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1 94 Journal of Exercise Physiologyonline December 2017 Volume 20 Number 6 Editor-in-Chief Official Research Journal of Tommy the American Boone, PhD, Society MBA of Review Exercise Board Physiologists Todd Astorino, PhD Julien Baker, ISSN PhD Steve Brock, PhD Lance Dalleck, PhD Eric Goulet, PhD Robert Gotshall, PhD Alexander Hutchison, PhD M. Knight-Maloney, PhD Len Kravitz, PhD James Laskin, PhD Yit Aun Lim, PhD Lonnie Lowery, PhD Derek Marks, PhD Cristine Mermier, PhD Robert Robergs, PhD Chantal Vella, PhD Dale Wagner, PhD Frank Wyatt, PhD Ben Zhou, PhD Official Research Journal of the American Society of Exercise Physiologists ISSN JEPonline Estimate of Body Fat Percentage of Trunk in Men William Alves Lima 1, Patrícia Espíndola Mota Venâncio 1, Grassyara Pinho Tolentino 1, Viviane Soares 1.2, Iransé Oliveira-Silva 1 1 Physical Education College, UniEVANGÉLICA, Anápolis-GO, Brazil, 2 Physical Education Post Graduate Program, Federal University of Goiás, Goiânia-GO, Brazil ABSTRACT Lima WA, Venâncio PEM, Tolentino GP, Soares V, Oliveira-Silva I. Estimate of Body Fat Percentage of Trunk in Men. JEPonline 2017;20(6): Dual-energy X-ray absorptiometry (DXA) is the most widely used testing method for assessing percent body fat, but it is expensive. The purpose of this study was to classify the percentile corresponding to the body fat accumulation in the torso region and the development of an equation for estimating body fat accumulated in the trunk. Subjects consisted of 138 adult men aged 20 to 58 yrs. A whole body evaluation was performed by DXA. The total body fat was measured and stratified by region (lower and upper limbs and trunk). A multiple linear regression equation was performed using the stepwise method with abdominal ST and waist perimeter. Bland Altman and the paired t test were used. Only the abdominal ST variable and waist perimeter entered the regression model. The sample had an average %F= 20.8 ± 8.2 and %F torso = 23.5 ± 9.7. The %F trunk can be estimated by the equation =.428 * (ST abdomen) * (waist perimeter) The equation presented an r =.95 and R 2 =.90. The standard error was 3.8 estimative of P = Individuals with %F trunk below the 5 th percentile (8.40%) were classified as very thin, between 8.40 and 14.74% were thin, between to 24.19% were acceptable, between 24.20% to 30.99% was classified as abdominal overweight, and 31% above was classified as abdominal obesity. The findings indicate that the equation finds satisfactorily the proposed estimation of torso body fat in Latin adult men. Key Words: Abdominal Fat, Body Composition, Body Fat Percentage (%F)

2 95 INTRODUCTION The literature (1,3,11,13,16,21) is clear that the excess of body fat is associated with the risk of developing cardiovascular disease and other chronic diseases. In particular, android fat distribution (i.e., fat in the abdomen, chest, and shoulders) is directly related to the increase in blood lipid concentration (24) that promotes the development of cardiovascular diseases (17,24). Several studies (2,7,15,19) have correlated a high body mass index with the development of chronic diseases, especially those with a metabolic profile that indicates an increased risk of atherogenesis. Other studies (6,8,12) have pointed to fat deposition in the abdominal region as the primary factor in the development of high blood pressure and coronary artery disease. In order to measure the fat amount deposited specifically in the trunk, there is a need for costly laboratory methods such as Dual Energy X-ray Absorptiometry (DXA), magnetic resonance imaging, or computed tomography (20), since the double-indirect equations used to calculate the body composition were constructed to calculate the body fat percentage (4) specifically the fat accumulated in the trunk region. Considering DXA the most reliable method (23), and seen as the gold standard (9) for estimating body fat separately by body segments, the purpose of this study was to develop a body fat estimation equation of accumulated fat in trunk region as well as to classify the percentile corresponding to it in this part of the body. METHODS Subjects One hundred and thirty-eight adult men with an average age of 35.5 ± 5.8 yrs (minimum of 20 and maximum of 58 yrs) were included in this study. The subjects were healthy as indicated by their medical report. They did not have diabetes or cardiovascular disease. None of the subjects was under any treatment for chronic pathology that might compromise the data collection process. In addition, they were not taking any type of continuous use of medication. All subjects were given detailed information about the research procedures, and this study complied with the Helsinki Declaration as well as the recommendations of resolution CNS 466/2012. Procedures Each subject s evaluation was individually scheduled, where clothing and prior feeding information was passed on. Upon arriving at the laboratory, body mass was measured on a scale (Premium, Filizola, Brazil) with a precision of 0.1 kg and a capacity of up to 300 kg. Then, the subject s height was measured using a stadiometer (206, Seca, Brazil). Both procedures were consistent with the parameters described by the International Society for Advancement in Kinanthropometry (14). The subcapular, abdominal, axillary, suprailiac, and supraspinous cutaneous folds (DC) were measured in a circuit with an adipometer (Lange, US Chemical, USA), with a 1 mm scale and constant pressure (8.9 g mm -2 ) in all bar opening angles. After the third measurement of each

3 fold, they presented a variation of less than 2% from one measurement to next. Then, the median for each measured point was identified. If the variation between the measures was greater than 2%, a fourth or fifth measure was performed. The abdomen and waist circumferences were measured three times with a metallic anthropometric measurement (TR4010, Sanny, Brazil). If the variation between the measurements was less than 5%, the median of the three measures was identified. For both procedures, the standardization of ISAK (14) was followed. This procedure was performed by a single evaluator with extensive experience and competence in this type of measurement and ISAK level 2 certified assessors. After the anthropometric measurements, the patient was submitted to a densitometry (DXA QDR-4500W fan-beam scanners, Hologic Inc., Bedford, MA - USA). Total body fat was measured and stratified by region (i.e., lower limbs and trunk). Segregation of body segments in DXA software was performed by a single laboratory technician with extensive experience using the equipment. Study Design This study was developed over a period of 134 days, starting with the contact of the people who went to an imaging clinic looking for body composition analysis in DXA. They were invited to participate in the research. If they agreed to do so, they were offered an anthropometric evaluation at no cost with a follow-up analysis and explanation of the results. Characterization the When an individual contacted the clinic to schedule the body composition assessment of the whole body by DXA, he was invited to participate in a sample by the clinic clerk. After scheduling an appointment for the patient, the researchers were informed about it. They went to the clinic at the time scheduled for the measurement of the anthropometric characteristics. Statistical Analysis The data were submitted to statistical analysis using SPSS v 20.0, IBM. The Kolmogorov- Smirnov test was used to determine the normality of the data. Descriptive statistics were used to present the average ± standard deviation and 95% confidence interval. A correlation between the dependent variables (%F and %F trunk) and independent variables (body mass, height, age, and anthropometric characteristics) was applied to identify the variables that were entered into the multiple linear regression model (using the Stepwise method, correction factor). The standard error estimate for the developed equation was then calculated, which was 3.8%. RESULTS Table 1 shows the descriptive characteristics of the sample. Thus, it is possible to perceive the heterogeneity of the sample as the measured variables, due to the calculated standard deviation. Then, it was determined that the greatest variation between the skin folds was in the abdominal. Among the parameters of the abdomen and waist, there was no significant variation between these measures. 96

4 97 Table 1. Descriptive Characteristics of the Sample (N = 138). Variables Average ± SD IC95% Age (yrs) 35.5 ± Body Mass (kg) ± Height (cm) ± DC Subscapular (mm) ± DC Supra-Spinal (mm) ± DC Axillary (mm) ± DC Suprailiac (mm) ± DC Abdominal (mm) ± Perimeter of Abdomen (cm) ± Waist Circumference (cm) ± %F DXA ± %F trunk DXA ± From the variables released in the regression model as independent, only the variables DC abdomen and waist circumference entered the regression model after the application of the correction factors. The equation generated showed a high correlation (r = 0.95, P = 0.001) and a very high coefficient of determination (R 2 = 0.907) as shown in Figure 1, together with the straight line of the regression equation. The standard error was 3.8% for P = Comparing the generalized equations developed and validated by Durnin and Womersley (10), they showed that 64.4% of body composition variations could be explained by the measurement of skin folds. The present study shows 90.7% of the variations presented in the accumulation of fat in the trunk could be explained by the regression equation developed and presented in Table 2. This is the result of a high correlation between the data. The standard error estimation (3.8%) was high, which was probably due to the variation amplitude of the sample regarding the subjects age. Figure 1. Regression for the Trunk Fat Percentage Calculation.

5 The comparison between the variables is relatively low, and it is shown by the Bland Altman in Figure 2 that the proposed equation resembles the gold standard (i.e., %GT DXA). 98 Figure 2. Bland Altman of the Measures Assessed. DISCUSSION The purpose of this study was to propose another option to search for associations between anthropometric variables and risk factors for cardiovascular diseases that are more prominent in men (5). Table 2 indicates that the subjects percentile and regression equation to calculate a subject s %F Trunk. Note that 21.6% of the subjects were classified as a low %F trunk; 32.0% were classified as acceptable; 24.8% were classified as %F of high trunk (indicating an excess of fat in the abdominal region); and 20.8% were classified as very high, which represents an abdominal obesity. Table 2. Sample Characterization in and the Regression Equation to Calculate the %F Trunk from Anthropometric Variables Classification Very Low Low Acceptable High Very High %F Trunk < 8.4% 8.4% to 14.74% 14.75% to 24.19% 24.20% to 31.00% >31.0% %F Trunk Estimate = * (DC Abdomen) * (Waist Circumference)

6 These data show that more than 45% of the subjects in the present study had excess fat in the abdominal region. Abdominal obesity, or android type of obesity, is considered an independent risk factor for several morbidities that represent a high health risk when compared to other forms of body fat distribution (18). We recommend the validation of this equation with individuals between 20 and 58 yrs of age, who were not part of the sample. Another suggestion is to recalculate the equation from quadratic models, as Vincent (22) states, these are more rigid in relation to linear regression, taking into account a greater variability of minimum and maximum values. CONCLUSION The equation developed satisfies the purpose of this study, which was to estimate body fat in Latin adult men between the ages of 20 to 58 yrs. Also, the established percentile ranges can rank the corporal fat accumulation for a population with similar characteristics. 99 Address for correspondence: William Alves Lima, Physical Education, UniEVANGÉLICA University Center, Anápolis, Goiás, Brazil, , williamfef@hotmail.com REFERENCES 1. Bacha F, Saad R, Gungor N, et al. Obesity, regional fat distribution, and syndrome X in obese black versus white adolescents: Race differential in diabetogenic and atherogenic risk factors. J Clin Endocrinol Metab. 2003;88(6): Bhupathiraju SN, Di Angelantonio E, Danesh J, Hu FB. Commentary on A metaanalysis but not a systematic review: An evaluation of the Global BMI Mortality Collaboration. J Clin Epidemiol. 2017;88: Björntorp P. The regulation of adipose tissue distribution in humans. Int J Obes Relat Meatb Disord. 1996;20(4): Bonnick SL, Lewis LA. Body Composition Analysis. Bone Densitometry for Technologists. Springer: New York, 2013, pp Brant LCC, Nascimento BR, Passos VMA, Duncan BB, Bensenõr IJM, Malta DC, et al. Variations and particularities in cardiovascular disease mortality in Brazil and Brazilian states in 1990 and 2015: Estimates from the global burden of disease. Rev Bras Epidemiol. 2017;20(1): Britton KA, Massaro JM, Murabito JM, Kreger BE, Hoffmann U, Fox CS. Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality. J Am Coll Cardiol. 2013;62(10):

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