Diabetic kidney diseases revisited: A new perspective for a new era

Abstract

Background: Globally, diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. As the most common microvascular complication of diabetes, DKD is a thorny, clinical problem in terms of its diagnosis and management. Intensive glucose control in DKD could slow down but not significantly halt disease progression. Revisiting the tremendous advances that have occurred in the field would enhance recognition of DKD pathogenesis as well as improve our understanding of translational science in DKD in this new era.

Scope of review: In this review, we summarize advances in the understanding of the local microenvironmental changes in diabetic kidneys and discuss the involvement of genetic and epigenetic factors in the pathogenesis of DKD. We also review DKD prevalence changes and analyze the challenges in optimizing the diagnostic approaches and management strategies for DKD in the clinic. As we enter the era of 'big data', we also explore the possibility of linking systems biology with translational medicine in DKD in the current healthcare system.

Major conclusion: Newer understanding of the structural changes of diabetic kidneys and mechanisms of DKD pathogenesis, as well as emergent research technologies will shed light on new methods of dealing with the existing clinical challenges of DKD.

Keywords: Diabetic kidney disease; Epigenetics; Genetics; Metabolic memory; Systems biology.

Figure 1

Pathologic lesions in human DKD. (A) Nonnodular diabetic glomerulosclerosis. The black arrow indicates ECM deposition in the mesangial area. Black arrowhead indicates a thickening of tubular basement membrane (TBM). (B) Electric microscopy shows the thickness of the GBM in human diabetic kidneys compared with healthy control. (C) Nodular diabetic glomerulosclerosis, thickening of TBM, ECM accumulation, and interstitium expansion in diabetic kidneys. The black star denotes Kimmelstiel-Wilson (KW) nodule. Black arrowhead denotes thickening of TBM. (D) Immunofluorescence staining of immunoglobulin G shows KW nodule (white star) in diabetic kidney. (E) Schematic diagram depicting a ‘sophisticated’ microenvironment formation in diabetic kidneys. ECM: extracellular matrix.

Figure 2

The pathogenesis of DKD. Inherited and acquired risk factors have become hotspots in DKD research.

Figure 3

Diagnostic data distributions of DKD, NDKD, or mixed forms in the clinic. The prevalence of DKD and NDKD varies among diabetes patients biopsied from different institutions worldwide. The diagnostic data of DKD in the top three countries (China, India, and the United States) of people with diabetes as well as the data of Europe and Africa are presented [35].

Figure 4

Systems biology links to translational medicine lighting the way to prevent DKD. Schematic diagrams show (A) the methods of big data collection, including OMICS data at different levels in available public database, and precise model construction, including model development, validation, and prediction in the current systems biology medical research and (B) the translational applications in the clinic. GEO, Gene Expression Omnibus; TCGA, The Cancer Genome Atlas; ENA, European Nucleotide Archive; MSigDB, Molecular Signatures Database; STRING, String Protein–Protein Interaction Networks; dbSNP: Single Nucleotide Polymorphism Database; MethBank, Methylation Bank; ODE, Ordinary Differential Equation; PDE, Partial Differential Equation; SDE, Stochastic Differential Equations.