A Reliable Estimate of Visceral Fat Area From Simple Anthropometric Measurements in Chinese Overweight and Obese Individuals.

OBJECTIVE

Visceral obesity, reflected by the amount of visceral adipose tissue (VAT), is associated with multiple chronic diseases and metabolic disorders. The visceral fat area (VFA), measured by MRI, is the 'gold standard' for diagnosis of visceral obesity. In this study, a simple model to predict VFA was constructed to facilitate the identification and monitoring of patients who are at high risk of visceral obesity.

METHODS

The 721 overweight and obese participants were divided into two groups according to sex, then randomly assigned to derivation and validation cohorts in a 1:2 ratio. Data from the derivation group were used to construct a multiple linear regression model; data from the validation group were used to verify the validity of the model.

RESULTS

The following prediction equations, applicable to both sexes, were developed based on age, waist circumference (WC) and neck circumference (NC) that exhibited strong correlations with the VFA: VFA=3.7×age+2.4×WC+5.5×NC-443.6 (R = 0.511, adjusted R = 0.481, for men) and VFA=2.8×age+1.7×WC+6.5×NC-367.3 (R = 0.442, adjusted R = 0.433, for women). The data demonstrated good fit for both sexes. A comparison of the predicted and actual VFA in the verification group confirmed the accuracy of the equations: for men, R = 0.489, adjusted R = 0.484 and intra-class correlation coefficient (ICC) = 0.653 (p < 0.001) and for women: R = 0.538, adjusted R= 0.536 and ICC = 0.672 (p < 0.001). The actual and predicted VFAs also showed good agreement in a Bland-Altman plot, indicating the significant correlations of both equations with the actual VFA.

CONCLUSIONS

Based on readily available anthropometric data, VFA prediction equations consisting of age, WC and NC were developed. The equations are robust, with good predictive power in both sexes; they provide ideal tools for the early detection of visceral obesity in Chinese overweight and obese individuals.