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

Abstract

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.

Fig 1

ROC-curves illustrating the discriminatory power of the different anthropometric indices to predict CMA in the total sample (A), among females (B), and among males (C).

Fig 2. Calibration plots of the anthropometric measures based on the contingency table for Hosmer-Lemeshow statistic in total sample.