SciELO - Scientific Electronic Library Online

vol.17 issue6Personal attributes of female basketball athletes in trainingThe work of physical education professionals in Family Health Support Centers (NASF): a national survey author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand



  • English (pdf)
  • Article in xml format
  • How to cite this article
  • SciELO Analytics
  • Curriculum ScienTI
  • Automatic translation


Related links


Revista Brasileira de Cineantropometria & Desempenho Humano

Print version ISSN 1415-8426On-line version ISSN 1980-0037

Rev. bras. cineantropom. desempenho hum. vol.17 no.6 Florianópolis Nov./Dec. 2015 

Original Article

Prevalence of metabolic syndrome and associated factors in 11- to 17-year-old adolescents

Prevalência de síndrome metabólica e fatores associados em adolescentes de 11 a 17 anos

Durcelina Schiavoni Bortoloti1  3 

Juliana Pizzi1 

Fernando Henrique Pavão1 

Patricia Amaral Gurgel Velasquez1 

Lediana Dalla Costa1 

Rômulo Araujo Fernandes2 

Edilson Serpeloni Cyrino3 

1Universidade Paranaense. Instituto de Ciências Biológicas Médicas e da Saúde. Francisco Beltrão, PR. Brasil.

2Universidade Estadual Paulista. Departamento de Educação Física. Presidente Prudente, SP. Brasil.

3Universidade Estadual de Londrina. Centro de Educação Física e Esporte. Grupo de Estudo e Pesquisa em Metabolismo, Nutrição e Exercício. Londrina, PR. Brasil.


The adoption of health risk behaviors (low level of habitual physical activity and inadequate food and nutrient intake) has increased the prevalence of overweight/ obesity, particularly among adolescents. Thus, the prevalence of disorders and metabolic diseases has increased in this population. The aim of this study was to identify the prevalence of metabolic syndrome (MS) and to analyze its association with sport participation and overweight in adolescents aged 11 to 17 years. A total of 683 adolescents (301 boys and 382 girls) were randomly selected to compose the sample. The prevalence of MS was established based on fasting glucose, triglycerides, high-density lipoprotein (HDL-c), waist circumference, and blood pressure according to the cut-offs recommended by the Brazilian Society of Cardiology (2005). Nutritional status, sport participation, socioeconomic status, and alcohol consumption were analyzed as risk factors associated with MS. The presence of one or more risk factors was identified in 39.5% and 22.5% of the sample, respectively. The most prevalent MS components were low HDL-c (44.7%), high systolic blood pressure (32.4%), and elevated triglycerides (18.6%). The presence of MS was associated with both overweight and the lack of participation in sport ( P < 0.05). The results indicated an overall prevalence of MS of 5.4% in adolescent boys and girls. Furthermore, overweight and lack of sport participation were the main factors associated with MS.

Key words: Adolescents; Metabolic syndrome; Obesity; Sports


A adoção de comportamentos de risco à saúde (reduzido nível de atividade física habitual, consumo inadequado de alimentos e nutrientes, entre outros) tem aumentado a prevalência de sobrepeso/obesidade, particularmente, em adolescentes. Assim, o número de disfunções e doenças metabólicas tem crescido nesta população. Portanto, o objetivo deste estudo foi identificar a prevalência de Síndrome Metabólica (SM) e analisar a sua associação com a prática esportiva e excesso de peso em adolescentes de 11 a 17 anos. Seiscentos e oitenta e três meninos (n = 301) e meninas (n = 382) foram selecionados aleatoriamente para comporem a amostra. A prevalência de SM foi estabelecida com base nos valores de glicose em jejum, triglicerídeos, lipoproteínas de alta densidade (HDL-c), circunferência de cintura e pressão arterial, de acordo com os pontos de corte recomendados pela Sociedade Brasileira de Cardiologia (2005). O estado nutricional, prática de esporte, nível socioeconômico e consumo de álcool foram analisados como fatores de risco associados a SM. A presença de um ou mais fatores de risco foi identificada em 39,5% e 22,5% da amostra, respectivamente. Os componentes da SM mais prevalentes foram HDL-c reduzida (44,7%), pressão arterial sistólica elevada (32,4%) e triglicérides elevado (18,6%). A presença de SM foi associada ao excesso de peso e a ausência da prática de esportes (P < 0,05). Os resultados indicaram uma prevalência total de SM na ordem de 5,4% em adolescentes de ambos os sexos. Adicionalmente, o excesso de peso e ausência da prática de esportes foram os principais fatores associados com a SM.

Palavras-chave Adolescentes; Esportes; Obesidade; Síndrome metabólica


The morbidity and mortality profile of different populations has changed considerably in recent decades1. Thus, there is growing interest in different countries to investigate risk behaviors (inadequate eating habits, reduced habitual physical activity, and alcohol and tobacco consumption, among others) in children and adolescents of both genders in an attempt to provide data and measures for the development of public policies for disease prevention and health promotion in this population2,3.

In this respect, the adoption of risk behaviors in childhood and adolecence4 has been associated with the development of important metabolic disorders (abdominal obesity, high blood pressure, insulin resistance, glucose intolerance, and dyslipidemias). The combined manifestation of these disorders gives origin to the so-called metabolic syndrome (MS), a phenomenon that is well known in the adult population and is being identified early among adolescents.

Despite the lack of consensus regarding the criteria and cut-off values to be used for the diagnosis of MS in children and adolescents, researchers have identified an important prevalence of MS in these populations5,6, especially among overweight and obese individuals7,8. This fact is a matter of concern since the rates of overweight/obesity have been growing over the years, particularly among Brazilian children and adolescents9. It is important to note that obesity is a leading cardiovascular risk factor, which shows a strong association with a series of comorbitidies10.

Brazil is a country of continental dimensions with an enormous socioeconomic and cultural diversity among municipalities, states and regions. However, the number of studies in the literature investigating the prevalence of MS and its main determinants in Brazilian adolescents is sparse. Therefore, the objective of this study was to evaluate the prevalence of MS and its association with the lack of sport participation and excess weight in a representative sample of adolescents aged 11 to 17 years enrolled in public schools in the town of Francisco Beltrão, Paraná, Brazil. Our hypothesis was that the prevalence of MS will be relatively high (> 4%), especially among overweight adolescents who do not participate in sports.



The study was conducted at public elementary schools and high schools in the municipality of Francisco Beltrão, southwestern region of the State of Paraná, Brazil. The population of the municipality is approximately 80,000 inhabitants. For sample size calculation, a predictive equation was adopted to estimate population parameters, establishing a prevalence of MS of 4% according to values reported in international studies11,12, a precision of 2.5%, statistical significance of 5% (z = 1.96), and a design effect of 2.0. Considering possible losses/refusals, 20% was added to the final calculation, which resulted in a minimum number of 566 adolescents.

For selection of the participants, all schools were first computed according to their geographic location (north, south, east, west, center, and periphery) and six schools (one per region) were selected randomly. At each school, the morning classes comprising the proposed age group (11 to 17 years: 6th grade of elementary school to 3rd year of high school) were selected randomly. All students of the selected classes were invited to participate in the study.

The following inclusion criteria were adopted during the selection process: 1) no frequent use of medications or treatment for some disease; 2) age of 11 to 17 years; 3) enrollment in the selected schools; 4) return of the free informed consent form signed by the parents or legal guardian and consent form signed by the participants. The present sample consisted of 683 adolescents (301 boys and 382 girls). The study was approved by the Ethic Committee of UNIPAR/CEPEH (Protocol No. 21714/2012) and was conducted according to the ethical principles for research involving humans adopted by the National Council on Ethics in Research (Conselho Nacional de Ética em Pesquisa - CONEP) and Resolution 196/96 of the Ministry of Health.


Body mass was measured with a digital scale to the nearest 0.1 kg and height was measured with a stadiometer attached to the scale to the nearest 0.1 cm according to literature recommendations13. The body mass index (BMI) was established as the ratio between body weight (kg) and the square of the height (m2). Waist circumference (WC) was determined with a nonelastic tape measure to the nearest 0.1 cm by a single examiner properly familiarized with the standard procedure described in the literature14, with high reproducibility (intraclass coefficient = 0.95). For this study, WC was adopted as an indicator of abdominal obesity.

Blood pressure measurement

The systolic (SBP) and diastolic (DBP) blood pressure was measured with an Omron HEM-742 digital blood pressure monitor according to literature recommendations15. Two measurements were obtained on the right arm of the participant in the sitting position after 5 minutes of rest, with an interval of 2 minutes between measurements. When the differences between measurements were 10 mmHg or higher, a third measurement was obtained. The final blood pressure was calculated as the arithmetic mean of the two measurements. The technical error of measurement was 3.3% for SBP and 7.1% for DBP. The normative blood pressure levels of the National High Blood Pressure Education Program15 were adopted for classification of the subjects. Adolescents exhibiting SBP or DBP values above the 90th percentile for sex and age were classified as having high blood pressure.

Blood collection

Blood was collected at the school itself in a room adapted for this purpose by three experienced nursing students and two laboratory technicians. The samples were collected in the morning after a 12-hour fast. Venous blood was collected in the elbow flexure into two vacuum tubes, one containing separator gel without anticoagulant and another containing fluoride for glucose measurement. Both samples were centrifuged for 10 minutes at 3000 rpm for the separation of serum and plasma. These samples were used to determine the concentrations of triglycerides, total cholesterol, high-density lipoprotein-cholesterol (HDL-c), and glucose in a biochemical autoanalyzer (Selectra 2-Vitalab). The assays were carried out at the Laboratory of Clinical Analysis, UNIPAR, Unidade de Francisco Beltrão, Paraná.

Diagnosis of metabolic syndrome

Metabolic syndrome was diagnosed according to the definitions recommended by the Brazilian Society of Cardiology16 and of the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III)17, which consist of the presence of three or more of the following components: central obesity, elevated triglycerides, low HDL-c levels, high blood pressure, and high fasting glycemia.

Sport participation and excess weight

Habitual physical activity was evaluated using the questionnaire of Baecke et al.18. For this purpose, an examiner completed the questionnaire together with the students in the classroom. Specifically for this study, an important variable was used to identify changes in habitual physical activity in children and adolescents, i.e., the participation in sport19. Thus, for analysis, the subjects were asked whether they participated in sport activities (yes or no).

Excess weight was determined based on BMI using the cut-off values proposed by Conde and Monteiro20, and the adolescents were classified as normal weight and excess weight (overweight or obesity). Finally, a polytomous variable of aggregation was created, which included sport participation and excess weight: i) no risk factor (sport participation and with normal weight, n = 218), ii) only lack of sport participation (n = 309), iii) only excess weight (n = 68), and iv) both (n = 88).

Confounding factors

In addition to the questionnaire of Baecke et al.18, other questionnaires containing additional data about possible confounding factors were used. The questionnaire proposed by the Brazilian Association of Market Research Companies (Associação Brasileira de Empresas de Pesquisa - ABEP)21 was used to verify the socioeconomic level. A question about the education level of the head of the household was also included. Alcohol consumption by the adolescents was evaluated using a food frequency questionnaire.

The confounding factors adopted for this study were: i) socioeconomic level of the household of the adolescent; ii) alcohol consumption evaluated with the food frequency questionnaire; iii) gender; iv) age; v) school unit (place of the school: center/periphery), and vi) education level of the household head.

Statistical analysis

The Mann-Whitney U test was used to compare numerical variables between genders. The prevalence of MS was determined by the analysis of frequency distribution. The chi-squared test (χ2) was used to identify associations between MS components, the lack of sport participation, and excess weight. Multivariate models were developed by binary logistic regression to illustrate the magnitude of the associations (expressed as odds ratio [OR] and 95% confidence interval [OR95%CI]) between MS, lack of sport participation, and excess weight. For this purpose, a hierarchical system of entry of the confounding variables was created to fit the models (Model 1: not adjusted; Model 2: inclusion of sociodemographic variables of the adolescent; Model 3: inclusion of socioeconomic variables of the household; Model 4: inclusion of behavioral variables of the adolescent). The Hosmer-Lemeshow test was applied to evaluate the goodness-of-fit of the multivariate models (values > 5% indicate adequate goodness-of-fit). The IBM Statistical Package for the Social Sciences 17.0 (SPSS) for Windows (Chicago, IL, USA) was used for statistical analysis. A level of significance of 5% was adopted for all analyses.


Table 1 shows the general characteristics of the girls and boys. Significant differences were observed between genders; adolescent boys had higher SBP, WC and glucose ( p < 0.05), while girls had higher triglyceride levels.

Table 1 General characteristics of the sample according to gender. 

Variable Girls (n = 382) Boys (n = 301) Total (n = 683)
Body weight (kg) 52.6 ± 11.9 56.4 ± 14.7 54.3 ± 13.3
Height (cm) 158.9 ± 7.6 164.3 ± 11.3 162.3 ± 9.8
BMI (kg/m2) 20.73 ± 3.99 20.66 ± 4.04 20.70 ± 4.01
WC (cm) 69.2 ± 8.8 71.2 ± 9.3* 70.1 ± 9.1
SBP (mmHg) 113 ± 11 118 ±14* 116 ± 13
DBP (mmHg) 66 ± 9 67 ± 10 66 ± 10
Glucose (mg/dl) 72 ± 8 75 ± 8* 74 ± 8
HDL-c (mg/dl) 48 ± 10 47 ± 10 47 ± 10
Triglycerides (mg/dl) 76 ± 36 71 ± 34* 74 ± 35

*p < 0.05 vs. girls. BMI = body mass index; WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure; HDL-c = high-density lipoprotein.

When the percentage of adolescents with inadequate levels of the different MS components were analyzed (Figure 1), the most prevalent components were reduced HDL-c (44.7%), high SBP (32.4%), elevated triglycerides (18.6%), high DBP (7%), and high WC (3.5%). The results showed that 37.9% of the adolescents did not have risk factors for MS, while one and two risk factors were identified in 39.5% and 17.2% of the sample, respectively. The prevalence of MS found in this study was 5.4%.

Figure 1 Percentage of adolescents with inadequate values of the different components of metabolic syndrome. WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure; HDL-c = high-density lipoprotein; TG = triglycerides. 

Table 2 shows the association between MS components, lack of sport participation and excess weight. With respect to abdominal obesity, excess weight was a determinant in this association. The same was observed for SBP ( p = 0.001) and triglycerides ( p = 0.001). On the other hand, alterations in HDL-c ( p = 0.052) or high DBP ( p = 0.267) and glucose were not associated with excess weight or lack of sport participation.

Table 2 Association between excess weight, lack of sport participation and alterations in components of metabolic syndrome in the adolescents studied (n = 683). 

WC SPB DBP Glucose HDL-c Triglycerides
Risk factor n (%) n (%) n (%) n (%) n (%) n (%)
None 1 (0.5) 57 (26.1) 18 (8.3) 2 (0.9) 95 (43.6) 32 (14.7)
Lack of sport participation 1 (0.3) 95 (30.7) 14 (4.5) 2 (0.6) 130 (42.1) 40 (12.9)
Excess weight 8 (11.8) 31 (45.6) 5 (7.4) 0 (--) 29 (42.6) 29 (42.6)
Both 14 (15.9) 38 (43.2) 11 (12.5) 0 (--) 51 (58) 26 (29.5)
Total 24 221 48 4 305
X2p-value 0.001 0.001 0.267 --- 0.052 0.001

WC = waist circumference; SBP = systolic blood pressure; DBP = diastolic blood pressure; HDL-c = high-density lipoprotein.

The models used to determine the magnitude of the associations between MS components, lack of sport participation and excess weight are shown in Table 3. Regardless of the possible confounding factors analyzed (socioeconomic level of the adolescent, alcohol consumption, gender, age, school unit, and education level of the household head), when compared to normal weight and physically active young people, adolescents with excess weight had a higher risk of developing MS (OR = 3.35 [OR95%CI = 1.10 – 10.1]) than adolescents not participating in sport activities, but with normal weight. Thus, the combination of excess weight and lack of sport participation maximized the risk of developing MS (OR = 8.70 [OR95%CI = 3.30 – 22.9]). Consequently, the inclusion of confounding factors did not result in saturation of the multivariate models.

Table 3 Association between excess weight, lack of sport participation and metabolic syndrome in the adolescents studied (n = 683). 

Risk factor n (%) M1 M2 M3 M4
None 7 (3.2)* 1.0 1.0 1.0 1.0
Lack of sport participation 7 (2.3) 0.69 (0.24 - 2.02) 0.89 (0.30 - 2.64) 0.88 (0.29 - 2.59) 0.90 (0.30 - 2.66)
Excess weight 7 (10.3) 3.45 (1.16 - 10.2) 3.49 (1.15 - 10.5) 3.34 (1.10 - 10.1) 3.35 (1.10 - 10.1)
Both 16 (18.2) 6.69 (2.64 - 16.9) 8.23 (3.18 - 21.5) 8.37 (3.23 - 21.6) 8.70 (3.30 - 22.9)
Model fit** p = 1.0 p = 0.877 p = 0.781 p = 0.726

*X2 = 0.001.

**Hosmer-Lemeshow test.

Model 1 (M1) = not adjusted; Model 2 (M2) = adjusted for gender, age, and school unit; Model 3 (M3) = adjusted for gender, age, school unit, education level of the household head, and household socioeconomic condition; Model 4 (M4) = adjusted for gender, age, school unit, education level of the household head, socioeconomic condition, and alcohol consumption of the adolescent.


The main finding of the present study was the worrisome prevalence of MS (5.4%) among adolescents of both genders living in a small town in Paraná. Furthermore, the combination of excess weight and the lack of sport participation resulted in an 8-fold increase in the risk of developing MS. Although the prevalence of MS and its association with morbidity and mortality are well documented for the adult and elderly populations22,23, the same does not apply to young people.

The prevalence of MS identified in this study was higher than the 4.2% reported in another study12, but lower than the rates found in other international studies (9.2%)24. Although the adolescents studied were from a municipality with a low population density, the prevalence of MS was only slightly lower than that reported by Stabelini Neto et al.8 for a sample of 601 adolescents living in three other cities of the State of Paraná (Curitiba, São Mateus do Sul, and Jacarezinho).

Part of the results found in this study corroborates the data of an interesting systematic review7 on the prevalence of SM among adolescents aged 10 to 19 years. The authors analyzed 16 studies involving samples of different nationalities and observed that, despite the lack of consensus regarding the diagnosis of MS in adolescents, the studies analyzed indicated relatively high prevalence rates, especially among obese adolescents.

One of the main factors associated with the presence of MS in adolescents is increased body fat deposition. Within this context, obesity plays a key role in the stimulation of a variety of metabolic pathways that can induce inflammatory processes and consequently trigger alterations in one or more components of MS10,25. While obesity plays a fundamental role in the genesis of MS, our results show that its association with the lack of sport participation maximizes the risk of developing MS.

If on the one hand, a low level of physical activity is an important risk factor for body weight gain and the subsequent development of obesity, on the other hand physical exercise seems to affect MS components without necessarily reducing body fat26. Although high-intensity exercise programs can induce positive changes in HDL-c concentrations, the same does not appear to occur when the main purpose is to reduce body fat, since highintensity exercises cannot be sustained for prolonged periods.

In addition to stimulating an increase in lipid oxidation, physical exercise increases vasodilatation, induces the release of anti-inflammatory agents, and improves the sensitivity of tissues to the action of insulin without affecting body fat stores27. These changes contribute to the prevention of cardiovascular diseases. Therefore, the absence of stimulation of the cited pathways may explain, at least in part, the synergistic action of excess weight and physical inactivity on increasing the risk of MS in adolescents.

Recent studies have shown that adults who were physically inactive in childhood and adolescents are at higher risk of developing arterial hypertension, dyslipidemias, and type 2 diabetes mellitus in adulthood28. Thus, although physical activities or sport participation and the adoption of sedentary behaviors are independent constructs29, it is believed that the combination of sedentary and other health-risk behaviors is determinant for the development of MS. However, this hypothesis should be tested in longitudinal studies.

This study has some limitations. The lack of control of biological maturation may have partially affected the results, since some of the variables analyzed are influenced by maturation. In addition to biological aspects, the process of maturation can also influence social, emotional and behavioral aspects30. Another important limitation is the cross-sectional design adopted in this study, which permits analysis of the product but not of the process, a fact impairing the understanding of the metabolic profile found. Additionally, the use of indirect measures for the evaluation of sport participation and the classification of the nutritional status of the subjects may have partially influenced our results.

On the other hand, the type of sample stratification and the number of subjects of both genders used in the present study increase the power of generalization of the results. Since our results showed that the combination of excess weight and lack of sport participation maximizes the risk of developing MS, particularly among adolescents, the absence of public policies designed to promote lifestyle changes in the family and school environment tends to increase public health expenditure at increasingly earlier ages.


The present results showed an overall prevalence of MS of 5.4% among adolescent boys and girls. Furthermore, the combination of excess weight and lack of sport participation was the main factor associated with MS.


1 World Health Organization. Global status report on noncommunicable diseases 2010. Geneva: World Health Organization; 2011. [ Links ]

2 Stabelini Neto A, Mascarenhas LPG, Vasconcelos IQA, Bozza R, Ulbrich AZ, Campos W. High blood pressure in the adolescence: relationship with cardiorespiratory fitness, BMI and waist circumference. Rev Bras Hipertens 2008;15:59-64. [ Links ]

3 Romanzini M, Reichert FF, Lopes AS, Petroski EL, Farias Júnior JC. Prevalência de fatores de risco cardiovascular em adolescentes. Cad Saúde Pública 2008;24(11):2573-81. [ Links ]

4 Berenson GS, Srinivasan SR, Bao W, Newman WP, Tracy RE, Wattigney WA. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. N Engl J Med 1998;338(23):1650-6. [ Links ]

5 Chen W, Berenson GS. Metabolic syndrome: definition and prevalence in children. J Pediatr. 2007;83:1-3. [ Links ]

6 Rodrigues AN, Perez AJ, Pires JGP, Carletti L, Araújo MT, Moyses MR, Abreu GRD. Fatores de risco cardiovasculares, suas associações e presença de síndrome metabólica em adolescentes. J Pediatr 2009;85(1):55-60. [ Links ]

7 Moraes ACF, Fulaz CS, Netto-Oliveira ER, Reichert FF. Prevalência de síndrome metabólica em adolescentes: uma revisão sistemática Cad Saúde Pública 2009;25(6):1195-202 [ Links ]

8 Stabelini NA, Bozza R, Ulbrich A, Mascarenhas LPG, Boguszewski MCS, Campos W. Síndrome metabólica em adolescentes de diferentes estados nutricionais. Arq Bras Endocrinol Metab 2012;56(2):104-9. [ Links ]

9 Cintra IP, Zanetti MA, Fisberg M, Machado HC. Evolução em duas séries históricas do índice de massa corporal em adolescentes. J Pediatr 2007;83(2):157-62. [ Links ]

10 Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature 2006;444(7121):875-80. [ Links ]

11 Goodman E, Daniels SR, Morrison JA, Huang B, Dolan LM. Contrasting prevalence of and demographic disparities in the World Health Organization and National Cholesterol Education Program Adult Treatment Panel III definitions of metabolic syndrome among adolescents. J Pediatr 2004;145(4):445-51. [ Links ]

12 Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med 2003;157:821-7. [ Links ]

13 Gordon CC, Chumlea WC, Roche AF. Stature, recumbent length, and weight. In: Lohman TG, Roche AF, Martorell R, editors. Anthropometric standardization reference manual. Champaign: Human Kinetics Books; 1988. p.3-8. [ Links ]

14 Katzmarzyk PT, Srinivasan SR, Chen W, Malina RM, Bouchard C, Berenson GS. Body mass index, waist circumference, and clustering of cardiovascular risk factors in a biracial sample of children and adolescents. Pediatrics 2004;114(2):198-205. [ Links ]

15 National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, treatment of high blood pressure in children and adolescents. Pediatrics 2004; 114(2):555-76. [ Links ]

16 Sociedade Brasileira de Cardiologia (SBC). I Diretriz Brasileira de diagnóstico e tratamento da síndrome metabólica. Arq Bras Cardiol 2005;84:1-28. [ Links ]

17 National Institutes of Health (NCEP-ATP III). Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Circulation 2002;02:5215 [ Links ]

18 Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 1982;36(6):936-42. [ Links ]

19 Eime RM, Young JA, Harvey JT, Charity MJ, Payne WR. A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of conceptual model of health through sport. Int J Beh Nutr Phys Activ 2013;98:1-21. [ Links ]

20 Conde WL, Monteiro CA. Valores críticos do índice de massa corporal para classificação do estado nutricional de crianças e adolescentes brasileiros. J Pediatr. 2006;82(4):266-72. [ Links ]

21 Associação Brasileira de Empresas de Pesquisa - ABEP [homepage na internet]. Critério de Classificação Econômica Brasil. Available from: [2011 mar 20]. [ Links ]

22 Rigo JC, Vieira JL, Dalacorte RR, Reichert CL. Prevalência de síndrome metabólica em idosos de uma comunidade: comparação entre três métodos diagnósticos. Arq Bras Cardiol 2009;93(2):85-91. [ Links ]

23 Pelegrini A, Santos-Silva DA, Petroski EL, Glaner MF. Prevalência de síndrome metabólica em homens. Rev. Salud Pública 2010;12(4):635-46. [ Links ]

24 Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the Metabolic syndrome in American adolescents: findings from the Third National Health and Nutrition Examination Survey. Circulation 2004;110:2494-7. [ Links ]

25 Guillaume, M. Defining obesity in childhood: current practice. Am J Clin Nutr 1999;70(suppl):126s-30s. [ Links ]

26 Zaros PR, Pires CE, Bacci M Jr, Moraes C, Zanesco A. Effect of 6-months of physical exercise on the nitrate/nitrite levels in hypertensive postmenopausal women. BMC Womens Health 2009;9:17. [ Links ]

27 De Moraes C, Davel AP, Rossoni LV, Antunes E, Zanesco A. Exercise training improves relaxation response and SOD-1 expression in aortic and mesenteric rings from high caloric diet-fed rats. BMC Physiol 2008;8:12. [ Links ]

28 Fernandes RA, Christofaro DG, Casonatto J, Codogno JS, Rodrigues EQ, Cardoso ML, Kawaguti SS, Zanesco A. Prevalence of dyslipidemia in individuals physically active during childhood, adolescence and adult age. Arq Bras Cardiol 2011;97(4):317-23. [ Links ]

29 Fernandes RA, Júnior IF, Cardoso JR, Vaz Ronque ER, Loch MR, de Oliveira AR. Association between regular participation in sports and leisure time behaviors in Brazilian adolescents: a cross-sectional study. BMC Public Health 2008;8:329. [ Links ]

30 Sherar LB, Cumming SP, Eisenmann JC, Baxter-Jones AD, Malina RM. Adolescent biological maturity and physical activity: biology meets behavior. Pediatr Exerc Sci 2010;33:332-49. [ Links ]

Recebido: 15 de Fevereiro de 2015; Aceito: 08 de Agosto de 2015

Durcelina Schiavoni Bortoloti, Rua: Julio Assis Cavalheiro, 2000 – Industrial, Francisco Beltrão – Paraná/Brasil, CEP: 86601-000.E-mail:

Creative Commons License This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.