Citations

Background: The bovine aortic arch is a vascular variant in which the left common carotid artery originates from the brachiocephalic trunk rather than directly from the aortic arch. The bovine anatomical variation may alter the blood flow dynamics, potentially contributing to the formation and progression of carotid plaques. In this study, we evaluated the association of the bovine aortic arch and the severity of carotid stenosis. Methods: In this retrospective study, we included patients with carotid artery disease undergoing carotid endarterectomy (CEA) and pre-surgical computed tomography angiography (CTA). CTAs were reviewed to assess the carotid stenosis and the anatomy of the aortic arch. The left carotid artery was classified as normal if it originated from the aortic arch and as bovine if it originated from the brachiocephalic trunk (Figure 1) . Unlimited propensity score matching was performed to balance baseline characteristics between patients with bovine and normal anatomies. The Mann-Whitney U test was applied to compare carotid stenosis between bovine and normal arteries. Results: A total of 203 patients (bovine:23, normal:180) were initially enrolled. Using propensity score matching, 20 patients with bovine anatomy were matched to 45 patients with normal anatomy (Figure 2) . Among the 65 patients included, 47 (74.6%) were male, with a median age of 70 [IQR:65.0-76.0] years. Left-sided carotid artery disease was more prevalent in the bovine compared to normal group (N=12, 66.7% vs. N=16, 36.4%; p=0.030). Additionally, the severity of carotid stenosis in CEA-treated side was higher in patients with bovine arch compared to normal controls (82.5% [76.2-88.7] vs. 70.0% [50.0-95.0]; p=0.030) (Figure 3) . Conclusions: The association of a bovine aortic arch with more severe carotid stenosis suggests that this anatomical variant may be a marker for increased cerebrovascular risk.

Metabolic syndrome (MetS) is associated with low-grade inflammation, which can be exacerbated by renal artery stenosis (RAS) and renovascular hypertension, potentially worsening outcomes through pro-inflammatory cytokines. This study investigated whether mesenchymal stem/stromal cells (MSCs) could reduce fat inflammation in pigs with MetS and RAS. Twenty-four pigs were divided into Lean (control), MetS, MetS + RAS, and MetS + RAS + MSCs. In the MSC-treated group, autologous adipose-derived MSCs (107 cells) were injected into the renal artery six weeks after RAS induction. After four weeks, fat volumes and inflammatory markers were assessed. MSC treatment reduced levels of pro-inflammatory cytokines (MCP-1, TNF-a, IL-6) in the renal vein blood and in perirenal fat. The MSCs also decreased fat fibrosis, restored adipocyte size, and altered adipogenesis-related gene expression, particularly in the perirenal fat. These effects were less pronounced in subcutaneous fat. The MSC therapy attenuated fat inflammation and improved metabolic outcomes in pigs with MetS + RAS, suggesting that adipose-derived MSCs may offer a promising therapeutic approach for metabolic disorders.

Background: We previously developed deep-learning algorithms that identify coronary artery disease (CAD) risk based on (i) coronary artery calcium (CAC), (ii) obstructive CAD by angiography, and (iii) left ventricular akinesis in ≥1 segment by echocardiogram, using a 12-lead electrocardiogram (CAD ECG-AI). We tested the hypothesis that those with increased probability of CAD by CAD ECG-AI algorithms will have an increased risk of atherosclerotic cardiovascular disease (ASCVD) events and would refine the AHA Predicting Risk of Cardiovascular Disease Events (PREVENT TM ) equations’ predictive capabilities. Methods: We assessed a group of consecutive patients who sought primary care in Olmsted County, MN, between 1997 and 2003. Passive follow-up was conducted using the Rochester Epidemiology Project's record linkage system. Patients included met the same criteria of the original PREVENT TM equations. The probability output of each CAD ECG-AI algorithm was used to predict ASCVD (fatal and non-fatal myocardial infarction and ischemic stroke) and ASCVD-Plus [further including percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and all-cause mortality]. Events were validated in duplicate. Cox proportional hazard models adjusted for age, sex, and risk factors while modeling each CAD ECG-AI algorithm as an individual and additive predictor (see footnote). PREVENT TM risk categories stratified analysis to evaluate the effect of the CAD ECG-AI models on predicted ASCVD risk. Results: We included 21,193 patients, with mean ± SD age 51.6 ± 12.0, 54% women and 95% white. After 14.2±12.4 years follow-up, 1,351 (6.4%) developed ASCVD, and 2,808 (13.2%) had ASCVD-Plus. The risk of ASCVD and ASCVD-Plus increased with positivity for the CAD ECG-AI algorithms after adjustments for age, sex, and PREVENT TM equations factors, all p for trend <0.001 (data not shown). In additive analyses, the risk of ASCVD and ASCVD-Plus increased with increased CAD ECG-AI factors, all p for trend <0.001 ( Fig. A-B ). Furthermore, the CAD ECG-AI algorithm enhanced the predictive capabilities of PREVENT TM equations across most risk subgroups ( Fig. C-E ). Conclusions: The deep-learning CAD ECG-AI algorithms displayed an independent and additive association with long-term ASCVD and ASCVD-Plus events in the community and improved the PREVENT TM predicted ASCVD risk. The CAD ECG-AI algorithms could help identify individuals at risk in primary prevention of cardiovascular events.

Background: Recent studies have reported associations between alterations in gut microbiome composition (GMC) and cardiovascular disease risk factors (CVR). However, data regarding the use of GMC as a biomarker of CVR is scarce. Aims: The current study was designed to assess the distinct patterns in GMC among patients with varying degrees of CVR and/or atherosclerotic cardiovascular disease (ASCVD). Methods: Patients with a range of CVR including hypertension (HTN), hyperlipidemia (HLD), diabetes (DM), and/or ASCVD referring to Mayo Clinic from 2013 to 2018 were prospectively enrolled. DNA extracted from stool samples was analyzed using the V3-V5 region of the 16s data. Microbial α-diversity was assessed by the observed taxonomic units, Shannon, and Chao1 indices. β-diversity was assessed using Bray-Curtis dissimilarity and plotted using principal coordinates analysis. Hierarchical clustering was used to identify patterns in the GMC samples. Random Forest analysis was used to identify the most important clinical factors differentiating the clusters. Results: A total of 211 patients with a median age of 60 [IQR: 50,70] years and with 90 (42.7%) males were included. Two clusters of GMC were identified (Figure 1A) . Cluster 1 and 2 had 104 (49.3%) and 107 (50.7%) patients, respectively. Among CVRs, age and body mass index were the most prominent factors contributing to the difference in GMC among clusters ( Figure 1B ). Cluster 2 had a better α diversity profile than Cluster 1 (Figure 1C-E) . There was no significant difference in TMAO between clusters (P=0.6). Cluster 2 patients were younger (P<0.001), leaner (P=0.007), more physically active (P<0.001), less male (P=0.009), and had a lower prevalence of ASCVD (P=0.003), HTN (P=0.010), and HLD (P=0.005). There was no significant difference in the prevalence of DM (P=0.063), smoking (P=0.446), and alcohol intake (P=0.134) between the clusters. Conclusion: This study suggests the potential of GMC profiling as a valuable biomarker for assessing CVRs, with age and BMI as the most prominent factors associated with GMC clustering.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Background: Cardiac allograft vasculopathy (CAV) is one of the leading causes of graft failure and death after heart transplantation (HTx). Conventionally, CAV is characterized by concentric intimal thickening, however the long-term progression patterns of plaque morphology are not yet well understood. Research Question: This study aimed to assess the plaque morphology, including histology and eccentricity, in a temporal manner post-HTx using virtual histology intravascular ultrasonography (VH-IVUS). Methods: We included 50 patients who underwent serial VH-IVUS assessment at three assessment points (A1:0-1 year, A2:3-5 years, A3:7-10 years) after HTx. Differences in VH-IVUS findings between timepoints A1 to A2 (early period) and A2 to A3 (late period) were investigated. Plaque volume index was defined as (plaque volume/vessel volume) × 100%. Eccentricity index (EI) was defined as (maximum intimal thickness – minimum intimal thickness)/maximum intimal thickness and was evaluated in 39 patients with significant plaque (maximal intimal thickness >0.50 mm at the A3) at each timepoint. The serial changes of EI and relationship between EI and other VH-IVUS findings were investigated. Results: Of 50 patients, mean age was 51±13 years, 36 (72%) were males. In both early and late periods, the plaque volume (PV) index showed a significant increase, however, the rate of progression was significantly slower in late period. (5.8% [0.9, 13.2] vs 2.4% [-0.2, 7.4], p=0.015). The EI showed a trend of decrease in the early period, compared to increases in the late period (-0.040 [-0.103, 0.004] vs 0.003 [-0.030, 0.052], p=0.036). Patients with EI change in late period higher than median value had significantly lower proportion of fibrous plaque (70.8% [67.2, 75.1] vs 62.7 [55.5, 66.9], p=0.002) and higher dense calcium (1.1% [0.9, 1.9], 4.5% [1.8, 6.9], p=0.022) at A3. Conclusion: In heart transplant patients, coronary artery plaque progression in the late period showed more eccentric progression with lower fibrous plaque composition, a pattern commonly observed in typical and conventional coronary atherosclerosis. These findings may provide a novel mechanism and therapeutic insight into the temporal patterns in CAV progression during long term follow up.

Background: Coronary microvascular disease (CMD) may be endothelium-independent (coronary flow reserve, CFR<2.5) or endothelium-dependent (<50% increase in coronary blood flow in response to acetylcholine). Endothelium-independent CMD has been subclassified into structural and functional groups based on hyperemic microvascular resistance (hMR, mmHg/cm/s). We hypothesized that endothelium-dependent CMD also has these subtypes. Methods: Patients with non-obstructive coronary artery disease who underwent coronary catheterization of the left anterior descending artery were included (n=112). Pressure and flow were recorded at rest, with adenosine, and with acetylcholine. Patients were classified by endothelial type and functional CMD (hMR<2.5) or structural CMD (hMR>2.5). Wave intensity analysis was performed and cumulative separated wave intensity (Joules/m 2 ) was calculated by standard formulae. Three dominant waves were analyzed (forward compression wave, FCW; backward compression wave, BCW; backward expansion wave, BEW). Results: Seventy-four of 109 patients had CMD, of which 59 had endothelium-independent and 38 had endothelium-dependent CMD (23 had both). Functional and structural subgroups were identified (Figure 1). All CMD subtypes had significantly different wave intensities at rest, adenosine, and acetylcholine (p<0.05). Comparing subtypes, there was no difference in wave intensities at rest. However, wave intensities significantly differed between all CMD subtypes with adenosine and acetylcholine, with higher energy waves in the endothelium-independent and functional groups (p<0.05, Figure 2). Conclusions: CMD is a heterogenous condition with distinct subtypes. Functional and structural groups exist in both endothelium-dependent and endothelium-independent CMD. These subgroups have distinct hemodynamic profiles as measured by wave intensity. A patient-specific approach to first line medical therapy may be improved by subgroup classification.

Introduction: Atrial fibrillation (AF) is prevalent in patients with atherosclerotic coronary and carotid artery disease and is associated with elevated plasma inflammatory markers, such as Interleukin (IL)-6. In carotid artery atherosclerosis, macrophages are considered a local marker of inflammation. Hypothesis: We hypothesize that AF is independently associated with carotid plaque inflammation. Aims: In patients undergoing carotid endarterectomy (CEA), we aimed to determine whether carotid plaques inflammation is associated with AF. Approach: In this prospective cohort study, consecutive patients with carotid artery disease undergoing CEA were enrolled. From the plaques removed during surgery, fixed in formalin and embedded in paraffin blocks, sections were stained with antibodies against CD68 to detect macrophages (Fig.1A) . Digital copies of the slides were created by Motic Easy Scan and histologically annotated with QuPath. The percentage of CD68+ cells was defined as (CD68+ positive cells/total nuclei) x 100. Flash-frozen plaque segments were analyzed by ELISA for IL-6 concentration. Baseline demographics and clinical data, including AF diagnosis, were extracted from the electronic health records. Results: A total of 106 patients undergoing CEA, with a median age of 73 [67-78] years and comprising 83 (78.3%) males, were included in the study. AF was diagnosed prior to CEA in 13 (12.3%) patients. Univariate logistic regression showed that plaque CD68+ cell content was associated with AF (OR 1.048 [95% CI 1.010 – 1.088]; p = 0.013). Additionally, in Mann-Whitney test IL-6 levels were significantly over twofold higher in plaques of patients with AF compared to those without AF (82.4 [64.9-111.7] pg/mg vs 37.0 [18.4-93.3] pg/mg; p=0.020) (Fig. 1B) . Conclusions: The current study demonstrates an association between carotid atherosclerotic plaque macrophage content, IL6 levels and AF, supporting the role of inflammation in the pathogenesis of AF in these patients. Further studies are needed to determine the potential of plaque inflammation as a therapeutic target in AF.

Background A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small‐vessel disease, implicating vascular disease in the development of depression. Cross‐sectional studies demonstrate a link between endothelial dysfunction and MDD, but the prospective association between peripheral endothelial dysfunction (PED) and an incident diagnosis of MDD is unknown. Methods and Results Patients undergoing a baseline assessment of cardiovascular risk were evaluated for PED using reactive hyperemia–peripheral arterial tonometry (≤1.8 consistent with PED). Patient medical records were reviewed to identify those who underwent a formal clinical evaluation of MDD after the index PED evaluation. The frequency of PED was compared in those with and without MDD. Logistic regression analyses were performed to assess the association between baseline PED and incident MDD. Between January 2006 and December 2020, 1614 patients underwent testing for PED. Four hundred eighty‐four (30.1%) patients underwent a formal evaluation for MDD after (0–15 years) the index procedure (mean±SD age, 52.8±13.8 years; 65.2% women). Of these, 157 (32.4%) had PED and 108 (31.0%) were diagnosed with MDD. Individuals with MDD had a higher frequency of PED (40.2% versus 30.2%; P =0.034) compared with those without MDD. In multivariable analyses, PED was significantly associated with MDD (odds ratio, 2.3 [95% CI, 1.4–3.8]; P <0.001). Conclusions PED is significantly associated with incident MDD. Thus, PED may be a useful marker to identify individuals at increased risk of depression who may benefit from more frequent and earlier management strategies.

Positive remodeling (PR) is an atherosclerotic plaque feature defined as an increase in arterial caliber at the level of an atheroma, in response to increasing plaque burden. The mechanisms that lead to its formation are incompletely understood, but its role in coronary atherosclerosis has major clinical implications. Indeed, plaques with PR have elevated risk of provoking acute cardiac events. Hence, PR figures among the high-risk plaque features that cardiac imaging studies should report. This review aims to provide an overview of the current literature on coronary PR. It outlines the pathophysiology of PR, the different techniques used to assess its presence, and the imaging findings associated to PR, on both noninvasive and invasive studies. This review also summarizes clinical observations, trials, and studies, focused on the impact of PR on clinical outcome.