Regression model for the prediction of risk of sarcopenia among older adults
Muscles, Ligaments and Tendons Journal
Background. Sarcopenia is a generalized loss of skeletal muscle mass and strength. Early identification is essential to minimize the adverse effects and consequences, which can help in the prevention and timely management of sarcopenia. Hence we developed a regression model for the prediction of sarcopenia. Methods. This study adopted a case-control design. The dependent variable was skeletal mass index and independent variables assessed were age, body mass index (BMI), physical activity status, depression status, alcohol consumption, cigarette smoking, type 2 diabetes mellitus, grip strength, quadriceps strength, gait speed, physical performance measures, and SARC F questionnaire. Binary logistic regression was used to determine the odds ratio and to develop the model. Results. One hundred and four older adults were included and analyzed in this study. Among the variables considered, age, BMI, physical activity, grip strength, quadriceps strength, balance, and SARC F showed a significant odds ratio (r = 0.724; p ≤ 0.05); thus they were considered for developing the regression model. Conclusion. A regression model for the risk of sarcopenia was developed, which can help in early detection and of individuals with sarcopenia at the community level. 2
Agnes, T.; Vishal, K.; and Girish, N., "Regression model for the prediction of risk of sarcopenia among older adults" (2019). Open Access archive. 655.