School of Education, University of Rhode Island, 142 Flagg Road, Kingston, RI 02881, United States.
Department of Kinesiology, University of Connecticut, Storrs, CT 06269, United States.
Nutr Metab Cardiovasc Dis. 2023 May;33(5):956-966. doi: 10.1016/j.numecd.2023.03.001. Epub 2023 Mar 4.
Abdominal adiposity indices have stronger associations with cardiometabolic risk factors compared to anthropometric measures but are rarely used in large scale studies due to the cost and efficiency. The aim of this study is to establish sex and race/ethnicity specific reference equations using anthropometric measures.
A secondary data analysis (n = 6589) of healthy adults was conducted using data from National Health and Nutrition Examination Survey 2011-2018. Variables included in the analyses were anthropometric measures (height; weight; waist circumference, WC) and abdominal adiposity indices (android percent fat; android to gynoid ratio, A/G ratio; visceral adipose tissue area, VATA; visceral to subcutaneous adipose area ratio, VSR). Multivariable prediction models were developed using quantile regression. Bland-Altman was used for external validation of prediction models. Reference equations to estimate android percent fat, A/G ratio, VATA and VSR from anthropometric measurements were developed using a randomly selected subsample of 4613. These reference equations for four abdominal adiposity indices were then cross-validated in the remaining subsample of 1976. The measured and predicted android percent fat, A/G ratio, VATA and VSR were not statistically different (p > 0.05) except for the A/G ratio in Asian males and VSR in White females. The results of Bland-Altman further revealed that ≥93% of predicted abdominal adiposity indices fell within the limits of agreement (±1.96 standard deviation).
The sex and race/ethnicity specific reference equations for abdominal adiposity indices established using anthropometrics in the present study have strong predictive ability in US healthy adults.
与人体测量学指标相比，腹部肥胖指数与心血管代谢危险因素的相关性更强，但由于成本和效率问题，很少在大规模研究中使用。本研究旨在使用人体测量学指标建立性别和种族/民族特异性参考方程。
对 2011-2018 年全国健康和营养调查（NHANES）的健康成年人进行了二次数据分析（n=6589）。分析中包括的变量有人体测量学指标（身高、体重、腰围、WC）和腹部肥胖指数（男性脂肪百分比、男性脂肪与女性脂肪比例、内脏脂肪组织面积、内脏与皮下脂肪面积比）。使用分位数回归建立多变量预测模型。Bland-Altman 用于外部验证预测模型。使用 4613 名随机选择的子样本开发了从人体测量学指标估计男性脂肪百分比、男性脂肪与女性脂肪比例、内脏脂肪组织面积和内脏与皮下脂肪面积比的参考方程。然后在剩余的 1976 个子样本中交叉验证了这四个腹部肥胖指数的参考方程。除了亚裔男性的男性脂肪与女性脂肪比例和白人女性的内脏与皮下脂肪面积比外，测量和预测的男性脂肪百分比、男性脂肪与女性脂肪比例、内脏脂肪组织面积和内脏与皮下脂肪面积比没有统计学差异（p>0.05）。Bland-Altman 的结果进一步表明，预测的腹部肥胖指数中≥93%落在了一致性界限（±1.96 标准差）内。
本研究使用人体测量学建立的性别和种族/民族特异性腹部肥胖指数参考方程在美国健康成年人中具有很强的预测能力。
