Prediction equations for fat and fat-free body mass in adolescents, based on body circumferences

• Autor(a) principal: Lyra, Clélia de Oliveira
• Data de Publicação: 2012
• Outros Autores: Lima, Severina Carla Vieira Cunha, Lima, Kenio Costa de, Arrais, Ricardo Fernando, Pedrosa, Lucia Fátima Campos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/22952
• topic: Body composition; Fat body mass; Fat-free body mass; Prediction equations

Fat mass (FM) and fat-free body mass (FFB) are important parameters for assessing nutritional status, since they are associated with higher prevalence of excess body fat and malnutrition worldwide. To develop prediction equations for fat and fat-free body mass in adolescents using body circumferences. This cross-sectional study included 218 adolescents (10-16 years) with normal weight as defined by body mass index. FM(Pred) and FFB(Pred) were estimated using stepwise multiple linear regression, considering age and body circumferences. Response variables, FM(BIA) and FFB(BIA) were estimated using bioelectric impedance analysis (BIA). The accuracy of the prediction equations was evaluated using the coefficient of determination (R(2)) and Akaike's Information Criterion (AIC). The best prediction equations for males were FM(Pred) = -7.114 - 0.592(age) - 0.958(wrist)+0.191(hip)+0.295(abdomen); R(2) = 0.552; AIC = 416.04 and FFB(Pred) = - 52.180+1.913(age)+1.954(wrist)+1.635(forearm); R(2) = 0.869; AIC = 578.24. For females, the best equations were FM(Pred) = -17.580 - 0.678(wrist)+0.221(abdomen)+0.241(hip)+0.202(proximal thigh) - 0.228(calf); R(2) = 0.838; AIC = 415.36 and FFB(Pred) = -31.066+0.90(age)+1.090(wrist) - 0.139(abdomen)+0.326(hip)+0.632(calf); R(2) = 0.878; AIC = 512.48. The equations developed to estimate fat body mass in females and fat-free body mass in both genders had high adjusted coefficients of determination and are therefore preferable to those derived using BIA.