A Metanalysis of genes impacted by Diabetic Kidney Disease
Background
Diabetic nephropathy is the leading cause of ESRD with a serious socio-economic burden on the society due to high morbidity and mortality(1). Diabetes affects approximately 537 million adults worldwide(2), 20% of which suffer from diabetic kidney disease (DKD)(3, 4). Over 30 years, approximately 30% of DKD patients develop end-stage-renal-disease (ESRD), of which about 50% die within two years of developing ESRD. Though DKD progression is considered an irreversible disease, therapeutical strategies for slowing and even preventing (if possible) the pace of DKD progression to ESRD are urgently needed. Relatively little is known about early molecular changes that precede overt diabetic nephropathy. Currently, available tests are not sufficiently sensitive to detect the earliest manifestations of diabetic kidney disease and efforts are underway to develop better biomarkers and develop novel therapeutic interventions(5). Recent molecular profiling studies, including single-cell RNA-Seq, have suggested pathways associated with DKD such as complement system, integrin(6), and regulation of endothelial cell proliferation and ion homeostasis(7). Unfortunately, details of these pathways and mechanisms and the critical players for underlying DKD progression are yet to be validated. The kidney glomerulus and proximal tubular cells are important functional sites for DKD-related lesions, currently classified as DKD I, DKD II, and DKD III(8, 9). Even though the progression of DKD is irreversible, a way to early detection and slowing down DKD progression will fill the current unmet need.
Hypothesis
We hypothesize that by performing metanalysis on publicly available gene expression data we will be able to identify early markers of DKD progression.
People Involved
Dr. Tara Sigdel – PI
Dr. Milan Bimali -PI
Dr. Bhikhari Tharu – Co-I
Mr. Nabin Simkhada – Student/Intern
Dr. Ritu Pokhrel – Volunteer/Intern
References
1. Kim SS, Kim JH, and Kim IJ. Current Challenges in Diabetic Nephropathy: Early Diagnosis and Ways to Improve Outcomes. Endocrinol Metab (Seoul). 2016;31(2):245-53.
2. IDF Diabetes Atlas 2021. IDF Diabetes Atlas 2021. 2022.
3. Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States, Atlanta, GA. US Department of Health and Human Services Centers for Disease Control and Prevention. 2019.
4. Murphy D, McCulloch CE, Lin F, Banerjee T, Bragg-Gresham JL, Eberhardt MS, et al. Trends in Prevalence of Chronic Kidney Disease in the United States. Ann Intern Med. 2016;165(7):473-81.
5. Colhoun HM, and Marcovecchio ML. Biomarkers of diabetic kidney disease. Diabetologia. 2018;61(5):996-1011.
6. Woroniecka KI, Park AS, Mohtat D, Thomas DB, Pullman JM, and Susztak K. Transcriptome analysis of human diabetic kidney disease. Diabetes. 2011;60(9):2354-69.
7. Wilson PC, Wu H, Kirita Y, Uchimura K, Ledru N, Rennke HG, et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci U S A. 2019;116(39):19619-25.
8. Reidy K, Kang HM, Hostetter T, and Susztak K. Molecular mechanisms of diabetic kidney disease. J Clin Invest. 2014;124(6):2333-40.
9. Badal SS, and Danesh FR. New insights into molecular mechanisms of diabetic kidney disease. Am J Kidney Dis. 2014;63(2 Suppl 2):S63-83.
