Correlation between body fat percentage and general obesity indexes in middle aged and old people in Guangzhou

Abstract

Objective: To examine the correlation between body fat percentage (BFP) and general obesity indexes, including body mass index (BMI), waist circumference (WC) and waist to hip ratio (WHR) and calculate the corresponding BFP cutoff values in the middle aged and old people in Guangzhou. The corresponding cut-point of optimal body fat percentage for Guangzhou older population. Methods: Based on the Guangzhou Biobank Cohort Study (GBCS), 3 490 relatively healthy Guangzhou residents aged ≥50 years were selected and were randomly divided into 2 groups. The equations between BFP and BMI, WC, WHR were set up with Curve fitting analysis in one group. The multiple regression analysis was undertaken to establish predictive equations between BFP and BMI, WC, WHR with stepwise model for adding gender, age, physical activity, drinking and smoking. Then, the optimal cut-points of BFP corresponding to BMI, WC and WHR to reflect the degree of obesity were calculated. The equations were then validated with another group. Results: BFP increased with the increase of WHR, WC and BMI. BMI was a better predictor of body fat percentage than WC and WHR. The final regression equation was BFP=(-23.47 -8.87×sex) +2.94× (BMI) - 0.024 × (BMI)2,the coefficient of determination was 0.805. Based on the equation, the BFP corresponding to overweight/obesity (24 kg/m2≤BMI<28 kg/m2) were 24.3% ≤BFP<31.1% in men and 33.2%≤BFP<40.0% in women, respectively. BMI had a better consistency with BFP in identify obesity compared with WC and WHR, obtained the area of ROC 0.909 in men and 0.919 in women respectively. The sensitivity and specificity were 70.3% and 85.5% in men; and 75.2% and 93.0% in women respectively. Conclusion: BFP has a better correlation with BMI. The study results indicated that BFP for middle aged and old males and females in Guangzhou corresponding to overweight/obesity (BMI≥24 kg/m2) were <24.0% and <33.0% respectively.