Estimating the Total and Regional Body Fat of Physically Active Men Is Not Appropriate for Sedentary Men
DOI:
https://doi.org/10.17309/tmfv.2024.3.06Keywords:
inactive men, young men, body composition, skinfold thickness, circumferenceAbstract
Objectives. The reliability of predictive body fat equations remains unclear due to their inappropriate use across different subject cohorts and conditions. The objective of this study was to validate and cross-validate equations to predict total and regional body fat in young physically active males.
Material and methods. Three hundred and five young male participants were divided into the following groups: active validation (n=165), active cross-validation (n=70), or sedentary cross-validation ones (n=70). The study used a stratified random sampling based on weekly physical activity level. The total and regional body fat mass were measured using dual-energy X-ray absorptiometry (DEXA) after an overnight fast. Simultaneous measurements of height, body mass, skinfold thickness, body mass index, and body circumferences were taken. The total and regional body fat predictive equations were generated using multiple linear stepwise regression models. The coefficient of determination (R²) and standard error of estimation (SEE) were calculated to examine the accuracy of the predictive equations. Furthermore, cross-validation groups were analysed.
Results. The percentage of total body fat, trunk fat, legs fat, arms fat, and body mass index of active cross-validation were found to be significantly lower than in the sedentary cross-validation groups. The total body fat percentage was highly associated with abdominal skinfold thickness (R=0.68-0.74, P<0.001), body mass index (R=0.55, P<0.001), and suprailiac skinfold thickness (R=0.67-0.71, P<0.001) in the active validation group. The predictive total and regional body fat equations of physically active men showed adjusted R² values ranging from 0.35 to 0.66, with standard error of estimation values between 2.74 to 4.35%. The standard error of estimation for the predictive percentage of total and regional body fat in the active cross-validation group was lower than in the sedentary cross-validation group.
Conclusions. The findings demonstrate that new predictive total and regional body fat equations can be used to accurately estimate body fat in healthy young active males under fast conditions.
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References
Chun, H., Suh, E., Byun, A. R., Park, H. R., & Shim, K. W. (2015). Epicardial fat thickness is associated to type 2 diabetes mellitus in Korean men: A cross-sectional study. Cardiovascular Diabetology, 14(46), 1-7. https://doi.org/10.1186/s12933-015-0210-7 DOI: https://doi.org/10.1186/s12933-015-0210-7
Ortega, F. B., Lavie, C. J., & Blair, S. N. (2016). Obesity and cardiovascular disease. Circulation Research, 118(11), 1752-1770. https://doi.org/10.1161/circresaha.115.306883 DOI: https://doi.org/10.1161/CIRCRESAHA.115.306883
Takeoka, A., Tayama, J., Yamasaki, H., Kobayashi, M., Ogawa, S., Saigo, T., . . . Shirabe, S. (2016). Intra-abdominal fat accumulation is a hypertension risk factor in young adulthood: A cross-sectional study. Medicine, 95(45), e5361. https://doi.org/10.1097/Md.0000000000005361 DOI: https://doi.org/10.1097/MD.0000000000005361
Ball, S., Cowan, C., Thyfault, J., & LaFontaine, T. (2014). Validation of a new skinfold prediction equation based on dual-energy X-ray absorptiometry. Measurement in Physical Education and Exercise Science, 18(3), 198-208. https://doi.org/10.1080/1091367X.2014.914518 DOI: https://doi.org/10.1080/1091367X.2014.914518
Leahy, S., O’Neill, C., Sohun, R., Toomey, C., & Jakeman, P. (2013). Generalised equations for the prediction of percentage body fat by anthropometry in adult men and women aged 18-81 years. British Journal of Nutrition, 109(4), 678-685. https://doi.org/10.1017/s0007114512001870 DOI: https://doi.org/10.1017/S0007114512001870
Liu, X., Sun, Q., Sun, L., Zong, G., Lu, L., Liu, G., . . . Lin, X. (2015). The development and validation of new equations for estimating body fat percentage among Chinese men and women. British Journal of Nutrition, 113(9), 1365-1372. https://doi.org/10.1017/s0007114515000616 DOI: https://doi.org/10.1017/S0007114515000616
Ganpule-Rao, A., Joglekar, C., Patkar, D., Chinchwadkar, M., Bhat, D., Lubree, H., . . . Yudkin, J. (2013). Associations of trunk fat depots with insulin resistance, beta cell function and glycaemia--a multiple technique study. PLoS One, 8(10), e75391. https://doi.org/10.1371/journal.pone.0075391 DOI: https://doi.org/10.1371/journal.pone.0075391
Kouda, K., Fujita, Y., Ohara, K., Tachiki, T., Tamaki, J., Yura, A., . . . Iki, M. (2021). Associations between trunk-to-peripheral fat ratio and cardiometabolic risk factors in elderly Japanese men: Baseline data from the Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) study. Environmental Health and Preventive Medicine, 26(1), 35. https://doi.org/10.1186/s12199-021-00959-9 DOI: https://doi.org/10.1186/s12199-021-00959-9
Lee, M., Choh, A. C., Demerath, E. W., Towne, B., Siervogel, R. M., & Czerwinski, S. A. (2012). Associations between trunk, leg and total body adiposity with arterial stiffness. American Journal of Hypertension, 25(10), 1131-1137. https://doi.org/10.1038/ajh.2012.92 DOI: https://doi.org/10.1038/ajh.2012.92
Ritchie, C. B., & Davidson, R. T. (2007). Regional body composition in college-aged Caucasians from anthropometric measures. Nutrition & Metabolism, 4, 29. https://doi.org/10.1186/1743-7075-4-29 DOI: https://doi.org/10.1186/1743-7075-4-29
Scafoglieri, A., Tresignie, J., Provyn, S., Marfell-Jones, M., George, K., Clarys, J. P., & Bautmans, I. (2013). Accuracy and concordance of anthropometry for measuring regional fat distribution in adults aged 20-55 years. American Journal of Human Biology, 25(1), 63-70. https://doi.org/10.1002/ajhb.22342 DOI: https://doi.org/10.1002/ajhb.22342
Davidson, L. E., Wang, J., Thornton, J. C., Kaleem, Z., Silva-Palacios, F., Pierson, R. N., . . . Gallagher, D. (2011). Predicting fat percent by skinfolds in racial groups: Durnin and Womersley revisited. Medicine and Science in Sports and Exercise, 43(3), 542-549. https://doi.org/10.1249/MSS.0b013e3181ef3f07 DOI: https://doi.org/10.1249/MSS.0b013e3181ef3f07
Jensen, B., Moritoyo, T., Kaufer-Horwitz, M., Peine, S., Norman, K., Maisch, M. J., . . . Bosy-Westphal, A. (2019). Ethnic differences in fat and muscle mass and their implication for interpretation of bioelectrical impedance vector analysis. Applied Physiology Nutrition and Metabolism, 44(6), 619-626. https://doi.org/10.1139/apnm-2018-0276 DOI: https://doi.org/10.1139/apnm-2018-0276
Stults-Kolehmainen, M. A., Stanforth, P. R., Bartholomew, J. B., Lu, T., Abolt, C. J., & Sinha, R. (2013). DXA estimates of fat in abdominal, trunk, and hip regions varies by ethnicity in men. Nutrition & Diabetes, 3(3), e64. https://doi.org/10.1038/nutd.2013.5 DOI: https://doi.org/10.1038/nutd.2013.5
Hastuti, J., Kagawa, M., Byrne, N. M., & Hills, A. P. (2013). Development and validation of anthropometric prediction equations for estimation of body fat in Indonesian men. Asia Pacific Journal of Clinical Nutrition, 22(4), 522-529. https://doi.org/10.6133/apjcn.2013.22.4.14
Coin, A., Ruggiero, E., Giannini, S., Pedrazzoni, M., Minisola, S., Rossini, M., . . . Sergi, G. (2012). Trunk and lower limb fat mass evaluated by dual-energy X-ray absorptiometry in a 20-to 80-year-old healthy Italian population. Annals of Nutrition and Metabolism, 61(2), 151-159. https://doi.org/10.1159/000342086 DOI: https://doi.org/10.1159/000342086
Ihasz, F., Finn, K. J., Lepes, J., Halasi, S., & Szabo, P. (2015). Body composition comparisons by age groups in Hungarian adults. International Journal of Morphology, 33(3), 850-854. https://doi.org/10.4067/S0717-95022015000300007 DOI: https://doi.org/10.4067/S0717-95022015000300007
Larsson, I., Lissner, L., Samuelson, G., Fors, H., Lantz, H., Naslund, I., . . . Bosaeus, I. (2015). Body composition through adult life: Swedish reference data on body composition. European Journal of Clinical Nutrition, 69(7), 837-842. https://doi.org/10.1038/ejcn.2014.268 DOI: https://doi.org/10.1038/ejcn.2014.268
Kyle, G. U., Morabia, A., Schutz, Y., & Pichard, C. (2004). Sedentarism affects body fat mass index and fat-free mass index in adults aged 18 to 98. Nutrition, 20(3), 255-260. https://doi.org/10.1016/j.nut.2003.11.019 DOI: https://doi.org/10.1016/j.nut.2003.11.019
Scheers, T., Philippaerts, R., & Lefevre, J. (2013). Objectively-determined intensity- and domain-specific physical activity and sedentary behavior in relation to percent body fat. Clinical Nutrition, 32(6), 999-1006. https://doi.org/10.1016/j.clnu.2013.03.014 DOI: https://doi.org/10.1016/j.clnu.2013.03.014
Henson, J., Edwardson, C. L., Morgan, B., Horsfield, M. A., Bodicoat, D. H., Biddle, S. J., . . . Yates, T. (2015). Associations of sedentary time with fat distribution in a high-risk population. Medicine and Science in Sports and Exercise, 47(8), 1727-1734. https://doi.org/10.1249/MSS.0000000000000572 DOI: https://doi.org/10.1249/MSS.0000000000000572
Kerr, A., Slater, G. J., & Byrne, N. (2017). Impact of food and fluid intake on technical and biological measurement error in body composition assessment methods in athletes. British Journal of Nutrition, 117(4), 591-601. https://doi.org/10.1017/S0007114517000551 DOI: https://doi.org/10.1017/S0007114517000551
Lee, D. H., Keum, N., Hu, F. B., Orav, E. J., Rimm, E. B., Sun, Q., . . . Giovannucci, E. L. (2017). Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. British Journal of Nutrition, 118(10), 858-866. https://doi.org/10.1017/s0007114517002665 DOI: https://doi.org/10.1017/S0007114517002665
Pongchaiyakul, C., Kosulwat, V., Rojroongwasinkul, N., Charoenkiatkul, S., Thepsuthammarat, K., Laopaiboon, M., . . . Rajatanavin, R. (2005). Prediction of percentage body fat in rural Thai population using simple anthropometric measurements. Obesity Research, 13(4), 729-738. https://doi.org/10.1038/Oby.2005.82 DOI: https://doi.org/10.1038/oby.2005.82
Lohman, G. T., Roche, F. A., & Martorell, R. (1991). Anthropometric standardization reference manual. Champaign: Human Kinetics. DOI: https://doi.org/10.1249/00005768-199208000-00020
Swain, D. P., Brawner, C. A., Chambliss, H. O., Nagelkirk, P. R., Bayles, M. P., & Swank, A. M. (2014). ACSM’s resource manual for guidelines for exercise testing and prescription (Seventh ed.). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
Dupont, W. D., & Plummer, W. D., Jr. (1998). Power and sample size calculations for studies involving linear regression. Controlled Clinical Trials, 19(6), 589-601. https://doi.org/10.1016/s0197-2456(98)00037-3 DOI: https://doi.org/10.1016/S0197-2456(98)00037-3
Ohta, M., Midorikawa, T., Hikihara, Y., Masuo, Y., Sakamoto, S., Torii, S., . . . Kanehisa, H. (2017). Validity of segmental bioelectrical impedance analysis for estimating fat-free mass in children including overweight individuals. Applied Physiology Nutrition and Metabolism, 42(2), 157-165. https://doi.org/10.1139/apnm-2016-0137 DOI: https://doi.org/10.1139/apnm-2016-0137
Bland, J. M., & Altman, D. G. (2010). Statistical methods for assessing agreement between two methods of clinical measurement. International Journal of Nursing Studies, 47(8), 931-936. https://doi.org/10.1016/j.ijnurstu.2009.10.001 DOI: https://doi.org/10.1016/j.ijnurstu.2009.10.001
Drey, M., Sieber, C. C., Degens, H., McPhee, J., Korhonen, M. T., Muller, K., . . . Rittweger, J. (2016). Relation between muscle mass, motor units, and type of training in master athletes. Clinical Physiology and Functional Imaging, 36(1), 70-76. https://doi.org/10.1111/cpf.12195 DOI: https://doi.org/10.1111/cpf.12195
Guiraudou, M., Maimoun, L., Dumas, J. M., Julia, M., Raingeard, I., & Brun, J. F. (2015). Body composition measured by bioimpedance segmental (BIAS) analysis and sprint performance in rugby players. Science & Sports, 30(5), 298-302. https://doi.org/10.1016/j.scispo.2015.08.002 DOI: https://doi.org/10.1016/j.scispo.2015.08.002
Rodriguez, F. J. R., de la Rosa, F. J. B., Flores, A. A. A., Zuleta, M. F. L., & Briceno, F. R. (2012). Comparison of body composition and muscle mass body segment in students of physical education and sports of different disciplines. International Journal of Morphology, 30(1), 7-14. https://doi.org/org/10.4067/S0717-95022012000100001
da Silva, B. G. C., da Silva, I. C. M., Ekelund, U., Brage, S., Ong, K. K., Rolfe, E. D., . . . Horta, B. L. (2019). Associations of physical activity and sedentary time with body composition in Brazilian young adults. Scientific Reports, 9(1), 5444. https://doi.org/10.1038/s41598-019-41935-2 DOI: https://doi.org/10.1038/s41598-019-41935-2
Suminski, R. R., Patterson, F., Perkett, M., Heinrich, K. M., & Poston, W. S. C. (2019). The association between television viewing time and percent body fat in adults varies as a function of physical activity and sex. BMC Public Health, 19(1), 736. https://doi.org/10.1186/s12889-019-7107-4 DOI: https://doi.org/10.1186/s12889-019-7107-4
Tarnus, E., & Bourdon, E. (2006). Anthropometric evaluations of body composition of undergraduate students at the University of la reunion. Advances in Physiology Education, 30(4), 248-253. https://doi.org/10.1152/advan.00069.2005 DOI: https://doi.org/10.1152/advan.00069.2005
Henry, C. J., Ponnalagu, S. D. O., Bi, X. Y., & Tan, S. Y. (2018). New equations to predict body fat in Asian-Chinese adults using age, height, skinfold thickness, and waist circumference. Journal of the Academy of Nutrition and Dietetics, 118(7), 1263-1269. https://doi.org/10.1016/j.jand.2018.02.019 DOI: https://doi.org/10.1016/j.jand.2018.02.019
Burns, R. D., Fu, Y., & Constantino, N. (2019). Measurement agreement in percent body fat estimates among laboratory and field assessments in college students: Use of equivalence testing. PLoS One, 14(3), e0214029. https://doi.org/10.1371/journal.pone.0214029 DOI: https://doi.org/10.1371/journal.pone.0214029
Nickerson, B. S., Esco, M. R., Bishop, P. A., Fedewa, M. V., Snarr, R. L., Kliszczewicz, B. M., & Park, K. S. (2018). Validity of BMI-based body fat equations in men and women: A 4-compartment model comparison. Journal of Strength and Conditioning Research, 32(1), 121-129. https://doi.org/10.1519/JSC.0000000000001774 DOI: https://doi.org/10.1519/JSC.0000000000001774
Durnin, J. V., & Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: Measurements on 481 men and women aged from 16 to 72 years. British Journal of Nutrition, 32(1), 77-97. DOI: https://doi.org/10.1079/BJN19740060
Jackson, A. S., & Pollock, M. L. (1978). Generalized equations for predicting body density of men. British Journal of Nutrition 40(3), 497-504. DOI: https://doi.org/10.1079/BJN19780152
Campos, R. G., Carrillo, J. P., Fierro, A. A., Albornoz, C. U., & Cossio-Bolanos, M. (2018). Validation of equations and proposed reference values to estimate fat mass in Chilean university students. Endocrinologia Diabetes Y Nutricion, 65(3), 156-163. https://doi.org/10.1016/j.endinu.2017.11.008 DOI: https://doi.org/10.1016/j.endien.2017.11.018
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