Deciphering the genetic and modular connections between coronary heart disease, idiopathic pulmonary arterial hypertension and pulmonary heart disease

Baseline triglyceride-glucose, body mass index, end-diastolic internal diameter of the left atria and creatinine are independent predictors for pulmonary hypertension in coronary artery disease patients after percutaneous coronary intervention treatments

Aim: To identify the predictive role of triglyceride-glucose (TyG) index in pulmonary hypertension (PH) in coronary artery disease (CAD) patients after percutaneous coronary intervention (PCI) treatment.Methods: Blood biomarkers have been measured at the cross-section of entrance. The baseline and followed-up echocardiography have been performed at both cross-sections.Results: The incidence of PH was 8.91%. The baseline myoglobin (MYO), was significantly higher among PH patients (p < 0.001). In the univariate regression, body mass index (BMI p = 0.020), left atria end-diastolic internal diameter (LAD, p = 0.083), creatinine (Cr, p = 0.005), triglyceride (TG, p < 0.001), high-density lipoprotein cholesterol (HDL-C, p = 0.056) and TyG index (p = 0.002) were potential predictors for PH. Finally, the adjusted COX regression indicated that BMI (p = 0.001), LAD (p = 0.030), Cr(p = 0.005) and TyG index (p = 0.002) were independent predictors of the onset of PH.Conclusion: Baseline TyG index, BMI, LAD, Cr level were independent predictors for PH in CAD patients after PCI treatment.

Advanced hemodynamic and cluster analysis for identifying novel RV function subphenotypes in patients with pulmonary hypertension

BACKGROUND

Quantifying right ventricular (RV) function is important to describe the pathophysiology of in pulmonary hypertension (PH). Current phenotyping strategies in PH rely on few invasive hemodynamic parameters to quantify RV dysfunction severity. The aim of this study was to identify novel RV phenotypes using unsupervised clustering methods on advanced hemodynamic features of RV function.

METHODS

Participants were identified from the University of Arizona Pulmonary Hypertension Registry (n = 190). RV-pulmonary artery coupling (Ees/Ea), RV systolic (Ees), and diastolic function (Eed) were quantified from stored RV pressure waveforms. Consensus clustering analysis with bootstrapping was used to identify the optimal clustering method. Pearson correlation analysis was used to reduce collinearity between variables. RV cluster subphenotypes were characterized using clinical data and compared to pulmonary vascular resistance (PVR) quintiles.

RESULTS

Five distinct RV clusters (C1-C5) with distinct RV subphenotypes were identified using k-medoids with a Pearson distance matrix. Clusters 1 and 2 both have low diastolic stiffness (Eed) and afterload (Ea) but RV-PA coupling (Ees/Ea) is decreased in C2. Intermediate cluster (C3) has a similar Ees/Ea as C2 but with higher PA pressure and afterload. Clusters C4 and C5 have increased Eed and Ea but C5 has a significant decrease in Ees/Ea. Cardiac output was high in C3 distinct from the other clusters. In the PVR quintiles, contractility increased and stroke volume decreased as a function of increased afterload. World Symposium PH classifications were distributed across clusters and PVR quintiles.

CONCLUSIONS

RV-centric phenotyping offers an opportunity for a more precise-medicine-based management approach.

Deciphering the Protein, Modular Connections and Precision Medicine for Heart Failure With Preserved Ejection Fraction and Hypertension Based on TMT Quantitative Proteomics and Molecular Docking

Background: Exploring the potential biological relationships between heart failure with preserved ejection fraction (HFpEF) and concomitant diseases has been the focus of many studies for the establishment of personalized therapies. Hypertension (HTN) is the most common concomitant disease in HFpEF patients, but the functional connections between HFpEF and HTN are still not fully understood and effective treatment strategies are still lacking.

Methods: In this study, tandem mass tag (TMT) quantitative proteomics was used to identify disease-related proteins and construct disease-related networks. Furthermore, functional enrichment analysis of overlapping network modules was used to determine the functional similarities between HFpEF and HTN. Molecular docking and module analyses were combined to identify therapeutic targets for HFpEF and HTN.

Results: Seven common differentially expressed proteins (co-DEPs) and eight overlapping modules were identified in HFpEF and HTN. The common biological processes between HFpEF and HTN were mainly related to energy metabolism. Myocardial contraction, energy metabolism, apoptosis, oxidative stress, immune response, and cardiac hypertrophy were all closely associated with HFpEF and HTN. Epinephrine, sulfadimethoxine, chloroform, and prednisolone acetate were best matched with the co-DEPs by molecular docking analyses.

Conclusion: Myocardial contraction, energy metabolism, apoptosis, oxidative stress, immune response, and cardiac hypertrophy were the main functional connections between HFpEF and HTN. Epinephrine, sulfadimethoxine, chloroform, and prednisolone acetate could potentially be effective for the treatment of HTN and HFpEF.

Clinical implications of idiopathic pulmonary arterial hypertension phenotypes defined by cluster analysis

BACKGROUND

>Despite advances in drug development, life expectancy in idiopathic pulmonary arterial hypertension (IPAH) remains unacceptable. Contemporary IPAH characterization is based on criteria that may not adequately capture disease heterogeneity and may be proposed as a possible explanation for why patient outcome is still unfavorable. The aim of this study was to apply cluster analysis to improve phenotyping of patients with IPAH and analyze long-term clinical outcome of derived clusters.

METHODS

Patients with IPAH from 2 referral centers (n = 252) were evaluated with clinical, hemodynamic, and echocardiographic assessment and cardiopulmonary exercise test. Patients were classified according to cluster analysis and followed for clinical worsening occurrence.

RESULTS

The cluster analysis identified 4 IPAH phenotypes. Cluster 1 was characterized by young patients, mild pulmonary hypertension (PH), mild right ventricular (RV) dilation and high oxygen (O₂) pulse; Cluster 2 by severe PH and RV dilation and high O₂ pulse; and Cluster 3 by male patients, severe PH and RV dilation, and low O₂ pulse. Cluster 4 patients were older and overweight, with mild PH and RV dilation and low O₂ pulse. After a mean follow-up of 995 ± 623 days, 123 (48.8%) patients had clinical worsening. Cluster 1 patients presented the best prognosis, whereas Cluster 3 had the highest rates of clinical worsening. Compared with Cluster 1, risk of clinical worsening ranged from 4.12 (confidence interval [CI] 1.43-11.92; p = 0.009) for Cluster 4 to 7.38 (CI 2.80-19.40) for Cluster 2 and 13.8 (CI 5.60-34.0; p = 0.0001) for Cluster 3.

CONCLUSIONS

Cluster analysis of clinical variables identified 4 distinct phenotypes of IPAH. Our findings underscore the high degree of disease heterogeneity that exists within patients with IPAH and the need for advanced clinical testing to define phenotypes to improve treatment strategy decision-making. CONDENSED ABSTRACT Idiopathic pulmonary arterial hypertension (IPAH) characterization is based on criteria that may not adequately capture disease heterogeneity. The aim of this study was to apply cluster analysis to improve phenotyping of IPAH. Patients with IPAH (n = 252) were evaluated with clinical, hemodynamic, and echocardiographic assessment and cardiopulmonary exercise test. Within the umbrella category of IPAH, it was the combination of mean pulmonary arterial pressure, right ventricular size, and oxygen pulse that further stratified patients into novel IPAH phenotypes that significantly associate with clinical worsening. These findings underscore the need for novel multidimensional IPAH phenotyping for improved patient care and trial quality.

Deciphering the scalene association among type-2 diabetes mellitus, prostate cancer, and chronic myeloid leukemia via enrichment analysis of disease-gene network

The potential biological relationship between type‐2 diabetes mellitus (T2DM) has been focused in numerous studies. To investigate the molecular associations among T2DM, prostate cancer (PCa), and chronic myeloid leukemia (CML), using a biomolecular network enrichment analysis. We obtained a list of disease‐related genes and constructed disease networks. Then, GO enrichment analysis was performed to identify the significant functions and pathways of overlapping modules in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. More than 75% of these overlapping genes were found to be consistent with the findings of previous studies. In the three diseases, we found that Sarcoglycan delta (SGCD) and Rho family GTPase 3 (RND3) were the overlapping genes and identified negative regulation of apoptotic process and negative regulation of transcription from RNA polymerase II promoter RNA as the two overlapping biological functions. CML and PCa were the most closely related, with 34 overlapping genes, five overlapping modules, 27 overlapping biological functions, and nine overlapping pathways. There were 13 overlapping genes, one overlapping modules, four overlapping biological functions and one overlapping pathway (FoxO signaling pathway) were found in T2DM and CML.And T2DM and PCa were the least related pair in our study, with only six overlapping genes, five overlapping modules, and one overlapping biological function. SGCD and RND3 were the main gene‐to‐gene relationship among T2DM, CML, and PCa; apoptosis, development, and transcription from RNA polymerase II promote processes were the main functional connections among T2DM, CML, and PCa by network enrichment analysis. There is a “scalene” relationship among T2DM, CML, and PCa at gene, pathway, biological process, and module levels: CML and PCa were the most closely related, the second were T2DM and PCa, and T2DM and PCa were the least related pair in our study. Our study provides a new avenue for further studies on T2DM and cancers, which may promote the discovery and development of novel therapeutic and can be used to treat multiple diseases.

Association between a Single Nucleotide Polymorphism in the 3'-UTR of ARHGEF18 and the Risk of Nonidiopathic Pulmonary Arterial Hypertension in Chinese Population

ARHGEF18 has been identified as upregulated in the lung tissues of rat models of pulmonary artery hypertension introduced by hypoxia or monocrotaline (MCT). We used online SNP function prediction tools to screen the candidate SNPs that might be associated with the regulation of the ARHGEF18 expression. The result suggested that rs3745357 located in the 3'-untranslated region of ARHGEF18 is probably a genetic modifier in the process. In the present study, we aimed to investigate the association between ARHGEF18 rs3745357 polymorphism and nonidiopathic pulmonary arterial hypertension susceptibility (niPAH). A total of 293 participants were included in the case-control study (117 patients and 176 healthy controls). The rs3745357 variant was discriminated by using cleaved amplification polymorphism (CAP) sequence-tagged site technology. Although the overall allele and genotype frequencies of rs3745357 in niPAH patients were close to those of the control group, significant differences have been identified when we further divided the niPAH patients into subgroups with or without coronary heart disease (CHD). Rs3745357 C allele frequency was significantly higher in niPAH patients without CHD history (p = 0.001), while the frequency was significantly lower in niPAH patients with CHD history (p = 0.017) when compared to control subjects. The distribution of genotype frequencies was also quite different. After adjustment by gender and age, significant differences were found between patients with CHD history and controls. The results suggest that the ARHGEF18 rs3745357 variant may be used as a marker for the genetic susceptibility to niPAH.