Rogelio González-Arellanes1, Rene Urquidez-Romero2, Alejandra Rodríguez-Tadeo2, Julián Esparza-Romero1, Rosa Olivia Méndez-Estrada1, Erik Ramírez-López3, Alma-Elizabeth Robles-Sardin1, Bertha-Isabel Pacheco-Moreno1, Heliodoro Alemán-Mateo4. 1. Centro de Investigación en Alimentación y Desarrollo, A.C. Coordinación de Nutrición. Carretera Gustavo Enrique Astiazarán Rosas #46, Col. La Victoria. C.P. 83304. Hermosillo, Sonora, Mexico. 2. Instituto de Ciencias Biomédicas. Departamento de Ciencias de la Salud, Universidad Autónoma de Ciudad Juárez, Ave. Plutarco Elías Calles #1210, Col. Fovissste Chamizal. C.P. 32310. Ciudad Juárez, Chihuahua, Mexico. 3. Facultad de Salud Pública y Nutrición, Universidad Autónoma de Nuevo León, Ave. Dr. Eduardo Aguirre Pequeño #905, Col. Mitras Centro. C.P. 64460, Monterrey, Nuevo León, Mexico. 4. Centro de Investigación en Alimentación y Desarrollo, A.C. Coordinación de Nutrición. Carretera Gustavo Enrique Astiazarán Rosas #46, Col. La Victoria. C.P. 83304. Hermosillo, Sonora, Mexico. helio@ciad.mx.
Abstract
BACKGROUND: Predictive equations are the best option for assessing fat mass in clinical practice due to their low cost and practicality. However, several factors, such as age, excess adiposity, and ethnicity can compromise the accuracy of the equations reported to date in the literature. OBJECTIVE: To develop and validate two predictive equations for estimating fat mass: one based exclusively on anthropometric variables, the other combining anthropometric and bioelectrical impedance variables using the 4C model as the reference method. SUBJECTS/ METHODS: This is a cross-sectional study that included 386 Hispanic subjects aged ≥60 with excess adiposity. Fat mass and fat-free mass were measured by the 4C model as predictive variables. Age, sex, and certain anthropometric and bioelectrical impedance data were considered as potential predictor variables. To develop and to validate the equations, the multiple linear regression analysis, and cross-validation protocol were applied. RESULTS: Equation 1 included weight, sex, and BMI as predictor variables, while equation 2 considered sex, weight, height squared/resistance, and resistance as predictor variables. R2 and RMSE values were ≥0.79 and ≤3.45, respectively, in both equations. The differences in estimates of fat mass by equations 1 and 2 were 0.34 kg and -0.25 kg, respectively, compared to the 4C model. This bias was not significant (p < 0.05). CONCLUSIONS: The new predictive equations are reliable for estimating body composition and are interchangeable with the 4C model. Thus, they can be used in epidemiological and clinical studies, as well as in clinical practice, to estimate body composition in older Hispanic adults with excess adiposity.
BACKGROUND: Predictive equations are the best option for assessing fat mass in clinical practice due to their low cost and practicality. However, several factors, such as age, excess adiposity, and ethnicity can compromise the accuracy of the equations reported to date in the literature. OBJECTIVE: To develop and validate two predictive equations for estimating fat mass: one based exclusively on anthropometric variables, the other combining anthropometric and bioelectrical impedance variables using the 4C model as the reference method. SUBJECTS/ METHODS: This is a cross-sectional study that included 386 Hispanic subjects aged ≥60 with excess adiposity. Fat mass and fat-free mass were measured by the 4C model as predictive variables. Age, sex, and certain anthropometric and bioelectrical impedance data were considered as potential predictor variables. To develop and to validate the equations, the multiple linear regression analysis, and cross-validation protocol were applied. RESULTS: Equation 1 included weight, sex, and BMI as predictor variables, while equation 2 considered sex, weight, height squared/resistance, and resistance as predictor variables. R2 and RMSE values were ≥0.79 and ≤3.45, respectively, in both equations. The differences in estimates of fat mass by equations 1 and 2 were 0.34 kg and -0.25 kg, respectively, compared to the 4C model. This bias was not significant (p < 0.05). CONCLUSIONS: The new predictive equations are reliable for estimating body composition and are interchangeable with the 4C model. Thus, they can be used in epidemiological and clinical studies, as well as in clinical practice, to estimate body composition in older Hispanic adults with excess adiposity.
Authors: Judith M M Meijers; Marian A E van Bokhorst-de van der Schueren; Jos M G A Schols; Peter B Soeters; Ruud J G Halfens Journal: Nutrition Date: 2009-12-01 Impact factor: 4.008
Authors: Amy Berrington de Gonzalez; Patricia Hartge; James R Cerhan; Alan J Flint; Lindsay Hannan; Robert J MacInnis; Steven C Moore; Geoffrey S Tobias; Hoda Anton-Culver; Laura Beane Freeman; W Lawrence Beeson; Sandra L Clipp; Dallas R English; Aaron R Folsom; D Michal Freedman; Graham Giles; Niclas Hakansson; Katherine D Henderson; Judith Hoffman-Bolton; Jane A Hoppin; Karen L Koenig; I-Min Lee; Martha S Linet; Yikyung Park; Gaia Pocobelli; Arthur Schatzkin; Howard D Sesso; Elisabete Weiderpass; Bradley J Willcox; Alicja Wolk; Anne Zeleniuch-Jacquotte; Walter C Willett; Michael J Thun Journal: N Engl J Med Date: 2010-12-02 Impact factor: 91.245