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Updated: Jun 10, 2026

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
Published on: June 7, 2024
Optimal cut-off values and population means of waist circumference in different populations
Zhiqiang Wang1, Jun Ma, Damin Si
1School of Medicine, Centre for Chronic Disease, University of Queensland, Brisbane, Australia. z.wang@uq.edu.au
Abstract:
Abdominal obesity is a risk factor for cardiometabolic disease, and has become a major public health problem in the world. Waist circumference is generally used as a simple surrogate marker to define abdominal obesity for population screening. An increasing number of publications solely rely on the method that maximises sensitivity and specificity to define 'optimal' cut-off values. It is well documented that the optimal cut-off values of waist circumference vary across different ethnicities. However, it is not clear if the variation in cut-off values is a true biological phenomenon or an artifact of the method for identifying optimal cut-off points. The objective of the present review was to assess the relationship between optimal cut-offs and population waist circumference levels. Among sixty-one research papers, optimal cut-off values ranged from 65·5 to 101·2 cm for women and 72·5 to 103·0 cm for men. Reported optimal cut-off values were highly correlated with population means (correlation coefficient: 0·91 for men and 0·93 for women). Such a strong association was independent of waist circumference measurement techniques or the health outcomes (dyslipidaemia, hypertension or hyperglycaemia), and existed in some homogeneous populations such as the Chinese and Japanese. Our findings raised some concerns about applying the sensitivity and specificity approach to determine cut-off values. Further research is needed to understand whether the differences among populations in waist circumference were genetically or environmentally determined, and to understand whether using region-specific cut-off points can identify individuals with the same absolute risk levels of metabolic and cardiovascular outcomes among different populations.
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