Dr Charlotte Edling

The cardiac extracellular matrix (ECM) is involved in several pathological conditions, and age itself is also associated with certain changes in the heart: it gets larger and stiffer, and it develops an increased risk of abnormal intrinsic rhythm. This, therefore, makes conditions such as atrial arrythmia more common. Many of these changes are directly related to the ECM, yet the proteomic composition of the ECM and how it changes with age is not fully resolved. The limited research progress in this field is mainly due to the intrinsic challenges in unravelling tightly bound cardiac proteomic components and also the time-consuming and costly dependency on animal models. This review aims to give an overview of the composition of the cardiac ECM, how different components aid the function of the healthy heart, how the ECM is remodelled and how it is affected by ageing.

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.