Pongchaiyakul Chatlert, Kosulwat Vongsvat, Rojroongwasinkul Nipa, Charoenkiatkul Somsri, Thepsuthammarat Kaewjai, Laopaiboon Malinee, Nguyen Tuan V, Rajatanavin Rajata
Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002 Thailand.
Obes Res. 2005 Apr;13(4):729-38. doi: 10.1038/oby.2005.82.
To develop and validate sex-specific equations for predicting percentage body fat (%BF) in rural Thai population, based on BMI and anthropometric measurements.
%BF (DXA; GE Lunar Corp., Madison, WI) was measured in 181 men and 255 women who were healthy and between 20 and 84 years old. Anthropometric measures such as weight (kilograms), height (centimeters), BMI (kilograms per meter squared), waist circumference (centimeters), hip circumference (centimeters), thickness at triceps skinfold (millimeters), biceps skinfold (millimeters), subscapular skinfold (millimeters), and suprailiac skinfold (millimeters) were also measured. The sample was randomly divided into a development group (98 men and 125 women) and a validation group (83 men and 130 women). Regression equations of %BF derived from the development group were then evaluated for accuracy in the validation group.
The equation for estimating %BF in men was: %BF(men) = 0.42 x subscapular skinfold + 0.62 x BMI - 0.28 x biceps skinfold + 0.17 x waist circumference - 18.47, and in women: %BF(women) = 0.42 x hip circumference + 0.17 x suprailiac skinfold + 0.46 x BMI - 23.75. The coefficient of determination (R2) for both equations was 0.68. Without anthropometric variables, the predictive equation using BMI, age, and sex was: %BF = 1.65 x BMI + 0.06 x age - 15.3 x sex - 10.67 (where sex = 1 for men and sex = 0 for women), with R2 = 0.83. When these equations were applied to the validation sample, the difference between measured and predicted %BF ranged between +/-9%, and the positive predictive values were above 0.9.
These results suggest that simple, noninvasive, and inexpensive anthropometric variables may provide an accurate estimate of %BF and could potentially aid the diagnosis of obesity in rural Thais.
基于体重指数（BMI）和人体测量数据，开发并验证用于预测泰国农村人口体脂百分比（%BF）的性别特异性方程。
对181名年龄在20至84岁之间的健康男性和255名健康女性测量了%BF（采用双能X线吸收法；通用电气公司，麦迪逊，威斯康星州）。还测量了体重（千克）、身高（厘米）、BMI（千克/米²）、腰围（厘米）、臀围（厘米）、肱三头肌皮褶厚度（毫米）、肱二头肌皮褶厚度（毫米）、肩胛下皮褶厚度（毫米）和髂上皮肤褶厚度（毫米）等人体测量指标。样本被随机分为开发组（98名男性和125名女性）和验证组（83名男性和130名女性）。然后评估开发组得出的%BF回归方程在验证组中的准确性。
男性估计%BF的方程为：%BF（男性）=0.42×肩胛下皮褶厚度+0.62×BMI-0.28×肱二头肌皮褶厚度+0.17×腰围-18.47；女性为：%BF（女性）=0.42×臀围+0.17×髂上皮肤褶厚度+0.46×BMI-23.75。两个方程的决定系数（R²）均为0.68。不使用人体测量变量时，使用BMI、年龄和性别的预测方程为：%BF = 1.65×BMI + 0.06×年龄 - 15.3×性别 - 10.67（男性性别=1，女性性别=0），R² = 0.83。当将这些方程应用于验证样本时，测量的和预测的%BF之间的差异在±9%之间，阳性预测值高于0.9。
这些结果表明，简单、无创且廉价的人体测量变量可能提供对%BF的准确估计，并可能有助于泰国农村地区肥胖症的诊断。
