Predictor Of Coronary Artery Disease Research Articles

Wrist Circumference as a Novel Predictor of Coronary Artery Disease and Metabolic Syndrome

Introduction: Wrist circumference is a good predictor of obesity and insulin resistance. Wrist circumference may be considered as an indirect determinant of hyperinsulinemia and insulin resistance. The present study aimed to assess wrist circumference as an alternative tool for differentiating patients with coronary artery disease and metabolic syndrome from those without coronary artery disease and metabolic syndrome. Materials and Methods: The study was conducted on patients ≥ 30 years old undergoing coronary angiography following acute coronary syndrome or for evaluation of coronary artery disease. Results: Diabetic, hypertensive, and obese patients had more wrist circumference. High TG, low HDL levels were associated with higher wrist circumference (p=0.005). Wrist circumference was positively correlated with waist-hip ratio (p<0.001). Patients with Metabolic Syndrome have a mean Wrist circumference of 16.54 whereas patients without Metabolic Syndrome have a mean wrist circumference of 15.89(p<0.001). Coronary artery disease patients had higher mean wrist circumference than non-coronary artery disease patients (16.59 vs 15.9) (p<0.001). Triple-vessel disease patients have higher wrist circumference than double-vessel-disease patients; Doublevessel- disease patients have higher wrist circumference than single-vessel disease (p<0.001). Multivariate logistic model shows the main predictors for coronary artery disease are male sex, smoking, high BMI, low HDL, and wrist circumference. Conclusion: Measurement of Wrist circumference can serve as an easy anthropometric marker to identify individuals at risk of cardio-metabolic disorder, and predict the severity of Coronary Artery Disease can be used in large epidemiological studies.

The role of cardiac adipose tissue in coronary artery disease: a narrative review of cardiac imaging studies.

Cardiac adipose tissue's (CAT) role in the pathogenesis of coronary artery disease (CAD) has become a hot topic in the literature in recent years. Cardiac computed tomography (CCT), a cutting-edge imaging modality, has become vital in quantifying and characterizing CAT. CAT is divided into paracardial adipose tissue (PAT), epicardial adipose tissue (EAT), and pericoronary adipose tissue (PCAT). Despite numerous studies investigating the association of EAT and PCAT with CAD, their connection remains equivocal. In this review, we clarify the relationship between EAT and PCAT with atherosclerotic burden, high-risk plaque, major adverse cardiovascular events, and the prediction of CAD through the use of CCT. A thorough literature research spanning from January 2004 to May 2024 of the PubMed database was conducted. Only publications in English which included reviews, clinical trials, meta-analyses, and systematic reviews on CAT's association with CAD were considered. The 63 articles in the review honed in on the influence of CAT metrics obtained mainly by CCT with multiple stages of CAD. Current evidence supports the interaction between dysfunctioning EAT and PCAT and the release of several molecules within the neighboring coronary arteries which is linked with the development and progression of CAD. EAT is related to coronary plaque synthesis. PCAT underlines ongoing inflammation within the plaques. CAT evaluation provides significant incremental value compared to existing assessment methods. It enables enhanced prognostic judgment and further stratified management of CAD patients. CAT metrics have the potential to become future biomarkers of CAD. Additional investigations are required to explore the potential of these metrics as composite scores CAD risk prediction.

Deep Learning for CAD Prediction: X-ray Angiography Insights

This study presents a deep learning-based approach to improve the prediction of coronary artery disease (CAD) using X-ray angiography images. The primary objective is to achieve accurate and automated CAD identification by employing a convolutional neural network (CNN) model. The methodology involves preprocessing the dataset through normalization and augmentation techniques and utilizes a U-Net architecture for precise detection of coronary stenosis. To ensure robustness and generalizability, hyperparameter tuning and dropout regularisation are applied during model training. The proposed model achieves high performance, with an average Dice coefficient of 0.57 and a Jaccard Index of 0.47 on a held-out test set, indicating its effectiveness in segmenting coronary artery stenosis. These findings support the integration of deep learning methods into clinical workflows for enhanced CAD diagnosis and early intervention.

Urinary metabolomics provide insights into coronary artery disease in individuals with type 1 diabetes.

Type 1 diabetes increases the risk of coronary artery disease (CAD). High-throughput metabolomics may be utilized to identify metabolites associated with disease, thus, providing insight into disease pathophysiology, and serving as predictive markers in clinical practice. Urine is less tightly regulated than blood, and therefore, may enable earlier discovery of disease-associated markers. We studied urine metabolomics in relation to incident CAD in individuals with type 1 diabetes. We prospectively studied CAD in 2501 adults with type 1 diabetes from the Finnish Diabetic Nephropathy Study. 209 participants experienced incident CAD within the 10-year follow-up. We analyzed the baseline urine samples with a high-throughput targeted urine metabolomics platform, which yielded 54 metabolites. With the data, we performed metabolome-wide survival analyses, correlation network analyses, and metabolomic state profiling for prediction of incident CAD. Urinary 3-hydroxyisobutyrate was associated with decreased 10-year incident CAD, which according to the network analysis, likely reflects younger age and improved kidney function. Urinary xanthosine was associated with 10-year incident CAD. In the network analysis, xanthosine correlated with baseline urinary allantoin, which is a marker of oxidative stress. In addition, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. Metabolomic state profiling supported the usage of CAD-associated urinary metabolites to improve prediction accuracy, especially during shorter follow-up. Furthermore, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. The network analysis further suggested glomerular filtration rate to influence the urinary metabolome differently between individuals with and without future CAD. We have performed the first high-throughput urinary metabolomics analysis on CAD in individuals with type 1 diabetes and found xanthosine, 3-hydroxyisobutyrate, trans-aconitate, and 4-deoxythreonate to be associated with incident CAD. In addition, metabolomic state profiling improved prediction of incident CAD.

Serum C-peptide as a Predictor of Coronary Artery Disease in Middle-Aged Patients With Type 2 Diabetes Mellitus: A Cross-Sectional Analytical Study

Serum C-peptide as a Predictor of Coronary Artery Disease in Middle-Aged Patients With Type 2 Diabetes Mellitus: A Cross-Sectional Analytical Study

Predictive value of triglyceride-glucose index for the evaluation of coronary artery disease severity and occurrence of major adverse cardiovascular events.

The triglyceride-glucose (TyG) index has been proposed as an independent predictor of coronary artery disease (CAD). In this retrospective study we further examine this association and its utility as a predictor for major adverse cardiovascular events (MACE). A total of 870 patients who underwent coronary angiography between May 2008 and June 2009 were included in this retrospective study. The TyG index was calculated using the formula Ln (fasting TG [mg/dL] × FBG [mg/dL]/2). The association of TyG index with the presence and severity of CAD, cardiovascular risk factors, and inflammatory markers was evaluated at baseline. In the longitudinal study, the multivariate adjusted Cox hazard model was used to investigate the associations of the TyG index with the occurrence of MACE during a 5-year follow-up, which was defined as endpoint. The TyG index was significantly associated with the presence and severity of CAD. Multiple linear regression analysis showed that a high TyG index, together with inflammatory markers and dyslipidemia, were independently associated with greater stenotic occlusion of coronary arteries (adjusted R2 = 0.031, p<0.001). Kaplan-Meier survival curve (free of MACE) by tertiles of the TyG index showed a higher incidence of MACE in the upper tertile (log-rank test, p = 0.02). Multivariate Cox analysis demonstrated that the risk of incident MACE during the follow-up was associated with higher levels of TyG index, even after adjusting for inflammatory parameters and cardiovascular risk factors: hazard ratio (HR), 1.54 (95% confidence interval 1.18-2.13; p<0.01). We conclude that elevated TyG index is independently associated with a higher risk of CAD and a poor prognosis for MACE.

Lymphocyte-based inflammatory markers: Novel predictors of significant coronary artery disease✰,✰✰

BackgroundLymphocyte-based inflammatory indices such as monocyte-to-lymphocyte ratio (MLR) have long been recognized as reliable coronary artery disease (CAD) predictors. More recently, novel indices like the Systemic Inflammatory Index (SII), Systemic Inflammatory Response Index (SIRI), and Systemic Immune-Inflammation Index (SIIRI) have emerged. These newer markers offer a more comprehensive assessment of inflammation by integrating multiple immune cell types, potentially enhancing the prediction of cardiovascular outcomes. ObjectivesWe evaluated the predictive value of novel inflammatory markers in estimating the pretest probability of severe CAD in high-risk patients. MethodsWe enrolled consecutive patients undergoing diagnostic coronary angiography in a single tertiary care hospital. Inflammatory markers were calculated based on pre-procedural complete blood count laboratory measurements. Severe CAD was defined as critical (>70 %) and actionable narrowing of a primary coronary artery. Classification performance was assessed using multivariate logistic regression. ResultsThe study sample included 363 patients (age 58.9± 11 years, 44.9 % females, 30 % severe CAD). In univariate analysis, MLR, SIRI, and SIIRI were significant predictors of severe CAD, with age- and sex-adjusted OR of 1.98 [1.25–3.14], 1.79 [1.24–2.59], and 1.63 [1.11–2.38], respectively. In multivariate analysis, SIRI remained an independent predictor of severe CAD (OR = 1.98, 95 % CI 1.13–3.46, p = 0.02). ConclusionOur results suggest that novel inflammatory markers derived from routine blood tests are predictive of severe CAD in high-risk patients. Such simple, practical, and cost-effective inflammatory markers may enhance cardiac risk stratification and prediction of severe CAD.

Abstract 4136463: Novel Systemic Inflammatory Biomarkers Can Predict Significant Coronary Disease in High Risk Patients

Background: Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. Chronic inflammation is a known risk factor for atherosclerosis, plaque formation, disease progression, and plaque instability. Novel inflammatory biomarkers have been recently linked to excess risk of adverse cardiovascular events, including incident myocardial infarction. Elevated inflammatory biomarkers may help identify patients at higher risk for severe CAD. Aim: This study aimed to evaluate the use of novel systemic inflammatory markers in predicting the pretest probability of severe CAD in patients referred to diagnostic coronary angiography in a single tertiary care center. Methods: This was a prospective observational cohort study enrolling consecutive patients referred to diagnostic coronary angiography in a tertiary care center. Preprocedural laboratory results were obtained from medical charts, including complete blood counts with differentials. Three novel systemic inflammatory markers were computed using existing mathematical formulas: systemic immune-inflammation index (SII = neutrophil count*platelet/lymphocyte); systemic inflammation response index (SIRI = neutrophil*monocyte/lymphocyte); and systemic immune inflammatory response index (SIIRI = neutrophil*monocyte*platelet/lymphocyte). The primary study outcome was severe CAD defined as the presence of ≥70% stenosis in one of three main coronary arteries requiring percutaneous coronary intervention). The outcome was adjudicated by an independent reviewers blinded to computations of biomarkers. Result: The sample included 264 patients (age 59.3±10.8, 47% Females). Overall, 84 patients (32%) met the primary study outcomes. In univariate analysis, those with severe CAD had significantly higher SIRI (2.9±0.7 vs. 2.6±0.6, p = 0.001) and SIIRI (8.4±0.8 vs. 8.1±0.7, p = 0.015) compared to their counterparts. In multivariate analysis adjusted for age and sex, only SIRI was an independent predictor of severe CAD. Using SIRI &gt; 20.8 (upper quartile) was associated with nearly three-fold excess risk of severe CAD (OR = 2.72, 95% CI 1.45-5.09). Conclusion: Elevated SIRI, a marker of ongoing systemic inflammatory response, could be indicative of severe CAD. As an inexpensive biomarker based exclusively on complete blood count, SIRI can be used to prioritize patients referred for diagnostic angiography and potentially expedite treatments in those at higher risk for coronary events.

Abstract 4136501: Machine Learning Uncovers the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population

Background: Genome-wide association studies (GWASs) have enhanced our understanding of the genetic basis of coronary artery disease (CAD), and polygenic risk scores (PRSs) have facilitated the assessment of genetic risk. However, these methods predominantly focus on common variants due to statistical power, potentially leaving rare variants insufficiently analyzed and thus limiting the predictive performance of PRS. Methods: We conducted whole genome sequencing (WGS) of 1,752 Japanese early-onset myocardial infarction (MI) patients and 3,019 controls from Biobank Japan (BBJ). We performed case-control association studies including GWAS and gene-based tests, as well as a novel machine learning-based framework. In this framework, we developed a penalized regression model to predict the CAD status from genome-wide rare nonsynonymous variants. The model identified the minimal set of most distinguishing features (genes) and generated a rare variant-based risk score (RVS). The RVS was evaluated on an independent validation WGS cohort of 200 cases and 824 controls. We also derived a PRS based on CAD-GWAS (25,668 CAD cases vs 141,667 controls from BBJ) to compare the properties and performance between the RVS and PRS. Results: In the case-control studies, only two common variants in chromosome 12 were identified in GWAS, with no genes in gene-based analysis (SKAT-O), suggesting the challenges in rare variant analysis. On the other hand, our machine-learning framework identified 59 CAD-related genes, including LDLR , a causal gene of familial hypercholesteremia. Functional analyses revealed that various biological pathways, including lipid metabolism, immune system, and vessel development, are involved in CAD. For the genetic risk prediction, RVS significantly predicted CAD (area under the curve [AUC], 0.58; p=0.001, pseudo-R 2 , 0.051; p=7.92*10 -9 ). RVS was significantly associated with LDL cholesterol levels and coagulation function (Pearson’s r, 0.21; p=4.5*10 -5 and 0.10; p=0.03, respectively) and MI patients with high RVS (top 5%) showed higher cardiovascular mortality rate (p=0.03, log-rank test), highlighting the clinical importance of RVS. Finally, the combined risk score (CRS) of RVS and PRS significantly improved CAD prediction compared to PRS (AUC, 0.66 (CRS) vs 0.61 (PRS); p=0.007, pseudo-R 2 , 0.093 (CRS) vs 0.040 (PRS); p=0.0018, Figure ). Conclusions: Our machine learning framework successfully characterized rare variants and enhanced genetic risk prediction in CAD.

The Role of Glucose-Lymphocyte Ratio in Evaluating the Severity of Coronary Artery Disease.

Background: Recently, a new inflammatory and prognostic marker called glucose/lymphocyte ratio (GLR) has been used in patients with coronary artery disease. In this study, we analyzed the correlation between GLR and coronary artery disease (CAD) severity in patients with chronic coronary syndrome (CCS). Methods: The study included 341 patients with CCS who underwent coronary angiography and documented coronary stenosis of 50% or more in at least one major coronary artery and 437 individuals with coronary atherosclerosis between 1% and 50% or no coronary atherosclerosis (control group). Blood samples for GLR and other laboratory parameters were obtained from all patients on admission. GLR was obtained by dividing the glucose level by the lymphocyte count. Results: There were more patients with diabetes mellitus (DM) in the critical CAD group, and glucose levels (p < 0.001), neutrophil counts (p < 0.001), C-reactive protein (CRP) levels (p < 0.001), neutrophil/lymphocyte ratio (NLR) (p < 0.001), platelet/lymphocyte ratio (PLR) (p < 0.001), and GLR (p < 0.001) were higher. In contrast, lymphocyte counts were lower (p < 0.034). Multivariate logistic regression analysis showed that DM and high CRP were independent predictors of critical CAD (p = 0.004 and p = 0.048, respectively). However, high GLR was found to be an independent predictor of critical CAD (p < 0.001). Conclusions: GLR, a simple and easily measured marker, has shown strong predictive value for CAD severity in CCS patients.

ELEVATED ILIAC CALCIUM SCORE AS A MARKER OF CORONARY CALCIFICATION AND OVERALL ATHEROSCLEROTIC RISK

BackgroundThe initial assessment of cardiovascular risk in patients undergoing lower limb revascularization surgery is crucial to minimize complications and improve outcomes. This study aims to determine if the iliac calcium score (ICS) serves as a cardiologic risk marker by examining its correlation with the coronary calcium score (CCS) in these patients. MethodsThis prospective observational single-center study included 248 patients with critical limb ischemia undergoing revascularization procedures from January 2022 to June 2023. Baseline characteristics such as age, gender, smoking status, comorbidities, and clinical status were recorded. CCS and ICS were calculated using preoperative computed tomography angiography. Multiple linear regression identified significant predictors of ICS, including CCS, iliac intima-media thickness (i-IMT), coronary artery disease (CAD), diabetes, chronic kidney disease (CKD), hypertension, dyslipidemia, and smoking. ResultsThe study population had a mean age of 74.4 years, 83.86% male. Significant correlations were found between ICS and CCS (Pearson r=0.34, p<0.001) and between ICS and i-IMT (Pearson r=0.35, p<0.001). Regression analysis revealed significant predictors for ICS, including CCS (coefficient=1.808, p<0.0001), i-IMT (coefficient=3.11, p<0.0001), CAD (coefficient=11.94, p=0.042), diabetes (coefficient=19.59, p=0.002), CKD (coefficient=11.79, p<0.0001), and hypertension (coefficient=22.10, p=0.001). Dyslipidemia and smoking did not show significant associations with ICS. ConclusionsThe iliac calcium score shows a statistically significant association with the coronary calcium score and iliac intima-media thickness in patients undergoing lower limb revascularization surgery. This correlation suggests that iliac calcium score reflects vascular calcification patterns like those observed in coronary arteries. Further studies are needed to explore this relationship in diverse patient populations and under varying clinical conditions

Machine-learning based prediction of obstructive coronary artery disease using integrated submodules of clinical information, chest x-ray, and electrocardiography

Abstract Background The early diagnosis of obstructive coronary artery disease (CAD) is critical. However, delays in diagnosis may occur due to subsequent high-cost tests, contributing to substantial medical expenses. A more insightful interpretation of fundamental diagnostic tools, such as chest x-ray (CXR) and electrocardiography (ECG), could mitigate medical costs and facilitate a timely diagnosis. Purpose This study explores the application of machine learning to interpret each modality and create a consolidated prediction model for significant CAD, aiming to construct a diagnostic model based on primary tests, including CXR, ECG, and clinical information. Methods Clinical information-ECG-CXR paired data were generated from 19,140 patients, with CAD confirmed through coronary angiography (CAG). Machine learning (ML) was employed to develop submodules for each modality, and various integration methods were explored. The area under the curve (AUC) served as the outcome metric for predicting obstructive CAD. Results The development set comprised data from 17,976 patients, with CAG-confirmed obstructive CAD observed in approximately 60% of patients. The obstructive CAD group exhibited characteristics such as advanced age, male predominance, a higher prevalence of chest pain, and more risk factors. The ML model, incorporating clinical information, CXR, and ECG with submodules, demonstrated the optimal prediction of obstructive CAD (AUC 0.722). This model significantly outperformed the model without submodules (0.722 vs. 0.638, p&amp;lt;0.0001) in obstructive CAD prediction. Conclusions Through the integration of submodules encompassing clinical information and basic tests, leveraging a high-quality database, the integrated ML algorithm offers improved prediction capabilities for CAG-confirmed obstructive CAD. This underscores the potential role of machine learning in clinical decision-making based on diverse modalities with distinct characteristics.

Multi-modality deep learning model for prediction of chronic obstructive coronary artery disease

Abstract Background/Introduction The incidence of coronary artery disease (CAD) is on the rise with an increasing burden of stable ischemic heart disease (SIHD) and high prevalence of obesity, diabetes, hyperlipidemia, and other cardiovascular risk factors. Pre-test probability estimation for CAD based on demographic features and characterization of chest pain is insufficient to accurately assess whether further evaluation for CAD is warranted. Purpose To develop and validate a deep learning (DL) algorithm utilizing electrocardiogram (ECG) waveforms and clinical features to predict obstructive CAD. Methods Clinical data from subjects undergoing invasive angiography over a 4-year period at a quaternary care center exclusive of subjects who had concern for acute coronary syndromes were used for model training. ECGs obtained up to 5 years prior to angiography were included; ECGs were partitioned 8:1:1 for training, validation, and test datasets. Obstructive CAD was defined as receiving percutaneous coronary intervention during elective angiography. Convolutional neural network models were developed for ECG waveforms alone (DL-ECG), clinical features from standard risk scores (DL-Clinical), and the combination of ECG waveforms and clinical features (DL-ECG+Clinical); a commonly used pre-test probability estimation tool derived from the CAD Consortium study was used as a comparison (PTP). The models were evaluated based on area under the curve receiver operating characteristics (AUC). Shapley additive explanations (SHAP) analysis was performed to understand feature importance in the DL models. Results A total of 40,602 ECGs and 8,361 coronary angiograms from 7,201 patients were included in the analysis. The median age was 66 (IQR 56-75), 71.9% were men, 34.9% had hypertension, 30.6% had diabetes mellitus, and 39.0% had dyslipidemia. The PTP model (AUC 0.733 [0.717-0.750]) had similar performance as the DL-Clinical model (AUC 0.762 [0.746-0.778]). The DL-ECG model (AUC 0.741 [0.726-0.758]) had similar performance as both the clinical feature models. The DL-ECG+Clinical model (AUC 0.807 [0.793-0.822]) trained on ECG waveforms and clinical features had a superior performance (Figure 1). SHAP analysis for the DL-ECG+Clinical model revealed that ECG waveforms had the largest influence on the model’s output followed by age and chest pain type (Figure 2). Conclusions Multi-modality DL model utilizing ECG and commonly used features can improve prediction of obstructive CAD in SIHD compared to traditional pre-test probability estimates. Prospective research is warranted to determine whether incorporation of DL methods to ECG analysis effectively improves diagnosis and outcomes in obstructive CAD.Figure 1.Figure 2.

Lipidomics-based deep learning algorithms can enhance prediction of obstructive coronary artery disease

Abstract Background Lipidomics emerge as a promising research field with the potential to help in personalized risk stratification and also improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). The existing technological challenges to detect diverse yet structurally similar lipids and their isomers, as well as the need for novel statistical and computational tools to handle the high-dimensional lipidome may be responsible for the reduced clinical translation of the very first lipidomics research outcomes. Methods To that end, this post-hoc analysis of the prospective CorLipid trial aimed to investigate the predictive capability of a lipidomics panel for obstructive CAD risk through an extreme gradient boosting (XGBoost) machine learning (ML) approach. The lipid profiles of 146 individuals with suspected CAD were investigated through liquid chromatography-mass spectrometry. Fasting blood samples were drawn prior to invasive coronary angiography execution. Obstructive CAD was defined as SYNTAX Score (SS) &amp;gt;0 compared to non-obstructive CAD (SS=0). Results Study participants (75.3% male, mean age: 61 ±10.5 years old) were divided into two categories based on the existence of obstructive CAD (54.8% with SS&amp;gt;0 and 45.2% with SS=0). In total, 517 lipid species were identified, from which 290 lipid species were finally quantified in participants’ serum [glycerophospholipids (52.1%), glycerolipids (28.6%) and sphingolipids (19.3%)]. The levels of glycerophospholipid, sphingolipid and glycerolipid classes were significantly different in patients with obstructive CAD. Finally, a ML XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, triacylglycerols, galectin-3, glucose, LDL and LDH) as totally sensitive (100% sensitivity, 62.1% specificity and 100% negative predictive value) for the prediction of obstructive CAD. Conclusions These findings provide molecular insights into the role of dysregulated lipid metabolism in the development and progression of CAD while validating the existing body of evidence from similar research studies. Further (ML-based) investigation of lipid metabolism could hold promises for novel therapeutic strategies and improvement of the existing risk stratification schemes.Lipidomics panel importance

ClinicAl verSus algorIthmic predictioN of Obstructive Coronary Artery Disease - the CASINO study

Abstract Background The estimation of pretest probability (PTP) is a key step when evaluating patients with suspected coronary artery disease (CAD). Current European guidelines recommend using an algorithm based on age, sex, and symptom typicality. However, many cardiologists do not regularly use this method, relying instead on overall clinical impression. The aim of this study was to compare clinical and algorithmic prediction of obstructive CAD in a set of symptomatic patients with suspected chronic coronary syndrome (CCS). Methods In this survey study, 10 cardiologists from were asked to estimate the probability of obstructive CAD (on a scale of 1-99% and without the aid of any score or algorithm) for 120 anonymized clinical vignettes of outpatients who underwent diagnostic workup for suspected CCS. The provided information included age, sex, symptom description, cardiovascular risk factors, and know comorbidities. Whenever non-invasive tests had been performed, physicians were also asked to estimate the probability of obstructive CAD after knowing that results. Obstructive CAD was defined as diameter stenosis ≥50% confirmed by invasive coronary angiography. Results Among the 120 included patients (62 women, mean age 61±10 years), symptoms consisted of chest pain in 104 and dyspnea in the remainder. The median PTP of obstructive CAD was 15% (IQR 10-25%) according to the ESC algorithm, and 30% (IQR 15-50%) according to cardiologists’ estimates (p&amp;lt;0.001). The observed prevalence of obstructive CAD was 20% (n=24), yielding suitable calibration for the ESC model (predicted vs. observed p=0.631), and significant overestimation for clinicians’ assessments (predicted vs. observed p=0.014) – Figure 1A. Despite using the same data, cardiologists’ PTP estimates varied widely within the same patients (mean inter-observer coefficient of variation 56%±22%). In a subset of 29 patients whose PTP was appraised twice by the same physicians, the mean absolute difference between assessments (intra-observer variability) was 22%±8%. The ESC algorithm showed fair discriminative power to identify patients with obstructive CAD (C-statistic 0.68, 95% CI 0.58-0.79, p=0.005), whereas the C-statistic for clinicians ranged from 0.53 to 0.72 (average 0.62±0.05, p values for comparisons 0.001-0.677) – Fig 1B. In the subset of 70 patients with prior non-invasive tests, clinicians significantly changed their predictions after knowing test results (mean absolute difference 22%±12%, p&amp;lt;0.001), improving their discriminative power significantly (mean change in C-statistic of 0.08±0.09, p=0.020). Conclusion Clinicians’ assessments of PTP in symptomatic patients display high inter- and intra-observer variability, and tend to overestimate the likelihood of obstructive CAD. Despite using more clinical data, cardiologists fail to outperform the ESC algorithm. The systematic use of this tool should be promoted in order to improve disease prediction and guide non-invasive testing.

Coronary artery disease polygenic risk score in patients undergoing coronary angiography and risk of future revascularization

Abstract Background Coronary artery disease (CAD) polygenic risk score (PRS) is a strong predictor of incident CAD. The role of CAD PRS in patients with known or suspected CAD already presenting for invasive coronary angiography remains poorly understood. Purpose We aimed to (i) study the association between CAD PRS and coronary angiography traits, and (ii) determine whether CAD PRS can predict risk of future revascularization after an initial coronary angiogram. Methods Patients who underwent coronary angiography at a single hospital and were part of its biobank were included in the study. Participants were classified into 3 risk groups based on CAD PRS distribution–low (0-20%), intermediate (21-80%), and high (81-100%)–computed from a recently reported PRS comprised of 1,296,172 variants (GPSMult, PGS003725). Revascularization was defined as a composite of coronary artery bypass graft and percutaneous coronary intervention with a 3-month blocking window after the initial angiogram. Associations between coronary stenosis severity and burden, and CAD PRS were assessed using multinomial logistic regression adjusted for age, sex, and genetic ancestry. The cumulative incidence of revascularization was tested using Fine-Gray regression accounting for competing risk of all-cause mortality and compared across CAD PRS strata by log-rank test. Results Among 3,518 participants (29.90% female, mean age 63.29±12.14 years), 2,568 (73%) had angiographic evidence of CAD – 340 (13.24%) mild, 346 (13.47%) moderate, and 1,882 (73.29%) severe. Out of those with CAD, 844 (32.87%) presented with acute coronary syndromes (ACS) and 1,724 (67.13%) with stable CAD. CAD PRS was strongly associated with angiographic presence of CAD (OR 2.62, 95%CI 2.08-3.29) and this did not differ among patients presenting with ACS vs. stable CAD (Fig1A). The association was weakest for mild CAD (OR 1.14, 95%CI 0.77-1.68) and strongest for severe CAD (OR 3.04, 95%CI 2.40-3.84). Besides severity, CAD PRS was also strongly associated with angiographic burden of CAD with the strongest association observed for presence of left main coronary stenosis (OR 2.63, 95%CI 1.86-3.73). Each standard deviation increase in CAD PRS was associated with a 10-point increase in the Gensini score, a continuous measure of angiographic burden of CAD (p&amp;lt;2e-16) (Fig1B). Over a mean follow-up period of 9.26±5.81 years, 659 (18.73%) patients required revascularization at a mean period of 5.31±4.69 years. Risk of revascularization varied dramatically by CAD PRS; HR was 1.64 (95%CI 1.28-2.10, p=8.7e-5) for high vs. low CAD PRS (Fig2). CAD PRS remained predictive of future revascularization even after adjusting for the angiographic burden of CAD at baseline (HR 1.41, 95%CI 1.09-1.82, p=7.9e-3). Conclusion CAD PRS is strongly associated with severity and burden of CAD assessed on coronary angiography and is predictive of future risk of revascularization even after accounting for baseline disease burden.Figure 1.CAD PRS and angiographic traitsFigure 2.CAD PRS and revascularization

Lipoprotein (a), coronary artery disease risk, and MACE in individuals without standard modifiable risk factors in the UK Biobank

Abstract Background A significant proportion of patients with coronary artery disease (CAD) lack standard modifiable risk factors (SMuRFs; smoking, hypercholesterolaemia, diabetes and hypertension), for example with recent analyses demonstrating that around 25% of patients with ST-elevation myocardial infarction are SMuRF-less(1). With continued focus on primary prevention in cardiovascular disease, the SMuRF-less subset is likely to increase over time. A body of evidence has implicated lipoprotein (a) in CAD, but no such studies have investigated this association in a purely SMuRF-less population. Purpose To investigate the role of lipoprotein (a) in predicting CAD risk and major adverse cardiovascular events (MACE) in a large, SMuRF-less cohort. Methods The UK Biobank is a large prospective multicentre study which recruited over 500,000 participants in the UK aged 37-73 years at entry between 2006-2010. SMuRFs were defined as 1) smoking (current or historical), 2) hypercholesterolaemia – lipid lowering medication on enrolment or total cholesterol &amp;gt;5.5mmol/L or LDL-C &amp;gt;3.5mmol/L, 3) previous diagnosis of diabetes mellitus, or HbA1c &amp;gt;48mmol/mol, 4) previous diagnosis of hypertension. MACE was defined as a composite of coronary artery disease – previous myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft surgery –, stroke, or all-cause mortality. Results Of the total UK Biobank cohort, 46,139 participants were identified as SMuRF-less together with a valid lipoprotein (a) level. Median follow-up duration was 12yrs. In univariate analyses (Table 1), there was no significant difference in lipoprotein (a) concentrations between individuals with and without MACE (16.9 vs. 17.1nmol/L respectively, p = 0.913). The overall event rate was low, with just 3% MACE (1381 events) overall. Older age, male gender, apolipoprotein A, apolipoprotein B, HbA1c level, HDL cholesterol level and body mass index were all associated with MACE. In regard to all-cause mortality alone, there was no association with lipoprotein (a) level, although the other aforementioned parameters were all significantly associated with this outcome. Similarly, lipoprotein (a) was not a significant predictor of stroke. However, there was a large difference in lipoprotein (a) levels between patients with and without CAD (27.2 vs. 17.1nmol/L respectively, p = 0.004). In Kaplan-Meier analysis, lipoprotein (a) level above the 77th percentile (60.3nmol/L) was significantly associated with CAD (p=0.032). In Cox regression models, despite the low event rate, lipoprotein (a) was a significant predictor of CAD (HR 1.00-1.01, p=0.01), and importantly the only potentially modifiable parameter (N = 39,433, 111 events, Table 2). Conclusion Lipoprotein (a) independently predicts CAD in those without SMuRFs, highlighting the importance of checking this parameter in such patients, particularly given the novel antisense technologies in development which may lower lipoprotein (a)(2).Univariate analysesCox regression for CAD

Polygenic prediction of coronary artery disease among 130,000 Mexican adults

Abstract Background Coronary artery disease (CAD) is a leading cause of premature mortality globally. Polygenic risk scores (PRSs) have been reported to enhance CAD prediction in populations of European ancestry but evidence from other populations is limited. We assessed the transferability and relevance of eight different external PRSs to the odds of premature CAD in a Mexican population with high levels of relatedness and ancestry admixture. Methods 128,388 genotyped participants aged 35-74 years who were recruited between 1998 and 2004 into the Mexico City Prospective Study (MCPS) were included. We selected and recreated eight previously-reported CAD PRSs. The number of single nucleotide polymorphism variants included in the PRSs varied from 44 to 6,472,620. Premature CAD was defined as either self-reported history of heart attack or angina at recruitment or death before age 75 with CAD recorded on the death certificate. Age and sex-adjusted logistic regression was used to estimate the ability of each PRS to predict CAD, both overall and separately by age, sex and estimated proportion of Indigenous American ancestry. Analyses were repeated after further adjustment for conventional risk factors (blood pressure, adiposity, smoking, diabetes and highest education achieved). Risk discrimination was assessed using C-statistic. Results Of the 128,388 participants, 68% were women, mean (SD) age was 50.2±10.9 years and the average proportion of Indigenous American ancestry was 67%. CAD was identified for 3,871 (3.0%) participants; prior CAD was reported by 1,669 (1.3%) participants while 2,360 (1.8%) participants died before age 75 years with CAD mentioned on the death certificate. All eight PRSs were positively and approximately log-linearly associated with the odds of CAD, but the strength of association varied between the PRSs, ranging from a 7% increase per 1SD higher PRS (odds ratio [OR] 1.07, 95% CI 1.04-1.11) to a 33% increase (OR 1.33, 95% CI 1.29-1.37). Collectively, age, sex and (any) PRS resulted in a C-statistic of 0.70-0.71. C-statistics were slightly lower among those with a higher proportion of indigenous American ancestry. For four of the eight PRSs (including three of the four multi-ancestry PRSs) the increase in odds of CAD per 1SD higher PRS was significantly greater for men than women (eg, for the PRS with the strongest overall association with CAD the OR per 1SD higher PRS was 1.43 [95% CI 1.36-1.49] in men and 1.25 [1.20-1.31] in women). Further inclusion of conventional vascular risk factors reduced the strengths of associations of each PRS with CAD only slightly but increased the model C statistic to 0.74 in all cases. Conclusions In this population of admixed Mexican adults, existing PRSs predicted CAD reasonably well and independently of conventional vascular risk factors. Ancestry-specific instruments that more closely represent genetic variation in Mexico may further enhance polygenic prediction of CAD risk.Table 1.Baseline characteristicsFigure 1.Main analysis results

Geographical variations and predictors of coronary artery disease mortality in Turkiye: an environmental and behavioral analysis

ObjectiveThis study aims to analyze the geographical variations and identify key environmental and behavioral predictors of coronary artery disease (CAD) mortality in Turkiye.MethodsA 10-year longitudinal province-level data was used to identify change trajectories of CAD mortality. Environmental determinants (such as air quality and climatic conditions) and behavioral factors of alcohol consumption and smoking were examined for their association with CAD mortality change trajectories using Ordinal Logistic Regression models.ResultsThe study revealed significantly different trajectoriesof CAD mortality across Turkiye. Environmental factors, particularly air quality (Particulate Matter-10 variation) and climatic conditions (humidity and temperature variations), were heavily associated with the level of CAD mortality. Behavioral factors, notably alcohol consumption and smoking, also exhibited a significantly positive association. Humidity, sunlight, and temperature remained as key predictors of CAD after controlling for smoking and alcohol consumption.ConclusionThe study underscores the importance of addressing environmental and lifestyle factors in CAD management and prevention strategies. The findings suggest the necessity for region-specific interventions and public health policies tailored to the unique characteristics of each province in Turkiye. This research contributes to a deeper understanding of the multifactorial nature of CAD mortality, providing valuable insights for future research to investigate causal associations, healthcare planning, and policy-making.Trial registrationOur study has been registered in ClinicalTrials.GOV system with a procotol ID of CAD001.

Genetic Susceptibility to Hidradenitis Suppurativa and Predisposition to Cardiometabolic Disease.

Hidradenitis suppurativa (HS) is associated with an increased prevalence of cardiovascular diseases compared with the general population. Any association between polygenic risk for HS, risk of incident cardiometabolic outcomes, and the plasma proteome is unclear. To investigate the genetic correlation between HS and cardiometabolic disease. This cohort study used a polygenic risk score (PRS) for HS to examine the risks of coronary artery disease (CAD) and diabetes and identify changes in the plasma proteome in individuals of European ancestry from the UK Biobank. Participants were enrolled from January 1, 2006, to December 31, 2010. End of follow-up was January 1, 2023. Correlations were assessed between HS susceptibility and cardiometabolic traits using linkage disequilibrium score regression. Odds ratios were assessed in logistic regressions. The risk of incident CAD and diabetes was estimated in cause-specific survival models designed as time-to-event analyses. The PRS for HS. Main outcomes were CAD and diabetes diagnosis measured by logistic regressions and incident disease measured by Cox proportional hazards regression models adjusted for sex, age, body mass index, and smoking status. The study included 391 481 individuals (median [IQR] age, 58 [51-64] years; 209 235 [53%] female). Genetic variants for HS correlated significantly with variants associated with CAD, diabetes, and plasma levels of high-density lipoprotein cholesterol, triglycerides, and C-reactive protein. Compared with the low-risk group, a high PRS for HS (≥75th percentile) conferred odds ratios of 1.09 (95% CI, 1.06-1.12; P < .001) for CAD and 1.13 (95% CI, 1.10-1.17; P < .001) for diabetes. Estimates remained consistent when examining only incident CAD and diabetes. The PRS for HS was significantly associated with altered expression of 58 plasma proteins. Integrating this proteomic profile and the PRS for HS in a machine learning model improved prediction of CAD and diabetes compared with a reference model based on sex, age, and body mass index. These findings suggest that a high genetic risk of HS is associated with increased risk of subsequent CAD and diabetes and altered composition of the plasma proteome. Additional investigation into the identified proteins and their potential roles as drug targets is warranted.