Predictor Of Coronary Heart Disease Research Articles

Gender-related impact of vitamin D status on severity of coronary artery disease

Abstract Funding Acknowledgements None. Background Vitamin D has cardioprotective properties. A status of hypovitaminosis D, defined as vitamin D ≤ 20 ng/ml, was found to have a comparable effect to diabetes mellitus (DM) in terms of adverse outcomes in patients with acute myocardial infarction (AMI). To date, it is known that hypovitaminosis D is more frequent in women than men but there is still a paucity of data on how gender differences in vitamin D levels are associated with the extent of coronary heart disease (CAD). Purpose To evaluate the relationship between vitamin D status on CAD incidence and severity in patients with AMI. Methods Blood samples were obtained from 1484 patients with AMI on hospital admission. Levels of vitamin D ≤ 10 ng/ml were considered as severe hypovitaminosis D. CAD was defined as the presence of at least one coronary vessel stenosis greater than 50%, while severe CAD was defined as left main disease and/or three-vessel disease. The endpoint was all-cause mortality. Results The cohort's mean age was 66.25 (11.47) years, the majority were male (71.8%) and 24.3% of patients had DM. Overall, 86.2% of patients had CAD and 32.9% severe CAD. The median vitamin D concentration was 17.25 [10.3 - 24.7] ng/ml, and 23.6% of patients had severe hypovitaminosis D. In female patients, hypovitaminosis D was significantly more common in whom with severe CAD (74.1% vs 58.1%, p=0.002), but not in men (61.3% vs 57.1%, p=0.193). On the other hand, severe hypovitaminosis D was significantly more frequent in patients with severe CAD in both genders (male and female respectively 26.6% vs 17.9%, p=0.005; 40.5% vs 26.4%, p=0.001). In female patients, severe CAD was predicted by hypovitaminosis D (OR: 1.85 [1.14 – 3.03], p=0.013) along with DM and older age after correction for confounders as GFR<60 mL/min/1.73 m2, higher cholesterol levels and body mass index. However, hypovitaminosis D was not an independent predictor of severe CAD in men. Moreover, a composite of hypovitaminosis D and DM was identified as a predictor of severe CAD when compared to patients with neither hypovitaminosis D nor DM (OR: 3.56 [1.8 – 7.03], p=0.021) only in female patients. Conclusions The identification of an association between hypovitaminosis D and severe CAD in women but not in men is a novelty in this field. Furthermore, we found that the presence of both hypovitaminosis D and DM has a greater contribution to severe CAD in female patients with AMI.

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning

ObjectiveThis study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).MethodsSixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model.ResultsThe LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814–0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD.ConclusionThis study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs.

Beyond the AJR: Coronary Calcium Remains the Best Risk Predictor for Coronary Heart Disease for Now, but the Polygenic Risk Score Is Fast Becoming a Contender Among Young Adults.

Beyond the AJR: Coronary Calcium Remains the Best Risk Predictor for Coronary Heart Disease for Now, but the Polygenic Risk Score Is Fast Becoming a Contender Among Young Adults.

Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study.

Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D. The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI). We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465). This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Characteristics of metabolic inflammatory syndrome among inpatients with type 2 diabetes: A cross-sectional study in China.

As meta-inflammation is a common feature for obesity, type 2 diabetes (T2D), nonalcoholic fatty liver disease and atherosclerosis, we have proposed a new concept, metabolic inflammatory syndrome (MIS), to cluster such diseases. We aimed to characterize MIS and explore its association with coronary heart disease (CHD) among T2D inpatients in China. A total number of 8344 T2D participants were enrolled. Each component of MIS and metabolic syndrome (MS) was analyzed. Their association with the risk of CHD was assessed using a binary logistic analysis. Among the T2D inpatients, the detection rate of MIS was much higher than that of MS (93.6 % vs. 53.2 %). Among all the components of MIS and MS, carotid atherosclerosis (71.9 %) was most commonly detected, which increased with aging in subgroups. Surprisingly, the most common combination of MIS was with all 4 components in T2D patients, with a constituent ratio of 30.9 %. According to the odds ratios (ORs), MIS was a better predictor of CHD than MS, especially after adjustment for age, sex, smoking, and alcohol consumption (adjusted OR for MIS: 3.083; for MS: 1.515). The presence of more components of MIS was associated with a higher detection rate of CHD (P<0.001). Among all the components of MIS and MS, carotid atherosclerosis best predicted the risk of CHD (adjusted OR: 1.787). MIS is an independent risk factor for CHD, with a bigger OR value than MS. Carotid atherosclerosis, with the highest detection rate, was the best individual predictor of CHD and thus a critical component of MIS. The concept of MIS represents the understanding of metabolic diseases from the perspective of holistic integrative medicine.

Coronary heart disease prediction based on hybrid deep learning.

Machine learning provides increasingly reliable assistance for medical experts in diagnosing coronary heart disease. This study proposes a deep learning hybrid model based coronary heart disease (CAD) prediction method, which can significantly improve the prediction accuracy compared to traditional solutions. This research scheme is based on the data of 7291 patients and proposes a hybrid model, which uses two different deep neural network models and a recurrent neural network model as the main model for training. The prediction results based on the main model training use a k-nearest neighbor model for secondary training so as to improve the accuracy of coronary heart disease prediction. The comparison between the model prediction results and the clinical diagnostic results shows that the prediction model has a prediction accuracy rate of 82.8%, a prediction precision rate of 87.08%, a prediction recall rate of 88.57%, a prediction F1-score of 87.82%, and an area under the curve value of 0.8 in the test set. Compared to single model machine learning predictions, the hybrid model has a significantly improved accuracy and has effectively solved the problem of overfitting. A deep learning based CAD prediction hybrid model that combines multiple weak models into a strong model can fully explore the complex inter-relationships between various features under limited feature values and sample size, improve the evaluation indicators of the prediction model, and provide effective auxiliary support for CAD diagnosis.

Coronary Heart Disease Prediction: A Novel Deep Learning-based Approach

Coronary Heart Disease (CHD) is reported to be one of the world’s deadliest diseases. Early prediction or detection and diagnosis of the disease can help prevent, cure, and reduce the damage it could cause. Artificial intelligent techniques such as Machine Learning and Deep Learning have proven useful for the early detection and prediction of disease. However, issues of irrelevant and redundant features in open datasets have contributed to low classification accuracy rates and high misclassification rates. This leaves a gap for continuous approaches for smart feature selection and high-performing models in the field for better results. This study investigates the impacts of cardiologist-inspired datasets and PCA dimensionality reduction techniques on the performance of CHD prediction. The compared results show that while PCA improves the CHD prediction accuracy for datasets obtained from cardiologists, there exist no statistically significant differences in the efficacy of the PCA method for CHD classification when applied to open datasets, however, MLP and LSTM offer promising results. The results further indicated that the effectiveness of expert-based feature selection techniques on CHD classification is relatively stable when compared with open-source datasets.

Elevated triglycerides and low triiodothyronine: Key risk factors for coronary artery calcification in patients with schizophrenia

ObjectiveCoronary artery calcification (CAC) is a well-established independent predictor of coronary heart disease, and patients with schizophrenia have significantly higher rates compared to the general population. We performed this study to examine the population-specific risk factors associated with CAC in patients with schizophrenia. MethodsIn this cross-sectional study, patients with schizophrenia who underwent low-dose chest CT scans between January 2020 and December 2021 were analyzed. Ordinary CAC scores and results of routine blood tests were obtained. Logistic regression was used to calculate the odds ratio (OR) for potential risk factors in patients with and without CAC, while the negative binomial additive model was used to explore the dose-response relationship between risk factors and CAC score. ResultsOf the 916 patients, 233 (25.4 %) had CAC, while 683 (74.6 %) did not. After adjusting for confounding factors, higher triglyceride levels (OR = 1.20, 95 % confidence interval (CI): 1.04 to 1.38, p = 0.013) and low triiodothyronine levels (OR = 0.50, 95 % CI: 0.29 to 0.84; p = 0.010) were identified as risk factors for CAC. Both triglycerides (p = 0.021) and triiodothyronine (p = 0.010) were also found to have significant dose-response relationships with CAC scores according to the negative binomial additive model in the exploratory analysis. ConclusionsThis study highlights elevated serum triglycerides and decreased triiodothyronine levels as population-specific risk factors for CAC in patients with schizophrenia, suggest the need for close monitoring of CAC in patients with schizophrenia and further prospective trials to provide additional evidence on this topic.

Lipidomics identified novel cholesterol-independent predictors for risk of incident coronary heart disease: Mediation of risk from diabetes and aggravation of risk by ambient air pollution

IntroductionPrevious lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency. ObjectivesTo search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention. MethodsWe conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry. ResultsIn search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P < 0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P < 0.05). ConclusionWe identified seven lipid species with greatest predictive increment so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.

Development and Validation of a Novel Model for Predicting Coronary Heart Disease in Snoring Hypertensive Patients with Hyperhomocysteinemia.

Hypertensive patients with snoring and elevated plasma homocysteine levels are common. When these factors are combined, the risk of coronary heart disease (CHD) is high. Herein, we developed and validated an easy-to-use nomogram to predict high-risk CHD in snoring hypertensive patients with elevated plasma homocysteine.Snoring patients (n = 1,962) with hyperhomocysteinemia and hypertension were divided into training (n = 1,373, 70%) and validation (n = 589, 30%) sets. We extracted CHD predictors using multivariate Cox regression analysis, then constructed a nomogram model. Internal validation using 1,000 bootstrap resampling was performed to assess the consistency and discrimination of the predictive model using the area under the receiver operating characteristic curve (AUC) and calibration plots.We constructed a nomogram model with the extracted predictors, including age, waist-height ratio, smoking, and low-density lipoprotein cholesterol levels. The AUCs of the training and validation cohorts at 80 months were 0.735 (95% CI: 0.678-0.792) and 0.646 (95% CI: 0.547-0.746), respectively. The consistency between the observed CHD survival and the probability of CHD survival in the training and validation sets was acceptable based on the calibration plots. A total of more than 151 points in the nomogram can be used in the identification of high-risk patients for CHD among snoring hypertensive patients with elevated plasma homocysteine.We developed a CHD risk prediction model for snoring hypertension patients with hyperhomocysteinemia. Our findings provide a useful clinical tool for the rapid identification of high-risk CHD at an early stage.

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

BackgroundThe association between periodontitis and cardiovascular disease is increasingly recognized. In this research, a prediction model utilizing machine learning (ML) was created and verified to evaluate the likelihood of coronary heart disease in individuals affected by periodontitis.MethodsWe conducted a comprehensive analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES) database, encompassing the period between 2009 and 2014.This dataset comprised detailed information on a total of 3,245 individuals who had received a confirmed diagnosis of periodontitis. Subsequently, the dataset was randomly partitioned into a training set and a validation set at a ratio of 6:4. As part of this study, we conducted weighted logistic regression analyses, both univariate and multivariate, to identify risk factors that are independent predictors for coronary heart disease in individuals who have periodontitis. Five different machine learning algorithms, namely Logistic Regression (LR), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Classification and Regression Tree (CART), were utilized to develop the model on the training set. The evaluation of the prediction models’ performance was conducted on both the training set and validation set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), Brier score, calibration plot, and decision curve analysis (DCA). Additionally, a graphical representation called a nomogram was created using logistic regression to visually depict the predictive model.ResultsThe factors that were found to independently contribute to the risk, as determined by both univariate and multivariate logistic regression analyses, encompassed age, race, presence of myocardial infarction, chest pain status, utilization of lipid-lowering medications, levels of serum uric acid and serum creatinine. Among the five evaluated machine learning models, the KNN model exhibited exceptional accuracy, achieving an AUC value of 0.977. The calibration plot and brier score illustrated the model's ability to accurately estimate probabilities. Furthermore, the model's clinical applicability was confirmed by DCA.ConclusionOur research showcases the effectiveness of machine learning algorithms in forecasting the likelihood of coronary heart disease in individuals with periodontitis, thereby aiding healthcare professionals in tailoring treatment plans and making well-informed clinical decisions.

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction.

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

FRI089 Massive Hypertriglyceridemia? Think Oral Contraceptive Pills!

Abstract Disclosure: I. Ali: None. M. Hamdi: None. Introduction: Oral contraceptive pills (OCPs) are common cause of Hypertriglyceridemia (TG) that should be recognized early by clinicians. Concern is raised regarding pursing further genetic test for Familial hypertriglyceridemia for female who developed massive hyper triglyceridemic after starting hormonal therapy. Case: A 37-year-old female patient with history of prediabetes with A1c of 5.7% and one attack of pancreatitis. Normal BMI, No regular medications, negative family history for lipid disorder. Rare social intake of alcohol. Routine annual lab showed triglycerides level of 164 in 2016, otherwise normal biochemical evaluation. No treatment pursued. Later She was started on OCPs (Kariva 0.15-0.02 mg) for contraception. Late 2019, blood work-up showed cholesterol level of 979, TG of 3170 and HDL level of 22. She was started on a statin therapy and fenofibrates beside stopping OCP, she maintained normal lipid profile while being off OCPs, until 2022 when she restarted an OCPs (Lo Loestrin 1mg-10 mcg) for 2 months that resulted in elevated cholesterol level of 1222, triglycerides of 5177 and HDL of 10.OCP was stopped, lipids level trended down with cholesterol of 961, triglycerides of 2005 and HDL of 22 then normalized after 2 months. Patient did not have any clinical symptom or complain. Discussion: High triglyceride concentration is an independent predictor of coronary heart disease and ischemic stroke. Many patients with triglycerides &amp;gt;1000 mg/dl have familial hypertriglyceridemia with superimposed additional exogenous factors which further raise triglyceride concentrations like exogenous estrogens, SERMs, hyperestrogenemia of pregnancy, poorly controlled diabetes mellitus, hypothyroidism, alcohol excess, exogenous corticosteroids, and drugs including isotretinoin and protease inhibitors. OCP induced hypertriglyceridemia occurs due to estrogen’s action in decreasing hepatic triglyceride lipase and lipoprotein lipase activity. This causes an overall increase in low and intermediate-density triglycerides and cholesterol that can increase free fatty acids to toxic level. The marked increase of TG and cholesterol in this patient while being on OCP is unusual and strongly indicate underlying familial hypertriglyceridemia exacerbated by starting OCP. Further genetic work up is being done. As OCP or other estrogen therapy are frequently given unwittingly to women with preexisting hypertriglyceridemia that lead to severe clinical consequences. Estrogen containing medication should not be considered if fasting triglyceride is above 500 mg/dl. In women on hormonal treatment with treatment-resistant hypertriglyceridemia, the likelihood that the hormonal treatment is nullifying drug efficacy is high so it should be stopped for a short trial while continuing modifying triglyceride-lowering medications. Presentation: Friday, June 16, 2023

Ratio of visceral fat area to body fat mass (VBR) is a superior predictor of coronary heart disease.

Ratio of visceral fat area to body fat mass (VBR) is a superior predictor of coronary heart disease.

Integration of Biomarker Polygenic Risk Score Improves Prediction of Coronary Heart Disease

There are several established biomarkers for coronary heart disease (CHD), including blood pressure, cholesterol, and lipoproteins. It is of high interest to determine how a combined polygenic risk score (PRS) of CHD-associated biomarkers (BioPRS) can further improve genetic prediction of CHD. We developed CHDBioPRS, combining BioPRS with PRS of CHD in the UK Biobank and tested it on FinnGen. We found that BioPRS was clearly predictive of CHD and that CHDBioPRS improved the standard CHD PRS. The largest effect was observed with early onset cases in FinnGen, with HRs above 2 per standard deviation of CHDBioPRS.

Plasma Fibronectin as a Novel Predictor of Coronary Heart Disease: A Retrospective Study.

Although fibronectin has been associated with the pathogenesis of atherosclerosis, little is currently known about the relationship between plasma fibronectin and coronary heart disease (CHD). This retrospective study aimed to determine the predictive value of plasma fibronectin for CHD and its severity. A total of 1644 consecutive patients who underwent selective coronary angiography were recruited into the present study. The characteristics and results of the clinical examination of all patients were collected. Logistic regression analyses were performed to determine the predictive value of plasma fibronectin for the presence and severity of CHD. Compared with non-CHD patients, the CHD patients showed significantly higher plasma levels of troponin I and creatine kinase isoenzyme, along with lower plasma levels of fibronectin. However, no significant differences were detected in plasma fibronectin among patients with different grades of CHD. The logistic regression model showed that plasma fibronectin remained an independent predictor of CHD after adjustment with a 1.39-fold increased risk for every 1 SD decrease in plasma fibronectin. Nevertheless, plasma fibronectin could not predict the severity of CHD determined by the number of stenosed vessels and the modified Gensini score. This study demonstrated that lower plasma fibronectin might be an independent predictor of CHD, but it may be of no value in predicting the severity of CHD.

A linear weighted combination of polygenic scores for a broad range of traits improves prediction of coronary heart disease

Polygenic scores (PGS) for coronary heart disease (CHD) are constructed using GWAS summary statistics for CHD. However, pleiotropy is pervasive in biology and disease-associated variants often share etiologic pathways with multiple traits. Therefore, incorporating GWAS summary statistics of additional traits could improve the performance of PGS for CHD. Using lasso regression models, we developed two multi-PGS for CHD: 1) multiPGSCHD, utilizing GWAS summary statistics for CHD, its risk factors, and other ASCVD as training data and the UK Biobank for tuning, and 2) extendedPGSCHD, using existing PGS for a broader range of traits in the PGS Catalog as training data and the Atherosclerosis Risk in Communities Study (ARIC) cohort for tuning. We evaluated the performance of multiPGSCHD and extendedPGSCHD in the Mayo Clinic Biobank, an independent cohort of 43,578 adults of European ancestry which included 4,479 CHD cases and 39,099 controls. In the Mayo Clinic Biobank, a 1 SD increase in multiPGSCHD and extendedPGSCHD was associated with a 1.66-fold (95% CI: 1.60–1.71) and 1.70-fold (95% CI: 1.64–1.76) increased odds of CHD, respectively, in models that included age, sex, and 10 PCs, whereas an already published PGS for CHD (CHD_PRSCS) increased the odds by 1.50 (95% CI: 1.45–1.56). In the highest deciles of extendedPGSCHD, multiPGSCHD, and CHD_PRSCS, 18.4%, 17.5%, and 16.3% of patients had CHD, respectively.

Impaired metabolism predicts coronary artery calcification in women with systemic lupus erythematosus

Patients with systemic lupus erythematosus (SLE) exhibit a high risk for cardiovascular diseases (CVD) which is not fully explained by the classical Framingham risk factors. SLE is characterized by major metabolic alterations which can contribute to the elevated prevalence of CVD. A comprehensive analysis of the circulating metabolome and lipidome was conducted in a large cohort of 211 women with SLE who underwent a multi-detector computed tomography scan for quantification of coronary artery calcium (CAC), a robust predictor of coronary heart disease (CHD). Beyond traditional risk factors, including age and hypertension, disease activity and duration were independent risk factors for developing CAC in women with SLE. The presence of coronary calcium was associated with major alterations of circulating lipidome dominated by an elevated abundance of ceramides with very long chain fatty acids. Alterations in multiple metabolic pathways, including purine, arginine and proline metabolism, and microbiota-derived metabolites, were also associated with CAC in women with SLE. Logistic regression with bootstrapping of lipidomic and metabolomic variables were used to develop prognostic scores. Strikingly, combining metabolic and lipidomic variables with clinical and biological parameters markedly improved the prediction (area under the curve: 0.887, p<0.001) of the presence of coronary calcium in women with SLE. The present study uncovers the contribution of disturbed metabolism to the presence of coronary artery calcium and the associated risk of CHD in SLE. Identification of novel lipid and metabolite biomarkers may help stratifying patients for reducing CVD morbidity and mortality in SLE. INSERM and Sorbonne Université.

Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN)

Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks. However, there are difficulties in transfer learning for graph representation learning and graph network models. Most GNNs work only in a single domain and cannot transfer the learned knowledge to other domains. Coronary Heart Disease (CHD) is a high-mortality disease, and there are non-public and significant differences in CHD datasets for current research, which makes it difficult to perform unified transfer learning. Therefore, in this paper, we propose a novel adversarial domain-adaptive multichannel graph convolutional network (DAMGCN) that can perform graph transfer learning on cross-domain tasks to achieve cross-domain medical knowledge transfer on different CHD datasets. First, we use a two-channel GCN model for feature aggregation using local consistency and global consistency. Then, a uniform node representation is generated for different graphs using an attention mechanism. Finally, we provide a domain adversarial module to decrease the discrepancies between the source and target domain classifiers and optimize the three loss functions in order to accomplish source and target domain knowledge transfer. The experimental findings demonstrate that our model performs best on three CHD datasets, and its performance is greatly enhanced by graph transfer learning.

An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study

BackgroundVarious predictive models have been developed for predicting the incidence of coronary heart disease (CHD), but none of them has had optimal predictive value. Although these models consider diabetes as an important CHD risk factor, they do not consider insulin resistance or triglyceride (TG). The unsatisfactory performance of these prediction models may be attributed to the ignoring of these factors despite their proven effects on CHD. We decided to modify standard CHD predictive models through machine learning to determine whether the triglyceride-glucose index (TyG-index, a logarithmized combination of fasting blood sugar (FBS) and TG that demonstrates insulin resistance) functions better than diabetes as a CHD predictor.MethodsTwo-thousand participants of a community-based Iranian population, aged 20–74 years, were investigated with a mean follow-up of 9.9 years (range: 7.6–12.2). The association between the TyG-index and CHD was investigated using multivariate Cox proportional hazard models. By selecting common components of previously validated CHD risk scores, we developed machine learning models for predicting CHD. The TyG-index was substituted for diabetes in CHD prediction models. All components of machine learning models were explained in terms of how they affect CHD prediction. CHD-predicting TyG-index cut-off points were calculated.ResultsThe incidence of CHD was 14.5%. Compared to the lowest quartile of the TyG-index, the fourth quartile had a fully adjusted hazard ratio of 2.32 (confidence interval [CI] 1.16–4.68, p-trend 0.04). A TyG-index > 8.42 had the highest negative predictive value for CHD. The TyG-index-based support vector machine (SVM) performed significantly better than diabetes-based SVM for predicting CHD. The TyG-index was not only more important than diabetes in predicting CHD; it was the most important factor after age in machine learning models.ConclusionWe recommend using the TyG-index in clinical practice and predictive models to identify individuals at risk of developing CHD and to aid in its prevention.