# Improving Kidney Function Assessment in Health and Disease

> **NIH NIH R01** · TUFTS MEDICAL CENTER · 2022 · $707,484

## Abstract

Accurate assessment of kidney function is fundamental to the care of all patients, but current methods to
estimate GFR have error rates of 35-60% in important populations as well as variable accuracy across race,
ethnicity and geography, leading to errors in critical decisions, drug dosing, and determination of prognosis.
Current GFR estimates include a coefficient for Black vs non-Black which may restrict access to care. As a
result, there are critical knowledge gaps in evaluation and management of GFR in health and disease.
Our goal is to develop a valid and robust GFR estimate optimized for an individual person that meets the
critical unmet need for a confirmatory test. The confirmatory test will use a panel of endogenous filtration
markers to estimate GFR (peGFR) from a single blood sample using an equation that does not require serum
creatinine or demographic characteristics. Our research team has extensive experience in biomarker
evaluation, GFR estimation, epidemiology, laboratory science and metabolomics. Our preliminary data
provide proof of concept that a panel of novel metabolites that does not include serum creatinine or
demographics can eliminate bias and greatly improve precision of GFR estimates in patients with heart failure
and reduced muscle mass; and that novel techniques can be used to produce a high-accuracy and
parsimonious prediction model.
Aim 1 Using global metabolomic discovery (~1000+ metabolites) on five cohorts (N=2583), we will select
candidate metabolites based on maximal joint association with mGFR as well as biological and physiological
assessment of their properties as filtration markers. Markers that show acceptable analytical properties in initial
testing will be incorporated into a liquid chromatography tandem mass spectrometer (LC-MS/MS) multiplex
assay. Aim 2: We propose to use a spectrum of novel approaches, such as marginalizing predictions to down-
weight outlier metabolites and kernel nearest neighbor weighted average predictions, to maximize precision as
well as robustness across multiple diverse populations and to compare these approaches to a benchmark
model developed using superlearner ensemble modeling techniques. Development and external validation in
~3036 and ~3465 participants across 8 and 12 studies, respectively. Aim 3: We propose to evaluate the
impact of panel eGFR in patients with heart and liver failure (N=1796) on clinical decisions and outcomes.
The expected outcome is development of panel eGFR that can be used as a confirmatory test and ultimately
incorporated into clinical practice guidelines. The proposal is highly innovative as it is comprehensive
spanning discovery, validation, assessment of clinical utility in populations not previously been studied and use
of novel statistical methods to compute individualized GFR estimates. The proposal is significant because it
will enable generalizable and accurate individualized GFR estimates in health and disease, particularly
diseases with u...

## Key facts

- **NIH application ID:** 10316264
- **Project number:** 5R01DK116790-02
- **Recipient organization:** TUFTS MEDICAL CENTER
- **Principal Investigator:** Lesley Ann Inker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $707,484
- **Award type:** 5
- **Project period:** 2020-12-15 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10316264

## Citation

> US National Institutes of Health, RePORTER application 10316264, Improving Kidney Function Assessment in Health and Disease (5R01DK116790-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10316264. Licensed CC0.

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