Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Abstract

Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease‐related proteins often interact with each other in functional modules, many advanced network‐oriented algorithms were applied to patient‐derived big data to identify the complex gene–environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin‐1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence‐based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non‐invasive AI‐derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI‐radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network‐based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.

Role of Epigenetics and Metabolomics in Predicting Endothelial Dysfunction in Type 2 Diabetes

“…The large volume of data obtained so far is complex and requires appropriate computational experience to understand disease progression and inflammatory signaling development, identify novel targets, and predicting how different therapeutics may influence the progression of disease. More studies are needed, possibly through worldwide collaborations [123,61,124,125] Furthermore, because of the cellspecific nature of epigenetic information, more human biopsies and samples could be useful. In this scenario, epigenomic and metabolomics, together with other omics technologies, would provide a valid tool to identify all biomolecular mechanisms underlying disease development and possibly categorize patients into endotypes based on common omics expression patterns.…”

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

“…Finally, novel network analyses, such as weighted correlation network analysis (WGCNA), could be used to search for clusters of highly correlated genes [47]. WGCNA algorithms have been used in dyslipidemias [48], cancer [49,50], andautismspectrum disorder [51].…”

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

“…Proteomics, the protein-based risk score, turned out to be a proper tool to predict the harm within 3 months. Benincasa et al [ 123 ] proposed a digitalized way to individualize the treatment of dyslipidemia and Tsigalou et al [ 124 ] presented a ML method to assess LDL plasma levels. Another study created a method to identify patients with familial hypercholesterolemia [ 125 ].…”