个性化医疗
生物标志物
疾病
精密医学
前蛋白转化酶
生物信息学
医学
计算生物学
生物
内科学
脂蛋白
病理
低密度脂蛋白受体
生物化学
胆固醇
作者
Giuditta Benincasa,Paola de Candia,Dário Alves da Silva Costa,Mario Faenza,Gelsomina Mansueto,Giuseppe Ambrosio,Claudio Napoli
出处
期刊:Lipids
[Wiley]
日期:2020-10-29
卷期号:56 (3): 259-268
被引量:9
摘要
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.
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