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Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol
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 nee...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323369/ https://www.ncbi.nlm.nih.gov/pubmed/32600374 http://dx.doi.org/10.1186/s12882-020-01901-x |
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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 |
author_facet | 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 |
author_sort | Gooding, Kim M. |
collection | PubMed |
description | 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/1.73m(2). 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 H(2)O(15) 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). |
format | Online Article Text |
id | pubmed-7323369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73233692020-06-29 Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 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 BMC Nephrol Study Protocol 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/1.73m(2). 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 H(2)O(15) 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). BioMed Central 2020-06-29 /pmc/articles/PMC7323369/ /pubmed/32600374 http://dx.doi.org/10.1186/s12882-020-01901-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Study Protocol 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 Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title | 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_short | Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol |
title_sort | prognostic imaging biomarkers for diabetic kidney disease (ibeat): study protocol |
topic | Study Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323369/ https://www.ncbi.nlm.nih.gov/pubmed/32600374 http://dx.doi.org/10.1186/s12882-020-01901-x |
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