Can prediabetes diagnosed using HemoglobinA1c or oral glucose tolerance test predict presence and severity of coronary artery disease in symptomatic patients?

We investigated whether prediabetes diagnosed by hemoglobinA1c (HbA1c) or oral glucose tolerance test (OGTT) could predict presence and severity of coronary artery disease (CAD) in symptomatic patients. The presence of plaque, stenosis, plaque characteristics, and coronary artery calcium (CAC) were evaluated by coronary CT angiography in 702 patients with suspicion of CAD. Patients were classified by glycemic status using the American Diabetes Association criteria for HbA1c and OGTT, and compared to their respective normal ranges. Prediabetes was observed in 24% by HbA1c and 72% by OGTT. Both prediabetes classifications were associated with increased presence of plaque, stenosis, calcified plaques, CAC >400, and a lower frequency of zero CAC compared to their respective normal range (all, p < 0.05). After adjusting for potential confounders, patients with HbA1c-prediabetes had an odds ratio of 2.1 (95% CI: 1.3-3.5) for CAC >400 and 1.5 (95% CI: 1.0-2.4) for plaque presence, while none of the associations for OGTT-prediabetes were significant. The receiver operating characteristic-curve for HbA1c-prediabetes showed an area under the curve of 0.81 for CAC >400 and 0.77 for plaque presence. Prediabetes defined by HbA1c predicts presence and severity of CAD. Although OGTT identified more patients with prediabetes, their risk of CAD were not explained by prediabetes using these diagnostic-criteria.

The Effect of Clinically Indicated Liraglutide on Pericoronary Adipose Tissue in Type 2 Diabetic Patients

Vascular inflammation can be detected in the pericoronary adipose tissue (PCAT) by coronary computed tomography angiography (CCTA) attenuation. Treatment with liraglutide is associated with anti-inflammatory effects and reduces cardiovascular risk in diabetic patients. This study is aimed at examining the effect of clinically indicated liraglutide on PCAT attenuation. Asymptomatic patients with type 2 diabetes mellitus (T2DM) and without known ischemic heart disease underwent clinical examination, blood analysis, and CCTA. The main coronary arteries were outlined and PCAT attenuation was measured on the proximal 40 mm. Patients treated with liraglutide on a clinical indication were compared to patients not receiving liraglutide. The study included 190 patients; 53 (28%) received liraglutide (Lira+) and 137 (72%) did not (Lira-). There were no significant differences in PCAT attenuation between the two groups in either artery. However, PCAT attenuation measured around the left anterior descending artery (LAD) was lower in the Lira+ group after adjustment for age, sex, body mass index, and T2DM duration (b coefficient -2.4, p = 0.029). In a population of cardiac asymptomatic T2DM patients, treatment with clinically indicated liraglutide was not associated with differences in PCAT attenuation compared to nonliraglutide treatment in the unadjusted model. An association was seen in the adjusted model for the left anterior descending artery, possibly indicating an anti-inflammatory effect.

Impact of a motion correction algorithm on image quality in patients undergoing CT angiography: A randomized controlled trial

AIMS

To investigate the motion correction algorithm Snapshot-Freeze (SSF) compared to standard reconstruction (STD) in patients randomized to receive beta-blockers (BB) or no beta-blockers (non-BB) before coronary CT, and to investigate if SSF can replace BB.

METHODS

One hundred and forty patients scheduled for coronary CT were randomized. All images were reconstructed by the SSF and STD algorithms. Image quality was evaluated according to Likert score (1: excellent, 2: good, 3: adequate, 4: non-diagnostic) and presence of artifacts was noted.

RESULTS

Images from 64 patients in the BB group (mean HR 56±4bpm) and 51 patients in the non-BB group (mean HR 67±7bpm) were analyzed. Twenty five patients were excluded because of tachycardia, bradycardia or reconstruction errors in SSF. SSF increased the number of excellent images in both groups compared to the STD algorithm (BB: 59% vs.44%; non-BB: 25% vs. 8%), but the number of non-diagnostic images was not significantly reduced. SSF reduced motion artifacts (BB: 11% vs. 31%; non-BB: 49% vs. 75%), but despite this reduction, motion artifacts in non-BB were still more frequent compared to the BB group analyzed by STD (49% vs. 31%).

CONCLUSION

SSF improves image quality and reduces motion artifacts, but does not compensate for the absence of BB.