Abstract: SA-PO137

Integrated Score from Glomerular Structure and Molecular Profiles Predicts ESRD in Diabetic Kidney Disease (DKD)

Session Information

Category: Diabetes

  • 503 Diabetes Mellitus and Obesity: Translational

Authors

  • Nair, Viji, University of Michigan, Ann Arbor, Michigan, United States
  • Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States
  • Harder, Jennifer L., University of Michigan, Ann Arbor, Michigan, United States
  • Guarnieri, Paolo, Boehringer Ingelheim, Ridgefield, Connecticut, United States
  • Hill, Jonathan, Boehringer Ingelheim, Ridgefield, Connecticut, United States
  • Godfrey, Brad A., University of Michigan, Ann Arbor, Michigan, United States
  • Boustany, Carine, Boehringer Ingelheim, Ridgefield, Connecticut, United States
  • Najafian, Behzad, University of Washington, Seattle, Washington, United States
  • Mauer, Michael, University of Minnesota, Minneapolis, Minnesota, United States
  • Nelson, Robert G., National Institutes of Health, Phoenix, Arizona, United States
Background

DKD progression is the major cause of ESRD and increased mortality risk globally. We used systems biology tools to identify predictors of DKD progression through the integration of kidney structural changes with transcriptional profiling.

Methods

Glomerular specific gene expression profiling and quantitative morphometric analyses were performed on protocol kidney biopsies from 70 Pima Indians with type 2 diabetes [iothalamate (iGFR) 145 ± 52 ml/min and ACR 25.9[139.5]) mg/g]. Transcriptional co-expression modules were associated with glomerular morphometric traits using Weighted Gene Coexpression Network Analysis (WGCNA). Traits included glomerular basement membrane width (GBMW), mesangial fractional volume per glomerulus [Vv(Mes)], foot process width (FPW) and % peripheral capillary endothelial surface which is fenestrated (%EF), all associated with clinical expression of DKD. A z-score was computed from the gene set that correlated with these traits and associated with the ESRD outcome. The prediction ability of this score for ESRD over a median of 15 years of follow up was evaluated using the Area Under the Curve Statistic (AUC).

Results

WGCNA analysis identified 14 modules of coexpressed genes. Glomerular structural traits of DKD showed strong associations with molecular signatures. Inflammatory responses and cellular signaling and proliferation pathways were the prominent functions enriched within these shared structural traits. The glomerular lesions gene score discriminated patients with and without progression to ESRD (AUC 0.85[0.75 - 0.96], p <0.001) where cross sectional iGFR performed poorly 0.43[0.27-0.56]. The score also correlated significantly with iGFR slope (r= -0.55, p <0.001).

Conclusion

Early glomerular lesions of DKD and the gene expression pathways associated with these lesions strongly predicted progressive DKD in type 2 diabetes. Further evaluation of these pathways could provide early intervention targets and novel noninvasive biomarkers with predictive clinical utility.

Funding

  • NIDDK Support