Fluid-based assays and precision medicine of cardiovascular diseases: the 'hope' for Pandora's box?
Giuditta BenincasaGelsomina MansuetoClaudio NapoliPublished in: Journal of clinical pathology (2019)
Progresses in liquid-based assays may provide novel useful non-invasive indicators of cardiovascular (CV) diseases. By analysing circulating cells or their products in blood, saliva and urine samples, we can investigate molecular changes present at specific time points in each patient allowing sequential monitoring of disease evolution. For example, an increased number of circulating endothelial cells may be a diagnostic biomarker for diabetic nephropathy and heart failure with preserved ejection fraction. The assessment of circulating cell-free DNA (cfDNA) levels may be useful to predict severity of acute myocardial infarction, as well as diagnose heart graft rejection. Remarkably, circulating epigenetic biomarkers, including DNA methylation, histone modifications and non-coding RNAs are key pathogenic determinants of CV diseases representing putative useful biomarkers and drug targets. For example, the unmethylated FAM101A gene may specifically trace cfDNA derived from cardiomyocyte death providing a powerful diagnostic biomarker of apoptosis during ischaemia. Moreover, changes in plasma levels of circulating miR-92 may predict acute coronary syndrome onset in patients with diabetes. Now, network medicine provides a framework to analyse a huge amount of big data by describing a CV disease as a result of a chain of molecular perturbations rather than a single defect (reductionism). We outline advantages and challenges of liquid biopsy with respect to traditional tissue biopsy and summarise the main completed and ongoing clinical trials in CV diseases. Furthermore, we discuss the importance of combining fluid-based assays, big data and network medicine to improve precision medicine and personalised therapy in this field.
Keyphrases
- network analysis
- big data
- dna methylation
- artificial intelligence
- acute coronary syndrome
- machine learning
- acute myocardial infarction
- clinical trial
- diabetic nephropathy
- endothelial cells
- high throughput
- genome wide
- cardiovascular disease
- cell cycle arrest
- gene expression
- percutaneous coronary intervention
- cell proliferation
- induced apoptosis
- cell death
- heart failure
- oxidative stress
- transcription factor
- long non coding rna
- endoplasmic reticulum stress
- angiotensin ii
- randomized controlled trial
- ionic liquid
- left ventricular
- coronary artery disease
- stem cells
- atrial fibrillation
- copy number
- emergency department
- open label
- cell therapy
- type diabetes
- risk assessment
- deep learning
- cardiovascular events
- phase ii