Optimization Of Community Screening Strategy For Sarcopenia For Older Women

Objectives:To develop and validate an anthropometric equation based on the least absolute shrinkage and selection operator(LASSO)regression to predict appendicular skeletal muscle(ASM)in the 60-70-year-old women,providing a simple and effective evaluation method for ASM in the community;to compare associations of the various relative skeletal muscle mass indexes(RSMI)obtained by ASM adjusted by different body size variables respectively with upper and lower extremities’ functions,so as to optimize the adjustment method of ASM,effectively reflect the association between muscle mass and adverse outcomes,and then promote the screening of sarcopenia in the community.Methods:A total of 1296 women aged 60-70 years were recruited in the community from March 2021 to June 2021(development group 648,validation group 648).Bioelectrical impedance analysis(BIA)was used to evaluate ASM.Eleven anthropometric variables including weight,height,sitting height,body mass index(BMI),calf circumference(CC),waist-to-hip ratio(WHR),upper limb length,lower limb length,total length of limbs,difference between the lengths of the lower and upper limbs,and the ratio of the lengths of lower and upper limbs were collected and used as candidate predictors of ASM.Upper and lower extremities’functions were assessed by grip strength and 5-time chair stand test(5-CST).General information questionnaire,Athens insomnia scale,Mini-mental State Examination,short-form mini-nutritional assessment,2-item Generalized Anxiety Disorder Scale,2-item Patient Health Questionnaire and the tri-axial accelerometer were used for the survey.In the development group,LASSO regression was used to select ASM predictors,and multiple linear regression was used to develop the ASM prediction model.In the validation group,paired-t test and Bland-Altman analysis were used to validate the agreement.Additionally,height2,sitting height2,weight,BMI and weight/sitting height2 were used to adjust ASM respectively to obtain RSMI.Univariate analysis,multiple linear regression and generalized linear model were used to explore the associations between various RSMI and upper and lower extremities’ functions.Results:1.In the development group,there were four variables selected by LASSO regression,including weight,WHR,CC,and sitting height,and the ASM prediction equation developed based on the multiple linear regression is ASM=0.2308 × weight(kg)-27.5652 × WHR+8.0179 × CC(m)+2.3772 ×sitting height(m)+ 22.2405.In the validation group,there was no significant difference between BIA-measured ASM and predicted ASM,and there is a high agreement between these two methods with the mean difference is-0.041 kg,and the 95%limits of agreement is-1.441 to 1.359 kg.2.After adjusting for age,education,history of fall,anxiety,depression,number of chronic disease,nutrition status,sleep score,status of fatigue,daily time of physical activity(PA),and daily accelerometer wear time,there were positive associations between upper extremity’s function and RSMI obtained by ASM adjusted by height2,sitting height2,BMI,and weight/sitting height2,respectively(all P<0.001).After adjusting for age,depression,history of falls,number of chronic disease,nutrition status,sleep score,status of fatigue,daily time of PA,and daily accelerometer wear time,ASM adjusted by height2 or sitting height2 was negatively associated with lower extremity’s function(P=0.029,P<0.001),and RSMI adjusted by weight or weight/sitting height2 was positively associated with lower extremity’s function(P<0.001,P=0.037).It was indicated that ASM adjusted by height2,sitting height2,BMI or weight was positively solely associated with upper or lower extremity’s function,but weight/sitting height2-adjusted ASM was positively associated with upper and lower extremities’ functions simultaneously.Conclusions:1.The ASM prediction model based on the weight,WHR,CC and sitting height for community-dwelling women aged 60-70 years has a high agreement and accuracy,which provide a practical method of ASM assessment for community to promote the widespread screening of sarcopenia.2.Compared to height2,sitting height2,weight and BMI,weight/sitting height2 was more suitable to adjust ASM to reflect effectively the associations of RSMI with upper and lower extremities’ functions,which further optimizes the muscle mass assessment method and provides new ideas and evidence for muscle mass related research in the older adults.