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Abstract: FR-PO423

The iBEAT MRI Biomarker Panel: Prognostic Imaging Biomarkers for DKD

Session Information

Category: Diabetic Kidney Disease

  • 602 Diabetic Kidney Disease: Clinical


  • Sharma, Kanishka, University of Leeds, Leeds, United Kingdom
  • Tagkalakis, Fotios, University of Leeds, Leeds, United Kingdom
  • Teh, Irvin, University of Leeds, Leeds, United Kingdom
  • Saunavaara, Virva, Turku PET centre c/o Turku University Hospital, Turku, Finland
  • Kuznetsov, Dmitry, SIB, Lausanne, Switzerland
  • Mansfield, Michael W., University of Leeds, Leeds, United Kingdom
  • Gilchrist, Mark, Royal Devon and Exeter Hospital, Devon, Exeter, United Kingdom
  • De blasi, Roberto, Pia Fondazione Panico, Tricase, Italy
  • Ibberson, Mark, SIB, Lausanne, Switzerland
  • Karihaloo, Anil K., Novo Nordisk, Seattle, Washington, United States
  • Grenier, Nicolas, Pellegrin Hospital, Bordeaux, France
  • Sourbron, Steven, University of Leeds, Leeds, United Kingdom

Group or Team Name

  • BEAt-DKD Work Package 4

Advances in functional Magnetic Resonance Imaging (MRI) have provided novel renal imaging biomarkers that inform on parenchymal perfusion, oxygenation, filtration, fibrosis, inflammation, and tissue composition.
We present a novel panel of MRI biomarkers designed to help predict DKD progression. The panel has been developed for iBEAT, a new cohort study in 500 patients with T2 diabetes and eGFR>30 mL/min, starting mid 2018. iBEAT is part of the BEAt-DKD project ( funded by IMI-JU (No 115974).


iBEAT will collect MRI in 5 sites at 3 Tesla scanners of 2 vendors; studies will be uploaded into a central database. QC and MRI post-processing will be performed centrally, and MRI biomarkers will be integrated with clinical data, demographics, blood-, urine-, and biopsy biomarkers, endothelial function, and nuclear medicine gold-standards. The MRI protocol was developed through a 6-month process involving measurements on volunteers and a reference object developed by NIST.


The iBEAT MRI panel characterizes both kidney parenchyma and general body composition. The latter includes liver fat (%, insulin resistance), pancreatic fat (%, insulin secretion), liver iron (mg/g, diabetes risk), visceral fat (mL, diabetes risk). Renal biomarkers cover three groups:
(1) Anatomical biomarkers include kidney volume (mL, increased in hyperfiltration), renal sinus fat (mL, predictor of albuminuria), cortical volume (mL, surrogate for nephron number);
(2) Microstructure biomarkers include T2* (ms, oxygenation), T1 (ms, fibrosis), T2 (ms, inflammation, oedema), blood volume (%, glomerular hypertrophy), tubular volume (%, tubular obstruction), fractional anisotropy (%, tubular dilatation, glomerulosclerosis);
(3) Dynamic biomarkers include cortical and medullary perfusion (ml/min/g), filtration fraction (%, hyperfiltration), tubular flow (ml/min/g, concentrating capacity), GFR density (ml/min/g).


The iBEAT MRI biomarker panel is designed to capture DKD progression on the level of renal tissue dynamics, microstructure and anatomy, as well as broader risk factors for diabetes. The iBEAT study will determine the utility of these biomarkers for DKD prognosis, and whether they may become part of the clinical endpoint in future trial designs.


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