Leonardo Pozza Santos1, Maria Cristina Gonzalez2,3, Silvana Paiva Orlandi4, Renata Moraes Bielemann4,5, Thiago G Barbosa-Silva5, Steven B Heymsfield3. 1. Nutrition College, Federal University of Pampa, Bagé, Brazil. 2. Postgraduate Program in Health and Behavior, Catholic University of Pelotas, Pelotas, Brazil. 3. Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA. 4. Nutrition College, Federal University of Pelotas, Pelotas, Brazil. 5. Postgraduate Program in Health and Behavior and in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.
Abstract
BACKGROUND: Low appendicular skeletal muscle mass (ASM) is associated with negative outcomes, but its assessment requires proper limb muscle evaluation. We aimed to verify how anthropometric circumferences are correlated to ASM and to develop new prediction equations based on calf circumference and other anthropometric measures, using dual-energy X-ray absorptiometry (DEXA) as the reference method. METHODS: DEXA and anthropometric information from 15,293 adults surveyed in the 1999-2006 NHANES were evaluated. ASM was defined by the sum of the lean soft tissue from the limbs. Anthropometric data included BMI and calf, arm, thigh, and waist circumferences. Correlations were assessed by Pearson's correlation, and multivariable linear regression produced 4 different ASM prediction equations. The concordance and the overall 95% limits of agreement between measured and estimated ASM were assessed using Lin's coefficient and Bland-Altman's approach. RESULTS: Calf and thigh circumferences were highly correlated with ASM, independent of age and ethnicity. Among the models, the best performance came from the equation constituted solely by calf circumference, sex, race, and age as independent variables, which was able to explain almost 90% of the DEXA-measured ASM variation. The inclusion of different anthropometric parameters in the model increased collinearity without improving estimates. Concordance between the four developed equations and DEXA-measured ASM was high (Lin's concordance coefficient >0.90). CONCLUSION: Despite the good performance of the four developed equations in predicting ASM, the best results came from the equation constituted only by calf circumference, sex, race, and age. This equation allows satisfactory ASM estimation from a single anthropometric measurement.
BACKGROUND:Low appendicular skeletal muscle mass (ASM) is associated with negative outcomes, but its assessment requires proper limb muscle evaluation. We aimed to verify how anthropometric circumferences are correlated to ASM and to develop new prediction equations based on calf circumference and other anthropometric measures, using dual-energy X-ray absorptiometry (DEXA) as the reference method. METHODS: DEXA and anthropometric information from 15,293 adults surveyed in the 1999-2006 NHANES were evaluated. ASM was defined by the sum of the lean soft tissue from the limbs. Anthropometric data included BMI and calf, arm, thigh, and waist circumferences. Correlations were assessed by Pearson's correlation, and multivariable linear regression produced 4 different ASM prediction equations. The concordance and the overall 95% limits of agreement between measured and estimated ASM were assessed using Lin's coefficient and Bland-Altman's approach. RESULTS:Calf and thigh circumferences were highly correlated with ASM, independent of age and ethnicity. Among the models, the best performance came from the equation constituted solely by calf circumference, sex, race, and age as independent variables, which was able to explain almost 90% of the DEXA-measured ASM variation. The inclusion of different anthropometric parameters in the model increased collinearity without improving estimates. Concordance between the four developed equations and DEXA-measured ASM was high (Lin's concordance coefficient >0.90). CONCLUSION: Despite the good performance of the four developed equations in predicting ASM, the best results came from the equation constituted only by calf circumference, sex, race, and age. This equation allows satisfactory ASM estimation from a single anthropometric measurement.
Authors: Lorenzo M Donini; Luca Busetto; Stephan C Bischoff; Tommy Cederholm; Maria D Ballesteros-Pomar; John A Batsis; Juergen M Bauer; Yves Boirie; Alfonso J Cruz-Jentoft; Dror Dicker; Stefano Frara; Gema Frühbeck; Laurence Genton; Yftach Gepner; Andrea Giustina; Maria Cristina Gonzalez; Ho-Seong Han; Steven B Heymsfield; Takashi Higashiguchi; Alessandro Laviano; Andrea Lenzi; Ibolya Nyulasi; Edda Parrinello; Eleonora Poggiogalle; Carla M Prado; Javier Salvador; Yves Rolland; Ferruccio Santini; Mireille J Serlie; Hanping Shi; Cornel C Sieber; Mario Siervo; Roberto Vettor; Dennis T Villareal; Dorothee Volkert; Jianchun Yu; Mauro Zamboni; Rocco Barazzoni Journal: Obes Facts Date: 2022-02-23 Impact factor: 4.807
Authors: Maria Cristina Gonzalez; Ali Mehrnezhad; Nariman Razaviarab; Thiago G Barbosa-Silva; Steven B Heymsfield Journal: Am J Clin Nutr Date: 2021-06-01 Impact factor: 8.472