Network medicine in Cardiovascular Research

Cardiovasc Res. 2021 Aug 29;117(10):2186-2202. doi: 10.1093/cvr/cvaa321.

Abstract

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

Keywords: Cardiovascular disease; Network medicine; Omics; Pathobiology; Precision medicine.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Biomarkers / metabolism
  • Biomedical Research / methods*
  • Cardiovascular Agents / therapeutic use
  • Cardiovascular Diseases* / drug therapy
  • Cardiovascular Diseases* / genetics
  • Cardiovascular Diseases* / metabolism
  • Cardiovascular Diseases* / physiopathology
  • Computational Biology*
  • Drug Repositioning
  • Gene Expression Profiling
  • Gene Regulatory Networks
  • Genetic Association Studies
  • Humans
  • Precision Medicine*
  • Predictive Value of Tests
  • Prognosis
  • Protein Interaction Maps
  • Proteome*
  • Proteomics
  • Risk Assessment
  • Risk Factors
  • Signal Transduction
  • Systems Biology
  • Transcriptome*

Substances

  • Biomarkers
  • Cardiovascular Agents
  • Proteome