Abstract
BACKGROUND: Anthropometric indexes offer a practical approach to identifying metabolic syndrome (MetS) and its components. However, there is a scarcity of research on anthropometric indexes tailored to predict MetS in populations from resource-limited regions. OBJECTIVE: This study aimed to examine the association between 8 easy-to-collect anthropometric indexes and MetS, and determine the most appropriate indexes to identify the presence of MetS for adults in resource-limited areas. METHODS: A total of 10,520 participants aged 18-85 years from Ningxia Hui Autonomous Region, China, were included in this cross-sectional study. Participants were recruited through a stratified sampling approach from January 1, 2020, to December 31, 2021. MetS was defined using the International Diabetes Federation (IDF) criteria. Eight anthropometric indexes were examined, including BMI, waist-to-height ratio (WHtR), weight-adjusted waist index (WWI), conicity index, a body shape index (ABSI), lipid accumulation products (LAP), visceral obesity index (VAI), and the triglyceride-glucose (TyG) index. Logistic regression analysis and restricted cubic splines (RCSs) were applied to identify the association between the anthropometric indexes. The receiver operating characteristic curve and the area under the curve (AUC) were analyzed to identify and compare the discriminative power of anthropometric indexes in identifying MetS. The Youden index was used to determine a range of optimal diagnostic thresholds. Logistic regression analysis was applied to identify the association between the anthropometric indexes. RESULTS: A total of 3324 (31.60%) participants were diagnosed with MetS. After adjusting for age, ethnicity, current residence, education level, habitual alcohol consumption, and tobacco use, all the 8 indexes were positively correlated with the risks of MetS (P<.05). LAP presented the highest adjusted odds ratios (adjOR 35.69, 95% CI 34.59-36.80), followed by WHtR (adjOR 29.27, 95% CI 28.00-30.55), conicity index (adjOR 11.58, 95% CI 10.95-12.22), TyG index (adjOR 5.53, 95% CI 5.07-6.04), BMI (adjOR 3.88, 95% CI 3.71-4.05), WWI (adjOR 3.23, 95% CI 3.02-3.46), VAI (adjOR 2.11, 95% CI 2.02-2.20), and ABSI (adjOR 1.71, 95% CI 1.62-1.80). Significantly nonlinear associations between the 8 indexes and the risk of MetS (all Pnonlinear<.001) were observed in the RCSs. WHtR was the strongest predictor of MetS for males (AUC 0.91, 95% CI 0.90-0.92; optimal cutoff 0.53). LAP were the strongest predictor of MetS for females (AUC 0.89, 95% CI 0.89-0.90; optimal cutoff 28.67). Statistical differences were present between WHtR and all other 7 anthropometric indexes among males and overall (all P<.05). In females, the AUC values between LAP and BMI, WWI, ABSI, conicity index, VAI, and TyG index were significantly different (P<.001). No statistical difference was observed between LAP and WHtR among females. CONCLUSIONS: According to 8 anthropometric and lipid-related indices, it is suggested that WHtR and LAP are the most appropriate indexes for identifying the presence of MetS in resource-limited areas.