Body fat estimated by equations based on anthropometric parameters correlates with bioelectrical impedance in patients undergoing bariatric surgery

Authors

  • Amanda Motta de Bortoli a Programa de Pós-graduação em Nutrição e Saúde, Centro de Ciências da Saúde, Universidade Federal do Espírito Santo, Vitória-ES, Brasil
  • Beatriz Bobbio de Brito b Departamento de Educação Integrada em Saúde, Centro de Ciências da Saúde, Universidade Federal do Espírito Santo, Vitória-ES, Brasil
  • Luís Lucas Vasconcelos Neves c Unidade Acadêmica de Serra Talhada, Universidade Federal Rural de Pernambuco, Serra Talhada – PB, Brasil
  • Ricardo Lucio de Almeida d Centro de Estudos e Pesquisas de Plantas Medicinais, Universidade Federal do Vale de São Francisco, Petrolina, PB, Brasil
  • Leandro dos Santos c Unidade Acadêmica de Serra Talhada, Universidade Federal Rural de Pernambuco, Serra Talhada – PB, Brasil
  • Valério Garrone Barauna e Programa de Pós-Graduação em Ciências Fisiológicas, Universidade Federal do Espírito Santo, Vitória-ES, Brasil
  • Fabiano Kenji Haraguchi a Programa de Pós-graduação em Nutrição e Saúde, Centro de Ciências da Saúde, Universidade Federal do Espírito Santo, Vitória-ES, Brasil; b Departamento de Educação Integrada em Saúde, Centro de Ciências da Saúde, Universidade Federal do Espírito Santo, Vitória-ES, Brasil

DOI:

https://doi.org/10.36311/jhgd.v32.13776

Keywords:

obesity, body composition, electric impedance, anthropometry

Abstract

Introduction: predictive equations to estimate body fat based on simple anthropometric parameters are easy to use in the clinical practice.

Objective: to evaluate the relationship between predictive equations based on anthropometric parameters and bioelectrical impedance to estimate body fat in individuals undergoing bariatric surgery.

Methods: a prospective and longitudinal study carried out with individuals undergoing bariatric surgery. Body weight, body mass index, waist circumference and body fat percentage estimated by anthropometric parameters and by impedance were evaluated at three moments, one month before, two and six months after surgery. Data were analyzed by one-way ANOVA for repeated measures with Holm-Sidak´s post hoc or Friedman test with Tukey´s post hoc, and Pearson or Spearman correlations, according to data distribution. Significance level adopted 5%.

Results: twenty-five subjects composed the final sample. All anthropometric parameters reduced significantly over time (p<0.001). Except for Lean et al equation before surgery, the body fat percentage estimated by other formulas showed a strong correlation with impedance in all moments, with the highest correlation strength observed in Gómez-Ambrosi et al. equation.

Conclusion: in the present study, the equations used showed a good correlation with bioelectrical impedance, and the Gómez-Ambrosi et al. equation as a better option to the use of bioimpedance to assess changes in body fat percentage of patients undergoing bariatric surgery for the treatment of severe obesity.

Downloads

Download data is not yet available.

References

Lin X, Li H. Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front Endocrinol (Lausanne). 2021; 12, 706978.

Farias ES, Moreira KFA, Santos JP, Gemelli IFB, Costa GM, Souza OF. Overweight and obesity: prevalence in children and adolescents in Northern Brazil. J. Hum. Growth Dev. 2020; 30(2): 266-273.

Camargo JSAA, Zamarchi TBO, Balieiro AAS, Pessoa FAC, Camargo LMA. Prevalence of obesity, high blood pressure, dyslipidemia and their associated factors in children and adolescents in a municipality in the Brazilian Amazon region. J. Hum. Growth Dev. 2021; 31(1): 37-46.

Castanha CR, Ferraz AAB, Castanha AR, Belo GQMB, Lacerda RMR, Vilar L. Avaliação da qualidade de vida, perda de peso e comorbidades de pacientes submetidos à cirurgia bariátrica. Rev. Col. Bras. Cir. 2018; 45(3).

Carey DG, Pliego GJ, Raymond RL, Skau KB. Body composition and metabolic changes following bariatric surgery: effects on fat mass, lean mass and basal metabolic rate. Obes Surg. 2006; 16(4): 469-477.

Nicoletti CF, Camelo JS Jr, dos Santos JE, Marchini JS, Salgado W Jr, Nonino CB. Bioelectrical impedance vector analysis in obese women before and after bariatric surgery: changes in body composition. Nutrition. 2014; 30(5): 569-574.

Shannon CA, Brown JR, Del Pozzi AT. Comparison of Body Composition Prediction Equations with Air Displacement Plethysmography in Overweight and Obese Caucasian Males. Int J Exerc Sci. 2019; 12(4): 1034-1044.

Lukaski HC, Johnson PE, Bolonchuk WW, Lykken GI. Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am J Clin Nutr. 1985; 41(4): 810-817.

Segal KR, Van Loan M, Fitzgerald PI, Hodgdon JA, Van Itallie TB. Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study. Am J Clin Nutr. 1988; 47(1): 7-14.

Feferbaun R, Leone C, Nogueira RC, Cavalcanti PN, Cardoso EB, Serra MA. A 10-month anthropometric and bioimpedance evaluation of a nutritional education program for 7 - to 14-year-old students. J. Hum. Growth Dev. 2012; 22(3): 283-290.

Lopes WA, Leite N, Silva LR, Consentino CLM, Coutinho P, Radominski RB, et al. Comparação de três equações para predição da gordura corporal por bioimpedância em jovens obesas. Rev Bras Med Esporte 2015; 21(4): 266-270.

Rodrigues MN, Silva SC, Monteiro WD, Farinatti PTV. Estimativa da gordura corporal através de equipamentos de bioimpedância, dobras cutâneas e pesagem hidrostática. Rev Bras Med Esporte 2001; 7(4): 125-131.

Martins GQ, Matheus SC, Santos DL, Both DR, Farinha JB, Martins MS. Comparação de equações antropométricas para estimativa da gordura corporal em indivíduos com excesso de peso. Nutr Clín Diet Hosp. 2015; 35(3): 27-33.

Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr. 1991; 65(2): 105-114.

Lean ME, Han TS, Deurenberg P. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr. 1996; 63(1): 4-14.

Gómez-Ambrosi J, Silva C, Catalán V, Rodríguez A, Galofré JC, Escalada J. et al. Clinical usefulness of a new equation for estimating body fat. Diabetes Care. 2012; 35(2): 383-388.

Woolcott O, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage – A cross-sectional study in American adult individuals: Sci Rep 2018; 8(1): 1-11.

Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr.2004; 23(6): 1430–53.

Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012; 24(3): 69-71.

Duren DL, Sherwood RJ, Czerwinski SA, Lee M, Choh AC, Siervogel RM. et al. Body composition methods: comparisons and interpretation. J Diabetes Sci Technol. 2008; 2(6): 1139-1146.

Guzmán-León AE, Velarde AG, Vidal-Salas M, Urquijo-Ruiz LG, Caraveo-Gutiérrez LA, Valencia ME. External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods. PLoS One. 2019; 14(12): e0226767.

Silveira EA, Barbosa LS, Noll M, Pinheiro HA, de Oliveira C. Body fat percentage prediction in older adults: Agreement between anthropometric equations and DXA. Clin Nutr. 2021; 40(4): 2091-2099.

Koehler KB, Moraes RAG, Rodrigues JB, Portela BSM, Miguel GPS, Pedrosa RG. et al. Bioimpedance phase angle is associated with serum transthyretin but not with prognostic inflammatory and nutritional index during follow-up of women submitted to bariatric surgery. Clin Nutr ESPEN. 2019; 33: 183-187.

Teixeira GPH, Moraes RA, Miguel GPS, Pedrosa RG, Haraguchi FK. Atherogenic index of plasma is reduced during follow-up among Roux-in-Y gastric bypass patients. Rev. chil. nutr. 2021; 48(5): 768-774.

Manoel R, Venâncio FA, Miguel GPS, Haraguchi FK, Pedrosa RG. A Higher Phase Angle Is Associated with Greater Metabolic Equivalents in Women 1 Year After Bariatric Surgery. Obes Surg. 2022; 32(6): 2003-2009.

Liu Y, Jin J, Chen Y, Chen C, Chen Z, Xu L. Integrative analyses of biomarkers and pathways for adipose tissue after bariatric surgery. Adipocyte. 2020; 9(1): 384-400.

Arterburn DE, Telem DA, Kushner RF, Courcoulas AP. Benefits and Risks of Bariatric Surgery in Adults: A Review. JAMA. 2020; 324(9): 879-887.

Published

2022-10-31

Issue

Section

ORIGINAL ARTICLES