Calcific aortic valve disease: Is it another face of atherosclerosis?

Sathyamurthy, I; Alex, Shaji

doi:10.1016/j.ihj.2015.07.033

Cited by 42 publications

(33 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although disrupted lipid metabolism and deposition contribute to the development of atherosclerosis and although CAVD resembles atherosclerosis in many ways (Sathyamurthy and Alex 2015), in the present work, neither GO terms nor KEGG pathways related to lipid metabolism were among the top-ranked lists in human samples. Moreover, statins (inhibitors of HMG Co-A reductase and reduces low-density lipoprotein cholesterol) have been shown to be unable to halt the progression of CAVD in recent randomized clinical trials (Chan et al 2010).…”

Section: Modulecontrasting

confidence: 64%

Integrated Bioinformatics Analysis Predicts the Key Genes Involved in Aortic Valve Calcification: From Hemodynamic Changes to Extracellular Remodeling

Liu

Luo

Sun

et al. 2017

Tohoku J. Exp. Med.

View full text Add to dashboard Cite

In our aging world, increasing numbers of people are suffering from calcific aortic valve disease (CAVD). In this study, we used integrated bioinformatics analysis to predict several key genes that are involved in the initiation and progression of CAVD. Expression profiles of 15 calcific and 14 normal human aortic valve samples were generated from two gene expression datasets (GSE12644 and GSE51472). Dataset GSE26953 from the human aortic valve fibrosa-derived endothelial cells cultured under laminar or oscillatory shear stress was also evaluated. Related R packages were used to process the data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for functional annotation. Hub genes were identified based on the protein-protein interaction network. CAVD-related gene modules were identified by Weighted Gene Co-expression Network Analysis (WGCNA). The predicted key genes were manually reviewed. In our present work, complex connections among mechano-response, oxidative stress, inflammation and extracellular remodeling pathways in the etiology of CAVD were revealed. The key genes, thus identified, encode a transcription factor KLF2 and phospholipid phosphatase 3 (PLPP3) that are involved in mechanoresponses; eNOS involved in oxidative stress; IL-8 involved in inflammation; and collagen triple helix repeat containing 1 (CTHRC1) and secretogranin II (SCG2) involved in extracellular remodeling. These gene products are predicted to play critical roles in CAVD development and progression. The present study provides valuable information for future research and drug development.

show abstract

Section: Modulecontrasting

confidence: 64%

Integrated Bioinformatics Analysis Predicts the Key Genes Involved in Aortic Valve Calcification: From Hemodynamic Changes to Extracellular Remodeling

Liu

Luo

Sun

et al. 2017

Tohoku J. Exp. Med.

View full text Add to dashboard Cite

show abstract

“…Autopsy studies have confirmed an association between coronary artery calcification and cardiac VC, suggesting that VC is a marker of atherosclerosis [3]. The pathophysiology of VC is also an active process akin to atherosclerosis and involves initiating factors, endothelial dysfunction, inflammatory responses, oxidative stress leading to remodeling and mineralization of valves [19, 20]. However, the molecular and cellular processes that contribute to VC are not fully characterized.…”

Section: Discussionmentioning

confidence: 99%

Early Left Ventricular Dysfunction Detected by Speckle Tracking in Long-Term Hemodialysis Patients with Valvular Calcification

Li¹,

Li²,

Wang³

et al. 2018

Cardiorenal Med

View full text Add to dashboard Cite

Purpose: Cardiac valve calcification (VC) is very common in patients on hemodialysis. However, the definite effect of VC on left ventricular (LV) geometry and function in this population is unknown, especially when LV ejection fraction (LVEF) is normal. The aim of this study was to determine the effect of VC on LV geometry and function in long-term hemodialysis patients by conventional echocardiography and two-dimensional speckle tracking echocardiography (2D-STE). Methods: A total of 47 hemodialysis patients (2–3 times weekly for 5 years or more) were enrolled in this study. Cardiac VC was defined as bright echoes of more than 1 mm on one or more cusps of the aortic valve or mitral valve or mitral annulus using echocardiography as the screening method. LV longitudinal global strain (GLS) was assessed on the apical four-chamber view and calculated as the mean strain of 6 segments. LV global circumferential strain was acquired on the LV short axis view at the level of papillary muscles. Results: Twenty-five patients with VC had higher mean values of interventricular septum thickness, LV posterior wall thickness, LV mass index, relative wall thickness, and LV mass/end-diastolic volume than 22 patients without VC (p < 0.05, respectively), indicating more obvious LV hypertrophy (LVH). VC patients had higher mitral annular E/E′ values, especially at the septal side representing increased LV filling pressure compatible with diastolic dysfunction, while only the E/E′ ratio of the septal side was significantly different between the 2 groups (16.7 ± 4.1 vs. 12.3 ± 4.4, p < 0.01). When assessed by GLS, LV longitudinal systolic function was also lower in in patients with VC compared with those without VC (–0.18 ± 0.03 vs. –0.25 ± 0.04; p < 0.01). Conclusions: Cardiac VC diagnosed by echocardiography when it occurs in long-term hemodialysis patients may indicate more severe LVH, myocardial damage, and worse heart function in comparison to those without VC. Tissue Doppler imaging and 2D-STE can detect the subtle change of heart function in this population in the early stage of LV dysfunction when LVEF is normal.

show abstract

“…However, emerging evidence has indicated that this condition is mediated by the interplay of complex biological processes that include the following: inflammation, cell apoptosis, lipids deposition, renin-angiotensin system activation, remodeling of the extracellular matrix, and bone formation [4–11]. However, the underlying mechanisms that regulate this process remain largely unknown [12, 13]. …”

Section: Introductionmentioning

confidence: 99%

MicroRNA Expression Signature in Degenerative Aortic Stenosis

Shi

Liu

Wang

et al. 2016

BioMed Research International

View full text Add to dashboard Cite

Degenerative aortic stenosis, characterized by narrowing of the exit of the left ventricle of the heart, has become the most common valvular heart disease in the elderly. The aim of this study was to investigate the microRNA (miRNA) signature in degenerative AS. Through microarray analysis, we identified the miRNA expression signature in the tissue samples from healthy individuals (n = 4) and patients with degenerative AS (n = 4). Six miRNAs (hsa-miR-193a-3p, hsa-miR-29b-1-5p, hsa-miR-505-5p, hsa-miR-194-5p, hsa-miR-99b-3p, and hsa-miR-200b-3p) were overexpressed and 14 (hsa-miR-3663-3p, hsa-miR-513a-5p, hsa-miR-146b-5p, hsa-miR-1972, hsa-miR-718, hsa-miR-3138, hsa-miR-21-5p, hsa-miR-630, hsa-miR-575, hsa-miR-301a-3p, hsa-miR-636, hsa-miR-34a-3p, hsa-miR-21-3p, and hsa-miR-516a-5p) were downregulated in aortic tissue from AS patients. GeneSpring 13.1 was used to identify potential human miRNA target genes by comparing a 3-way comparison of predictions from TargetScan, PITA, and microRNAorg databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to identify potential pathways and functional annotations associated with AS. Twenty miRNAs were significantly differentially expressed between patients with AS samples and normal controls and identified potential miRNA targets and molecular pathways associated with this morbidity. This study describes the miRNA expression signature in degenerative AS and provides an improved understanding of the molecular pathobiology of this disease.

show abstract

Calcific aortic valve disease: Is it another face of atherosclerosis?

Cited by 42 publications

References 24 publications

Integrated Bioinformatics Analysis Predicts the Key Genes Involved in Aortic Valve Calcification: From Hemodynamic Changes to Extracellular Remodeling

Integrated Bioinformatics Analysis Predicts the Key Genes Involved in Aortic Valve Calcification: From Hemodynamic Changes to Extracellular Remodeling

Early Left Ventricular Dysfunction Detected by Speckle Tracking in Long-Term Hemodialysis Patients with Valvular Calcification

MicroRNA Expression Signature in Degenerative Aortic Stenosis

Contact Info

Product

Resources

About