Patient-specific computational modeling of subendothelial LDL accumulation in a stenosed right coronary artery: effect of hemodynamic and biological factors

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.

Differential sensitivities to blood pressure variations in internal carotid and intracranial arteries: a numerical approach to stroke prediction

Stroke remains a global health concern, necessitating early prediction for effective management. Atherosclerosis-induced internal carotid and intra cranial stenosis contributes significantly to stroke risk. This study explores the relationship between blood pressure and stroke prediction, focusing on internal carotid artery (ICA) branches: middle cerebral artery (MCA), anterior cerebral artery (ACA), and their role in hemodynamics. Computational fluid dynamics (CFD) informed by the Windkessel model were employed to simulate patient-specific ICA models with introduced stenosis. Central to our investigation is the impact of stenosis on blood pressure, flow velocity, and flow rate across these branches, incorporating Fractional Flow Reserve (FFR) analysis. Results highlight differential sensitivities to blood pressure variations, with M1 branch showing high sensitivity, ACA moderate, and M2 minimal. Comparing blood pressure fluctuations between ICA and MCA revealed heightened sensitivity to potential reverse flow compared to ICA and ACA comparisons, emphasizing MCA's role. Blood flow adjustments due to stenosis demonstrated intricate compensatory mechanisms. FFR emerged as a robust predictor of stenosis severity, particularly in the M2 branch. In conclusion, this study provides comprehensive insights into hemodynamic complexities within major intracranial arteries, elucidating the significance of blood pressure variations, flow attributes, and FFR in stenosis contexts. Subject-specific data integration enhances model reliability, aiding stroke risk assessment and advancing cerebrovascular disease understanding.

Computational modeling of low-density lipoprotein accumulation at the carotid artery bifurcation after stenting

Restenosis typically occurs in regions of low and oscillating wall shear stress, which also favor the accumulation of atherogenic macromolecules such as low-density lipoprotein (LDL). This study aims to evaluate LDL transport and accumulation at the carotid artery bifurcation following carotid artery stenting (CAS) by means of computational simulation. The computational model consists of coupled blood flow and LDL transport, with the latter being modeled as a dilute substance dissolved in the blood and transported by the flow through a convection-diffusion transport equation. The endothelial layer was assumed to be permeable to LDL, and the hydraulic conductivity of LDL was shear-dependent. Anatomically realistic geometric models of the carotid bifurcation were built based on pre- and post-stent computed tomography (CT) scans. The influence of stent design was investigated by virtually deploying two different types of stents (open- and closed-cell stents) into the same carotid bifurcation model. Predicted LDL concentrations were compared between the post-stent carotid models and the relatively normal contralateral model reconstructed from patient-specific CT images. Our results show elevated LDL concentration in the distal section of the stent in all post-stent models, where LDL concentration is 20 times higher than that in the contralateral carotid. Compared with the open-cell stents, the closed-cell stents have larger areas exposed to high LDL concentration, suggesting an increased risk of stent restenosis. This computational approach is readily applicable to multiple patient studies and, once fully validated against follow-up data, it can help elucidate the role of stent strut design in the development of in-stent restenosis after CAS.

Detailed behaviour of endothelial wall shear stress across coronary lesions from non-invasive imaging with coronary computed tomography angiography

AIMS

Evolving evidence suggests that endothelial wall shear stress (ESS) plays a crucial role in the rupture and progression of coronary plaques by triggering biological signalling pathways. We aimed to investigate the patterns of ESS across coronary lesions from non-invasive imaging with coronary computed tomography angiography (CCTA), and to define plaque-associated ESS values in patients with coronary artery disease (CAD).

METHODS AND RESULTS

Symptomatic patients with CAD who underwent a clinically indicated CCTA scan were identified. Separate core laboratories performed blinded analysis of CCTA for anatomical and ESS features of coronary atherosclerosis. ESS was assessed using dedicated software, providing minimal and maximal ESS values for each 3 mm segment. Each coronary lesion was divided into upstream, start, minimal luminal area (MLA), end and downstream segments. Also, ESS ratios were calculated using the upstream segment as a reference. From 122 patients (mean age 64 ± 7 years, 57% men), a total of 237 lesions were analyzed. Minimal and maximal ESS values varied across the lesions with the highest values at the MLA segment [minimal ESS 3.97 Pa (IQR 1.93-8.92 Pa) and maximal ESS 5.64 Pa (IQR 3.13-11.21 Pa), respectively]. Furthermore, minimal and maximal ESS values were positively associated with stenosis severity (P < 0.001), percent atheroma volume (P < 0.001), and lesion length (P ≤ 0.023) at the MLA segment. Using ESS ratios, similar associations were observed for stenosis severity and lesion length.

CONCLUSIONS

Detailed behaviour of ESS across coronary lesions can be derived from routine non-invasive CCTA imaging. This may further improve risk stratification.

Spatiotemporal changes of local hemodynamics and plaque components during atherosclerotic progression in rabbit

BACKGROUND AND OBJECTIVE

Recent evidence demonstrates that the atherogenic process is discontinuous. Our goal is to study changes of plaque components and local hemodynamics during atherosclerotic progression.

METHODS

The histological and immunohistochemical staining of high-fat diet rabbit aorta were evaluated at 0, 8, 10 and 12 weeks, respectively. In addition, the blood flow and LDL transport were simulated at the above four time points.

RESULTS

The plaque thickness at different characteristic regions increased at different rates. The collagen continued to increase, while the elastin, fibronectin, macrophages and smooth muscle cells increased first and then decreased. The relative surface LDL concentration decreased at 8 weeks, and then it increased first and decreased slightly. Meanwhile, the hemodynamic environment became better firstly at 8 weeks, then got slightly worse and lastly improved again.

CONCLUSIONS

The local hemodynamics and plaque components vary nonlinearly during atherosclerotic progression in rabbit aorta.

A proof-of-concept study for the prediction of the de-novo atherosclerotic plaque development using finite elements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:4354-4357. [PMID: 34892184 DOI: 10.1109/embc46164.2021.9629792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The type of the atherosclerotic plaque has significant clinical meaning since plaque vulnerability depends on its type. In this work, we present a computational approach which predicts the development of new plaques in coronary arteries. More specifically, we employ a multi-level model which simulates the blood fluid dynamics, the lipoprotein transport and their accumulation in the arterial wall and the triggering of inflammation using convection-diffusion-reaction equations and in the final level, we estimate the plaque volume which causes the arterial wall thickening. The novelty of this work relies on the conceptual approach that using the information from 94 patients with computed tomography coronary angiography (CTCA) imaging at two time points we identify the correlation of the computational results with the real plaque components detected in CTCA. In the next step, we use these correlations to generate two types of de-novo plaques: calcified and non-calcified. Evaluation of the model's performance is achieved using eleven patients, who present de-novo plaques at the follow-up imaging. The results demonstrate that the computationally generated plaques are associated significantly with the real plaques indicating that the proposed approach could be used for the prediction of specific plaque type formation.

Mechanosensitive molecular interactions in atherogenic regions of the arteries: development of atherosclerosis

A terrible disease of the cardiovascular system, atherosclerosis, develops in the areas of bends and branches of arteries, where the direction and modulus of the blood flow velocity vector change, and consequently so does the mechanical effect on endothelial cells in contact with the blood flow. The review focuses on topical research studies on the development of atherosclerosis – mechanobiochemical events that transform the proatherogenic mechanical stimulus of blood flow – low and low/oscillatory arterial wall shear stress in the chains of biochemical reactions in endothelial cells, leading to the expression of specific proteins that cause the progression of the pathological process. The stages of atherogenesis, systemic risk factors for atherogenesis and its important hemodynamic factor, low and low/oscillatory wall shear stress exerted by blood flow on the endothelial cells lining the arterial walls, have been described. The interactions of cell adhesion molecules responsible for the development of atherosclerosis under low and low/oscillating shear stress conditions have been demonstrated. The activation of the regulator of the expression of cell adhesion molecules, the transcription factor NF-κB, and the factors regulating its activation under these conditions have been described. Mechanosensitive signaling pathways leading to the expression of NF-κB in endothelial cells have been described. Studies of the mechanobiochemical signaling pathways and interactions involved in the progression of atherosclerosis provide valuable information for the development of approaches that delay or block the development of this disease. Key words: atherogenesis; shear stress; transcription factor NF-κB; RelA expression; mechanosensitive receptors; cell adhesion molecules; signaling pathways; mechanotransduction.

Relationship of Endothelial Shear Stress with Plaque Features with Coronary CT Angiography and Vasodilating Capability with PET

Background Advances in three-dimensional reconstruction techniques and computational fluid dynamics of coronary CT angiography (CCTA) data sets make feasible evaluation of endothelial shear stress (ESS) in the vessel wall. Purpose To investigate the relationship between CCTA-derived computational fluid dynamics metrics, anatomic and morphologic characteristics of coronary lesions, and their comparative performance in predicting impaired coronary vasodilating capability assessed by using PET myocardial perfusion imaging (MPI). Materials and Methods In this retrospective study, conducted between October 2019 and September 2020, coronary vessels in patients with stable chest pain and with intermediate probability of coronary artery disease who underwent both CCTA and PET MPI with oxygen 15-labeled water or nitrogen 13 ammonia and quantification of myocardial blood flow were analyzed. CCTA images were used in assessing stenosis severity, lesion-specific total plaque volume (PV), noncalcified PV, calcified PV, and plaque phenotype. PET MPI was used in assessing significant coronary stenosis. The predictive performance of the CCTA-derived parameters was evaluated by using area under the receiver operating characteristic curve (AUC) analysis. Results There were 92 coronary vessels evaluated in 53 patients (mean age, 65 years ± 7; 31 men). ESS was higher in lesions with greater than 50% stenosis versus those without significant stenosis (mean, 15.1 Pa ± 30 vs 4.6 Pa ± 4 vs 3.3 Pa ± 3; P = .004). ESS was higher in functionally significant versus nonsignificant lesions (median, 7 Pa [interquartile range, 5-23 Pa] vs 2.6 Pa [interquartile range, 1.8-5 Pa], respectively; P ≤ .001). Adding ESS to stenosis severity improved prediction (change in AUC, 0.10; 95% CI: 0.04, 0.17; P = .002) for functionally significant lesions. Conclusion The combination of endothelial shear stress with coronary CT angiography (CCTA) stenosis severity improved prediction of an abnormal PET myocardial perfusion imaging result versus CCTA stenosis severity alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Kusmirek and Wieben in this issue.

Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors.

Prediction of atherosclerotic disease progression combining computational modelling with machine learning

Non-invasive serial computed tomography coronary angiography (CTCA) was acquired from 32 patients and 3D reconstruction of 58 coronary arteries was achieved. The arterial geometries were utilized for blood flow and LDL transport modelling. Navier-Stokes and convection-diffusion equations were employed for simulation of blood flow and LDL transport, respectively. Disease progression was assessed comparing the follow-up and baseline arterial models after co-registration using side branches as anatomical landmarks. A machine learning model for predicting disease progression was built using the Gradient Boosted Trees (GBT) algorithm. The Accuracy, Sensitivity, Specificity and AUC of the developed methodology for predicting lumen area decrease equal was 0.68, 0.56, 0.34 and 0.59, respectively. The best results were found for the prediction of plaque area increase by 20%, with 0.73, 0.67, 0.86, and 0.76 accuracy, sensitivity, specificity andAUC, respectively. This approach outperforms significantly the predictive capability of models based on binary logistic regression.

Predictive Models of Coronary Artery Disease Based on Computational Modeling: The SMARTool System

SMARTool aims to the development of Decision Support Systems (DSS) for the risk stratification, diagnosis, prediction and treatment of coronary artery disease (CAD). In this work, we present the results of the prediction DSS, which utilizes clinical data, imaging morphological characteristics and computational modeling results. More specifically, 263 patients were recruited in the SMARTool clinical trial and 196 patients were selected for the DSS development. Traditional risk factors, blood examinations and computed coronary tomography angiography (CCTA) were performed at two different time points with an interscan period 6.22 ± 1.42 years. Computational modeling of blood flow and LDL transport was performed at the baseline. Predictive models are built for the prediction of CAD at the follow-up. The results show that CAD can be predicted with 83% accuracy, when low ESS, high accumulation of LDL and imaging data are included in the model.

A computational multi-level atherosclerotic plaque growth model for coronary arteries

In this work, we present a novel computational approach for the prediction of atherosclerotic plaque growth. In particular, patient-specific coronary computed tomography angiography (CCTA) data were collected from 60 patients at two time points. Additionally, blood samples were collected for biochemical analysis. The CCTA data were used for 3D reconstruction of the coronary arteries, which were then used for computational modeling of plaque growth. The model of plaque growth is based on a multi-level approach: i) the blood flow is modeled in the lumen and the arterial wall, ii) the low and high density lipoprotein and monocytes transport is included, and iii) the major atherosclerotic processes are modeled including the foam cells formation, the proliferation of smooth muscle cells and the formation of atherosclerotic plaque. Validation of the model was performed using the follow-up CCTA. The results show a correlation of the simulated follow-up arterial wall area to be correlated with the corresponding realistic follow-up with r²=0.49, P<; 0.0001.

Modeling of the early stage of atherosclerosis with emphasis on the regulation of the endothelial permeability

In this paper, we develop a mathematical model for the early stage of atherosclerosis, as a chronic inflammatory disease. It includes also processes that are relevant for the "thickening" of the vessel walls, and prepares a more complete model including also the later stages of atherosclerosis. The model consists of partial differential equations: Navier-Stokes equations modeling blood flow, Biot equations modeling the fluid flow inside the poroelastic vessel wall, and convection/chemotaxis-reaction-diffusion equations modeling transport, signaling and interaction processes initiating inflammation and atherosclerosis. The main innovations of this model are: a) quantifying the endothelial permeability to low-density-lipoproteins (LDL) and to the monocytes as a function of WSS, cytokines and LDL on the endothelial surface; b) transport of monocytes on the endothelial surface, mimicking the monocytes adhesion and rolling; c) the monocytes influx in the lumen, as a function of factor increasing monocytopoiesis; d) coupling between Navier-Stokes system, Biot system and convection/chemotaxis-reaction-diffusion equations. Numerical simulations of a simplified model were performed in an idealized two-dimensional geometry in order to investigate the dynamics of endothelial permeability, and the growth and spread of immune cells populations and their dependence in particular on low-density-lipoprotein and wall-shear stress.

Effects of different non-Newtonian models on unsteady blood flow hemodynamics in patient-specific arterial models with in-vivo validation

The aim of this study is to demonstrate the implications of using different blood rheological models in the simulation of blood flow dynamics in atherosclerotic coronary arteries. Computational fluid dynamics simulation was performed using three-dimensional (3D) patient-specific models of diseased left anterior descending (LAD) coronary arteries with varying degrees of stenosis severity. The three-dimensional arterial models were reconstructed from 3D quantitative coronary angiography, and input flow conditions were prescribed with blood flow conditions measured in-vivo. Different blood viscosity models were used for the simulations, and they include Newtonian and also non-Newtonian models such as Bingham, Carreau, Carreau-Yasuda, Casson, modified Casson, Cross, modified Cross, simplified Cross, Herschel Bulkley, Kuang-Luo (K-L), PowellErying, modified PowellErying, Power-law, Quemada and Walburn-Schneck models. Results from this study show that the time-averaged velocity at the centre of the arteries produced in the CFD simulations that uses the Carreau, modified Casson or Quemada blood viscosity models corresponded exceptionally well with the clinical measurements regardless of stenosis severities and hence, highlights the usefulness of these models to determine the potential determinants of blood vessel wall integrity such as dynamic blood viscosity, blood velocity and wall shear stress.

Mathematical Modelling and Simulation of Atherosclerosis Formation and Progress: A Review

Cardiovascular disease (CVD) is a major threat to human health since it is the leading cause of death in western countries. Atherosclerosis is a type of CVD related to hypertension, diabetes, high levels of cholesterol, smoking, oxidative stress, and age. Atherosclerosis primarily occurs in medium and large arteries, such as coronary and the carotid artery and, in particular, at bifurcations and curvatures. Atherosclerosis is compared to an inflammatory disease where a thick, porous material comprising cholesterol fat, saturated sterols, proteins, fatty acids, calcium etc., is covered by an endothelial membrane and a fragile fibrous tissue which makes atheromatic plaque prone to rupture that could lead to the blockage of the artery due to the released plaque material. Despite the great progress achieved, the nature of the disease is not fully understood. This paper reviews the current state of modelling of all levels of atherosclerosis formation and progress and discusses further challenges in atherosclerosis modelling. The objective is to pave a way towards more precise computational tools to predict and eventually reengineer the fate of atherosclerosis.

A Clinical Decision Support Platform for the Risk Stratification, Diagnosis, and Prediction of Coronary Artery Disease Evolution

SMARTool aims to perform accurate risk stratification of coronary artery disease patients as well as to provide early diagnosis and prediction of disease progression. This is achieved by the acquisition of data from about 263 patients including computed tomography angiographic images, clinical, molecular, biohumoral, exposome, inflammatory and omics data. Data are collected in two time points with a followup period of approximately 5 years. In the first step, data mining techniques are implemented for the estimation of risk stratification. In the next step, patients, who are classified as medium to high risk are considered for coronary imaging and computational modelling of blood flow, plaque growth and stenosis severity assessment. Additionally, patients with increased stenosis are selected for stent deployment. All the above modules are integrated in a cloud-based platform for the clinical decision support (CDSS) of patients with coronary artery disease. The work presents preliminary results employing the SMARTool dataset as well as the concept and architecture of the under development platform.

Lipid Deposition in Kidney Diseases: Interplay Among Redox, Lipid Mediators, and Renal Impairment

Significance: The relationship between lipid disturbances and renal diseases has been studied for several decades, and it is well recognized that when the balance of renal lipid uptake, synthesis, oxidation, and outflow is disrupted, lipids will undergo oxidation, be sequestrated as lipid droplets, generate toxic metabolites, and cause nephrotoxicity in diverse renal diseases. Recent Advances: During renal disorders, redox signaling is a pivotal event promoting or resulting from lipid disorders. Accordingly, a vicious cycle of lipid redox dysregulation could be developed, accelerating the renal damage. Critical Issues: The aim of this concise review is to introduce the connection among redox, lipid abnormalities and kidney damage in various conditions. And we summarized current understanding of the lipid redox loop implicated in acute kidney injury, chronic kidney disease, metabolic abnormalities, aging, and genetic pitfalls. Future Directions: Despite recent advances, further investigations are required to clarify the complicated molecular and regulatory mechanisms among redox, lipid mediators and renal disorders. Moreover, exploring an ideal target for potential therapies should be discussed and studied in future. Antioxid. Redox Signal. 28, 1027-1043.

SMARTool: A tool for clinical decision support for the management of patients with coronary artery disease based on modeling of atherosclerotic plaque process

SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide decision support for patients with stenosed arteries. Finally, it integrates modeling of stent deployment. In this work preliminary results are presented. More specifically, the reconstruction methodology has mean value of Dice Coefficient and Hausdorff Distance is 0.749 and 1.746, respectively, while low ESS and high LDL concentration can predict plaque progression.

Prediction of Atherosclerotic Plaque Development in an In Vivo Coronary Arterial Segment Based on a Multilevel Modeling Approach

OBJECTIVE

The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model.

METHODS

To this aim, a human reconstructed left circumflex coronary artery is utilized for a multilevel modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low-density lipoprotein (LDL) and high-density lipoprotein and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation, and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter, and plaque components using follow-up data.

RESULTS

The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [R² = 0.365 (P = 0.029, adjusted R² = 0.307) and R² = 0.368 (P = 0.015, adjusted R² = 0.342), respectively], whereas the introduction of the variables of oxidized LDL, macrophages, and foam cells as independent predictors improves the accuracy in predicting regions potential for atherosclerotic plaque development [R² = 0.847 (P = 0.009, adjusted R² = 0.738)].

CONCLUSION

Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development.

SIGNIFICANCE

Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.

Natural History of Carotid Atherosclerosis in Relation to the Hemodynamic Environment

Carotid atherosclerosis may lead to devastating clinical outcomes such as stroke. Data on the value of local factors in predicting progression in carotid atherosclerosis are limited. Our aim was to investigate the association of local endothelial shear stress (ESS) and low-density lipoprotein (LDL) accumulation with the natural history of atherosclerotic disease using a series of 3 time points of human magnetic resonance data. Three-dimensional lumen/wall reconstruction was performed in 12 carotids, and blood flow and LDL mass transport modeling were performed. Our results showed that an increase in plaque thickness and a decrease in lumen size were associated with low ESS and high LDL accumulation in the arterial wall. Low ESS (odds ratio [OR]: 2.99; 95% confidence interval [CI]: 2.31-3.88; P < .001 vs higher ESS) and high LDL concentration (OR: 3.26; 95% CI: 2.44-4.36; P < .001 vs higher LDL concentration) were significantly associated with substantial local plaque growth. Low ESS and high LDL accumulation both presented a diagnostic accuracy of 67% for predicting plaque growth regions. Modeling of blood flow and LDL mass transport show promise in predicting progression of carotid atherosclerosis.

Blood Brothers: Hemodynamics and Cell-Matrix Interactions in Endothelial Function

SIGNIFICANCE

Alterations in endothelial function contribute to a variety of vascular diseases. In pathological conditions, the endothelium shows a reduced ability to regulate vasodilation (endothelial dysfunction) and a conversion toward a proinflammatory and leaky phenotype (endothelial activation). At the interface between the vessel wall and blood, the endothelium exists in a complex microenvironment and must translate changes in these environmental signals to alterations in vessel function. Mechanical stimulation and endothelial cell interactions with the vascular matrix, as well as a host of soluble factors, coordinately contribute to this dynamic regulation.

RECENT ADVANCES

Blood hemodynamics play an established role in the regulation of endothelial function. However, a growing body of work suggests that subendothelial matrix composition similarly and coordinately regulates endothelial cell phenotype such that blood flow affects matrix remodeling, which affects the endothelial response to flow.

CRITICAL ISSUES

Hemodynamics and soluble factors likely affect endothelial matrix remodeling through multiple mechanisms, including transforming growth factor β signaling and alterations in cell-matrix receptors, such as the integrins. Likewise, differential integrin signaling following matrix remodeling appears to regulate several key flow-induced responses, including nitric oxide production, regulation of oxidant stress, and activation of proinflammatory signaling and gene expression. Microvascular remodeling responses, such as angiogenesis and arteriogenesis, may also show coordinated regulation by flow and matrix.

FUTURE DIRECTIONS

Identifying the mechanisms regulating the dynamic interplay between hemodynamics and matrix remodeling and their contribution to the pathogenesis of cardiovascular disease remains an important research area with therapeutic implications across a variety of conditions. Antioxid. Redox Signal. 25, 415-434.

Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling

High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

Prediction of atherosclerotic disease progression using LDL transport modelling: a serial computed tomographic coronary angiographic study

AIM

To investigate the efficacy of low-density lipoprotein (LDL) transport simulation in reconstructed arteries derived from computed tomography coronary angiography (CTCA) to predict coronary segments that are prone to progress.

METHODS AND RESULTS

Thirty-two patients admitted with an acute coronary event who underwent 64-slice CTCA after percutaneous coronary intervention and at 3-year follow-up were included in the analysis. The CTCA data were used to reconstruct the coronary anatomy of the untreated vessels at baseline and follow-up, and LDL transport simulation was performed in the baseline models. The computed endothelial shear stress (ESS), LDL concentration, and CTCA-derived plaque characteristics were used to identify predictors of substantial disease progression (defined as an increase in the plaque burden at follow-up higher than two standard deviations of the intra-observer variability of the expert who performed the analysis). Fifty-eight vessels were analysed. High LDL concentration [odds ratio (OR): 2.16; 95% confidence interval (CI): 1.64-2.84; P = 0.0054], plaque burden (OR: 1.40; 95% CI: 1.13-1.72; P = 0.0017), and plaque area (OR: 3.46; 95% CI: 2.20-5.44; P≤ 0.0001) were independent predictors of a substantial disease progression at follow-up. The ESS appears as a predictor of disease progression in univariate analysis but was not an independent predictor when the LDL concentration was entered into the multivariate model. The accuracy of the model that included the LDL concentration was higher than the accuracy of the model that included the ESS (65.1 vs. 62.5%).

CONCLUSIONS

LDL transport modelling appears a better predictor of atherosclerotic disease progression than the ESS, and combined with the atheroma characteristics provided by CTCA is able to detect with a moderate accuracy segments that will exhibit a significant plaque burden increase at mid-term follow-up.

Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry

Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.

A proof-of-concept study for predicting the region of atherosclerotic plaque development based on plaque growth modeling in carotid arteries

In this work, we present a computational model for plaque growth utilizing magnetic resonance data of a patient's carotid artery. More specifically, we model blood flow utilizing the Navier-Stokes equations, as well as LDL and HDL transport using the convection-diffusion equation in the arterial lumen. The accumulated LDL in the arterial wall is oxidized considering the protective effect of HDL. Macrophages recruitment and foam cells formation are the final step of the proposed multi-level modeling approach of the plaque growth. The simulated results of our model are compared with the follow-up MRI findings in 12 months regarding the change to the arterial wall thickness. WSS and LDL may indicate potential regions of plaque growth (R(2)=0.35), but the contribution of foam cells formation, macrophages and oxidized LDL increased the prediction significantly (R(2)=0.75).

Image-based computational simulation of sub-endothelial LDL accumulation in a human right coronary artery

Accumulation of low density lipoproteins (LDL) in the vessel wall is suggested as the initiator of atherosclerosis and coronary stenosis. This process is associated with the performance of endothelium layer that regulates entering of macromolecules to the vessel wall. Therefore, the present study aims to investigate sub-endothelial accumulation of LDL molecules in a coronary tree and predict atherosclerosis prone sites. Non-Newtonian blood flow is simulated for normal and hypertensive conditions through the lumen of a right coronary artery reconstructed from computed tomography (CT) images. A three-pore model is implemented as the endothelium boundary condition and hence, plasma flow and LDL transport are simulated within the arterial wall. Based on the pore model, endothelium pathways divide into normal junctions, vesicles and leaky junctions. Most of LDL molecules pass through the leaky junctions that arise at locations with low wall shear stress (WSS). Results indicate that increase in the number of leaky junctions at branch points with low WSS can lead to both elevated levels of sub-endothelial LDL accumulation and atherosclerosis risk. Findings reveal that at the branch points with disturbed flow, sub-endothelial concentration of LDL for the hypertensive condition is higher than the normal condition, however for the rest of regions with uniform geometry and unidirectional flow, this is reversed. Comparisons of non-Newtonian and Newtonian flows show mean increases of 34% and 13% in the sub-endothelial concentrations of Newtonian flows during the normal and hypertensive conditions, respectively.

Mass transport of low density lipoprotein in reconstructed hemodynamic environments of human carotid arteries: the role of volume and solute flux through the endothelium

The accumulation of low density lipoprotein (LDL) in the arterial intima is a critical step in the initiation and progression of atheromatous lesions. In this study we examine subject-specific LDL transport into the intima of carotid bifurcations in three human subjects using a three-pore model for LDL mass transfer. Subject-specific carotid artery computational models were derived using magnetic resonance imaging (MRI) to obtain the geometry and phase-contract MRI (PC-MRI) to acquire pulsatile inflow and outflow boundary conditions for each subject. The subjects were selected to represent a wide range of anatomical configurations and different stages of atherosclerotic development from mild to moderate intimal thickening. A fluid-solid interaction (FSI) model was implemented in the computational fluid dynamics (CFD) approach in order to consider the effects of a compliant vessel on wall shear stress (WSS). The WSS-dependent response of the endothelium to LDL mass transfer was modeled by multiple pathways to include the contributions of leaky junctions, normal junctions, and transcytosis to LDL solute and plasma volume flux from the lumen into the intima. Time averaged WSS (TAWSS) over the cardiac cycle was computed to represent the spatial WSS distribution, and wall thickness (WTH) was determined from black blood MRI (BBMRI) so as to visualize intimal thickening patterns in the bifurcations. The regions which are exposed to low TAWSS correspond to elevated WTH and higher mass and volume flux via the leaky junctions. In all subjects, the maximum LDL solute flux was observed to be immediately downstream of the stenosis, supporting observations that existing atherosclerotic lesions tend to progress in the downstream direction of the stenosis.

Using a Systems Pharmacology Approach to Study the Effect of Statins on the Early Stage of Atherosclerosis in Humans

More than 100,000 people have participated in controlled trials of statins (lowering cholesterol drugs) since the introduction of lovastatin in the 1980s. Meta-analyses of this data have shown that statins have a beneficial effect on treated groups compared to control groups, reducing cardiovascular risk. Inhibiting the HMG-CoA reductase in the liver, statins can reduce cholesterol levels, thus reducing LDL levels in circulation. Published data from intravascular ultrasound studies (IVUS) was used in this work to develop and validate a unique integrative system model; this consisted of analyzing control groups from two randomized controlled statins trials (24/97 subjects respectively), one treated group (40 subjects, simvastatin trial), and 27 male subjects (simvastatin, pharmacokinetic study). The model allows to simulate the pharmacokinetics of statins and its effect on the dynamics of lipoproteins (e.g., LDL) and the inflammatory pathway while simultaneously exploring the effect of flow-related variables (e.g., wall shear stress) on atherosclerosis progression.