Predictor Of Coronary Artery Disease Research Articles

Exploring the Link Between Genetic Predictors of Cardiovascular Disease and Psoriasis.

The epidemiological link between immune-mediated diseases (IMIDs) and cardiovascular disease has often been attributed to systemic inflammation. However, the direction of causality and the biological mechanisms linking cardiovascular disease with IMIDs are incompletely understood. Given the robust epidemiological association and the growing body of supportive mechanistic evidence, psoriasis is an exemplary IMID model for exploring this relationship. To assess the bidirectional relationships between genetic predictors of psoriasis and the 2 major forms of cardiovascular disease, coronary artery disease (CAD) and stroke, and to evaluate the association between genetic predictors of cardiovascular disease with 9 other IMIDs. This was a genetic association study using mendelian randomization (MR), a powerful genetic tool to help distinguish causation from associations observed in epidemiological studies, to provide supportive evidence for causality between traits. The study conducted 2-sample MR analyses using summary-level data from large-scale genome-wide association meta-analysis studies (GWAS) for each trait. The analysis focused on individuals of European descent from GWAS meta-analyses, involving CAD, stroke, psoriasis, and 9 other IMIDs. Data were analyzed from January 2023 to May 2024. Genetic predictors of CAD, stroke, psoriasis, and 9 other IMIDs. The primary outcomes were the associations of genetic predictors of CAD and stroke with the risk of psoriasis and 9 other IMIDs, determined using inverse-variance weighted (IVW) MR estimates. This study included 181 249 cases and 1 165 690 controls with CAD, 110 182 cases and 1 503 898 controls with stroke, 36 466 cases and 458 078 controls with psoriasis, for a total of approximately 3 400 000 individuals, and 9 other IMIDs. In contrast to previous assumptions, genetic predictors of psoriasis were found to have no association with CAD or stroke. In the reverse direction, genetic predictors of both CAD (MR estimate IVW odds ratio [OR], 1.07; 95% CI, 1.04-1.10; P = .003) and stroke (IVW OR, 1.22; 95% CI, 1.05-1.41; P = .01) were found to have risk-increasing associations with psoriasis. Adjusting for stroke rendered the associations of genetically predicted CAD with psoriasis risk nonsignificant (and vice versa), suggesting that a shared effect underlying genetic risk for CAD and stroke associates with increased psoriasis risk. No risk-increasing associations were observed for genetic predictors of cardiovascular disease with other common IMIDs, including rheumatoid arthritis and inflammatory bowel disease. Findings of this mendelian randomization study indicate that genetic predictors of cardiovascular disease were associated with increased psoriasis risk with no reciprocal effect or association with other IMIDs. Elucidating mechanisms underpinning this association could lead to novel therapeutic approaches in both diseases.

Regional wall motion abnormalities on focused transthoracic echocardiography in patients presenting with acute chest pain: a predefined post hoc analysis of the prospective single-centre observational EPIC-ACS study

ObjectivesWe evaluated the ability of the assessment of regional wall motion abnormalities (RWMA) detected via transthoracic echocardiography to predict the presence of obstructive coronary artery disease (CAD) in patients presenting...

Is There a Need for Sex-Tailored Lipoprotein(a) Cut-Off Values for Coronary Artery Disease Risk Stratification?

Lipoprotein(a) [Lp(a)] plasma level is a well-known risk factor for coronary artery disease (CAD). Existing data regarding the influence of sex on the Lp(a)-CAD relationship are inconsistent. To investigate the relationship between Lp(a) and CAD in men and women and to elucidate any sex-specific differences that may exist. Data of patients with Lp(a) measurements who were admitted to a tertiary university hospital, Koc University Hospital, were analyzed. The relationship between Lp(a) levels and CAD was explored in all patients and in subgroups created by sex. Two commonly accepted Lp(a) thresholds ≥ 30 and ≥ 50 mg/dL were analyzed. A total of 1858 patients (mean age 54 ± 17 years; 53.33% females) were included in the analysis. Lp(a) was an independent predictor of CAD according to the multivariate regression model for the entire cohort. In all cohort, both cut-off values (≥ 30 and ≥ 50 mg/dL) were detected as independent predictors of CAD (p < 0.001). In sex-specific analysis, an Lp(a) ≥ 30 mg/dL was an independent predictor of CAD only in women (p < 0.001), but Lp(a) ≥ 50 mg/dL was a CAD predictor both in men and women (men, p = 0.004; women, p = 0.047). The findings of this study may suggest that different thresholds of Lp(a) level can be employed for risk stratification in women compared to men.

QTc dispersion as a predictor of coronary artery disease severity in Bangladeshi patients with chronic coronary syndromes

Background: Coronary artery disease (CAD) is a leading global and national health burden. Accurate assessment of CAD severity is crucial for effective management. QTc dispersion, a simple electrocardiographic parameter, has been explored as a potential predictor of CAD. This study aimed to evaluate the association between QTc dispersion and coronary angiographic severity in Bangladeshi patients with chronic coronary syndromes (CCS). Methods: A cross-sectional study was conducted on 92 CCS patients undergoing coronary angiography at Sir Salimullah Medical College Mitford Hospital, Dhaka, from November 2020 to October 2021. Patients were divided into two groups based on QTc dispersion (≥50 ms or &lt;50 ms). Coronary angiographic severity was assessed using the Gensini score. Results: Patients with increased QTc dispersion (Group B) had a significantly higher mean age, were predominantly male, and exhibited significantly higher Gensini scores compared to those with normal QTc dispersion (Group A). A strong positive correlation was observed between QTc dispersion and Gensini score (r=0.742, p&lt;0.001). Conclusion: Our findings suggest that QTc dispersion is a significant predictor of CAD severity in Bangladeshi patients with CCS. Incorporating QTc dispersion into routine clinical practice may aid in risk stratification and the development of personalized treatment strategies for these patients.

Utility of Magnetocardiography and Stress Speckle Tracking in Detection of Coronary Artery Disease.

Introduction: Coronary artery disease (CAD) is a leading cause of death and disability in developed countries. While exercise testing is recommended for diagnosing stable angina pectoris, its limited sensitivity and specificity have been questioned. Myocardial strain is a promising predictor of significant CAD. Aim: To evaluate the utility of myocardial strain obtained by 2D speckle tracking at rest and under stress combined with stress magnetocardiography for detecting CAD in patients with stable or low-risk unstable angina pectoris. Methods: A total of 108 patients meeting the inclusion criteria underwent coronary angiography within 48 h of admission. Myocardial strain was assessed using 2D speckle tracking at rest and during dobutamine stress alongside stress magnetocardiography. Results: Global longitudinal strain at stress showed a moderate correlation with significant CAD (r = 0.41, p <0.0001) and with coronary occlusion severity (r = 0.62, p <0.0001). Strain at stress had a sensitivity of 74.1% and specificity of 76.7% for detecting CAD at a cut-off value of -19.1. The ST fluctuation rate from magnetocardiography demonstrated the highest sensitivity for CAD detection. Conclusions: Longitudinal strain parameters and stress magnetocardiography are effective non-invasive methods for predicting CAD in patients with stable angina, potentially reducing the need for invasive assessments.

Detection of Coronary Artery Disease With Coronary Computed Tomography Angiography and Stress Testing in Candidates for Liver Transplant

Cardiac complications are the leading cause of morbidity and mortality in liver transplant (LT) recipients. Prior guidelines recommended stress testing to exclude coronary artery disease (CAD), though recent guidelines recommend coronary computed tomography angiography (CCTA). We aimed to assess the prevalence and predictors of CAD on CCTA and compare CCTA with stress testing in consecutive adult LT candidates undergoing CAD non-invasive assessment between 2020 to 2023. Patients who underwent a stress test between 01/2020 and 12/2020 formed the stress cohort, and patients who underwent CCTA between 01/2021 and 09/2023 formed the CCTA cohort. There were 141 in the stress test cohort and 269 patients in the CCTA cohort. Stress tests were non-diagnostic or inconclusive in 18 (12.8%) whereas CCTA was non-diagnostic in 6 (2.2%) patients. In patients evaluated with CCTA, mean coronary artery calcium (CAC) score was 332 ± 716 AU, with moderate or greater (>50%) stenosis in 33 (12.3%) patients. New CAD was diagnosed in 158 (58.7%) using CCTA and in 5 (3.5%) patients using stress tests. Clinically actionable CAD (CAC >100) on CCTA was present in 96 (35.7%) patients. The number of CAD risk factors were associated with the presence of CAD on CCTA. In conclusion, there was a high burden of CAD, mainly non-obstructive, in a large cohort of LT candidates undergoing CAD testing over a 4-year period. The current recommended risk-based evaluation of LT candidates using CCTA as a first line test was feasible and effective. Diagnosis of clinically actionable CAD on CCTA provides a vast opportunity for optimizing cardiac care in LT candidates and recipients.

Exploring the interplay between epicardial fat, coronary artery calcium score, and nonalcoholic fatty liver disease through non-ECG-gated chest computed tomography: A cross-sectional study.

This study examines the relationships between epicardial adipose tissue (EAT), nonalcoholic fatty liver disease (NAFLD), and coronary artery calcium score (CACS) using non-ECG-gated CT scans. It aims to determine the effectiveness of EAT measurements and NAFLD as predictors for coronary artery disease (CAD). This cross-sectional study was conducted at a specialized center, focusing on individuals who underwent non-ECG-gated chest CT scans without contrast. We evaluated EAT thickness and density in three areas: the right atrioventricular groove, the free wall of the right ventricle, and the central area of the right anterior interventricular groove. Additionally, we measured CACS and determined the presence of NAFLD by comparing liver-to-spleen density ratios. Statistical analyses, including regression models, were performed using SPSS (version 26). In this study, we enrolled 365 participants, including 203 males with an average age of 47 ± 17.93 years. EAT thickness was 6.28 ± 1.62 mm, and EAT density was -96.07 ± 12.47 Hounsfield units (HU). The mean CACS was 22.27 ± 79.01, and the mean liver density was 50.01 ± 10.76 HU. A significant positive correlation was observed between EAT thickness and CACS (r = 0.208, p < 0.001). EAT density showed a significant negative correlation with CACS (r = -0.155, p = 0.003). No correlation was found between NAFLD and CACS. Univariate logistic regression analysis identified significant predictors of increased CACS, including EAT thickness (OR: 1.803), EAT density (OR: 0.671), diabetes mellitus (DM) (OR: 5.921), and hypertension (HTN) (OR: 7.414). Multivariate analysis confirmed the significance of EAT thickness (OR: 0.682), DM (OR: 3.66), and HTN (OR: 2.79) as predictors of elevated CACS. Our findings demonstrate that increased EAT thickness and decreased density are associated with higher CACS. Also, both DM and HTN significantly contribute to increased CACS. These results support the inclusion of EAT measurements in cardiovascular risk assessment models to enhance diagnostic accuracy.

Do Positive Psychosocial Factors Contribute to the Prediction of Coronary Artery Disease? A UK Biobank-Based Machine Learning Approach.

Most prediction models for coronary artery disease (CAD) compile biomedical and behavioural risk factors, using linear multivariate models. This study explored the potential of integrating positive psychosocial factors (PPFs), including happiness, satisfaction with life, and social support, into conventional and machine learning-based CAD prediction models. We included UK Biobank participants without CAD at baseline. First, we estimated associations of individual PPFs with subsequent acute myocardial infarction (AMI) and chronic ischaemic heart disease (CIHD) using logistic regression. Then, we compared the performances of logistic regression and eXtreme Gradient Boosting (XGBoost) prediction models when adding PPFs as predictors to the Framingham Risk Score (FRS). Based on a sample size between 160,226 and 441,419 of UK Biobank participants, happiness, satisfaction with health and life, and participation in social activities were linked to lower AMI and CIHD risk (all p-for-trend ≤ 0.04), while social support was not. In a validation sample, adding PPFs to the FRS using logistic regression and XGBoost prediction models improved neither AMI (AUC change: 0.02% and 0.90%, respectively) nor CIHD (AUC change: -1.10% and -0.88%, respectively) prediction. PPFs were individually linked to CAD risk, in line with previous studies, and as reflected by the new European Society of Cardiology guidelines on cardiovascular disease prevention. However, including available PPFs in CAD-prediction models did not improve prediction compared to the FRS alone. Future studies should explore whether PPFs may act as CAD-risk modifiers, especially if the individual's risk is close to a decision threshold.

Lipoprotein(a) as a cardiovascular risk factor among patients with and without diabetes Mellitus: the Mass General Brigham Lp(a) Registry

BackgroundDiabetes mellitus (DM) and Lp(a) are well-established predictors of coronary artery disease (CAD) outcomes. However, their combined association remains poorly understood.ObjectiveTo investigate the relationship between elevated Lp(a) and DM with CAD outcomes.MethodsRetrospective analysis of the MGB Lp(a) Registry involving patients ≥ 18 years who underwent Lp(a) measurements between 2000 and 2019. Exclusion criteria were severe kidney dysfunction, malignant neoplasms, and prior atherosclerotic cardiovascular disease (ASCVD). The primary outcome was a combination of cardiovascular death or myocardial infarction (MI). Elevated Lp(a) was defined as > 90th percentile (≥ 216 nmol/L).ResultsAmong 6,238 patients who met the eligibility criteria, the median age was 54, 45% were women, and 12% had DM. Patients with DM were older, more frequently male, and had a higher prevalence of additional cardiovascular risk factors. Over a median follow-up of 12.9 years, patients with either DM or elevated Lp(a) experienced higher rates of the primary outcome. Notably, those with elevated Lp(a) had a higher incidence of the primary outcome regardless of their DM status. The annual event rates were as follows: No-DM and Lp(a) < 90th% − 0.6%; No-DM and Lp(a) > 90th% − 1.3%; DM and Lp(a) < 90th% − 1.9%; DM and Lp(a) > 90th% − 4.7% (p < 0.001). After adjusting for confounders, elevated Lp(a) remained independently associated with the primary outcome among both patients with DM (HR = 2.66 [95%CI: 1.55–4.58], p < 0.001) and those without DM (HR = 2.01 [95%CI: 1.48–2.74], p < 0.001).ConclusionsElevated Lp(a) constitutes an independent and incremental risk factor for CAD outcomes in patients with and without DM.

Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease.

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques.

Heart disease is a severe health issue that results in high fatality rates worldwide. Identifying cardiovascular diseases such as coronary artery disease (CAD) and heart attacks through repetitive clinical data analysis is a significant task. Detecting heart disease in its early stages can save lives. The most lethal cardiovascular condition is CAD, which develops over time due to plaque buildup in coronary arteries, causing incomplete blood flow obstruction. Machine Learning (ML) is progressively used in the medical sector to detect CAD disease. The primary aim of this work is to deliver a state-of-the-art approach to enhancing CAD prediction accuracy by using a DL algorithm in a classification context. A unique ML technique is proposed in this study to predict CAD disease accurately using a deep learning algorithm in a classification context. An ensemble voting classifier classification model is developed based on various methods such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Bidirectional LSTM and Long Short-Term Memory (LSTM). The performance of the ensemble models and a novel model are compared in this study. The Alizadeh Sani dataset, which consists of a random sample of 216 cases with CAD, is used in this study. Synthetic Minority Over Sampling Technique (SMOTE) is used to address the issue of imbalanced datasets, and the Chi-square test is used for feature selection optimization. Performance is assessed using various assessment methodologies, such as confusion matrix, accuracy, recall, precision, f1-score, and auc-roc. When a novel algorithm achieves the highest accuracy relative to other algorithms, it demonstrates its effectiveness in several ways, including superior performance, robustness, generalization capability, efficiency, innovative approaches, and benchmarking against baselines. These characteristics collectively contribute to establishing the novel algorithm as a promising solution for addressing the target problem in machine learning and related fields. Implementing the novel model in this study significantly improved performance, achieving a prediction accuracy rate of 92% in the detection of CAD. These findings are competitive and on par with the top outcomes among other methods.

Evaluation of Plasma Atherogenic Index, Triglyceride-Glucose Index and Other Lipid Ratios as Predictive Biomarkers of Coronary Artery Disease in Different Age Groups.

(1) Background: Dyslipidaemia and insulin resistance are major risk factors for coronary artery disease (CAD). This study investigated the relationship between plasma atherogenic index (PA-I), triglyceride-glucose index (TGI) and other lipid ratios with the presence and prediction of CAD among different age categories. (2) Methods: The study included 223 participants diagnosed with CAD and those with normal coronary arteries (normal group) by coronary computed tomography angiography (CCTA). Participants were categorised by age and sex: premature CAD (PCAD) for men under 55 and women under 65, and older groups as elderly. (3) Results: PA-I, Lipid Combined Index, Castelli Risk Indices, and TGI were significantly higher in the PCAD group compared to the control group (p < 0.05). ROC analysis showed that a PA-I cut-off of 0.41 had a sensitivity of 62% and a specificity of 58% for predicting PCAD, while a TGI cut-off of 8.74 had a sensitivity of 68% and a specificity of 62%. In the elderly, no significant differences in these indices were found between the CAD and normal groups. (4) Conclusions: Traditional lipid profiles and non-traditional lipid indices such as PA-I and TGI show significant differences in predicting CAD in younger populations but not in older groups. TGI and PA-I may be promising biomarkers for the prediction of PAD, although further validation is needed.

Subclinical Coronary Artery Disease in Patients with Idiopathic Inflammatory Myopathy: An Evaluation by CT Coronary Angiography

Idiopathic inflammatory myopathy (IIM) poses elevated risk of cardiovascular event and mortality, similar to other autoimmune rheumatic diseases. We conducted a cross-sectional study to examine the prevalence and risk factor of subclinical coronary artery disease (CAD) in patients with IIM, using CT coronary angiogram (CTCA). The prevalence of obstructive CAD and CAD in IIM (13.3% and 66.7%, respectively) were significantly higher than age and sex-matched controls (0% and 30%, respectively, both [Formula: see text]). Diabetes mellitus and calcium calcification score [Formula: see text] units were found to be the independent predictors of obstructive CAD. Screening of high-risk patients with aggressive treatment of cardiovascular risk factors should be considered in managing IIM patients.

Evaluating the American Heart Association/American College of Cardiology Guideline-Recommended and Contemporary Pretest Probability Models in a Mixed Asian Cohort: The Contribution of Coronary Artery Calcium.

Most pretest probability (PTP) tools for obstructive coronary artery disease (CAD) were Western -developed. The most appropriate PTP models and the contribution of coronary artery calcium score (CACS) in Asian populations remain unknown. In a mixed Asian cohort, we compare 5 PTP models: local assessment of the heart (LAH), CAD Consortium (CAD2), risk factor-weighted clinical likelihood, the American Heart Association/American College of Cardiology and the European Society of Cardiology PTP and 3 extended versions of these models that incorporated CACS: LAH(CACS), CAD2(CACS), and the CACS-clinical likelihood. The study cohort included 771 patients referred for stable chest pain. Obstructive CAD prevalence was 27.5%. Calibration, area under the receiver-operating characteristic curves (AUC) and net reclassification index were evaluated. LAH clinical had the best calibration (χ2 5.8; P=0.12). For CACS models, LAH(CACS) showed least deviation between observed and expected cases (χ2 37.5; P<0.001). There was no difference in AUCs between the LAH clinical (AUC, 0.73 [95% CI, 0.69-0.77]), CAD2 clinical (AUC, 0.72 [95% CI, 0.68-0.76]), risk factor-weighted clinical likelihood (AUC, 0.73 [95% CI: 0.69-0.76) and European Society of Cardiology PTP (AUC, 0.71 [95% CI, 0.67-0.75]). CACS improved discrimination and reclassification of the LAH(CACS) (AUC, 0.88; net reclassification index, 0.46), CAD2(CACS) (AUC, 0.87; net reclassification index, 0.29) and CACS-CL (AUC, 0.87; net reclassification index, 0.25). In a mixed Asian cohort, Asian-derived LAH models had similar discriminatory performance but better calibration and risk categorization for clinically relevant PTP cutoffs. Incorporating CACS improved discrimination and reclassification. These results support the use of population-matched, CACS-inclusive PTP tools for the prediction of obstructive CAD.

Linking Diabetic Retinopathy Severity to Coronary Artery Disease Risk Factors in Type 2 Diabetic Patients.

Diabetes mellitus (DM) is a common metabolic disorder characterized by hyperglycemia, leading to chronic complications, notably cardiovascular diseases such as coronary artery disease (CAD). Diabetic retinopathy (DR), a leading cause of blindness, may serve as a non-invasive marker for CAD. This study investigates the correlation between DR and CAD to explore its diagnostic potential in diabetic populations. A cross-sectional study was conducted over one year in a general hospital, involving 100 type 2 DM patients with retinopathy. DR was classified as mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative retinopathy, based on fundus examinations. Data on age, duration of diabetes, cholesterol levels, glycated hemoglobin (HbA1C), and ECG (electrocardiography) findings were collected. Statistical analysis included frequency analysis, chi-square tests for association between categorical variables, and significance testing with p-values. Data were analyzed using IBM SPSS Statistics for Windows, Version 20.0 (Released 2011; IBM Corp., Armonk, New York, United States). Descriptive statistics were characterized by categorical and continuous variables. The chi-square test determined associations between qualitative variables, with significance set at p<0.05. The mean age of patients was 57.13 ± 8.51 years. Age and duration of diabetes were significant predictors of retinopathy severity (p<0.001). Proliferative retinopathy was found exclusively in patients over 70 years. Lower cholesterol levels (<200 mg/dL) were significantly associated with less severe retinopathy (p=0.033), whereas higher cholesterol levels (>200 mg/dL) did not show a statistically significant association with retinopathy severity (p=0.772). Patients with HbA1C levels between 6.5% and 8.5% predominantly had milder forms of retinopathy, as indicated by the significant p-value (<0.001). In contrast, patients with HbA1C levels above 8.5% are more likely to have severe NPDR or proliferative diabetic retinopathy (PDR), but this association was not statistically significant (p=0.582). ECG abnormalities increased with retinopathy severity (p=0.002). Hypertension was significantly linked to cardiac changes in retinopathy patients (p<0.001), while smoking and family history of CAD were not significant factors.This study's cross-sectional design limits causality inference. The single-center sample of 100 patients may not be broadly generalizable. Reliance on self-reported data introduces potential recall bias, and confounding factors such as diet, physical activity, and additional comorbidities were not accounted for. The lack of a control group further limits comparative analysis. Future longitudinal studies with larger, diverse samples are needed. Retinopathy in DM patients is significantly associated with cardiac changes and other risk factors such as hypertension, dyslipidemia, and poor glycemic control. Aggressive management of these factors is essential. Retinopathy can serve as a predictor of CAD in diabetic patients.

Polygenic Risk Scores in Predicting Coronary Artery Disease in Symptomatic Patients. A Validation Study.

Clinical risk scores for coronary artery disease (CAD) are used in clinical practice to select patients for diagnostic testing and therapy. Several studies have proposed that polygenic risk scores (PRSs) can improve the prediction of CAD, but the scores need to be validated in clinical populations with accurately characterized phenotypes. We assessed the predictive power of the three most promising PRSs for the prediction of coronary atherosclerosis and obstructive CAD. This study was conducted on 943 symptomatic patients with suspected CAD for whom the phenotype was accurately characterized using anatomic and functional imaging. Previously published genome-wide polygenic scores were generated to compare a genetic model based on PRSs with a model based on clinical data. The test and PRS cohorts were predominantly Caucasian of northern European ancestry. All three PRSs predicted coronary atherosclerosis and obstructive CAD statistically significantly. The predictive accuracy of the models combining clinical data and different PRSs varied between 0.778 and 0.805 in terms of the area under the receiver operating characteristic (AUROC), being close to the model including only clinical variables (AUROC 0.769). The difference between the clinical model and combined clinical + PRS model was not significant for PRS1 (p=0.627) and PRS3 (p=0.061). Only PRS2 slightly improved the predictive power of the model (p=0.04). The likelihood ratios showed the very weak diagnostic power of all PRSs. The addition of PRSs to conventional risk factors did not clinically significantly improve the predictive accuracy for either coronary atherosclerosis or obstructive CAD, showing that current PRSs are not justified for routine clinical use in CAD.

ApoA1/HDL-C ratio as a predictor for coronary artery disease in patients with type 2 diabetes: a matched case-control study

IntroductionThis study investigated the possible relationship between the Apo lipoprotein A1 /high-density lipoprotein cholesterol (ApoA1/HDL-C) ratio and coronary artery disease (CAD) in patients with type 2 diabetes (T2D).MethodsThis was a matched case-control study of 482 patients with T2D in two groups of CAD and (n = 241) non-CAD (n = 241). The patients were classified into four quartiles according to the ApoA1/HDL-C ratio, and multivariate logistic regression analysis was performed to assess the relationship between ApoA1/HDL-C and CAD. ROC analysis was also conducted.ResultsThis study showed that the ApoA1/HDL-C ratio has an independent association with CAD in individuals with T2D. The CAD group exhibited a significantly higher ApoA1/HDL-C ratio than those without CAD (p-value = 0.004). Moreover, the risk of CAD increased significantly across the ApoA1/HDL-C ratio quartiles, with the highest odds in the fourth quartile. The second quartile showed an odds ratio (OR) of 2.03 (p-value = 0.048) compared to the first. Moving to the third quartile, the OR increased to 2.23 (p-value = 0.023). The highest OR was noted in the fourth, reaching 3.41 (p-value = 0.001). Employing a cut-off value of 2.66 and an area under the curve (AUC) of 0.885, the ApoA1/HDL-C ratio predicts CAD among patients with T2D with a sensitivity of 75% and a specificity of 91% (p-value < 0.001).ConclusionThe current study revealed an independent association between ApoA1/HDL-C ratio and CAD in patients with T2D. This ratio can be a promising tool for predicting CAD during the follow-up of patients with T2D, aiding in identifying those at higher risk for CAD.

Validity of the Triglyceride-Glucose Indices for Predicting the Severity of Coronary Artery Disease in Patients With Nondiabetic Chronic Coronary Syndrome.

The triglyceride-glucose (TyG) index was shown to be an independent predictor of coronary artery disease (CAD) progression and prognosis. However, whether the TyG index can predict the severity of CAD in nondiabetic patients with chronic coronary syndrome remains unclear. A total of 118 individuals who underwent elective coronary angiography were classified into group A (59 with coronary lesions) and group B (59 with normal coronary arteries; as a control group) after coronary angiography and laboratory tests for fasting and the postprandial (PP) TyG index. The complexity of CAD was determined by the Synergy Between Percutaneous Coronary Intervention (SYNTAX) score (SYNTAX score >22 indicated moderate-high risk), and patients diagnosed with diabetes or prediabetes were excluded. The TyG index was not related to the SYNTAX score in groups A and B; however, in the CAD group with an low-density lipoprotein (LDL) concentration <70 mg/dL (group A1), a fasting TyG index ≥8.25 and a PP TyG index ≥11 could predict moderate-high SYNTAX risk score; in addition, the odds ratio (OR) was 4.3× higher and the relative risk (RR) was 1.8× greater (OR = 4.3, RR = 1.8, 95% confidence interval = 1.4-13.5, P < 0.05) for individuals with a higher fasting TyG index ≥8.25 to have a moderate-high SYNTAX risk score. Individuals with a higher PP TyG index ≥11 had OR of 2.6× higher and a RR of 1.4× greater to have moderate-high SYNTAX risk score. Both fasting and PP TyG levels were associated with greater coronary anatomical complexity (SYNTAX score >22) in nondiabetic chronic coronary patients with LDL <70 mg/dL. Fasting and the PP TyG indices can serve as noninvasive predictors of CAD complexity in nondiabetic patients with LDL <70 mg/dL and could change the management and therapeutic approaches.

CADCare: Smart System for CHD Identification &amp; Sensor Alerts

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.

Atherogenic index of plasma as a predictor of coronary artery disease: a cohort study in south of Iran

BackgroundCoronary artery disease (CAD) is an atherosclerotic disease of an inflammatory nature. Previous studies examining the relationship between triglycerides and high-density lipoprotein cholesterol have highlighted the importance of plasma atherogenic index (AIP) as an important predictor of coronary heart disease. However, due to the lack of adequate information on this topic, this study aimed to investigate the relationship between AIP and coronary heart disease risk.ResultsThis study included 2,226 women and 1,690 men aged 35–70 years who participated in the Bandar Kong Cohort study and met the eligibility criteria. The data was collected using a checklist and questionnaires, which were designed by experienced individuals. After participants completed a registration form and gave informed consent, face-to-face interviews were conducted by trained experts. The validity and reliability of the questionnaire had been verified by the national cohort team prior to its use. The Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1400.171) approved the study. Data from the initial cohort survey using SPSS software version 25, were analyzed to include several factors, including age, sex, smoking status, body mass index (BMI), physical activity level, socioeconomic status, AIP, systolic blood pressure, and diastolic blood pressure. The prevalence of coronary heart disease was found to be 7.5% higher in people with a BMI of 25 or higher. Also, Individuals with low physical activity had a higher prevalence. Individuals with CAD had significantly higher mean values for the AIP, age, systolic blood pressure, and diastolic blood pressure (0.46, 57.50, 128.43, and 81.10, respectively) compared to those without CAD. Furthermore, patients with CAD had lower years of education (2649.45 and 3.59) than individuals without CAD (P < 0.05). Importantly, our findings showed that AIP increased the odds ratio of coronary heart disease by 1.86 as an independent risk factor.ConclusionsBased on our investigation, the AIP is a valuable and independent predictive risk factor for coronary artery disease. This index can be utilized effectively due to its accessibility and affordability, making it a promising tool for risk assessment in clinical settings.