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.

Public Health Relevance

Kidney function is usually estimated from substances found in the blood, called filtration markers, but these estimates are not individualized. The current project will target this important gap in knowledge by evaluating whether the combination of existing and novel filtration markers will lead to more accurate estimates of kidney function. More accurate estimates will enable better clinical decision making, more rigorous research designs, and better estimates of the public health burden of chronic kidney disease in the United States.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK097020-03
Application #
8726978
Study Section
Special Emphasis Panel (ZDK1)
Program Officer
Narva, Andrew
Project Start
2012-09-28
Project End
2015-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Tufts University
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02111
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