# Environmental Metal Toxicity and Kidney Tubule Measures in Diverse Populations

> **NIH NIH R01** · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · 2024 · $738,587

## Abstract

PROJECT SUMMARY / ABSTRACT
 There is a high burden of environmental metal exposure in the U.S. In rural communities, this is often
from contamination of groundwater from mining and natural sources and use of well water for cooking and
drinking. In urban settings, recent water contamination events in Flint, Michigan and Jackson, Mississippi,
among other communities, have highlighted vulnerabilities to failures in water safety. Metal exposure remains
disproportionately high among minoritized populations and those with lower socioeconomic status in both rural
or urban settings. Across a broad panel of different metals, including arsenic, cadmium, lead, and uranium,
high levels of exposure are known to damage kidney tubules. Yet, the health consequences of lower levels of
metal exposure have not been elucidated, principally because sensitive markers of kidney tubule damage had
been lacking. Recently, methods for assessing metal levels in biospecimens and in the water supply, have
markedly progressed, including sensitive methods to detect kidney tubule damage non-invasively.
 Our ultimate goal is to develop a kidney monitoring panel that can detect and quantify damage to the kidney
from any of the metal exposures that have

## Key facts

- **NIH application ID:** 10821096
- **Project number:** 1R01DK138542-01
- **Recipient organization:** NORTHERN CALIFORNIA INSTITUTE/RES/EDU
- **Principal Investigator:** Joachim H Ix
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $738,587
- **Award type:** 1
- **Project period:** 2024-03-01 → 2028-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10821096

## Citation

> US National Institutes of Health, RePORTER application 10821096, Environmental Metal Toxicity and Kidney Tubule Measures in Diverse Populations (1R01DK138542-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10821096. Licensed CC0.

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