Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass

Objective: The aim of this cross-sectional study was to develop new regression equations for estimating fat mass (FM) and skeletal muscle mass (SMM) in a heterogeneous Caucasian population, using the phase angle (PhA) as a bioelectrical parameter and DXA as the reference method. We also aimed to cro...

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Authors: Rojano Ortega, Daniel, Berral Aguilar, Antonio J., Moya Amaya, Heliodoro, Molina López, Antonio, Berral de la Rosa, Francisco José
Format: article
Publication Date:2025
Country:España
Institution:Universidad Pablo de Olavide (UPO)
Repository:RIO. Repositorio Institucional Olavide
Language:English
OAI Identifier:oai:rio.upo.es:10433/24200
Online Access:https://hdl.handle.net/10433/24200
Access Level:Open access
Keyword:Body composition
Fat mass
Skeletal muscle mass
DXA
BIA
Health
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spelling Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle massRojano Ortega, DanielBerral Aguilar, Antonio J.Moya Amaya, HeliodoroMolina López, AntonioBerral de la Rosa, Francisco JoséBody compositionFat massSkeletal muscle massDXABIAHealthObjective: The aim of this cross-sectional study was to develop new regression equations for estimating fat mass (FM) and skeletal muscle mass (SMM) in a heterogeneous Caucasian population, using the phase angle (PhA) as a bioelectrical parameter and DXA as the reference method. We also aimed to cross-validate the new equations, and to compare them with the manufacturers’ equations. Methods: The 212 healthy Caucasian participants aged 20–65 years were randomly distributed into two groups: development group (n = 141) and validation group (n = 71). Bioelectrical parameters were obtained with a 50 kHz foot-to-hand phase-sensitive body composition analyzer. The new FM percentage (FM%) and SMM percentage (SMM%) equations were developed by performing multiple forward regression analyses. Agreement between DXA and the different equations was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland–Altman plots. Results: The proposed equations explained 89.2% of the variance in the DXA-derived FM% and 91.8% in the DXA-derived SMM%, with low random errors (SEE = 3.04% and 1.92%, respectively), and a very strong agreement (CCC = 0.93 and 0.94, respectively). In addition, they demonstrated no fixed bias and a relatively low individual variability. However, the manufacturer’s equations described a lower percentage of the variance, with higher random errors, obtained fixed bias of -5.77% for FM% and 4.91% for SMM%, as well as higher individual variability. Conclusions: The new regression equations, which include the PhA as a bioelectrical parameter, can accurately predict DXA-derived FM% and SMM% in a heterogeneous Caucasian population, and are better options than the manufacturer’s equations.Elsevier20252025-06-1120252025-01-0120252025-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10433/24200reponame:RIO. Repositorio Institucional Olavideinstname:Universidad Pablo de Olavide (UPO)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:rio.upo.es:10433/242002026-06-13T12:46:27Z
dc.title.none.fl_str_mv Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
title Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
spellingShingle Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
Rojano Ortega, Daniel
Body composition
Fat mass
Skeletal muscle mass
DXA
BIA
Health
title_short Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
title_full Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
title_fullStr Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
title_full_unstemmed Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
title_sort Association between phase angle and body composition: New equations to predict fat mass and skeletal muscle mass
dc.creator.none.fl_str_mv Rojano Ortega, Daniel
Berral Aguilar, Antonio J.
Moya Amaya, Heliodoro
Molina López, Antonio
Berral de la Rosa, Francisco José
author Rojano Ortega, Daniel
author_facet Rojano Ortega, Daniel
Berral Aguilar, Antonio J.
Moya Amaya, Heliodoro
Molina López, Antonio
Berral de la Rosa, Francisco José
author_role author
author2 Berral Aguilar, Antonio J.
Moya Amaya, Heliodoro
Molina López, Antonio
Berral de la Rosa, Francisco José
author2_role author
author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Body composition
Fat mass
Skeletal muscle mass
DXA
BIA
Health
topic Body composition
Fat mass
Skeletal muscle mass
DXA
BIA
Health
description Objective: The aim of this cross-sectional study was to develop new regression equations for estimating fat mass (FM) and skeletal muscle mass (SMM) in a heterogeneous Caucasian population, using the phase angle (PhA) as a bioelectrical parameter and DXA as the reference method. We also aimed to cross-validate the new equations, and to compare them with the manufacturers’ equations. Methods: The 212 healthy Caucasian participants aged 20–65 years were randomly distributed into two groups: development group (n = 141) and validation group (n = 71). Bioelectrical parameters were obtained with a 50 kHz foot-to-hand phase-sensitive body composition analyzer. The new FM percentage (FM%) and SMM percentage (SMM%) equations were developed by performing multiple forward regression analyses. Agreement between DXA and the different equations was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland–Altman plots. Results: The proposed equations explained 89.2% of the variance in the DXA-derived FM% and 91.8% in the DXA-derived SMM%, with low random errors (SEE = 3.04% and 1.92%, respectively), and a very strong agreement (CCC = 0.93 and 0.94, respectively). In addition, they demonstrated no fixed bias and a relatively low individual variability. However, the manufacturer’s equations described a lower percentage of the variance, with higher random errors, obtained fixed bias of -5.77% for FM% and 4.91% for SMM%, as well as higher individual variability. Conclusions: The new regression equations, which include the PhA as a bioelectrical parameter, can accurately predict DXA-derived FM% and SMM% in a heterogeneous Caucasian population, and are better options than the manufacturer’s equations.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-06-11
2025
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10433/24200
url https://hdl.handle.net/10433/24200
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RIO. Repositorio Institucional Olavide
instname:Universidad Pablo de Olavide (UPO)
instname_str Universidad Pablo de Olavide (UPO)
reponame_str RIO. Repositorio Institucional Olavide
collection RIO. Repositorio Institucional Olavide
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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