Assessment of the Level of Matrix Metalloproteinases, VEGF and MicroRNA-34a in Patients With Non-obstructive and Obstructive Lesions of the Coronary Arteries.

AIM: To assess the levels of matrix metalloproteinases (MMP), vascular endothelial growth factor (VEGF), and miRNA-34a expression in patients with ischemic heart disease (IHD) and obstructive and nonobstructive coronary artery (CA) disease. MATERIAL AND METHODS: This cross-sectional observational study included 64 patients with IHD (diagnosis verified by coronary angiography or multislice computed tomography coronary angiography), of which 33 (51.6%) were men aged 64.9±8.1 years. 20 patients had nonobstructive CA disease (stenosis <50%), and 44 had hemodynamically significant stenoses. The control group consisted of 30 healthy volunteers. MMP-1, -9, -13, and -14, miRNA-34a, and VEGF were measured in all patients. RESULTS: The concentration of MMP-1 was significantly higher in patients with ischemia and nonobstructive CA disease (INOCAD) (p=0.016), and the concentration of MMP-9 was the highest in the group with obstructive CA disease (p<0.001). The concentrations of MMP-13 and MMP-14 did not differ significantly between the groups. The highest VEGF concentrations were observed in the INOCAD group (p<0.001). The expression of miRNA-34a significantly differed between the IHD groups with different types of CA disease and controls (p <0.001). Patients with hemodynamically significant stenosis showed moderate relationships between the concentrations of MMP-14 and VEGF (ρ=0.418; p=0.024), as well as between VEGF and miRNA-34a (ρ=0.425; p=0.022). Patients with INOCAD had a significant negative correlation between the concentrations of MMP-13 and VEGF (ρ= -0.659; p=0.003). Correlation analysis showed in all IHD patients a moderate relationship of the concentrations of MMP-1 and MMP-14 with VEGF (ρ=0.449; p=0.002 and p=0.341; p=0.019, respectively). According to ROC analysis, a MMP-9 concentration above 4.83 ng/ml can be a predictor for the presence of hemodynamically significant CA obstruction in IHD patients; a VEGF concentration higher than 27.23 pg/ml suggests the absence of hemodynamically significant CA stenosis. CONCLUSION: IHD patients with INOCAD had the greatest increase in MMP-1, whereas patients with obstructive CA disease had the highest level of MMP-9. According to our data, concentrations of MMP-9 and VEGF can be used to predict the degree of CA obstruction. The expression of miRNA-34a was significantly higher in IHD patients with INOCAD and CA obstruction than in the control group, which suggested a miRNA-34a contribution to the development and progression of coronary atherosclerosis. In the future, it may be possible to use this miRNA as a diagnostic marker for IHD.

Whole exome sequencing and proteomics-based investigation of the pathogenesis of coronary artery disease with diffuse long lesion.

OBJECTIVES: The long-term prognosis of patients with coronary artery disease (CAD) with diffuse long lesion underwent coronary artery bypass graft (CABG) or percutaneous coronary intervention (PCI) remains worse. Here, we aimed to identify distinctive genes involved and offer novel insights into the pathogenesis of diffuse long lesion. MATERIALS AND METHODS: Whole exome sequencing was performed on peripheral blood samples from 20 CAD patients with diffuse long lesion (CAD-DLL) and from 10 controls with focal lesion (CAD-FL) through a uniform pipeline. Proteomics analysis was conducted on the serum samples from 10 CAD-DLL patients and from 10 controls with CAD-FL by mass spectrometry. Bioinformatics analysis was performed to elucidate the involved genes, including functional annotation and protein-protein interaction analysis. RESULTS: A total of 742 shared variant genes were found in CAD-DLL patients but not in controls. Of these, 46 genes were identified as high-frequency variant genes (≥ 4/20) distinctive genes. According to the consensus variant site, 148 shared variant sites were found in the CAD-DLL group. The lysosome and cellular senescence-related pathway may be the most significant pathway in diffuse long lesion. Following the DNA-protein combined analysis, eight genes were screened whose expression levels were altered at both DNA and protein levels. Among these genes, the MAN2A2 gene, the only one that was highly expressed at the protein level, was associated with metabolic and immune-inflammatory dysregulation. CONCLUSIONS: Compared to individuals with CAD-FL, patients with CAD-DLL show additional variants. These findings contribute to the understanding of the mechanism of CAD-DLL and provide potential targets for the diagnosis and treatment of CAD-DLL.

Mendelian randomisation and mediation analysis of self-reported walking pace and coronary artery disease.

The aim of this study was to assess the causal relationship between habitual walking pace and cardiovascular disease risk using a Mendelian randomisation approach. We performed both one- and two-sample Mendelian randomisation analyses in a sample of 340,000 European ancestry participants from UK Biobank, applying a range of sensitivity analyses to assess pleiotropy and reverse causality. We used a latent variable framework throughout to model walking pace as a continuous exposure, despite being measured in discrete categories, which provided more robust and interpretable causal effect estimates. Using one-sample Mendelian randomisation, we estimated that a 1 mph (i.e., 1.6 kph) increase in self-reported habitual walking pace corresponds to a 63% (hazard ratio (HR) = 0.37, 95% confidence interval (CI), 0.25-0.55, P = 2.0 × 10-6) reduction in coronary artery disease risk. Using conditional analyses, we also estimated that the proportion of the total effect on coronary artery disease mediated through BMI was 45% (95% CI 16-70%). We further validated findings from UK Biobank using two-sample Mendelian randomisation with outcome data from the CARDIoGRAMplusC4D consortium. Our findings suggest that interventions that seek to encourage individuals to walk more briskly should lead to protective effects on cardiovascular disease risk.

Transcriptional profiles associated with coronary artery disease in type 2 diabetes mellitus.

Background: Coronary artery disease (CAD) is a common complication of Type 2 diabetes mellitus (T2DM). Understanding the pathogenesis of this complication is essential in both diagnosis and management. Thus, this study aimed to characterize the presence of CAD in T2DM using molecular markers and pathway analyses. Methods: The study is a sex- and age-frequency matched case-control design comparing 23 unrelated adult Filipinos with T2DM-CAD to 23 controls (DM with CAD). Healthy controls served as a reference. Total RNA from peripheral blood mononuclear cells (PBMCs) underwent whole transcriptomic profiling using the Illumina HumanHT-12 v4.0 expression beadchip. Differential gene expression with gene ontogeny analyses was performed, with supporting correlational analyses using weighted correlation network analysis (WGCNA). Results: The study observed that 458 genes were differentially expressed between T2DM with and without CAD (FDR<0.05). The 5 top genes the transcription factor 3 (TCF3), allograft inflammatory factor 1 (AIF1), nuclear factor, interleukin 3 regulated (NFIL3), paired immunoglobulin-like type 2 receptor alpha (PILRA), and cytoskeleton-associated protein 4 (CKAP4) with AUCs >89%. Pathway analyses show differences in innate immunity activity, which centers on the myelocytic (neutrophilic/monocytic) theme. SNP-module analyses point to a possible causal dysfunction in innate immunity that triggers the CAD injury in T2DM. Conclusion: The study findings indicate the involvement of innate immunity in the development of T2DM-CAD, and potential immunity markers can reflect the occurrence of this injury. Further studies can verify the mechanistic hypothesis and use of the markers.

European and US Guideline-Based Statin Eligibility, Genetically Predicted Coronary Artery Disease, and the Risk of Major Coronary Events.

BACKGROUND: A study was designed to investigate whether the coronary artery disease polygenic risk score (CAD-PRS) may guide lipid-lowering treatment initiation as well as deferral in primary prevention beyond established clinical risk scores. METHODS AND RESULTS: Participants were 311 799 individuals from the UK Biobank free of atherosclerotic cardiovascular disease, diabetes, chronic kidney disease, and lipid-lowering treatment at baseline. Participants were categorized as statin indicated, statin indication unclear, or statin not indicated as defined by the European and US guidelines on statin use. For a median of 11.9 (11.2-12.6) years, 8196 major coronary events developed. CAD-PRS added to European-Systematic Coronary Risk Evaluation 2 (European-SCORE2) and US-Pooled Cohort Equation (US-PCE) identified 18% and 12% of statin-indication-unclear individuals whose risk of major coronary events were the same as or higher than the average risk of statin-indicated individuals and 16% and 12% of statin-indicated individuals whose major coronary event risks were the same as or lower than the average risk of statin-indication-unclear individuals. For major coronary and atherosclerotic cardiovascular disease events, CAD-PRS improved C-statistics greater among statin-indicated or statin-indication-unclear than statin-not-indicated individuals. For atherosclerotic cardiovascular disease events, CAD-PRS added to the European evaluation and US equation resulted in a net reclassification improvement of 13.6% (95% CI, 11.8-15.5) and 14.7% (95% CI, 13.1-16.3) among statin-indicated, 10.8% (95% CI, 9.6-12.0) and 15.3% (95% CI, 13.2-17.5) among statin-indication-unclear, and 0.9% (95% CI, 0.6-1.3) and 3.6% (95% CI, 3.0-4.2) among statin-not-indicated individuals. CONCLUSIONS: CAD-PRS may guide statin initiation as well as deferral among statin-indication-unclear or statin-indicated individuals as defined by the European and US guidelines. CAD-PRS had little clinical utility among statin-not-indicated individuals.

Identification of diagnostic biomarkers and immune cell infiltration in coronary artery disease by machine learning, nomogram, and molecular docking.

Background: Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD. Methods: GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes. Results: There were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method. Conclusions: CSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score.

BACKGROUND: The extent to which the relationships between clinical risk factors and coronary artery disease (CAD) are altered by CAD polygenic risk score (PRS) is not well understood. Here, we determine whether the interactions between clinical risk factors and CAD PRS further explain risk for incident CAD. METHODS: Participants were of European ancestry from the UK Biobank without prevalent CAD. An externally trained genome-wide CAD PRS was generated and then applied. Clinical risk factors were ascertained at baseline. Cox proportional hazards models were fitted to examine the incident CAD effects of CAD PRS, risk factors, and their interactions. Next, the PRS and risk factors were stratified to investigate the attributable risk of clinical risk factors. FINDINGS: A total of 357,144 individuals of European ancestry without prevalent CAD were included. During a median of 11.1 years of follow-up (interquartile range 10.4-14.1 years), CAD PRS was associated with 1.35-fold (95% confidence interval [CI] 1.332-1.368) risk per SD for incident CAD. The prognostic relevance of the following risk factors was relatively diminished for those with high CAD PRS on a continuous scale: type 2 diabetes (hazard ratio [HR]interaction 0.91, 95% CIinteraction 0.88-0.94), increased body mass index (HRinteraction 0.97, 95% CIinteraction 0.96-0.98), and increased C-reactive protein (HRinteraction 0.98, 95% CIinteraction 0.96-0.99). However, a high CAD PRS yielded joint risk increases with low-density lipoprotein cholesterol (HRinteraction 1.05, 95% CIinteraction 1.04-1.06) and total cholesterol (HRinteraction 1.05, 95% CIinteraction 1.03-1.06). CONCLUSION: The CAD PRS is associated with incident CAD, and its application improves the prognostic relevance of several clinical risk factors. FUNDING: P.N. (R01HL127564, R01HL151152, and U01HG011719) is supported by the National Institutes of Health.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Single-Cell RNA Sequencing Reveals an Immune Landscape of CD4+ T Cells in Coronary Culprit Plaques With Acute Coronary Syndrome in Humans-Brief Report.

BACKGROUND: Acute coronary syndrome (ACS) involves plaque-related thrombosis, causing primary ischemic cardiomyopathy or lethal arrhythmia. We previously demonstrated a unique immune landscape of myeloid cells in the culprit plaques causing ACS by using single-cell RNA sequencing. Here, we aimed to characterize T cells in a single-cell level, assess clonal expansion of T cells, and find a therapeutic target to prevent ACS. METHODS: We obtained the culprit lesion plaques from 4 patients with chronic coronary syndrome (chronic coronary syndrome plaques) and the culprit lesion plaques from 3 patients with ACS (ACS plaques) who were candidates for percutaneous coronary intervention with directional coronary atherectomy. Live CD45+ immune cells were sorted from each pooled plaque samples and applied to the 10× platform for single-cell RNA sequencing analysis. We also extracted RNA from other 3 ACS plaque samples and conducted unbiased TCR (T-cell receptor) repertoire analysis. RESULTS: CD4+ T cells were divided into 5 distinct clusters: effector, naive, cytotoxic, CCR7+ (C-C chemokine receptor type 7) central memory, and FOXP3 (forkhead box P3)+ regulatory CD4+ T cells. The proportion of central memory CD4+ T cells was higher in the ACS plaques. Correspondingly, dendritic cells also tended to express more HLAs (human leukocyte antigens) and costimulatory molecules in the ACS plaques. The velocity analysis suggested the differentiation flow from central memory CD4+ T cells into effector CD4+ T cells and that from naive CD4+ T cells into central memory CD4+ T cells in the ACS plaques, which were not observed in the chronic coronary syndrome plaques. The bulk repertoire analysis revealed clonal expansion of TCRs in each patient with ACS and suggested that several peptides in the ACS plaques work as antigens and induced clonal expansion of CD4+ T cells. CONCLUSIONS: For the first time, we revealed single cell-level characteristics of CD4+ T cells in patients with ACS. CD4+ T cells could be therapeutic targets of ACS. REGISTRATION: URL: https://upload.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000046521; Unique identifier: UMIN000040747.

Single-cell 'omic profiles of human aortic endothelial cells in vitro and human atherosclerotic lesions ex vivo reveal heterogeneity of endothelial subtype and response to activating perturbations.

Heterogeneity in endothelial cell (EC) sub-phenotypes is becoming increasingly appreciated in atherosclerosis progression. Still, studies quantifying EC heterogeneity across whole transcriptomes and epigenomes in both in vitro and in vivo models are lacking. Multiomic profiling concurrently measuring transcriptomes and accessible chromatin in the same single cells was performed on six distinct primary cultures of human aortic ECs (HAECs) exposed to activating environments characteristic of the atherosclerotic microenvironment in vitro. Meta-analysis of single-cell transcriptomes across 17 human ex vivo arterial specimens was performed and two computational approaches quantitatively evaluated the similarity in molecular profiles between heterogeneous in vitro and ex vivo cell profiles. HAEC cultures were reproducibly populated by four major clusters with distinct pathway enrichment profiles and modest heterogeneous responses: EC1-angiogenic, EC2-proliferative, EC3-activated/mesenchymal-like, and EC4-mesenchymal. Quantitative comparisons between in vitro and ex vivo transcriptomes confirmed EC1 and EC2 as most canonically EC-like, and EC4 as most mesenchymal with minimal effects elicited by siERG and IL1B. Lastly, accessible chromatin regions unique to EC2 and EC4 were most enriched for coronary artery disease (CAD)-associated single-nucleotide polymorphisms from Genome Wide Association Studies (GWAS), suggesting that these cell phenotypes harbor CAD-modulating mechanisms. Primary EC cultures contain markedly heterogeneous cell subtypes defined by their molecular profiles. Surprisingly, the perturbations used here only modestly shifted cells between subpopulations, suggesting relatively stable molecular phenotypes in culture. Identifying consistently heterogeneous EC subpopulations between in vitro and ex vivo models should pave the way for improving in vitro systems while enabling the mechanisms governing heterogeneous cell state decisions.

Causal associations of particulate matter 2.5 and cardiovascular disease: A two-sample mendelian randomization study.

BACKGROUND: According to epidemiological studies, particulate matter 2.5 (PM2.5) is a significant contributor to cardiovascular disease (CVD). However, making causal inferences is difficult due to the methodological constraints of observational studies. In this study, we used two-sample Mendelian randomization (MR) to examine the causal relationship between PM 2.5 and the risk of CVD. METHODS: Genome-wide association study (GWAS) statistics for PM2.5 and CVD were collected from the FinnGen and UK Biobanks. Mendelian randomization analyses were applied to explore the causal effects of PM2.5 on CVD by selecting single-nucleotide polymorphisms(SNP) as instrumental variables. RESULTS: The results revealed that a causal effect was observed between PM2.5 and coronary artery disease(IVW: OR 2.06, 95% CI 1.35, 3.14), and hypertension(IVW: OR 1.07, 95% CI 1.03, 1.12). On the contrary, no causal effect was observed between PM2.5 and myocardial infarction(IVW: OR 0.73, 95% CI 0.44, 1.22), heart failure(IVW: OR 1.54, 95% CI 0.96, 2.47), atrial fibrillation(IVW: OR 1.03, 95% CI 0.71, 1.48), and ischemic stroke (IS)(IVW: OR 0.98, 95% CI 0.54, 1.77). CONCLUSION: We discovered that there is a causal link between PM2.5 and coronary artery disease and hypertension in the European population, using MR methods. Our discovery may have the significance of public hygiene to improve the understanding of air quality and CVD risk.

Association of different milk fat content with coronary artery disease and myocardial infarction risk: A Mendelian randomization study.

BACKGROUND: Numerous observational studies have investigated on the correlation of whole, semi-skimmed, and skimmed milk with coronary artery disease (CAD) and myocardial infarction (MI) risk; However, no consensus has been reached and evidence on any causal links between these exposures and outcomes remains unclear. This study aimed to conduct univariate and multivariate Mendelian randomization (MR) analyses, using publicly released genome-wide association study summary statistics (GWAS) from the IEU GWAS database, to ascertain the causal association of milk with various fat content with CAD and MI risk. METHODS: For the exposure data, 29, 15, and 30 single-nucleotide polymorphisms for whole milk, semi-skimmed milk, and skimmed milk, respectively, obtained from 360,806 Europeans, were used as instrumental variables. CAD and MI comprised 141,217 and 395,795 samples, respectively. We used inverse variance weighted (IVW), weighted median, MR-Egger regression, and MR Pleiotropy Residual Sum and Outlier analyses to determine whether pleiotropy and heterogeneity could skew the MR results. Sensitivity tests were conducted to verify the robustness of the results. RESULTS: After adjusting for false discovery rates (FDR), we discovered proof that skimmed milk intake is a genetically predicted risk factor for CAD (odds ratio [OR] = 5.302; 95% confidence interval [CI] 2.261-12.432; P < 0.001; FDR-corrected P < 0.001) and MI (OR = 2.287; 95% CI 1.218-4.300; P = 0.010; FDR-corrected P = 0.009). Most sensitivity assessments yielded valid results. Multivariable MR for CAD and MI produced results consistent with those obtained using the IVW method. There was no causal relationship between whole or semi-skimmed milk, and CAD or MI. CONCLUSION: Our findings indicate that the consumption of skimmed milk may increase the risk of CAD and MI. This evidence may help inform dietary recommendations for preventing cardiovascular disease. Further studies are required to elucidate the underlying mechanisms.

Identification of PPARG as key gene to link coronary atherosclerosis disease and rheumatoid arthritis via microarray data analysis.

BACKGROUND: Inflammation is the common pathogenesis of coronary atherosclerosis disease (CAD) and rheumatoid arthritis (RA). Although it is established that RA increases the risk of CAD, the underlining mechanism remained indefinite. This study seeks to explore the molecular mechanisms of RA linked CAD and identify potential target gene for early prediction of CAD in RA patients. MATERIALS AND METHODS: The study utilized five raw datasets: GSE55235, GSE55457, GSE12021 for RA patients, and GSE42148 and GSE20680 for CAD patients. Gene Set Enrichment Analysis (GSEA) was used to investigate common signaling pathways associated with RA and CAD. Then, weighted gene co-expression network analysis (WGCNA) was performed on RA and CAD training datasets to identify gene modules related to single-sample GSEA (ssGSEA) scores. Overlapping module genes and differentially expressed genes (DEGs) were considered as co-susceptible genes for both diseases. Three hub genes were screened using a protein-protein interaction (PPI) network analysis via Cytoscape plug-ins. The signaling pathways, immune infiltration, and transcription factors associated with these hub genes were analyzed to explore the underlying mechanism connecting both diseases. Immunohistochemistry and qRT-PCR were conducted to validate the expression of the key candidate gene, PPARG, in macrophages of synovial tissue and arterial walls from RA and CAD patients. RESULTS: The study found that Fc-gamma receptor-mediated endocytosis is a common signaling pathway for both RA and CAD. A total of 25 genes were screened by WGCNA and DEGs, which are involved in inflammation-related ligand-receptor interactions, cytoskeleton, and endocytosis signaling pathways. The principal component analysis(PCA) and support vector machine (SVM) and receiver-operator characteristic (ROC) analysis demonstrate that 25 DEGs can effectively distinguish RA and CAD groups from normal groups. Three hub genes TUBB2A, FKBP5, and PPARG were further identified by the Cytoscape software. Both FKBP5 and PPARG were downregulated in synovial tissue of RA and upregulated in the peripheral blood of CAD patients and differential mRNAexpreesion between normal and disease groups in both diseases were validated by qRT-PCR.Association of PPARG with monocyte was demonstrated across both training and validation datasets in CAD. PPARG expression is observed in control synovial epithelial cells and foamy macrophages of arterial walls, but was decreased in synovial epithelium of RA patients. Its expression in foamy macrophages of atherosclerotic vascular walls exhibits a positive correlation (r = 0.6276, p = 0.0002) with CD68. CONCLUSION: Our findings suggest that PPARG may serve as a potentially predictive marker for CAD in RA patients, which provides new insights into the molecular mechanism underling RA linked CAD.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases.

BACKGROUND: Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS: Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS: Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS: This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

Receptor for advanced glycation end products polymorphisms in coronary artery ectasia.

BACKGROUND: Although the implication of receptor of advanced glycation endproducts (RAGE) has been reported in coronary artery disease, its roles in coronary artery ectasia (CAE) have remained undetermined. Furthermore, the effect of RAGE polymorfisms were not well-defined in scope of soluble RAGE (sRAGE) levels. Thus, we aimed to investigate the influence of the functional polymorphisms of RAGE -374T > A (rs1800624) and G82S (rs2070600) in CAE development. METHODS: This prospective observational study was conducted in 2 groups selected of 2452 patients who underwent elective coronary angiography (CAG) for evaluation after positive noninvasive heart tests. Group-I included 98 patients with non-obstructive coronary artery disease and CAE, and Group-II (control) included 100 patients with normal coronary arteries. SNPs were genotyped by real-time PCR using Taqman® genotyping assay. Serum sRAGE and soluble lectin-like oxidized receptor-1 (sOLR1) were assayed by ELISA and serum lipids were measured enzymatically. RESULTS: The frequencies of the RAGE -374A allele and -374AA genotype were significantly higher in CAE patients compared to controls (p < 0.001). sRAGE levels were not different between study groups, while sOLR1 levels were elevated in CAE (p = 0.004). In controls without systemic disease, -374A allele was associated with low sRAGE levels (p < 0.05), but this association was not significant in controls with HT. Similarly, sRAGE levels of CAE patients with both HT and T2DM were higher than those no systemic disease (p = 0.02). The -374A allele was also associated with younger patient age and higher platelet count in the CAE group in both total and subgroup analyses. In the correlation analyses, the -374A allele was also negatively correlated with age and positively correlated with Plt in all of these CAE groups. In the total CAE group, sRAGE levels also showed a positive correlation with age and a negative correlation with HDL-cholesterol levels. On the other hand, a negative correlation was observed between sRAGE and Plt in the total, hypertensive and no systemic disease control subgroups. Multivariate logistic regression analysis confirmed that the -374A allele (p < 0.001), hyperlipidemia (p < 0.05), and high sOLR1 level (p < 0.05) are risk factors for CAE. ROC curve analysis shows that RAGE -374A allele has AUC of 0.713 (sensitivity: 83.7 %, specificity: 59.0 %), which is higher than HLD (sensitivity: 59.2 %, specificity: 69.0 %), HT (sensitivity: 62.4 %, specificity: 61.1 %) and high sOLR1 level (≥0.67 ng/ml)) (sensitivity: 59.8 %, specificity: 58.5 %). CONCLUSION: Beside the demonstration of the relationship between -374A allele and increased risk of CAE for the first time, our results indicate that antihypertensive and antidiabetic treatment in CAE patients causes an increase in sRAGE levels. The lack of an association between the expected -374A allele and low sRAGE levels in total CAE group was attributed to the high proportion of hypertensive patients and hence to antihypertensive treatment. Moreover, the RAGE -374A allele is associated with younger age at CAE and higher Plt, suggesting that -374A may also be associated with platelet activation, which plays a role in the pathogenesis of CAE. However, our data need to be confirmed in a large study for definitive conclusions.

Single-nucleus transcriptomics of epicardial adipose tissue from female pigs reveals effects of exercise training on resident innate and adaptive immune cells.

BACKGROUND: Coronary artery disease (CAD) is a leading cause of death in women. Epicardial adipose tissue (EAT) secretes cytokines to modulate coronary artery function, and the release of fatty acids from EAT serves as a readily available energy source for cardiomyocytes. However, despite having beneficial functions, excessive amounts of EAT can cause the secretion of proinflammatory molecules that increase the instability of atherosclerotic plaques and contribute to CAD progression. Although exercise mitigates CAD, the mechanisms by which exercise impacts EAT are unknown. The Yucatan pig is an excellent translational model for the effects of exercise on cardiac function. Therefore, we sought to determine if chronic aerobic exercise promotes an anti-inflammatory microenvironment in EAT from female Yucatan pigs. METHODS: Sexually mature, female Yucatan pigs (n = 7 total) were assigned to sedentary (Sed, n = 3) or exercise (Ex, n = 4) treatments, and coronary arteries were occluded (O) with an ameroid to mimic CAD or remained non-occluded (N). EAT was collected for bulk (n = 7 total) and single nucleus transcriptomic sequencing (n = 2 total, 1 per exercise treatment). RESULTS: Based on the bulk transcriptomic analysis, exercise upregulated S100 family, G-protein coupled receptor, and CREB signaling in neurons canonical pathways in EAT. The top networks in EAT affected by exercise as measured by bulk RNA sequencing were SRC kinase family, fibroblast growth factor receptor, Jak-Stat, and vascular endothelial growth factor. Single nucleus transcriptomic analysis revealed that exercise increased the interaction between immune, endothelial, and mesenchymal cells in the insulin-like growth factor pathway and between endothelial and other cell types in the platelet endothelial cell adhesion molecule 1 pathway. Sub-clustering revealed nine cell types in EAT, with fibroblast and macrophage populations predominant in O-Ex EAT and T cell populations predominant in N-Ex EAT. Unlike the findings for exercise alone as a treatment, there were not increased interactions between endothelial and mesenchymal cells in O-Ex EAT. Coronary artery occlusion impacted the most genes in T cells and endothelial cells. Genes related to fatty acid metabolism were the most highly upregulated in non-immune cells from O-Ex EAT. Sub-clustering of endothelial cells revealed that N-Ex EAT separated from other treatments. CONCLUSIONS: According to bulk transcriptomics, exercise upregulated pathways and networks related to growth factors and immune cell communication. Based on single nucleus transcriptomics, aerobic exercise increased cell-to-cell interaction amongst immune, mesenchymal, and endothelial cells in female EAT. Yet, exercise was minimally effective at reversing alterations in gene expression in endothelial and mesenchymal cells in EAT surrounding occluded arteries. These findings lay the foundation for future work focused on the impact of exercise on cell types in EAT.

Impacts of pro-inflammatory cytokines variant on cardiometabolic profile and premature coronary artery disease: A systematic review and meta-analysis.

Interleukin-6 (IL-6), a pivotal pro-inflammatory cytokine, is closely linked to vascular wall thickening and atherosclerotic lesion. Since serum IL-6 levels are largely determined by the genetic variant in IL-6, this study was conducted to investigate whether the IL-6 variant impacts cardiometabolic profile and the risk of premature coronary artery disease (PCAD). PubMed, Cochrane Library, Central, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and ClinicalTrials.gov were searched from May 13, 2022 to June 28, 2023. In total, 40 studies (26,543 individuals) were included for the analysis. The rs1800795 (a function variant in the IL-6 gene) C allele was linked to higher levels of low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), fasting plasma glucose (FPG), body mass index (BMI), and waist circumference (WC), and a lower levels of high-density lipoprotein cholesterol (HDL-C). However, no significant association was observed of rs1800795 with triglycerides (TG), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Interestingly, a significant association was detected between rs1800795 and PCAD. Subgroup analyses indicted that the impacts of rs1800795 on cardiometabolic risk factors were significant in Caucasians but stronger in obese patients. In contrast, the impact of rs1800795 on PCAD was significant in brown race population. In summary, rs1800795 had a slight but significant impact on cardiometabolic risk factors and PCAD. IL-6 inhibition with ziltivekimab or canakinumab may benefit high-risk populations (e.g. brown race population, Caucasians, obese patients, etc.) with rs1800795 to prevent PCAD.

Circulating miR-6821-5p levels and coronary calcification in asymptomatic familial hypercholesterolemia patients.

BACKGROUND AND AIMS: Premature atherosclerotic cardiovascular disease (CVD) is a clinic characteristic of familial hypercholesterolemia (FH). Coronary calcium score (CCS) is a highly used imaging modality to evidence atherosclerotic plaque burden. microRNAs (miRNAs) are non-coding RNAs that epigenetically regulate gene expression. Here, we investigated whether CCS associates with a specific miRNA-signature in FH-patients. METHODS: Patients with genetic diagnosis of FH (N = 86) from the nationwide SAFEHEART-cohort were investigated by computed tomography angiography imaging and classified depending on the presence of coronary calcification in FH-CCS (+) and FH-CCS (-) groups by the Agatston score. Differential miRNA profiling was performed in two stages: first by Affymetrix microarray technology (high-throughput differential profiling-studies) and second by RT-PCR using TaqMan-technology (analytical RT-qPCR study) in plasma of the two patient groups. RESULTS: miR-193a-5p, miR-30e-5p and miR-6821-5p levels were significantly higher in FH-CCS (+) compared to FH-CCS (-). miR-6821-5p was the best miRNA to discriminate FH-patients CCS(+), according to receiver operating characteristic (ROC) analysis (AUC: 0.70 ± 0.06, p = 0.006). High miR-6821-5p levels were associated with older age (p = 0.03) and high LDL-burden (p = 0.014) using a ROC-derived cut-off value. However, miR-6821-5p did not correlate with age in either the CCS- or CCS + group. Genes involved in calcification processes were identified by in silico analysis. The relation of cell-calcification and expression levels of miR-6821-5p, BMP2 and SPP1 was validated experimentally in human vascular smooth muscle cell cultures. CONCLUSIONS: Up-regulated levels of miR-6821-5p are found in the plasma of asymptomatic FH-patients with coronary calcified atherosclerotic plaques, as well as in isolated human vascular smooth muscle cells expressing the pro-calcification genes BMP2 and SPP1. These findings highlight the impact of epigenetic regulation on the development of subclinical atherosclerosis.

Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults.

BACKGROUND: The relevance of iron status biomarkers for coronary artery disease (CAD), heart failure (HF), ischemic stroke (IS), and type 2 diabetes (T2D) is uncertain. We compared the observational and Mendelian randomization (MR) analyses of iron status biomarkers and hemoglobin with these diseases. METHODS AND RESULTS: Observational analyses of hemoglobin were compared with genetically predicted hemoglobin with cardiovascular diseases and diabetes in the UK Biobank. Iron biomarkers included transferrin saturation, serum iron, ferritin, and total iron binding capacity. MR analyses assessed associations with CAD (CARDIOGRAMplusC4D [Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus The Coronary Artery Disease Genetics], n=181 522 cases), HF (HERMES [Heart Failure Molecular Epidemiology for Therapeutic Targets), n=115 150 cases), IS (GIGASTROKE, n=62 100 cases), and T2D (DIAMANTE [Diabetes Meta-Analysis of Trans-Ethnic Association Studies], n=80 154 cases) genome-wide consortia. Observational analyses demonstrated J-shaped associations of hemoglobin with CAD, HF, IS, and T2D. In contrast, MR analyses demonstrated linear positive associations of higher genetically predicted hemoglobin levels with 8% higher risk per 1 SD higher hemoglobin for CAD, 10% to 13% for diabetes, but not with IS or HF in UK Biobank. Bidirectional MR analyses confirmed the causal relevance of iron biomarkers for hemoglobin. Further MR analyses in global consortia demonstrated modest protective effects of iron biomarkers for CAD (7%-14% lower risk for 1 SD higher levels of iron biomarkers), adverse effects for T2D, but no associations with IS or HF. CONCLUSIONS: Higher levels of iron biomarkers were protective for CAD, had adverse effects on T2D, but had no effects on IS or HF. Randomized trials are now required to assess effects of iron supplements on risk of CAD in high-risk older people.