Open AccessPredicting Total Body Fat from Anthropometry in Latino Children
Author(s) -
Huang Terry T.K.,
Watkins Michael P.,
Goran Michael I.
Publication year - 2003
Publication title -
obesity research
Language(s) - English
Resource type - Journals
eISSN - 1550-8528
pISSN - 1071-7323
DOI - 10.1038/oby.2003.164
Subject(s) - medicine , anthropometry , body weight , waist , skinfold thickness , abdominal fat , demography , body height , zoology , abdomen , body adiposity index , body mass index , classification of obesity , fat mass , surgery , sociology , biology
Abstract Objective : To develop prediction equations for total body fat specific to Latino children, using demographic and anthropometric measures. Research Methods and Procedures : Ninety‐six Latino children (7 to 13 years old) were studied. Two‐thirds of the sample was randomized into the equation development group; the remainder served as the cross‐validation group. Total body fat was measured by DXA. Measures included weight, height, waist and hip circumferences, and skinfolds (suprailiac, triceps, abdomen, subscapula, thigh, and calf). Results : The previously published equation from Dezenberg et al. did not accurately predict total body fat in Latino children. However, newly developed equations with either body weight alone (intercept ± SE = 1.78 ± 1.53 kg, p > 0.05; slope ± SE = 0.90 ± 0.07, p > 0.05 against slope = 1.0; R 2 = 0.86), weight plus age and gender (intercept ± SE = 2.28 ± 1.20 kg, p > 0.05; slope ± SE = 0.91 ± 0.05, p > 0.05; against slope = 1.0; R 2 = 0.92), or weight plus height, gender, Tanner stage, and abdominal skinfold (intercept ± SE = 1.47 ± 1.01 kg, p > 0.05; slope ± SE = 0.93 ± 0.04, p > 0.05; against slope = 1.0, R 2 = 0.97) predicted total body fat without bias. Discussion : Unique prediction equations of total body fat may be needed for Latino children. Weight, as the single most significant predictor, can be used easily to estimate total body fat in the absence of any additional measures. Including age and gender with weight produces an equally stable prediction equation with increasing precision. Using a combination of demographic and anthropometric measures, we were able to capture 97% of the variance in measured total body fat.