Accuracy of anthropometric measurements in estimating fat mass in individuals with 21-hydroxylase deficiency

Gonçalves, Ezequiel Moreira; Silva, Analiza M.; Santos, Diana A.; Lemos-Marini, Sofia Helena Valente de; Santos, Allan de Oliveira; Mendes‐dos‐Santos, Carolina T.; Mello, Maricilda Palandi de; Guerra‐Júnior, Gil

doi:10.1016/j.nut.2011.12.014

Cited by 8 publications

(9 citation statements)

References 36 publications

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“…However, it is also subject to propagation of measurement errors, is time consuming, and requires sophisticated and highly specialized technical equipment. Moreover, it is restricted to research facilities and has high costs, making it difficult to use in large samples [ 37 ]. Nevertheless, DXA has been used by various researchers to develop pediatric equations [ 19 , 38 – 41 ], therefore considered as a valid and reliable method [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Anthropometric multicompartmental model to predict body composition In Brazilian girls

Machado

Silva

Gobbo³

et al. 2017

BMC Sports Sci Med Rehabil

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BackgroundAnthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt, with restrictions to a multicompartimental approach as a quantitative growth. DXA has great potential to determine pediatric body composition in more than one component (3-C), but has limited use in field settings. Thus, the aim of this study was to propose and validate an anthropometric model for simultaneous estimation of lean soft tissue (LST), bone mineral content (BMC) and fat mass (FM) in healthy girls, from a multivariate approach of densitometric technique, as the criterion method.MethodsA sample of 84 Brazilian girls (7-17 years) was defined by chronological age and maturity offset. Whole total and regional DXA body scan were performed and, the components were defined (LST, BMC and FM) and considered as dependent variables. Twenty-one anthropometric measures were recorded as independent variables. From a multivariate regression, an anthropometric multicompartmental model was obtained.ResultsIt was possible to predict DXA body components with only four predictive measurements: body weight (BW); supra-iliac skinfold (SiSk); horizontal abdominal skinfold (HaSk) and contracted arm circumference (CaCi) with high coefficients of determination and low estimation errors (LST = 0.6662657 BW - 0. 2157279 SiSk - 0.2069373 HaSk + 0.3411678 CaCi - 1.8504187; BMC = 0.0222185 BW - 0.1001097 SiSk - 0.0064539 HaSk - 0.0084785 CaCi + 0.3733974 and FM = 0.3645630 BW + 0.1000325 SiSk - 0.2888978 HaSk - 0.4752146 CaCi + 2.8461916). The cross-validation was confirmed through the sum of squares of residuals (PRESS) method, presenting accurate coefficients (Q2 PRESS from 0.81 to 0.93) and reduced error reliability (SPRESS from 0.01 to 0.30).ConclusionsWhen sophisticated instruments are not available, this model provides valid estimates of multicompartmental body composition of girls in healthy Brazilian pediatric populations.

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Section: Discussionmentioning

confidence: 99%

Anthropometric multicompartmental model to predict body composition In Brazilian girls

Machado

Silva

Gobbo³

et al. 2017

BMC Sports Sci Med Rehabil

View full text Add to dashboard Cite

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“…A four-compartment model (4C model) is actually the most strong model for accurately assesses body composition in children as it accounts for the variability of the main FFM components [31]. Though its use is recommended as criterion, this method is time-consuming and requires sophisticated equipment, specialized technicians, and high costs which make it difficult for use in large samples [32]. In addition, the 4C model is not free of errors, considering the number of required techniques necessary for determining the main FFM constituents (water and mineral) [8].…”

Section: Discussionmentioning

confidence: 99%

The Multicomponent Anthropometric Model for Assessing Body Composition in a Male Pediatric Population: A Simultaneous Prediction of Fat Mass, Bone Mineral Content, and Lean Soft Tissue

2013

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The aim of this study was to propose and cross-validate an anthropometric model for the simultaneous estimation of fat mass (FM), bone mineral content (BMC), and lean soft tissue (LST) using DXA as the reference method. A total of 408 boys (8–18 years) were included in this sample. Whole-body FM, BMC, and LST were measured by DXA and considered as dependent variables. Independent variables included thirty-two anthropometrics measurements and maturity offset determined by the Mirwald equation. From a multivariate regression model (Ymn = x(r + 1)(r + 1)nβ m + εnm), a matrix analysis was performed resulting in a multicomponent anthropometric model. The cross-validation was executed through the sum of squares of residuals (PRESS) method. Five anthropometric variables predicted simultaneously FM, BMC, and LST. Cross-validation parameters indicated that the new model is accurate with high R PRESS 2 values ranging from 0.94 to 0.98 and standard error of estimate ranging from 0.01 to 0.09. The newly proposed model represents an alternative to accurately assess the body composition in male pediatric ages.

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“…Historically, large cohort studies and national surveys have relied on relatively inexpensive and generally reliable anthropometric measures, such as weight, height, waist circumference and skinfold measures, to estimate body composition in children and adults (12)(13)(14)(15) . However, these methods are generally not generalisable and may create biased estimates especially when body composition compartments, such as percentage body fat, have been calculated using prediction equations developed from populations that differ by age, race and/or ethnicity (16)(17)(18)(19)(20)(21) . For example, Hoffman et al (15) reported that skinfold prediction equations consistently underestimated body fat mass (FM) compared with dual-energy X-ray absorptiometry (DXA) and 2 H dilution.…”

mentioning

confidence: 99%

Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children

et al. 2020

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ABSTRACT There is a need for accurate, inexpensive and field-friendly methods to assess body composition in children. Bioelectrical impedance analyzer (BIA) is a promising approach; however, there has been limited validation and use among young children in resource poor settings. We aim to develop and validate population-specific prediction equations for estimating total fat mass (FM), fat free-mass (FFM), and percent body fat (PBF) in Vietnamese children (4-7 years) using reactance and resistance from BIA, anthropometric variables and demographic information. We conducted a cross-sectional survey of 120 children. Body composition was measured using dual energy x-ray absorptiometry (DXA), BIA and anthropometry. To develop prediction equations, we split all data into development (70%) and validation datasets (30%). The model performance was evaluated using PRESS (Predicted residual error sum of squares), RMSE (Root mean squared error), MEA (Mean absolute error) and R2. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index or resistance and height), low RMSE (FM 0.70, FFM 0.74, PBF 3.10), low MAE (FM 0.55, FFM 0.62, PBF 2.49), high R2 (FM 0.95, FFM 0.92, PBF 0.82), and the least difference between predicted values and actual values from DEXA (FM 0.03kg or 0.01SD, FFM 0.06kg or 0.02SD, PBF 0.27% or 0.04SD). In conclusion, we developed the first valid and highly predictive equations to estimate FM, FFM and PBF in Vietnamese children using BIA. These findings have important implications for future research on the double burden of disease and risks associated with overweight and obesity in young children.

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Accuracy of anthropometric measurements in estimating fat mass in individuals with 21-hydroxylase deficiency

Abstract: In children and adolescents with 21OHD, %BF as estimated by skinfold measurements was associated more strongly with DXA-assessed %BF than both BMI and WC. However, still, the skinfold-based assessment underestimated DXA %BF and showed moderate agreement.

Cited by 8 publications

References 36 publications

Anthropometric multicompartmental model to predict body composition In Brazilian girls

Anthropometric multicompartmental model to predict body composition In Brazilian girls

The Multicomponent Anthropometric Model for Assessing Body Composition in a Male Pediatric Population: A Simultaneous Prediction of Fat Mass, Bone Mineral Content, and Lean Soft Tissue

Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children

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