Identifier to cite or link to this item: http://hdl.handle.net/20.500.13003/10904
A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults
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ISSN: 1932-6203
WOS ID: 000352134700162
Scopus EID: 2-s2.0-84926292396
PMID: 25821960
Embase PUI: L603554891
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2015-03-30Document type
research articleCitation
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.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.
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https://dx.doi.org/10.1371/journal.pone.0122291MeSH
AnthropometryAged
European Continental Ancestry Group
Young Adult
Adult
Adipose Tissue
Humans
Middle Aged
Cross-Sectional Studies
Adiposity
Body Mass Index
Models, Statistical
Regression Analysis
DeCS
Índice de Masa CorporalModelos Estadísticos
Grupo de Ascendencia Continental Europea
Tejido Adiposo
Estudios Transversales
Humanos
Persona de Mediana Edad
Adulto Joven
Anciano
Antropometría
Adulto
Análisis de Regresión
Adiposidad
This item appears in following Docusalut collections
Hospital Universitario Son Espases - HUSE > Comunicación científicaHospital de Manacor - HMAN > Comunicación científica
Instituto de Investigación Sanitaria Islas Baleares - IDISBA > Comunicación científica