Patterns of Muscle-Related Risk Factors for Sarcopenia in Older Mexican Women

María Fernanda Carrillo-Vega, Mario Ulises Pérez-Zepeda, Guillermo Salinas-Escudero, Carmen García-Peña, Edward Daniel Reyes-Ramírez, María Claudia Espinel-Bermúdez, Sergio Sánchez-García and Lorena Parra-Rodríguez ()
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María Fernanda Carrillo-Vega: Instituto Nacional de Geriatría, Dirección de Investigación, Av. Contreras 428, Ciudad de México 10200, Mexico
Mario Ulises Pérez-Zepeda: Instituto Nacional de Geriatría, Dirección de Investigación, Av. Contreras 428, Ciudad de México 10200, Mexico
Guillermo Salinas-Escudero: Hospital Infantil de Mexico Federico Gómez, Centro de Estudios Económicos y Sociales en Salud, Calle Doctor Márquez 162, Ciudad de Mexico 06720, Mexico
Carmen García-Peña: Instituto Nacional de Geriatría, Dirección de Investigación, Av. Contreras 428, Ciudad de México 10200, Mexico
Edward Daniel Reyes-Ramírez: Instituto Nacional de Geriatría, Dirección de Investigación, Av. Contreras 428, Ciudad de México 10200, Mexico
María Claudia Espinel-Bermúdez: Instituto Mexicano del Seguro Social, Centro Mexico Nacional de Occidente, Unidad Médica de Alta Especialidad Hospital de Especialidades, Unidad de Investigación Biomédica 02 y División de Investigación en Salud, Av. Belisario Domínguez 1000, Guadalajara 44340, Mexico
Sergio Sánchez-García: Instituto Mexicano del Seguro Social, Centro Médico Nacional Siglo XXI, Unidad de Investigación en Epidemiología y Servicios de Salud, Área de Envejecimiento, Av. Cuauhtémoc 330, Ciudad de México 06720, Mexico
Lorena Parra-Rodríguez: Instituto Nacional de Geriatría, Dirección de Investigación, Av. Contreras 428, Ciudad de México 10200, Mexico

IJERPH, 2022, vol. 19, issue 16, 1-11

Abstract: Early detriment in the muscle mass quantity, quality, and functionality, determined by calf circumference (CC), phase angle (PA), gait time (GT), and grip strength (GSt), may be considered a risk factor for sarcopenia. Patterns derived from these parameters could timely identify an early stage of this disease. Thus, the present work aims to identify those patterns of muscle-related parameters and their association with sarcopenia in a cohort of older Mexican women with neural network analysis. Methods: Information from the functional decline patterns at the end of life, related factors, and associated costs study was used. A self-organizing map was used to analyze the information. A SOM is an unsupervised machine learning technique that projects input variables on a low-dimensional hexagonal grid that can be effectively utilized to visualize and explore properties of the data allowing to cluster individuals with similar age, GT, GSt, CC, and PA. An unadjusted logistic regression model assessed the probability of having sarcopenia given a particular cluster. Results: 250 women were evaluated. Mean age was 68.54 ± 5.99, sarcopenia was present in 31 (12.4%). Clusters 1 and 2 had similar GT, GSt, and CC values. Moreover, in cluster 1, women were older with higher PA values ( p < 0.001). From cluster 3 upward, there is a trend of worse scores for every variable. Moreover, 100% of the participants in cluster 6 have sarcopenia ( p < 0.001). Women in clusters 4 and 5 were 19.29 and 90 respectively, times more likely to develop sarcopenia than those from cluster 2 ( p < 0.01). Conclusions: The joint use of age, GSt, GT, CC, and PA is strongly associated with the probability women have of presenting sarcopenia.

Keywords: aged; body composition; sarcopenia; functional decline; physical performance tests (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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