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Abstract: SA-PO464

Preliminary Results from iBeat Ancillary Study: A Novel Bari Classification of Renal Damage in Diabetic Patients with CKD

Session Information

Category: Diabetic Kidney Disease

  • 702 Diabetic Kidney Disease: Clinical

Authors

  • Pontrelli, Paola, University of Bari, Bari, Italy
  • Rossini, Michele, University of Bari, Bari, Italy
  • Conserva, Francesca, University of Bari, Bari, Italy
  • Pesce, Francesco, University of Bari, Bari, Italy
  • Giorgino, Francesco, University of Bari, Bari, Italy
  • Laviola, Luigi, University of Bari, Bari, Italy
  • McCown, Phillip J., University of Michigan, Ann Arbor, Michigan, United States
  • Otto, Edgar A., University of Michigan, Ann Arbor, Michigan, United States
  • Godfrey, Brad A., University of Michigan, Ann Arbor, Michigan, United States
  • Sourbron, Steven, University of Sheffield, Sheffield, United Kingdom
  • Gooding, Kim, University of Exeter, Exeter, United Kingdom
  • Gomez, Maria F., Lunds Universitet, Lund, Sweden
  • Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States
  • Gesualdo, Loreto, University of Bari, Bari, Italy
Background

Renal damage in diabetes is heterogeneous, kidney biopsy remains the gold standard for diagnosis. In this iBEAt sub-study, within the BEAt-DKD project (https://www.beat-dkd.eu/), we aimed to correlate imaging, clinical, molecular and histopathological data to dissect renal damage phenotypes.

Methods

We enrolled 83 patients to perform kidney biopsy (carried out in 64 patients), US and MRI, biofluids and clinical data collection.

Results

According to KDIGO 13 patients were in stage A1, 26 in A2 and 25 in A3, all ranging from G1 to G4. Histological classification by Mazzucco et al (PMID: 11920336) identified 31% of patients with class 1 (diabetic nephropathy-DN); 33% with class 2 (vascular and ischemic glomerular changes); 4% with class 3a (glomerular diseases and DN) and 31% with class 3b (other glomerulonephritis in the absence of DN). Since we observed heterogeneous histological lesions, we here propose a novel classification for diabetic patients with CKD. We recognized 3 different classes (DKD, NDKD, DKD+NDKD) and 6 different phenotypes including: a) in DKD (33%): pure metabolic (7%) and vascular metabolic (26%); b) in NDKD (63%): pure vascular (35%), immunological vascular (IgA, FSGS, MN; 9%), pure immunological (IgAN, FSGS; 19%); c) in DKD+NDKD overlapping phenotypes (4%). We observed differences of uACR (p=0.02) and proteinuria (p=0.005) among the different phenotypes of DKD and NDKD. PAS staining confirmed the increased mesangial expansion in DKD phenotype (p=0.04); vascular wall-to-lumen ratio discriminated vascular vs metabolic (p=0.04) and immunologic damage (p=0.05); renal resistive index discriminated among the phenotypes (p=0.02). We also performed single-cell RNASeq analysis of kidney biopsies mapping more than 61000 nuclei in different cell types, identifying differences between classes and phenotypes along the nephron and the vasculature (i.e. TNNT2 expression in podocytes was associated with vascular damage).

Conclusion

The integration of renal pathology, imaging and molecular data from the same patient has the potential to unlock new diagnostic criteria to address the heterogeneity of renal phenotypes, providing a new classification of renal damage in diabetes.