Quantitative analysis of the magnitude and time delay of cyclic variation of myocardial backscatter from asymptomatic type 2 diabetes mellitus subjects.

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.