Brooks C Wingo1, Valene Garr Barry2, Amy C Ellis3, Barbara A Gower4. 1. Department of Occupational Therapy, University of Alabama at Birmingham, 1720 2nd Ave S, SHPB 385, Birmingham, AL, 35294, USA. Electronic address: bcwingo@uab.edu. 2. Department of Nutrition Sciences, University of Alabama at Birmingham, 1720 2nd Ave S, Birmingham, AL, 35294, USA. Electronic address: vgarr@uab.edu. 3. Department of Nutrition and Hospitality Management, University of Alabama, 412 Russell Hall, Box 870311, Tuscaloosa, AL, 35487, USA. Electronic address: aellis@ches.ua.edu. 4. Department of Nutrition Sciences, University of Alabama at Birmingham, 1720 2nd Ave S, Birmingham, AL, 35294, USA. Electronic address: bgower@uab.edu.
Abstract
BACKGROUND & AIMS: Segmental body composition may be an important indicator of health and nutritional status in conditions where variations in fat and lean mass are frequently isolated to a particular body segment (e.g. paralysis, sarcopenia). Until recently, segment-specific body composition could only be assessed using invasive and expensive methods such as dual-energy x-ray absorptiometry (DXA), magnetic resonance imaging (MRI), or computed tomography (CT). Bioelectrical impedance analysis (BIA) may be a rapid, inexpensive alternative for assessing segmental composition, but it has not been fully validated for this purpose. The purpose of this study was to compare segmental estimates of lean and fat mass using BIA versus a criterion standard of DXA. METHODS: A cross-sectional pilot study was conducted in n = 30 healthy adults. Outcome measures included total mass, fat mass and lean mass of arm, leg and trunk. Pearson correlation coefficients (r) and paired-samples t-tests (t) were used to assess relationships between each outcome as measured by BIA and DXA. RESULTS: Although the methods were strongly correlated for all measures, (r > .87 for all segments) BIA routinely overestimated lean mass for arm and trunk (mean difference arm: 0.97 kg, p = .008; trunk: 5.58 kg, p < .0001); and underestimated fat mass for arm and leg (mean difference arm: 0.42 kg, p < .0001; leg: 1.94 kg p < .0001). BIA overestimated total body lean mass in 93% of participants and underestimated total body fat mass in 90% of participants. CONCLUSIONS: Significant discrepancies were noted between DXA and BIA in all body segments. Further research is needed to refine BIA methods for segmental composition estimates in heterogeneous samples and disease-specific populations before this methods can be used reliably in a clinical setting.
BACKGROUND & AIMS: Segmental body composition may be an important indicator of health and nutritional status in conditions where variations in fat and lean mass are frequently isolated to a particular body segment (e.g. paralysis, sarcopenia). Until recently, segment-specific body composition could only be assessed using invasive and expensive methods such as dual-energy x-ray absorptiometry (DXA), magnetic resonance imaging (MRI), or computed tomography (CT). Bioelectrical impedance analysis (BIA) may be a rapid, inexpensive alternative for assessing segmental composition, but it has not been fully validated for this purpose. The purpose of this study was to compare segmental estimates of lean and fat mass using BIA versus a criterion standard of DXA. METHODS: A cross-sectional pilot study was conducted in n = 30 healthy adults. Outcome measures included total mass, fat mass and lean mass of arm, leg and trunk. Pearson correlation coefficients (r) and paired-samples t-tests (t) were used to assess relationships between each outcome as measured by BIA and DXA. RESULTS: Although the methods were strongly correlated for all measures, (r > .87 for all segments) BIA routinely overestimated lean mass for arm and trunk (mean difference arm: 0.97 kg, p = .008; trunk: 5.58 kg, p < .0001); and underestimated fat mass for arm and leg (mean difference arm: 0.42 kg, p < .0001; leg: 1.94 kg p < .0001). BIA overestimated total body lean mass in 93% of participants and underestimated total body fat mass in 90% of participants. CONCLUSIONS: Significant discrepancies were noted between DXA and BIA in all body segments. Further research is needed to refine BIA methods for segmental composition estimates in heterogeneous samples and disease-specific populations before this methods can be used reliably in a clinical setting.
Authors: Ursula G Kyle; Ingvar Bosaeus; Antonio D De Lorenzo; Paul Deurenberg; Marinos Elia; José Manuel Gómez; Berit Lilienthal Heitmann; Luisa Kent-Smith; Jean-Claude Melchior; Matthias Pirlich; Hermann Scharfetter; Annemie M W J Schols; Claude Pichard Journal: Clin Nutr Date: 2004-12 Impact factor: 7.324
Authors: O Ortiz; M Russell; T L Daley; R N Baumgartner; M Waki; S Lichtman; J Wang; R N Pierson; S B Heymsfield Journal: Am J Clin Nutr Date: 1992-01 Impact factor: 7.045
Authors: Christie L Ward; Yoojin Suh; Abbi D Lane; Huimin Yan; Sushant M Ranadive; Bo Fernhall; Robert W Motl; Ellen M Evans Journal: J Rehabil Res Dev Date: 2013
Authors: Dana L Duren; Richard J Sherwood; Stefan A Czerwinski; Miryoung Lee; Audrey C Choh; Roger M Siervogel; Wm Cameron Chumlea Journal: J Diabetes Sci Technol Date: 2008-11
Authors: Ellen W Demerath; Shumei S Sun; Nikki Rogers; Miryoung Lee; Derek Reed; Audrey C Choh; William Couch; Stefan A Czerwinski; W Cameron Chumlea; Roger M Siervogel; Bradford Towne Journal: Obesity (Silver Spring) Date: 2007-12 Impact factor: 5.002
Authors: Sanjiv Kaul; Megan P Rothney; Dawn M Peters; Wynn K Wacker; Cynthia E Davis; Michael D Shapiro; David L Ergun Journal: Obesity (Silver Spring) Date: 2012-01-26 Impact factor: 5.002
Authors: Luís B Sardinha; Gil B Rosa; Megan Hetherington-Rauth; Inês R Correia; João P Magalhães; Analiza M Silva; Henry Lukaski Journal: Eur J Clin Nutr Date: 2022-10-17 Impact factor: 4.884
Authors: Camille R Schneider-Worthington; Jessica S Bahorski; David A Fields; Barbara A Gower; José R Fernández; Paula C Chandler-Laney Journal: J Hum Lact Date: 2020-10-09 Impact factor: 2.219
Authors: Paola N Cruz Rivera; Rebekah L Goldstein; Madeline Polak; Antonio A Lazzari; Marilyn L Moy; Emily S Wan Journal: Sci Rep Date: 2022-02-04 Impact factor: 4.996
Authors: Malgorzata Kwissa; Tomasz Krauze; Agnieszka Mitkowska-Redman; Beata Banaszewska; Robert Z Spaczynski; Andrzej Wykretowicz; Przemyslaw Guzik Journal: J Clin Med Date: 2022-10-03 Impact factor: 4.964