John B Dixon1, Aparna G Bhasker2, Gavin W Lambert3, Muffazal Lakdawala2. 1. Clinical Obesity Research, Baker IDI Heart and Diabetes Institute, Melbourne, Australia. Electronic address: John.Dixon@bakeridi.edu.au. 2. Centre for Obesity and Digestive Surgery, Gamdevi, Mumbai, India; Institute of Minimally Invasive Surgical Sciences and Research Centre, Saifee Hospital, Mumbai, India. 3. Human Neurotransmitter Laboratory, Baker IDI Heart and Diabetes Institute, Melbourne, Australia.
Abstract
BACKGROUND: Bioelectrical impedance analysis (BIA) is well tolerated, inexpensive, and readily available, but can it be used to detect with clinical precision aberrant changes in the proportion of fat mass to fat-free mass during weight loss? OBJECTIVES: To assess the variance in percentage body fat mass explained by the readily available inputs and assess residual variance provided by leg-to-leg BIA scales. METHODS: Using cross-sectional data from a cohort of 665 patients of Indian ethnicity presenting for bariatric surgery, we examine the determinants of percentage body fat as provided by leg-to-leg output from Tanita SC-330 BIA scales. RESULTS: Four input factors-sex, weight, height, and age-contributed to provide 92% and 95% explanation in output variance for percentage fat mass (%FM) and actual fat mass, respectively, in 665 patients. Body mass index alone explained 89% and 81% of variance in %FM output for women and men, respectively. Neither weight distribution, as indicated by waist and hip circumference or waist to hip ratio, nor plasma lipids or markers of glucose metabolism contributed additional variance in %FM when controlled for the 4 key inputs. CONCLUSIONS: Simple, known input variables dominate the leg-to-leg BIA output of %FM, and this may compromise the detection of aberrant changes in %FM and fat-free mass with substantial weight loss. For clinical research, validated methods not largely dependent on known inputs should be used for evaluating changes in body composition after substantial weight loss.
BACKGROUND: Bioelectrical impedance analysis (BIA) is well tolerated, inexpensive, and readily available, but can it be used to detect with clinical precision aberrant changes in the proportion of fat mass to fat-free mass during weight loss? OBJECTIVES: To assess the variance in percentage body fat mass explained by the readily available inputs and assess residual variance provided by leg-to-leg BIA scales. METHODS: Using cross-sectional data from a cohort of 665 patients of Indian ethnicity presenting for bariatric surgery, we examine the determinants of percentage body fat as provided by leg-to-leg output from Tanita SC-330 BIA scales. RESULTS: Four input factors-sex, weight, height, and age-contributed to provide 92% and 95% explanation in output variance for percentage fat mass (%FM) and actual fat mass, respectively, in 665 patients. Body mass index alone explained 89% and 81% of variance in %FM output for women and men, respectively. Neither weight distribution, as indicated by waist and hip circumference or waist to hip ratio, nor plasma lipids or markers of glucose metabolism contributed additional variance in %FM when controlled for the 4 key inputs. CONCLUSIONS: Simple, known input variables dominate the leg-to-leg BIA output of %FM, and this may compromise the detection of aberrant changes in %FM and fat-free mass with substantial weight loss. For clinical research, validated methods not largely dependent on known inputs should be used for evaluating changes in body composition after substantial weight loss.
Authors: Gabriel Cunha Beato; Michele Novais Ravelli; Alex Harley Crisp; Maria Rita Marques de Oliveira Journal: Obes Surg Date: 2019-01 Impact factor: 4.129
Authors: R Vilallonga; J L Pereira-Cunill; S Morales-Conde; I Alarcón; I Breton; E Domínguez-Adame; J V Ferrer; A Garcia Ruiz-de-Gordejuela; A Goday; A Lecube; E Martín García-Almenta; M Á Rubio; F J Tinahones; P P García-Luna Journal: Obes Surg Date: 2019-12 Impact factor: 4.129
Authors: Rebeca Rocha de Almeida; Márcia Ferreira Cândido de Souza; Dihogo Gama de Matos; Larissa Monteiro Costa Pereira; Victor Batista Oliveira; Joselina Luzia Menezes Oliveira; José Augusto Soares Barreto-Filho; Marcos Antonio Almeida-Santos; Raphael Fabrício de Souza; Aristela de Freitas Zanona; Victor Machado Reis; Felipe J Aidar; Antônio Carlos Sobral Sousa Journal: Int J Environ Res Public Health Date: 2019-11-27 Impact factor: 3.390