What is the best anthropometry index to evaluate the risk of metabolic abnormalities in Chinese adults?

Abdominal volume index, waist-to-height ratio, and waist circumference are optimal predictors of cardiometabolic abnormalities in a sample of Lebanese adults: A cross-sectional study

The prevalence of cardiometabolic abnormalities is high globally. This is concerning since these abnormalities increase the risk of morbidity and mortality. Using noninvasive, low-cost, and ethnic-specific anthropometric indices is crucial for widespread screening and early detection of cardiometabolic abnormalities. In this cross-sectional study, we enrolled 221 Lebanese participants (62.9% females; mean age: 43.36 ± 16.05 years; mean body mass index (BMI): 28.43 ± 6.10 Kg/m2). The main outcome measure was cardiometabolic abnormality (CMA), defined as the presence of at least two or more non-anthropometric components of the Metabolic Syndrome. Several anthropometric indices: Total body fat percent, Conicity index, Abdominal volume index (AVI), Weight-adjusted-waist index, Waist circumference (WC), Neck circumference, Hip circumference, Waist-to-hip ratio, Waist-to-height ratio (WtHR), Neck-to-height ratio, and BMI were assessed in their prediction of CMA, using logistic regression modelling and c-statistic [95% confidence intervals (CIs)], and calibration plots, as well sensitivity, specificity, and negative and positive predictive values measures. The Benjamini-Hochberg correction procedure was used to correct for multiple testing. The prevalence of CMA was 52.0% (47.5% in females and 59.8% in males). Significant associations were found between all the anthropometric indices and CMA, except for neck-to-height ratio. AVI and WC were most predictive for CMA in the total sample. WtHR and WC were most predictive in females with suggested cut-off values of 0.58 and 91.25 cm, whereas AVI and WC were most predictive in males with suggested cut-off values of 19.61 and 101.50 cm. The neck-to-height measurement had the lowest predictive ability for CMA. Adding anthropometric indices to sociodemographic variables did not significantly improve model discrimination. AVI, WHtR, and WC best predicted CMA in a sample of Lebanese adults. These less invasive, low-cost, easy-to-measure indices can be used to screen widely for CMA to better manage and prevent disease and subsequent morbidity and mortality.