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Vol. 3 No. 2 (2023): Volume 3, Issue 2, Year 2023

Articles

Diseño Y Validación De Un Modelo Antropométrico Para Evaluar La Masa Grasa Corporal En Mujeres Mexicanas

  • David Yair Martínez Romero,
  • Marco Antonio Hernández Lepe,
  • Arnulfo Ramos-Jiménez,
  • Juan Benito Martínez Romero
David Yair Martínez Romero
Facultad de Medicina y Psicología, Universidad Autónoma de Baja California Circuito Universitario Insurgentes, 22424 Tijuana, B.C., México
Marco Antonio Hernández Lepe
Facultad de Medicina y Psicología, Universidad Autónoma de Baja California Circuito Universitario Insurgentes, 22424 Tijuana, B.C., México
Arnulfo Ramos-Jiménez
Instituto de Ciencias Biomédicas, Universidad Autónoma de Ciudad Juárez, Chihuahua, México. Av. Benjamín Franklin no. 4650, Zona Pronaf Condominio La Plata, 32310 Cd Juárez, Chihuahua, México.
Juan Benito Martínez Romero
Escuela Superior de Ingeniería Química e Industrias Extractivas, Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional, Lindavista, Gustavo A. Madero, 07700 Ciudad de México, CDMX.
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DOI: https://doi.org/10.34256/ijk23213

Published 30-12-2023

Keywords

  • Anthropometry,
  • Body composition,
  • Fat mass,
  • Air displacement plethysmography,
  • Overweight

How to Cite

Martínez Romero, D. Y., Hernández Lepe, M. A., Ramos-Jiménez, A., & Martínez Romero, J. B. (2023). Diseño Y Validación De Un Modelo Antropométrico Para Evaluar La Masa Grasa Corporal En Mujeres Mexicanas. International Journal of Kinanthropometry, 3(2), 112–126. https://doi.org/10.34256/ijk23213

Copyright (c) 2023 David Yair Martínez Romero, Marco Antonio Hernández Lepe, Arnulfo Ramos-Jiménez, Juan Benito Martínez Romero

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

Objective: Develop a multiple linear model, using the least squares method to correlate fat mass (kg), using anthropometric variables obtained from a sample of women from northwest Mexico. Materials: ISAK standardization was used in this study to collect measurements. The statistical criteria R², EER, VIF, Cp, and RMSE were used to evaluate the performance of the model. Method: Descriptive observational cross-sectional study to determine the fat mass of a sample of 95 women from the northwest of Mexico with normal weight and overweight. Results: The adjusted model (M8p) is made up of eight predictors that are statistically most representative in this study: weight, 6 skinfolds, and biliocrestal diameter. The fat mass of the sample was determined using air displacement plethysmography (reference), the mean obtained for the fat mass was 21.3 kg with a standard deviation of ±9.3, the M8p model predicts 20.9±9.9 kg which is 2% below the reference method used. The statistical criteria of the adjusted model are, R²Adj=0.92, SER= 2.9 kg, VIF 4.8, Cp= 7.8, and RMSE= 3.08 obtained with the adjustment sample (70 women), the validation sample (25 women) obtained a value RMSE of 3.15, so the model has predictive capacity. Conclusions: The developed model adequately predicts the fat mass of women with and without excess body fat mass, which makes it valid for use in similar samples, giving the health professional one more option to adequately evaluate this tissue, which will allow giving a optimal treatment on an individualized basis.

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References

  1. Ghatak, A. (2017). Machine learning with R. Springer, Singapore. https://doi.org/10.1007/978-981-10-6808-9
  2. Castro-Porras, L., Rojas-Russell, M., Villanueva-Sánchez, J., & López-Cervantes, M. (2019). An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity. Public Health Nutrition, 22(7): 1250-1258. https://doi.org/10.1017/S1368980018004044
  3. Torres, D., Izquierdo, G., Cruz, B. (2015). Panorama de la obesidad en México. Revista Médica del Instituto Mexicano del Seguro Social, 53(2): 240-249.
  4. Dempster, P., Aitkens, S. (1995). A new air displacement method for the determination of human body composition. Medicine and science in sports and exercise, 27(12): 1692–1697. https://doi.org/10.1249/00005768-199512000-00017
  5. Denis, D. (2020). Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science. John Wiley & Sons. https://doi.org/10.1002/9781119549963
  6. Durnin, J. V., & Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. British journal of nutrition, 32(1): 77–97. https://doi.org/10.1079/bjn19740060
  7. Ros, F.E., Cristóbal, R.V., Marfell-Jones, M. (2019). Protocolo internacional para la valoración antropométrica. Publicado por Sociedad Internacional para el Avance de la Cineantropometría.
  8. Faraway, J.J. (2004). Linear models with R. Chapman and Hall/CRC, Florida.
  9. Fosbøl, M.Ø., Zerahn, B. (2015). Contemporary methods of body composition measurement. Clinical physiology and functional imaging, 35(2): 81–97. https://doi.org/10.1111/cpf.12152
  10. Huerta Huerta, R., Esparza-Romero, J., Urquidez, R., Pacheco, B.I., Valencia, M.E., Alemán-Mateo, H. (2007). Validez de una ecuación basada en antropometría para estimar la grasa corporal en adultos mayores. Archivos latinoamericanos de nutrición, 57(4): 357-365.
  11. Kasper, A.M., Langan-Evans, C., Hudson, J.F., Brownlee, T.E., Harper, L.D., Naughton, R.J., Morton, J.P., Close, G.L. (2021). Come Back Skinfolds, All Is Forgiven: A Narrative Review of the Efficacy of Common Body Composition Methods in Applied Sports Practice. Nutrients, 13(4): 1075. https://doi.org/10.3390/nu13041075
  12. Kerr, D.A. (1988). An anthropometric method for fractionation of skin, adipose, bone, muscle and residual tissue masses in males and females age 6 to 77 years. M.Sc Thesis, Simon Fraser University.
  13. Lee, D.H., Keum, N., Hu, F.B., Orav, E.J., Rimm, E.B., Sun, Q., Willett, W.C., Giovannucci, E.L. (2017). Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. British journal of nutrition, 118(10) 858–866. https://doi.org/10.1017/S0007114517002665
  14. Lee, D.H., Keum, N., Hu, F.B., Orav, E.J., Rimm, E.B., Willett, W.C., Giovannucci, E.L. (2018). Predicted lean body mass, fat mass, and all cause and 90 cause specific mortality in men: prospective US cohort study. BMJ, 362, k2575. https://doi.org/10.1136/bmj.k2575
  15. Martin, A. (1984). An anatomical basis for assessing human body composition: evidence from 25 dissections, M.Sc thesis, Simon Fraser University.
  16. Macias, N., Alemán-Mateo, H., Esparza-Romero, J., Valencia, M.E. (2007). Body fat measurement by bioelectrical impedance and air displacement plethysmography: a cross-validation study to design bioelectrical impedance equations in Mexican adults. Nutrition journal, 6, 18. https://doi.org/10.1186/1475-2891-6-18
  17. Ramos-Jiménez, A., Hernandez-Torres, R.P. Murguía-Romero, M. (2018). Anthropometric equations for calculating body fat in young adults. Archivos Latinoamericanos de Nutricion, 68. 111-121. https://doi.org/10.37527/2018.68.2.002
  18. Romero-Martínez, M., Shamah-Levy, T., Vielma-Orozco, E., Heredia-Hernández, O., Mojica-Cuevas, J., Cuevas-Nasu, L., Rivera-Dommarco, J. (2019). Encuesta Nacional de Salud y Nutrición (Ensanut 2018): metodología y perspectivas. Salud Pública De México, 61(6), 917-923.
  19. Sandhu, J.S., Gupta, G., Shenoy, S. (2010). Prediction equation for calculating fat mass in young Indian adults. Asian journal of sports medicine, 1(2), 101–107. https://doi.org/10.5812/asjsm.34862
  20. Verzani, J. (2004). Using R for Introductory Statistics. Chapman and Hall/CRC. https://doi.org/10.4324/9780203499894
  21. Werdein, E.J., Kyle, L.H. (1960). Estimation of the constancy of density of the fat-free body. Journal of Clinical Investigation, 39(4), 626–629. https://doi.org/10.1172/JCI104077