Cardiology Research and Practice

Background. Cardiomyopathy encompasses a broad spectrum of diseases affecting myocardial tissue, characterized clinically by abnormalities in cardiac structure, heart failure, and/or arrhythmias. Clinically heterogeneous, major types include dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RM), ischemic cardiomyopathy (ICM), among which DCM is more prevalent, while ICM exhibits higher incidence and mortality rates. Myocardial injury during cardiomyopathy progression may lead to myocardial fibrosis. Failure to intervene early and inhibit the process of myocardial fibrosis may culminate in heart failure. Cardiac fibroblasts constitute crucial cellular components determining the extent and quality of myocardial fibrosis, with various subpopulations exerting diverse roles in cardiomyopathy progression. Despite this, understanding of the cellular plasticity and transcriptional regulatory networks of cardiac fibroblasts in cardiomyopathy remains limited. Therefore, in this study, we conducted comprehensive single-cell analysis of cardiac fibroblasts in cardiomyopathy to explore differences in cellular plasticity and transcriptional regulatory networks among fibroblast subpopulations, with the aim of providing as many useful references as possible for the diagnosis, prognosis, and treatment of cardiomyopathy. Materials and Methods. Cells with mitochondrial gene expression comprising >20% of total expressed genes were excluded. Differential expression genes (DEGs) and stemness genes within cardiac fibroblast subpopulations were subjected to Gene Ontology (GO) analysis of biological processes (BP) and AUCell analysis. Monocle software was employed to analyze the pseudo-temporal trajectory of cardiac fibroblasts in cardiomyopathy. Additionally, the Python package SCENIC was utilized to assess enrichment of transcription factors and activity of regulators within cardiac fibroblast subpopulations in cardiomyopathy. Results. Following batch effect correction, 179,927 cells were clustered into 32 clusters, designated as T_NK cells, endothelial cells, myeloid cells, fibroblasts, pericytes, SMCs, CMs, proliferating cells, EndoCs, and EPCs. Among them, 8148 fibroblasts were further subdivided into 4 subpopulations, namely C0 THBS4+ Fibroblasts, C1 LINC01133+ Fibroblasts, C2 FGF7+ Fibroblasts, and C3 AGT + Fibroblasts. Results from GO_BP and AUCell analyses suggest that C3 AGT + Fibroblasts may be associated with immune response activation, protein transport, and myocardial contractile function, correlating with disease progression in cardiomyopathy. Transcription factor enrichment analysis indicates that FOS is the most significant TF in C3 AGT + Fibroblasts, also associated with the M1 module, possibly implicated in protein hydrolysis, intracellular DNA replication, and cell proliferation. Moreover, correlation analysis of transcriptional regulatory activity between fibroblast subpopulations reveals a more pronounced heterogeneity within C3 AGT + Fibroblasts in cardiomyopathy. Conclusion. C3 AGT + Fibroblasts exhibit increased sensitivity towards adverse outcomes in cardiomyopathy, such as myocardial fibrosis and impaired cardiac contractile function, compared to other cardiac fibroblast subpopulations. The differential cellular plasticity and transcriptional regulatory activity between C3 AGT + Fibroblasts and other subgroups offer new perspectives for targeting fibroblast subpopulation activity to treat cardiomyopathy. Additionally, stemness genes EPAS1 and MYC, along with the regulator FOS, may play roles in modulating the biological processes of cardiac fibroblasts in cardiomyopathy.

Objective. To explore the feasibility of serum albumin (Alb) and left ventricular ejection fraction (LVEF) in predicting all-cause death (ACD) in patients with stable coronary artery disease (SCAD). Methods. Patients with SCAD were divided into 4 groups according to their Alb and LVEF levels: Group A: Alb ≤4 g/dL and LVEF > 50%; Group B: Alb ≤4 g/dL and LVEF ≤50%; Group C: Alb >4 g/dL and LVEF ≤50%; Group D: Alb >4 g/dL and LVEF >50%. The K–M curve and log-rank test were used to compare ACD among the four groups over three years. Receiver operating characteristic (ROC) curves were used to compare the efficacy of predicting ACD among the combination of Alb and LVEF and either Alb or LVEF alone. Cox regression analysis identified the influencing factors of ACD in patients with SCAD and detected the correlation between Alb and LVEF. Results. ACD occurred in 18 (8.9%) of 203 patients with SCAD, with an average follow-up of 26.53 ± 14.34 months. In the Kaplan‒Meier analysis, the risk of ACD in the four groups ranged from high to low: Group B (17.6%) > Group A (26.7%) > Group D (0.9%) > Group C (0%, ). The ROC curve showed that the combination of Alb and LVEF (AUC = 0.888) had better predictive value for ACD than either Alb (AUC = 0.879) or LVEF alone (AUC = 0.651), . Multivariate Cox regression analysis showed that Alb ≤4 g/dL predicted ACD events after adjusting for baseline (HR: 12.16, 95% CI: 1.57 to 94.41; ) and treatment (HR: 19.36, 95% CI: 2.53–147.78, ). Alb was positively correlated with LVEF (r = 0.22, ). Conclusions. Alb combined with LVEF is more effective than a single index in predicting ACD in SCAD and could be used as a new model to judge the prognosis of SCAD.

Vascular calcification (VC) has a high incidence in patients with chronic kidney disease, which is a worldwide public health problem and presents a heavy burden to society. Hypoxia-inducible factor (HIF)-1α, the active subunit of HIF-1, has been reported to play a vital role in high phosphate-induced VC. However, the underlying mechanism is still undetermined, and effective treatment is unavailable. In the present study, human aortic smooth muscle cells (HASMCs) were cultured under normal or high phosphate media conditions. HIF-1α small interfering RNA and overexpression plasmids were employed to regulate HIF-1α expression. Phosphonoformic acid was employed to restrain the function of type III sodium-dependent phosphate cotransporter 1 (Pit-1). The expression levels of HIF-1α, Pit-1, runt-related transcription factor 2 (Runx2), and smooth muscle 22 alpha (SM22α) were evaluated, and the calcium contents were also examined. Cell growth was assessed using an MTT assay. High phosphate stimulation caused an upregulation in HIF-1α and Pit-1 expression levels and induced calcium depositions in HASMCs. Upregulation of Runx2 expression accompanied by downregulation of SM22α expression was observed in the high phosphate group. Following the suppression of HIF-1α expression, there was a concomitant attenuation in Pit-1 expression, calcium deposition, the alteration of phenotypic transition marker genes, and vice versa. The most serious calcium deposition was noted in HASMCs cultured under high phosphate conditions which were pretreated with a HIF-1α overexpression plasmid. However, when the biological functions of Pit-1 were restrained, the putative serious calcium deposition was not formed even in HASMCs transfected with a HIF-1α overexpression plasmid. The findings confirmed that HIF-1α regulated Pit-1 expression and exerted its pro-calcifying effect through Pit-1, which identified HIF-1α and Pit-1 as therapeutic targets for high phosphate-induced VC.

Background. Coronary heart disease (CHD) is the leading cause of death and disability worldwide. Accumulating evidence reveals that atherosclerosis (AS), characterized by systemic, chronic, and multifocal disease, and is the primary pathological basis of cardiovascular diseases, including CHD. However, the molecular underpinnings of CHD are still far from well understood. Our study attempted to identify aberrant plasma exosome-derived circRNAs and key exosomal circRNA biomarkers for CHD. Methods. The expression profiles of mRNAs, circRNAs, and lncRNAs in the blood exosomes of CHD patients and healthy controls were obtained from the exoRBase database. The corresponding miRNAs of the differentially expressed mRNAs, circRNAs, and lncRNAs were predicted via ENCORI and the miRcode database. LncRNAs/circRNAs and mRNAs with the cotargeted miRNAs were selected to construct an interaction network. Multiple machine learning algorithms have been used to explore potential biomarkers, followed by verification in patients with CHD using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR). Results. Based on the cutoff criterion of , we identified 85 differentially expressed circRNAs (4 upregulated and 81 downregulated), 43 differentially expressed lncRNAs (24 upregulated and 19 downregulated), and 312 differentially expressed mRNAs (55 upregulated and 257 downregulated). Functional enrichment analysis revealed that the differentially expressed mRNAs were involved mainly in neutrophil extracellular trap (NET) formation and the nucleotide-binding oligomerization domain- (NOD-) like receptor signaling pathway. Further analysis revealed that the DEGs in the circRNA/lncRNA-miRNA-mRNA interaction network were closely related to lipid and atherosclerotic signaling pathways. Hsa_circ_0001360 and hsa_circ_0000038 were identified as potential biomarkers for CHD based on three machine learning algorithms. The relative expression levels of hsa_circ_0001360 and hsa_circ_0000038 were significantly altered in plasma exosomes from patients with CHD. ROC curve analysis revealed that the areas under the curve (AUCs) were 0.860, 0.870, and 0.940 for hsa_circ_0001360, hsa_circ_0000038, and the two-gene combination, respectively. Conclusion. The circRNA/lncRNA-miRNA-mRNA interaction network might help to elucidate the pathogenesis of CHD. Hsa_circ_0001360 combined with hsa_circ_0000038 might be an important diagnostic biomarker.

Objectives. To examine the relationship of QRS voltages and left ventricular (LV) mass across the spectrum of individuals with different LV mass. Methods. Twenty QRS voltage measurements or combinations were determined in a consecutive series of 159 adults with an ECG and echocardiogram without previous myocardial infarction, left or right bundle branch block, pre-excitation, or electronic pacemaker. Results. The four strongest and significant correlations between QRS and LV mass were S in V4, deepest S wave in any precordial lead plus S in V4, S in V3, and S in V3 plus R in AVL times QRS duration. For men, the strength of the relationships were S in V3 (F = 33.8), deepest S wave in any precordial lead plus S V4 (F = 33.7), S in V3 plus R aVL (F = 29.9), S in V4 (F = 29.79), and deepest S in precordial leads (F = 17.9). The R wave in AVL alone did not correlate with LV mass. Criteria using the R wave in lateral precordial leads did not correlate as strongly with LV mass. For women, only S in V4 significantly correlated with LV mass. Overall, the R wave voltage in limb leads (AVL I or II) did not correlate with precordial S wave amplitudes. Univariate and multivariate analysis showed that some but not all QRS voltages correlated with each other. In multivariate analysis, using only single variables and not combination of QRS variables, the only significant relationship between QRS voltage and left ventricular mass was for men the S in V3 () and for women S in V4 () and R in V6 (). Conclusion. The S wave in V3 and V4 correlate most strongly with LV mass while the R wave in limb leads, including AVL, do not correlate.

Background. Nepal, currently facing a high burden of noncommunicable diseases (NCDs), including cardiovascular diseases (CVDs), which poses the highest mortality rate in the country, does not seem to have a proper referral strategy. This study explored the wide range of factors and challenges that affect the referral system of CVD cases in Nepal. Methods. In this qualitative study, we conducted face-to-face and telephone interviews with purposely selected 57 key participants which included 35 healthcare professionals from tertiary, secondary, and primary levels from Bagmati Province and 22 CVD patients (myocardial infarction and stroke) from Bagmati and Madhesh Provinces. We interviewed them using an interview guide with open-ended questions for in-depth information in a local language and in a private space. The interviews were audio-recorded, transcribed verbatim, coded, and analyzed using the thematic approach. Results. The findings indicated that the referral system for CVD cases from primary- to secondary- to tertiary-level care is inadequate and malfunctioning. The major factors affecting referral of CVD cases are centralization of CVD-specific services in few urban areas, inadequate systematic communication between the centers, self-referential, lack of human resources for CVD care, and obstacles to patient transfer due to geographical and financial reasons. Conclusion. A referral system for CVD patients is absent in the context of Nepal. Understanding and addressing key factors that affect the referral system of CVD patients may help to improve cardiac outcomes and ultimately save lives.