A community-based approach to lean body mass and appendicular skeletal muscle mass prediction using body circumferences in community-dwelling elderly in Taiwan. 2020

Kuei-Yu Chien, and Chiao-Nan Chen, and Shu-Chen Chen, and Hsiu-Hua Wang, and Wen-Sheng Zhou, and Lee-Hwa Chen
Graduate Institute of Sports Science, National Taiwan Sport University, Kueishan, Taoyuan, Taiwan. Email: chienkueiyu@gmail.com.

OBJECTIVE To develop and validate the prediction equations for lean body mass (LBM) and appendicular skeletal muscle mass (ASM) using body circumference measurements of community-dwelling adults older than 50 years old. METHODS Four hundred and ninety-eight community-dwelling adults older than 50 years old were recruited for this study. Participants were randomly assigned to a development group (DG, n=332) and validation group (VG, n=166). Lean body mass and ASM were assessed using dualenergy x-ray absorptiometry along with the anthropometric parameters. The Pearson correlation coefficient was used to examine the associations between ASM, LBM and anthropometric parameters in the DG. Prediction equations for LBM and ASM were established from DG data using multiple regression analyses. Paired t-test and Bland-Altman test were used to validate the equations in the VG. RESULTS Forearm circumference had the highest correlation with LBM and ASM. The developed prediction models were: LBM (kg) = 27.479 + 0.726 * weight (kg) - 3.383 * gender (male = 1, female = 2) - 0.672 * BMI + 0.514 * forearm circumference (cm) - 0.245 * hip circumference (cm)(r2=0.90); ASM (kg) = -4.287 + 0.202 * weight (kg) - 0.166 * hip circumference (cm) - 1.484 * gender (male = 1, female = 2) + 0.173 * calf circumference (cm) + 0.096 * height + 0.243 * forearm circumference (cm)(r2=0.85). CONCLUSIONS Prediction equations using only a measuring tape provide accurate, inexpensive, practical methods to assess LBM and ASM in Asians older than 50 years old.

UI MeSH Term Description Entries
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D011897 Random Allocation A process involving chance used in therapeutic trials or other research endeavor for allocating experimental subjects, human or animal, between treatment and control groups, or among treatment groups. It may also apply to experiments on inanimate objects. Randomization,Allocation, Random
D012016 Reference Values The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality. Normal Range,Normal Values,Reference Ranges,Normal Ranges,Normal Value,Range, Normal,Range, Reference,Ranges, Normal,Ranges, Reference,Reference Range,Reference Value,Value, Normal,Value, Reference,Values, Normal,Values, Reference
D001823 Body Composition The relative amounts of various components in the body, such as percentage of body fat. Body Compositions,Composition, Body,Compositions, Body
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly
D000886 Anthropometry The technique that deals with the measurement of the size, weight, and proportions of the human or other primate body.

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