JVS-vascular science最新文献

The 2023 Vascular Research Initiatives Conference (VRIC) was held in Boston, Massachusetts, and coincided with the first day of Vascular Discovery 2023, hosted by the American Heart Association. VRIC attracts vascular clinicians, surgeon-scientists, and basic science researchers to a common arena to facilitate the exchange of basic and translational science to stimulate and inspire participants to study and find solutions to vascular conditions. The theme of the conference this year was “Structural and Immune Cells in Vascular Disease.” Abstract sessions focused on venous disease, vascular regeneration, stem cells and wound healing, aortopathies, and the role of the immune system in atherosclerosis. A moderated translational science panel included talks from Dr Chiara Giannarelli and Dr Bhama Ramkhelawon. Recipients of Society for Vascular Surgery partner grants and National Institutes of Health K08 awardees presented their progress reports, and the Resident Research and VRIC Trainee Awardees were acknowledged. The Alexander W. Clowes Distinguished Lecture, entitled “Co-stimulatory Immune Checkpoints in Atherosclerosis: Novel Immunotherapeutic Targets to Combat Atherosclerotic Cardiovascular Disease” was given by Dr Ester Lutgens. VRIC continues to foster interdisciplinary collaborations across the translational field of vascular disease.

Objective

Characterization of the atherosclerotic process fully relies on histological evaluation and staging through a consensus grading system. So far, a head-to-head comparison of atherosclerotic process in experimental models and tissue resources commonly applied in atherosclerosis research with the actual human atherosclerotic process is missing.

Material and Methods

Aspects of the atherosclerotic process present in established murine atherosclerosis models and human carotid endarterectomy specimen were systematically graded using the modified American Heart Association histological classification (Virmani classification). Aspects were aligned with the atherosclerotic process observed in human coronary artery and aortic atherosclerosis reference tissues that were available through biobanks based on human tissue/organ donor material.

Results

Apart from absent intraplaque hemorrhages in aortic lesions, the histological characteristics of the different stages of human coronary and aortic atherosclerosis are similar. Carotid endarterectomy samples all represent end-stage “fibrous calcified plaque” lesions, although secondary, progressive, and vulnerable lesions with gross morphologies similar to coronary/aortic lesions occasionally present along the primary lesions. For the murine lesions, clear histological parallels were observed for the intermediate lesion types (“pathological intimal thickening,” and “early fibroatheroma”). However, none of the murine lesions studied progressed to an equivalent of late fibroatheroma or beyond. Notable contrasts were observed for disease initiation: whereas disease initiation in humans is characterized by a mesenchymal cell influx in the intima, the earliest murine lesions are exclusively intimal, with subendothelial accumulation foam cells. A mesenchymal (and medial) response are absent. In fact, it is concluded that the stage of “adaptive intimal thickening” is absent in all mouse models included in this study.

Conclusions

The Virmani classification for coronary atherosclerosis can be applied for systematically grading experimental and clinical atherosclerosis. Application of this histological grading tool shows clear parallels for intermediate human and murine atherosclerotic lesions. However, clear contrasts are observed for disease initiation, and late stage atherosclerotic lesions. Carotid endarterectomy all represent end-stage fibrous calcified plaque lesions, although secondary earlier lesions may present in a subset of samples.

Objective

Takayasu arteritis (TA) is a difficult disease to deal with because there are neither reliable clinical signs, laboratory biomarkers, nor a single noninvasive imaging technique that can be used for early diagnosis and disease activity monitoring. Knowledge of aortic hemodynamics in TA is lacking. This study aimed to fill this gap by assessing hemodynamics in patients with TA using image-based computational fluid dynamics (CFD) simulations.

Methods

Eleven patients with TA were included in the present study. Patient-specific geometries were reconstructed from either clinical aortic computed tomography angiography or magnetic resonance angiography studies and coupled with physiological boundary conditions for CFD simulations. Key anatomical and hemodynamic parameters were compared with a control group consisting of 18 age- and sex-matched adults without TA who had healthy aortas.

Results

Compared with controls, patients with TA had significantly higher aortic velocities (0.9 m/s [0.7, 1.1 m/s] vs 0.6 m/s [0.5, 0.7 m/s]; P = .002), maximum time-averaged wall shear stress (14.2 Pa [9.8, 20.9 Pa] vs 8.0 Pa [6.2, 10.3 Pa]; P = .004), and maximum pressure drops between the ascending and descending aorta (36.9 mm Hg [29.0, 49.3 mm Hg] vs 28.5 mm Hg [25.8, 31.5 mm Hg]; P = .004). These significant hemodynamic alterations in patients with TA might result from abnormal anatomical features including smaller arch diameter (20.0 mm [13.8, 23.3 mm] vs 25.2 mm [23.3, 26.8 mm]; P = .003), supra-aortic branch diameters (21.9 mm [18.5, 24.6 mm] vs 25.7 mm [24.3, 28.3 mm]; P = .003) and descending aorta diameter (14.7 mm [12.2, 16.8 mm] vs 22.5 mm [19.8, 24.0 mm]; P < .001).

Conclusions

CFD analysis reveals hemodynamic changes in the aorta of patients with TA. The applicability of CFD technique coupled with standard imaging assessments in predicting disease progression of such patients will be explored in future studies. Future large cohort study with outcome correlation is also warranted.

Clinical Relevance

Based on patient-specific computational fluid dynamics simulations, the present retrospective study revealed significant difference in aortic hemodynamics between the patients with and without Takayasu arteritis (TA). To the best of our knowledge, this study is the first to evaluate hemodynamic conditions within TA, demonstrating the potential of computational flow modeling in capturing abnormal hemodynamic forces, such as high wall shear stress, resulted from irregular morphological changes. In the future, assessing the hemodynamic parameters within patients with TA during the prestenotic period, together with longitudinal computational fluid dynamics studies may allow better monitoring and management of TA.

Objective

The objective of this investigation was to demonstrate that in vivo induction of hypertension (HTN) and in vitro cyclic stretch of aortic vascular smooth muscle cells (VSMCs) can cause serum and glucocorticoid-inducible kinase (SGK-1)-dependent production of cytokines to promote macrophage accumulation that may promote vascular pathology.

Methods

HTN was induced in C57Bl/6 mice with angiotensin II infusion (1.46 mg/kg/day × 21 days) with or without systemic infusion of EMD638683 (2.5 mg/kg/day × 21 days), a selective SGK-1 inhibitor. Systolic blood pressure was recorded. Abdominal aortas were harvested to quantify SGK-1 activity (pSGK-1/SGK-1) by immunoblot. Flow cytometry quantified the abundance of CD11b⁺/F480⁺ cells (macrophages). Plasma interleukin (IL)-6 and monocyte chemoattractant protein-1 (MCP-1) was assessed by enzyme-linked immunosorbent assay. Aortic VSMCs from wild-type mice were subjected to 12% biaxial cyclic stretch (Stretch) for 3 or 12 hours with or without EMD638683 (10 μM) and with or without SGK-1 small interfering RNA with subsequent quantitative polymerase chain reaction for IL-6 and MCP-1 expression. IL-6 and MCP-1 in culture media were analyzed by enzyme-linked immunosorbent assay. Aortic VSMCs from SGK-1^flox+/+ mice were transfected with Cre-Adenovirus to knockdown SGK-1 (SGK-1KD VSMCs) and underwent parallel tension experimentation. Computational modeling was used to simulate VSMC signaling. Statistical analysis included analysis of variance with significance at a P value of <.05.

Results

SGK-1 activity, abundance of CD11b⁺/F4-80⁺ cells, and plasma IL-6 were increased in the abdominal aorta of mice with HTN and significantly reduced by treatment with EMD638683. This outcome mirrored the increased abundance of IL-6 in media from Stretch C57Bl/6 VSMCs and attenuation of the effect with EMD638683 or SGK-1 small interfering RNA. C57Bl/6 VSMCs also responded to Stretch with increased MCP-1 expression and secretion into the culture media. Further supporting the integral role of mechanical signaling through SGK-1, target gene expression and cytokine secretion was unchanged in SGK-1KD VSMCs with Stretch, and computer modeling confirmed SGK-1 as an intersecting node of signaling owing to mechanical strain and angiotensin II.

Conclusions

Mechanical activation of SGK-1 in aortic VSMCs can promote inflammatory signaling and increased macrophage abundance, therefore this kinase warrants further exploration as a pharmacotherapeutic target to abrogate hypertensive vascular pathology.

Objective

Restenosis is a significant complication of revascularization treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing when restenosis will happen is extremely difficult due to the interplay of multiple variables and factors. Standard clinical and Doppler ultrasound scans surveillance follow-ups are the only tools clinicians can rely on to monitor intervention outcomes. However, implementing efficient surveillance programs is hindered by health care system limitations, patients’ comorbidities, and compliance. Predictive models classifying patients according to their risk of developing restenosis over a specific period will allow the development of tailored surveillance, prevention programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models.

Methods

We carried out a comprehensive literature review by accessing the PubMed/MEDLINE database according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy consisted of a combination of keywords and included studies focusing on predictive models of restenosis published between January 1993 and April 2023. One author independently screened titles and abstracts and checked for eligibility. The rest of the authors independently confirmed and discussed in case of any disagreement. The search of published literature identified 22 studies providing two perspectives—clinical and biomechanical engineering—on restenosis and comprising distinct methodologies, predictors, and study designs. We compared predictive models’ performance on discrimination and calibration aspects. We reported the performance of models simulating reocclusion progression, evaluated by comparison with clinical images.

Results

Clinical perspective studies consider only routinely collected patient information as restenosis predictors. Our review reveals that clinical models adopting traditional statistics (n = 14) exhibit only modest predictive power. The latter improves when machine learning algorithms (n = 4) are employed. The logistic regression models of the biomechanical engineering perspective (n = 2) show enhanced predictive power when hemodynamic descriptors linked to restenosis are fused with a limited set of clinical risk factors. Biomechanical engineering studies simulating restenosis progression (n = 2) are able to capture its evolution but are computationally expensive and lack risk scoring for individual patients at specific follow-ups.

Conclusions

Restenosis predictive models, based solely on routine clinical risk factors and using classical statistics, inadequately predict the oc