Personalized medicine in cardiovascular diseases: a biomarker signature for high and very high patients

Cardiovascular (CV) diseases (CVDs) are the leading cause of death worldwide. It is essential to identify patients at higher risk to develop an event that could result in a fatal outcome or in a worsening clinical condition highly impacting the costs for patient management. Currently available risk scores showed a limited accuracy in CV risk prediction and need to be improved to better identify patient mostly needing a therapeutic intervention, especially due to the high cost of new therapies. New methodologies promise to be able to identify innovative biomarkers, suggesting the possibility that an integrated analysis of multiple-approaches, and then of different biomarker types, can further improve CV risk prediction. The main project aim is to identify a biomarker signature of very high CV risk based on a multiple approach including a multiplicity of biological, genetic and clinical data obtained by the most innovative laboratory methods. Specific aims of the project are: 1. The identification of biomarkers different among patients in primary (no event), secondary (atherosclerotic CV- ASCVD- but no CV event) or tertiary (previous CV event) prevention; 2. Identification of biomarkers useful for risk evaluation according to specific patient stratification criteria at recruitment (TO) and at a clinical follow-up (T1); 3. Development of the signature, namely the definition of a weighted role of each of the biomarkers identified as associated with a greater CV risk according to the project results.
The most up-to-date laboratory investigations will be applied to investigate wide groups of innovative biomarkers: a. RNA sequencing of exosomes and other extracellular vesicles; b. analysis proteins content of exosomes; c. proteomic analysis by quantitative proximity assay focused on proteins linked to CV disease and inflammation; d. metabolomic analysis allowing to evaluate the end products of the different altered biochemical pathway; e. analysis of genetic risk scores, that could represent a predisposition to CVD since the birth.
Due to the high-dimensional data set expected by these wide approaches (also omic approaches), advanced data analysis, such as machine learning, will be carried out allowing to define the biomarkers most impacting risk prediction. Data Analysis will be made according to the patient status at recruitment and at follow-up.
The different expertise of the team researchers will allow to carry out the clinical evaluations (including the evaluation of endocrine disorders), the innovative laboratory analysis and the final data analysis.
The most relevant biomarkers could be considered as potential new therapeutic targets. However, the final goal of the project will be the creation of a signature, validated on a population subgroup, that could be immediately translated in clinical practice. The signature developed by this study could be immediately translated in clinical practice to improve the identification of very high-risk patients among those with CV risk factors and/or with a previous CVD, allowing to a more focused therapeutic strategy and saving at the same time both public money and patient lives.