The prevalence of diabetes and associated complications is increasing and accounts for much of Australia’s healthcare burden. Current clinical biomarkers do not allow efficient prediction of diabetes progression. There is, therefore, a need to obtain an improved understanding of molecular signatures of diabetes onset and vascular damage.
In order to understand the potential of miRNAs as circulating biomarkers of diabetes progression, we carried out discovery analyses using an OpenArray platform for profiling of 754 microRNAs followed by validation in well-characterized clinical samples. microRNAs are short, non-coding RNA molecules that are important regulators of several pathophysiological functions. In the past few years, circulating microRNAs have been looked upon as important biomarkers of disease progression. Although such miRNA biomarkers are well described in cancer progression, the understanding of similar biomarkers in diabetes research is lacking. Using systematic approaches in sample processing, assaying and data analyses, we identified microRNA signatures that are highly associated with insulin expression, pancreatic beta cell death (in vitro and in vivo), presented at increasing abundance during human pancreas development or dysregulated in individuals with/without diabetes. I will present our new findings on the analysis of these miRNAs in >600 individuals from multiple cohorts/groups with and without diabetes, followed by independent validation in other clinical study samples. I will also present our data on derivation and validation of RNA-based signature of endothelial damage/vascular complications in diabetes.
These data demonstrate that our microRNA signatures not only facilitate the potential prediction of diabetes progression, but also help in understanding treatment efficacies in trials aiming to retard beta-cell death. Hopefully, our study results will provide basic scientists with a tool for selecting treatments to selectively block beta-cell death and inform medical researchers/clinicians as to how to predict the development of diabetes and monitor response to interventions.