Comparison of Carotid Atherosclerotic Plaque Characteristics between Symptomatic Patients with Transient Ischemic Attack and Stroke using High-Resolution Magnetic Resonance Imaging

Background:This study aimed to compare the characteristics of carotid plaques between patients with transient ischemic attack (TIA) and ischemic stroke using magnetic resonance (MR) imaging.Methods：Patients with a recent ischemic stroke or TIA who exhibited atherosclerotic plaques of carotid arteries in the symptomatic sides determined by MR vessel wall imaging were recruited. The plaque morphology and compositions including intraplaque hemorrhage (IPH), lipid-rich necrotic-core (LRNC) and calcification were compared between TIA and stroke patients. Logistic regression was performed to relate the plaque characteristics to the types of ischemic events.Results:A total of 270 patients with TIA or ischemic stroke were recruited. Stroke patients had significantly higher prevalence of diabetes (42.2% vs. 28.2%, p=0.021), greater mean wall area (35.1 ± 10.1 mm2 vs. 32.0 ± 7.7 mm2, p = 0.004), mean wall thickness (1.3 ± 0.2 mm vs. 1.2 ± 0.2 mm, p=0.001), maximum normalized wall index (NWI)(63.9% ± 6.0% vs. 62.2% ± 5.9%, p=0.023) and %volume of LRNC (9.7%± 8.2% vs. 7.4% ±7.9%, p=0.025) in carotid arteries compared to those with TIA. After adjusted for clinical factors, above characteristics of carotid arteries were significantly associated with the type of ischemic events. After further adjusted for maximum NWI, this association remained statistically significant (OR, 1.41; CI, 1.01-1.96; p=0.041).Conclusions:Ischemic stroke patients had larger plaque burden and greater proportion of LRNC in carotid plaques compared to those with TIA. This study suggests that ischemic stroke patients had more vulnerable plaques compared to those with TIA.

Identification of Symptomatic Carotid Artery Plaque: A Three-Item Scale Combined Angiography With Optical Coherence Tomography

Introduction: Symptomatic carotid disease conveys a high risk of recurrent stroke. Plaque morphology and specific plaque characteristics are associated with the risk of stroke. This study aimed to evaluate the detailed plaque features by optical coherence tomography (OCT) and develop a simple scale combining clinical indicators, digital subtraction angiography (DSA), and OCT imaging markers to identify symptomatic carotid plaque.Methods: Carotid plaques from consecutive patients who underwent carotid OCT imaging between June 2017 and June 2021 were evaluated. Clinical characteristics, DSA, and OCT data were compared between the symptomatic and asymptomatic groups. Logistic regression was performed to identify the factors associated with symptomatic carotid plaque and to develop a scale. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the scale.Results: A total of 90 carotid plaques from 90 patients were included (symptomatic 35.6%, asymptomatic 64.4%). Three main factors were found to be associated with symptomatic carotid plaque: high-density lipoprotein cholesterol (HDL-C) <0.925 mmol/L (OR, 4.708; 95% CI, 1.640 to 13.517; P = 0.004), irregular plaque (OR, 4.017; 95% CI, 1.250 to 12.910; P = 0.020), and white thrombus (OR, 4.594; 95% CI, 1.141 to 18.487; P = 0.032). The corresponding score of three items produced a scale with good discrimination (AUC, 0.768; 95% CI, 0.665 to 0.871). The optimal cutoff value of the scale was 1.5 points with 59.4% sensitivity and 84.5% specificity.Conclusion: The three-item scale comprising HDL-C <0.925 mmol/L, angiographical irregular plaque, and white thrombus detected by OCT may provide information to identify symptomatic carotid plaque. Further large-scale studies are required to validate whether the symptomatic carotid plaque scale is clinically valuable in recognizing carotid atherosclerosis in the early stages.

CT Angiography-Based Radiomics as a Tool for Carotid Plaque Characterization and Surgery Patient Selection: A Pilot Study

Objectives: Radiomics is a quantitative method able to analyze a high-throughput extraction of minable imaging features. Herein, we aim to develop a CT angiography-based radiomics analysis and machine learning model for carotid plaques to discriminate vulnerable from no vulnerable plaques.Methods: Thirty consecutive patients with carotid atherosclerosis were enrolled in this pilot study. At surgery, a binary classification of plaques was adopted (“hard” vs “soft”). Feature extraction was performed using the R software package Moddicom. Pairwise feature interdependencies were evaluated using the Spearman rank correlation coefficient. A univariate analysis was performed to assess the association between each feature and the plaque classification and chose top-ranked features. The feature predictive value was investigated using binary logistic regression. A stepwise backward elimination procedure was performed to minimize the Akaike information criterion (AIC). The final significant features were used to build the models for binary classification of carotid plaques, including logistic regression (LR), support vector machine (SVM), and classification and regression tree analysis (CART). All models were cross-validated using 5-fold cross validation. Class-specific accuracy, precision, recall and F-measure evaluation metrics were used to quantify classifier output quality.Results: A total of 230 radiomics features were extracted from each plaque. Pairwise Spearman correlation between features reported a high level of correlations, with more than 80% correlating with at least one other feature at |ρ| > 0.8. After a stepwise backward elimination procedure, the entropy and volume features were found to be the most significantly associated with the two plaque groups (p< 0.001), with AUCs of 0.92 and 0.96, respectively. The best performance was registered by the SVM classifier with the RBF kernel, with accuracy, precision, recall and F-score equal to 86.7, 92.9, 81.3 and 86.7%, respectively. The CART classification tree model for the entropy and volume features model achieved 86.7% well-classified plaques and an AUC of 0.987.Conclusion: This pilot study highlighted the potential of CTA-based radiomics and machine learning to discriminate plaque composition. This new approach has the potential to provide a reliable method to improve risk stratification in patients with carotid atherosclerosis.

Factors associated with the elasticity of asymptomatic carotid plaques in hypertension Tibetan population

Aims Hypertension is a major risk factor for coronary, cerebrovascular, and the greatest cause of stroke. Half of stroke events are the result of cerebrovascular atherosclerosis, including carotid plaques. It is of major importance to detect which plaques are vulnerable, even though not yet ruptured. Due to the particularity of the Tibetan population, this study evaluated the elasticity of asymptomatic carotid plaques in Tibetan hypertension patients using shear wave elastography (SWE) and explored associated risk factors. Methods A total of 91 Tibetan patients were divided into normotension, grade 1-2, and grade 3 hypertension groups based on the level of blood pressure. All subjects underwent common duplex ultrasonic examination and SWE evaluation for carotid plaques. Elasticity of carotid plaque was assessed by Young’s modulus. Results The final analysis included 126 plaques as representative plaques according to Total Plaque Risk Score. The mean and maximum Young’s modulus in the grade 3 hypertension group were smaller, and more plaques with irregularity surface compared with the other two groups ( p < 0.05). Multivariate regression analysis showed drinking butter tea (β = −0.220, p = 0.009; β = −0.240, p = 0.004, respectively) was the independent factor associated with mean and maximum Young’s modulus. Conclusions SWE is feasible for measurement of Young’s modulus of carotid plaques. Plaques in the grade 3 hypertension group were more likely to become vulnerable ones. In hypertension Tibetan patients, drinking butter tea was an independent factor associated with mean and maximum Young’s modulus of asymptomatic carotid plaque.

Association of Circulating miR-145-5p and miR-let7c and Atherosclerotic Plaques in Hypertensive Patients

Aim: Hypertension is a strong risk factor for atherosclerosis. Increased carotid intima-media thickness (cIMT) and carotid plaques are considered subclinical markers of atherosclerosis. This study aimed at evaluating the serum expression of miRNAs previously related to adverse vascular remodeling and correlating them with carotid plaques and cIMT in hypertensive patients. Methods: We cross-sectionally evaluated the clinical and carotid characteristics as well as serum expression of miR-145-5p, miR-let7c, miR-92a, miR-30a and miR-451 in 177 hypertensive patients. Carotid plaques and cIMT were evaluated by ultrasound, and the expression of selected miRNAs was evaluated by a quantitative polymerase chain reaction. Results: Among all participants (age = 60.6 ± 10.7 years, 43% males), there were 59% with carotid plaques. We observed an increased expression of miR-145-5p (Fold Change = 2.0, p = 0.035) and miR-let7c (Fold Change = 3.8, p = 0.045) in participants with atherosclerotic plaque when compared to those without plaque. In the logistic regression analysis adjusted for relevant covariates, these miRNAs showed a stronger association with carotid plaques (miR-145-5p: Beta ± SE = 0.050 ± 0.020, p = 0.016 and miR-let7c: Beta ± SE = 0.056 ± 0.019, p = 0.003). Conclusions: Hypertensive patients with carotid plaques have an increased expression of miR-145-5p and miR-let7c, suggesting a potential role of these miRNAs as a biomarker for subclinical atherosclerosis in hypertensive individuals.

Carotid plaque thickness is increased in chronic kidney disease and associated with carotid and coronary calcification

Background Chronic kidney disease accelerates both atherosclerosis and arterial calcification. The aim of the present study was to explore whether maximal carotid plaque thickness (cPTmax) was increased in patients with chronic kidney disease compared to controls and associated with cardiovascular disease and severity of calcification in the carotid and coronary arteries. Methods The study group consisted of 200 patients with chronic kidney disease stage 3 from the Copenhagen Chronic Kidney Disease Cohort and 121 age- and sex-matched controls. cPTmax was assessed by ultrasound and arterial calcification by computed tomography scanning. Results Carotid plaques were present in 58% of patients (n = 115) compared with 40% of controls (n = 48), p = 0.002. Among participants with plaques, cPTmax (median, interquartile range) was significantly higher in patients compared with controls (1.9 (1.4–2.3) versus 1.5 (1.2–1.8) mm), p = 0.001. Cardiovascular disease was present in 9% of patients without plaques (n = 85), 23% of patients with cPTmax 1.0–1.9 mm (n = 69) and 35% of patients with cPTmax >1.9 mm (n = 46), p = 0.001. Carotid and coronary calcium scores >400 were present in 0% and 4%, respectively, of patients with no carotid plaques, in 19% and 24% of patients with cPTmax 1.0–1.9 mm, and in 48% and 53% of patients with cPTmax >1.9 mm, p<0.001. Conclusions This is the first study showing that cPTmax is increased in patients with chronic kidney disease stage 3 compared to controls and closely associated with prevalent cardiovascular disease and severity of calcification in both the carotid and coronary arteries.

Cerebrovascular Risk-Factors of Prevalent and Incident Brain Infarcts in the General Population: The AGES-Reykjavik Study

Background and Purpose: Studies on the association of cerebrovascular risk factors to magnetic resonance imaging detected brain infarcts have been inconsistent, partly reflecting limits of assessment to infarcts anywhere in the brain, as opposed to specific brain regions. We hypothesized that risk-factors may differ depending on where the infarct is located in subcortical-, cortical-, and cerebellar regions. Methods: Participants (n=2662, mean age 74.6±4.8) from the longitudinal population-based AGES (Age, Gene/Environment Susceptibility)-Reykjavik Study underwent brain magnetic resonance imaging at baseline and on average 5.2 years later. We assessed the number and location of brain infarcts (prevalent versus incident). We estimated the risk-ratios of prevalent (PRR) and incident (IRR) infarcts by baseline cerebrovascular risk-factors using Poisson regression. Results: Thirty-one percent of the study participants had prevalent brain infarcts and 21% developed new infarcts over 5 years. Prevalent subcortical infarcts were associated with hypertension (PRR, 2.7 [95% CI, 1.1–6.8]), systolic blood pressure (PRR, 1.2 [95% CI, 1.1–1.4]), and diabetes (PRR, 2.8 [95% CI, 1.9–4.1]); incident subcortical infarcts were associated with systolic (IRR, 1.2 [95% CI, 1.0–1.4]) and diastolic (IRR, 1.3 [95% CI, 1.0–1.6]) blood pressure. Prevalent and incident cortical infarcts were associated with carotid plaques (PRR, 1.8 [95% CI, 1.3–2.5] and IRR, 1.9 [95% CI, 1.3–2.9], respectively), and atrial fibrillation was significantly associated with prevalent cortical infarcts (PRR, 1.8 [95% CI, 1.2–2.7]). Risk-factors for prevalent cerebellar infarcts included hypertension (PRR, 2.45 [95% CI, 1.5–4.0]), carotid plaques (PRR, 1.45 [95% CI, 1.2–1.8]), and migraine with aura (PRR, 1.6 [95% CI, 1.1–2.2]). Incident cerebellar infarcts were only associated with any migraine (IRR, 1.4 [95% CI, 1.0–2.0]). Conclusions: The risk for subcortical infarcts tends to increase with small vessel disease risk-factors such as hypertension and diabetes. Risk for cortical infarcts tends to increase with atherosclerotic/coronary processes and risk for cerebellar infarcts with a more mixed profile of factors. Assessment of risk-factors by location of asymptomatic infarcts found on magnetic resonance imaging may improve the ability to target and optimize preventive therapeutic approaches to prevent stroke.