# Remote Exposome Monitoring for Skin Diseases through Digital Health Devices and Home-Based Multiomics

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $444,125

## Abstract

Project Summary
The exposome, defined as the measure of all exposures for an individual and how those exposures relate to
an individual's health, plays a key role in many immune-mediated inflammatory skin diseases. Measuring the
exposome over time and understanding its effect on human health using sophisticated readouts such as -
omics assays is a key priority for future clinical and translational studies. However, one of the major barriers to
conducting such longitudinal exposome studies is their frequent reliance on in-person clinical research visits.
In-person research visits, often requiring time-consuming travel to an academic medical center and absence
from work, can significantly limit the geographic, racial, and socioeconomic representation of research
participants. These in-person visits also typically occur every few months, limiting the temporal resolution of
exposome data that can be measured. This proposal focuses on the implementation of innovative strategies
for remote, home-based exposome monitoring and -omics measurements. Successful completion of this work
will provide valuable resources for a future collaborative network studying the exposome.

## Key facts

- **NIH application ID:** 10871108
- **Project number:** 1R21AR084041-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Wilson Liao
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $444,125
- **Award type:** 1
- **Project period:** 2023-09-18 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10871108

## Citation

> US National Institutes of Health, RePORTER application 10871108, Remote Exposome Monitoring for Skin Diseases through Digital Health Devices and Home-Based Multiomics (1R21AR084041-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10871108. Licensed CC0.

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