Citations

Introduction: Atherosclerosis, the underlying cause of cardiovascular disease (CVD), is a lipid-driven chronic inflammatory disease. We have identified the co-stimulatory CD40-CD40L immune checkpoint dyad as a key driver of inflammation during atherosclerosis. CD40 and CD40L exert cell-type divergent functions via different signaling pathways. Hypothesis: We hypothesize that CD40 on endothelial cell (ECs) induces endothelial-to-mesenchymal transition (EndMT)-mediated vascular inflammation in atherosclerosis. Methods & Results: Using single cell RNAseq, we found that CD40 (TNFRSF5) is expressed on PDE3A + GJA5 + and KDR + COL15A1 + ECs of advanced human carotid atherosclerotic plaques. CD40 is highly expressed in the endothelial lining of human and mouse atherosclerotic plaques, and colocalizes with the EndMT markers fibronectin and collagen and adhesion molecule ICAM-1. CD40L activation of human aortic endothelial cells (HAECs) results in expression of the EndMT markers fibronectin, vimentin, sm22α, FSP1 and snail. Activation of CD40 in EC induces filamin A dependent translocation of CD40 into lipid rafts, increases TNF-receptor associated factor (TRAFs) -2, -3 and 6 levels, induces Akt signaling and increases expression of VCAM1 and CCL2. Conclusions: The endothelial cell has been more and more regarded as a cell type that plays a major role in driving inflammation. Our data suggest that the CD40-CD40L axis plays a significant role in EC-mediated inflammation in atherosclerosis by driving EndMT.

Endothelial progenitor cells (EPCs) constitute a promising focus for research in regenerative medicine. These cells originate from the bone marrow and other circulating hematopoietic cells and regulate tissue regeneration and vascular integrity. EPC mobilization serves for neovascularization and re-endothelialization after injury in multiple organ systems, including the renal and cardiovascular systems. Both their number and function may vary in disease states such as CKD, depending on severity, comorbid conditions, and other factors. Therefore, circulating EPC count and function have been proposed as markers for vascular health, and their mobilization or replenishment may offer a potential therapy. Delivery of EPCs has shown success in repairing injured kidneys in animal models of both chronic and AKIs. This approach may be limited by EPC heterogeneity and incomplete characterization that may be addressed by standardization, engineering, or combination with other therapies. The aim of this article was to review the current state and recent advances in our understanding of the role of EPCs in homeostasis and conditions that may lead to their dysregulation in kidney diseases.

Abstract Renal artery stenosis (RAS) is the leading cause of secondary hypertension worldwide. However, current medical and surgical treatment modalities provide minimal benefits for kidney injury. Recent preclinical RAS models have demonstrated promising potential of human mesenchymal stem cells (MSC) and their daughter extracellular vesicles (EV) in improving murine renal function and attenuating inflammation. However, the extent and mechanisms underlying immune rejection of xenogeneic MSCs or EVs are yet undetermined. Therefore, adipose tissue was harvested from adult healthy patients. Adipose-derived MSCs were extracted and cultured, and EVs were isolated from their supernatants via ultra-centrifugation. Then, mice randomly assigned to RAS or sham surgery were divided into 6 groups: sham surgery, RAS, sham + MSC, RAS + MSC, sham + EV, and RAS + EV. Two weeks after intra-aortic injection of MSCs (5 × 105) or EVs (20 µg protein), we compared the intrarenal T-cell and macrophage accumulation, splenic B-cell numbers, circulating cytokines and anti-human antibodies levels among the groups. MSCs and EVs did not influence intrarenal immune cell infiltrations. However, MSCs significantly increased circulating anti-human antibodies. In the spleen, RAS + EV mice showed higher memory IgM+ B-cells but reduced CD19+ B-cells compared to RAS + MSC. In vitro T-cell recall assay showed that both MSCs and EVs exhibited reduced IFN-γ release upon re-stimulation, indicating an immunosuppressive effect. Therefore, xenogeneic MSCs induced a greater humoral response in mice, while EVs triggered a splenic cellular response, but neither elicits discernible kidney rejection. Our results provide key insights into the immunomodulatory mechanisms of MSCs and EVs and immune mechanisms underlying xenograft rejection.

Abstract Introduction Acoustic analysis of speech has discriminated decompensated acute heart failure (HF). Speech rate (SR) and cepstral peak prominence (CPP) variation are among features previously evaluated. However, the association between SR and CPP and chronic stable HF with and without pulmonary hypertension (PH) as well as PH alone have not been previously studied. Methods Patients evaluated for HF and/or PH in the outpatient setting recorded a standardized text read out loud from which a sentence was extracted and analysed to extract pre-specified acoustic features including SR and CPP calculated for voiced speech (CPP-V) and in all speech (CPP-All). Patients were grouped depending on the presence or absence of disease (HF and/or PH) and symptoms. Linear regression models were fitted to determine the association between each acoustic feature and disease status. Results In total, 2153 patients were included: age 65.32 ± 17.18 years; male n = 1246 (57.9%); 879 had HF (40.8%), 542 had PH (25.2%) and 777 had no disease and no symptoms (36.1%). After adjustment for age and sex, SR was significantly lower in patients with PH only [estimated coefficient, 95% confidence interval (CI): −0.14, −0.21 to −0.06, P = 0.0006], HF only (−0.11, −0.17 to −0.05, P = 0.0002) and HF with PH (−0.17, −0.24 to −0.10, P < 0.0001) compared with no disease. CPP-V differed in patients with PH only (0.37, 0.16–0.57, P = 0.0004) and CPP-All differed significantly compared with patients without disease (0.23, 0.08–0.38, P = 0.0025). Conclusions SR is significantly slower in patients with HF alone, PH alone and HF and PH combined compared with patients without disease. CPP also differs significantly in patients with PH compared with controls. These findings suggest that acoustic analysis may be useful in discriminating chronic stable HF and PH, offering promise for the development of non-invasive screening methods for HF and PH.

Methods: From a sample cohort of 274,539 consecutive adults who sought care at Mayo Clinic between 2018-2022, we ascertained demographic and clinical information, including clinically indicated TTE within 15 days and up to a year after the index clinical visit. The Area Deprivation Index (ADI), a tool that captures neighborhood-level disadvantages that are linked to cardiovascular morbidity and outcomes, was extracted from the Neighborhood Atlas and ranked accordingly as state percentiles stratified in quintiles, with the highest values representing “most disadvantaged” areas. We performed non-parametric multivariate regression models that adjusted for cardiovascular risk factors expressed in odds ratio (OR) and 95% confidence interval (CI). Results: A total of 45,823 (16.9%) subjects had a TTE performed, with a mean age of 63±16 years, 48% were women, and 86% were non-Hispanic white. According to the TTE, 4% of subjects had LVSD, while 11% had a high probability of LVSD by AI-ECG. Median state ADI was 4 IQR (2-7). In the whole cohort, subjects residing in the most disadvantaged areas were less likely to have a TTE [OR 0.94 CI (0.91- 0.97)] see Fig. 1-A ; conversely, they were more likely to have LVSD by TTE [OR 1.80 CI (1.52-2.13)] see Fig. 1-B , and higher probability of LVSD by AI-ECG [OR 1.43 CI (1.36-1.51)] when compared to those with the least disadvantage see Fig. 1-C , independent of age, sex, and cardiovascular risk factors, p for trend<0.001. Conclusion: Individuals living in socially disadvantaged areas were slightly less likely to have an echocardiogram despite a significantly higher likelihood of having an LVSD (suspected by AI-ECG and confirmed by TTE), independent of cardiovascular risk factors. Highlighting an important disparity in the diagnosis of LVSD.

Background: Previous studies have linked pericoronary artery adipose tissue attenuation to major adverse cardiac event (MACE). However, the association between pericarotid artery fat and prognosis remains underexplored. This study aimed to investigate the association between preoperative pericarotid fat characteristics and the subsequent prognosis in patients undergoing carotid endarterectomy (CEA). Methods: This study included patients with preoperative computed tomography angiography (CTA) followed by CEA. Preoperative neck CTA images were obtained and analyzed with novel methods using 3D Slicer software. We segmented the common and internal carotid arteries. The segment was horizontally trimmed, isolating the region around the bifurcation, extending 15 mm above and 10 mm below. Pericarotid fat was defined as adipose tissue within a 5 mm radial distance from the outer vessel wall, with Hounsfield units (HU) between -190 and -30 (Figure 1, carotid segmentation [green] and pericarotid fat [yellow]) . For each patient, we calculated the HU average and the volume (mm3) of the pericarotid fat. MACE was defined as a composite of all-cause mortality, myocardial infarction, and cerebrovascular events. The relationship between both pericarotid fat characteristics and MACE was investigated. Results: Of the 43 patients initially included, one was excluded due to an inability to perform segmentation caused by chronic total occlusion, leaving 42 patients (age 73 [67-78] years, 33 [76.7%] male). The comparison of mean pericarotid fat HU between the ipsilateral and contralateral sides showed no significant difference (-60.3±11.0 HU vs. -60.9 ±11.3 HU, p=0.437). However, the median ipsilateral fat volume was significantly higher than the contralateral volume (528 [248, 1100] mm3 vs. 328 [184, 697] mm3, p=0.020). When comparing patients with and without MACE, those with MACE had a significantly lower ipsilateral fat density (-59.4±10.2 HU vs. -66.8 ±11.1 HU, p=0.022) and a higher fat volume (468 [233, 746] mm3 vs. 845 [458, 1451] mm3, p=0.044). Kaplan-Meier analysis revealed that higher volume and lower fat density than the median were significant predictors of MACE (log-rank p=0.015 and p=0.042, respectively) (Figure 2). Conclusions: Assessment of pericarotid fat using a novel CTA analytic method in patients undergoing CEA identified ipsilateral lower fat density and higher fat volume as predictors of adverse outcomes.

Background: Atherosclerotic carotid artery disease is associated with a high risk of major adverse cardiac events (MACE) even with optimal risk factor management and surgical interventions such as carotid endarterectomy (CEA). Aortic stenosis (AS) is an age-related valve disease that is associated with a high rate of MACE. An artificial intelligence-based AS estimation algorithm (AI-AS) can identify AS with high accuracy using electrocardiogram. This study evaluates the prognostic value of AI-AS in patients with carotid artery disease undergoing CEA. Methods: We included patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 if they had >30 days of follow-up and an electrocardiogram within one year before CEA. The electrocardiogram closest to the CEA date was used to calculate AI-AS (AS probability ranging from 0-1). MACE was defined as the composite of cardiac events, ischemic cerebrovascular accidents, and all-cause mortality during follow-up after CEA. A receiver operating characteristic curve and the Youden index were used to identify the optimal AI-AS cut-off value for predicting MACE. Kaplan Meier plot was used to depict MACE-free survival. Regression models were used to evaluate the predictive value of AI-AS for future MACE adjusting for clinical covariates. Results: A total of 665 patients with a median age of 72 [IQR:66, 78] years and 438 (65.9%) males were included. Over a median follow-up time of 7.1 [IQR:2.7, 11.5] years, MACE occurred in 419 (63%) of the patients, including 187 (44.6%) mortality, 161 (38.4%) cardiac events, and 71 (16.9%) cerebrovascular accidents. Median AI-AS was 0.32 [IQR:0.09, 0.55] in the total cohort, 0.34 [IQR:0.12, 0.56] in patients with MACE, and 0.30 [IQR:0.07, 0.53] in patients without MACE (P=0.038). AI-AS was a significant predictor of MACE (HR:2.38, 95%CI:1.64-3.46, P<0.001) in univariate Cox regression analysis. The optimal cut-off value of AI-AS for MACE was 0.419 (area under the curve: 0.55). Patients with high AI-AS (≥0.419) had a significantly lower MACE-free survival in the Log-rank test ( Figure 1 ) and in the multivariable Cox regression model adjusted for covariates compared to low AI-AS (<0.419) patients ( Table 1 ). Conclusion: AI-AS is an independent predictor of MACE in patients with carotid artery disease after CEA and can be used as a risk-stratifying tool in this patient population. Additional studies are needed to elucidate the mechanistic link between AS and MACE in these patients.

Background: Previous research has suggested pericarotid fat density to be significantly linked to atherosclerotic plaque components and cerebrovascular events. Inflammatory markers within the plaques are critical in the pathogenesis and progression of carotid artery disease. However, the association between pericarotid fat and inflammatory processes within atherosclerotic plaques remains underexplored. This study hypothesizes that pericarotid fat density is directly associated with the levels of inflammatory markers within atherosclerotic plaques. Methods: Carotid plaques were prospectively collected from patients with carotid artery disease undergoing carotid endarterectomy (CEA). The levels of inflammatory plaque cytokines (MCP-1, IL-8, IFN-γ, IL-10, IL-1β, IL-6, PDGF-AA, PDGF-AB/BB, TNF-α, VEGF) were measured in tissue homogenates using a customized 10-plex Luminex assay. Baseline demographics and clinical data of our population were extracted from electronic health records. We analyzed preoperative neck computed tomography angiography (CTA) images placing regions-of-interest (ROI) in the pericarotid fat on the slice showing maximal stenosis. For each patient, we calculated the average of Hounsfield Units (HU) within the ROI. Spearman correlation test was used to assess the correlation between variables. Results: Among the 43 patients included, the median age was 73 (67, 78) years, and 33 (76.7%) patients were male. Plaque levels of PDGF-AA and VEGF showed significant positive correlations with HU (r=0.330, p=0.030; r=0.380, p=0.011, respectively), as illustrated in Figure 1 . Although plaque levels of IFN-γ demonstrated a significant negative correlation with HU (r=-0.390, p=0.010), the most cases exhibited values close to zero. Conclusions: The assessment of pericarotid fat density on CTA may allow for the estimation of plaque inflammation, suggesting that more fat density is associated with increased inflammatory activity within atherosclerotic plaques.

Introduction: Patients with atherosclerotic carotid artery disease are at high risk of mortality in the long-term follow-up after carotid endarterectomy (CEA), partly due to dysrhythmia. Atrial fibrillation (AF) is a common cardiac dysrhythmia linked to stroke and cardiovascular events. An artificial intelligence-based electrocardiogram (ECG) AF algorithm (AI-AF) can effectively identify silent AF. We aimed to assess the effectiveness of AI-AF in predicting mortality in patients with carotid artery disease undergoing CEA. Methods: Patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 were included if they had >30 days follow-up and an ECG within one year before CEA. The ECG closest to the CEA date was used to calculate AI-AF (probability of AF ranging from 0 to 1). All-cause mortality rates after CEA were obtained from clinical records. The optimal AI-AF cut-off value for predicting mortality was calculated using a receiver operating characteristic curve and the Youden index. Long-term survival after CEA was depicted with a Kaplan Meier plot and compared with the Log-rank test. Univariate and multivariate Cox Regression models were used to assess the predictive value of AI-AF for mortality, adjusting for age, sex, body mass index, diabetes mellitus, hypertension, and hyperlipidemia. Results: A total of 636 patients with a median age of 72 [IQR: 66, 78] years and 414 (65.1%) males were included. The median AI-AF was 0.056 [IQR: <0, 0.171]. Over a median follow-up time of 7.1 [IQR: 2.7, 11.5] years, mortality occurred in 326 (51.3%) patients. AI-AF significantly predicted mortality in univariate and multivariate Cox regression analysis (HR: 3.47, 95%CI: 2.34-5.16, P<0.001; HR: 2.30, 95%CI: 1.44-3.68, P<0.001, respectively). The best cut-off value for AI-AF for mortality prediction was 0.025 (area under the curve: 0.608). High and low AI-AF were defined as AI-AF≥ 0.025 and <0.025, respectively. Mortality was significantly higher in high AI-AF compared to low AI-AF patients (P<0.001, Figure 1A,1B), and high AI-AF was a significant predictor of mortality (univariate: Figure 2; multivariate Cox regression: HR: 2.11, 95%CI: 1.56-2.84, P<0.001). Conclusion: AI-AF can identify patients with a higher risk of mortality after CEA. This finding supports a role for AI algorithms in risk stratification of patients with carotid artery disease following CEA. Future studies are needed to elucidate the contribution of AF to all-cause mortality.