Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Non-Communicable Disease Control, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
BMC Public Health. 2018 Jun 5;18(1):691. doi: 10.1186/s12889-018-5611-6.
To determine the anthropometric indices that would predict type 2 diabetes (T2D) and delineate their optimal cut-points.
In a cohort study, 7017 Iranian adults, aged 20-60 years, free of T2D at baseline were investigated. Using Cox proportional hazard models, hazard ratios (HRs) for incident T2D per 1 SD change in body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR), waist to hip ratio (WHR), and hip circumference (HC) were calculated. The area under the receiver operating characteristics (ROC) curves (AUC) was calculated to compare the discriminative power of anthropometric variables for incident T2D. Cut-points of each index were estimated by the maximum value of Youden's index and fixing the sensitivity at 75%. Using the derived cut-points, joint effects of BMI and other obesity indices on T2D hazard were assessed.
During a median follow-up of 12 years, 354 men, and 490 women developed T2D. In both sexes, 1 SD increase in anthropometric variables showed significant association with incident T2D, except for HC in multivariate adjusted model in men. In both sexes, WHtR had the highest discriminatory power while HC had the lowest. The derived cut-points for BMI, WC, WHtR, WHR, and HC were 25.56 kg/m, 89 cm, 0.52, 0.91, and 96 cm in men and 27.12 kg/m, 87 cm, 0.56, 0.83, and 103 cm in women, respectively. Assessing joint effects of BMI and each of the obesity measures in the prediction of incident T2D showed that among both sexes, combined high values of obesity indices increase the specificity for the price of reduced sensitivity and positive predictive value.
Our derived cut-points differ between both sexes and are different from other ethnicities.
确定预测 2 型糖尿病(T2D)的人体测量学指标,并描绘其最佳临界点。
在一项队列研究中,对 7017 名年龄在 20-60 岁、基线时无 T2D 的伊朗成年人进行了调查。使用 Cox 比例风险模型,计算每 1 SD 变化的体重指数(BMI)、腰围(WC)、腰高比(WHtR)、腰臀比(WHR)和臀围(HC)的 T2D 发生率的风险比(HR)。计算接受者操作特征(ROC)曲线下面积(AUC),以比较人体测量学变量对 T2D 发生率的判别能力。通过最大约登指数值和固定敏感性为 75%来估计每个指数的切点。使用得出的切点,评估 BMI 和其他肥胖指标对 T2D 风险的联合作用。
在中位随访 12 年期间,354 名男性和 490 名女性发生了 T2D。在男女两性中,除了男性多变量调整模型中的 HC 外,人体测量学变量的 1 SD 增加与 T2D 发生率显著相关。在男女两性中,WHtR 的判别能力最高,而 HC 的判别能力最低。男性 BMI、WC、WHtR、WHR 和 HC 的切点分别为 25.56kg/m、89cm、0.52、0.91 和 96cm,女性分别为 27.12kg/m、87cm、0.56、0.83 和 103cm。评估 BMI 和每种肥胖指标在预测 T2D 发生率方面的联合作用表明,在两性中,肥胖指标的综合高值增加了特异性,但降低了敏感性和阳性预测值。
我们得出的切点在男女两性之间存在差异,与其他种族也不同。