Anthropometric indices and cut-off points for screening of metabolic syndrome among South African taxi drivers - Sekgala, Machoene Derrick; Opperman, Maretha; Mpahleni, Buhle; Mchiza, Zandile June-Rose (Frontiers in Nutrition, 2022)
Background: Detecting the early onset of metabolic syndrome (metS) allows for quick intervention which may slow progression to a variety of health consequences, hence, determining the best measurement to detect metS is essential. Aim: This research aimed at examining the metS predictive power of anthropometric indices, such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body shape index (ABSI), body roundness index (BRI), percentage body fat (%BF), conicity index (CI), and Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE) to determine the cut-off points to identify male South African taxi drivers with metS. Method: A cross-sectional study was conducted among 185 male taxi drivers. Their weight, height, WC, blood lipid profile were measured. International Diabetes Federation (IDF) definition was used to define metS. Receiver Operating Characteristic (ROC) curves were used to compare the predictive ability of Anthropometric indices to detect metS. Results: The mean age of the participants was 39.84 years. Overall, 41.6% (N=77) of the participants presented with metS. The mean values for BMI, WC, WHtR, %BF, BRI, CUN-BAE, ABSI and CI were 28.60±6.20kg/m2, 99.13±17.59cm, 0.58±0.10, 27.28±8.28%, 5.09±2.33, 27.78±8.34, 0.08±0.01 and 1.70±0.19, respectively. The mean values for these indices were significantly (p<0.001) higher in participants with metS. The highest area under the curve (AUC) outcomes for screening metS were for the %BF and CUN-BAE, followed by the BMI and WHtR, and lastly the BRI. All these anthropometric indices had outstanding discriminatory powers for predicting metS with AUCs and sensitivity values above 80%. While all the indices displayed outstanding capabilities to predict metS, ABSI showed significantly (p<0.001) less predictive power. In terms of predicting individual risk factors for metS, only the BMI and CUN_BAE could predict the risk of abnormal HDL-C (AUC outcomes >70%). Based on the logistic regression models abnormal BMI, WHtR, %BF, BRI, CUN-BAE, TG, FBG, systolic BP, diastolic BP and WC showed increased risk of metS. Conclusion: While the %BF, CUN-BAE, BMI, WC, WHtR, BRI, CI and CUN-BAE could predict metS among South African male taxi drivers, these indices were less effective in predicting the individual metS risk factors such as TG, BP, and FBG.