Abstract
Genetics and environmental and lifestyle factors deeply affect cardiovascular diseases, with atherosclerosis as the etiopathological factor (ACVD) and their early recognition can significantly contribute to an efficient prevention and treatment of the disease. Due to the vast number of these factors, only the novel “omic” approaches are surmised. In addition to genomics, which extended the effective therapeutic potential for complex and rarer diseases, the use of “omics” presents a step-forward that can be harnessed for more accurate ACVD prediction and risk assessment in larger populations. The analysis of these data by artificial intelligence (AI)/machine learning (ML) strategies makes is possible to decipher the large amount of data that derives from such techniques, in order to provide an unbiased assessment of pathophysiological correlations and to develop a better understanding of the molecular background of ACVD. The predictive models implementing data from these “omics”, are based on consolidated AI best practices for classical ML and deep learning paradigms that employ methods (e.g., Integrative Network Fusion method, using an AI/ML supervised strategy and cross-validation) to validate the reproducibility of the results. Here, we highlight the proposed integrated approach for the prediction and diagnosis of ACVD with the presentation of the key elements of a joint scientific project of the University of Milan and the Almazov National Medical Research Centre.
Highlights
The most common forms of cardiovascular disease, presenting with atherosclerosis as the etiopathological factor (ACVD) (e.g., secondary prevention patients complicated by metabolic alterations, severe heterozygous forms of Familial Hypercholesterolemia (FH)
Multi-Omics Tools for Cardiovascular Risk Prediction Tools: Transcriptomic and Markersshown here is related to ACVD risk
ANRIL in particular showed to be the leading candidate contributing to the relationship between 9p21 regions and CAD, since a transcriptomic analysis of circulating leukocytes demonstrated that the expression of short variants of ANRIL increased by 2.2 fold whereas the expression of the long ANRIL variants decreased by 1.2 fold in healthy subjects homozygous for the risk allele
Summary
The most common forms of cardiovascular disease, presenting with atherosclerosis as the etiopathological factor (ACVD) (e.g., secondary prevention patients complicated by metabolic alterations, severe heterozygous forms of Familial Hypercholesterolemia (FH). Notwithstanding other models of genomic data have been developed to explore “peripheral genes” in which the up or downregulation, not biologically involved in the pathway of the disease, contributes to an increased risk of the diseases, because of perturbations in regulatory gene networks that are co-segregated during inception [7] The identification of such networks is of particular interest for the identification of the most severe forms of FH or in the identification of subjects that, beyond both LDL-C and a well-characterized monogenic form causal of the disease, harbor an additional genetic contribution of a higher risk of ACVD. MR have shown that long-term exposure to lower LDL-C is associated with a much greater reduction in the risk of cardiovascular events as compared to the same reduction in LDL-C achieved with medications started much later in life as evaluated in randomized trials This finding implies that the causal effect of LDL-C accumulates over time.
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