Estimating GFR from a Panel of Endogenous Filtration Markers (Panel eGFR)

根据内源性滤过标记物组估算 GFR(eGFR 组)

基本信息

  • 批准号:
    8550040
  • 负责人:
  • 金额:
    $ 56.68万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-28 至 2015-08-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Assessing kidney function is an integral part of the practice of medicine. However, even the most accurate GFR estimates based on serum creatinine and cystatin C are biased in selected populations and imprecise in all. Measured GFR is the only confirmatory test for decreased estimated GFR, but is not practical and consequently, is not performed in most clinical practice or research settings. Our long-term goal is to develop GFR estimates that are as accurate as measured GFR, requiring fewer demographic or clinical variables and only a single blood sample to assay a panel of endogenous filtration markers, which can be reported automatically by clinical laboratories for use as a confirmatory test in clinical practice and research. We think that a critical flaw in past attempts to improve GFR estimation is the search for a single ideal filtration marker. Our objective is to evaluate novel endogenous filtration markers and to identify a "GFR panel" consisting of 4-7 markers (2-4 novel and 2-3 well-established markers) for use with GFR estimating equations to report a "panel eGFR". Our central hypothesis, based on statistical concepts and confirmed by our preliminary data, is that the panel eGFR can be substantially more accurate than current GFR estimates even if each novel marker is not more accurate than creatinine or cystatin C. The rationale for including multiple markers in a panel is to diminish bias from non-correlated non-GFR determinants of each marker, reduce the need for inclusion of demographic or clinical variables, thereby increasing precision with each additional marker. The expected outcome is a GFR panel and GFR estimating equations that can be used for reporting panel eGFR that approaches the accuracy of measured GFR. Our research team, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), has extensive experience in evaluation of biomarkers, GFR estimation, and CKD epidemiology. In the current proposal, we will use specimens from 5390 subjects in 9 studies to examine 3 established (urea, creatinine and cystatin C) and 4 novel filtration markers [3 low molecular weight serum proteins: ¿-trace protein (BTP), ¿-2 microglobulin (B2M), and tumor associated trypsin inhibitor (TATI); and 1 metabolite: 2-(¿-mannopyranosyl)-L-tryptophan (MPT, also known as Tryp-Man), or another metabolite]. Our specific aims are (1) to evaluate novel endogenous filtration markers for inclusion in a panel with well-established filtration markers (GFR panel). (2) To develop GFR estimating equations for use with multiple filtration markers to report a panel eGFR. We have adequate power to test our hypotheses within studies, and within subgroups in the pooled dataset. The proposed work is highly innovative because it augments the traditional strategy of estimating GFR using a single marker, and will enable development of GFR estimates that are as accurate as measured GFR using a single blood sample. The proposed work is significant because it will facilitate development of GFR estimating equations for confirmation of decreased estimated GFR in clinical practice and research.
描述(申请人提供):评估肾功能是医学实践中不可或缺的一部分。然而,即使是基于血清肌酐和胱抑素C的最准确的GFR估计在选定的人群中也是有偏见的,而且总体上是不准确的。测量的肾小球滤过率是估计肾小球滤过率降低的唯一确证试验,但不实用,因此在大多数临床实践或研究环境中不进行。我们的长期目标是开发出与测量的GFR一样准确的GFR估计,需要更少的人口统计学或临床变量,只需一份血液样本就可以检测一组内源性滤过标志物,临床实验室可以自动报告这些标记物,用于临床实践和研究中的验证性测试。我们认为过去的一个关键缺陷 改善GFR估计的尝试是寻找一种单一的理想的滤过标志物。我们的目标是评估新的内源性滤过标志物,并确定由4-7个标志物(2-4个新标志物和2-3个公认的标志物)组成的“GFR小组”,用于GFR估计方程以报告“小组EGFR”。我们的中心假设,基于统计学概念,并被我们的初步数据证实,即使每个新的标记物都不比肌酐或胱抑素C更准确,小组EGFR也可以比当前的GFR估计更准确。在一个小组中包括多个标记物的理由是减少每个标记物的不相关的非GFR决定因素的偏差,减少纳入人口统计学或临床变量的需要,从而提高每个额外标记物的精确度。预期结果是一个GFR小组和GFR估计公式,可用于报告小组EGFR,接近测量的GFR的准确性。我们的研究团队,慢性肾脏疾病流行病学协作组(CKD-EPI),在生物标志物评估、GFR评估和CKD流行病学方面拥有丰富的经验。在目前的方案中,我们将使用9项研究中5,390名受试者的样本来检测3种已建立的滤过性标志物(尿素、肌酐和胱抑素C)和4种新的滤过标志物[3种低分子血清蛋白:微量蛋白(BTP)、-2微球蛋白(B2M)和肿瘤相关胰蛋白酶抑制物(TATI);以及1种代谢物:2-(甘露糖基)-L-色氨酸(MPT,也称为Tryp-Man)或另一种代谢物]。我们的具体目标是(1)评估新的内源性滤过标志物,以纳入具有公认的滤过标志物的小组(GFR小组)。(2)建立与多个滤过性标志物联合使用的GFR估计方程,以报告一组EGFR。我们有足够的能力在研究中以及在汇集的数据集中的子组中测试我们的假设。这项拟议的工作具有很高的创新性,因为它补充了使用单一标记物估计GFR的传统策略,并将使GFR估计能够与使用单一血液样本测量的GFR一样准确。建议的工作具有重要意义,因为它将促进GFR估计方程的发展,以在临床实践和研究中证实减少的估计GFR。

项目成果

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ANDREW S LEVEY其他文献

ANDREW S LEVEY的其他文献

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{{ truncateString('ANDREW S LEVEY', 18)}}的其他基金

Estimating GFR from a Panel of Endogenous Filtration Markers (Panel eGFR)
根据内源性滤过标记物组估算 GFR(eGFR 组)
  • 批准号:
    8418921
  • 财政年份:
    2012
  • 资助金额:
    $ 56.68万
  • 项目类别:
Estimating GFR from a Panel of Endogenous Filtration Markers (Panel eGFR)
根据内源性滤过标记物组估算 GFR(eGFR 组)
  • 批准号:
    8726978
  • 财政年份:
    2012
  • 资助金额:
    $ 56.68万
  • 项目类别:
Effects of Age and Race on GFR Estimation in a Population-based Cohort
基于人群的队列中年龄和种族对 GFR 估计的影响
  • 批准号:
    8540412
  • 财政年份:
    2011
  • 资助金额:
    $ 56.68万
  • 项目类别:
Effects of Age and Race on GFR Estimation in a Population-based Cohort
基于人群的队列中年龄和种族对 GFR 估计的影响
  • 批准号:
    8919877
  • 财政年份:
    2011
  • 资助金额:
    $ 56.68万
  • 项目类别:
Effects of Age and Race on GFR Estimation in a Population-based Cohort
基于人群的队列中年龄和种族对 GFR 估计的影响
  • 批准号:
    8041349
  • 财政年份:
    2011
  • 资助金额:
    $ 56.68万
  • 项目类别:
Effects of Age and Race on GFR Estimation in a Population-based Cohort
基于人群的队列中年龄和种族对 GFR 估计的影响
  • 批准号:
    8722546
  • 财政年份:
    2011
  • 资助金额:
    $ 56.68万
  • 项目类别:
Effects of Age and Race on GFR Estimation in a Population-based Cohort
基于人群的队列中年龄和种族对 GFR 估计的影响
  • 批准号:
    8334051
  • 财政年份:
    2011
  • 资助金额:
    $ 56.68万
  • 项目类别:
Kidney Function, Aortic Stiffness and Aging
肾功能、主动脉僵硬和衰老
  • 批准号:
    8110885
  • 财政年份:
    2010
  • 资助金额:
    $ 56.68万
  • 项目类别:
Kidney Function, Aortic Stiffness and Aging
肾功能、主动脉僵硬和衰老
  • 批准号:
    8066727
  • 财政年份:
    2010
  • 资助金额:
    $ 56.68万
  • 项目类别:
Kidney Function, Aortic Stiffness and Aging
肾功能、主动脉僵硬和衰老
  • 批准号:
    7785456
  • 财政年份:
    2010
  • 资助金额:
    $ 56.68万
  • 项目类别:

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