Cardiovascular diseases (CVD) remain the leading cause of death globally. Accurately assessing the risk of CVD manifestation is thus vital to enable early detection, prevention strategies, and clinical decision-making to prevent both future events and associated deaths. In the clinic, risk prediction primarily relies on demographic characteristics, lifestyle factors, health parameters, and family history. However, although there is a consensus among cardiologists that genetics contribute to common adult-onset disease appearance, routine genetic screening is currently absent from clinical care, when genetics are the earliest measurable contributor that can be assessed to estimate lifetime predisposition to CVD. To non-invasively and routinely estimate such predisposition, a novel genetic profiling tool has been developed, relying on the Polygenic Risk Score (PRS) to measure the probabilistic susceptibility of an individual to disease. Specifically, the PRS can be defined as the weighted sum of the number of risk alleles carried by an individual, where the risk alleles and their weights are defined by their measured effects as detected by Genome Wide Association Studies (GWAS). The aim of this study was to develop a novel PRS for a series of CVDs, comprising coronary artery disease, dilated cardiomyopathy, hypertrophic cardiomyopathy, atrial fibrillation, ischemic stroke, and heart failure, and to further improve its risk estimation through the incorporation of chronological age and behavioral parameters, which would allow us to calculate an adjusted PRS.

Background: Exosomes are a group of extracellular vesicles that are released by almost all mammalian cell types and engage in intracellular communication. Studies conducted in recent years have shown that exosomes are involved in a variety of diseases, where they may act as “vehicles” for the transmission of biomolecules and biomolecular information. Amyloidoses constitute a critical subgroup of these diseases, caused by extracellular deposition or intracellular inclusions of insoluble protein fibrils in cells and tissues. However, how exosomes are involved in these diseases remains largely unexplored. Methods: To detect possible links between amyloid proteins and exosomes, protein data from amyloidosis-isolated exosomes were collected and visualized using biological networks. Results: This biomedical informatics approach for the analysis of interaction networks, in combination with the existing literature, highlighted the involvement of exosomes in amyloidosis while strengthening existing hypotheses regarding their mechanism of action. Conclusion: This work is focused on exosomes from patients with Alzheimer’s disease and identifies important amyloidogenic proteins found in exosomes. These proteins can be used for future research in the field of exosome-based biomarkers of amyloidosis and potential prognostic or preventive approaches.