Biomechanical models and numerical studies of atherosclerotic plaque_Journal of Biomedical Engineering

Authors：

LIU Mengchen , PAN Jichao , CAI Yan ,  LI Zhiyong

Biomechanics Laboratory, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P.R.China;

Corresponding author：

Keywords：

atherosclerosis; biomechanics; numerical simulation; vulnerable plaque

DOI：

10.7507/1001-5515.202008038

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Atherosclerosis is a complex and multi-factorial pathophysiological process. Researches over the past decades have shown that the development of atherosclerotic vulnerable plaque is closely related to its components, morphology, and stress status. Biomechanical models have been developed by combining with medical imaging, biological experiments, and mechanical analysis, to study and analyze the biomechanical factors related to plaque vulnerability. Numerical simulation could quantify the dynamic changes of the microenvironment within the plaque, providing a method to represent the distribution of cellular and acellular components within the plaque microenvironment and to explore the interaction of lipid deposition, inflammation, angiogenesis, and other processes. Studying the pathological mechanism of plaque development would improve our understanding of cardiovascular disease and assist non-invasive inspection and early diagnosis of vulnerable plaques. The biomechanical models and numerical methods may serve as a theoretical support for designing and optimizing treatment strategies for vulnerable atherosclerosis.

Citation： LIU Mengchen, PAN Jichao, CAI Yan, LI Zhiyong. Biomechanical models and numerical studies of atherosclerotic plaque. Journal of Biomedical Engineering, 2020, 37(6): 948-955. doi: 10.7507/1001-5515.202008038 Copy

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2. 严金川. 脆性斑块的基础与临床. 北京: 人民卫生出版社, 2015.
3. Stary H. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. Circulation, 1995, 15(9): 1512-1531.
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11. Burke A P, Farb A, Malcom G T, et al. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med, 1997, 336(18): 1276-1282.
12. Zhao X Q, Kerwin W S. Utilizing imaging tools in lipidology: examining the potential of MRI for monitoring cholesterol therapy. Clin Lipidol, 2012, 7(3): 329-343.
13. Rusanov S E. The affection of the disturbance of the hydrodynamics of blood in case of stress on pathological increase of level of low density lipoproteins in blood. Med Hypotheses, 2017, 106: 61-70.
14. Sluimer J C, Kolodgie F D, Bijnens A P, et al. Thin-walled microvessels in human coronary atherosclerotic plaques show incomplete endothelial junctions relevance of compromised structural integrity for intraplaque microvascular leakage. J Am Coll Cardiol, 2009, 53(17): 1517-1527.
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16. Moreno P R, Purushothaman K R, Sirol M, et al. Neovascularization in human atherosclerosis. Circulation, 2006, 113(18): 2245-2252.
17. de Vries M R, Quax P H. Plaque angiogenesis and its relation to inflammation and atherosclerotic plaque destabilization. Curr Opin Lipidol, 2016, 27(5): 499-506.
18. Xu J, Lu X, Shi G P. Vasa vasorum in atherosclerosis and clinical significance. Int J Mol Sci, 2015, 16(5): 11574-11608.
19. van der Veken B, Guido R M, Wim M. Intraplaque neovascularization as a novel therapeutic target in advanced atherosclerosis. Expert Opin Ther Targets, 2016, 20(10): 1247-1257.
20. Plank M J, Wall D J, David T. The role of endothelial calcium and nitric oxide in the localisation of atherosclerosis. Math Biosci, 2007, 207(1): 26-39.
21. Cilla M, Peña E, Martínez M A. Mathematical modelling of atheroma plaque formation and development in coronary arteries. J R Soc Interface, 2014, 11(90): 20130866.
22. Bulelzai M, Dubbeldam J, Meijer H. Bifurcation analysis of a model for atherosclerotic plaque evolution. Physica D, 2014, 278-279: 31-43.
23. Ougrinovskaia A, Thompson R S, Myerscough M R. An ODE model of early stages of atherosclerosis: mechanisms of the inflammatory response. Bull Math Biol, 2010, 72(6): 1534-1561.
24. Cohen A, Myerscough M R, Thompson R S. Athero-protective effects of high density lipoproteins (HDL): an ODE model of the early stages of atherosclerosis. Bull Math Biol, 2014, 76(5): 1117-1142.
25. Bulelzai M, Dubbeldam J L. Long time evolution of atherosclerotic plaques. J Theor Biol, 2012, 297: 1-10.
26. Chung S, Vafai K. Low-density lipoprotein transport within a multi-layered arterial wall--effect of the atherosclerotic plaque/stenosis. J Biomech, 2013, 46(3): 574-585.
27. Siogkas P, Sakellarios A, Exarchos T P, et al. Multiscale-patient-specific artery and atherogenesis models. IEEE Trans Biomed Eng, 2011, 58(12): 3464-3468.
28. Khatib N E, Génieys S, Volpert V. Atherosclerosis initiation modeled as an inflammatory process. Math Model Nat Phenom, 2007, 2(2): 126-141.
29. Zohdi T I, Holzapfel G A, Berger S A. A phenomenological model for atherosclerotic plaque growth and rupture. J Theor Biol, 2004, 227(3): 437-443.
30. Vincent C, Gabriel H J, Nicolas M, et al. Mathematical and numerical modeling of early atherosclerotic lesions. Esaim Proceedings, 2010, 30: 1-14.
31. Hao W, Friedman A. The LDL-HDL profile determines the risk of atherosclerosis: a mathematical model. PLoS One, 2014, 9(3): e90497.
32. Friedman A, Hao W. A mathematical model of atherosclerosis with reverse cholesterol transport and associated risk factors. Bull Math Biol, 2015, 77(5): 758-781.
33. Direnzo D, Owens G K, Leeper N J. “attack of the clones”: commonalities between cancer and atherosclerosis. Circ Res, 2017, 120(4): 624-626.
34. Meijers W C, de Boer R A. Common risk factors for heart failure and cancer. Cardiovasc Res, 2019, 115(5): 844-853.
35. Camaré C, Pucelle M, Nègre-Salvayre A, et al. Angiogenesis in the atherosclerotic plaque. Redox Biol, 2017, 12: 18-34.
36. 李玉林, 高绪兰, 于洪藻, 等. 肿瘤微血管及其对血管活性物质的反应性--组织血流测定和病理形态学研究. 中华肿瘤杂志, 1994(1): 3-6.
37. Araujo R P, Mcelwain D L. A history of the study of solid tumour growth: the contribution of mathematical modelling. Bull Math Biol, 2004, 66(5): 1039-1091.
38. Anderson A, Chaplain M. A mathematical model for capillary network formation in the absence of endothelial cell proliferation. Appl Math Lett, 1998, 11(3): 109-114.
39. Orme M E, Chaplain M A. A mathematical model of the first steps of tumour-related angiogenesis: capillary sprout formation and secondary branching. IMA J Math Appl Med Biol, 1996, 13(2): 73-98.
40. Orme M E, Chaplain M A. Two-dimensional models of tumour angiogenesis and anti-angiogenesis strategies. IMA J Math Appl Med Biol, 1997, 14(3): 189-205.
41. Gevertz J L, Torquato S. Modeling the effects of vasculature evolution on early brain tumor growth. J Theor Biol, 2006, 243(4): 517-531.
42. Markus M, Böhm D, Schmick M. Simulation of vessel morphogenesis using cellular automata. Math Biosci, 1999, 156(1/2): 191-206.
43. Bartha K, Rieger H. Vascular network remodeling via vessel cooption, regression and growth in tumors. J Theor Biol, 2006, 241(4): 903-918.
44. Bauer A L, Jackson T L, JIANG Y. A cell-based model exhibiting branching and anastomosis during tumor-induced angiogenesis. Biophys J, 2007, 92(9): 3105-3121.
45. Addison-Smith B, Mcelwain D L, Maini P K. A simple mechanistic model of sprout spacing in tumour-associated angiogenesis. J Theor Biol, 2008, 250(1): 1-15.
46. Anderson A, Chaplain M. Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull Math Biol, 1998, 60(5): 857-899.
47. Alarcón T, Byrne H M, Maini P K. A cellular automaton model for tumour growth in inhomogeneous environment. J Theor Biol, 2003, 225(2): 257-274.
48. Owen M R, Alarcón T, Maini P K, et al. Angiogenesis and vascular remodelling in normal and cancerous tissues. J Math Biol, 2009, 58(4/5): 689-721.
49. Perfahl H, Byrne H M, Chen T, et al. Multiscale modelling of vascular tumour growth in 3D: the roles of domain size and boundary conditions. PLoS One, 2011, 6(4): e14790.
50. Huang X, Yang C, Zheng J, et al. Higher critical plaque wall stress in patients who died of coronary artery disease compared with those who died of other causes: a 3D FSI study based on ex vivo MRI of coronary plaques. J Biomech, 2014, 47(2): 432-437.
51. Tang D, Teng Z, Canton G, et al. Sites of rupture in human atherosclerotic carotid plaques are associated with high structural stresses: an in vivo MRI-based 3D fluid-structure interaction study. Stroke, 2009, 40(10): 3258-3263.
52. Canton G, Hippe D S, SUN J, et al. Characterization of distensibility, plaque burden, and composition of the atherosclerotic carotid artery using magnetic resonance imaging. Med Phys, 2012, 39(10): 6247-6253.
53. Yang C, Tang D, Atluri S. Patient-Specific carotid plaque progression simulation using 3D meshless generalized finite difference models with fluid-structure interactions based on serial in vivo MRI data. Comput Model Eng Sci, 2011, 72(1): 53-77.
54. Li Z Y, Taviani V, Tang T, et al. The hemodynamic effects of in-tandem carotid artery stenosis: implications for carotid endarterectomy. J Stroke Cerebrovasc Dis, 2010, 19(2): 138-145.
55. Li Z Y, Tan F P, Soloperto G, et al. Flow pattern analysis in a highly stenotic patient-specific carotid bifurcation model using a turbulence model. Comput Methods Biomech Biomed Engin, 2015, 18(10): 1099-1107.
56. Tang Dalin, Li Z Y, Gijsen F, et al. Cardiovascular diseases and vulnerable plaques: data, modeling, predictions and clinical applications. Biomed Eng Online, 2015, 14(Suppl 1): S1.
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