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.

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.

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.

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.

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.

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.

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.

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.

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

BACKGROUND: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS: Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS: HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Low HDL cholesterol and the eNOS Glu298Asp polymorphism are associated with inducible myocardial ischemia in patients with suspected stable coronary artery disease.

BACKGROUND: The endothelial nitric oxide synthase (eNOS) gene deficiency is known to cause impaired coronary vasodilating capability in animal models. In the general clinical population, the eNOS gene polymorphisms, able to affect eNOS activity, were associated with cardiometabolic risk features and prevalence of coronary artery disease (CAD). AIM: To investigate the association of eNOS Glu298Asp gene polymorphism, cardiometabolic profile, obstructive CAD and inducible myocardial ischemia in patients with suspected stable CAD. METHODS: A total of 506 patients (314 males; mean age 62 ± 9 years) referred for suspected CAD was enrolled. Among these, 325 patients underwent stress ECG or cardiac imaging to assess the presence of inducible myocardial ischemia and 436 patients underwent non-invasive computerized tomography or invasive coronary angiography to assess the presence of obstructive CAD. Clinical characteristics and blood samples were collected for each patient. RESULTS: In the whole population, 49.6% of patients were homozygous for the Glu298 genotype (Glu/Glu), 40.9% heterozygotes (Glu/Asp) and 9.5% homozygous for the 298Asp genotype (Asp/Asp). Obstructive CAD was documented in 178/436 (40.8%) patients undergoing coronary angiography while myocardial ischemia in 160/325 (49.2%) patients undergoing stress testing. Patients with eNOS Asp genotype (Glu/Asp + Asp/Asp) had no significant differences in clinical risk factors and in circulating markers. Independent predictors of obstructive CAD were age, gender, obesity, and low HDL-C. Independent predictors of myocardial ischemia were gender, obesity, low HDL-C and Asp genotype. In the subpopulation in which both stress tests and coronary angiography were performed, the Asp genotype remained associated with increased myocardial ischemia risk after adjustment for obstructive CAD. CONCLUSION: In this population, low-HDL cholesterol was the only cardiometabolic risk determinant of obstructive CAD. The eNOS Glu298Asp gene polymorphism was significantly associated with inducible myocardial ischemia independently of other risk factors and presence of obstructive CAD.

Association of rs5051 and rs699 polymorphisms in angiotensinogen with coronary artery disease in Iranian population: A case-control study.

Coronary artery disease (CAD) is the third most common cause of mortality globally (with 17.8 million deaths annually). Angiotensinogen (AGT) and polymorphisms in this gene can be considered as susceptibility factors for CAD. We performed a retrospective case-control study to determine the correlation of AGT rs5051 and rs699 polymorphisms with CAD in an Iranian population. We genotyped 310 CAD patients and 310 healthy subjects using polymerase chain reaction-based methods. To confirm the accuracy of the screening approach, 10% of genotyped subjects were validated using gold-standard Sanger Sequencing. To evaluate the effect of the candidate polymorphisms, white blood cells were randomly purified from the subjects and AGT expression was measured by quantitative reverse transcriptase-polymerase chain reaction. Sex stratification indicated a significant correlation between CAD and male sex (P = .0101). We found a significant association between the rs5051 A allele (P = .002) and the rs699 C allele, and CAD (P = .0122) in recessive and dominant models. Moreover, our findings showed a significant association of the haplotype, including the rs5051 A/A and rs699 T/C genotypes, with CAD (P = .0405). Finally, AGT mRNA levels were significantly decreased in patients harboring the candidate polymorphisms (P = .03). According to our findings The AGT rs5051 A and AGT rs699 C alleles are predisposing variants of CAD risk and severity in the Iranian population.

Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies.

The study aimed to identify the biomarkers for predicting coronary atherosclerotic lesions progression in patients with inflammatory bowel disease (IBD). Related transcriptome datasets were seized from Gene Expression Omnibus database. IBD-related modules were identified via Weighted Gene Co-expression Network Analysis. The 'Limma' was applied to screen differentially expressed genes between stable coronary artery disease (CAD) and acute myocardial infarction (AMI). Subsequently, we employed protein-protein interaction (PPI) network and three machine-learning strategies to further screen for candidate hub genes. Application of the receiver operating characteristics curve to quantitatively evaluate candidates to determine key diagnostic biomarkers, followed by a nomogram construction. Ultimately, we performed immune landscape analysis, single-gene GSEA and prediction of target-drugs. 3227 IBD-related module genes and 570 DEGs accounting for AMI were recognized. Intersection yielded 85 shared genes and mostly enriched in immune and inflammatory pathways. After filtering through PPI network and multi-machine learning algorithms, five candidate genes generated. Upon validation, CTSD, CEBPD, CYP27A1 were identified as key diagnostic biomarkers with a superior sensitivity and specificity (AUC > 0.8). Furthermore, all three genes were negatively correlated with CD4+ T cells and positively correlated with neutrophils. Single-gene GSEA highlighted the importance of pathogen invasion, metabolism, immune and inflammation responses during the pathogenesis of AMI. Ten target-drugs were predicted. The discovery of three peripheral blood biomarkers capable of predicting the risk of CAD proceeding into AMI in IBD patients. These identified biomarkers were negatively correlated with CD4+ T cells and positively correlated with neutrophils, indicating a latent therapeutic target.

Genetic Counseling and Genetic Testing for Familial Hypercholesterolemia.

Familial hypercholesterolemia (FH) is one of the most common autosomal codominant Mendelian diseases. The major complications of FH include tendon and cutaneous xanthomas and coronary artery disease (CAD) associated with a substantial elevation of serum low-density lipoprotein levels (LDL). Genetic counseling and genetic testing for FH is useful for its diagnosis, risk stratification, and motivation for further LDL-lowering treatments. In this study, we summarize the epidemiology of FH based on numerous genetic studies, including its pathogenic variants, genotype-phenotype correlation, prognostic factors, screening, and usefulness of genetic counseling and genetic testing. Due to the variety of treatments available for this common Mendelian disease, genetic counseling and genetic testing for FH should be implemented in daily clinical practice.

Association of the ESR1 (rs9340799), OLR1 (rs3736234), LIPC (rs2070895), VDR (rs2228570), and CETP (rs708272) Polymorphisms With Risk of Coronary Artery Disease in Iranian Patients.

BACKGROUND: Coronary artery disease (CAD) is a devastating illness and a leading cause of death worldwide, primarily caused by atherosclerosis resulting from a genetic-environmental interaction. This study aimed to investigate the relationship between the ESR1 (rs9340799), OLR1 (rs3736234), LIPC (rs2070895), VDR (rs2228570), and CETP (rs708272) polymorphisms, lipid profile parameters, and CAD risk in a southeast Iranian population. METHODS: A total of 400 subjects (200 CAD patients with hyperlipidemia and 200 healthy controls) were enrolled in this case-control study. Five selected polymorphisms were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. RESULTS: For all single nucleotide polymorphisms (SNPs), the population under study was in the Hardy-Weinberg equilibrium. The T-risk allele frequency of rs2228570 was associated with an increased risk of CAD. The TT and CT genotypes of rs2228570 had also been associated with the risk of CAD. Additionally, the TT genotype was associated with higher serum low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) levels. The GG genotype of the rs3736234 was associated with higher body mass index (BMI) and triglyceride (TG) levels, and the AA genotype of the rs708272 was associated with higher HDL-c levels. Based on these findings, we propose that the VDR (rs2228570) polymorphism was associated with serum HDL-c and LDL-c levels and may serve as potential risk factors for CAD within the Iranian population. Moreover, rs3736234 and rs708272 influence the concentrations of TG and HDL-c, respectively. CONCLUSION: These findings provided insights into the complex interplay between genetic variations, cardiovascular risk, and lipid metabolism.

Integration of polygenic and gut metagenomic risk prediction for common diseases.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Association between CBS gene T833C, G919A and 844ins68 polymorphisms in the 8th exon region and coronary artery disease: a meta-analysis.

BACKGROUND: Several studies indicate that the cystathionine ß-synthase (CBS) gene T833C, G919A and 844ins68 polymorphisms in the 8th exon region may be correlated with coronary artery disease (CAD) susceptibility, but the results have been inconsistent and inconclusive. Thus, a meta-analysis was conducted to provide a comprehensive estimate of these associations. METHODS: On the basis of searches in the PubMed, EMBASE, Cochrane Library, Wanfang, VIP, and CNKI databases, we selected 14 case - control studies including 2123 cases and 2368 controls for this meta-analysis. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated accordingly using a fixed-effect or random-effect model. RESULTS: The results indicated an increased risk between the CBS T833C gene polymorphisms and susceptibility to CAD under the dominant model (CC+CT vs. TT: OR = 1.92, 95% CI: 1.11 ~ 3.32), recessive model (CC vs. CT+TT: OR = 1.88, 95% CI: 1.17 ~ 3.03), and homozygous model (CC vs. TT: OR = 2.46, 95% CI: 1.04 ~ 5.83). In these three genetic models, no significant association was identified for CBS G919A (AA+AG vs. GG: OR = 1.48, 95% CI: 0.45 ~ 4.82),(AA vs. AG+GG: OR = 1.58, 95% CI: 0.93 ~ 2.70),(AA vs. GG: OR = 1.66, 95% CI: 0.40 ~ 6.92) or CBS 844ins68 (II+ID vs. DD: OR = 1.04, 95% CI: 0.80 ~ 1.35),(II vs. ID+DD: OR = 1.09, 95% CI: 0.51 ~ 2.36),(II vs. DD: OR = 1.10, 95% CI: 0.51 ~ 2.39). CONCLUSIONS: This meta-analysis suggests that the CBS T833C gene polymorphism is significantly associated with the risk of CAD and it shows a stronger association in Asian populations. Individuals with the C allele of the CBS gene T833C polymorphism might be particularly susceptible to CAD.

An update on the cell-free DNA-derived methylome as a non-invasive biomarker for coronary artery disease.

Cardiovascular diseases are the foremost contributor to global mortality, presenting a complex etiology and an expanding array of risk factors. Coronary artery disease characterized by atherosclerotic plaque build-up in the coronary arteries, imposes significant mortality and financial burdens, especially in low- and middle-income nations. The pathogenesis of coronary artery disease involves a multifaceted interplay of genetic, environmental, and epigenetic factors. Epigenetic regulation contributes to the dynamic control of gene expression without altering the underlying DNA sequence. The mounting evidence that highlights the pivotal role of epigenetic regulation in coronary artery disease development and progression, offering potential avenues for the development of novel diagnostic biomarkers and therapeutic targets. Abnormal DNA methylation patterns are linked to the modulation of gene expression involved in crucial processes like lipid metabolism, inflammation, and vascular function in the context of coronary artery disease. Cell-free DNA has become invaluable in tumor biology as a liquid biopsy, while its applications in coronary artery disease are limited, but intriguing. Atherosclerotic plaque rupture causes myocardial infarction, by depriving heart muscles of oxygen, releasing cell-free DNA from dead cardiac cells, and providing a minimally invasive source to explore tissue-specific epigenetic alterations. We discussed the methodologies for studying the global methylome and hydroxy-methylome landscape, their advantages, and limitations. It explores methylome alterations in coronary artery disease, considering risk factors and their relevance in coronary artery disease genesis. The review also details the implications of MI-derived cell-free DNA for developing minimally invasive biomarkers and associated challenges.

APOE ε4 carriage associates with improved myocardial performance from adolescence to older age.

BACKGROUND: Although APOE ε4 allele carriage confers a risk for coronary artery disease, its persistence in humans might be explained by certain survival advantages (antagonistic pleiotropy). METHODS: Combining data from ~ 37,000 persons from three older age British cohorts (1946 National Survey of Health and Development [NSHD], Southall and Brent Revised [SABRE], and UK Biobank) and one younger age cohort (Avon Longitudinal Study of Parents and Children [ALSPAC]), we explored whether APOE ε4 carriage associates with beneficial or unfavorable left ventricular (LV) structural and functional metrics by echocardiography and cardiovascular magnetic resonance (CMR). RESULTS: Compared to the non-APOE ε4 group, APOE ε4 carriers had similar cardiac phenotypes in terms of LV ejection fraction, E/e', posterior wall and interventricular septal thickness, and LV mass. However, they had improved myocardial performance resulting in greater LV stroke volume generation per 1 mL of myocardium (higher myocardial contraction fraction). In NSHD (n = 1467) and SABRE (n = 1187), ε4 carriers had a 4% higher MCF (95% CI 1-7%, p = 0.016) using echocardiography. Using CMR data, in UK Biobank (n = 32,972), ε4 carriers had a 1% higher MCF 95% (CI 0-1%, p = 0.020) with a dose-response relationship based on the number of ε4 alleles. In addition, UK Biobank ε4 carriers also had more favorable radial and longitudinal strain rates compared to non APOE ε4 carriers. In ALSPAC (n = 1397), APOE ε4 carriers aged < 24 years had a 2% higher MCF (95% CI 0-5%, p = 0.059). CONCLUSIONS: By triangulating results in four independent cohorts, across imaging modalities (echocardiography and CMR), and in ~ 37,000 individuals, our results point towards an association between ε4 carriage and improved cardiac performance in terms of LV MCF. This potentially favorable cardiac phenotype adds to the growing number of reported survival advantages attributed to the pleiotropic effects APOE ε4 carriage that might collectively explain its persistence in human populations.