# University of Pittsburgh Clinical and Translational Science Institute

> **NIH NIH UL1** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $842,907

## Abstract

ABSTRACT
The pandemic caused by the SARS-CoV-2 virus continues to impact public health and the health of individuals,
families, and communities. Those with a rare disease may be disproportionately affected because they may
have a high risk of exposure to SARS-CoV-2 from their caregivers, housing situations, and need to attend in-
person medical appointments. They may also be particularly vulnerable to complications from infection due to
their underlying disease condition, immunosuppressive therapies, genetic susceptibility, and/or other factors.
The scope of infection among those with rare diseases is unknown. The present proposal will investigate the
sero-prevalence of immunity against SARS-CoV-2 among asymptomatic individuals with rare diseases.
Specifically, this study will determine the prevalence of detectable antibodies to SARS-CoV-2 (Aim 1) and
investigate the immune attributes associated with health outcomes across the life course (Aim 2) among
asymptomatic individuals across the United States with one of >280 rare diseases. The anticipated results will
provide crucial insights into the magnitude of the COVID-19 pandemic in the context of rare disease and will
contribute to the identification of potential targets for a vaccine.

## Key facts

- **NIH application ID:** 10216856
- **Project number:** 3UL1TR001857-05S2
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** STEVEN E REIS
- **Activity code:** UL1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $842,907
- **Award type:** 3
- **Project period:** 2020-09-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10216856

## Citation

> US National Institutes of Health, RePORTER application 10216856, University of Pittsburgh Clinical and Translational Science Institute (3UL1TR001857-05S2). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10216856. Licensed CC0.

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