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Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol.
BMC Nephrology ( IF 2.2 ) Pub Date : 2020-06-29 , DOI: 10.1186/s12882-020-01901-x
Kim M Gooding 1, 2 , Chrysta Lienczewski 3 , Massimo Papale 4 , Niina Koivuviita 5, 6 , Marlena Maziarz 7 , Anna-Maria Dutius Andersson 7 , Kanishka Sharma 8 , Paola Pontrelli 4 , Alberto Garcia Hernandez 9 , Julie Bailey 10 , Kay Tobin 11 , Virva Saunavaara 12 , Anna Zetterqvist 7 , David Shelley 10, 13 , Irvin Teh 14 , Claire Ball 2 , Sapna Puppala 10 , Mark Ibberson 15 , Anil Karihaloo 16 , Kaj Metsärinne 5 , Rosamonde E Banks 17 , Peter S Gilmour 18 , Michael Mansfield 10 , Mark Gilchrist 1 , Dick de Zeeuw 19, 20 , Hiddo J L Heerspink 19, 20 , Pirjo Nuutila 5, 6 , Matthias Kretzler 3, 21 , Matthew Welberry Smith 11 , Loreto Gesualdo 4 , Dennis Andress 22 , Nicolas Grenier 23 , Angela C Shore 1, 2 , Maria F Gomez 7 , Steven Sourbron 8 ,
Affiliation  

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). 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. 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. Clinicaltrials.gov ( NCT03716401 ).

中文翻译:

糖尿病肾病预后成像生物标志物 (iBEAt):研究方案。

糖尿病肾病 (DKD) 仍然是糖尿病患者过早死亡的主要原因之一。DKD 根据蛋白尿和肾功能降低(估计肾小球滤过率 (eGFR))进行分类,但这些对预测未来的肾脏状态具有中等价值。对可用于临床环境的生物标志物的需求未得到满足,这些生物标志物还可以在常规可用数据的基础上提高对肾功能衰退的预测,特别是在早期阶段。BEAt-DKD 项目的 iBEAt 研究旨在确定肾脏成像生物标志物(磁共振成像 (MRI) 和超声 (US))是否有助于深入了解 DKD(主要目标)的发病机制和异质性,以及它们是否具有作为预后生物标志物的潜力在 DKD(次要目标)。iBEAt 是一项前瞻性多中心观察性队列研究,招募了 500 名 2 型糖尿病 (T2D) 和 eGFR ≥30 ml/min/1.73m2 的患者。在基线时,将收集血液和尿液,进行临床检查,并获得病史。这些评估将每年重复一次,持续 3 年。在基线时,每位参与者还将接受定量肾脏 MRI 和 US,并对 MRI 图像进行中央处理。生物样本将存储在中央实验室进行生物标志物和验证研究,并将数据存储在中央数据存储库中。数据分析将探索成像生物标志物与肾功能之间的潜在关联,以及成像生物标志物是否能改善对 DKD 进展的预测。辅助子研究将: (1) 针对肾脏组织病理学验证成像生物标志物;(2) 根据 H2O15 正电子发射断层扫描 (PET) 验证基于 MRI 的肾血流量测量值;(3) 验证肾脏 MRI(半)自动处理的方法;(4) 检查成像生物标志物的纵向变化;(5) 检查糖萼和微血管测量是否与成像生物标志物和 eGFR 下降有关;(6) 探讨 T2D 的发现是否可以外推到 1 型糖尿病。iBEAt 是迄今为止最大的 DKD 成像研究,将为 DKD 的进展和异质性提供有价值的见解。结果可能有助于为 T2D 患者提供更个性化的 DKD 管理方法。Clinicaltrials.gov (NCT03716401)。(4) 检查成像生物标志物的纵向变化;(5) 检查糖萼和微血管测量是否与成像生物标志物和 eGFR 下降有关;(6) 探讨 T2D 的发现是否可以外推到 1 型糖尿病。iBEAt 是迄今为止最大的 DKD 成像研究,将为 DKD 的进展和异质性提供有价值的见解。结果可能有助于为 T2D 患者提供更个性化的 DKD 管理方法。Clinicaltrials.gov (NCT03716401)。(4) 检查成像生物标志物的纵向变化;(5) 检查糖萼和微血管测量是否与成像生物标志物和 eGFR 下降有关;(6) 探讨 T2D 的发现是否可以外推到 1 型糖尿病。iBEAt 是迄今为止最大的 DKD 成像研究,将为 DKD 的进展和异质性提供有价值的见解。结果可能有助于为 T2D 患者提供更个性化的 DKD 管理方法。Clinicaltrials.gov (NCT03716401)。iBEAt 是迄今为止最大的 DKD 成像研究,将为 DKD 的进展和异质性提供有价值的见解。结果可能有助于为 T2D 患者提供更个性化的 DKD 管理方法。Clinicaltrials.gov (NCT03716401)。iBEAt 是迄今为止最大的 DKD 成像研究,将为 DKD 的进展和异质性提供有价值的见解。结果可能有助于为 T2D 患者提供更个性化的 DKD 管理方法。Clinicaltrials.gov (NCT03716401)。
更新日期:2020-06-29
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