Early Detection of Undiagnosed Abdominal Aortic Aneurysm and Sub-Aneurysmal Aortic Dilatations in Patients with High-Risk Coronary Artery Disease: The Value of Targetted Screening Programme

Cellular, Molecular and Clinical Aspects of Aortic Aneurysm-Vascular Physiology and Pathophysiology

A disturbance of the structure of the aortic wall results in the formation of aortic aneurysm, which is characterized by a significant bulge on the vessel surface that may have consequences, such as distention and finally rupture. Abdominal aortic aneurysm (AAA) is a major pathological condition because it affects approximately 8% of elderly men and 1.5% of elderly women. The pathogenesis of AAA involves multiple interlocking mechanisms, including inflammation, immune cell activation, protein degradation and cellular malalignments. The expression of inflammatory factors, such as cytokines and chemokines, induce the infiltration of inflammatory cells into the wall of the aorta, including macrophages, natural killer cells (NK cells) and T and B lymphocytes. Protein degradation occurs with a high expression not only of matrix metalloproteinases (MMPs) but also of neutrophil gelatinase-associated lipocalin (NGAL), interferon gamma (IFN-γ) and chymases. The loss of extracellular matrix (ECM) due to cell apoptosis and phenotype switching reduces tissue density and may contribute to AAA. It is important to consider the key mechanisms of initiating and promoting AAA to achieve better preventative and therapeutic outcomes.

Predicting multi-vascular diseases in patients with coronary artery disease

Background: Because of its systemic nature, the occurrence of atherosclerosis in the coronary arteries can also indicate a risk for other vascular diseases.  However, screening program targeted for all patients with coronary artery disease (CAD) is highly ineffective and no studies have assessed the risk factors for developing multi-vascular diseases in general. This study constructed a predictive model and scoring system to enable targeted screening for multi-vascular diseases in CAD patients. Methods: This cross-sectional study includes patients with CAD, as diagnosed during coronary angiography or percutaneous coronary intervention from March 2021 to December 2021. Coronary artery stenosis (CAS) and abdominal aortic aneurysm (AAA) were diagnosed using Doppler ultrasound while peripheral artery disease (PAD) was diagnosed based on ABI score. Multivariate logistic regression was conducted to construct the predictive model and risk scores. Validation was conducted using ROC analysis and Hosmer-Lemeshow test. Results: Multivariate analysis showed that ages of >60 years (OR [95% CI] = 1.579 [1.153-2.164]), diabetes mellitus (OR = 1.412 [1.036-1.924]), cerebrovascular disease (OR = 3.656 [2.326-5.747]), and CAD3VD (OR = 1.960 [1.250-3.073]) increased the odds for multi-vascular disease. The model demonstrated good predictive capability (AUC = 0.659) and was well-calibrated (Hosmer-Lemeshow p = 0.379). Targeted screening for high-risk patients reduced the number needed to screen (NNS) from 6 in the general population to 3 and has a high specificity of 96.5% Conclusions: Targeted screening using clinical risk scores was able to decrease NNS with good predictive capability and high specificity.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

The Important Role of Endothelium and Extracellular Vesicles in the Cellular Mechanism of Aortic Aneurysm Formation

Homeostasis is a fundamental property of biological systems consisting of the ability to maintain a dynamic balance of the environment of biochemical processes. The action of endogenous and exogenous factors can lead to internal balance disorder, which results in the activation of the immune system and the development of inflammatory response. Inflammation determines the disturbances in the structure of the vessel wall, connected with the change in their diameter. These disorders consist of accumulation in the space between the endothelium and the muscle cells of low-density lipoproteins (LDL), resulting in the formation of fatty streaks narrowing the lumen and restricting the blood flow in the area behind the structure. The effect of inflammation may also be pathological dilatation of the vessel wall associated with the development of aneurysms. Described disease entities strongly correlate with the increased migration of immune cells. Recent scientific research indicates the secretion of specific vesicular structures during migration activated by the inflammation. The review focuses on the link between endothelial dysfunction and the inflammatory response and the impact of these processes on the development of disease entities potentially related to the secretion of extracellular vesicles (EVs).

Identification of a Plasma Microrna Signature as Biomarker of Subaneurysmal Aortic Dilation in Patients with High Cardiovascular Risk

Patients with subaneurysmal aortic dilation (SAD; 25–29 mm diameter) are likely to progress to true abdominal aortic aneurysm (AAA). Despite these patients having a higher risk of all-cause mortality than subjects with aortic size <24 mm, early diagnostic biomarkers are lacking. MicroRNAs (miRs) are well-recognized potential biomarkers due to their differential expression in different tissues and their stability in blood. We have investigated whether a plasma miRs profile could identify the presence of SAD in high cardiovascular risk patients. Using qRT-PCR arrays in plasma samples, we determined miRs differentially expressed between SAD patients and patients with normal aortic diameter. We then selected 12 miRs to be investigated as biomarkers by construction of ROC curves. A total of 82 significantly differentially expressed miRs were found by qPCR array, and 12 were validated by qRT-PCR. ROC curve analyses showed that seven selected miRs (miR-28-3p, miR-29a-3p, miR-93-3p, miR-150-5p, miR-338-3p, miR-339-3p, and miR-378a-3p) could be valuable biomarkers for distinguishing SAD patients. MiR-339-3p showed the best sensitivity and specificity, even after combination with other miRs. Decreased miR-339-3p expression was associated with increased aortic abdominal diameter. MiR-339-3p, alone or in combination with other miRs, could be used for SAD screening in high cardiovascular risk patients, helping to the early diagnosis of asymptomatic AAA.