#5662 PRELIMINARY RESULTS FROM IBEAT ANCILLARY-STUDY: A NOVEL CLASSIFICATION OF RENAL DAMAGE IN DIABETES
Authors: Pontrelli Paola , Rossini Michele , Conserva Francesca , Pesce Francesco , Ardillo Teodora , Moschetta Marco , Giorgino Francesco , Laviola Luigi , Sourbron Steven , Gooding Kim , Gomez Maria , Kretzler Matthias , Gesualdo Loreto 
Published: June 2023

Background and Aims

Renal damage in diabetes can manifest as real diabetic glomerulosclerosis (DN) or non-diabetic renal disease (NDRD). DN and NDRD differ in terms of prognosis and treatment thus kidney biopsy remains the gold standard. The iBEAt study conducted within the frame of IMI project BEAt-DKD (https://www.beat-dkd.eu/; GA No 115974) aims to determine whether renal imaging biomarkers capture by magnetic resonance (MRI) and ultrasound (US) can dissect DKD heterogeneity. This ancillary sub-study in iBEAt aims to explore correlations between imaging biomarkers, clinical, molecular and renal histopathological data, in order to identify different DKD phenotypes.

Method

Up to January 2023 we enrolled 69 patients with: ages18-80; diagnosis of type 2 diabetes; eGFR &gt; = 15 ml/min/1.73m2. Patients underwent kidney biopsy, US and MRI, along with biofluids and clinical data collection. For each patient, a total of three renal cores were collected, one was fixed in formalin, embedded in paraffin and used for routine diagnostics, the other was split and either included in OCT or used for electron microscopy, while the last was stored in RNA later for omics analysis.

Results

Among the 69 patients enrolled, 57 underwent kidney biopsy and US, with 47 also undergoing MRI. According to the KDIGO guidelines our cohort included: 12 A1 stage patients (1 G1, 1 G2, 2 G3a, 6 G3b, 2 G4); 27 in A2 stage (7 G1, 7 G2, 4 G3a, 7 G3b, 2 G4) and 21 in A3 (2 G1, 6 G3a, 6 G3b, 7 G4). According to the histological classification by Mazzucco et al (Am J Kidney Disease, 2002), 31% of DKD patients were categorized as Class 1 (diabetic glomerulosclerosis); 33% as Class 2 (vascular and ischemic glomerular changes); 4% as Class 3a (glomerular diseases superimposed on DN); 31% as Class 3b (other glomerulonephritis in the absence of DN).

Pathologists assessment evidenced the presence of heterogeneous lesions among patients included within the same Mazzucco class. We thus proposed a novel classification of DKD, based both on renal pathology and according to the pathogenetic drivers. We identified 7 different histological classes of DKD: i) Class I: pure DN; ii) Class II: DN and nephroangiosclerosis; iii) Class III: DN and acute tubular necrosis; iv) Class IV: DN and Focal Segmental Glomerulosclerosis (FSGS); v) Class V: Nephroangiosclerosis; vi) Class VI: FSGS and other GN; vii) Class VII: FSGS and nephroangiosclerosis. According to the pathogenic drivers of DKD, we then grouped these classes in: pure metabolic damage (class I), metabolic damage and other drivers (including classes II, III, IV), pure vascular damage (class V) and immunological damage (classes VI, VII). Applying the newly proposed classification, we observed a significantly different distribution of key clinical parameters, uACR (p = 0.02), eGFR (p = 0.012), serum creatinine (p = 0.002) and proteinuria (p = 0.004). PAS glomerular staining positivity confirmed the more severe glomerular damage in the metabolic classes (p = 0.02); the wall-to-lumen ratio was able to significantly discriminate patients with vascular from those with immunological damage (p = 0.002). Finally, the renal resistive index measured through US significantly discriminated pure DN from mixed forms (p = 0.02)

Conclusion

The classification of renal damage in diabetes could represent a key strategy in the stratification of patients for precision medicine. The integration of renal pathology, US, ongoing MRI and omics data from the same patient has the potential to unlock new diagnostic tools and criteria to more accurately define the variety of renal phenotypes in diabetes.