SciELO - Scientific Electronic Library Online

 
vol.30 número6Análisis comparativo de la respuesta glicémica e índice glicémico del puré de papas instantáneo determinado en sujetos sometidos a gastrectomía vertical en manga laparoscópica y en sujetos controlesIngesta de fibra soluble e insoluble y factores de riesgo de síndrome metabólico y enfermedad cardiovascular en adultos de mediana edad: la cohorte AWHS índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • No hay articulos similaresSimilares en SciELO
  • En proceso de indezaciónSimilares en Google

Compartir


Nutrición Hospitalaria

versión On-line ISSN 1699-5198versión impresa ISSN 0212-1611

Nutr. Hosp. vol.30 no.6 Madrid dic. 2014

http://dx.doi.org/10.3305/nh.2014.30.6.7793 

ORIGINAL / Pediatría

 

Prediction of body fat in adolescents: comparison of two electric bioimpedance devices with dual-energy X-ray absorptiometry

Predicción de la grasa corporal en adolescentes: comparación de dos dispositivos de bioimpedancia eléctrica con absorciometría dual de rayos X

 

 

Eliane Rodrigues de Faria1, Franciane Rocha de Faria2, Vivian Siqueira Santos Gonçalves2, Sylvia do Carmo Castro Franceschini3, Maria do Carmo Gouveia Peluzio3, Luciana Ferreira da Rocha Sant'Ana3 and Silvia Eloiza Priore3

1Department of Pharmacy and Nutrition - Universidade Federal do Espirito Santo.
2Universidade Federal de Viçosa.
3Department of Nutrition and Health - Universidade Federal de Viçosa. Brazil.

This study was funded in part by CNPq (Process no 485986/20116) and FAPEMIG (Process no APQ-01618-10) as well as CAPES for the PhD fellowship granted to the Graduate Program in Nutritional Science.

Correspondence

 

 


ABSTRACT

Introduction: An accurate estimate of body composition is important in assessing and monitoring the nutritional status of adolescents.
Objectives: To compare the accuracy of 2 electrical bioimpedance devices with that of dual-energy X-ray absorptiometry (DXA) to predict body fat in Brazilian adolescents.
Methods: We evaluated 500 adolescents aged between 10 and 19 years, stratified by sex and divided into overweight and non-overweight groups. The percentage of body fat (%BF) was estimated using 2 types of electrical bioimpedance devices: BIA1 (horizontal tetrapolar bioimpedance equipment) and BIA2 (vertical 8-electrode bioimpedance equipment), as well as by DXA. A Bland-Altman plot was used to calculate the total errors and standard errors of estimate.
Results: Considering BMI for age, 19.4% were overweight and 47.4% as assessed by %BF of DXA were overweight. The %BF estimated by BIA2 correlated well (p < 0.05) with the %BF predicted by DXA, and only the total errors for BIA2 in the overweight group were acceptable (≤2.5%). The standard errors of estimate was <3.5%, with the lowest values observed for BIA2. Both BIA1 and BIA2 underestimated the %BF in overweight adolescents, while overestimating the %BF in male adolescents of normal weight.
Conclusions: The BIA2 was found to be more effective in the evaluation of body fat. Regardless of the method used, the results should be carefully interpreted when assessing the body composition of adolescents.

Key words: Adolescents. Electric impedance. Body composition.


RESUMEN

Introducción: Una estimación precisa de la composición corporal es importante para evaluar y monitorear el estado nutricional de los adolescentes.
Objetivos: Comparar la exactitud de 2 dispositivos de bioimpedancia eléctrica con la absortometria de rayos X de doble energía (AXD) para predecir la grasa corporal en adolescentes brasileños.
Métodos: Se evaluaron 500 adolescentes entre 10 y 19 años, estratificados por sexo y divididos en grupos con sobrepeso y sin sobrepeso. El porcentaje de grasa corporal (%GC) se estimó utilizando 2 tipos de bioimpedancia eléctrica: BIA1 (equipo de bioimpedancia tetrapolar horizontal) y BIA2 (vertical equipo de bioimpedancia 8 electrodos), así como por AXD. Un gráfico de Bland-Altman se utilizó para calcular los errores totales y errores estándar de estimación.
Resultados: Teniendo en cuenta el IMC para la edad, el 19,4% tenían sobrepeso y el 47,4% según la evaluación de %GC de DXA tenían sobrepeso. El %GC estimado por BIA2 buena correlación (p<0,05) con el %GC pronosticado por AXD, y sólo los errores totales para BIA2 en el grupo de sobrepeso eran aceptables (<2,5%). Los errores estándar de estimación fue <3,5%, con los valores más bajos observados para BIA2. Tanto BIA1 y BIA2 subestimaron el %GC en los adolescentes con sobrepeso, mientras que sobreestimar el %GC de los adolescentes varones de peso normal.
Conclusiones: El BIA2 se encontró que era más eficaz en la evaluación de la grasa corporal. Independientemente del método utilizado, los resultados deben interpretarse con cautela al evaluar la composición corporal de los adolescentes.

Palabras clave: Adolescentes. Impedancia eléctrica. Composición corporal.


Abbreviations
%BF: Proportion of body fat.
BIA: Electrical bioimpedance analysis.
BIA1: Horizontal tetrapolar bioimpedance equipment.
BIA2: Vertical 8-electrode bioimpedance equipment.
BMI: Body mass index.
DXA: Dual-energy X-ray absorptiometry.
G1: Group 1, not overweight.
G2: Group 2, overweight.
κ: Kappa coefficient.
NPV: Negative predictive value.
PPV: Positive predictive value.
r: Pearson linear correlation coefficient.
r2: Determinant coefficient.
SD: Standard deviation.
SDS: Standard deviation score.
SEE: Standard error of estimate.
Sens: Sensivity.
Spec: Specificity.
TBM: Total body mass.

 

Introduction

Adolescence is defined as the period between 10 and 19 years of age and is characterized by rapid growth marked by the onset of puberty, which promotes physiological, corporal, psychological, and social changes that occur unevenly among individuals1. During the maturation process, body composition changes in a sex-specific manner, with female adolescents developing a higher proportion of fatty tissue1.

An accurate estimate of body composition is important in assessing and monitoring the nutritional status of adolescents, and acts as a predictor of cardiovascular disease, diabetes mellitus, dyslipidemia, hypertension, metabolic syndrome, and excess body fat2-4, all of which can persist in adulthood5.

The prevalence of obesity and related metabolic disorders has been increasing worldwide, and both are evenly distributed with respect to sex, age, and ethnicity6. The Household Budget Survey, conducted in the metropolitan areas of Brazil between 2008 and 2009, indicated that 20.5% of adolescents were overweight (21.5% of male and 19.4% of female adolescents), which represents an increase of approximately 4% when compared with the results of the same survey in 2002-20037. Moreover, excess body fat has been found even in adolescents of normal weight8-10.

There are different methods to assess body composition, including electrical bioimpedance analysis (BIA), which measures the resistance or impedance of a low-intensity electrical current passed through body tissue. It is a simple, fast, relatively inexpensive, noninvasive, portable, and safe method for assessing the proportion of body fat (%BF)3,11-13. However, BIA devices using 4 electrodes can give different %BF values from those using 8 tactile electrodes. This discrepancy was apparent in a study of children and adolescents between 6 and 13 years of age14, as well of young adults between 18 and 29 years of age12, but has not yet been studied in adolescents between 10 and 19 years of age.

Given the frequent use of BIA in clinical practice and in population studies, it is important to accurately and reproducibly assess the proportion of body fat. The aim of this study was to determine which of the electrical bioimpedance devices, when compared to DXA, would more accurately determine the amount of body fat in male and female Brazilian adolescents who were of normal weight or were overweight.

 

Methods

Study design and data collection

This was an epidemiological, cross-sectional study conducted between March 2010 and April 2012 involving adolescents of both sexes aged between 10 and 19 years of age, selected from public and private schools in both urban and rural areas of Viçosa, Minas Gerais, Brazil. Inclusion criteria were the absence of chronic disease, no regular use of medicines that alter blood pressure, fasting glucose levels, or lipid metabolism, no continuous use of diuretics or laxatives, no pacemaker or implant, and, for female adolescents, no use of oral contraceptives for at least 2 months before the study commenced, as well as no current or previous pregnancy.

Sample selection was based on the total number of adolescents who lived in the city and met the age criterion in 2012. The sample size was calculated using software Epi Info version 6.04 based on a specific formula for cross-sectional studies. We considered the population of 12080 adolescents at the age studied, in Viçosa-Minas Gerais18, the expected frequency of excess body fat of 28.5%16, and a variability of 5%. We anticipated a loss of 20% of subjects from the study, indicating a minimum enrollment of 480 adolescents, with a confidence interval of 99.9%.

The adolescents were selected by simple random sampling with the school as a means of access. The study included a total of 27 public and private schools, with students in the age group of interest. During selection, the principal of each school was contacted, and after providing their permission, invitations were distributed to the adolescents to participate. Those adolescents who accepted received an informed consent form explaining the study. Study participants were randomly selected from among those who returned the signed informed consent form.

The project was approved by the Ethics Committee on Human Research of the Federal University of Viçosa (Of. Ref. No. 0140/2010). Participation was voluntary, and required verbal clarification and written informed consent from both the adolescents and their parents.

On the basis of a previously described classification of nutritional status17, adolescents were grouped as follows: Group 1, not overweight (underweight and normal weight, body mass index [BMI]/age ≤+1 standard deviation); Group 2, overweight, BMI/age >+1 standard deviation; and these were further stratified by sex.

Anthropometric assessment

Subjects were weighed using a digital scale with a maximum capacity of 150 kg in 50-g subdivisions, and height was measured using a portable stadiometer with a bracket coupled to one end, with a scale up to a maximum of 2.13 m in 0.1-cm gradations. Measurements were taken twice and the average value was used. If the difference between them exceeded 0.5 cm, further assessments were performed. Weight and height were measured according to the techniques described by Jellife18.

Nutritional status was assessed using the result obtained from z-score calculations adjusted for age and gender to give a BMI standard deviation score (SDS) using the cutoff and anthropometric reference points recommended by the World Health Organization19. Overweight and obese adolescents were classified as overweight (>+1 standard deviation)19.

Body composition

In this study, %BF was estimated using 2 electrical BIA devices: BIA1 (horizontal tetrapolar bioimpedance equipment; Biodynamics Model 450®) and BIA2 (vertical 8-electrode bioimpedance equipment; InBody 230®), as well as DXA (dual-energy X-ray absorptiometry) (Lunar Prodigy Advance DXA System - analysis version: 13.31, GE Healthcare). All assessments were conducted in the morning as outlined in specific protocols for testing20.

The electrical bioimpedance method relies on the conduction of low-intensity (500 to 800 μA) and high-frequency (50 kHz) electrical current and on the calculation of impedance, which is determined by the sum of resistance and reactance. The impedance varies with body tissue composition, being higher in leaner bodies due to the higher concentration of water and electrolytes in this tissue3.

In addition to the %BF, the total body mass according to DXA (the sum of the fatty and lean tissue, and bone) was evaluated. The %BF was analyzed according to the classification proposed by Lohman21, defining overweight as a value ≥20% for boys and ≥25% for girls.

The protocol was also designed to standardize the hydration status of subjects prior to BIA assessment. Subjects needed to be assessed at least 7 days after their last menstrual period and 7 days before the next. They also needed to have undergone complete fasting and refrained from physical exercise in the previous 12 hours, not to have consumed alcohol in the previous 48 hours, not to have used diuretics for at least 7 days before the assessment, and to have urinated 30 minutes before the assessment. Adolescents were also asked to remove metal objects such as earrings, rings, and watches, which could interfere with the passage of electrical current.

Statistical analysis

The database was doubly entered in Microsoft Office Excel 2007 and, after checking the consistency of the data, analyses were performed in SPSS for Windows 13.0 and Stata 11.0. The Kolmogorov-Smirnov normality test was used to assess the distribution of variables and the Kappa index was used to assess the agreement between the measurements provided by BIA1 and BIA2, as well as the DXA, in accordance with Lohman's criteria21.

We calculated the sensitivity, specificity, and positive and negative predictive values of BIA1 and BIA2 for excess body fat in adolescents. A simple linear regression model was used to assess the relationship between the %BF, as estimated by electrical bioimpedance (independent variable), and the measurements provided by DXA (dependent variable), stratified by sex and nutritional status.

To test the accuracy of the electrical bioimpedance methods compared to DXA, we used the criteria proposed by Lohman21. These were a Pearson linear correlation coefficient (r) >0.79; a paired t-test to detect differences between the mean %BF as estimated by electric bioimpedance and by DXA; a total error ≤2.5%, and a standard error of estimate (SEE) <3.5% for the prediction of %BF. The limits of agreement of the %BF estimated by the different devices were assessed using a Bland-Altman plot. The level of rejection of the null hypothesis for all tests was 5%.

 

Results

Characteristics of the subjects

This study enrolled 500 participants with a mean age of 13.79 years (range, 10.02-19.99 years), of whom 279 (55.8%) were female. On the basis of the BMI corrected for age, 22 subjects (4.4%) were underweight, 381 were normal weight (76.2%), and 97 were overweight (19.4%), of which 27 (5.7%) were obese17. The proportion of overweight subjects was very similar amongst male (20%) and female (18.6%) adolescents.

The %BF as assessed using DXA showed that 47.4% of participants were overweight21, with female participants significantly more likely to be overweight (62.7%) than male participants (28.1%) (p<0.001), which was expected given the physiological differences between men and women. Among subjects who were not overweight, excess body fat was predicted in 53.7% and 11.9% of female and male participants, respectively.

Comparison of %BF according to bioimpedance and DXA

The characterization of body composition and the relationship between the %BF as assessed using BIA1 and BIA2, or DXA, are shown in table I. Significant differences (p<0.05) were found between the %BF as measured by DXA compared with BIA1 and BIA2 for both sexes and the overweight and non-overweight groups. For overweight and non-overweight female adolescents, BIA1 underestimated the %BF by -6.68 ± 3.01% and -1.89 ± 3.93%, respectively, whereas BIA2 underestimated the %BF by -0.82 ± 2.32% and -1.35 ± 2.63%, respectively. In non-overweight male adolescents, BIA1 and BIA2 overestimated the %BF by 0.91 ± 3.08% and 1.23 ± 2.68%, respectively, and underestimated the %BF in overweight male adolescents by -4.55 ± 4.00 and -0.90 ± 2.54%, respectively.

The %BF estimated by BIA2 correlated more closely (p < 0.05) with the %BF measured by DXA for male and female adolescents irrespective of whether they were overweight (Table II). Only the total errors of BIA2 for overweight adolescents were considered statistically significant (≤ 2.5%) for female (2.21%) and male (2.5%) adolescents. The SEE was <3.5% and deemed to be sufficiently low by the criterion of accuracy suggested by Lohman21, with lower values for BIA2 (Table II).

Strong and positive correlations were found, exceeding 0.79, between the %BF estimated by BIA2 and DXA for both sex and weight groups. The BIA2 measurements showed a higher correlation with the DXA values than did the BIA1 measurements (p<0.05) for the whole cohort (Table II).

The Bland-Altman plot for BIA1 and BIA2 are shown in figure 1. Evaluating BIA1, BIA2, and DXA resulted in an r-value close to zero, with no statistically significant difference between the BIA1 and DXA measurements for male adolescents who were not overweight (Fig. 1), or between the BIA2 and DXA measurements for both male and female overweight adolescents (Fig. 1). Therefore, BIA1 can be used to assess male adolescents who are not overweight, and BIA2 can be used to assess both male and female adolescents who are overweight. Both BIA devices tended to underestimate the %BF in female participants (Fig. 1), and overestimate the %BF in male participants who were underweight or of normal weight, while underestimating it in those who were overweight. The agreement between the body composition estimates provided by DXA, BIA1, and BIA2 are shown in table III. For all groups, BIA2 estimates agreed better with DXA measurements for the determination of %BF than did those for BIA1.

Overall higher sensitivity, specificity, and positive and negative predictive values were achieved using BIA2, which proved more accurate at both the individual and population level than did BIA1 (Table IV). Therefore, although the results are differentiated by group and sex, it is clear that the BIA2 is better predictor of excess body fat when compared to BIA1.

 

Discussion

In this study, we found significant, but also previously well-characterized differences between sexes with respect to their proportion of body fat (Table I). During adolescence and maturation, body composition changes in a sex-specific manner, whereby female adolescents develop a greater proportion of body fat1; these effects are largely attributable to changes in estrogen and testosterone during puberty22,23.

The higher prevalence of adolescents with excess body fat seen in this study is concordant with the findings from other studies conducted in Brazil19,24,25 and elsewhere22,26, especially with regard to adolescent girls, which highlights this change even in adolescents with normal weight in terms of BMI8-10,19,24. The number of adolescents with excess weight explains the higher percentage of dystrophic adolescents by BMI, as reported by the Household Budget Survey 2008-20097 and studies with adolescents from Viçosa, MG/Brazil27.

There are several methods of assessing body composition and the appropriate method depends on which body compartments are to be evaluated, the population of interest, cost, validity/reliability, and the degree of training necessary for the evaluator28. Electrical BIA analysis has been used in clinical and epidemiological studies for the evaluation of body compartments and results in fewer errors in the estimation of body composition than do skinfold equations19,29. However, results between different types of bioimpedance equipment can vary widely. In this study, the 2 pieces of bioimpedance equipment tests (tetrapolar BIA1 and the 8-tactile electrode BIA2) gave differing values for body fat composition. Thus, despite being an easy, noninvasive, and highly reproducible method, the accuracy of BIA analysis may have been affected in certain situations, such as those involving alcohol consumption and intense physical activity performed prior to the test, the presence of edema or water retention30, obesity31, and ingestion before meals32, thereby emphasizing the need for more defined protocols.

Several studies have compared different methods for assessing body composition11,26,33 but only a few have compared the 2 types of BIA equipment with DXA in adolescents between 10 and 19 years of age, as in the present study.

Gupta11 compared horizontal tetrapolar BIA with DXA in Chinese adults and found that there was good agreement between the methods. However, its use was only recommended for epidemiological studies because the confidence intervals ranged widely. In this study, BIA tended to overestimate the %BF of the total population, as well as of men, and underestimate it in women, as compared to DXA. Kim et al.4 compared the 8-electrode BIA method with DXA in 174 adults. Correlations between the %BF according to BIA and DXA were 0.956 and 0.960 for men and women, respectively, and the total error was 2.1% and 2.3% fat in men and women, respectively. The mean difference between methods was small, but significant (p<0.05), as in our study, and resulted in an overestimation of 1.2 ± 2.2% fat (95% confidence interval: -3.2-6.2%) in men and an underestimation of -2.0 ± 2.4% fat (95% confidence interval: -2.3-7.1%) in women. Using the Bland-Altman analysis, the %BF (86.3% in men and 66.0% in women) was found to be an accurate estimate within the accepted range of error of 3.5% fat. They concluded that BIA2 measurements generally agreed with those obtained using DXA in predicting the %BF in Korean adults. However, this equipment had small but systematic errors concerning the accuracy of individual %BF estimates. The total of errors led to an overestimation of the %BF in lean men and an underestimation of the %BF in obese women.

On the basis of these previous findings and those of the present study, the assessment of body composition by different methods should be interpreted with caution, and it is important to consider the sex and nutritional status of the individual.

In this study, subjects were stratified by sex and group (overweight or not overweight). We found that BIA2 gave the best results, regardless of sex, especially in the overweight group, wherein this equipment was more accurate than BIA1 (Fig. 1; Tables III and IV). In addition, when BIA2 was compared to BIA1, it proved to be more sensitive for the detection of excess body fat (Table IV), identifying more adolescents who required monitoring, regardless of sex. It was also more accurate, correlating better with the values generated using DXA, and resulting in a correspondingly greater agreement in the Bland-Altman analysis, particularly for overweight adolescents. Conversely, BIA1 was not adequate for male and female adolescents of any weight, and gave a correlation of <0.79, a larger difference with the DXA measurements, and an error of >2.5%.

In a similar study to ours, Kriemler et al.14 evaluated 333 Swiss children and adolescents between 6 and 13 years of age with the aim of validating the measurements of BIA1 and BIA2 equipment. They found that BIA2 was more accurate in the assessment of lean mass and segmental body fat. In a related study, Leahy et al.12 compared BIA2 to DXA amongst 403 subjects aged 18 to 29 years in Ireland, and found that BIA2 underestimated the percentage of total fat in both men and women. The underestimation was higher in men with a %BF above 24.6% and higher in women with a %BF above 32%. Therefore, the BIA should be used with caution in the assessment of body composition, especially in individuals with a %BF >25%.

Another important issue is the lack of studies using BIA2, especially in the age range of this study, making comparisons with this method difficult. Nonetheless, a careful analysis of this equipment could be of great importance in generating more accurate results, which in turn could enable its wider use in population studies and in clinical practice. However, owing to its high cost, simpler and cheaper equipment is often chosen. BIA1 resulted in a SEE of <3.5% and reasonable sensitivities and positive predictive values, and can be used in the absence of more sensitive methods of assessing body composition.

Therefore, it is important to consider the method used, the age range of the study subjects, and the nutritional status of the individual when assessing %BF, because the errors made by the equipment may lead to an estimation of excess body fat that is too far from the true value. Consequently, many teenagers may not receive appropriate nutritional treatment. Excess body fat is a risk factor for insulin resistance and related metabolic disorders and must be diagnosed early to prevent current and future problems22,24.

This study has a number of limitations. These include a lack of pubertal staging, as it is well established that fat mass is highly dependent on pubertal development. For example, on average the fat mass of a pre-pubertal 10-year-old girl differs significantly from that of a pubertal girl; however, the evaluation of children and adolescents between 10 and 19 years of age is important owing to the lack of data in the literature for this age group. Furthermore, although many studies have compared the performance of electrical bioimpedance equipment to that of gold standard methods for assessing body fat, relatively little work has been done to assess the accuracy of body composition using the vertical, 8-tactile-electrode BIA equipment, especially in adolescents, which makes our findings particularly relevant.

 

Conclusions

We found that the vertical 8-electrode bioimpedance equipment (BIA2) was more accurate in assessing body fat than the horizontal tetrapolar bioimpedance equipment (BIA1), as the former underestimated/overestimated the %BF less often, gave a higher correlation and agreement with DXA measurements, and resulted in a lower error and higher sensitivity and specificity. This was more evident in overweight adolescents than among those of normal weight. Therefore, BIA2 appears to be a superior method for measuring body fat compositions in adolescents at both the population and individual levels.

The horizontal tetrapolar bioimpedance equipment (BIA1), although less accurate than BIA2, can also be used with caution, in the absence of more sensitive methods for assessing body fat composition. BIA1 is less expensive, more widely available to health services, and results in a SEE of <2.5%. Care should be taken when assessing body composition in adolescents, regardless of the method used, but should also be considered as a clinical priority, as it is likely to play a role in the prevention of metabolic abnormalities.

 

Acknowledgments

We would like to acknowledge all the schools, adolescents and their parents/guardians and teachers involved in this study, as well as the Department of Nutrition and Health and the Division of Health of the Universidade Federal de Viçosa, who allowed their involvement and made this study possible.

 

Authors' contribution

Eliane Rodrigues de Faria, Franciane Rocha de Faria and Vivian Siqueira Santos Gonçalves were responsible for data acquisition and transcription. Statistical analysis was performed by Eliane Rodrigues de Faria and Franciane Rocha de Faria. Eliane Rodrigues de Faria wrote most of the manuscript, including the discussion. Sylvia do Carmo Castro Franceschini, Maria do Carmo Gouveia Peluzio, Luciana Ferreira da Rocha Sant'Ana and Silvia Eloiza Priore were involved in planning and organization, and reviewed the manuscript before submission. All authors were involved in writing the paper and approved the submitted and published versions.

 

References

1. World Health Organization. Nutrition in adolescence - issues and challenges for the health sector: issues in adolescent health and development. Geneva: World Health Organization, 2005. 115p.         [ Links ]

2. Siervogel RM, Demerath EW, Schubert CH, Remsberg KE, Chumlea WC, Sun S, et al. Puberty and Body Composition. Horm Res 2003;60(Suppl 1):S36-S45.         [ Links ]

3. Jaeger AS, Barón MA. Uso de la bioimpedancia eléctrica para la estimación de la composición corporal en niños y adolescentes. An Venez Nutr 2009; 22 (2): 105-110.         [ Links ]

4. Kim H, Kim CH, Kim DW, Park M, Park HS, Min SS, et al. External cross-validation of bioelectrical impedance analysis for the assessment of body composition in Korean adults. Nutr Res Pract 2011;5(3):246-252.         [ Links ]

5. Oliveira RMS, Franceschini SCC, Rosado GP, Priore SE. Influence of prior nutritional status on the development of the metabolic syndrome in adults. Arq Bras Cardiol 2009;92(2):107-112.         [ Links ]

6. Hearst MO, Biskeborn K, Christensen M, Cushing C. Trends of overweight and obesity among white and american indian school children in south dakota, 1998-2010. Obesity 2013; 21:E26-E32.         [ Links ]

7. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de Orçamentos Familiares 2008-2009. Antropometria e estado nutricional de crianças, adolescentes e adultos no Brasil. Rio de Janeiro: IBGE, 2010.         [ Links ]

8. Carvalho GQ, Pereira PF, Serrano HMS, Franceschini SCC, de Paula SO, Priore SE, et al. Peripheral expression of inflammatory markers in overweight female adolescents and eutrophic female adolescents with a high percentage of body fat. Appl Physiol Nutr Metab 2010; 35: 1-7.         [ Links ]

9. Serrano HMS, Carvalho GQ, Pereira PF, Peluzio MCG, Franceschini SCC, Priore SE. Composição corpórea, alterações bioquímicas e clínicas de adolescentes com excesso de adiposidade. Arq Bras Cardiol 2010; 13 (1): 1-9.         [ Links ]

10. Vieira PR, de Faria E, de Faria F, Sperandio N, Araujo C, Stofeles R, et al. Fatores associados ä adiposidade em adolescentes do sexo feminino eutróficas com adequado e elevado percentual de gordura corporal: elaboração de um modelo de risco. Arch Latinoam Nutr 2011; 61 (3): 279-287.         [ Links ]

11. Gupta N, Balasekaran G, Govindaswamyb VV, Hwa CY, Shuna LM. Comparison of body composition with bioelectric impedance (BIA) and dual energy X-ray absorptiometry (DXA) among Singapore Chinese. J Sci Med Sport 2011; 14: 33-35.         [ Links ]

12. Leahy S, O'Neill C, Sohun R, Jakeman P. A comparison of dual energy X-ray absorptiometry and bioelectrical impedance analysis to measure total and segmental body composition in healthy young adults. Eur J Appl Physiol 2012; 112:589-595.         [ Links ]

13. Verdich C; Barbe P; Petersen M; Grau K; Ward L; Macdonald I, et al. Changes in body composition during weight loss in obese subjects in the NUGENOB study: comparison of bioelectrical impedance vs. dual-energy X-ray absorptiometry. Diabetes Metab 2011; 37(3): 222-9.         [ Links ]

14. Kriemler S, Puder J, Zahner L, Roth R, Braun-Fahrlander C, Bedogni G. Cross-validation of bioelectrical impedance analysis for the assessment of body composition in a representative sample of 6-to 13-year-old children. Eur J Clin Nutr 2008,1-8.         [ Links ]

15. Ministério da Saúde. 2012. População residente Viçosa, MG. URL: http://tabnet.datasus.gov.br/cgi/tabcgi.exe?ibge/cnv/popmg.def.         [ Links ]

16. Crizel MM. Indicadores antropométricos, bioquímicos, de composição corporal e pressão arterial como preditores de risco cardiovascular e síndrome metabólica em adolescentes. 2010-(Dissertação de Mestrado) Departamento de Nutrição e Saude, Universidade Federal de Viçosa, Viçosa.         [ Links ]

17. World Health Organization. de 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: 660-667.         [ Links ]

18. Jelliffe DB. Evolución del estado de nutrición de la comunidad. Ginebra, Organización Mundial de la Salud, 1968.         [ Links ]

19. Faria ER, Franceschini SCC, Peluzio MCG, Sant'Ana, LFR, Priore SE. Correlação entre variáveis de composição corporal e metabólica em adolescentes do sexo feminino. Arq Bras Cardiol 2009; 93 (2): 119-127.         [ Links ]

20. Barbosa KBF. Consumo Alimentar e marcadores de risco para a síndrome metabólica em adolescentes do sexo feminino: comparacäo entre instrumentos de inquérito dietético. 2006 - (Dissertação de Mestrado) Departamento de Nutrição e Saude, Universidade Federal de Viçosa, Viçosa.         [ Links ]

21. Lohman TG. Assessing fat distribuition. In: Advances in body composition assessment: current issues in exercise science. Illinois, Human Kinetics. Champaign 1992: 57-63.         [ Links ]

22. Kim HA, Lee Y, Kwon HS, Lee SH, Jung MH, Han K, et al. Gender differences in the association of insulin resistance with metabolic risk factors among Korean adolescents: Korea National Health and Nutrition Examination Survey 2008-2010. Diabetes Res Clin Pr 2013; 99 (1): 54-62.         [ Links ]

23. Oliveira CL, Mello MT, Cintra IP, Fisberg M. Obesidade e síndrome metabólica na infancia e adolescencia. Rev Nutr 2004;17(2): 237-245.         [ Links ]

24. Pereira PF, Serrano HMS, Carvalho GQ, Lamounier JA, Peluzio MCG, Franceschini SCC, et al. Body fat location and cardiovascular disease risk factors in overweight female adolescents and eutrophic female adolescents with a high percentage of body fat. Cardiology in the Young. 2011; 1-8.         [ Links ]

25. Chaves OC, Franceschini SCC, Ribeiro SMR, Sant'Ana LFR, Faria CG, Priore SE. Comparison of the biochemical, anthropometric and body composition variables between adolescents from 10 to 13 years old and their parents. Nutr Hosp 2012;27:1127-1133.         [ Links ]

26. Vicente-Rodríguez G, Rey-López JP, Mesana MI, Poortvliet E, Ortega FB, Polito A, et al. Reliability and Intermethod Agreement for Body Fat Assessment Among Two Field and Two Laboratory Methods in Adolescents. Obesity 2012; 20 (1):221-228.         [ Links ]

27. Gontijo CA, Faria ER, Oliveira RMS, Priore SE. Síndrome Metabólica em Adolescentes Atendidos em Programa de Saude de Viçosa-MG. Rev Bras Cardiol 2010; 23 (6): 324-333.         [ Links ]

28. Heyward V. ASEP methods recommendation: body composition assessment. J Exer Physiol 2001; 4(4):1-12.         [ Links ]

29. Sardinha LB, Lohman TG, Teixeira PJ, Guedes DP, Going SB. Comparison of air displacement plethysmography with dual-energy X-ray absortiometry and 3 field methods for estimating body composition in middle-age men. Am J Clin Nutr 1998; 68:786-93.         [ Links ]

30. Eickemberg M, Oliveira CC, Roriz AKC, Sampaio LR. Bioimpedancia elétrica e sua aplicação em avaliação nutricional. Rev Nutr 2011;24(6):873-82.         [ Links ]

31. Baumgartner RN, Ross R, Heymsfield SB. Does adipose tissue influence bioelectric impedance in obese men and women. J Appl Physiol 1998; 84(1):257-62.         [ Links ]

32. Slinde F, Rossander-Hultén L. Bioelectrical impedance: effect of 3 identical meals on diurnal impedance variation and calculation of body composition. Am J Clin Nutr 2001; 74:474-478.         [ Links ]

33. Rech CR, Glaner MF. Impedancia bioelétrica bipolar: falta acuracidade para estimar a gordura relativa em homens. Rev Bras Cineantropom Desempenho Hum 2011, 13(2):100-105.         [ Links ]

 

 

Correspondence:
Eliane Rodrigues de Faria.
Street Afonso Pena 257, Neighborhood Centro.
Viçosa-MG, CEP: 36570-000, Brazil.
E-mail: elianefariaufes@gmail.com

Recibido: 17-VII-2014.
Aceptado: 16-VIII-2014.