Circulating microRNA as a Biomarker for Coronary Artery Disease.
Ibrahim Talal FazminZakaria AchercoukCharlotte E EdlingAsri B SaidKamalan JeevaratnamPublished in: Biomolecules (2020)
Coronary artery disease (CAD) is the leading cause of sudden cardiac death in adults, and new methods of predicting disease and risk-stratifying patients will help guide intervention in order to reduce this burden. Current CAD detection involves multiple modalities, but the consideration of other biomarkers will help improve reliability. The aim of this narrative review is to help researchers and clinicians appreciate the growing relevance of miRNA in CAD and its potential as a biomarker, and also to suggest useful miRNA that may be targets for future study. We sourced information from several databases, namely PubMed, Scopus, and Google Scholar, when collating evidentiary information. MicroRNAs (miRNA) are short, noncoding RNAs that are relevant in cardiovascular physiology and pathophysiology, playing roles in cardiac hypertrophy, maintenance of vascular tone, and responses to vascular injury. CAD is associated with changes in miRNA expression profiles, and so are its risk factors, such as abnormal lipid metabolism and inflammation. Thus, they may potentially be biomarkers of CAD. Nevertheless, there are limitations in using miRNA. These include cost and the presence of several confounding factors that may affect miRNA profiles. Furthermore, there is difficulty in the normalisation of miRNA values between published studies, due to pre-analytical variations in samples.
Keyphrases
- coronary artery disease
- percutaneous coronary intervention
- risk factors
- coronary artery bypass grafting
- cardiovascular events
- randomized controlled trial
- end stage renal disease
- cardiovascular disease
- type diabetes
- heart failure
- palliative care
- newly diagnosed
- ejection fraction
- chronic kidney disease
- health information
- prognostic factors
- machine learning
- peritoneal dialysis
- big data
- systematic review
- mass spectrometry
- atrial fibrillation
- left ventricular
- deep learning