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Bioimpedance spectroscopy (BIS) devices utilize biophysical modeling to generate body composition data. The addition of body mass index (BMI) to modified Xitron-Hanai-based mixture equations improved BIS estimates of intracellular water (ICW), particularly at the extremes of BMI. A 3-compartment model for distinguishing excess fluid (ExF) from normally hydrated lean (NHLT) and adipose tissue may further improve BIS estimates.We aimed to validate a BIS approach based on the Chamney model for determining fat mass (FM) in healthy individuals (NHANES) and for measuring FM changes in individuals undergoing massive weight loss.Using adult NHANES 1999-2004 (2821 female, 3063 male) and longitudinal pre-to-post-RYGB (15F) data, we compared dual-energy-X-ray absorptiometry (DXA) and BIS for FM. We applied BIS adiposity-corrected values to Chamney equations for normally hydrated lean and adipose tissue (NHLT, NHAT) and FM. Method agreement was evaluated by correlations, paired t-tests, root mean square error (RMSE), Bland-Altman (B-A) analysis, and concordance correlation coefficients (CCC).Method agreement between BIS and DXAFM was good in healthy adults (r=0.96, CCC=0.93, p.0001), and pre-to-post-RYGB (r=0.93-0.98, CCC=0.81-0.86, p.001). Although cross-sectional FM measures differed, FM change measures post-RYGB did not (35.6±8.9 vs. 35.2±9.2 kg, BIS vs. DXA) and agreed well (r=0.84, p.0001). The 15 subjects with follow-up measurements at 1 year lost 11.5±9.8 kg FFM by DXA, but only 1.3±2.5 kg of NHLT by BIS, suggesting that the FFM loss may have been mostly adipose tissue water.Incorporation of the Chamney model into BIS algorithms is a major conceptual advancement for assessing and monitoring body composition. Its ability to differentiate ICW and extracellular water (ECW) in NHLT and NHAT, as well as excess ECW is promising, and would facilitate lean tissue monitoring in obesity and acute/chronic disease.Os dispositivos de espectroscopia de bioimpedância (DEB) utilizam modelagem biofísica para gerar dados de composição corporal. A adição do índice de massa corporal (IMC) às equações de mistura modificadas com Xitron-Hanai modificadas melhorou as estimativas de DEB de água intracelular (AI), particularmente nos casos extremos do IMC. Um modelo de 3 compartimentos para distinguir o excesso de fluido (ExF) de magro normalmente hidratado (NHLT) e tecido adiposo pode ainda melhorar as estimativas do DEB.Pretendemos validar uma abordagem do DEB com base no modelo de Chamney para determinar a massa de gordura (MG) em indivíduos saudáveis (NHANES) e para medir mudanças de MG em indivíduos submetidos à perda de peso maciça.Usando o NHANES adulto 1999–2004 (2821 mulheres, 3063 homens) e dados longitudinais pré-pós-RYGB (15 F), comparamos a absorção de raios-X de dupla energia (DXA) e DEB para MG. Aplicamos os valores corrigidos de adiposidade do BIS às equações de Chamney para tecidos magros e adiposos normalmente hidratados (NHLT, NHAT) e FM. O acordo de método foi avaliado por correlações, testes t pareados, erro quadrado médio (EQM), análise Bland-Altman (B-A) e coeficientes de correlação de concordância (CCC).O acordo de método entre DEB e DXA MG foi bom em adultos saudáveis (r=0,96, CCC=0,93, p.0001) e pré-pós-RYGB (r=0,93–0,98, CCC=0,81–0,86, p0,001). Embora as medidas de MG transversais diferissem, as medidas de mudança de MG pós-RYGB não (35,6±8,9 vs. 35,2±9,2 kg, DEBvs. DXA) e concordaram bem (r=0,84, p.0001). Os 15 sujeitos com medidas de seguimento ao 1 ano perderam 11,5±9,8 kg FFM por DXA, mas apenas 1,3±2,5 kg de NHLT pelo DEB, sugerindo que a perda de FFM pode ter sido principalmente água do tecido adiposo.A incorporação do modelo de Chamney em algoritmos DEB é um grande avanço conceitual para avaliar e monitorar a composição corporal. A sua capacidade de diferenciar AI e água extracelular (AE) no NHLT e NHAT, bem como o excesso de AE é promissor e facilitará a monitorização do tecido magro na obesidade e doença aguda/crônica.

Assessment of body composition, both at single time points and longitudinally, is particularly important in clinical nutrition practice. It provides a means for the clinician to characterize nutrition status at a single time point, aiding in the identification and diagnosis of malnutrition, and to monitor changes over time by providing real-time information on the adequacy of nutrition interventions. Objective body composition measurement tools are available clinically but are often underused in nutrition care, particularly in the United States. This is, in part, due to a number of factors concerning their use in a clinical context: cost and accessibility of equipment, as well as interpretability of the results. This article focuses on the factors influencing interpretation of results in a clinical setting. Body composition assessment, regardless of the method, is inherently limited by its indirect nature. Therefore, an understanding of the strengths and limitations of any method is essential for meaningful interpretation of its results. This review provides an overview of body composition technologies available clinically (computed tomography, dual-energy x-ray absorptiometry, bioimpedance, ultrasound) and discusses the strengths and limitations of each device.