Response to New Prediction Equations to Estimate Appendicular Skeletal Muscle Mass Using Calf Circumference on NHANES Data: Methodological Issues

2019 ◽  
Vol 43 (8) ◽  
pp. 958-959 ◽  
Author(s):  
Leonardo Pozza Santos ◽  
Maria Cristina Gonzalez ◽  
Silvana Paiva Orlandi ◽  
Renata Moraes Bielemann ◽  
Thiago G. Barbosa‐Silva ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
Calf Circumference ◽  
Prediction Equations ◽  
Methodological Issues ◽  
Appendicular Skeletal Muscle ◽  
Appendicular Skeletal Muscle Mass

New Prediction Equations to Estimate Appendicular Skeletal Muscle Mass Using Calf Circumference: Results From NHANES 1999–2006

2019 ◽  
Vol 43 (8) ◽  
pp. 998-1007 ◽  
Author(s):  
Leonardo Pozza Santos ◽  
Maria Cristina Gonzalez ◽  
Silvana Paiva Orlandi ◽  
Renata Moraes Bielemann ◽  
Thiago G. Barbosa‐Silva ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
Calf Circumference ◽  
Prediction Equations ◽  
Appendicular Skeletal Muscle ◽  
Appendicular Skeletal Muscle Mass

scholarly journals Prediction Equations of the Multifrequency Standing and Supine Bioimpedance for Appendicular Skeletal Muscle Mass in Korean Older People

2020 ◽  
Vol 17 (16) ◽  
pp. 5847
Author(s):  
Kwon Chan Jeon ◽  
So-Young Kim ◽  
Fang Lin Jiang ◽  
Sochung Chung ◽  
Jatin P. Ambegaonkar ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Predictive Value ◽  
Skeletal Muscle Mass ◽  
Predictive Accuracy ◽  
Future Research ◽  
Entire Group ◽  
Prediction Equations ◽  
Appendicular Skeletal Muscle ◽  
Appendicular Skeletal Muscle Mass

Bioimpedance analysis (BIA) has been demanded for the assessment of appendicular skeletal muscle mass (ASM) in clinical and epidemiological settings. This study aimed to validate BIA equations for predicting ASM in the standing and supine positions; externally to cross-validate the new and published and built-in BIA equations for group and individual predictive accuracy; and to assess the overall agreement between the measured and predicted ASM index as sarcopenia diagnosis. In total, 199 healthy older adults completed the measurements of multifrequency BIA (InBody770 and InBodyS10) and dual-energy X-ray absorptiometry (DXA). Multiple regression analysis was used to validate the new multifrequency bioelectrical impedance analysis (MF-BIA) prediction equations. Each MF-BIA equation in the standing and supine position developed in the entire group included height2/resistance, sex, and reactance as predictors (R2 = 92.7% and 92.8%, SEE = 1.02 kg and 1.01 kg ASM for the standing and supine MF-BIA). The new MF-BIA equations had a specificity positive predictive value and negative predictive value of 85% or more except for a sensitivity of about 60.0%. The new standing and supine MF-BIA prediction equation are useful for epidemiological and field settings as well as a clinical diagnosis of sarcopenia. Future research is needed to improve the sensitivity of diagnosis of sarcopenia using MF-BIA.

APPENDICULAR SKELETAL MUSCLE MASS: DEVELOPMENT AND VALIDATION OF ANTHROPOMETRIC PREDICTION EQUATIONS

2012 ◽  
pp. 1-5
Author(s):  
R. VISVANATHAN ◽  
S. YU ◽  
J. FIELD ◽  
I. CHAPMAN ◽  
R. ADAMS ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
South Australia ◽  
Aged Care ◽  
Prediction Equations ◽  
Appendicular Skeletal Muscle ◽  
And Function ◽  
Development And Validation ◽  
Appendicular Skeletal Muscle Mass

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.

Relationship between Calf Circumference and Skeletal Muscle Index among Community-Dwelling Thai Elderly

2021 ◽  
Vol 104 (11) ◽  
pp. 1814-1820
Keyword(s):  
Skeletal Muscle ◽  
Linear Regression ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
Community Dwelling ◽  
Calf Circumference ◽  
Skeletal Muscle Index ◽  
Appendicular Skeletal Muscle ◽  
The Relationship ◽  
Appendicular Skeletal Muscle Mass

Background: A strong association between calf circumference (CC) and skeletal muscle index (SMI) has been established worldwide in the elderly, however, these data in the Thai population are lacking. Objective: To evaluate the relationship between CC and SMI, as well as to identify the important predictors of SMI among the community-dwelling Thai elderly. Materials and Methods: The present study was an analytic cross-sectional study performed in 110 community-dwelling adults aged 60 years and older who lived in Sriracha, Chonburi, Thailand. Weight, height, and the maximum CC were measured in standing position. Body composition was measured using the bioelectrical impedance analysis (BIA) and the SMI was calculated as the appendicular skeletal muscle mass (ASM) divided by the height squared (kg/m²). Pearson’s correlation was used to indicate the relationship between CC and SMI. Multiple linear regression was developed to predict SMI. Results: The prevalence of low muscle mass in men and women were 23.5% and 33.3%, respectively. CC had a positive correlation with SMI (r=0.75; p<0.001). The cut-off values for predicting low muscle mass using CC were 34.0 cm (sensitivity 85.5%, specificity 71.8%, AUC 0.895) in women, and 33.4 cm (sensitivity 75.0%, specificity 92.3%, AUC 0.925) in men. Multiple linear regression analysis revealed age, gender, weight, and CC as the key predictors for SMI with adjusted r² of the model equal to 0.80. CC and weight had a direct effect on SMI. On the other hand, age was inversely related to SMI. Women had lower SMI than men. Conclusion: CC was positively associated with SMI, and it could be used as a screening tool to identify the community-dwelling Thai elderly with low muscle mass in the field settings. Important predictors of SMI were age, gender, weight, and CC. Keywords: Calf circumference; Skeletal muscle index; Sarcopenia; Low muscle mass; Aging; Appendicular skeletal muscle mass

scholarly journals Development of prediction equations for estimating appendicular skeletal muscle mass in Japanese men and women

2017 ◽  
Vol 36 (1) ◽  
Author(s):  
Taishi Furushima ◽  
Motohiko Miyachi ◽  
Motoyuki Iemitsu ◽  
Haruka Murakami ◽  
Hiroshi Kawano ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
Prediction Equations ◽  
Japanese Men ◽  
Men And Women ◽  
Appendicular Skeletal Muscle ◽  
Appendicular Skeletal Muscle Mass

scholarly journals Physical activity, protein intake, and appendicular skeletal muscle mass in older men

1999 ◽  
Vol 70 (1) ◽  
pp. 91-96 ◽  
Author(s):  
Raymond D Starling ◽  
Philip A Ades ◽  
Eric T Poehlman
Keyword(s):  
Physical Activity ◽  
Skeletal Muscle ◽  
Muscle Mass ◽  
Protein Intake ◽  
Skeletal Muscle Mass ◽  
Older Men ◽  
Appendicular Skeletal Muscle ◽  
Appendicular Skeletal Muscle Mass
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