Study Population

Patients with diabetes ( n = 21) were recruited from individuals presenting to the noninvasive cardiac laboratory at Ben Taub General Hospital (Houston, TX) for routine transthoracic echocardiography. A diagnosis of diabetes was confirmed using criteria from the American Diabetes Association’s 2010 guidelines or the active use of insulin and/or oral hypoglycemic therapies with a historical diagnosis. Healthy individuals without any previously known cardiovascular risk factors ( n = 20) were also recruited as a control comparison group ( Table 1 ). All volunteers were recruited on presentation to the noninvasive cardiac laboratory and thus were not asked specifically to refrain from food, caffeine, alcohol, tobacco, or vasoactive medications before the study. This scenario was considered to represent conditions encountered in a clinical setting.

Exclusion criteria included age < 18 years, history of carotid stenting or carotid endarterectomy, history of radiation therapy to the neck, or a life expectancy < 6 months. The study was approved by the institutional review board of the Baylor College of Medicine and the Harris County Hospital District. Written consent was obtained before study entry. All coronary heart disease risk factors as defined by the Adult Treatment Panel III guidelines were noted for each subject.

Acquisition of Ultrasound Images

All subjects were interviewed and scanned by the same study investigator. The subjects were positioned supine with elevation of the head of the bed at a 30° incline. Electrocardiographically gated B-mode bilateral carotid artery ultrasound scans were obtained (carotid artery presets, Vivid 7; GE Healthcare, Waukesha, WI) over three cardiac cycles at 14 MHz with an M12L vascular transducer. Images were acquired at a mean of 78.7 ± 8.9 frames/sec and at a dynamic range of 72 dB. The gain was set to just below the presence of echoes in blood. The probe marker was always oriented to the left of the patient, regardless of the side imaged.

The probe was placed anteriorly over the carotid artery, and image sweeps from the proximal common carotid artery (CCA) to the carotid bifurcation were used to survey the CCA anatomy in transverse (cross-section). The transverse distal CCA, approximately 1 cm inferior to the carotid bulb, was used for our analysis. Subjects were instructed to perform a breath-hold maneuver and to refrain from swallowing during the acquisition. The presence of significant motion artifact was noted on each scan, because motion would interfere with speckle-tracking and thus measured strain values. If motion artifacts occurred in a scan, image acquisition was repeated for that view when possible. Images were exported in Digital Imaging and Communications in Medicine format for offline analysis with an anonymization code unique to each subject.

Speckle-Tracking Strain Analysis

Ultrasound images were uploaded to a computer workstation with a software package designed to measure myocardial strain using speckle-tracking algorithms (2D Cardiac Performance Analysis; TomTec Imaging, Munich, Germany). TomTec provided the analysis software but otherwise had no input into the study design, data accrual, analysis, or manuscript preparation. On images of the transverse distal CCA, the image frame at the end of electrical diastole (the start of the QRS wave) was identified, and the vessel on this frame was manually marked with a contour at the lumen-intima boundary ( Figure 1 A). The analysis tool was then run to obtain strain data.

Measuring circumferential CAS using speckle-tracking. Contour generated after manual marking of intima-luminal border of the right distal CCA (A) . Strain values depicted by software system over three cardiac cycles (B) . The top graph shows the change in circumferential strain over time at each of the 48 points along a closed loop contour. The graph below it illustrates a color map representation, with each of the 48 points represented on the y axis and time on the x axis, red indicating positive strain values and blue indicating negative values.

The speckle-tracking algorithm divided a manually marked contour into 48 equidistant points and tracked the vessel intimal wall using a cross-correlation velocity vector imaging algorithm, which was accelerated with a fast Fourier transform function and optimized for myocardial strain analysis. In principle, the algorithm would enable tracking of the smallest strain corresponding to the motion of a fraction of the ultrasound wavelength (on the order of micrometers). Strain was calculated with reference to the end of electrical diastole of each cardiac cycle ( Figure 1 B). The raw data were exported to Excel spreadsheets (Microsoft Corporation, Redmond, WA) for further processing.

Postprocessing of Speckle-Tracking Strain Analysis

Because a myocardial analysis tool was used, the six-segment convention of the myocardial short-axis views was adopted for the identification of vessel wall segments. Anterior regional segments were identified as near wall segments and inferior regional segments as far wall segments. The septal segment was identified as medial for the right CCA and lateral for the left CCA, because the probe marker always pointed to the left, and similarly, the lateral segment was identified as lateral for the right CCA but medial for the left CCA. The anterior segment and the inferior segment themselves were identified as mid segments (i.e., near wall mid and far wall mid, respectively).

For each arterial wall segment, the peak strain value (i.e., the most positive value) was determined for each of the three cardiac cycles, and the mean peak strain value was calculated. With this method, the circumferential strain values for the six segments corresponding to myocardial segments on short-axis views were obtained.

Superficial probe pressure may affect vessel wall segments near the probe. We observed medial-lateral echo dropout in our images ( Supplemental Figure 1 ), and the reliability of strain measurements has been shown in phantom models to be lower in medial-lateral segments, likely because of heterogeneity in the density of ultrasonic lines or echo dropout that can lead to unreliable strain measurements when the ultrasound beam is not aligned with direction of strain. Thus, machine settings were optimized to achieve frame rates not too low to result in errors in lateral displacement and not too high to result in a decreased number of ultrasound scan lines. Furthermore, we opted, a priori, to use the far wall as the primary segment for analysis. We also examined the global net average of all segments for comparison with traditional luminal measures. We later tested intervisit repeatability and intravisit and intervisit reproducibility of the strain measures in each segment and of their averages.

Distensibility Measurement

For comparison of speckle-tracking–derived strain with ultrasound-based distensibility measurements, we measured the minimum and maximum of mean arterial diameters along 1 cm of the distal CCA on longitudinal views with an analysis system (Carotid Analyzer; Medical Imaging Applications, LLC, Coralville, IA), which has been shown to have a mean difference of 0.02 pixels (95% confidence interval, −2.98 to 3.02 pixels) between repeat measurements. Luminal strain was calculated by taking the difference between the two and dividing the minimum of mean arterial diameters. This method differs from previous methods that have used M-mode assessments of the carotid artery or aorta, by assessing diameters over a length of the CCA instead of a single-beam axis.

Statistical Methods

Statistical analyses were performed using Stata version 11 (StataCorp LP, College Station, TX). The Shapiro-Wilk test was used to determine nonnormality of the data. Appropriate parametric and nonparametric tests (two-tailed two-sample t tests and Wilcoxon’s rank-sum tests) were used to compare the characteristics and strain measures between the groups of patients with diabetes and healthy controls. Values for each side were examined separately and as an average. Regression models were then used to adjust for covariates in the side-averaged strain comparisons (i.e., the mean of the left and right measures), with normalization of the dependent and predictor variables as needed. Two models were examined adjusting for participant characteristics and hemodynamic parameters, because these may account for variations in CAS, and both models included age, race, gender, heart rate, current smoking, and history of smoking. The first model added systolic and diastolic blood pressures as covariates, and the second model added pulse pressure. These analyses were conducted with and without exclusion of image scans having motion artifacts.

Evaluation of Reproducibility

A separate set of healthy volunteers ( n = 10; mean age, 34.1 years; SE, 3.2 years; five men, five women; 20 carotid arteries in total) underwent ultrasound imaging as described in the above sections to allow testing for reproducibility. After a baseline scan, all the volunteers returned 2 to 4 days later for repeat imaging at approximately the same time of the day as the baseline scan. All carotid artery wall segments (the lateral, mid, and medial segments of the near and far wall regions) were analyzed. One reader conducted the speckle-tracking strain analysis on the images acquired at both visits for intervisit reproducibility analyses and repeated the analysis for the first visit for the intraobserver repeatability analyses. Another reader analyzed the same set of images for the interobserver reproducibility analyses. Intraclass correlation coefficients (ICCs; type [2,1]) and coefficients of variation (CVs) were used to assess intervisit repeatability and intrareader and interreader reproducibility. Bland-Altman plots were also used to assess for bias.

Reproducibility Analyses

Given the potential influence of heart rate and pulse pressure on strain values, we tested repeatability with and without consideration of these hemodynamic parameters. When segmental CAS values were examined for significant intervisit differences, all wall segmental CAS values and their averages showed no significant difference between visits ( P > .05). This trend persisted after accounting for heart rate and pulse pressure ( Supplemental Table 1 ).

For the primary repeatability study, test-retest analysis showed the greatest correlation for the far wall segments (ICC = 0.63) and the global net averaged strain of all segments (ICC = 0.40) ( Figure 2 ). The intervisit CVs for the averages of the far wall segments and of all segments were 27% and 26%, respectively. When the ratio of strain to heart rate–pulse pressure product (i.e., heart rate times pulse pressure) was used, test-retest analysis showed the highest correlation again for far wall average (ICC = 0.59; Supplemental Table 2 ). These observations supported our a priori decision to examine the average strain of the three far wall segments for our primary analysis.

Reproducibility of CAS measures using speckle-tracking. Test-retest of image acquisition, intrareader repeatability, and interreader reproducibility by ICC type (2,1) of CAS measures by arterial wall segments on a separate set of volunteers ( n = 10, 20 carotid scans).

Intrareader and interreader reproducibility was assessed for each segment, with agreement given as ICCs. The highest agreements for within-reader (intrareader) and between-reader (interreader) correlations were again observed for the far wall average and the net average of all arterial wall segments ( Figure 2 ). The intrareader CVs for these averages were 15% and 12%, respectively, and the interreader CVs were 14% and 10%, respectively. Bland-Altman plots showed no systematic bias for all reproducibility measures and reasonable intrareader and interreader limits of agreement ( Supplemental Figure 2 , Supplemental Table 3 ).

Comparison of Patients with Diabetes and Healthy Controls

The mean age of all individuals (patients with diabetes and healthy volunteers) was 56.8 years. As a whole, the group consisted of 49% men and 29% Caucasians; had mean systolic, diastolic, and pulse pressures of 123.3 mm Hg (SE, 2.7 mm Hg), 74.5 mm Hg (SE, 1.6 mm Hg), and 48.8 mm Hg (SE, 1.7 mm Hg), respectively; and had a mean heart rate of 70.6 beats/min (SE, 1.9 beats/min) ( Table 1 ). No significant differences between the two groups were noted in baseline characteristics, except that patients with diabetes were more likely to be hypertensive, to be on antihypertensive medications, to be current smokers, and to have higher heart rates than controls ( Table 1 ). Of the 21 patients with diabetes, nine were on β-blockers, two were on nondihydropyridine calcium channel blockers, and one was on amiodarone. No controls were on medications that affect heart rate. Despite repeated acquisitions, motion artifacts were still present in scans of the right carotid artery in two patients with diabetes. When the two individuals with motion artifacts were excluded, the baseline differences between the two groups remained similar.

Some participants (seven patients with diabetes, two controls) had atherosclerotic plaques at or above the level of the carotid artery bulb, but none had plaques in the carotid artery segments analyzed. No subject had a history of aortic valve disease, which may influence arterial hemodynamic measurements and potentially strain measurements, or any irregular heart rhythm such as atrial fibrillation.

Overall, side-averaged CAS values (i.e., the individual mean of left and right measures together) were normally distributed among healthy controls ( P > .05) but positively skewed among patients with diabetes ( P < .01) for all segments and their averages. Therefore, all comparison tests were nonparametric. CAS values were lower in patients with diabetes than in controls for the far wall average (4.30% [SE, 0.44%] vs 5.58% [SE, 0.29%], P = .003) and global net average (4.29% [SE, 0.29%] vs 5.48% [SE, 0.30%], P = .001) ( Figure 3 ). When each far wall segment was examined, CAS values continued to be significantly lower in patients with diabetes than in controls for the far wall lateral ( P = .004) and mid segments ( P = .0008) but not for the far wall medial segment ( P = .18) ( Figure 4 ). When scans with motion artifacts were excluded, side-averaged CAS values continued to remain lower in patients with diabetes ( n = 19) than in controls ( n = 20) for the global net average (3.95% [SE, 0.18%] vs 5.48% [SE, 0.30%], P = .0001) and for the far wall average (3.78% [SE, 0.25%] vs 5.58% [SE, 0.29%], P = .0002).

Comparison of peak circumferential CAS in controls and patients with diabetes by net average and far wall average values. Strain values listed are means with standard error bars. Tests of comparison were performed using Wilcoxon’s rank-sum test. ^# P < .05; ^∗ P < .001.

Comparison of peak CAS in controls and patients with diabetes by medial, mid, and lateral far wall segments. Strain values listed are means with standard error bars. Tests of comparison were performed using Wilcoxon’s rank-sum test. ^# P < .05; ^∗ P < .001.

For the entire group (patients with diabetes and healthy controls), the CAS values on the left were much lower than on the right for the global net average ( P = .003), far wall average ( P = .0007), and the far wall segments except for the far wall medial segment, which had no difference ( P < .0001 for far wall mid segment, P = .005 for far wall lateral segment, and P = .33 for far wall medial segment) ( Figure 3 , Supplemental Table 4 ). After exclusion of the two individuals with motion artifacts, the lower left-sided strain values persisted.

There was no significant difference in CAS values derived from measures of luminal dimensions between patients with diabetes (mean CAS of left and right sides, 8.00%; SE, 0.55%) and healthy controls (mean CAS of left and right sides, 7.14%; SE, 0.51%) ( P = 0.32), even after exclusion of the two participants with motion artifact (CAS in patients with diabetes, 8.25% [SE, 0.58%] vs CAS in controls, 7.14% [SE, 0.51%]; P = .16).

Regression analyses were performed on log-transformed values of CAS to normalize the variables. On univariate analysis, diabetic status, current smoking status, and heart rate were significant predictors of lower far wall average and global net average strain values; age and blood pressure measurements, notably pulse pressures ( Supplemental Figure 3 ), were not ( Supplemental Table 5 ). After the exclusion of scans with motion artifacts, this relationship between CAS and predictors persisted ( Supplemental Table 5 ). When the comparisons were adjusted for covariates (model 1, including age, gender, race, heart rate, current and past smoking, and systolic and diastolic blood pressures; model 2, including age, gender, race, heart rate, current and past smoking, and pulse pressure), diabetic status was no longer significantly associated with speckle-tracking–derived CAS values for both models. However, when the two participants with motion artifacts were excluded, diabetic status became significantly associated with the global net average strain ( P = .01 for both models) and the far wall average strain ( P = .01 for model 1, P = .02 for model 2) ( Table 2 ). Overall, these results suggest that diabetes significantly and adversely affects circumferential CAS, even after adjustments for covariates.

Reproducibility Analyses

Given the potential influence of heart rate and pulse pressure on strain values, we tested repeatability with and without consideration of these hemodynamic parameters. When segmental CAS values were examined for significant intervisit differences, all wall segmental CAS values and their averages showed no significant difference between visits ( P > .05). This trend persisted after accounting for heart rate and pulse pressure ( Supplemental Table 1 ).

For the primary repeatability study, test-retest analysis showed the greatest correlation for the far wall segments (ICC = 0.63) and the global net averaged strain of all segments (ICC = 0.40) ( Figure 2 ). The intervisit CVs for the averages of the far wall segments and of all segments were 27% and 26%, respectively. When the ratio of strain to heart rate–pulse pressure product (i.e., heart rate times pulse pressure) was used, test-retest analysis showed the highest correlation again for far wall average (ICC = 0.59; Supplemental Table 2 ). These observations supported our a priori decision to examine the average strain of the three far wall segments for our primary analysis.

Reproducibility of CAS measures using speckle-tracking. Test-retest of image acquisition, intrareader repeatability, and interreader reproducibility by ICC type (2,1) of CAS measures by arterial wall segments on a separate set of volunteers ( n = 10, 20 carotid scans).

Intrareader and interreader reproducibility was assessed for each segment, with agreement given as ICCs. The highest agreements for within-reader (intrareader) and between-reader (interreader) correlations were again observed for the far wall average and the net average of all arterial wall segments ( Figure 2 ). The intrareader CVs for these averages were 15% and 12%, respectively, and the interreader CVs were 14% and 10%, respectively. Bland-Altman plots showed no systematic bias for all reproducibility measures and reasonable intrareader and interreader limits of agreement ( Supplemental Figure 2 , Supplemental Table 3 ).

Comparison of Patients with Diabetes and Healthy Controls

The mean age of all individuals (patients with diabetes and healthy volunteers) was 56.8 years. As a whole, the group consisted of 49% men and 29% Caucasians; had mean systolic, diastolic, and pulse pressures of 123.3 mm Hg (SE, 2.7 mm Hg), 74.5 mm Hg (SE, 1.6 mm Hg), and 48.8 mm Hg (SE, 1.7 mm Hg), respectively; and had a mean heart rate of 70.6 beats/min (SE, 1.9 beats/min) ( Table 1 ). No significant differences between the two groups were noted in baseline characteristics, except that patients with diabetes were more likely to be hypertensive, to be on antihypertensive medications, to be current smokers, and to have higher heart rates than controls ( Table 1 ). Of the 21 patients with diabetes, nine were on β-blockers, two were on nondihydropyridine calcium channel blockers, and one was on amiodarone. No controls were on medications that affect heart rate. Despite repeated acquisitions, motion artifacts were still present in scans of the right carotid artery in two patients with diabetes. When the two individuals with motion artifacts were excluded, the baseline differences between the two groups remained similar.

Some participants (seven patients with diabetes, two controls) had atherosclerotic plaques at or above the level of the carotid artery bulb, but none had plaques in the carotid artery segments analyzed. No subject had a history of aortic valve disease, which may influence arterial hemodynamic measurements and potentially strain measurements, or any irregular heart rhythm such as atrial fibrillation.

Overall, side-averaged CAS values (i.e., the individual mean of left and right measures together) were normally distributed among healthy controls ( P > .05) but positively skewed among patients with diabetes ( P < .01) for all segments and their averages. Therefore, all comparison tests were nonparametric. CAS values were lower in patients with diabetes than in controls for the far wall average (4.30% [SE, 0.44%] vs 5.58% [SE, 0.29%], P = .003) and global net average (4.29% [SE, 0.29%] vs 5.48% [SE, 0.30%], P = .001) ( Figure 3 ). When each far wall segment was examined, CAS values continued to be significantly lower in patients with diabetes than in controls for the far wall lateral ( P = .004) and mid segments ( P = .0008) but not for the far wall medial segment ( P = .18) ( Figure 4 ). When scans with motion artifacts were excluded, side-averaged CAS values continued to remain lower in patients with diabetes ( n = 19) than in controls ( n = 20) for the global net average (3.95% [SE, 0.18%] vs 5.48% [SE, 0.30%], P = .0001) and for the far wall average (3.78% [SE, 0.25%] vs 5.58% [SE, 0.29%], P = .0002).

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