Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol
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 u...
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2020-06-01
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Online Access: | http://link.springer.com/article/10.1186/s12882-020-01901-x |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kim M. Gooding Chrysta Lienczewski Massimo Papale Niina Koivuviita Marlena Maziarz Anna-Maria Dutius Andersson Kanishka Sharma Paola Pontrelli Alberto Garcia Hernandez Julie Bailey Kay Tobin Virva Saunavaara Anna Zetterqvist David Shelley Irvin Teh Claire Ball Sapna Puppala Mark Ibberson Anil Karihaloo Kaj Metsärinne Rosamonde E. Banks Peter S. Gilmour Michael Mansfield Mark Gilchrist Dick de Zeeuw Hiddo J. L. Heerspink Pirjo Nuutila Matthias Kretzler Matthew Welberry Smith Loreto Gesualdo Dennis Andress Nicolas Grenier Angela C. Shore Maria F. Gomez Steven Sourbron for the BEAt-DKD consortium |
spellingShingle |
Kim M. Gooding Chrysta Lienczewski Massimo Papale Niina Koivuviita Marlena Maziarz Anna-Maria Dutius Andersson Kanishka Sharma Paola Pontrelli Alberto Garcia Hernandez Julie Bailey Kay Tobin Virva Saunavaara Anna Zetterqvist David Shelley Irvin Teh Claire Ball Sapna Puppala Mark Ibberson Anil Karihaloo Kaj Metsärinne Rosamonde E. Banks Peter S. Gilmour Michael Mansfield Mark Gilchrist Dick de Zeeuw Hiddo J. L. Heerspink Pirjo Nuutila Matthias Kretzler Matthew Welberry Smith Loreto Gesualdo Dennis Andress Nicolas Grenier Angela C. Shore Maria F. Gomez Steven Sourbron for the BEAt-DKD consortium Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol BMC Nephrology Diabetic kidney disease Type 2 diabetes Magnetic resonance imaging Ultrasound Albuminuria Chronic kidney disease stages 1–3 |
author_facet |
Kim M. Gooding Chrysta Lienczewski Massimo Papale Niina Koivuviita Marlena Maziarz Anna-Maria Dutius Andersson Kanishka Sharma Paola Pontrelli Alberto Garcia Hernandez Julie Bailey Kay Tobin Virva Saunavaara Anna Zetterqvist David Shelley Irvin Teh Claire Ball Sapna Puppala Mark Ibberson Anil Karihaloo Kaj Metsärinne Rosamonde E. Banks Peter S. Gilmour Michael Mansfield Mark Gilchrist Dick de Zeeuw Hiddo J. L. Heerspink Pirjo Nuutila Matthias Kretzler Matthew Welberry Smith Loreto Gesualdo Dennis Andress Nicolas Grenier Angela C. Shore Maria F. Gomez Steven Sourbron for the BEAt-DKD consortium |
author_sort |
Kim M. Gooding |
title |
Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title_short |
Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title_full |
Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title_fullStr |
Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title_full_unstemmed |
Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title_sort |
prognostic imaging biomarkers for diabetic kidney disease (ibeat): study protocol |
publisher |
BMC |
series |
BMC Nephrology |
issn |
1471-2369 |
publishDate |
2020-06-01 |
description |
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 ). |
topic |
Diabetic kidney disease Type 2 diabetes Magnetic resonance imaging Ultrasound Albuminuria Chronic kidney disease stages 1–3 |
url |
http://link.springer.com/article/10.1186/s12882-020-01901-x |
work_keys_str_mv |
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doaj-e94cbf7fb6ec48ce9d18b5b1e307048f2020-11-25T03:07:24ZengBMCBMC Nephrology1471-23692020-06-0121111110.1186/s12882-020-01901-xPrognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocolKim M. Gooding0Chrysta Lienczewski1Massimo Papale2Niina Koivuviita3Marlena Maziarz4Anna-Maria Dutius Andersson5Kanishka Sharma6Paola Pontrelli7Alberto Garcia Hernandez8Julie Bailey9Kay Tobin10Virva Saunavaara11Anna Zetterqvist12David Shelley13Irvin Teh14Claire Ball15Sapna Puppala16Mark Ibberson17Anil Karihaloo18Kaj Metsärinne19Rosamonde E. Banks20Peter S. Gilmour21Michael Mansfield22Mark Gilchrist23Dick de Zeeuw24Hiddo J. L. Heerspink25Pirjo Nuutila26Matthias Kretzler27Matthew Welberry Smith28Loreto Gesualdo29Dennis Andress30Nicolas Grenier31Angela C. Shore32Maria F. Gomez33Steven Sourbron34for the BEAt-DKD consortiumDiabetes and Vascular Medicine, University of Exeter Medical SchoolDepartment of Nephrology, University of MichiganDepartment of Emergency and Organ Transplantation, Nephrology Unit, University of Bari Aldo MoroDepartment of Medicine, Division of Nephrology, Turku University HospitalDepartment of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund UniversityDepartment of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund UniversityDepartment of Imaging, Infection, Immunity and Cardiovascular Disease, University of SheffieldDepartment of Emergency and Organ Transplantation, Nephrology Unit, University of Bari Aldo MoroAstellas Pharma Europe B.VLeeds Teaching Hospitals NHS TrustDepartment of Renal Medicine and Renal Transplantation, Leeds Teaching Hospitals NHS TrustDepartment of Medical Physics, Division of Medical Imaging, Turku University HospitalDepartment of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund UniversityLeeds Teaching Hospitals NHS TrustLeeds Institute of Cardiovascular and Metabolic Medicine, University of LeedsNIHR Exeter Clinical Research Facility, Royal Devon and Exeter NHS Foundation TrustLeeds Teaching Hospitals NHS TrustSwiss Institute of BioinformaticsNovo Nordisk Research Center Seattle, Inc.Department of Medicine, Division of Nephrology, Turku University HospitalLeeds Institute of Medical Research at St James’s, University of LeedsThe Drug Development TeamLeeds Teaching Hospitals NHS TrustDiabetes and Vascular Medicine, University of Exeter Medical SchoolDepartment of Clinical Pharmacy and Pharmacology, University Medical Center GroningenDepartment of Clinical Pharmacy and Pharmacology, University Medical Center GroningenDepartment of Medicine, Division of Nephrology, Turku University HospitalDepartment of Nephrology, University of MichiganDepartment of Renal Medicine and Renal Transplantation, Leeds Teaching Hospitals NHS TrustDepartment of Emergency and Organ Transplantation, Nephrology Unit, University of Bari Aldo MoroAbbVieService de Radiologie, CHU de Bordeaux, Université de BordeauxDiabetes and Vascular Medicine, University of Exeter Medical SchoolDepartment of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund UniversityDepartment of Imaging, Infection, Immunity and Cardiovascular Disease, University of SheffieldAbstract 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 ).http://link.springer.com/article/10.1186/s12882-020-01901-xDiabetic kidney diseaseType 2 diabetesMagnetic resonance imagingUltrasoundAlbuminuriaChronic kidney disease stages 1–3 |