Anthropometric multicompartmental model to predict body composition In Brazilian girls

Abstract

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.

Prediction of maturity offset and age at peak height velocity in a longitudinal series of boys and girls

“…American youth of European (White), African (Black) and Hispanic ancestry vary in the proportions of sitting height and estimated leg length (Malina, Brown, & Zavaleta, 1987;Martorell, Malina, Castillo, Mendoza, & Pawson, 1988). As the Mirwald equations require sitting height and estimated leg length, applications to different ethnic groups can perhaps be questioned (Calabro, Stewart, & Welk, 2013;Dishman, McIver, Dowda, Saunders, & Pate, 2015;Farr et al, 2011;Machado et al, 2017;Machado, Oikawa, & Barbanti, 2013;Pienaar, Coetzee, & Monyeki, 2015;Silva et al, 2016;Wickel, Eisenmann, & Welk, 2009) and need validation. Studies of youth athletes do not ordinarily indicate the ethnic composition of samples, although a recent study noted that non-European soccer players were taller with a lower sitting height/height ratio, that is, proportionally longer legs, than European players (Parr et al, 2020).…”

Estimating resting energy expenditure from dual‐energy X‐ray absorptiometry: A cross‐sectional study in healthy young adults

“…An initial analysis (ANOVA) of ethnicity did not present differences ( F (5, 150) = 0.469, P = .799). The Brazilian racial miscegenation is striking, and sometimes difficult to justify as an ethnic distinction (Machado et al, 2017).…”

“…Additionally, predictive modeling where organs and tissues are included could decrease biases in REE estimates for different body tissues (Hayes et al, 2002) and better determine interpersonal differences (Bosy‐Westphal, Braun, Schautz, & Muller, 2013). Imaging BC analysis methods provide a good estimation of REE at the organ tissue level (Wang, Heshka, Zhang, Boozer, & Heymsfield, 2001a; Wang, O'Connor, Heshka, & Heymsfield, 2001b), and dual‐energy X‐ray absorptiometry (DXA), in particular, imposes advantages over other methods including minimal radiation exposure (Machado et al, 2017), greater precision in the simultaneous definition of different tissues, and minimal participant burden (Fosbol & Zerahn, 2015). All of which are important factors for estimating EE from BC (Heymsfield et al, 2018).…”

Multicompartment body composition analysis in older adults: a cross-sectional study

“…Comparisons between sex were performed using Student's t-test for independent samples. For the Multicompartmental anthropometric equation development, we adopted previous procedures (50,51), brie y described below.…”