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Introduction: This population-based cross-sectional study aimed to identify the best predictor of the 10-year cardiovascular (CV) high risk among old and new anthropometric indices. Methods: We investigated 76,915 adults older than 18 years of age living in southwest China. Ten obesity indices were calculated. The 10-year cardiovascular disease (CVD) risk was estimated using the Framingham risk score. Receiver operating characteristic curve analysis was performed to assess the ability of the anthropometric index to predict the 10-year high risk of CVD events. Results: The waist-to-hip ratio (WHR) had the highest area under the curve (AUC) value (0.711; sensitivity: 62.22%, specificity: 42.73%) in men, while the body fat index (BAI) had the lowest AUC value (0.624, sensitivity: 49.07%, specificity: 54.84%). The waist-to-height ratio (WHtR) and the body roundness index (BRI) showed the highest AUC value (0.751, sensitivity: 39.24%, 39.83%, specificity: 75.68%, 68.59%) in women, while the BAI showed the lowest AUC value (0.671, sensitivity: 53.15%, specificity: 57.14%). Conclusions: The WHR was the best anthropometric measure for assessing the 10-year high risk of CVD in men, while the WHtR and BRI were the best measures for women. In men, the WHR should be < 0.88, and in women, the WHtR should be < 0.502 or the BRI should be < 3.41. [ABSTRACT FROM AUTHOR]

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., (© Danyu Yang, Ling Ma, Yin Cheng, Hongjuan Shi, Yining Liu, Chao Shi. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

Objectives: The digit ratio (2D:4D) is a possible marker of prenatal hormone exposure. The purpose of this study was to explore the relationships between digit ratio (2D:4D) and anthropometric indexes in Chinese children and adolescents., Methods: This study is a cross-sectional study. A school-based survey among 685 children and adolescents aged 8-15 years were conducted by stratified cluster sampling. The length of index finger (2D) and ring finger (4D) of the left hand, height, sitting height (ST), weight, chest circumference (CC), waist circumference (WC), hip circumference (HC), and abdominal skinfold thickness (AST) were measured. Pearson correlation and multivariate linear regression were used to analyze associations between 2D:4D and above indexes., Results: In girls, 2D:4D was positively related to WC, AST, waist-to-height (WHtR), waist-to-hip ratio (WHR) after adjusting for ages (p < 0.05). The WC, AST, WHtR, and WHR among girls with 2D:4D ≥ 1 were significantly higher than those among girls with 2D:4D < 1, respectively (p < 0.05). However, there was no correlations between digit ratio (2D:4D) and above anthropometric indexes in boys (p > 0.05)., Conclusions: The 2D:4D was related to anthropometric indexes in girls, which suggests that the maternal prenatal hormone exposure might be related to the anthropometric indexes of their female offspring., (© 2024 Wiley Periodicals LLC.)

Purpose: Assessing and comparing the ability of the hypertriglyceridemic waist (HW) phenotype and anthropometric obesity indexes to identify subjects at high risk of chronic kidney disease (CKD) in a relatively lean population in South China., Methods: Using data from a community-based, cross-sectional study conducted in Zhuhai City, Southern China, we examined associations between the HW phenotype, anthropometric obesity indexes, and incident CKD risk in a relatively lean population. Multiple logistic regression analyses were used to evaluate the associations., Results: The HW phenotype associated with CKD significantly in the unadjusted analysis (OR 3.53, 95% CI 1.65-7.52, P = 0.001). Further adjustment for gender, age, and other potential confounding variables had an impact on the odd ratios (OR); the OR decreased but still existed (OR 2.91, 95% 1.23-6.87, P = 0.016). The association of the HW phenotype with CKD remained significant after further adjustment for hypertension and diabetes. No significant association between the anthropometric indexes and incident CKD was found., Conclusion: The HW phenotype, but not the anthropometric indexes, is associated with an elevated risk of CKD in relatively lean subjects. The HW phenotype appears to be a better predictor of CKD than the anthropometric indexes., Level of Evidence: Level V, descriptive study.