Atherosclerosis Risk in Communities (ARIC) is a new prospective study to investigate the etiology of atherosclerosis and its clinical sequelae and variation in cardiovascular risk factors, medical care, and disease by race, sex, place, and time. In each of four US communities--Forsyth County, North Carolina, Jackson, Mississippi, suburbs of Minneapolis, Minnesota, and Washington County, Maryland--4,000 adults aged 45-64 years will be examined twice, three years apart. ARIC has coordinating, ultrasound, pulmonary, and electrocardiographic centers and three central laboratories. Three cohorts represent the ethnic mix of their communities; the Jackson cohort, its black population. Examinations include ultrasound scanning of carotid and popliteal arteries; lipids, lipoproteins, and apolipoproteins assayed in the Lipid Laboratory; and coagulation, inhibition, and platelet and fibrinolytic activity assayed in the Hemostasis Laboratory. Surveillance for coronary heart disease will involve review of hospitalizations and deaths among community residents aged 35-74 years. ARIC aims to study atherosclerosis by direct observation of the disease and by use of modern biochemistry.
In this work, we study the problem pertaining to personalized classification of subclinical atherosclerosis by developing a hierarchical graph neural network framework to leverage two characteristic modalities of a patient: clinical features within the context of the cohort, and molecular data unique to individual patients. Current graph-based methods for disease classification detect patient-specific molecular fingerprints, but lack consistency and comprehension regarding cohort-wide features, which are an essential requirement for understanding pathogenic phenotypes across diverse atherosclerotic trajectories. Furthermore, understanding patient subtypes often considers clinical feature similarity in isolation, without integration of shared pathogenic interdependencies among patients. To address these challenges, we introduce ATHENA: Atherosclerosis Through Hierarchical Explainable Neural Network Analysis, which constructs a novel hierarchical network representation through integrated modality learning; subsequently, it optimizes learned patient-specific molecular fingerprints that reflect individual omics data, enforcing consistency with cohort-wide patterns. With a primary clini
As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow pro
The BReast CAncer type 2 susceptibility protein (BRCA2) responds to DNA damage by participating in homology-directed repair. BRCA2 deficiency culminates in defective DNA damage repair (DDR) that when prolonged leads to the accumulation of DNA damage causing cancer or apoptosis. Oxidative stress promotes DNA damage and apoptosis and is a common mechanism through which cardiovascular risk factors lead to endothelial dysfunction and atherosclerosis. Herein, we show that endothelial BRCA2 plays a protective role against atherosclerosis under hypercholesterolemic stress. We successfully generated and characterized endothelial cell (EC)-specific BRCA2 knockout (BRCA2endo) mice. To study the effect of EC-specific BRCA2-loss in atherosclerosis, we generated and characterized BRCA2endo mice on apolipoprotein E null background (ApoE-/-), fed them with high-fat diet (HFD) and evaluated atherosclerosis. Baseline phenotyping of BRCA2endo mice did not show any adverse effects in terms of DNA damage and apoptosis as well as cardiac and metabolic function. However, using HFD-fed apolipoprotein E knockout (ApoE-/-) background, we demonstrated that EC-specific loss of BRCA2 resulted in aortic plaque
Atherosclerosis is a major cardiovascular complication of diseases associated with elevated oxidative stress such as type 2 diabetes and metabolic syndrome. In these situations, low density lipoproteins (LDL) undergo oxidation. Oxidized LDL display proatherogenic activities through multiple and complex mechanisms which lead to dysfunctions of vascular cells (endothelial cells, smooth muscle cells and macrophages). Oxidized LDL are enriched in oxidized products of cholesterol called oxysterols formed either by autoxidation, enzymatically, or by both mechanisms. Several oxysterols have been shown to accumulate in atheroma plaques and to play a key role in atherogenesis. Depending on the type of oxysterols, various biological effects are exerted on vascular cells to regulate the formation of macrophage foam cells, endothelial integrity, adhesion and transmigration of monocytes, plaque progression and instability. Most of these effects are linked to the ability of oxysterols to induce cellular oxidative stress and cytotoxicity mainly through apoptosis and proinflammatory mediators. Like for excess cholesterol, high density lipoproteins (HDL) can exert antiatherogenic activity stimulati
Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3
The objective of this study is to develop a deep-learning based detection and diagnosis technique for carotid atherosclerosis using a portable freehand 3D ultrasound (US) imaging system. A total of 127 3D carotid artery scans were acquired using a portable 3D US system which consisted of a handheld US scanner and an electromagnetic tracking system. A U-Net segmentation network was firstly applied to extract the carotid artery on 2D transverse frame, then a novel 3D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume. Furthermore, a convolutional neural network was used to classify healthy and diseased cases qualitatively. 3D volume analysis methods including longitudinal image acquisition and stenosis grade measurement were developed to obtain the clinical metrics quantitatively. The proposed system achieved sensitivity of 0.714, specificity of 0.851 and accuracy of 0.803 respectively for diagnosis of carotid atherosclerosis. The automatically measured stenosis grade illustrated good correlation (r=0.762) with the experienced expert measurement. The developed technique based on 3D US imag
We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to model pathological changes (e.g., atherosclerotic plaques) in coronary vessels. The system learns the discriminatory features between vessels with and without atherosclerosis. The discriminative features at the final convolutional layer are visualized with a saliency map approach to provide visual clues related to atherosclerosis likelihood and location. We have evaluated the system on a reference dataset representing247 patients with atherosclerosis and 246 patients free of atherosclerosis. With five-fold cross-validation,an Accuracy = 90.9%, Positive Predictive Value = 58.8%, Sensitivity = 68.
Atherosclerotic plaques are accumulations of cholesterol-engorged macrophages in the artery wall. Plaque growth is initiated and sustained by the deposition of low density lipoproteins (LDL) in the artery wall. High density lipoproteins (HDL) counterbalance the effects of LDL by accepting cholesterol from macrophages and removing it from the plaque. In this paper, we develop a free boundary multiphase model to investigate the effects of LDL and HDL on early plaque development. We examine how the rates of LDL and HDL deposition affect cholesterol accumulation in macrophages, and how this impacts cell death rates and emigration. We identify a region of LDL-HDL parameter space where plaque growth stabilises for low LDL and high HDL influxes, due to macrophage emigration and HDL clearance that counterbalances the influx of new cells and cholesterol. We explore how the efferocytic uptake of dead cells and the recruitment of new macrophages affect plaque development for a range of LDL and HDL levels. Finally, we consider how changes in the LDL-HDL profile can change the course of plaque development. We show that changes towards lower LDL and higher HDL can slow plaque growth and even ind
Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D continuous labels for training 3D segmentation model. With the trained model, we generate pseudo labels for unlabeled slices to incorporate them for model training. Then we use the whole MR scans and the propagated labels to re-train the segmentation model and improve its robustness. We evaluated the label propagation framework on the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score of 83.41\% on the testing dataset, which got the 1-st place on the online evaluation leaderboard. The results demonstrate the effectiveness of th
Atherosclerosis, a chronic lesion of vascular wall, remains a leading cause of death and loss of life years. Classical hypotheses for atherosclerosis are long-standing mainly to explain atherogenesis. Unfortunately, these hypotheses may not explain the variation in the susceptibility to atherosclerosis. These issues are controversial over the past 150 years. Atherosclerosis from human coronary arteries was examined and triangle of media was found to be a true portraiture of cells injury in the media, and triangle of intima was a true portraiture of myofibroblast proliferation, extracellular matrix (ECM) secretion, collagen fiber formation and intimal thickening to repair media dysfunction. Myofibroblasts, ECM and lumen (intima)/vasa vasorum (VV) (adventitia) constitute granulation tissue repair. With granulation tissue hyperplasia, lots of collagen fibers (normal or denatured), foam cells and new capillaries formed. Thus, the following theory was postulated: Risk factors induce smooth muscle cells (SMCs) injury/loss, and fibrosis or structure destruction could be developed in the media, which lead to media dysfunction. Media dysfunction prompts disturbed mechanical properties of bl
Atherosclerosis is one of the principle pathologies of cardiovascular disease with blood cholesterol a significant risk factor. The World Health Organisation estimates that approximately 2.5 million deaths occur annually due to the risk from elevated cholesterol with 39% of adults worldwide at future risk. Atherosclerosis emerges from the combination of many dynamical factors, including haemodynamics, endothelial damage, innate immunity and sterol biochemistry. Despite its significance to public health, the dynamics that drive atherosclerosis remain poorly understood. As a disease that depends on multiple factors operating on different length scales, the natural framework to apply to atherosclerosis is mathematical and computational modelling. A computational model provides an integrated description of the disease and serves as an in silico experimental system from which we can learn about the disease and develop therapeutic hypotheses. Although the work completed in this area to-date has been limited, there are clear signs that interest is growing and that a nascent field is establishing itself. This paper discusses the current state of modelling in this area, bringing together ma
This review focuses on the role of oxidative processes in atherosclerosis and its resultant cardiovascular events. There is now a consensus that atherosclerosis represents a state of heightened oxidative stress characterized by lipid and protein oxidation in the vascular wall. The oxidative modification hypothesis of atherosclerosis predicts that low-density lipoprotein (LDL) oxidation is an early event in atherosclerosis and that oxidized LDL contributes to atherogenesis. In support of this hypothesis, oxidized LDL can support foam cell formation in vitro, the lipid in human lesions is substantially oxidized, there is evidence for the presence of oxidized LDL in vivo, oxidized LDL has a number of potentially proatherogenic activities, and several structurally unrelated antioxidants inhibit atherosclerosis in animals. An emerging consensus also underscores the importance in vascular disease of oxidative events in addition to LDL oxidation. These include the production of reactive oxygen and nitrogen species by vascular cells, as well as oxidative modifications contributing to important clinical manifestations of coronary artery disease such as endothelial dysfunction and plaque disruption. Despite these abundant data however, fundamental problems remain with implicating oxidative modification as a (requisite) pathophysiologically important cause for atherosclerosis. These include the poor performance of antioxidant strategies in limiting either atherosclerosis or cardiovascular events from atherosclerosis, and observations in animals that suggest dissociation between atherosclerosis and lipoprotein oxidation. Indeed, it remains to be established that oxidative events are a cause rather than an injurious response to atherogenesis. In this context, inflammation needs to be considered as a primary process of atherosclerosis, and oxidative stress as a secondary event. To address this issue, we have proposed an "oxidative response to inflammation" model as a means of reconciling the response-to-injury and oxidative modification hypotheses of atherosclerosis.
Atherosclerosis is a state wherein plaque (fat, cholesterol, and different substances) develops inside the veins that in the end prompts carotid artery stenosis which is a phase of narrowing in the huge courses situated on either side of the neck that convey blood to the brain, face, and head. Carotid stenosis is regularly connected with perpetual injury of an aspect of the brain (strokes) because of loss of its blood flexibly. For instance, ischemia generally brings about serious handicap or demise. Hematocrit or pressed cell volume (PCV) is the volume of red blood corpuscles according to that of entire blood. The reason for our exploration is to carry out a Computational Fluid Dynamics (CFD) investigation of blood stream with the rate changes of hematocrit to examine the hemodynamic and physiological conduct of atherosclerosis. Our examination a developed 2D calculation model that has been investigated utilizing Finite volume technique (FVM) for interesting phases of atherosclerosis. The point of this investigation is to investigate the social bits of knowledge into the velocity slope, wall shear stress, and pressure gradient of carotid corridor under various rates of hematocrit
Atherosclerosis of the carotid artery increases stroke risk. Atherosclerosis assessment with MRI requires multimodal and multidimensional segmentation of the carotid artery, reproducible extraction of biomarkers, and the visualization of segmentations and biomarkers. We developed CaroTo, a tool that allows for standardized carotid atherosclerosis assessment. It combines the capabilities of MEVISFlow with specialized tools for carotid geometry and vessel wall assessment. It supports manual and automatic segmentation for 2D, 2D+time, and 3D images, facilitating precise and consistent evaluations of carotid artery stenosis.
Atherosclerosis is a chronic inflammatory disease of the artery wall. The early stages of atherosclerosis are driven by interactions between lipids and monocyte-derived-macrophages (MDMs). The mechanisms that govern the spatial distribution of lipids and MDMs in the lesion remain poorly understood. In this paper, we develop a spatially-resolved and lipid-structured model for early atherosclerosis. The model development and analysis are guided by images of human coronary lesions by Nakashima et al. 2007. Consistent with their findings, the model predicts that lipid initially accumulates deep in the intima due to a spatially non-uniform LDL retention capacity. The model also qualitatively reproduces the global internal maxima in the Nakashima images only when the MDM mobility is sufficiently sensitive to lipid content, and MDM lifespan sufficiently insensitive. Introducing lipid content-dependence to MDM mobility and mean lifespan produced minimal impact on model behaviour at early times, but strongly impacted lesion composition at steady state. Increases to the sensitivity of MDM lifespan to lipid content yield lesions with fewer MDMs, less total lesion lipid content and reduced mea
Amateur marathon runners who exercise excessively over time have pathological structural changes in their hearts and aortas. Amateur marathon runners' cardiovascular system adaptations and dangers are discussed in this article. After completing endurance races, amateur athletes experience a series of cardiac modifications, including temporary elevation and changes in biomarkers of cardiac damage associated with an increased risk of coronary atherosclerosis, arrhythmias, and sudden cardiac death. As a result of the high prevalence of "false positive" biomarkers in athletes, the health benefits of aerobic activity are questioned, and treatment is complicated. Reports on long-term aerobic exercise contradict atherosclerosis risk. Differences may influence the results in lifestyle characteristics among participants. By comparing runners with their non-runner wives, we find that regular, high intensity run training improves many components of the cardiovascular profile but does not reduce atherosclerosis. Although metabolomic approaches have been developed to evaluate the physiological response of marathon runners, there is still controversy concerning the biomarkers of cardiovascular s
The analysis of carotid arteries, particularly plaques, in multi-sequence Magnetic Resonance Imaging (MRI) data is crucial for assessing the risk of atherosclerosis and ischemic stroke. In order to evaluate metrics and radiomic features, quantifying the state of atherosclerosis, accurate segmentation is important. However, the complex morphology of plaques and the scarcity of labeled data poses significant challenges. In this work, we address these problems and propose a semi-supervised deep learning-based approach designed to effectively integrate multi-sequence MRI data for the segmentation of carotid artery vessel wall and plaque. The proposed algorithm consists of two networks: a coarse localization model identifies the region of interest guided by some prior knowledge on the position and number of carotid arteries, followed by a fine segmentation model for precise delineation of vessel walls and plaques. To effectively integrate complementary information across different MRI sequences, we investigate different fusion strategies and introduce a multi-level multi-sequence version of U-Net architecture. To address the challenges of limited labeled data and the complexity of carot
Observational cohort data is an important source of information for understanding the causal effects of treatments on survival and the degree to which these effects are mediated through changes in disease-related risk factors. However, these analyses are often complicated by irregular data collection intervals and the presence of longitudinal confounders and mediators. We propose a causal mediation framework that jointly models longitudinal exposures, confounders, mediators, and time-to-event outcomes as continuous functions of age. This framework for longitudinal covariate trajectories enables statistical inference even at ages where the subject's covariate measurements are unavailable. The observed data distribution in our framework is modeled using an enriched Dirichlet process mixture (EDPM) model. Using data from the Atherosclerosis Risk in Communities cohort study, we apply our methods to assess how medication -- prescribed to target cardiovascular disease (CVD) risk factors -- affects the time-to-CVD death.
A mathematical model of atherosclerosis of a blood vessel is advanced with regard for the entry of low-density lipoproteins (LDLs) into blood. For the first time, the influence of cytokines on the inflammation of a blood vessel at the formation of atherosclerotic plaques is taken into account. With the help of the expansion in a Fourier series and the calculation of an invariant measure, the scenario of the appearance of strange attractors depending on a change in the parameter of the dissipation of cholesterol is studied. The conclusion is made about the interconnection of the dynamics of the metabolic process in a blood vascular system and its physical state.