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Revista chilena de nutrición

versão On-line ISSN 0717-7518

Rev. chil. nutr. vol.46 no.1 Santiago fev. 2019

http://dx.doi.org/10.4067/s0717-75182019000100032 

Artículos Originales

Factors associated with the lipid profile of adolescents

Factores asociados con el perfil lipídico de adolescentes

Larissa Carvalho Ribeiro de Sá1 

Larisse Monteles Nascimento1 

Márcio Dênis Medeiros Mascarenhas1 

Malvina Thaís Pacheco Rodrigues1 

Keila Rejane Oliveira Gomes1 

Karoline de Macêdo Gonçalves Frota1 

1Programa de Pós-Graduação em Saúde e Comunidade, Universidade Federal do Piauí, Av. Frei Serafim 2280, Centro, Teresina, Brasil

ABSTRACT

There is a high prevalence of dyslipidemia in adolescence. The aim of this study was to determine the lipid profile of adolescents and associated factors. We conducted a cross-sectional study with male and female adolescents from public and private schools in Teresina, aged 14 to 19 years. Body mass index (BMI) z-score was obtained and adjusted for age, in addition to waist circumference (WC) percentile values. Lipid profile was determined by enzymatic colorimetric method; LDL-C was calculated. The statistical tests Mann-Whitney U, Student's t, and odds ratio were used. The sample was comprised of 327 adolescents with a mean age of 16.5 years, 59.6% were female and 65.7% from public schools. The prevalence of dyslipidemia was 85.6%, especially hypoalphalipoproteinemia. TG levels were significantly higher and HDL levels were lower among participants who attended public schools (P< 0.05). BMI and WC were associated with dyslipidemia; a higher mean BMI and overweight was observed in girls with dyslipidemia. In the adjusted regression, being from a public school increased the odds for low HDL-C and dyslipidemia, while overall higher BMI and abdominal excess weight represented a risk for higher triglycerides. Thus, overweight increased the chances of hypertriglyceridemia and studying in a public school increased the odds for dyslipidemia and hypoalphalipoproteinemia.

Keywords: Adolescent; Dyslipidemia; Hypertriglyceridemia; Obesity; Obesity, abdominal

RESUMEN

Las dislipidemias tienen una elevada prevalencia en la adolescencia. Nuestro objetivo fue determinar el perfil lipídico de adolescentes y sus factores asociados. Realizamos un estudio transversal que abarcó adolescentes de escuelas públicas y privadas de Teresina, con edad entre 14 y 19 años, de los dos sexos. El índice de masa corporal (escore-z, IMC-Z) se obtuvo conforme a la edad, y la circunferencia de la cintura (CC) en percentil. La determinación del perfil lipídico se realizó por método enzimático colorimétrico; LDL-C fue calculado. Las pruebas estadísticas Mann-Whitney U, T de Student y odds ratio fueran utilizadas. La muestra comprendió 327 adolescentes con edad media de 16,5 años, siendo 59,6% del sexo femenino y el 65,7% de escuelas públicas. La prevalencia de dislipidemia fue 85,6%, destacándose la hipo-alfa-lipoproteinemia. Los niveles de TC fueron significativamente mayores y los de HDL menores en las escuelas públicas (P< 0,05). Al asociar IMC y CC con dislipidemia, se observó mayores valores medios de IMC en las niñas con dislipidemia y sobrepeso. En la regresión ajustada, ser de escuela pública aumentó las posibilidades de bajo HDL-C y dislipidemia, mientras que el exceso de peso global y abdominal fueron factores de riesgo de cambios en los triglicéridos. Así, el exceso de peso elevó las posibilidades de hipertrigliceridemia, y estudiar en escuela pública elevó las posibilidades de dislipidemia e hipo-alfa-lipoproteinemia.

Palabras clave: Adolescente; Dislipidemia; Hipertrigliceridemia; Obesidad; Obesidad abdominal

INTRODUCTION

Dyslipidemia has become an important public health problem in many countries because of its high prevalence and due to its causal relationship with chronic non-communicable diseases. At early ages, it may cause an atherosclerotic process, and contribute to the development of cardiovascular disease in adulthood. In Brazil, research shows a high prevalence of lipid disorders in adolescence1-4.

The increased trend of obesity in adolescence, as it has occurred in developed countries, has been related to increased prevalence of lipid disorders5. In Brazil, there is a lack of studies of national scope that show dyslipidemia in adolescents. The Study of Cardiovascular Risks in Adolescents (ERICA) showed that the changes with highest prevalence in Brazilian adolescents were HDL-C reduction, hypercholesterolemia (total cholesterol-TC) and hypertriglyceridemia1. In this sense, the early identification of dyslipidemia and associated factors may improve long-term health outcomes6,7.

Overweight, physical inactivity, inadequate diet and improper lifestyle habits in general, in addition to secondary causes such as diabetes mellitus, hypothyroidism, among others, are involved in the onset of dyslipidemia8-11.

Among the factors associated with dyslipidemia, overweight is highlighted. An exaggerated expansion of adipose tissue, typical of obesity, causes deregulation of adipocytokines, which are substances produced in this tissue, subsequently triggering low-grade chronic inflammation and altering lipid homeostasis. More specifically, the increase of intra-abdominal fat is a major contributor to the development of changes in serum lipids4,12,13.

In this perspective, attention must be drawn to the importance of controlling risk factors that lead to the development of lipid changes in adolescence, reducing the risk of development of chronic non-communicable diseases. In view of the above, the present study aimed to determine the lipid profile of adolescents and associated factors.

MATERIALS AND METHODS

This was a cross-sectional study, part of the project entitled “Saúde na escola: diagnóstico situacional no ensino médio” (“Health at school: situational diagnosis in high school”), of the Post-Graduation Course in Health and Community of the Federal University of Piauí (UFPI). This research included high school students, aged 14 to 19 years, from the public and private education networks of the municipality of Teresina-PI.

One hundred sixty-nine schools with regular high school courses in the city of Teresina were listed for inclusion in the study, totaling 101 publics and 68 private institutions. They were organized according to the type of management (public or private), the four geographical areas in which the city was divided, and for size as small, medium and large, so that a public and a private school of each size could be drawn for each geographical area, totaling 24 schools (12 publics and 12 private). After that, students were drawn from each school.

The sample of students was a stratified probabilistic sample, proportional to the size of the four geographic areas in which Teresina was divided, to the type of management and size of the school, in addition to grade, gender, and age, in that order.

The calculation of the minimum required sample size was performed using the Epi Info 6.04d program (Centers for Disease Control and Prevention, Atlanta, USA). According to the School Census data of 201414, the were 40,136 high school students in public (state) and private schools. We adopted a 95% confidence interval, prevalence of 17.1% overweight15, accuracy of 5%, design effect of 1.416 and significance level of 5%17. The design effect of 1.4 adopted was aimed at correcting the sample size by 40%, necessary to keep the desired precision of a stratified sample. With this, the minimum required sample size was 316 adolescents. Due to the losses of cases that could occur, 10% more were drawn from the sample of each school, using the same selection criteria, making up a sample of 348 adolescents.

The anthropometric data of weight and height were obtained according to Cameron18 and Jelliffe & Jelliffe19, and waist circumference (WC) according to Callaway et al. (1988), at the midpoint between the last rib and the iliac crest20. Weight (in kilograms) was measured using a portable electronic scale (Seca®) with 100g accuracy and height (cm) was measured using a Seca® stadiometer with 0.1 cm accuracy. WC was measured using an inelastic tape measure (Seca®, model 201, Hamburg, Germany) with 0.1 cm accuracy, with the Fredriks et al.21 curve being used as a reference, since that study covers the entire age range of the present study. Thus, abdominal obesity was considered as WC> 90th percentile, according to gender and age. All evaluations were performed in triplicate.

Body mass index (BMI) was obtained by the ratio between weight (kg) and squared height (m2), being expressed in z-score, according to the curves of the World Health Organization (WHO)22. In the nutritional diagnosis, low and normal weight were combined, and the categories of overweight and obesity corresponded to the overweight group. The following classification was considered according to z-score values: severe thinness, z-score values < −3; thinness, z-score values ≥ −3 and < −2; normal weight, z-score values ≥ −1 and ≤ +1; overweight, z-score values > +1 and ≤ +2; obesity, z-score values > +2 and ≤ +3; and severe obesity, z-score values > +3.

For serum lipid analyses, 5 mL of venous blood was collected from the adolescents after a fast of at least 12 hours. Disposable plastic syringes and sterile, disposable, stainless steel needles were used. The material was then placed in Vacuette® tubes without anticoagulant. The concentrations of total cholesterol (TC), HDL-C and triglycerides (TG) were determined according to enzymatic colorimetric method using Labtest® kits. LDL-C was calculated by the formula of Friedwald et al.23, valid for TG values below 400 mg/dL. The analyses were performed in the Laboratory of the Nucleus of Research in Medicinal Plants of the Post-Graduation Program in Pharmacology of UFPI. The cut-off values for serum lipids of the V Brazilian Guideline for Dyslipidemia and Prevention of Atherosclerosis24 were used, whose reference values are: TC< 150, TG< 100 mg/dL, and LDL-c < 100 mg/dL. For HDL-C, the reference value of the National Cholesterol Education Program25 was used, which is ≥ 35 mg/dL. Dyslipidemia was characterized by the presence of at least one altered lipid parameter.

The collected data were arranged in Excel® spreadsheets and later exported to the SPSS program (for Windows®, version 22.0), in which the statistical analyses were performed. The Kolmogorov-Smirnov normality test was performed for each of the variables. The differences between group means/ medians were tested by Mann-Whitney U test or Student's t-test, depending on the normality of variables. To assess the association between anthropometric indicators and school type (independent variables) and dyslipidemia (dependent variable), the odds ratio (OR) was calculated, with a 95% confidence interval (CI). The significance level adopted for the tests was p< 0.05.

The project was approved by the Research Ethics Committee of UFPI, under opinion No. 1.495.975, and by the Piauí Secretariat of Education and Culture26. Parents and/or guardians of adolescents authorized their participation in the research by signing the informed consent, and the adolescents confirmed their acceptance by signing the informed consent.

RESULTS

Of the 348 adolescents selected, 21 did not complete all stages of the study because they did not respond completely to the questionnaires or due to blood hemolysis. Thus, the final sample consisted of 327 students, 40.4% male and 59.6% female, and 65.7% were from public schools. The mean age of the students was 16.5 ± 1.2 years. Adolescents with at least one altered lipid parameter accounted for 85.6% of the sample. Dyslipidemia was found in 91.2% of students in public schools and in 75% of students in private schools. The most frequent lipid alteration was hypoalphalipoproteinemia, or low HDL-C (68.8%), followed by total hypercholesterolemia (56.3%). Individuals with high TG and LDL-C accounted for 17.4% and 48%, respectively.

Table 1 shows the mean and median values of the serum lipids evaluated. There was no statistically significant difference in blood lipid levels between genders. On the other hand, there was a significant difference (p< 0.05) in relation to the school type for TG, HDL-C and LDL-C, with higher concentrations of TG and lower levels of HDL-C in adolescents in public schools. As for LDL-C, the levels were higher among students in private schools.

Table 1 Concentrations of serum lipids (mg/dL) in adolescents. Teresina-PI, Brazil, 2016. 

All Public School Private School p
Mean (SD) Median (P25-P75) Mean (SD) Median (P25-P75) Mean (SD) Median (P25-P75)
n (M/F) 327 (131/195)
TG
All 74 (35) 77(36) 67 (33) 0.015*
Male 73 (33)
Female 75 (36)
P 0.634*
HDL-C
All 30 (22-37) 29 (21-35) 34 (27-40) 0.000**
Male 30 (22-35)
Female 30 (23-38)
P 0.219**
TC
All 159(133-198) 156 (134-202) 162 (130-195) 0.519**
Male 149 (127-205)
Female 162 (138-196)
P 0.408**
LDL-C
All 95 (67-140) 86 (57-132) 114 (85-145) 0.000**
Male 97 (71-156)
Female 95 (64-130)
P 0.079**

SD= standard deviation; P25= 25th percentile; P75= 75th percentile; M/F= male/female; TG = triglycerides; HDL-C= high-density lipoprotein cholesterol; TC= total cholesterol; LDL-C= low-density lipoprotein cholesterol.

*P-value derived from Student's t-test.

**P-value derived from Mann-Whitney U test.

The mean BMI in overweight individuals was significantly higher for females with dyslipidemia. There were no differences between subjects with and without dyslipidemia in relation to WC according to gender (Table 2). For both genders, TC levels were significantly higher and HDL-C levels were lower (both p<0.05) for adolescents with dyslipidemia.

Table 2 Anthropometric variables and serum lipid levels (mg/dL) in adolescents with or without diagnosis of dyslipidemia, by gender*. Teresina-PI, Brazil, 2016. 

Male Dyslipidemia Female Dyslipidemia
- (n=14) + (n=118) - (n=33) + (n=162)
Mean (SD) Median (P25-P75) Mean (SD) Median (P25-P75) P Mean (SD) Median (P25-P75) Mean (SD) Median (P25-P75) P
BMI (kg/m2)
UW/Normal
weight
20.3 (1.9) 20.4 (1.9) 0.908* 20.1 (2.1) 20.7 (2.4) 0,111*
Overweight 26.6 (2.7) 28.1 (3.7) 0.283* 25.1 (3.3) 28,3 (2,7) 0,008*
WC (cm)
< 90th percentile 73.6 (9.8) 74.7 (8.3) 0.654* 70.1 (8.8) 70,6 (7.7) 0.769*
> 90th percentile 99 (2.5) 107.3 (14.4) 0.424* 94.6 (2.2) †;
TC 129 (115-141) 162 (130-208) 0.001** 147 (129-159) 172 (141-199) 0.000**
LDL-C 70 (56-82) 107 (75-160) 0.000** 86 (69-101) 98 (63-140) 0.105**
HDL-C 38 (37-45) 29 (21-33) 0.000** 41 (37-47) 29 (21-36) 0.000**
TG 64 (25) 74 (34) 0.299** 63 (22) 77 (38) 0.048*

SD=standard deviation; P25= 25th percentile; P75= 75th percentile; BMI= body mass index; UW= underweight; WC= waist circumference; TC= total cholesterol; LDL-C= low-density lipoprotein cholesterol; HDL-C= high-density lipoprotein cholesterol; TG= triglycerides. †= not applicable.

*P-value derived from Student's t-test.

**P-value derived from Mann-Whitney U test

A logistic regression analysis of the lipid profile was performed with anthropometric variables and school type (Table 3). The risk of having altered triglyceride levels was associated with overall excess adiposity (BMI) (OR: 3.32; CI: 1.37-8.04) and excess abdominal fat (WC) (OR: 5.04; CI: 1.24-20.54). On the other hand, overweight, high WC (>90th percentile), and being from public school did not increase the chances of having changes in LDL-C and TC levels. The odds of dyslipidemia were 3.71 times higher in public school students (Or: 3.71; CI: 1.94-7.09). Being from a public school was also associated with increased odds of low HDL-C (OR: 2.81; CI: 1.69-4.66).

Table 3 Anthropometric variables and school type of adolescents in relation to the lipid profile*. Teresina-PI, Brazil, 2016. 

Variable ADJUSTED HDL-C
OR (CI) P-value
BMI Underweight/normal weight 1 Reference
Overweight 1.18 (0.63-2.18) 0.61
School Type Private 1 Reference
Public 2.81 (1.69-4.66) 0.00
WC < 90th percentile 1 Reference
> 90th percentile 3.04 (0.91-10.14) 0.07
LDL-C
OR (CI) P-value
BMI Underweight/normal weight 1 Reference
Overweight 1.41 (0.78-2.54) 0.25
School Type Private 1 Reference
Public 1.24 (0.77-1.99) 0.38
WC < 90th percentile 1 Reference
> 90th percentile 1.58 (0.49-5.03) 0.44
Triglycerides
OR (CI) P-value
BMI Underweight/normal weight 1 Reference
Overweight 3.32 (1.37-8.04) 0.00
School Type Private 1 Reference
Public 1.67 (0.64-4.32) 0.29
WC < 90th percentile 1 Reference
> 90th percentile 5.04 (1.24-20.54) 0.02
TC
OR (CI) P-value
BMI Underweight/normal weight 1 Reference
Overweight 1.25 (0.70-2.24) 0.44
School Type Private 1 Reference
Public 0.98 (0.52-1.56) 0.94
WC < 90th percentile 1 Reference
> 90th percentile 1.28 (0.40-4.07) 0.67
Dyslipidemia
OR (CI) P-value
BMI Underweight/normal weight 1 Reference
Overweight 1.37 (0.55-3.44) 0.50
School Type Private 1 Reference
Public 3.71 (1.94-7.09) 0.00
WC < 90th percentile 1 Reference
> 90th percentile 1.23 (0.26-5.83) 0.79

*Logistic regression analysis considering the effect of an explanatory variable. OR= odds ratio; CI = confidence interval; BMI = body mass index; WC= waist circumference; TC= total cholesterol; LDL-C= low-density lipoprotein cholesterol; HDL-C= high-density lipoprotein cholesterol.

DISCUSSION

The high prevalence of dyslipidemia in adolescents is a relevant fact, since changes in serum lipids can cause damage to current and adult health, due to the association with atherosclerosis and, consequently, with cardiovascular disease.

Brazilian studies showed high levels of dyslipidemia in the adolescent population, ranging from 24.2% to 71.4%27-32. ERICA verified that the lipid alteration with the highest prevalence in Brazilian adolescents was low HDL-C1, which was also observed in the present investigation. The high prevalence of lipid disorders in the study population may be due to several factors, such as obesity, unhealthy eating habits, and physical inactivity.

Students who attended public schools had higher TG levels and lower HDL-C levels, while LDL-C levels were also lower. Two Brazilian studies have indicated higher prevalence of dyslipidemia in public schools33,27, similar to the present study. The socioeconomic factors to which public school adolescents are exposed differ from those of students from private institutions and may have caused this higher prevalence of lipid alteration. Income, for example, is a condition that can affect food intake, and thus the lower the income, the higher the prevalence of food and nutrition insecurity, which implies losses in the quality of meals and, consequently, in health status34. Food insecurity is associated with high consumption of foods with a higher energy density, and with a low intake of fruits and vegetables, predisposing the individual to the appearance of cardiometabolic diseases35. Nascimento et al. (2018)36 demonstrated that the low consumption of antioxidant nutrients, many of which are present in foods of plant origin, was associated with lipid alterations in adolescents. Maternal schooling is another factor that relates to the health conditions of adolescents7. Therefore, socioeconomic factors that can lead to this situation in public institutions should be better investigated for effective prevention and treatment actions. Furthermore, these findings are relevant and suggest that public school students are at greater risk for cardiovascular disease, but data from private school adolescents are also of concern.

As in other studies37,9, gender did not influence lipid levels. On the other hand, some studies showed higher mean levels of TC, LDL-C, TG and HDL-C in females1,38. The literature shows that in girls a difference in growth and sexual maturation from 14 to 15 years of age can increase lipid levels, including TC38. This study included adolescents older than 15 years, which may explain why this difference in lipid levels between genders was not observed.

In the present study, obese adolescents with dyslipidemia had a higher BMI as compared to subjects without dyslipidemia. Ghomari-Boukhatem et al. (2015)39 found that higher BMI relates to higher LDL-C levels and lower HDL-C. The mean values of WC did not differ significantly considering adolescents from both genders with and without dyslipidemia.

In another study37, boys with dyslipidemia had a higher BMI and waist-height ratio, when compared to the group without dyslipidemia. On the other hand, there was no difference between anthropometric variables in female adolescents, unlike the present study.

A recent study showed through regression analysis that there is a relationship between BMI and TC, LDL-C, HDL-C and TG levels30. The literature, in general, points to a strong association between obesity in adolescence and changes in the lipid profile38,40,41. The present study indicated that triglyceride levels are associated with measures of nutritional status, such as high BMI and WC. Obesity can cause inflammatory cascades in the body, which generate different metabolic alterations, such as dyslipidemia and insulin resistance42. In addition, changes in the adipose tissue of adolescents, common in obesity, can lead to early sexual maturation, growth abnormalities, hepatic steatosis, and other impairments43. Therefore, obesity should be better prevented and treated in adolescence, because of its relationship with changes in serum lipids and cardiovascular disease and because it compromises quality of life and health in general.

One of the limitations of this study was that we did not address other risk factors for lipid changes, such as food consumption, physical activity, and alcohol consumption. In addition, the fact that we conducted a cross-sectional study requires great caution in the analysis of results, especially regarding causality.

Preventive and more effective measures should be adopted by the health and education sectors in the prevention of dyslipidemia in adolescents, through programs with greater effectiveness. It is necessary to consider that the primary prevention of cardiovascular disease should be started at an early age, and in this sense investments in adolescent health education aimed at combating obesity and other factors associated with dyslipidemia should be greater.

CONCLUSIONS

This study demonstrated a high prevalence of dyslipidemia in adolescents, which can lead to chronic diseases and harmful cardiovascular events in adulthood. Another interesting fact was the higher prevalence of dyslipidemia among adolescents who attended public schools, with higher levels of TG and lower concentrations of HDL-C. With respect to overweight individuals, mean BMI was significantly higher for female adolescents with dyslipidemia.

It has been shown that the risk of hypertriglyceridemia was associated with excess weight (overweight/obesity) and abdominal obesity. Studying at a public school was associated with greater odds of dyslipidemia and low HDL-C.

REFERENCES

1. Faria-Neto JR, Bento VRF, Baena CP, Olandoski M, Gonçalves LGO, Abreu GA, Kucshnir MCC, Bloch KV. ERICA: prevalence of dyslipidemia in Brazilian adolescents. Rev Saúde Pública. 2016;50(supl 1):10s. [ Links ]

2. Lozano P, Henrikson NB, Morrison CC, Dunn J, Nguyen M, Blasi PR, Whitlock EP. Lipid screening in childhood and adolescence for detection of multifactorial dyslipidemia. Evidence report and systematic review for the us preventive services task force. JAMA 2016; 316(6): 634-644. [ Links ]

3. Reuter CP, Silva PT, Renner JDP, Mello ED, Valim ARM, Pasa L, Silva R, Burgos M S. Dyslipidemia is associated with unfit and overweight-obese children and adolescents Arq Bras Cardiol 2016; 106 (3): 188-193. [ Links ]

4. Yin R, Wu D, Miao L, Aung LHH, Cao X, Yan T, Long X, Liu W, Zhang L, Li M. Several genetic polymorphisms interact with overweight/obesity to influence serum lipid levels. Cardiovasc Diabetol 2012; 11: 123. [ Links ]

5. Bhalavi V, Deshmukh PR, Goswami K, Garg N. Prevalence and correlates of metabolic syndrome in the adolescents of rural Wardha. Indian J Community Med 2015; 40(1): 43-48. [ Links ]

6. Kit BK, Kuklina E, Carroll MD, Ostchega Y, Freedman DS, Ogden CL. Prevalence of and trends in dyslipidemia and blood pressure among us children and adolescents, 1999-2012. JAMA Pediatr. 2015; 169(3): 272-279. [ Links ]

7. Alcântara Neto OD, Silva RCR, Assis AMO, Pinto EJ. Factors associated with dyslipidemia in children and adolescents enrolled in public schools of Salvador, Bahia. Rev Bras Epidemiol 2012; 15(2): 335-345. [ Links ]

8. Bamba V. Update on screening, etiology, and treatment of dyslipidemia in children. J Clin Endocrinol Metab 2014; 99(9): 3093-3102. [ Links ]

9. Araki MVR, Martins ICR, Santos, EGS, Barros C. Evaluation of non-HDL cholesterolemia in schoolchildren and teenagers. Rev Med Minas Gerais. 2015;25(1):59-64. [ Links ]

10. Mansour M, Nassef YE, Shady,MA, Aziz AA, Malt HAE. Metabolic syndrome and cardiovascular risk factors in obese adolescent. Maced J Med Sci 2016; 4(1): 118-121. [ Links ]

11. Sonestedt E, Hellstrand S, Schulz C, Wallström P, Drake I, Ericson U, Gullberg B, Hedblad B, Orho-melander M. Foods and risk of cardiovascular disease is not modified by genetic susceptibility to dyslipidemia as determined by 80 validated variants. PLoSOne 2015; 10(4): 1-16. [ Links ]

12. Jung UJ, Choi M. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci 2014; 15: 6184-6223. [ Links ]

13. Misra A, Shrivastava U. Obesity and dyslipidemia in southasians. Nutrients 2013; 5: 2708-2733. [ Links ]

14. Brazil. National Institute of Educational Studies and Research Anísio Teixeira [INEP]. Basic education. School Census 2014. http://www.dataescolabrasil.inep.gov.br/dataEscolaBrasil/home.seam. [ Links ]

15. Bloch KV, Klein CH, Szklo M, Kuschnir MCC, Abreu GA, Barufaldi LA, Veiga GV, Schaan B, Silva TLN. ERICA: prevalence of hypertension and obesity in Brazilian adolescentes. Rev Saúde Pública 2016; 50(supl 1): 9s [ Links ]

16. Luiz RR, Torres TG, Magnanini MMF. Sample planning. In: Luiz RR, Costa AJL, Nadanovsky P (org.). Epidemiology and biostatistics in dental research. São Paulo: Atheneu 2005; 473: 91-130. [ Links ]

17. Armitage P. Statistical method in medical research. New York: John Wiley and Sons; 1981. [ Links ]

18. Cameron N. Anthropometric Measurements. In: Cameron N. The measurement of human growth, Coom Helm. (1984). London, 56-99. [ Links ]

19. Jelliffe DB, Jelliffe PEF. Anthropometry: major measurements. In: Jelliffe DB, Patrice Jelliffe EF. Community nutritional assessment. 1989, Oxford University Press, Oxford, 68-105. [ Links ]

20. Callaway CW, Chumlea WC, Bouchard C, Himes JH, Lohman TG, Martin AD, et al. Circunferences. In: Lohman TG, Roche AF, Martorell R, editors. Anthropometric standardization reference manual. Champaign: Human Kinetics 1988; p.39-54. [ Links ]

21. Fredriks AM et al. Are age references for waist circumference, hip circumference and waist-hip ratio in Dutch children useful in clinical practice? European Journal Pediatrics. 2005; 164(4): 216-222. [ Links ]

22. World Health Organization. Multicentre Growth Reference Study Group. WHO child growth standards: Length/height-forage, weight-for-age, weight-for-length, weight-for height and body mass index-for-age: Methods and development. Geneva: World Health Organization; 2007. [ Links ]

23. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18(6): 499-502. [ Links ]

24. Xavier HT, Izar MC, Faria Neto JR, Assas MH, Rocha VZ, Sposito AC, Fonseca FA, dos Santos JE, Santos RD, Bertolami MC, Faludi AA, Martinez TLR, Diament J, Guimarães A, Forti NA, Moriguchi E, Chagas ACP, Coelho OR, Ramires JAF. V brazilian guidelines on dyslipidemias and prevention of atherosclerosis. Arq Bras Cardiol 2013; 101(4): 1-20. [ Links ]

25. National Cholesterol Education Program. Highlights of the report of the expert panel on blood cholesterol levels in children and adolescents. Pediatrics 1992; 89: 495-501. [ Links ]

26. Brazil. Ministry of Health. National Health Council. Regulatory guidelines and norms on research involving human beings. Resolution 466/12. Brasília: CNS, 2012. [ Links ]

27. Quadros TM, Gordia AP, Silva RC, Silva LR. Predictive capacity of anthropometric indicators for dyslipidemia screening in children and adolescents. J Pediatr 2015; 91: 455-463. [ Links ]

28. Romero A, Rezende LFM, Romero SCS, Villar BS. Relationship between obesity and biochemical markers in Brazilian adolescentes. Rev Bras Cineantropom Desempenho Hum. 2014; 16(3): 268-276. [ Links ]

29. Tomeleri CM, Ronque ERV, Silva DRP, Júnior CGC, Fernandes RA, Teixeira DC, Barbosa DS, Venturini D, Okinoc AM, Oliveira J A, Cyrino ES. Prevalence of dyslipidemia in adolescents: comparison between definitions. Rev Port Cardiol 2015; 34(2): 103-109. [ Links ]

30. Garcez MR, Pereira JL, Fontanelli MM, Marchioni DML, Fisberg RM. Prevalence of dyslipidemia according to the nutritional status in a representative sample of São Paulo. Arq Bras Cardiol 2014; 103(6): 476-484. [ Links ]

31. Ribas SA, Silva LCS. Anthropometric indices; predictors of dyslipidemia in children and adolescents from north of Brazil. Nutr Hosp 2012; 27(4): 1228-1235. [ Links ]

32. Lima SCVC, Lyra CO, Pinheiro LGB, Azevedo PRM, Arrais RF, Pedrosa LFC. Association between dyslipidemia and anthropometric indicators in adolescents. Nutr Hosp 2011; 26(2): 304-310. [ Links ]

33. Corrêa JD, Bertollo C, Sehn AP, Kern DG, Welser L, Silva CF, Weis GF, Reuter CP, Burgos MS. Association between dyslipidemia, sociodemographic data, sedentary behavior and improper feeding in schoolchildren from Southern of Brazil. Cinergis 2017; 18(2): 146-150. [ Links ]

34. Pedraza DF, Queiroz D, Menezes TN. Food security in families with children attending public daycare centers in the State of Paraíba, Brazil. Rev Nutr 2013; 26(5): 517-527. [ Links ]

35. Rocha NP, Milagres LC, Novaes JF, Franceschini CC. Association of food and nutritional insecurity with cardiometabolic risk factors in childhood and adolescence: a systematic review. Rev Paul Pediatr 2016; 34(2): 225-233. [ Links ]

36. Nascimento LM, Gomes KRO, Mascarenhas MDM, Miranda CES, Araújo TME, Frota KMG. Association between the consumption of antioxidant nutrients with lipid alterations and cardiometabolic risk in adolescentes. Rev Nutr 2018; 31(2): 183-197. [ Links ]

37. Bibiloni MM, Salas R, Garza Y, Villarreal JZ, Sureda A, Tur JA. Serum lipid profile, prevalence of dyslipidaemia, and associated risk factors among northern mexican adolescentes. JPGN 2016; 63(5): 544-549. [ Links ]

38. Bandara KMGK, Kumarasiri PVR, Nugegoda DB. Lipid profile and related factors among adolescents in an urban setting in Sri Lanka: the situation in 2006. Sri Lanka J Med 2015; 25(1): 11-19. [ Links ]

39. Ghomari-Boukhatem H, Bouchouicha A, Mekki K, Chenni K, Belhadj M, Bouchenak, M. Bloodpressure, dyslipidemia and inflammatory factors are related to body mass index in scholar adolescentes. Arch Med Sci 2015; 13(1): 46-52. [ Links ]

40. Huriyati E, Luglio HF, Ratrikaningtyas PD, Tsani AFA, Sadewa AH, Juffrie M. Dyslipidemia, insulin resistance and dietary fat intake in obese and normal weight adolescents: the role of uncoupling protein 2 −866G/A gene polymorphism. Int J Mol Epidemiol Genet 2016; 7(1): 67-73. [ Links ]

41. Pavão FH, Schiavoni D, Pizzi J, Silva KES, Junior HS. Dyslipidemia in adolescents living in a city of Paraná and association with abdominal obesity. Rev Educ Fis 2015; 26(3): 474-481. [ Links ]

42. Castro AVB, Kolka CM, Kim SP, Bergman RN. Obesity, insulin resistance and comorbidities - mechanisms of association. Arq Bras Endocrinol Metab 2014; 58(6): 600-609. [ Links ]

43. Ciampo LA, Ferraz IS, Ricco RG, Daneluzzi JC, CIAMPO IRL, Martinelli Junior CE. Dyslipidemia among obese adolescentes followed in a hebiatry program. Alim Nutr 2012; 23(3): 349-353. [ Links ]

Recibido: 18 de Diciembre de 2017; Revisado: 08 de Julio de 2018; Aprobado: 09 de Agosto de 2018

Dirigir correspondencia a: Larissa Carvalho Ribeiro de Sá. Programa de Pós-Graduação em Saúde e Comunidade, Universidade Federal do Piauí, Av. Frei Serafim 2280, Centro, Teresina, Brasil. Teléfono: 086 3215 4647 E-mail: larissacarvalho100@hotmail.com

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