39 Woodbury MA, Clive J, Garson A. Mathematical technique typology: a grade of membership for obtaining disease definition. Comput Biomed Res 1978; 11(3):277-298.^,4040 Sawyer DO, Leite I C, Alexandrino R. Perfis de utilização de serviços de saúde no Brasil. Cien Saude Colet 2002; 7(4):757-776..

Although λ_kjl directly describes the extreme profiles in probabilistic terms, the λ_kjl/marginal frequency ratio (expected/observed ratio) is more often used, since it sets an objective criterion for profile featuring based on the prevalence of attributes. Marginal frequency can be understood as the likely incidence of a particular feature in the total population. Cutoff value of 1.2, which was recorded for the Expected/Observed ratio, means that the likely incidence of an l-th response to a j-th variable in a k-th profile among pure types of that profile must be at least 20% higher than the observed marginal likelihood³⁹39 Woodbury MA, Clive J, Garson A. Mathematical technique typology: a grade of membership for obtaining disease definition. Comput Biomed Res 1978; 11(3):277-298.^,4040 Sawyer DO, Leite I C, Alexandrino R. Perfis de utilização de serviços de saúde no Brasil. Cien Saude Colet 2002; 7(4):757-776.. Risk factors coexisted in the current study when there were at least two risk factors for CVD in the generated profile⁴¹41 Whitaker V, Oldham M, Boyd J, Fairbrother H, Curtis P, Meier P, Holmes J. Clustering of health-related behaviours within children aged 11-16: a systematic review. BMC Public Health 2021; 21:137..

GoM parameters in the current study (g_ik and λ_kjl) were estimated in the GoMRcpp.R software for R⁴²42 Pinto JS, Caetano AJ. A heterogeneidade da vulnerabilidade social das juventudes: uma perspectiva empírica através do método grade of membership. Mediações Rev Cien Soc 2013; 18(1):164-82.. Created profiles and the G_iK found for each teenager were separated based on the highest degree of belonging to the profile. They were categorized as belonging to profile 1 when G_iK was ≤ 0.5 and as belonging to profile 2, when G_iK was > 0.5.

Statistical analysis

Population featuring was based on descriptive analysis. Collected data were analyzed in Stata software, version 16.0.

Multilevel logistic regression was used to estimate the odds ratio (OR) adjusted based on sociodemographic and school variables in order to check the significance of the association between the coexistence profile of CVD risk factors and overweight.

The modeling process has followed the steps suggested by Laros and Marciano⁴³43 Laros J, Marciano J. Análise multinível aplicada a dados do NELS: 88. Estud Aval Educ 2008; 19(40):263-278. and it was carried out in 3 different stages. The first stage is called the Null Model (M0).

Stage 2 consisted in analyzing the model based on variables at individual level such as participants’ age, sex, race/color, whether, or not, they live with their parents, and maternal education. Subsequently, stage 3 took into consideration variables at school level such as managerial dependency - public or private; sale of snacks at school and macro-region where the school is located in. Variance reduction was calculated at the end of the modeling process, based on the introduction of variables at individual and school level in the models in order to check their fit⁴³43 Laros J, Marciano J. Análise multinível aplicada a dados do NELS: 88. Estud Aval Educ 2008; 19(40):263-278.. Akaike information criterion (AIC) was used to calculate model fit - the best model was the one recording the lowest value for this criterion⁴⁴44 Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, Råstam L, Larsen K. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health 2006; 60(4):290-297. https://doi.org/10.1136/jech.2004.029454.
https://doi.org/10.1136/jech.2004.029454... .

The gllamm command - which enables performing statistical analysis by taking into consideration the multilevel structure of data, as well as including the necessary weighting at the time to analyze complex samples - was used to perform the multilevel model. Adolescents’ school was the herein adopted cluster unit.

All analyses were performed at 5% significance level.

Results

In total, 71,552 adolescents were analyzed in the current study. Based on participants’ BMI, 26.17% (95%CI 25.03%-27.34%) of them were classified as overweight. Among overweight adolescents, 53.96% were girls, and aged between 14 and 15 years (35.91%), 50.19% declared themselves to be brown and 6.40% lived alone. With respect to the place of study, 73.53% of overweight adolescents studied in public schools and 52.00% of them studied in more economically favored regions such as Southern, Southeastern and Midwestern Brazil (Table 1).

Table 2 shows the λ_kjl estimates (alcohol intake, tobacco smoking, UPF intake, and exercising) performed for each extreme profile of the investigated adolescents. Adolescents belonging to Profile 2 for Brazil (pure types, g_ik = 1) have shown behavioral features such as smoking, alcohol intake, and diet rich in UPF ≥ 45.60% of TCV. This profile can be categorized as CVD risk profile because it comprises three simultaneous risk factors (Table 1).

The null model is shown in Table 3. The M0 intercept variance (0.29; 95%CI 0.28-0.30) has shown overweight rates differed among schools (p < 0.001). Coefficient of variance partition (CVP) reached 0.051, or approximately 5.10% of total variance was attributed to adolescents’ school features.

Figure 1 shows the multilevel logistic regression model for overweight and its association with CVD risk profile. Model 1, which only comprised adolescent-level variables, has shown that participants belonging to the CVD risk profile presented significantly high likelihood of being overweight (Figure 1).

After school-level variables were included in the model (Figure 1 - Model 2), it was possible observing that adolescents presenting CVD risk profile have shown 1.067 times the likelihood of being overweight in comparison to adolescents who did not have this profile.

Discussion

The current study has shown that Brazilian adolescents presented more than one simultaneous risk factor for CVD. These concomitant risk factors were represented by profile comprising behavioral patterns such as smoking, alcohol intake, and UPF intake ≥ 45.60% of TCV, with weighted prevalence of 30.46% for girls and of 28.35%, for boys. It has also shown that the CVD risk profile was associated with higher likelihood of overweight in adolescence.

The understanding that risk factors for CVD coexist in adolescents remains recent, which is the reason why only few risk factors were analyzed, so far⁴⁵45 McClure JB, Divine G, Alexander G, Tolsma D, Rolnick SJ, Stopponi M, Richards J, Johnson CC. A comparison of smokers' and nonsmokers' fruit and vegetable intake and relevant psychosocial factors. Behav Med 2009; 35(1):14-22.^,4646 Ferreira NL, Claro RM, Mingoti SA, Lopes ACS. Coexistence of risk behaviors for being overweight among Brazilian adolescents. Prev Med 2017; 100:135-142.. A study carried out in 2019, with sample representative of Brazilian adolescents, recorded the highest prevalence (79%) of smoking and alcohol intake in clusters of risk factors for chronic noncommunicable diseases (NCDs)⁴⁶46 Ferreira NL, Claro RM, Mingoti SA, Lopes ACS. Coexistence of risk behaviors for being overweight among Brazilian adolescents. Prev Med 2017; 100:135-142.; this rate was higher than that found in the current study. Another study investigated this very same national sample and found that 2.9% of adolescents did not present any risk factor, whereas 38.0%, 32.9%, 9.4% and 1.8% of them accumulated two, three, four and five risk factors, respectively⁴⁷47 Ricardo CZ, Azeredo CM, Rezende LFM, Levy RB. Co-occurrence and clustering of the four major non-communicable disease risk factors in Brazilian adolescents: analysis of a national school-based survey. PLoS One 2019; 14(7):e0219370.; this outcome was similar to results found in the present study.

Another study has shown that 68.9% of adolescents presented at least two risk factors, whereas more than 10% of them had more than four risk factors⁴⁸48 Jardim TV, Gaziano TA, Nascente FM, Carneiro CS, Morais P, Roriz V, Mendonça KL, Póvoa TIR, Barroso WKS, Sousa ALL, Jardim PCBV. Multiple cardiovascular risk factors in adolescents from a middle-income country: prevalence and associated factors. PLoS One 2018; 13(7):e0200075.. According to most generated profiles, CVD-protective and risk behaviors existed in simultaneous way - this finding that was also observed in other studies⁴⁹49 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(1):1283.^,5050 Alamian A, Paradis G. Clustering of chronic disease behavioral risk factors in Canadian children and adolescents. Prev Med 2009; 48(5):493-499.. Furthermore, 65% of adolescents in Canada presented two, or more, risk factors for NCDs, namely: insufficient exercising levels, alcohol intake, tobacco smoking, sedentary lifestyle and high body mass rate⁵⁰50 Alamian A, Paradis G. Clustering of chronic disease behavioral risk factors in Canadian children and adolescents. Prev Med 2009; 48(5):493-499..

A study²⁴24 Wiium N, Breivik K, Wold B. Growth trajectories of health behaviors from adolescence through young adulthood. Int J Environ Res Public Health 2015; 12(11):13711-13729. carried out with different developmental trajectories in Norwegian adolescents until adult life has found that adolescents had three likely trajectories: one of them resembled the risk profile identified in the current study, according to which adolescents presented unhealthy habits leading to CVD²⁴24 Wiium N, Breivik K, Wold B. Growth trajectories of health behaviors from adolescence through young adulthood. Int J Environ Res Public Health 2015; 12(11):13711-13729.. Adolescents were highly likely to start smoking early in life, as well as to increase smoking levels in adult life. In addition, there was moderate-to-high lack of fruit intake on a daily basis in late adolescence and this habit persisted until early adulthood. Accordingly, these adolescents presented high alcohol intake levels, which further increased in adult life²⁴24 Wiium N, Breivik K, Wold B. Growth trajectories of health behaviors from adolescence through young adulthood. Int J Environ Res Public Health 2015; 12(11):13711-13729..

With respect to the association between CVD-risk profile and overweight, it is known that overweight has multiple causal factors such as the individual, school environment and community environment ones⁵¹51 Tyrrell RL, Greenhalgh F, Hodgson S, Wills WJ, Mathers JC, Adamson AJ, Lake AA. Food environments of young people: linking individual behaviour to environmental context. J Public Health (Oxf) 2017; 39(1):95-104.. Studies have already shown association between overweight in adolescence and factors such as alcohol intake, tobacco smoking and ultra-processed food intake²²22 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(Suppl. 1):5s.^,5252 Croezen S, Visscher TLS, Ter Bogt NCW, Veling ML, Haveman-Nies A. Skipping breakfast, alcohol consumption and physical inactivity as risk factors for overweight and obesity in adolescents: results of the E-MOVO project. Eur J Clin Nutr 2009; 63(3):405-412.

53 Håglin L, Törnkvist B, Bäckman L. Obesity, smoking habits, and serum phosphate levels predicts mortality after life-style intervention. PLoS One 2020; 15(1):e0227692.^-5454 Azeredo CM, Rezende LFM, Canella DS, Claro RM, Castro IRR, Luiz OC, Levy RB. Dietary intake of Brazilian adolescents. Public Health Nutr 2015; 18(7):1215-1224. however, they evidenced this association at individual level.

Studies have also shown the influence of multiple risk factors on overweight⁵⁵55 Cureau F V, Sparrenberger K, Bloch K V, Ekelund U, Schaan BD. Associations of multiple unhealthy lifestyle behaviors with overweight/obesity and abdominal obesity among Brazilian adolescents: a country-wide survey. Nutr Metab Cardiovasc Dis 2018; 28(7):765-774.^,5656 Souza Dantas M, Santos MC, Lopes LAF, Guedes DP, Guedes MRA, Oesterreich SA. Clustering of excess body weight-related behaviors in a sample of Brazilian adolescents. Nutrients 2018; 10(10) :1505.. Another study also using ERICA data, conducted with Brazilian adolescents has shown that adolescents categorized in the “unhealthy lifestyle” profile (comprising physical inactivity, long screen time, low fiber intake, excessive alcohol intake and smoking) were associated with overweight incidence in a dose-response gradient⁵⁵55 Cureau F V, Sparrenberger K, Bloch K V, Ekelund U, Schaan BD. Associations of multiple unhealthy lifestyle behaviors with overweight/obesity and abdominal obesity among Brazilian adolescents: a country-wide survey. Nutr Metab Cardiovasc Dis 2018; 28(7):765-774..

It is known that obesity-prevention initiatives individually aimed at adolescents do not achieve effective weight loss⁵⁷57 Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S, Abbafati C, Abera SF, Abraham JP, Abu-Rmeileh NM, Achoki T, AlBuhairan FS, Alemu ZA, Alfonso R, Ali MK, Ali R, Guzman NA, Ammar W, Anwari P, Banerjee A, Barquera S, Basu S, Bennett DA, Bhutta Z, Blore J, Cabral N, Nonato IC, Chang JC, Chowdhury R, Courville KJ, Criqui MH, Cundiff DK, Dabhadkar KC, Dandona L, Davis A, Dayama A, Dharmaratne SD, Ding EL, Durrani AM, Esteghamati A, Farzadfar F, Fay DF, Feigin VL, Flaxman A, Forouzanfar MH, Goto A, Green MA, Gupta R, Hafezi-Nejad N, Hankey GJ, Harewood HC, Havmoeller R, Hay S, Hernandez L, Husseini A, Idrisov BT, Ikeda N, Islami F, Jahangir E, Jassal SK, Jee SH, Jeffreys M, Jonas JB, Kabagambe EK, Khalifa SE, Kengne AP, Khader YS, Khang YH, Kim D, Kimokoti RW, Kinge JM, Kokubo Y, Kosen S, Kwan G, Lai T, Leinsalu M, Li Y, Liang X, Liu S, Logroscino G, Lotufo PA, Lu Y, Ma J, Mainoo NK, Mensah GA, Merriman TR, Mokdad AH, Moschandreas J, Naghavi M, Naheed A, Nand D, Narayan KM, Nelson EL, Neuhouser ML, Nisar MI, Ohkubo T, Oti SO, Pedroza A, Prabhakaran D, Roy N, Sampson U, Seo H, Sepanlou SG, Shibuya K, Shiri R, Shiue I, Singh GM, Singh JA, Skirbekk V, Stapelberg NJ, Sturua L, Sykes BL, Tobias M, Tran BX, Trasande L, Toyoshima H, van de Vijver S, Vasankari TJ, Veerman JL, Velasquez-Melendez G, Vlassov VV, Vollset SE, Vos T, Wang C, Wang X, Weiderpass E, Werdecker A, Wright JL, Yang YC, Yatsuya H, Yoon J, Yoon SJ, Zhao Y, Zhou M, Zhu S, Lopez AD, Murray CJ, Gakidou E. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384(9945):766-781.^,5858 Laxer RE, Brownson RC, Dubin JA, Cooke M, Chaurasia A, Leatherdale ST. Clustering of risk-related modifiable behaviours and their association with overweight and obesity among a large sample of youth in the COMPASS study. BMC Public Health 2017; 17(1):102.. The identification of CVD-risk profile, as well as its influence on overweight, enables making interventions in multiple modifiable behaviors and, consequently, it increases the likelihood of reducing overweight rates among adolescents⁵⁸58 Laxer RE, Brownson RC, Dubin JA, Cooke M, Chaurasia A, Leatherdale ST. Clustering of risk-related modifiable behaviours and their association with overweight and obesity among a large sample of youth in the COMPASS study. BMC Public Health 2017; 17(1):102.. The current study heads towards the analysis of association between CVD-risk factors and health outcomes, such as overweight, since 80% of obese adolescents will remain obese in adulthood and approximately 70% of them will be obese at the age of 30 years³3 Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev 2016; 17(2):95-107..

The study has some limitations, such as the “social desirability” bias, according to which adolescents may tend to answer the questionnaire according to previously normalized social behaviors. Furthermore, behaviors were self-reported, which may have led to information bias and to likely underestimated prevalence of risky behaviors. It is worth mentioning that participants were informed about the secrecy of the questionnaire and that their data would not be identified.

In addition, the 24-hour dietary recall may not fairly represent participants’ usual intake; therefore, it can be influenced by their memory bias. However, this limitation is addressed by sample’s representativeness, since it has external validity and allows generalizing the Brazilian population of adolescents in the age group 12-17 years, because ERICA is a school-based study with national representativeness of the Brazilian population. It is worth emphasizing that the study design does not allow inferring causality; thus, results in the current study should be carefully evaluated.

The present study helped improving the identification of coexistence of CVD-risk factors in Brazilian adolescents; moreover, it was pioneer in using ERICA data to identify risk profiles. In addition, GoM using to assess CVD-risk profiles is unprecedented, since this method enables determining fuzzy clusters of individuals who are not organized in well-defined sets, but who partly belong to more than one group. Based on this approach, it was possible estimating a more realistic representation of simultaneous risk factor profiles and epidemiological prevalence.

Conclusion

The current study has observed coexistence of risk factors for CVD in Brazilian adolescents - tobacco smoking and alcohol intake were the most prevalent factors. CVD-risk profile was associated with increased likelihood of overweight in adolescence. These findings reinforce the need of taking specific preventive measures aimed at this population. These measures must be strategic, effective and assertive, as well as include as many CVD-risk factors as possible, since adolescents tend to simultaneously present multiple risk factors, which can be used as strategic point to reduce overweight rates in this population. Therefore, these measures are essential to help changing the behavior of young individuals, since engaging in health-risk behaviors in adolescence can have significant impact on individuals’ adult life.

Abbreviations
• (IBGE) Brazilian Institute of Geography and Statistics
• (IMC) Body mass index
• (CVD) Cardiovascular disease
• (CDC) Center for Disease Control and Prevention in the United States
• (GYTS) Global Youth Tobacco Surveillance
• (GoM) Grade of membership
• (POF, acronym in Portuguese) Household Budget Survey
• (INA, acronym in Portuguese) National Dietary Surve
• (NCDs) Noncommunicable diseases
• (PDA) Personal Digital Assistants
• (ERICA) Study of Cardiovascular Risks in Adolescents
• (TCV) Total caloric value
• (UPF) Ultra-processed food

References

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