Future Atherosclerotic Cardiovascular Disease Research Articles

Abstract 4136273: A Machine Learning Approach to Simplify Risk Stratification of Patients with Atherosclerotic Cardiovascular Disease

Introduction: The 2018 AHA/ACC/Multisociety Blood Cholesterol Guideline recommends that patients with atherosclerotic cardiovascular disease (ASCVD) be classified as very high-risk (VHR) or not very high-risk (NVHR). This designation is based on the presence of > 2 major ASCVD events (recent acute coronary syndrome [ACS], history of myocardial infarction [MI], history of ischemic stroke, symptomatic peripheral artery disease [PAD]) or 1 major ASCVD event and > 2 high-risk conditions (age ≥65 years, heterozygous familial hypercholesterolemia, history of coronary revascularization, diabetes, hypertension, chronic kidney disease, current smoking, persistently elevated LDL-cholesterol [LDL-C], history of heart failure). While this approach was initially instituted to better predict future ASCVD events alone, more intensive LDL-C lowering is now recommended for those at VHR (LDL-C treatment threshold of 55 mg/dL for VHR patients vs 70 mg/dL for NVHR patients). We aimed to simplify ASCVD risk stratification in patients with clinical ASCVD using machine learning models. Methods: We used electronic health record data (Providence Health system) from 2022 to identify patients with ASCVD based on ICD-10 codes. Patient demographics, comorbidities, and laboratory data stratified patients as VHR or NVHR. Then, a classification and regression tree analysis was performed, with the outcome being VHR classification. Patients were randomly assigned to either the testing or training dataset in equal proportion. Variables in the model included age, sex, race, ethnicity, and the Guideline criteria. Model performance was assessed using misclassification rate and area under the receiver operative curve (AUC). Results: A total of 491,514 patients with ASCVD were included, of which 56% were male, 77% were white, and the mean (SD) age was 72.5 (13.0) years. Over half of patients (59%) were VHR. Those with recent ACS, history of MI, history of ischemic stroke, or PAD were classified as VHR 93%, 92%, 84%, and 91%, respectively. Presence of hypertension in these patients increased the likelihood of being VHR to 97%, 97%, 94%, and 98%, respectively. The misclassification rate and AUC for the testing set were 6.8% and 96.2%, respectively. Conclusions: Most patients with a major ASCVD event and hypertension were found to be VHR. Relying on these two factors alone can help simplify risk assessment. This approach may also help increase use of guideline-recommended LDL-C lowering therapy.

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients. AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome. AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation. British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

Adding traditional and emerging biomarkers for risk assessment in secondary prevention: A prospective cohort study of 20,656 patients with cardiovascular disease.

This study aims to explore whether conventional and emerging biomarkers could improve risk discrimination and calibration in secondary prevention of recurrent atherosclerotic cardiovascular disease (ASCVD), based on a model using predictors from SMART2. In a cohort of 20,658 UK Biobank participants with medical history of ASCVD, we analysed any improvement in C indices and net reclassification index (NRI) for future ASCVD events, following addition of LP-a, ApoB, cystatin C, HbA1c, GGT, AST, ALT, and ALP, to a model with predictors used in SMART2 for the outcome of recurrent major cardiovascular event. We also examined any improvement in C indices and NRIs replacing creatinine based estimated glomerular filtration rate (eGFR) with cystatin C based estimates. Calibration plots between different models were also compared. Compared with the baseline model (C index=0.663), modest increment in C indices were observed when adding HbA1c (ΔC=0.0064, p<0.001), cystatin C (ΔC=0.0037, p<0.001), GGT (ΔC=0.0023, p<0.001), AST (ΔC= 0.0007, p<0.005) or ALP (ΔC=0.0010, p<0.001) or replacing eGFRCr with eGFRCysC (ΔC=0.0036, p<0.001) or eGFRCr-CysC (ΔC=0.00336, p<0.001). Similarly, the strongest improvements in NRI were observed with the addition of HbA1c (NRI=0.014), or cystatin C (NRI= 0.006) or replacing eGFRCr with eGFRCr-CysC (NRI=0.001) or eGFRCysC (NRI=0.002). There was no evidence that adding biomarkers modify calibration. Adding several biomarkers, most notably cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination.

Prediction of mortality, future arrhythmia and cardiovascular disease: an artificial intelligence-enhanced electrocardiography platform

Abstract Background Artificial intelligence-enhanced electrocardiography (AI-ECG) can be used to identify existing disease, but could additionally be used to predict occurrence of future disease and death. Purpose We developed an AI-ECG platform that predicts mortality and a wide spectrum of future arrhythmia and cardiovascular disease. Methods The AI-ECG risk estimation platform (AIRE) was developed in a dataset of 1,163,401 ECGs from 189,539 patients from a secondary care setting in the USA. Uniquely, AIRE uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves (Fig 1A). AIRE was fine-tuned to additionally predict the endpoints described below. Results AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776) and can differentiate prognosis in high- and low-risk subjects, even when only cardiologist-defined normal ECGs are considered (Fig 2B). Additionally, in Cox regression analyses, AIRE was superior to conventional risk factors (Fig 1B). AIRE accurately predicted future ventricular arrhythmia (c-index 0.760 (0.756-0.763)), future complete heart block (CHB) (0.809 (0.805-0.814), future atrial fibrillation (0.753 (0.751-0.756), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.789)) . AIRE was superior to left ventricular ejection fraction (LVEF) in predicting risk of future ventricular arrhythmias in subjects with LVEF &amp;lt;50% (C-index 0.649 (0.638 – 0.660) vs 0.615 (0.603 – 0.626) p &amp;lt; 0.0001 ). For CHB prediction, AIRE was able to further risk stratify subjects with bifascicular block and syncope into low (1.9% 5 year CHB), intermediate (8.8%), and high risk (20.7%) groups, and could guide treatment decisions. We externally validated our findings in four diverse, transnational cohorts from Brazil and the UK, including volunteers, primary care subjects and patients with Chagas disease, and found AIRE accurately predicted all-cause mortality in all four external cohorts (Fig 2). Non-mortality endpoints were successful externally validated in the UKB. In explainability analyses, we found features of QRS morphology, low QRS voltage, poor precordial R wave progression, inverted and biphasic T waves and ST segment changes were the most significant morphological features associated with high predicted mortality (Fig 1D). Through phenome- and genome-wide association studies (GWAS), we identified candidate biological pathways including changes in cardiac structure (LV mass, LV trabeculation and left atrial size). GWAS identified variants adjacent to VGLL2, KCNQ1, CCDC91 and TBX3 which have been described in association with QT interval, biological aging and metabolic syndrome (Fig 1E). Conclusion AIRE could be used worldwide across a range of clinical contexts for risk estimation of not only mortality but a wide spectrum of future arrhythmia and cardiovascular diseases.Figure 1Figure 2

Artificial intelligence-enhanced electrocardiography predicts 10-year risk of atherosclerotic cardiovascular disease

Abstract Background The ACC/AHA pooled cohort equations (PCE) calculates the 10-year primary risk of atherosclerotic cardiovascular disease (ASCVD), an indication to initiate primary prevention statin therapy in international guidelines. The validity of PCE is limited by its overestimation of risk in high-risk cohorts and an ethnic heterogeneity. Electrocardiography (ECG) has not been incorporated in current ASCVD risk estimation tools. Artificial Intelligence (AI)-based models are capable to capture patterns that are informative for cardiovascular prognosis. Purpose We explored the predictive value of a discrete-time survival model in 10-year ASCVD risk in comparison to PCE. Methods We developed an AI-ECG model to predict the 10-year ASCVD risk in an unselected secondary care population from the USA comprised of 1,163,401 ECGs from 189,539 patients. Data was split into training/validation and hold-out test sets by patient ID. Our AI-ECG model uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves, accounting for both time to event and censorship (i.e., loss to follow up). We compared the performance of out AI-ECG model using a subset of outpatient data (n=4590) of the BIDMC dataset to PCE and ASCVD risk factors including systolic blood pressure, total cholesterol, HDL cholesterol, hypertension, smoking history, diabetes mellitus, and ethnicity. Performances were evaluated using cox survival analysis. Our AI-ECG model was externally validated in the UKB Biobank (UKB), where we compared the performance of our AI-ECG model to predictions of existing AI-ECG models such as the Stanford Estimator of Electrocardiogram Risk (SEER) model. Results Our AI-ECG successfully predicted future ASCVD events in individuals (C-index 0.721 (0.719-0.723), 5-year AUC 0.761 (0.758-0.763), and differentiated between high and low risk group with an adjusted HR of 2.33 (2.26-2.41). Upon internal validation our AI-ECG model showed a superior performance (C-index 0.679 (0.651-0.708)) than PCE (0.605 (0.577-0.634)) and all risk factors combined (C index 0.642 (0.613-0.672)). When combining AI-ECG predictions with all risk factors, the model showed additional predictive value (0.686 (0.657-0.715)) (Figure 1). Our AI-ECG model showed modest predictive value within the UKB dataset (C-index 0.655 (0.637-0.673)), but significantly outperformed the SEER model (C-index 0.547 (0.527-0.567), p &amp;lt;0.0001 for comparison with AIRE). Conclusion We have shown the potential of AI-ECG models in predicting future ASCVD risks. AI-ECG models demonstrated accurate discrete time survival prediction and good classification performance superior to that of the SEER model, as well as PCE and ASCVD risk factors. . Accurate risk assessment of ASCVD may help prevent adverse events in high risk subjects and save unnecessary pharmacological interventios in low risk subjects.Figure 1

A-228 Evaluation of an Equation for Low-Density Lipoproteins-Triglycerides in Predicting ASCVD Risk in the UK Biobank Population

Abstract Background Low-density lipoprotein-cholesterol (LDL-C) is the main univariate risk marker recommended for initial atherosclerotic cardiovascular disease (ASCVD) risk stratification. LDL, however, transports other constituents that can serve as pro-atherogenic risk markers. Like LDL-C, the triglyceride content of LDL (LDL-TG) can also be estimated from the lipid panel. Using data from UK Biobank (UKB), we compared LDL-C to LDL-TG for predicting future ASCVD events. Methods A UKB dataset (N=270,436) of primary prevention individuals not on lipid lowering therapy was used for analysis. LDL-C was calculated by the following equations: Friedewald, Extended Martin-Hopkins, Sampson-NIH, and Enhanced Sampson-NIH. LDL-TG was calculated by our newly devised equation (eLDL-TG = TG/38.46 + nonHDL-C/5.75 + (TG/nonHDL-C)/0.103 + (1/HDL-C)/0.0041 - 2.95)), which was derived from a dyslipidemic cohort (N=40,202) analyzed by the beta-quantification reference method. Results Although there was only a modest comparison between eLDL-TG and measured LDL-TG (R2=0.601, slope=0.611), eLDL-TG appeared to be a better risk marker than LDL-C in UKB. When the population was divided into quintiles and analyzed by survival curve analysis, eLDL-TG was better than LDL-C in segregating patients into low- and high-risk groups (Figure). Similar findings on the relative value of LDL-TG over other risk markers were also found when analyzed by ROC analysis (AUC: eLDL-TG&amp;gt;HDL-C&amp;gt;remnant-cholesterol&amp;gt;NonHDL-C&amp;gt;TG&amp;gt;apoB&amp;gt;Sampson-NIH-LDL-C). By Cox-proportional hazard analysis eLDL-TG showed alongside apoB and NonHDL-C the strongest association with future ASCVD events. Discordance analysis showed that when stratified as LDL-TG/LDL-C as High/Low there was an 26% increased risk for ASCVD, and when as High/High showed an 40% increased risk for ASCVD. Conclusions Like LDL-C, LDL-TG levels can also be estimated from the standard lipid panel. If our findings hold in other populations, the superiority of LDL-TG over LDL-C as a risk marker suggest that the reporting of calculated LDL-TG along with LDL-C could be a new strategy for improving ASCVD risk stratification.

Plasma C-reactive protein is associated with a pro-inflammatory and adverse plaque phenotype

Background and aimsSystemic low-grade inflammation, measured by plasma high-sensitivity C-reactive protein (hsCRP) levels, is an important risk factor for atherosclerotic cardiovascular disease (ASCVD). To date, however, it is unknown whether plasma hsCRP is associated with adverse histological plaque features. MethodsPlaques were derived during carotid endarterectomy. Patients with hsCRP levels ≥2 mg/L were evaluated for pro-inflammatory and adverse plaque characteristics, as well as future ASCVD events, and compared with patients with low hsCRP levels. Logistic and linear regression analyses in addition to subdistribution hazard ratios were conducted, adjusted for cardiovascular risk factors. ResultsA total of 1096 patients were included, of which 494 (46.2 %) had hsCRP levels ≥2 mg/L. Elevated hsCRP levels 2 mg/L were independently associated with levels of plaque interleukin 6, beta coefficient of 109.8 (95 % confidence interval (CI): 33.4, 186.5; p = 0.005) pg/L, interleukin 8 levels, 194.8 (110.4, 378.2; p = 0.03) pg/L and adiponectin plaque levels, −16.8 (−30.1, −3.6; p = 0.01) μg/L, compared with plaques from patients with low hsCRP levels. Histological analysis revealed increased vessel density in high hsCRP patients, odds ratio (OR) of 1.57 (1.20, 2.09; p = 0.001), larger lipid core, 1.35 (1.02, 1.73; p = 0.04), and increased macrophage content, 1.32 (1.02, 1.73; p = 0.04). Over a 3-year follow-up period, hsCRP levels ≥2 mg/L were associated with a hazard ratio of 1.81 (1.03, 3.16; p = 0.04) for coronary artery disease event risk. ConclusionsThe distinct inflammatory and histological features observed in carotid plaques among individuals with hsCRP levels ≥2 mg/L underscore the utility of plasma hsCRP as a potent identifier for patients harboring high-risk plaques.

Association of Autoimmune Diseases With Coronary Atherosclerosis Severity and Ischemic Events

BackgroundSome autoimmune diseases carry elevated risk for atherosclerotic cardiovascular disease (ASCVD), yet the underlying mechanism and the influence of traditional risk factors remain unclear. ObjectivesThis study sought to determine whether autoimmune diseases independently correlate with coronary atherosclerosis and ASCVD risk and whether traditional cardiovascular risk factors modulate the risk. MethodsThe study included 85,512 patients from the Western Denmark Heart Registry undergoing coronary computed tomography angiography. A diagnosis of 1 of 18 autoimmune diseases was assessed. Adjusted OR (aOR) for any plaque, any coronary artery calcification (CAC), CAC of >90th percentile, and obstructive coronary artery disease as well as adjusted HR (aHR) for ASCVD were calculated. ResultsDuring 5.3 years (Q1-Q3: 2.8-8.2 years) of follow-up, 3,832 ASCVD events occurred. A total of 4,064 patients had a diagnosis of autoimmune disease, which was associated with both presence of any plaque (aOR: 1.29; 95% CI: 1.20-1.40), any CAC (aOR: 1.28; 95% CI: 1.19-1.37), and severe CAC of >90th percentile (aOR: 1.53; 95% CI: 1.39-1.68), but not with having obstructive coronary artery disease (aOR: 1.04; 95% CI: 0.91-1.17). Patients with autoimmune diseases had a 46% higher risk (aHR: 1.46; 95% CI: 1.29-1.65) for ASCVD. Traditional cardiovascular risk factors were strongly associated with future ASCVD events, and a favorable cardiovascular risk factor profile in autoimmune patients was associated with ∼54% lower risk compared to patients with presence of risk factors (aHR: 0.46; 95% CI: 0.27-0.81). ConclusionsAutoimmune diseases were independently associated with higher burden of coronary atherosclerosis and higher risk for future ASCVD events, with risk accentuated by traditional cardiovascular risk factors. These findings suggest that autoimmune diseases increase risk through accelerated atherogenesis and that cardiovascular risk factor control is key for improving prognosis in patients with autoimmune diseases.

Artificial intelligence enabled electrocardiogram for mortality and ventricular arrhythmia prediction

Abstract Background Artificial intelligence-enhanced electrocardiograms (AI-ECG) can be used to predict risk of events but existing models suffer from lack of actionability, explainability and biological plausibility, and therefore are not used clinically. Purpose We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE). Methods AIRE was developed in a dataset of 1,163,401 ECGs from 189,539 patients from a secondary care setting in the USA. Uniquely, AIRE uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to create a subject-specific survival curve using a single ECG. AIRE was trained to predict time-to-mortality and fine-tuned for the additional endpoints described below. Results AIRE outputs a subject-specific survival curve that predicts not only mortality but time-to-mortality, using only a single ECG (Figure 1A). AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776), Figure 2) and can differentiate prognosis in high and low risk subjects even when only cardiologist reported normal ECGs are considered (Figure 2). AIRE also accurately predicted cardiovascular (CV) death 0.832 (0.831-0.834), future ventricular arrhythmia (0.760 (0.756-0.763)), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.889))). In particular AIRE was superior to left ventricular ejection fraction (LVEF) in predicting risk of future ventricular arrhythmias in subjects with LVEF &amp;lt;50% (C-index 0.649 (0.638 – 0.660) vs 0.615 (0.603 – 0.626) p &amp;lt; 0.0001 ) and in dilated cardiomyopathy (C-index 0.690 (0.669-0.711) vs 0.608 (0.584 – 0.631), p &amp;lt; 0.0001). We used a variational autoencoder (VAE) to visualise the features most correlated with the mortality predictions. Features of QRS morphology, particularly broader and more left bundle branch block morphologies, inverted and biphasic T waves as well as ST segment changes were associated with high predicted mortality (Figure 1B). We externally validated our findings in four diverse, transnational cohorts from Brazil and the UK including volunteers and primary care subjects (Figure 2), and found AIRE accurately predicted all-cause mortality in all four external cohorts. Through phenome- and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function (LV mass, LVEF, LV trabeculation), and genes associated with cardiac structure, QT interval, biological aging and metabolic syndrome (VGLL2, KCNQ1, CCDC91, TBX3). Conclusion AIRE is an explainable, biologically plausible and actionable AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short- and long-term risk estimation.Figure 1Figure 2

Abstract P407: Decreased Peripheral Mitochondrial Respiration in Pre-Adolescent Hispanic Children With Hepatic Steatosis

Introduction: Mitochondria plays a significant role in the functioning of the liver. Its function and structure have been associated with diseases outside the organelle by producing reactive oxygen species (ROS), causing generalized oxidative stress, improper function to several metabolic pathways, and perpetuating a pro-inflammatory state in conditions like obesity, diabetes, and metabolic dysfunction associated steatotic liver disease (MASLD). These diseases are on the rise and increase future atherosclerotic cardiovascular disease. Little is known regarding the pathophysiology of the earliest stages of hepatic steatosis, although mitochondrial dysfunction has been shown in later stages. In young children with and without MASLD, we aimed to measure mitochondrial respiration and compare it to hepatic steatosis and other cardiometabolic parameters. The hypothesis of the sub-study was that children with MASLD will have a decreased mitochondrial function compared with healthy children. Methods: We report data on 9 subjects, (all Latino subjects, mean age of 8.1 years, mean BMIp of 78%) who participated in screening for an ongoing clinical trial (ClinicalTrials No. NCT05292352). After consent and assent, procedures included drawing whole blood and transferring it immediately to the lab. Monocyte isolation was performed using RosetteSep™ Cocktail and measurement of fatty acid oxidation and glycolysis using the Seahorse Bioscience XFp extracellular flux analyzer. We measured the Oxygen Consumption Rate (OCR) as a proxy for mitochondrial respiration. Association with the mitochondrial parameters was evaluated with the percentage of hepatic steatosis (Hepafat), levels of ALT, insulin, and BMI percentile (BMIp). Data were analyzed using the R software program version 4.2.3 Results: Mean hepatic steatosis (HS) was 5.97% with a low of 2.4% and a high of 13%. The mean OCR for the subjects without MASLD (n=4, HS ≤5% by MRI) was 404.64 and the mean for subjects with MASLD (n=5, HS ≥5% by MRI) was 349.88. The mean OCR of subjects with a normal, overweight, and obese BMIp was 441.58, 397.45, 309.08, respectively. We found peripheral mitochondrial respiration tends to decrease as hepatic steatosis, BMIp, ALT and insulin levels increase. Conclusion: Hepatic steatosis is associated with decreased mitochondrial respiration and perhaps perpetuates an inflammatory state at very young ages in this vulnerable population. More subjects from the NAFLD prevention study will increase the significance of the study in the future.

Comparing coronary artery cross-sectional area among asymptomatic South Asian, White, and Black participants: the MASALA and CARDIA studies

BackgroundSouth Asian individuals have high risk of atherosclerotic cardiovascular disease (ASCVD). Some investigators suggest smaller coronary artery size may be partially responsible.MethodsWe compared the left anterior descending (LAD) artery cross-sectional area (CSA) (lumen and arterial wall) among South Asians in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study with White and Black participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study, adjusting for BMI, height, and other ASCVD risk factors. We used thin-slice non-contrast cardiac computed tomography to measure LAD CSA. We used linear regression models to determine whether race/ethnicity was associated with LAD CSA after adjusting for demographic factors, BMI, height, coronary artery calcium (CAC), and traditional cardiovascular risk factors.ResultsOur sample included 3,353 participants: 513 self-identified as South Asian (44.4% women), 1286 as Black (59.6% women), and 1554 as White (53.5% women). After adjusting for age, BMI, height, there was no difference in LAD CSA between South Asian men and women compared to White men and women, respectively. After full adjustment for CVD risk factors, LAD CSA values were: South Asian women (19.9 mm2, 95% CI [18.8 – 20.9]) and men (22.3 mm2, 95% CI [21.4 – 23.2]; White women (20.0 mm2, 95% CI [19.4—20.5]) and men (23.6 mm2, 95% CI [23.0—24.2]); and Black women (21.6 mm2, 95% CI [21.0 – 22.2]) and men (26.0 mm2, 95% CI [25.3 – 26.7]). Height, BMI, hypertension, CAC, and age were positively associated with LAD CSA; current and former cigarette use were inversely associated.ConclusionsSouth Asian men and women have similar LAD CSA to White men and women, and smaller LAD CSA compared to Black men and women, respectively, after accounting for differences in body size. Future studies should determine whether LAD CSA is associated with future ASCVD events.

Subclinical atherosclerosis in young adults predicting cardiovascular disease: The Cardiovascular Risk in Young Finns Study

Background and aimsAtherosclerosis is accompanied by pre-clinical vascular changes that can be detected using ultrasound imaging. We examined the value of such pre-clinical features in identifying young adults who are at risk of developing atherosclerotic cardiovascular disease (ASCVD). MethodsA total of 2641 individuals free of ASCVD were examined at the mean age of 32 years (range 24–45 years) for carotid artery intima-media thickness (IMT) and carotid plaques, carotid artery elasticity, and brachial artery flow-mediated endothelium-dependent vasodilation (FMD). The average follow-up time to event/censoring was 16 years (range 1–17 years). ResultsSixty-seven individuals developed ASCVD (incidence 2.5%). The lowest incidence (1.1%) was observed among those who were estimated of having low risk according to the SCORE2 risk algorithm (<2.5% 10-year risk) and who did not have plaque or high IMT (upper decile). The highest incidence (11.0%) was among those who were estimated of having a high risk (≥2.5% 10-year risk) and had positive ultrasound scan for carotid plaque and/or high IMT (upper decile). Carotid plaque and high IMT remained independently associated with higher risk in multivariate models. The distributions of carotid elasticity indices and brachial FMD did not differ between cases and non-cases. ConclusionsScreening for carotid plaque and high IMT in young adults may help identify individuals at high risk for future ASCVD.

Abstract 14460: Impact of Carotid Artery Plaque Detection on Cardiovascular Disease Risk and the Importance of Age in the Multi-Ethnic Study of Atherosclerosis

Background Carotid artery plaque (CAP) predicts future atherosclerotic cardiovascular disease (ASCVD) events; however, its prevalence increases with age and differs by sex and race/ethnicity, so how its presence influences ASCVD risk in clinical settings is uncertain. Hypothesis: We hypothesized that the impact of CAP detection on relative ASCVD risk declines with age. Methods: CAP was detected and quantified by ultrasound in adults free of clinical ASCVD at Exam 1 of the Multi-Ethnic Study of Atherosclerosis who were followed longitudinally for coronary death, myocardial infarction, angina, stroke, and ASCVD death. Cox proportional hazards models investigated associations of CAP and incident ASCVD events in subsets defined by age categories. These models were adjusted for confounders and an age*CAP interaction. Results: The 5198 participants were a mean (standard deviation) 61.8 (10.1) years old (53% female, 27% Black, 22% Hispanic, 12% Chinese) and experienced 880 ASCVD events over 14.3 (5.7) years. Prevalence of CAP and the age at which 50% of participants had detectable CAP differed by age, sex, and race/ethnicity, ranging from 13% in Chinese women &lt;50 years old to 97% in Non-Hispanic White men 80-84 years old (Figure). CAP presence independently predicted ASCVD events (hazard ratio, 1.35, [95% confidence interval 1.17-1.57], p&lt;0.001), however the strength of this association declined with increasing age (p interaction =0.029, Figure). CAP detection in younger individuals conferred higher relative ASCVD risk than in older participants who had higher absolute risk, regardless of CAP. Conclusions: Prevalence of CAP varies by sex and race/ethnicity. CAP becomes very common with increasing age and its relative association with future ASCVD events declines. These data can assist in guiding ASCVD risk assessment in younger adults and provide perspective for incidentally detected CAP on clinical imaging studies in older adults who have higher absolute ASCVD risk.

Abstract 11370: Comparing Coronary Artery Cross-Sectional Area Among Asymptomatic South Asian, White, and Black Participants: The Masala and Cardia Studies

Introduction: South Asian individuals have high risk of atherosclerotic cardiovascular disease (ASCVD). Some investigators suggest smaller coronary artery size may be partially responsible. Hypothesis: We hypothesized that South Asian participants would have smaller left anterior descending artery (LAD) cross sectional area (CSA) than Black and White participants, and that this association would be attenuated after adjusting for body size (BMI and height) and cardiovascular risk factors. Methods: We compared the LAD CSA (lumen and arterial wall) among South Asians in the Mediators of Atherosclerosis in South Asians Living in America study with White and Black participants in the Coronary Artery Risk Development in Young Adults study, adjusting for BMI, height, and other ASCVD risk factors. We used thin-slice non-contrast cardiac computed tomography to measure LAD CSA. We used linear regression models to explore differences by race/ethnicity in LAD CSA after adjusting for demographic, BMI, height, coronary artery calcium (CAC), and ASCVD risk factors. Results: We included 3,353 participants: 513 South Asian (44% women), 1286 Black (60% women), and 1554 White (54% women). After adjusting for age, BMI, height, there was no difference in LAD CSA between South Asian and White participants by sex. However, South Asian participants had smaller LAD CSA compared to Black participants by sex. After full adjustment for ASCVD risk factors, LAD CSA values were: South Asian women (19.9 mm 2 , 95% CI [18.8 - 20.9]) and men (22.3 mm 2 , 95% CI [21.4 - 23.2]; White women (20.0 mm 2 , 95% CI [19.4 - 20.5]) and men (23.6 mm 2 , 95% CI [23.0 - 24.2]); and Black women (21.6 mm 2 , 95% CI [21.0 - 22.2]) and men (26.0 mm 2 , 95% CI [25.3 - 26.7]) ( Figure 1 ). Conclusions: After accounting for differences in body size, South Asian participants had smaller LAD CSA compared to Black participants, but similar to White participants. Future studies should determine whether LAD CSA is associated with future ASCVD events.

Abstract 12346: Atherosclerotic Cardiovascular Disease Risk Associated With Social Determinants of Health at Individual and Area Levels

Introduction: Adverse social determinants of health (SDOH) are associated with an increased risk for atherosclerotic cardiovascular disease (ASCVD). However, it is unclear if the ASCVD risks associated with area-level SDOH are similar to those with individual-level measures. Further, SDOH are not included in the Pooled Cohort Equations (PCEs) when estimating 10-year ASCVD risk. Studies have shown that PCEs systematically underestimate risk for socially deprived individuals. Aims To examine the association of individual- and area-level SDOH including education, income, and employment with incident ASCVD. Also, we assessed if adding SDOH to the PCEs improves risk prediction accuracy. Methods: We pooled data from four cohorts: Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, Framingham Heart Study Offspring Cohort, and Reasons for Geographic and Racial Differences in Stroke Study. We excluded participants with a history of ASCVD. Individual-level SDOH were based on participants’ self-report and area-level SDOH were derived by linking participants’ addresses to US Census tract data. We used Cox proportional hazards models to assess the associations between SDOH and ASCVD. We also assessed the changes in discrimination (Harrell’s C-index) and calibration (Integrated Brier Score) after adding SDOH to risk prediction models. Results: We analyzed data from 27,603 participants (mean age 61.4 years, 59% women, and 49% White). During a median follow-up of 13 years, 2,839 incident ASCVD events occurred. Both individual- and area-level SDOH were associated with ASCVD ( Figure, Panel A ). Adding both individual- and area-level SDOH to risk prediction modestly improved model discrimination and calibration in Black men and women ( Figure, Panels B &amp; C ), and also improved calibration in non-Black women. Conclusions: The findings suggest that SDOH may be considered in the development of future ASCVD risk assessment tools, particularly among Black individuals.

Abstract 17498: Association Between 5-Year Change in Cardiovascular Risk and Atherosclerotic Cardiovascular Disease Incidence: A Multicohort Study

Introduction: Early measurement of atherosclerotic cardiovascular disease (ASCVD) risk factors contains valuable information about an individual's risk history. Hypothesis: Individuals with different historical cardiovascular risk statuses have varied risks of future ASCVD incidence. Methods: We analyzed pooled data from 7 community-based prospective cohort studies with up to 20-year follow-ups ( panel A ). White or Black participants aged 40-75 years without prevalent ASCVD were included. Cardiovascular risk was assessed using the pooled cohort equation and was categorized into non-high (&lt;20%) or high risk (≥20%). Changes in CVD risk over a 5-year interval were considered. The main outcome is incident ASCVD. Results: Among 11026 participants (mean [SD] age,60.0 [8.1] years). 4272 (38.7%) were female and 3127 (28.4%) were Black. During a median follow-up of 9.9 years, 2560 (23.2%) ASCVD events occurred. Compared to individuals with consistently high risk, participants with non-high to high risk (HR 0.67; 95%CI 0.59-0.77), high to non-high risk (HR 0.57; 95%CI 0.41-0.80), and consistently non-high risk (HR 0.33; 95%CI 0.29-0.37) had a lower risk of incident ASCVD. Compared to individuals with consistently non-high risk, participants with high to non-high risk (HR 1.74; 95%CI 1.26-2.41), non-high to high risk (HR 2.04; 95%CI 1.84-2.27), and consistently high risk (HR 3.03; 95%CI 2.69-3.42) had an increased risk of incident ASCVD ( panel B ). Conclusions: Changes in the CVD risk status over time inform dissimilar ASCVD risk than a single risk assessment. Dynamic risk evaluation might enable more accurate cardiovascular risk stratification and decision on preventive interventions should take historical risk status into account.

Serum proteomic profiling of carotid arteriopathy: A population outcome study

Background and aimsCirculating proteins reflecting subclinical vascular disease may improve prediction of atherosclerotic cardiovascular disease (ASCVD). We applied feature selection and unsupervised clustering on proteomic data to identify proteins associated with carotid arteriopathy and construct a protein-based classifier for ASCVD event prediction. Methods491 community-dwelling participants (mean age, 58 ± 11 years; 51 % women) underwent carotid ultrasonography and proteomic profiling (CVD II panel, Olink Proteomics). ASCVD outcome was collected (median follow-up time: 10.2 years). We applied partial least squares (PLS) to identify proteins linked to carotid intima-media thickness (cIMT). Next, we assessed the association between future ASCVD events and protein-based phenogroups derived by unsupervised clustering (Gaussian Mixture modelling) based on proteins selected in PLS. ResultsPLS identified 19 proteins as important, which were all associated with cIMT in multivariable-adjusted linear regression. 8 of the 19 proteins were excluded from the clustering analysis because of high collinearity. Based on the 11 remaining proteins, the clustering algorithm subdivided the cohort into two phenogroups. Compared to the first phenogroup (n = 177), participants in the second phenogroup (n = 314) presented: i) a more unfavorable lipid profile with higher total cholesterol and triglycerides and lower HDL cholesterol (p ≤ 0.014); ii) higher cIMT (p = 0.0020); and iii) a significantly higher risk for future ASCVD events (multivariable-adjusted hazard ratio (95 % CI) versus phenogroup 1: 2.05 (1.26–3.52); p = 0.0093). The protein-based phenogrouping supplemented ACC/AHA 10-year ASCVD risk scoring for prediction of a first ASCVD event. ConclusionsFocused protein-based phenogrouping identified individuals at high risk for future ASCVD and may complement current risk stratification strategies.

Association between 5-year change in cardiovascular risk and the incidence of atherosclerotic cardiovascular diseases: a multi-cohort study

BackgroundThe influence of the historical cardiovascular risk status on future risk of atherosclerotic cardiovascular disease (ASCVD) is poorly understood. We aimed to investigate the association between 5-year changes in cardiovascular risk and ASCVD incidence.MethodsWe analyzed pooled data from seven community-based prospective cohort studies with up to 20 years of follow-up data. The study populations included White or Black participants aged 40–75 years without prevalent ASCVD. Cardiovascular risk was assessed using the pooled cohort equation and was categorized into non-high (< 20%) or high risk (≥ 20%). Changes in cardiovascular disease (CVD) risk over a 5-year interval were recorded. The main outcome was incident ASCVD.ResultsAmong 11,026 participants (mean [SD] age, 60.0 [8.1] years), 4272 (38.7%) were female and 3127 (28.4%) were Black. During a median follow-up period of 9.9 years, 2560 (23.2%) ASCVD events occurred. In comparison with individuals showing a consistently high CVD risk, participants whose CVD risk changed from non-high to high (hazard ratio [HR], 0.67; 95% confidence interval [CI] 0.59–0.77) or high to non-high (HR, 0.57; 95% CI 0.41–0.80) and those with a consistently non-high risk (HR, 0.33; 95% CI 0.29–0.37) had a lower risk of incident ASCVD. In comparison with individuals showing a consistently non-high CVD risk, participants whose CVD risk changed from high to non-high (HR, 1.74; 95% CI 1.26–2.41) or from non-high to high risk (HR, 2.04; 95% CI 1.84–2.27) and those with a consistently high risk (HR 3.03; 95% CI 2.69–3.42) also showed an increased risk of incident ASCVD.ConclusionsIndividuals with the same current CVD risk status but different historical CVD risks exhibited varying risks of future ASCVD incidents. Dynamic risk evaluation may enable more accurate cardiovascular risk stratification, and decision-making regarding preventive interventions should take the historical risk status into account.Graphical

Chronic Electronic Cigarette Use and Atherosclerosis Risk in Young People: A Cross-Sectional Study-Brief Report.

Little is known whether electronic cigarettes (ECIG) increase vulnerability to future atherosclerotic cardiovascular disease. We determined, using an ex vivo mechanistic atherogenesis assay, whether proatherogenic changes including monocyte transendothelial migration and monocyte-derived foam cell formation are increased in people who use ECIGs. In a cross-sectional single-center study using plasma and peripheral blood mononuclear cells from healthy participants who are nonsmokers or with exclusive use of ECIGs or tobacco cigarettes (TCIGs), autologous peripheral blood mononuclear cells with patient plasma and pooled peripheral blood mononuclear cells from healthy nonsmokers with patient plasma were utilized to dissect patient-specific ex vivo proatherogenic circulating factors present in plasma and cellular factors present in monocytes. Our main outcomes were monocyte transendothelial migration (% of blood monocyte cells that undergo transendothelial migration through a collagen gel) and monocyte-derived foam cell formation as determined by flow cytometry and the median fluorescence intensity of the lipid-staining fluorochrome BODIPY in monocytes of participants in the setting of an ex vivo model of atherogenesis. Study participants (N=60) had median age of 24.0 years (interquartile range [IQR], 22.0-25.0 years), and 31 were females. Monocyte transendothelial migration was increased in people who exclusively used TCIGs (n=18; median [IQR], 2.30 [ 1.29-2.82]; P<0.001) and in people who exclusively used ECIGs (n=21; median [IQR], 1.42 [ 0.96-1.91]; P<0.01) compared with nonsmoking controls (n=21; median [IQR], 1.05 [0.66-1.24]). Monocyte-derived foam cell formation was increased in people who exclusively used TCIGs (median [IQR], 2.01 [ 1.59-2.49]; P<0.001) and in people who exclusively used ECIGs (median [IQR], 1.54 [ 1.10-1.86]; P<0.001) compared with nonsmoker controls (median [IQR], 0.97 [0.86-1.22]). Both monocyte transendothelial migration and monocyte-derived foam cell formation were higher in TCIG smokers compared with ECIG users and in ECIG users who were former smokers versus ECIG users who were never smokers (P<0.05 for all comparisons). The finding of alterations in proatherogenic properties of blood monocytes and plasma in TCIG smokers compared with nonsmokers validates this assay as a strong ex vivo mechanistic tool with which to measure proatherogenic changes in people who use ECIGs. Similar yet significantly less severe alterations in proatherogenic properties of monocytes and plasma were detected in the blood from ECIG users. Future studies are necessary to determine whether these findings are attributable to a residual effect of prior smoking or are a direct effect of current ECIG use.

State of current management of the heightened risk for atherosclerotic cardiovascular events in an established cohort of patients with lupus erythematosus

ObjectivePatients with lupus erythematosus (LE) are at heightened risk for clinical events, chiefly heart attacks and strokes, from atherosclerotic cardiovascular disease (ASCVD). We recently proposed new guidelines to assess and...