Approved Research

Applying integrative multi-omics and clinical analysis to define patient-stratifying biomarkers in diabetic kidney disease

Lay summary

Diabetic Kidney Disease ("DKD"), is a frequent complication of type 2 diabetes mellitus. It is characterized as a diabetes-related decline in kidney function that often results in end stage renal disease, dialysis and/or kidney transplantation. In addition, this disease often reduces the ability of the physicians to treat other diabetes-related conditions such as cardiovascular complications, owing to a decrease in the kidney's capacity to remove harmful substances and medications from the bloodstream and into the urine.

So far, there have been few attempts to examine the early to middle stages of DKD in patients, as most of the time this condition is identified very late in the development of the disease. Hence, there has been little understanding of what triggers DKD and what drives the progression of the disease. We also know that not all DKD patients progress at the same trajectory but there are no current ways to predict or group these patients.

Recent advances in methods to analyse molecular-level disease factors combined with the advancement so-called artificial intelligence-enabled computational techniques can now enable scientists to examine this disease in much more detail and understand what drives it - an opportunity to reduce the impact of DKD on patients and their families.

This research project will investigate the biological and molecular factors that influence the early and middle stages of DKD, using data available from the UK Biobank. Assuming we are successful, the results of this project will help the broader scientific and medical community to improve the health of DKD patients and to drastically upgrade their quality of life.