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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Gooding, Kim M. Lienczewski, Chrysta Papale, Massimo Koivuviita, Niina Maziarz, Marlena Dutius Andersson, Anna-Maria Sharma, Kanishka Pontrelli, Paola Garcia Hernandez, Alberto Bailey, Julie Tobin, Kay Saunavaara, Virva Zetterqvist, Anna Shelley, David Teh, Irvin Ball, Claire Puppala, Sapna Ibberson, Mark Karihaloo, Anil Metsärinne, Kaj Banks, Rosamonde E. Gilmour, Peter S. Mansfield, Michael Gilchrist, Mark de Zeeuw, Dick Heerspink, Hiddo J. L. Nuutila, Pirjo Kretzler, Matthias Welberry Smith, Matthew Gesualdo, Loreto Andress, Dennis Grenier, Nicolas Shore, Angela C. Gomez, Maria F. Sourbron, Steven |
| Abstract | Background Diabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project 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 (secondary aim). Methods iBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes. Discussion iBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D. Trial registration Clinicaltrials.gov ( NCT03716401 ). |
| Related Links | https://bmcnephrol.biomedcentral.com/counter/pdf/10.1186/s12882-020-01901-x.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712369 |
| DOI | 10.1186/s12882-020-01901-x |
| Journal | BMC Nephrology |
| Issue Number | 1 |
| Volume Number | 21 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2020-06-29 |
| Access Restriction | Open |
| Subject Keyword | Nephrology Internal Medicine Diabetic kidney disease Type 2 diabetes Magnetic resonance imaging Ultrasound Albuminuria Chronic kidney disease stages 1–3 Prospective cohort Renal decline Biomarkers Progression |
| Content Type | Text |
| Resource Type | Article |
| Subject | Nephrology |
| Journal Impact Factor | 2.2/2023 |
| 5-Year Journal Impact Factor | 2.6/2023 |
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