Objectives:Sarcopenia is the loss of muscle mass and function seen with increasing age. Central tomaking the diagnosis of sarcopenia is the assessment of appendicular skeletal muscle mass (ASM). The objectiveof this study was to develop and validate novel anthropometric prediction equations (PEs) for ASM that would beuseful in primary or aged care. Design:PEs were developed using best subset regression analysis. Three bestperforming PEs (PE1, PE2, PE3) were selected and validated using the Bland-Altman and Sheiner & Bealmethods. Setting:Community dwelling adults in South Australia. Participants:188 healthy subjects wereinvolved in the development study. 2275 older(age >50years) subjects were involved in the validation study.Measurements:ASM was assessed using dual x-ray abosrptiometry (DEXA). Weight and height was measuredand body mass index (BMI) estimated. Results: A strong correlation between PE derived ASM and the DEXAderived ASM was seen for the three selected PEs. PE3: ASM= 10.047427 + 0.353307(weight) - 0.621112(BMI) -0.022741(age) + 5.096201(if male) performed the best. PE3 over-estimated (P<0.001) ASM by 0.36 kg (95% CI0.28-0.44 Kg) and the adjusted R2was 0.869. The 95% limit of agreement was between -3.5 and 4.35 kg and thestandard error of the estimate was 1.95. The root mean square error was 1.91(95% CI 1.80-2.01). PE3 alsoperformed the best across the various age (50-65, 65-<80, 80+ years) and weight (BMI <18.5, 18.5-24.9, 25-29.9,>30 kg/m2) groups. Conclusions:A new anthropometric PE for ASM has been developed for use in primary oraged care but is specific to Caucasian population groups.
Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass
