Background/Objectives: Whether fat-free mass (FFM) and its components are depleted in eating-disorder (ED) patients is uncertain. Dual energy X-ray absorptiometry (DXA) is widely used to assess body composition in pediatric ED patients; however, its accuracy in underweight populations remains unknown. We aimed (1) to assess body composition of young females with ED involving substantial weight loss, relative to healthy controls using the four-component (4C) model, and (2) to explore the validity of DXA body composition assessment in ED patients. Subjects/Methods: Body composition of 13 females with ED and 117 controls, aged 10–18 years, was investigated using the 4C model. Accuracy of DXA for estimation of FFM and fat mass (FM) was tested using the approach of Bland and Altman. Results: Adjusting for age, height and pubertal stage, ED patients had significantly lower whole-body FM, FFM, protein mass (PM) and mineral mass (MM) compared with controls. Trunk and limb FM and limb lean soft tissue were significantly lower in ED patients. However, no significant difference in the hydration of FFM was detected. Compared with the 4C model, DXA overestimated FM by 5±36% and underestimated FFM by 1±9% in ED patients. Conclusion: Our study confirms that ED patients are depleted not only in FM but also in FFM, PM and MM. DXA has limitations for estimating body composition in individual young female ED patients.

Body composition techniques are required for monitoring response to treatment in individual obese children, and assessing the efficacy of weight loss programmes. Densitometry is readily undertaken, using air displacement plethysmography (ADP), but requires appropriate information on the density of lean tissue (DLT). The aims of this study were to develop predictive equations for DLT in obese children and adolescents, and to test the accuracy of ADP when using such predicted DLT values in an independent longitudinal sample using the four-component model as the reference method. Equations for the prediction of DLT from age, gender and body mass index standard deviation score were developed in 105 children (39 boys). Accuracy of ADP, when incorporating predicted DLT values, was tested for baseline body composition and its change over time in a separate sample of 51 children (20 boys). The predictive equation explained 33% of the variance in DLT. Fat mass obtained from ADP using such predicted values had a mean (s.d.) bias of 0.32 (1.39) kg, nonsignificant, whereas change in fat mass had an error of −0.25 (1.38) kg, nonsignificant. Hydration was strongly correlated with DLT. Use of ADP with predicted DLT values was associated with nonsignificant bias when estimating fat mass and its change over time. This study aids the application of ADP in childhood obesity research and clinical practise. The limits of agreement (±2.8 kg) relative to four-component values are moderately better than those for X-ray absorptiometry (±3.2 kg). Further improvement to accuracy would require assessment of lean tissue hydration by bioelectrical impedance.

Low-birth weight has been proposed to programme central adiposity in childhood. However, there is little information on associations between fetal weight gain and fat distribution within obese individuals. To investigate associations between birth weight and postnatal weight gain with body composition in a sample of obese children and adolescents. Body composition was measured using anthropometry, dual-emission X-ray absorptiometry and the 4-component model in 45 male and 76 female obese individuals aged 5–22 years. General linear models were used to investigate associations between birth weight standard deviation score (SDS), or change in weight SDS between birth and follow-up, and body composition, adjusting for age, pubertal status, height and gender. Birth weight SDS ranged from −1.86 to 3.46, and was inversely associated with current weight SDS after adjustment for height SDS. Birth weight SDS was weakly associated with waist and hip girths, but not waist–hip ratio or trunk fat, after adjusting for age, height, pubertal status and gender. Change in weight SDS was strongly associated with total and central adiposity. Despite incorporating substantial variability, birth weight SDS was only a weak predictor of tissue masses and their distribution in obese children. Variability in central adiposity was more strongly associated with the magnitude of postnatal growth, which in turn was weakly inversely associated with birth weight SDS. In a population uniformly characterised by excess body weight, postnatal weight gain exerted the dominant impact on adiposity and fat distribution.

Body composition is increasingly measured in pediatric obese patients. Although dual-energy X-ray absorptiometry (DXA) is widely available, and is precise, its accuracy for body composition assessment in obese children remains untested. We aimed to evaluate DXA against the four-component (4C) model in obese children and adolescents in both cross-sectional and longitudinal contexts. Body composition was measured by DXA (Lunar Prodigy) and the 4C model in 174 obese individuals aged 5–21 years, of whom 66 had a second measurement within 1.4 years. The Bland–Altman method was used to assess agreement between techniques for baseline body composition and change therein. A significant minority of individuals (n=21) could not be scanned successfully due to their large size. At baseline, in 153 individuals with complete data, DXA significantly overestimated fat mass (FM; Δ=0.9, s.d. 2.1 kg, P<0.0001) and underestimated lean mass (LM; Δ=−1.0, s.d. 2.1 kg, P<0.0001). Multiple regression analysis showed that gender, puberty status, LM and FM were associated with the magnitude of the bias. In the longitudinal study of 51 individuals, the mean bias in change in fat or LM did not differ significantly from zero (FM: Δ=−0.02, P=0.9; LM: Δ=0.04, P=0.8), however limits of agreement were wide (FM: ±3.2 kg; LM: ±3.0 kg). The proportion of variance in the reference values explained by DXA was 76% for change in FM and 43% for change in LM. There are limitations to the accuracy of DXA using Lunar Prodigy for assessing body composition or changes therein in obese children. The causes of differential bias include variability in the magnitude of tissue masses, and stage of pubertal development. Further work is required to evaluate this scenario for other DXA models and manufacturers.