Association between ADAMTS7 polymorphism and carotid artery plaque vulnerability

The Association of ADAMTS7 Gene Polymorphisms with the Risk of Coronary Artery Disease Occurrence and Cardiovascular Survival in the Polish Population: A Case-Control and a Prospective Cohort Study

The aim of this study was to investigate whether the polymorphisms of the ADAMTS7 gene affect the risk of occurrence and mortality due to CAD. The study group included 231 patients diagnosed with CAD and 240 control blood donors. The genotyping of specified polymorphisms, i.e., rs1994016, rs3825807, and rs7173743, was performed using the TaqMan-PCR. We found that the C allele carriers of the rs1994016 and A allele carriers of the rs3825807 polymorphisms increased the risk of CAD, respectively: OR = 1.72, p = 0.036; OR = 1.64, p = 0.04. Moreover, we studied the biological interactions of specified variants, i.e., rs3825807, rs1994016, and rs7173743, and previously approved risk factors of CAD. We demonstrated here that selected polymorphisms of ADAMTS7 increased the risk of CAD altogether with abnormalities of total cholesterol and LDL concentrations in serum. Although survival analyses did not reveal statistical significance, we observed a trend for the AA genotype of the rs3825807 ADAMTS7, which may predispose to death due to CAD in a 5-year follow-up. In conclusion, the ADAMTS7 polymorphisms investigated in this study may increase the risk of occurrence and/or death due to CAD in the Polish population.

A Machine Learning Model for the prediction of the progression of carotid arterial stenoses

Atherosclerotic carotid plaque development results in a steady narrowing of the artery lumen, which may eventually trigger catastrophic plaque rupture leading to thromboembolism and stroke. The primary cause of ischemic stroke in the EU is carotid artery disease, which increases the demand for tools for risk stratification and patient management in carotid artery disease. Additionally, advancements in cardiovascular modeling over the past few years have made it possible to build accurate three-dimensional models of patient-specific primary carotid arteries. Computational models then incorporate the aforementioned 3D models to estimate either the development of atherosclerotic plaque or a number of flow-related parameters that are linked to risk assessment. This work presents an attempt to provide a carotid artery stenosis prognostic model, utilizing non-imaging and imaging data, as well as simulated hemodynamic data. The overall methodology was trained and tested on a dataset of 41 cases with 23 carotid arteries with stable stenosis and 18 carotids with increasing stenosis degree. The highest accuracy of 71% was achieved using a neural network classifier. The novel aspect of our work is the definition of the problem that is solved, as well as the amount of simulated data that are used as input for the prognostic model.Clinical Relevance-A prognostic model for the prediction of the trajectory of carotid artery atherosclerosis is proposed, which can support physicians in critical treatment decisions.

Identification Markers of Carotid Vulnerable Plaques: An Update

Vulnerable plaques have been a hot topic in the field of stroke and carotid atherosclerosis. Currently, risk stratification and intervention of carotid plaques are guided by the degree of luminal stenosis. Recently, it has been recognized that the vulnerability of plaques may contribute to the risk of stroke. Some classical interventions, such as carotid endarterectomy, significantly reduce the risk of stroke in symptomatic patients with severe carotid stenosis, while for asymptomatic patients, clinically silent plaques with rupture tendency may expose them to the risk of cerebrovascular events. Early identification of vulnerable plaques contributes to lowering the risk of cerebrovascular events. Previously, the identification of vulnerable plaques was commonly based on imaging technologies at the macroscopic level. Recently, some microscopic molecules pertaining to vulnerable plaques have emerged, and could be potential biomarkers or therapeutic targets. This review aimed to update the previous summarization of vulnerable plaques and identify vulnerable plaques at the microscopic and macroscopic levels.

A Machine Learning Model for the Identification of High risk Carotid Atherosclerotic Plaques

Carotid artery disease is an inflammatory condition involving the deposition and accumulation of lipid species and leucocytes from blood into the arterial wall, which causes the narrowing of the carotid arteries on either side of the neck. Different imaging modalities can by implemented to determine the presence and the location of carotid artery stenosis, such as carotid ultrasound, computed tomography angiography (CTA), magnetic resonance angiography (MRA), or cerebral angiography. However, except of the presence and the degree of stenosis of the carotid arteries, the vulnerability of the carotid atherosclerotic plaques constitutes a significant factor for the progression of the disease and the presence of disease symptoms. In this study, our aim is to develop and present a machine learning model for the identification of high risk plaques using non imaging based features and non-invasive imaging based features. Firstly, we implemented statistical analysis to identify the most statistical significant features according to the defined output, and subsequently, we implemented different feature selection techniques and classification schemes for the development of our machine learning model. The overall methodology has been trained and tested using 208 cases of 107 cases of low risk plaques and 101 cases of high risk plaques. The highest accuracy of 0.76 was achieved using the relief feature selection technique and the support vector machine classification scheme. The innovative aspect of the proposed machine learning model is both the different categories of the utilized input features and the definition of the problem to be solved.

Coronary Disease Association With ADAMTS7 Is Due to Protease Activity

[Figure: see text].

Profile of genetic variations in severely calcified carotid plaques by whole-exome sequencing

Background:

The precise mechanisms of carotid calcification and its clinical significance have not been established.

Methods:

We classified ten plaques from carotid endarterectomy patients into high- and low-calcified plaques based on the Agatston calcium scores. We performed whole-exome sequencing for genetic profiles with single nucleotide variations (SNVs), insertions, and deletions. Bioinformatic data mining was then conducted to disclose specific gene variations to either high- or low-calcified carotid plaques.

Results:

In the carotid plaques, G:C>A:T/C:G>T:A transitions as SNVs, insT after C/insC after A as insertions, and delA after G/delT after C as deletions were most frequently observed, but no significant difference was observed between the high- and low-calcified plaque groups in their proportion of base-pair substitution types. In the bioinformatic analysis, SNVs of ATP binding cassette subfamily C member 6 (ADCC6) were more commonly found in high-calcified plaques and SNVs of KLKB1 were more commonly found in low-calcified plaques compared to the other group. No new genetic variants related to calcification or atherosclerosis among those not registered in dbSNP was detected.

Conclusion:

Our findings clarified the features of base-pair substitutions in carotid plaques, showing no relation to calcification. However, genetic variants in ADCC6 relating to vascular calcification for high-calcified plaques, and in KLKB1 encoding kallikrein associated with vascular regulation of atherosclerosis for low-calcified plaques were more specifically extracted. These results contribute to a better understanding of the genetic basis of molecular activity and calcium formation in carotid plaques.