Harnessing Genomic Analysis to Explore the Role of Telomeres in the Pathogenesis and Progression of Diabetic Kidney Disease.
Claire HillSeamus DuffyTiernan CoulterAlexander Peter MaxwellAmy Jayne McKnightPublished in: Genes (2023)
The prevalence of diabetes is increasing globally, and this trend is predicted to continue for future decades. Research is needed to uncover new ways to manage diabetes and its co-morbidities. A significant secondary complication of diabetes is kidney disease, which can ultimately result in the need for renal replacement therapy, via dialysis or transplantation. Diabetic kidney disease presents a substantial burden to patients, their families and global healthcare services. This review highlights studies that have harnessed genomic, epigenomic and functional prediction tools to uncover novel genes and pathways associated with DKD that are useful for the identification of therapeutic targets or novel biomarkers for risk stratification. Telomere length regulation is a specific pathway gaining attention recently because of its association with DKD. Researchers are employing both observational and genetics-based studies to identify telomere-related genes associated with kidney function decline in diabetes. Studies have also uncovered novel functions for telomere-related genes beyond the immediate regulation of telomere length, such as transcriptional regulation and inflammation. This review summarises studies that have revealed the potential to harness therapeutics that modulate telomere length, or the associated epigenetic modifications, for the treatment of DKD, to potentially slow renal function decline and reduce the global burden of this disease.
Keyphrases
- type diabetes
- cardiovascular disease
- healthcare
- glycemic control
- end stage renal disease
- case control
- chronic kidney disease
- risk factors
- primary care
- dna methylation
- newly diagnosed
- oxidative stress
- ejection fraction
- acute kidney injury
- wound healing
- single cell
- genome wide
- peritoneal dialysis
- climate change
- mental health
- big data
- weight loss
- small molecule
- machine learning
- artificial intelligence
- human health
- bone marrow
- cell therapy
- mesenchymal stem cells
- combination therapy
- genome wide identification
- affordable care act