Differential epigenetic factors in the prediction of cardiovascular risk in diabetic patients

Eur Heart J Cardiovasc Pharmacother. 2020 Jul 1;6(4):239-247. doi: 10.1093/ehjcvp/pvz062.

Abstract

Hyperglycaemia can strongly alter the epigenetic signatures in many types of human vascular cells providing persistent perturbations of protein-protein interactions both in micro- and macro-domains. The establishment of these epigenetic changes may precede cardiovascular (CV) complications and help us to predict vascular lesions in diabetic patients. Importantly, these epigenetic marks may be transmitted across several generations (transgenerational effect) and increase the individual risk of disease. Aberrant DNA methylation and imbalance of histone modifications, mainly acetylation and methylation of H3, represent key determinants of vascular lesions and, thus, putative useful biomarkers for prevention and diagnosis of CV risk in diabetics. Moreover, a differential expression of some micro-RNAs (miRNAs), mainly miR-126, may be a useful prognostic biomarker for atherosclerosis development in asymptomatic subjects. Recently, also environmental-induced chemical perturbations in mRNA (epitranscriptome), mainly the N6-methyladenosine, have been associated with obesity and diabetes. Importantly, reversal of epigenetic changes by modulation of lifestyle and use of metformin, statins, fenofibrate, and apabetalone may offer useful therapeutic options to prevent or delay CV events in diabetics increasing the opportunity for personalized therapy. Network medicine is a promising molecular-bioinformatic approach to identify the signalling pathways underlying the pathogenesis of CV lesions in diabetic patients. Moreover, machine learning tools combined with tomography are advancing the individualized assessment of CV risk in these patients. We remark the need for combining epigenetics and advanced bioinformatic platforms to improve the prediction of vascular lesions in diabetics increasing the opportunity for CV precision medicine.

Keywords: Cardiovascular prevention; Diabetic vasculature; Epigenetics; Machine learning; Network medicine; Personalized therapy.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Blood Glucose / metabolism*
  • Cardiovascular Diseases / epidemiology
  • Cardiovascular Diseases / genetics*
  • Cardiovascular Diseases / prevention & control
  • Diabetes Mellitus / blood
  • Diabetes Mellitus / epidemiology
  • Diabetes Mellitus / genetics*
  • Diabetes Mellitus / therapy
  • Epigenesis, Genetic*
  • Humans
  • Precision Medicine
  • Primary Prevention
  • Risk Assessment
  • Risk Factors
  • Secondary Prevention
  • Systems Analysis
  • Transcriptome*

Substances

  • Blood Glucose