Serum parathyroid hormone as a potential novel biomarker of coronary heart disease

Pathophysiology and Clinical Impacts of Chronic Kidney Disease on Coronary Artery Calcification

The global prevalence of chronic kidney disease (CKD) has increased in recent years. Adverse cardiovascular events have become the main cause of life-threatening events in patients with CKD, and vascular calcification is a risk factor for cardiovascular disease. Vascular calcification, especially coronary artery calcification, is more prevalent, severe, rapidly progressive, and harmful in patients with CKD. Some features and risk factors are unique to vascular calcification in patients with CKD; the formation of vascular calcification is not only influenced by the phenotypic transformation of vascular smooth muscle cells, but also by electrolyte and endocrine dysfunction, uremic toxin accumulation, and other novel factors. The study on the mechanism of vascular calcification in patients with renal insufficiency can provide a basis and new target for the prevention and treatment of this disease. This review aims to illustrate the impact of CKD on vascular calcification and to discuss the recent research data on the pathogenesis and factors involved in vascular calcification, mainly focusing on coronary artery calcification, in patients with CKD.

Identification of potential M2 macrophage-associated diagnostic biomarkers in coronary artery disease

BACKGROUND

M2 macrophages have been reported to be important in the progression of coronary artery disease (CAD). Thus, the present study aims at exploring the diagnostic value of M2 macrophage-associated genes in CAD.

METHODS

Transcriptome profile of CAD and control samples were downloaded from Gene Expression Omnibus database. The proportion of immune cells was analyzed using cell type identification by estimating relative subsets of RNA transcripts. Weighted Gene Co-expression Network Analysis (WGCNA) was carried out to screen the relevant module associated with M2 macrophages. Differential CAD and control samples of expressed genes (DEGs) were identified by the limma R package. Functional enrichment analysis by means of the clusterProfiler R package. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were carried out to select signature genes. Receiver operating curves (ROC) were plotted to evaluate the diagnostic value of selected signature genes. The expressions of potential diagnostic markers were validated by RT-qPCR. The ceRNA network of diagnostic biomarkers was constructed via miRwalk and Starbase database. CMap database was used to screen candidate drugs in the treatment of CAD by targeting diagnostic biomarkers.

RESULTS

A total of 166 M2 macrophage-associated genes were identified by WGCNA. By intersecting those genes with 879 DEGs, 53 M2 macrophage-associated DEGs were obtained in the present study. By LASSO, RF, and ROC analyses, C1orf105, CCL22, CRYGB, FRK, GAP43, REG1P, CALB1, and PTPN21 were identified as potential diagnostic biomarkers. RT-qPCR showed the consistent expression patterns of diagnostic biomarkers between GEO dataset and clinical samples. Perhexiline, alimemazine and mecamylamine were found to be potential drugs in the treatment of CAD.

CONCLUSION

We identified eight M2 macrophage-associated diagnostic biomarkers and candidate drugs for the CAD treatment.

Personalized Management of Cardiovascular Disorders

Personalized management of cardiovascular disorders (CVD), also referred to as personalized or precision cardiology in accordance with general principles of personalized medicine, is selection of the best treatment for an individual patient. It involves the integration of various "omics" technologies such as genomics and proteomics as well as other new technologies such as nanobiotechnology. Molecular diagnostics and biomarkers are important for linking diagnosis with therapy and monitoring therapy. Because CVD involve perturbations of large complex biological networks, a systems biology approach to CVD risk stratification may be used for improving risk-estimating algorithms, and modeling of personalized benefit of treatment may be helpful for guiding the choice of intervention. Bioinformatics tools are helpful in analyzing and integrating large amounts of data from various sources. Personalized therapy is considered during drug development, including methods of targeted drug delivery and clinical trials. Individualized recommendations consider multiple factors - genetic as well as epigenetic - for patients' risk of heart disease. Examples of personalized treatment are those of chronic myocardial ischemia, heart failure, and hypertension. Similar approaches can be used for the management of atrial fibrillation and hypercholesterolemia, as well as the use of anticoagulants. Personalized management includes pharmacotherapy, surgery, lifestyle modifications, and combinations thereof. Further progress in understanding the pathomechanism of complex cardiovascular diseases and identification of causative factors at the individual patient level will provide opportunities for the development of personalized cardiology. Application of principles of personalized medicine will improve the care of the patients with CVD.