PDF(Pubmed)
Abstract：
BACKGROUND: This study aimed to compare anthropometric indices to predict type 2 diabetes mellitus (T2DM) among first-degree relatives of diabetic patients in the Iranian community.
METHODS: In this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC).
RESULTS: The overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes.
CONCLUSIONS: The WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.
METHODS: In this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC).
RESULTS: The overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes.
CONCLUSIONS: The WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.
摘要：
背景：本研究旨在比较在伊朗社区糖尿病患者一级亲属中预测2型糖尿病（T2DM）的人体测量指标。
方法：在本研究中，从伊斯法罕医科大学内分泌和代谢研究中心的数据库中提取了3483名糖尿病患者一级亲属(FDRs)的信息.总的来说,分析中包括2082个FDR。使用逻辑回归模型来评估人体测量指标与患糖尿病的几率之间的关联。此外，根据每个指标的敏感性和特异性,应用受试者工作特征(ROC)曲线估算最佳截止点.此外,基于曲线下面积(AUC)比较指标.
结果：糖尿病的总患病率为15.3%。男性人体测量的最佳临界点是体重指数（BMI）25.09（AUC=0.573），腰围与身高比（WHtR）为0.52（AUC=0.648），腰臀比（WHR）为0.91（AUC=0.654），身体形状指数(ABSI)为0.08(AUC=0.599)，体圆度指数（BRI）为3.92（AUC=0.648），身体肥胖指数（BAI）27.27（AUC=0.590），内脏肥胖指数（VAI）为8（AUC=0.596）。人体测量指数的最佳截止点是BMI的28.75（AUC=0.610），WHtR为0.55（AUC=0.685），WHR为0.80（AUC=0.687），ABSI为0.07（AUC=0.669），4.34对于BRI（AUC=0.685），BAI为39.95（AUC=0.583），VAI为6.15(AUC=0.658)。WHR,WHTR,和BRI被发现具有公平的AUC值，并且相对高于男性和女性的其他指数。此外，在女性中,ABSI和VAI也有公平的AUC。然而,BMI和BAI的AUC值在两种性别的指数中最低。
结论：WHTR，BRI,VAI,在预测糖尿病患者一级亲属(FDRs)的T2DM方面,WHR优于其他人体测量指标。然而,可能需要在不同人群中进行进一步的调查，以证明其在临床实践中的广泛采用。
方法：在本研究中，从伊斯法罕医科大学内分泌和代谢研究中心的数据库中提取了3483名糖尿病患者一级亲属(FDRs)的信息.总的来说,分析中包括2082个FDR。使用逻辑回归模型来评估人体测量指标与患糖尿病的几率之间的关联。此外，根据每个指标的敏感性和特异性,应用受试者工作特征(ROC)曲线估算最佳截止点.此外,基于曲线下面积(AUC)比较指标.
结果：糖尿病的总患病率为15.3%。男性人体测量的最佳临界点是体重指数（BMI）25.09（AUC=0.573），腰围与身高比（WHtR）为0.52（AUC=0.648），腰臀比（WHR）为0.91（AUC=0.654），身体形状指数(ABSI)为0.08(AUC=0.599)，体圆度指数（BRI）为3.92（AUC=0.648），身体肥胖指数（BAI）27.27（AUC=0.590），内脏肥胖指数（VAI）为8（AUC=0.596）。人体测量指数的最佳截止点是BMI的28.75（AUC=0.610），WHtR为0.55（AUC=0.685），WHR为0.80（AUC=0.687），ABSI为0.07（AUC=0.669），4.34对于BRI（AUC=0.685），BAI为39.95（AUC=0.583），VAI为6.15(AUC=0.658)。WHR,WHTR,和BRI被发现具有公平的AUC值，并且相对高于男性和女性的其他指数。此外，在女性中,ABSI和VAI也有公平的AUC。然而,BMI和BAI的AUC值在两种性别的指数中最低。
结论：WHTR，BRI,VAI,在预测糖尿病患者一级亲属(FDRs)的T2DM方面,WHR优于其他人体测量指标。然而,可能需要在不同人群中进行进一步的调查，以证明其在临床实践中的广泛采用。
