Atherosclerosis and arteriosclerosis

Transcriptomic and Multi-scale Network Analyses Reveal Key Drivers of Cardiovascular Disease

Cardiovascular diseases (CVDs) and pathologies are often driven by changes in molecular signaling and communication, as well as in cellular and tissue components, particularly those involving the extracellular matrix (ECM), cytoskeleton, and immune response. The fine-wire vascular injury model is commonly used to study neointimal hyperplasia and vessel stiffening, but it is not typically considered a model for CVDs. In this paper, we hypothesize that vascular injury induces changes in gene expression, molecular communication, and biological processes similar to those observed in CVDs at both the transcriptome and protein levels. To investigate this, we analyzed gene expression in microarray datasets from injured and uninjured femoral arteries in mice two weeks post-injury, identifying 1,467 significantly and differentially expressed genes involved in several CVDs such as including vaso-occlusion, arrhythmia, and atherosclerosis. We further constructed a protein-protein interaction network with seven functionally distinct clusters, with notable enrichment in ECM, metabolic processes, actin-based process, and immune response. Significant molecular communications were observed between the clusters, most prominently among those involved in ECM and cytoskeleton organizations, inflammation, and cell cycle. Machine Learning Disease pathway analysis revealed that vascular injury-induced crosstalk between ECM remodeling and immune response clusters contributed to aortic aneurysm, neovascularization of choroid, and kidney failure. Additionally, we found that interactions between ECM and actin cytoskeletal reorganization clusters were linked to cardiac damage, carotid artery occlusion, and cardiac lesions. Overall, through multi-scale bioinformatic analyses, we demonstrated the robustness of the vascular injury model in eliciting transcriptomic and molecular network changes associated with CVDs, highlighting its potential for use in cardiovascular research.

Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning

Introduction

Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the specific molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS.

Methods

BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identified. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosis-related genes (DE-ASRGs). The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune infiltration was performed in BD.

Results

Overall, 67 DE-ASRGs were found to be overlapping between the DEGs and AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identified seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1). Eventually, two hub genes (CX3CR1 and ST6GAL1) were identified as BD and AS co-biomarkers by using machine learning algorithms. Immune infiltration had revealed the disorder of immunocytes.

Discussion

This study identified the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice.

The impact of platelet indices on ischemic stroke: a Mendelian randomization study and mediation analysis

Introduction

Platelet indices (PIs) are hematological parameters that indicate the number, morphology, and activation of platelets. Although some clinical trials suggest an association between PIs and the risk of stroke, the lack of robust evidence is attributed to confounding effects and reverse causation.

Objective

This study aimed to evaluate the association between PIs and stroke risk through Mendelian randomization (MR) while exploring the mediating effect of blood pressure in this association.

Methods

We identified genetic variants associated with PIs, including platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), and platelet crit (PCT), in the UK Biobank (n = 350,474). Relevant genome-wide association studies were utilized to gather summary statistics pertaining to the traits of interest. We primarily used the inverse-variance weighted analysis to obtain estimates for individual causal power.

Result

We observed a positive correlation between genetically predicted increases in PCT levels with the stroke onset [PCT: OR (95%CI) = 1.113(1.047, 1.183), p < 0.001]. However, no significant causal relationship was found between PLT, PDW, and MPV and the risk of stroke [PLT: OR (95%CI) = 1.037(0.979, 1.098), p = 0.221; PDW: OR (95%CI) = 0.973(0.923, 1.024), p = 0.294; MPV: OR (95%CI) = 0.990(0.945, 1.038), p = 0.675]. Multivariable MR analyses and mediation analysis found that the proportion mediated by systolic blood pressure (SBP) is 23.71% [95%CI (10.85-33.31%)] and the proportion mediated by diastolic blood pressure (DBP) is 28.09% [95%CI (12.92-39.63%)].

Conclusion

This large MR study presents evidence for the potential causal relationship between the PCT level and the risk of ischemic stroke, which might be mediated by blood pressure.

Assessment of aortic to peripheral vascular stiffness and gradient by segmented upper limb PWV in healthy and hypertensive individuals

Theoretically pulse wave velocity (PWV) is obtained by calculating the distance between two waveform probes divided by the time difference, and PWV ratio is used to assess the arterial stiffness gradient (SG) from proximal to distal. The aim was to investigate segmental upper-limb PWV (ulPWV) differences and the effects of hypertension and or aging on each ulPWV and SG. The study collected multi-waveform signals and conduction distances from 167 healthy individuals and 92 hypertensive patients. The results showed significant differences between ulPWVs (P < 0.001), with increased and then decreased vascular stiffness along the proximal transmission to the distal peripheral artery and then to the finger. Adjusted for age and sex, ulPWVs in hypertension exceeded that of healthy individuals, with significant differences between groups aged ≥ 50 years (P < 0.05). The hrPWV/rfPWV (heart-radial/radial-finger) was reduced in hypertension and differed significantly between the aged ≥ 50 years (P = 0.015); the ratio of baPWV (brachial-ankle) to ulPWV differed significantly between groups (P < 0.05). Hypertension affected the consistency of rfPWV with hfPWV (heart-finger). The findings suggest that segmented ulPWV is instrumental in providing stiffness corresponding to the physiological structure of the vessel. The superimposition of hypertension and or aging exacerbates peripheral arterial stiffness, as well as alteration in stiffness gradient.

Aortopathies: From Etiology to the Role of Arterial Stiffness

The aorta and aortic wall have a complex biological system of structural, biochemical, biomolecular, and hemodynamic elements. Arterial stiffness could be considered a manifestation of wall structural and functional variations, and it has been revealed to have a strong connection with aortopathies and be a predictor of cardiovascular risk, especially in patients affected by hypertension, diabetes mellitus, and nephropathy. Stiffness affects the function of different organs, especially the brain, kidneys, and heart, promoting remodeling of small arteries and endothelial dysfunction. This parameter could be easily evaluated using different methods, but pulse-wave velocity (PWV), the speed of transmission of arterial pressure waves, is considered the gold standard for a good and precise assessment. An increased PWV value indicates an elevated level of aortic stiffness because of the decline in elastin synthesis and activation of proteolysis and the increase in fibrosis that contributes to parietal rigidity. Higher values of PWV could also be found in some genetic diseases, such as Marfan syndrome (MFS) or Loeys-Dietz syndrome (LDS). Aortic stiffness has emerged as a major new cardiovascular disease (CVD) risk factor, and its evaluation using PWV could be very useful to identify patients with a high cardiovascular risk, giving some important prognostic information but also being used to value the benefits of therapeutic strategies.