Prediction of percentage body fat from anthropometry and bioelectrical impedance in Singaporean and Beijing Chinese

Body composition was measured in 205 male and female Beijing Chinese and in 148 male and female Singaporean Chinese, age 34 (mean) (range 18-68) years and body mass index (BMI) 22.3 (15.9-38.5) kg/m(2). In Beijing Siri's two-compartment model based on densitometry was used as a reference technique and in Singapore Siri's three-compartment model based on densitometry and deuterium oxide dilution was used. In addition, body composition was predicted using equations based on anthropometry and bioelectrical impedance developed in Caucasian populations. Percentage body fat (BF%) predicted from BMI was systematically underestimated by about 1% in Beijing Chinese and by about 3.5% in Singaporean Chinese. The difference in bias (measured minus predicted BF%) between the two population groups could be explained by differences in frame size. The Durnin and Womersley equations for BF% based on skinfold thickness predicted BF% in the male and female Chinese groups adequately, with only a slight (less than 1% body fat) and not significant bias. The prediction of BF% based on the waist circumference (Lean's formula) resulted in an unbiased estimate of BF% in females (bias about 1% body fat), whereas in males the formula systematically underestimated BF% by 3.5-5%. Bioelectrical impedance underestimated BF% systematically by 3%, in males and females to about the same extent. The bias of all prediction formulas was positively correlated with the level of body fatness and, except for impedance, also negatively correlated with age. The negative association of the bias with age indicates that the age-related increase in body fatness is lower in Chinese than in Caucasians. It can be concluded of the studied prediction techniques that only the skinfold methodology using the equations of Durnin and Womersley give valid mean estimates for both Chinese males and females. The other techniques require the development of population-specific prediction formula.