Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
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
_version_ 1783551770674331648
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
work_keys_str_mv AT goodingkimm prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT lienczewskichrysta prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT papalemassimo prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT koivuviitaniina prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT maziarzmarlena prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT dutiusanderssonannamaria prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT sharmakanishka prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT pontrellipaola prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT garciahernandezalberto prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT baileyjulie prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT tobinkay prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT saunavaaravirva prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT zetterqvistanna prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT shelleydavid prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT tehirvin prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT ballclaire prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT puppalasapna prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT ibbersonmark prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT karihalooanil prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT metsarinnekaj prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT banksrosamondee prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT gilmourpeters prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT mansfieldmichael prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT gilchristmark prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT dezeeuwdick prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT heerspinkhiddojl prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT nuutilapirjo prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT kretzlermatthias prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT welberrysmithmatthew prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT gesualdoloreto prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT andressdennis prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT greniernicolas prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT shoreangelac prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT gomezmariaf prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT sourbronsteven prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol
AT prognosticimagingbiomarkersfordiabetickidneydiseaseibeatstudyprotocol