Analysis In Cognitive Disorder Predicting Sarcopenia And Prevention Strategy

PURPOSE: To understand the risk factors that lead to sarcopenia and explore the proportion of cognitive functions in predicting elderly sarcopenia. METHODS: Design a case-control study.A total of 90 individuals aged 60 years and above were recruited from the community of Hebei Yanda Nursing Home in June 2023. Sarcopenia was diagnosed according to the Asian Sarcopenia Working Group (AWGS) 2019 diagnostic criteria, the sarcopenia group includes those with possible sarcopenia, sarcopenia and severe sarcopenia.Skeletal muscle mass was measured by the bioelectrical impedance analysis (BIA), other tests for muscle laxation include grip, 6-meter step speed, and calf surrounding. Use the Berg Balance Scale(Berg) for balance function test and Montreal Cognitive Assessment(MOCA) for cognitive assessment. RESULTS: Among the 90 residents enrolled in the study, 44 in sarcopenia group, including 18 with possible sarcopenia,21 with sarcopenia and 5 with severe sarcopenia. Single factor analysis shows that among 10 variables including age, gender, height, weight, BMI, grip, MOCA score, Berg score, waist and hip ratio and self -fatigue, only age, height, grip, MOCA score, Berg score have significant differences in 2 group (P < 0.05). According to the binary logistic regression, grip (OR = 0.777, 95%CI: 6.68-8.81, P < 0.05) and MOCA scores(OR = 0.717, 95%CI: 5.64-8.78, P < 0.05) are independent protection factor in predicting sarcopenia, the regression equation: logit (P) = 8.591-0.252 × grip-0.332× MOCA score. The results of the ROC curve analysis show that the background value is 0.871, sensitivity is 90.91%, and specificity is 71.74%. CONCLUSIONS: Cognitive disorder may be a factor in predicting sarcopenia. Paying attention to cognitive function in elderly is making great contributions for delaying the decline in physical ability and the development of cognitive impairment.