Prognostic Imaging Biomarkers for Diabetic Kidney Disease (iBEAt): Study protocol

Gooding, K. M.; Lienczewski, C.; Papale, M.; Koivuviita, N.; Maziarz, M.; Dutius Andersson, A.-M.; Sharma, K.; Pontrelli, P.; Garcia Hernandez, A.; Bailey, J.; Tobin, K.; Saunavaara, V.; Zetterqvist, A.; Shelley, D.; Teh, I.; Ball, C.; Puppala, S.; Ibberson, M.; Karihaloo, A.; Metsärinne, K.; Banks, R.; Gilmour, P. S.; Mansfield, M.; Gilchrist, M.; de Zeeuw, D.; Heerspink, H. J.; Nuutila, P.; Kretzler, M.; Wellberry-Smith, M.; Gesualdo, L.; Andress, D.; Grenier, N.; Shore, A. C.; Gomez, M. F.; Sourbron, S.; Investigators, iBEAT

Diabetic kidney disease (DKD) is traditionally classified based on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)), but these have limitations as prognostic biomarkers due to the heterogeneity of DKD. Novel prognostic markers are needed to improve stratification of patients based on risk of disease progression. The iBEAT study, part of the BEAt-DKD consortium, aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim), and whether they have potential as prognostic biomarkers in DKD progression (secondary aim). iBEAT is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR > 30ml/min/1.73m2. At baseline each participant will undergo quantitative renal MRI and US imaging with central processing for MRI images. Blood sampling, urine collection and clinical examinations will be performed and medical history obtained at baseline, and these assessments will be repeated annually for 3 years. Biological samples will be stored in a central laboratory for later biomarker and validation studies. All data will be stored in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers may improve the prediction of DKD progression rates. Embedded within iBEAT are ancillary substudies that will (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow against water-labelled positron-emission tomography (PET); (3) develop machine-learning methods for automated processing of renal MRI images; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether the glycocalyx, microvascular function and structure are associated with imaging biomarkers and eGFR decline; (6) a pilot study to examine whether the findings in T2D can be extrapolated to type 1 diabetes. The iBEAT study, the largest DKD imaging study to date, will provide invaluable insights into the progression and heterogeneity of DKD, and aims to contribute to a more personalized approach to the management of DKD in patients with type 2 diabetes.