<jats:sec xml:lang="en"> <jats:title>Background</jats:title> <jats:p xml:lang="en"> The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases ( <jats:styled-content style="fixed-case">CVD</jats:styled-content> s) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity‐related anthropometric indices (body mass index, waist circumference, hip circumference, waist‐to‐hip ratio, waist‐to‐height ratio) in Chinese population. </jats:p> </jats:sec> <jats:sec xml:lang="en"> <jats:title>Methods and Results</jats:title> <jats:p xml:lang="en"> A total of 44 048 participants in the study were derived from the baseline data of the <jats:styled-content style="fixed-case">PURE</jats:styled-content> ‐China (Prospective Urban and Rural Epidemiology) study in China. All participants’ anthropometric parameters, <jats:styled-content style="fixed-case">CVD</jats:styled-content> s, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity‐related anthropometric indices to the cardiometabolic risk factors and <jats:styled-content style="fixed-case">CVD</jats:styled-content> s. A positive association was observed between each anthropometric index and cardiometabolic risk factors and <jats:styled-content style="fixed-case">CVD</jats:styled-content> s in all models ( <jats:italic>P</jats:italic> <0.001). Compared with other anthropometric indices (body mass index, waist circumference, hip circumference, waist‐to‐hip ratio, and A Body Shape Index), waist‐to‐height ratio had significantly higher areas under the curve ( <jats:styled-content style="fixed-case">AUC</jats:styled-content> s) for predicting dyslipidemia ( <jats:styled-content style="fixed-case">AUC</jats:styled-content> s: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia ( <jats:styled-content style="fixed-case">AUC</jats:styled-content> s: 0.595, sensitivity: 60%, specificity: 45%), and <jats:styled-content style="fixed-case">CVD</jats:styled-content> s ( <jats:styled-content style="fixed-case">AUC</jats:styled-content> s: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure ( <jats:styled-content style="fixed-case">AUC</jats:styled-content> s: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or <jats:styled-content style="fixed-case">CVD</jats:styled-content> s than that of any other traditional obesity‐related indices. </jats:p> </jats:sec> <jats:sec xml:lang="en"> <jats:title>Conclusions</jats:title> <jats:p xml:lang="en"> Waist‐to‐height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and <jats:styled-content style="fixed-case">CVD</jats:styled-content> s, while waist circumference had a better prediction for abnormal blood pressure. </jats:p> </jats:sec>