# Advancing the Phenotyping of Acute Kidney Injury for the Million Veterans Program

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2021 · —

## Abstract

Acute kidney injury (AKI) is a complex and deadly disease that is strongly associated with progressive loss
of kidney function, cardiovascular disease, poor quality of life, and death. The most severe forms of AKI cause
parenchymal damage, which manifests as a persistent loss of kidney function. This condition, termed intrinsic
AKI (iAKI), carries the highest mortality and risk for long-term loss of kidney function. Due to their older age
and high prevalence of risk factors, Veterans are at especially high risk for experiencing iAKI compared to the
general population. Despite decades of investment, no successful treatments have been translated from
preclinical studies into routine clinical practice. The latter has led to calls for greater understanding of the
mechanisms responsible for defining risk in human iAKI.
 A growing area of investigation is understanding the genetic basis for susceptibility to iAKI. Early studies
have been limited by small sample sizes, a lack of unbiased approaches (e.g. genome wide association
(GWA)) and predicted gene expression studies, and most importantly, superficial phenotyping that does not
distinguish between causes of AKI. As iAKI is a heterogeneous condition, this critical deficiency can dilute
biological signals and treatment effects in large-scale studies. Lastly, few studies in AKI have explored
identifying novel phenotypes, or endophenotypes of iAKI, which have shown promise for improving
understanding of other complex and heterogeneous conditions. The overarching goals of this proposal are to
a) advance the clinical phenotyping of the most common and severe forms of intrinsic AKI (iAKI), and b)
leverage these phenotypes to identify genetic variants associated with iAKI.
 In Aim 1, we will apply a data-driven deep learning algorithm to dense structured data and narrative text to
discover data patterns that will likely represent a mixture of previously recognized and unrecognized
endophenotypes of AKI. In Aim 2, we will complement this strategy by generating probabilistic phenotype
algorithms that use manual chart review to identify traditional iAKI phenotypes in 3 clinical settings where iAKI
is common: cardiovascular surgery, cardiac catheterization, and sepsis. In Aim 3, we will perform a series of
GWA studies within these settings comparing cases identified by our iAKI phenotyping algorithms in Aim 2 to
patients without AKI within the Million Veteran Program. We will also conduct the same analyses using the
most promising Aim 1 data-driven endophenotypes. We will evaluate the top associated regions using
PrediXcan to examine predicted gene expression in kidney tissues.
 The proposed studies will be performed within the VA Million Veterans Gamma Program by a
multidisciplinary team of experts in AKI phenotyping, informatics-based phenotype developers, and genetic
epidemiologists. The deliverables from this proposal will advance the computational phenotyping of iAKI,
expand the rigor and scale of large-sc...

## Key facts

- **NIH application ID:** 9939306
- **Project number:** 5I01HX002489-02
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** MICHAEL E. MATHENY
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-04-01 → 2025-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9939306

## Citation

> US National Institutes of Health, RePORTER application 9939306, Advancing the Phenotyping of Acute Kidney Injury for the Million Veterans Program (5I01HX002489-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9939306. Licensed CC0.

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