# Epigenetic Age Measures to Predict COVID-19 Symptom Progression and Severity

> **NIH NIH R00** · YALE UNIVERSITY · 2020 · $139,448

## Abstract

PROJECT SUMMARY
 The risk of fatality and/or severe complications due to COVID-19 infection is strongly age
dependent. Data from the CDC suggests that those ages 85 and older have predicted mortality rates
that is 100-fold higher than for those under the age of 50 and currently, 8 out of 10 COVID-19 deaths
in the United States were in adults age 65 or older. While the exact etiology underlying this age
disparity is unknown, evidence suggests that vulnerability may be due to changes that occur as a
function of the aging process. This is further evidenced by the pattern of increased vulnerability
among persons with pre-existing diseases of aging—cardiovascular disease, diabetes, COPD,
chronic kidney disease, liver disease—suggesting that it isn't just chronological age that determines
risk, but rather, biological age.
 In recent years, our group has helped develop some of the most robust biomarkers available,
namely the epigenetic clocks. These measures estimate biological age in a sample based on DNA
methylation levels at hundreds to thousands of CpG sites across the genome. Not only do epigenetic
clocks track with age in diverse tissues and cell types, but discrepancies between epigenetic age and
actual age have also been shown to predict risk of mortality and incidence of major chronic disease,
including those which appear to be major risk factors for COVID-19. However, in order for these
measures to be informative for assessing COVID-19 risk clinically, or in the general population, 1)
they need to be re-optimized to capture the aspects of biological aging specific to COVID-19
susceptibility, and 2) advances in technology need to be made to ensure lower costs and rapid
turnaround.
 This proposal aims to build on our team's multidisciplinary strengths to develop and validate a
targeted, lab-developed, readily-available test to predict COVID-19 symptomology and mortality risk.
If successful, this test will have widespread applications—from informing triage and treatment
decisions in the clinic, to guiding social and pollical decisions when it comes to lifting “stay-at-home”
orders for certain individuals.

## Key facts

- **NIH application ID:** 10158592
- **Project number:** 3R00AG052604-04S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Morgan Elyse Levine
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $139,448
- **Award type:** 3
- **Project period:** 2017-03-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158592

## Citation

> US National Institutes of Health, RePORTER application 10158592, Epigenetic Age Measures to Predict COVID-19 Symptom Progression and Severity (3R00AG052604-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10158592. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*