Gibson Allyson A, Schaffer Jean E, Peterson Linda R, Bilhorn Kyle R, Robert Karla M, Haider Troy A, Farmer Marsha S, Holland Mark R, Miller James G
Washington University, St. Louis, MO 63130, USA.
Ultrasound Med Biol. 2009 Sep;35(9):1458-67. doi: 10.1016/j.ultrasmedbio.2009.04.003. Epub 2009 Jul 17.
Early detection of diabetic patients at high risk for developing diabetic cardiomyopathy may permit effective intervention. The goal of this work is to determine whether measurements of the magnitude and time delay of cyclic variation of myocardial backscatter, individually and in combination, can be used to discriminate between subgroups of individuals including normal controls and asymptomatic type 2 diabetes subjects. Two-dimensional parasternal long-axis echocardiographic images of 104 type 2 diabetic patients and 44 normal volunteers were acquired. Cyclic variation data were produced by measuring the mean myocardial backscatter level within a region-of-interest in the posterior wall, and characterized in terms of the magnitude and normalized time delay. The cyclic variation parameters were analyzed using Bayes classification and a nonparametric estimate of the area under the receiver operating characteristic (ROC) curve to illustrate the relative effectiveness of using one or two features to segregate subgroups of individuals. The subjects were grouped based on glycated hemoglobin (HbA1c), the homeostasis model assessment for insulin resistance (HOMA-IR) and the ratio of triglyceride to high-density lipoprotein cholesterol (TG/HDL-C). Analyses comparing the cyclic variation measurements of subjects in the highest and lowest quartiles of HbA1c, HOMA-IR and TG/HDL-C showed substantial differences in the mean magnitude and normalized time delay of cyclic variation. Results show that analyses of the cyclic variation of backscatter in young asymptomatic type 2 diabetics may be an early indicator for the development of diabetic cardiomyopathy.
早期发现有患糖尿病性心肌病高风险的糖尿病患者可能会允许进行有效的干预。这项工作的目标是确定单独或联合测量心肌背向散射的周期性变化的幅度和时间延迟，是否可用于区分包括正常对照和无症状2型糖尿病受试者在内的个体亚组。获取了104例2型糖尿病患者和44例正常志愿者的二维胸骨旁长轴超声心动图图像。通过测量后壁感兴趣区域内的平均心肌背向散射水平产生周期性变化数据，并根据幅度和归一化时间延迟进行表征。使用贝叶斯分类和受试者操作特征（ROC）曲线下面积的非参数估计对周期性变化参数进行分析，以说明使用一个或两个特征来区分个体亚组的相对有效性。根据糖化血红蛋白（HbA1c）、胰岛素抵抗的稳态模型评估（HOMA-IR）以及甘油三酯与高密度脂蛋白胆固醇的比值（TG/HDL-C）对受试者进行分组。对HbA1c、HOMA-IR和TG/HDL-C最高和最低四分位数的受试者的周期性变化测量进行比较分析，结果显示周期性变化的平均幅度和归一化时间延迟存在显著差异。结果表明，对年轻无症状2型糖尿病患者背向散射的周期性变化进行分析可能是糖尿病性心肌病发生的早期指标。
