Hamidabad, Negin Mahmoudi

Abstract Aims Social isolation (SI) is associated with a higher risk of cardiovascular disease (CVD). One mechanism linking SI and CVD is accelerated biological ageing, which can be assessed using artificial intelligence-enabled electrocardiography (AI-ECG). We investigated whether longitudinal changes in SI status are associated with changes in biological ageing patterns. Methods and results A retrospective longitudinal cohort study including 17 663 individuals without significant CVD who completed ≥2 Berkman and Syme Social Network Index and had paired ECGs within 1 year of each questionnaire. Longitudinal SI status was defined by comparing baseline and follow-up SI status. Ageing trends were assessed using Δage-gap, defined as the change in the gap between AI-ECG-predicted age and chronological age from baseline to follow-up. Linear regression models predicting Δage-gap and survival analyses with Kaplan–Meier and multivariable Cox regression for all-cause mortality were performed. Persistently connected individuals were older [63.1 (52.1, 70.6) years] and had a higher prevalence of hyperlipidaemia and hypertension, yet demonstrated more favourable ageing trajectories. Similarly, those who became connected showed a non-significant attenuation of accelerated ageing. In multivariable linear regression, being persistently isolated [β = +0.412 years, P < 0.001] or becoming isolated [β = +0.438 years, P = 0.012] was associated with accelerated ageing compared to being persistently connected. In survival analyses, becoming isolated (aHR = 1.94, 95% CI = 1.53–2.46, P < 0.001) and being persistently isolated (aHR = 1.66, 95% CI = 1.40–1.96, P < 0.001) were associated with significantly higher all-cause mortality, independent of chronological age and comorbidities. Conclusion Persistent social connection is associated with slower biological ageing and lower mortality. The effects of SI on ageing and survival appear to be potentially reversible, calling for a prospective study to evaluate interventions aimed at improving both the social and biological burden of SI.

Background Inadequate preventive dental care may contribute to inflammatory conditions such as periodontitis, increasing cardiovascular disease risk. Artificial intelligence–enabled electrocardiography (AI‐ECG) algorithms accurately estimate cardiovascular disease risk. We investigated the association between inadequate preventive dental care and the AI‐ECG–estimated risk of atrial fibrillation, aortic stenosis, low ejection fraction, cardiac amyloid, cardiovascular aging, and all‐cause mortality. Methods We retrospectively analyzed 266 637 Mayo Clinic patients (2018–2024) who completed a Social Determinants of Health Survey and had an ECG performed within 1 year of survey completion. AI‐ECG algorithms generated continuous risk scores (0 or 1) for atrial fibrillation, aortic stenosis, low ejection fraction, and amyloid. Cardiac aging was assessed by delta age, calculated as AI‐ECG–estimated physiological age minus the chronological age. Associations with preventive dental care were analyzed using logistic regression. Cox regression evaluated dental care predictiveness on all‐cause mortality. Results Among 266 637 individuals (mean age, 59.5±16.4 years; 51.1% women), 49 918 (18.7%) reported inadequate preventive dental care. These individuals had higher delta age and increased AI‐ECG–predicted risks of atrial fibrillation, aortic stenosis, amyloid, and reduced ejection fraction (all P <0.001). In multivariable logistic regression models including all AI‐ECG scores, inadequate preventive dental care was associated with higher probabilities of low ejection fraction (odds ratio [OR], 1.80 [95% CI, 1.69–1.91]), atrial fibrillation (OR, 1.10 [95% CI, 1.04–1.17]), aortic stenosis (OR, 1.90 [95% CI, 1.64–2.20]), amyloid (OR, 1.18 [95% CI, 1.12–1.25]), and higher delta age (OR, 1.03 [95% CI, 1.02–1.03]) (all P <0.001). Inadequate preventive dental care predicted all‐cause mortality after adjusting for demographics, risk factors, and AI‐ECG algorithms (HR, 1.45 [95% CI, 1.38–1.53], P <0.001). Conclusions Inadequate preventive dental care is associated with subclinical cardiovascular disease. AI‐ECG algorithms may facilitate cardiovascular risk stratification among individuals with limited preventive dental care access.

Background α‐Klotho is a multifunctional protein implicated in vascular aging. Declining Klotho levels are associated with arterial stiffness, endothelial dysfunction, and vascular calcification. We investigated the associations between circulating klotho, aortoiliac calcification, endothelial progenitor cells, and 10‐year survival. Methods In this retrospective cohort, blinded clinicians scored calcification on computed tomography (0–10 scale: aorta 6 points, iliac 4 points). Plasma klotho was measured by ELISA; endothelial progenitor cell subsets were quantified via flow cytometry. Mortality data were obtained from the Minnesota Death Registry. Results This study included 148 participants (mean age 72.4±9.6 years; 41% female). Median calcification score was 3 (interquartile range, 1–5). Patients with low calcification had higher Klotho levels (669 [567–841] versus 556 [457–678] pg/mL; P <0.01), with Klotho inversely correlated with calcification (r=−0.315, P <0.01). Stratified by calcification location and severity, the highest Klotho levels were seen in patients without aortic or iliac calcification ( P =0.015). Over 10 years, low Klotho was independently associated with higher mortality (hazard ratio [HR], 3.056 [95% CI 1.288–7.878]; P =0.014). Klotho inversely correlated with mature osteocalcin‐positive EPCs; B=−0.16 (95% CI, −0.29 to −0.02; P =0.027). Immunohistochemistry and immunofluorescence confirmed endothelial Klotho expression in internal mammary artery. Conclusions Lower circulating Klotho levels are associated with increased aortoiliac calcification and higher 10‐year mortality, supporting a role for Klotho in vascular aging.

Background: An increasing recognition of angina and nonobstructive coronary artery disease (ANOCA) underscores the importance of this patient group. While inflammation is a well-established key driver in the initiation and progression of atherosclerosis, its role in early stage is less clear. The current study was designed to assess the predictive value of monocyte-lymphocyte ratio(MLR) in comparison with hs-CRP in these patients. Method: This observational cohort study included patients with chest pain and nonobstructive coronary artery disease <40% stenosis in major coronary arteries on clinically indicated elective coronary angiography who underwent invasive physiological coronary reactivity testing for evaluation of coronary vasomotor function between 1997-2021. Preprocedural MLR was categorized as high (≥0.30) or low (<0.30) using the best cutoff value for predicting major adverse cardiovascular and cerebrovascular events (MACCE) determined by ROC curves. Preprocedural Hs-CRP levels ≥1 and <1 mg/L were classified as high or low, respectively. MACCE comprised cardiovascular death, nonfatal myocardial infarction, heart failure, stroke, and late revascularization. Kaplan-Meier plots, Log-rank test, and Cox regression analysis were used for analysis. Result: Among 892 patients (median age 52 [IQR: 43- 61] years, 69% females), 40% had high MLR and 49% had high hs-CRP, respectively. Over a 10-year follow-up, 125 patients (14%) experienced MACCE. In univariable analysis, high MLR level ( p < 0.001 ) and high hs-CRP ( p = 0.045) were associated with worse outcomes. (Figure 1) In multivariate Cox regression after adjusting for age, body mass index, creatinine, hypertension, hyperlipidemia, and diabetes mellitus, MLR remained a significant predictor of MACCE (HR=1.75, 95% CI=1.20- 2.55, p = 0.004), whereas hs-CRP lost its significance (HR=1.26, 95% CI=0.857- 1.848, p=0.241). Stratifying patients into four groups of high MLR/high hs-CRP (21%), high MLR/low hs-CRP (19%), low MLR/high hs-CRP (28%), and low MLR/low hs-CRP (32%) revealed that high MLR was associated with increased MACCE in both groups with high and low hs-CRP, while in both high MLR and low MLR groups, hs-CRP levels made no difference in MACCE incidences. (Figure 2). Conclusion: elevated MLR is a strong independent predictor of MACCE in patients with ANOCA, superior to hs-CRP. These findings highlight the potential of MLR as a valuable, readily available marker for risk stratification in early atherosclerosis.

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.

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.

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.