The role of muscle mass in university students’ metabolic health: an approach using multivariate models

Authors

DOI:

https://doi.org/10.47197/retos.v79.118381

Keywords:

Body composition, college students, metabolic risk, muscle mass, visceral fat

Abstract

Introduction: Metabolic health in university students has gained relevance due to hidden risk factors not detected by traditional metrics.

Objective: To determine the influence of relative muscle mass and visceral fat on metabolic risk in college students.

Methodology: A cross-sectional study was conducted with 325 students aged 18 to 35. Body composition was assessed by bioimpedance, and multivariate logistic regression was performed adjusting for age, sex, body fat percentage, and visceral fat.

Results: 68.31% of participants showed high metabolic risk. Visceral fat was the strongest predictor (OR = 3.18; p < 0.001). Male sex was protective (OR = 0.25; p = 0.031), while relative muscle mass was not statistically significant.

Discussion: These findings align with recent literature emphasizing visceral fat as a superior marker to BMI or subcutaneous fat. The lack of significance for muscle mass may reflect the predominant effect of visceral adiposity.

Conclusions: Visceral fat is a key determinant of metabolic risk in university students. Early detection and interventions focusing on visceral fat reduction and muscle strengthening are recommended.

References

Alfonso Cardozo, E. B., González Vázquez, G. S., Viveros, G., Ortiz Rolón, A., Méndez Romero, J., & Galeano, D. (2024). Presencia de factores de riesgo para el desarrollo del síndrome metabólico en estudiantes universitarios, Paraguay, 2023. Revista de Nutrición Clínica y Metabolismo (RNCM), 7(4 (Diciembre)), 18-27. https://dialnet.unirioja.es/servlet/articulo?codigo=9954652

Badillo-Meléndez, R. A., Rangel-Caballero, L. G., Martínez-Rueda, R., & Espinoza-Gutiérrez, R. (2021). Prevalencia de factores de riesgo metabólico en estudiantes universitarios latinoamericanos: Una revisión sistemática. Revista de Salud Pública, 23(3), 1. http://www.scielo.org.co/scielo.php?pid=S0124-00642021000300012&script=sci_arttext

Bosy‐Westphal, A., & Müller, M. J. (2021). Diagnosis of obesity based on body composition‐associated health risks—Time for a change in paradigm. Obesity Reviews, 22(S2), e13190. https://doi.org/10.1111/obr.13190

Bushita, H., Ozato, N., Mori, K., Kawada, H., Katsuragi, Y., Osaki, N., Mikami, T., Itoh, K., Murashita, K., Nakaji, S., & Tamada, Y. (2025). Effect of visceral fat on onset of metabolic syndrome. Scientific Reports, 15(1), 19012. https://doi.org/10.1038/s41598-025-01389-1

Chicco, D., Starovoitov, V., & Jurman, G. (2021). The benefits of the Matthews correlation coefficient (MCC) over the diagnostic odds ratio (DOR) in binary classification assessment. Ieee Access, 9, 47112-47124. https://ieeexplore.ieee.org/abstract/document/9385097/

Denis, D. J. (2021). Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner’s Guide to Advanced Data Analysis. John Wiley & Sons. https://books.google.com/books?hl=es&lr=&id=hUIoEAAAQBAJ&oi=fnd&pg=PR12&dq=cluster+analysis+multivariate+techniques+%2B+Python&ots=0WbkXtBijN&sig=fickUxiVhVVnsfTBy5P0NBXp3VA

Espinosa Brito, A. D. (2023). Salud, complejidad y enfermedades no transmisibles. Revista Finlay, 13(2), 216-230. http://scielo.sld.cu/scielo.php?pid=S2221-24342023000200216&script=sci_arttext&tlng=en

García Morante, S. (2025). Modelización predictiva en situación de alarma sanitaria. https://docta.ucm.es/entities/publication/511c20b2-d9ff-4d17-8ded-ad69c59a256f

James, N. M., & Stanford, K. I. (2025). Obesity and Exercise: New insights and perspectives. Endocrine Reviews, bnaf017. https://academic.oup.com/edrv/advance-article-abstract/doi/10.1210/endrev/bnaf017/8163868

Katchunga, P. B., Hermans, M., Bamuleke, B. A., Katoto, P. C., & Kabinda, J. M. (2013). Relationship between waist circumference, visceral fat and metabolic syndrome in a Congolese community: Further research is still to be undertaken. Pan African Medical Journal, 14(1). https://www.ajol.info/index.php/pamj/article/view/88117

Lee, C., & Jang, K. (2025). Vitamin D, C-Reactive Protein, and Cardiometabolic Risk Clustering in Middle-Aged Adults: Results from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES). Biomedicines, 13(11), 2762. https://doi.org/10.3390/biomedicines13112762

Lera, L., Leyton, B., & Lizana, P. A. (2025). La Regresión Logística y su Aplicación en la Investigación Biomédica. International Journal of Morphology, 43(5), 1545-1552. https://www.scielo.cl/scielo.php?pid=S0717-95022025000501545&script=sci_arttext&tlng=pt

Manterola, C., Hernández-Leal, M. J., Otzen, T., Espinosa, M. E., & Grande, L. (2023). Estudios de corte transversal. Un diseño de investigación a considerar en ciencias morfológicas. International Journal of Morphology, 41(1), 146-155. https://www.scielo.cl/scielo.php?pid=S0717-95022023000100146&script=sci_arttext&tlng=en

Neeland, I. J., Lim, S., Tchernof, A., Gastaldelli, A., Rangaswami, J., Ndumele, C. E., Powell-Wiley, T. M., & Després, J.-P. (2024). Metabolic syndrome. Nature Reviews Disease Primers, 10(1), 77. https://www.nature.com/articles/s41572-024-00563-5

Nishikawa, H., Asai, A., Fukunishi, S., Nishiguchi, S., & Higuchi, K. (2021). Metabolic syndrome and sarcopenia. Nutrients, 13(10), 3519. https://www.mdpi.com/2072-6643/13/10/3519

Nuñez-Leyva, R. E., Lozano-López, T. E., Calizaya-Milla, Y. E., Calizaya-Milla, S. E., & Saintila, J. (2022). Excess Weight and Body Fat Percentage Associated with Waist Circumference as a Cardiometabolic Risk Factor in University Students. Scientifica, 2022, 1310030. https://doi.org/10.1155/2022/1310030

Parra-Gómez, L. A., Puerta Rojas, J. P., Vásquez, A. J., Escalante Remolina, M. A., Lora Mantilla, A. J., Villa-bona Flórez, S. J., & Camacho López, P. A. (2025). Prevalence of metabolic syndrome in Latin America: A systematic review and meta-analysis of observational studies. Diabetes & Metabolic Syndrome, 19(7), 103282. https://doi.org/10.1016/j.dsx.2025.103282

Posso-Yépez, M., Flores-Bosmediano, E., Buitrón-Jácome, P., Osejos, E., Posso-Astudillo, M., & Zambrano, Z. E. R. (2026). Índice de masa corporal y consumo máximo de oxígeno: Análisis co-rrelacional y diferencias según sexo y edad de estudiantes universitarios. Retos, 76, 754-768. https://doi.org/10.47197/retos.v76.118265

Prado, C. M., Landi, F., Chew, S. T., Atherton, P. J., Molinger, J., Ruck, T., & Gonzalez, M. C. (2022). Ad-vances in muscle health and nutrition: A toolkit for healthcare professionals. Clinical Nutrition, 41(10), 2244-2263. https://www.sciencedirect.com/science/article/pii/S0261561422002825

Sakuma, K., Yamaguchi, A., & Matsuo, H. (2026). Molecular Mechanism of Age-Related Disorder of Skel-etal Muscle (Sarcopenia). En Cellular Aging and Consequences in Humans (pp. 189-202). CRC Press. https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003190875-14&type=chapterpdf

Torregroza-Diazgranados, E. de J. (2022). Nuevo índice de desempeño global de una prueba diagnósti-ca: El índice T. Revista Colombiana de Cirugía, 37(1), 33-42. http://www.scielo.org.co/scielo.php?pid=S2011-75822022000100033&script=sci_arttext

Torun, C., Ankaralı, H., Caştur, L., Uzunlulu, M., Erbakan, A. N., Akbaş, M. M., Gündüz, N., Doğan, M. B., & Oğuz, A. (2023). Is Metabolic Score for Visceral Fat (METS-VF) a Better Index Than Other Adiposity Indices for the Prediction of Visceral Adiposity. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 16, 2605-2615. https://doi.org/10.2147/DMSO.S421623

Tovar, M. G. (2021). Efectos del estrés agudo en la ingesta de alimentos en estudiantes universitarios emocionalmente sanos. Ansiedad y Estrés, 27, 160-171. https://ansiedadyestres.es/sites/default/files/rev/2021/anyes2021a21.pdf

Wang, R., Cao, L., Zhang, P., Chen, H., Zhong, Q., Sang, Z., Wu, C., & He, Z. (2025). Association of the skele-tal muscle mass to visceral fat area ratio with metabolically healthy obesity and metabolically unhealthy non-obesity: A cross-sectional study based on NHANES 2011–2018. Lipids in Health and Disease, 24(1), 334. https://doi.org/10.1186/s12944-025-02744-x

Yaguachi-Alarcón, R. A., Yaguachi-Alarcón, A. L., Burgos-Angulo, D. J., Vera-Unda, R. G., Morales-Prado, E. S., Boza-Mendoza, J. G., & González-Garcia, W. A. (2025). Calidad de la dieta y sueño en estu-diantes universitarios. Retos, 73, 1490-1498. https://doi.org/10.47197/retos.v73.117943

Yahia, N., Brown, C. A., Snyder, E., Cumper, S., Langolf, A., Trayer, C., & Green, C. (2017). Prevalence of Metabolic Syndrome and Its Individual Components Among Midwestern University Students. Journal of Community Health, 42(4), 674-687. https://doi.org/10.1007/s10900-016-0304-5

Downloads

Published

02-06-2026

Issue

Section

Original Research Article

How to Cite

Armijo Valverde, K., Morales Caluña, E., Vargas Olalla, V., Novillo Luzuriaga, N., Suarez Gonzalez, K., Ruiz Polit, P., Saltos Atiencia, D., & Velez Pillco, E. (2026). The role of muscle mass in university students’ metabolic health: an approach using multivariate models. Retos, 79, 202-213. https://doi.org/10.47197/retos.v79.118381