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A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

Identificadores del recurso

Fuster-Parra P, Bennasar-Veny M, Tauler Riera P, Yañez AM, Lopez Gonzalez AA, Aguilo A. A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults. PLoS One. 2015 Mar 30;10(3):e0122291.

1932-6203

http://hdl.handle.net/20.500.13003/10904

10.1371/journal.pone.0122291

25821960

L603554891

2-s2.0-84926292396

000352134700162

Procedencia

(Docusalut. Repositorio institucional del sistema sanitario público de las Islas Baleares)

Ficha

Título:: A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults
Tema:: Anthropometry; Aged; European Continental Ancestry Group; Young Adult; Adult; Adipose Tissue; Humans; Middle Aged; Cross-Sectional Studies; Adiposity; Body Mass Index; Models, Statistical; Regression Analysis; Índice de Masa Corporal; Modelos Estadísticos; Grupo de Ascendencia Continental Europea; Tejido Adiposo; Estudios Transversales; Humanos; Persona de Mediana Edad; Adulto Joven; Anciano; Antropometría; Adulto; Análisis de Regresión; Adiposidad
Descripción:: Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (rho = 0:87 vs rho= 0:86 for the whole sample and rho= 0:88 vs rho= 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.; This work was supported by the Spanish Ministry of Science and Innovation (PI 13/01477). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Idioma:: English
Relación:: https://dx.doi.org/10.1371/journal.pone.0122291
Autor/Productor:: Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yáñez, Aina M.; López-González, Angel Arturo; Aguilo, Antoni
Editor:: Public Library Science
Derechos:: Attribution 4.0 International; http://creativecommons.org/licenses/by/4.0/; open access
Fecha:: 2021-08-25T06:50:22Z; 2015-03-30
Tipo de recurso:: research article

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16. <dc:subject>Cross-Sectional Studies</dc:subject>
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8. <dcterms:abstract>Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (rho = 0:87 vs rho= 0:86 for the whole sample and rho= 0:88 vs rho= 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.</dcterms:abstract>
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  8. <dc:description>Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (rho = 0:87 vs rho= 0:86 for the whole sample and rho= 0:88 vs rho= 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.</dc:description>
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