Equations based on anthropometry to predict body fat measured by absorptiometry in schoolchildren and adolescents

Hernández, Luis Ortíz; Lopez, Antoine-Guy; Ramos-Ibáñez, Norma; Lara, Loreto; Gomez, Rigoberto; Pérez-Salgado, Diana

doi:10.1016/j.jped.2016.08.008

Cited by 18 publications

(17 citation statements)

References 22 publications

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“…Further, the method has been applied in the estimation of FM% in diverse ethnic groups such as Caucasians, Latinos, Asians and Africans in 111 countries. Nowadays, however, we suggest that body fat distribution would be better represented by equations that consider other fat deposition sites such as the abdominal region, measured at the waist level, and validated by DXA or MRI as this is likely to have more relevance to the metabolic syndrome, type 2 diabetes and cardiovascular diseases [37–39].…”

Section: Discussionmentioning

confidence: 99%

External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods

et al. 2019

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BackgroundAnalysis of body composition is becoming increasingly important for the assessment, understanding and monitoring of multiple health issues. The body mass index (BMI) has been questioned as a tool to estimate whole-body fat percentage (FM%). Recently, a simple equation described as relative fat mass (RFM) was proposed by Woolcott & Bergman. This equation estimates FM% using two anthropometric measurements: height and waist circumference (WC). The authors state that due to its simplicity and better performance than BMI, RFM could be used in daily clinical practice as a tool for the evaluation of body composition. The aim of this study was to externally validate the equation of Woolcott & Bergman to estimate FM% among adults from north-west Mexico compared with Dual-energy X-ray absorptiometry (DXA) as an alternative to BMI and secondly, to make the same comparison using air displacement plethysmography (ADP), Bioelectrical Impedance Analysis (BIA) and a 4-compartment model (4C model).MethodsWeight, height and WC were measured following standard procedures. The RFM index was calculated for each of the 61 participating subjects (29 females and 32 males, ages 20–37 years). The RFM was then regressed against each of the four body composition methods for estimating FM%.ResultsCompared with BMI, RFM was a better predictor of FM% determined by each of the body composition methods. In terms of precision the best equation was RFM regressed against DXA (y = 1.12 + 0.99 x; R2 = 0.84 p<0.001). Accuracy (represented by the closeness to the zero-intercept) was 1.12 (95% CI: -2.44, to 4.68) and thus, not significantly different from zero. For the rest of the methods, precision in the prediction of FM% was improved compared to BMI, with significant increases in the R2 and reduction of the root mean squared error (RMSE). However, the intercepts of each regression did not show accuracy since they were different from zero, for ADP: -9.95 (95%CI: -15.7 to -4.14), for BIA: -12.6 (95%CI: -17.5 to -7.74) and for the 4C model: -13.6 (95%CI: -18.6 to -8.60). Irrespectively, FM% measured by each of the body composition methods was higher for DXA than the other three methods (p<0.001).ConclusionsThis external validation proved that the performance of the RFM equation used in this study to estimate FM% was more consistent than BMI in this Mexican population, showing a stronger correlation with DXA than with the other body composition methods.

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Section: Discussionmentioning

confidence: 99%

External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods

et al. 2019

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“…A significant association was found between trunk fat mass and BMI and height in boys; in addition to BMI, this variable also had a significant relationship with weight in girls over 13 years of age and waist circumference in girls under 13 years of age. The literature review showed that BMI and waist circumference had a significant role in predicting fat mass [ 12 , 15 , 16 ]. A cross-sectional study in Brazilian adolescents reported that body circumferences, including waist circumference, had high adjusted coefficients of determination to estimate fat mass in girls [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…A cross-sectional study in Brazilian adolescents reported that body circumferences, including waist circumference, had high adjusted coefficients of determination to estimate fat mass in girls [ 15 ]. Another study in schoolchildren and adolescents found that BMI and waist circumference are among the best predictors of body fat percentage [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…Statistical methods that use predictive algorithms for assessing body composition have also been developed, which are thought to be more applicable in large studies in non-laboratory settings [5]. Numerous studies have been conducted throughout the world on this topic [5,[12][13][14][15][16][17][18][19][20][21][22]. The literature review revealed that multivariate regression models [12][13][14][15][16], receiver operating characteristic (ROC) curves [17][18][19], neural networks [21], and Bayesian networks (BN) [5,22] are among the methods used to predict body composition.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic prediction of segmental body composition in Iranian children and adolescents

et al. 2022

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Background Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods. Objectives To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements. Methods A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9–18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy. Results Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001). Conclusions The results revealed that, based on the present study’s predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing.

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“…Similarly, although ANT is a widely used method and scientific publications from this field are numerous, the comparison of body composition results obtained with different regression equations has, surprisingly, not been really extensive; most often, only a single or a limited number [21] of selected anthropometrical methods for body composition assessment were compared with a reference method of choice. The purpose of comparisons was most often to check for the accuracy of selected anthropometric method [35, 36], or to create new, population-specific regression equations [37]. Robič et al [24] performed a comparison of sample-specific and non-specific regression equations for BF calculation in pregnant women and clearly demonstrated that the differences in body composition, as determined with different regression equations (even with those specifically developed for the selected sample), go to the extent that the same subject can easily be (miss)classified into different nutritional categories.…”

Section: Discussionmentioning

confidence: 99%

Direct Comparison of (Anthropometric) Methods for the Assessment of Body Composition

et al. 2020

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Objective: Several methods for the assessment of body composition exist, yet they yield different results. The present study aimed to assess the extent of these differences on a sample of young, healthy subjects. We hypothesised that differences in body composition results obtained with different methods will vary to the extent that a subject can be misclassified into different nutritional categories. Research Methods and Procedures: Underwater weighing (UWW), bioelectrical impedance analysis (BIA), anthropometry (ANT), and dual-energy X-ray absorptiometry (DXA) were used to assess body composition. An extensive list of ANT regression equations (or sets of equations) was analysed in terms of accuracy and precision relative to DXA. Results: When DXA-determined body fat (BF) values were taken as a reference, UWW overestimated BF in both genders. In contrast, BIA (measured with a given bioimpedance analyser) underestimated BF in females, although BIA-determined BF did not differ from DXA in males. A huge difference in BF estimates (8-29% for females and 6-29% for males, for DXA-determined BF of 25.5% and 13.9% for females in males, respectively) was observed across a number of ANT regression equations; yet, ANT proved not to be inferior to DXA, provided that regression equations with the highest combinations of accuracy and precision were chosen. Conclusions: The study proved grounds for comparison of body composition results of young, healthy subjects, obtained with different methods and across a wide range of ANT regression equations. It also revealed a list of the most appropriate ANT regression equations for the selected sample and reported their accuracy and precision.

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Equations based on anthropometry to predict body fat measured by absorptiometry in schoolchildren and adolescents

Abstract: The equations developed in this study can be used to assess the percentage of body fat of Mexican schoolchildren and adolescents, as they demonstrate greater validity and lower error compared with previously published equations.

Cited by 18 publications

References 22 publications

External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods

External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods

Probabilistic prediction of segmental body composition in Iranian children and adolescents

Direct Comparison of (Anthropometric) Methods for the Assessment of Body Composition

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