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Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias
Author(s) -
Benincasa Giuditta,
Candia Paola,
Costa Dario,
Faenza Mario,
Mansueto Gelsomina,
Ambrosio Giuseppe,
Napoli Claudio
Publication year - 2021
Publication title -
lipids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.601
H-Index - 120
eISSN - 1558-9307
pISSN - 0024-4201
DOI - 10.1002/lipd.12290
Subject(s) - personalized medicine , biomarker , disease , precision medicine , proprotein convertase , bioinformatics , medicine , computational biology , biology , lipoprotein , pathology , biochemistry , ldl receptor , cholesterol
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.