Imaging in Coronary Artery Disease Risk Stratification

Phosphate and Coronary Artery Disease in Patients with Chronic Kidney Disease

Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). Both traditional and CKD-related factors are associated with CVD in CKD patients. Traditional factors that play an important role in the atherosclerotic process directly contribute to a higher risk of coronary artery disease in patients with early-stage CKD. Among CKD-related factors, CKD-mineral and bone disorder plays a critical role in the pathomechanism of nonatherosclerotic diseases, which increases the risk of cardiovascular morbidity and mortality in patients with advanced CKD. Higher serum phosphate levels were significantly associated with cardiovascular events and all-cause mortality in patients with or without CKD. An increased phosphate load, directly and indirectly, promotes arterial medial calcification and left ventricular hypertrophy, both of which predispose patients to coronary artery disease. Calciprotein particles that form in a hyperphosphatemic state promote the transformation of vascular smooth muscle cells (VSMCs) into osteoblastic cells, thereby providing a scaffold for medial calcification in the artery. Increases in fibroblast growth factor-23 and disturbed vitamin D metabolism induced by an excessive phosphate load play a significant role in the development of cardiomyocyte hypertrophy and cardiac fibrosis. Recently, hyperphosphatemia was reported to promote de novo cholesterol synthesis in VSMCs and macrophages, which is likely to contribute to statin resistance in patients with end-stage kidney disease. This review outlines the association between increased phosphate load and coronary artery disease in patients with CKD.

5mC modification patterns provide novel direction for early acute myocardial infarction detection and personalized therapy

Background

Most deaths from coronary artery disease (CAD) are due to acute myocardial infarction (AMI). There is an urgent need for early AMI detection, particularly in patients with stable CAD. 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of cardiovascular diseases, while little research examined 5mC regulators in CAD to AMI progression.

Method

Two datasets (GSE59867 and GSE62646) were downloaded from Gene Expression Omnibus (GEO) database, and 21 m5C regulators were extracted from previous literature. Dysregulated 5mC regulators were screened out by "limma." The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm were employed to identify hub 5mC regulators in CAD to AMI progression, and 43 clinical samples (Quantitative real-time PCR) were performed for expression validation. Then a logistic model was built to construct 5mC regulator signatures, and a series of bioinformatics algorithms were performed for model validation. Besides, 5mC-associated molecular clusters were studied via unsupervised clustering analysis, and correlation analysis between immunocyte and 5mC regulators in each cluster was conducted.

Results

Nine hub 5mC regulators were identified. A robust model was constructed, and its prominent classification accuracy was verified via ROC curve analysis (area under the curve [AUC] = 0.936 in the training cohort and AUC = 0.888 in the external validation cohort). Besides, the clinical effect of the model was validated by decision curve analysis. Then, 5mC modification clusters in AMI patients were identified, along with the immunocyte infiltration levels of each cluster. The correlation analysis found the strongest correlations were TET3-Mast cell in cluster-1 and TET3-MDSC in cluster-2.

Conclusion

Nine hub 5mC regulators (DNMT3B, MBD3, UHRF1, UHRF2, NTHL1, SMUG1, ZBTB33, TET1, and TET3) formed a diagnostic model, and concomitant results unraveled the critical impact of 5mC regulators, providing interesting epigenetics findings in AMI population vs. stable CAD.

Predicting Diagnostic Gene Biomarkers Associated With Immune Checkpoints, N6-Methyladenosine, and Ferroptosis in Patients With Acute Myocardial Infarction

This study aimed to determine early diagnosis genes of acute myocardial infarction (AMI) and then validate their association with ferroptosis, immune checkpoints, and N6-methyladenosine (m6A), which may provide a potential method for the early diagnosis of AMI. Firstly, we downloaded microarray data from NCBI (GSE61144, GSE60993, and GSE42148) and identified differentially expressed genes (DEGs) in samples from healthy subjects and patients with AMI. Also, we performed systematic gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used STRING to predict protein interactions. Moreover, MCC and MCODE algorithms in the cytoHubba plug-in were used to screen nine key genes in the network. We then determined the diagnostic significance of the nine obtained DEGs by plotting receiver operating characteristic curves using a multiscale curvature classification algorithm. Meanwhile, we investigated the relationship between AMI and immune checkpoints, ferroptosis, and m6A. In addition, we further validated the key genes through the GSE66360 dataset and consequently obtained nine specific genes that can be used as early diagnosis biomarkers for AMI. Through screening, we identified 210 DEGs, including 53 downregulated and 157 upregulated genes. According to GO, KEGG, and key gene screening results, FPR1, CXCR1, ELANE, TLR2, S100A12, TLR4, CXCL8, FPR2 and CAMP could be used for early prediction of AMI. Finally, we found that AMI was associated with ferroptosis, immune checkpoints, and m6A and FPR1, CXCR1, ELANE, TLR2, S100A12, TLR4, CXCL8, FPR2 and CAMP are effective markers for the diagnosis of AMI, which can provide new prospects for future studies on the pathogenesis of AMI.