Comparison of Three Adiposity Indexes and Cutoff Values to Predict Metabolic Syndrome Among University Students

Purpose: Obesity and high body fat are related to diabetes and metabolic syndrome (MetS) in all ethnic groups. Based on the International Diabetes Federation (IDF) definition of MetS, the aim of the present study was to compare body adiposity indexes (BAIs) and to assess their various cutoff values for the prediction of MetS in university students from Colombia. Methods: A cross-sectional study was conducted on 886 volunteers (51.9% woman; age mean 21.4 years). Anthropometric characteristics (height, weight, waist circumference [WC], and hip circumference [HC]) were measured, and body composition was assessed by bioelectrical impedance analysis. MetS was defined as including ≥3 of the metabolic abnormalities (WC, high-density lipoprotein cholesterol [HDL-C], triglycerides, fasting glucose, and systolic and diastolic blood pressure [BP]) in the definition provided by the IDF. The BAIs (i.e., BAI-HC [BAI], BAI-WC [BAI-w], and [BAI-p]) were calculated from formulas taking into account, height, weight, and WC, and for the visceral adiposity indexes, a formula, including WC, HDL-C, and triglycerides, was used. Results: The overall prevalence of MetS was 5.9%, higher in men than in women. The most prevalent components were low HDL-C, high triglyceride levels, WC, and BP levels. The receiver operating characteristic curves analysis showed that BAI, BAI-w, and BAI-p could be useful tools to predict MetS in this population. Conclusion: For women, the optimal MetS threshold was found to be 30.34 (area under curve [AUC] = 0.720-0.863), 19.10 (AUC = 0.799-0.925), and 29.68 (AUC = 0.779-0.901), for BAI, BAI-w, and BAI-p, respectively. For men, the optimal MetS threshold was found to be 27.83 (AUC = 0.726-0.873), 21.48 (AUC = 0.755-0.906), and 26.18 (AUC = 0.766-0.894), for BAI, BAI-w, and BAI-p, respectively. The three indexes can be useful tools to predict MetS according to the IDF criteria in university students from Colombia. Data on larger samples are needed. © Copyright 2017, Mary Ann Liebert, Inc. 2017.