About: Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol
Creator	Smith, Matthew Andersson, Anna-Maria Dutius Andress, Dennis Bailey, Julie Ball, Claire Banks, Rosamonde Dick De Zeeuw, Garcia Hernandez, Alberto Gesualdo, Loreto Gilchrist, Mark Gilmour, Peter Gomez, Maria Gooding, Kim Grenier, Nicolas Heerspink, Hiddo Ibberson, Mark Karihaloo, Anil Koivuviita, Niina Kretzler, Matthias Lienczewski, Chrysta Mansfield, Michael Maziarz, Marlena Metsärinne, Kaj Nuutila, Pirjo Papale, Massimo Pontrelli, Paola Puppala, Sapna Saunavaara, Virva Sharma, Kanishka Shelley, David Shore, Angela Sourbron, Steven Teh, Irvin Tobin, Kay Zetterqvist, Anna
Source	PMC
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/1.73m(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).
has issue date	2020-06-29(xsd:dateTime)
bibo:doi	10.1186/s12882-020-01901-x
has license	no-cc
sha1sum (hex)	cebbf362426b3f58fb2eedb09c25ab96438ab969
schema:url	https://doi.org/10.1186/s12882-020-01901-x
resource representing a document's title	Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol
has PubMed Central identifier	PMC7323369
schema:publication	BMC Nephrol
resource representing a document's body	covid:cebbf362426b3f58fb2eedb09c25ab96438ab969#body_text
is schema:about of	named entity 'classified' named entity 'leading' named entity 'improve' named entity 'heterogeneity' named entity 'magnetic resonance imaging (MRI)' named entity 'aim' named entity 'study' named entity 'reduced' named entity 'biomarkers' named entity 'aims' named entity 'biomarkers' named entity 'premature death' named entity 'albuminuria' named entity 'DKD' named entity 'prognostic' named entity 'Diabetic kidney disease' named entity 'heterogeneity' named entity 'diabetic kidney disease' named entity 'Prognostic' named entity 'CKD' named entity 'Kidney Diseases' named entity 'T2D' named entity 'urine' named entity 'ADPKD' named entity 'DKD' named entity 'quality assurance' named entity 'biomarkers' named entity 'MRI' named entity 'immunofluorescence microscopy' named entity 'medical records' named entity 'kidney' named entity 'end stage renal disease' named entity 'longitudinal analysis' named entity 'infection' named entity 'glycocalyx' named entity 'diabetes' named entity 'hyperfiltration' named entity 'TKV' named entity 'physical activity' named entity 'biomarkers' named entity 'albuminuria' named entity 'DKD' named entity 'DKD' named entity 'biopsies' named entity 'Clinical study' named entity 'DKD' named entity 'people with diabetes' named entity 'mixed effects models' named entity 'Lund University'

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