# Predicting  Alcohol Withdrawal using DNA Methylation

> **NIH NIH R21** · UNIVERSITY OF IOWA · 2022 · $183,469

## Abstract

The placement of intoxicated patients from the emergency room into inpatient hospital settings to monitor
and/treat possible alcohol withdrawal syndrome (AWS) is common occurrence for most urban hospitals.
Although the circumstances that lead to this outcome vary, a contributor to many of these hospitalizations is
the lack of reliable clinical information to predict whether a patient is likely to suffer medically severe AWS.
Because of this, clinicians are often forced to hospitalize patients, often against their will, unnecessarily. A
method predicting who will experience AWS could address this predicament, improve outcomes, avoid
unnecessary alienation of patients and decrease healthcare costs.
 Newly developed epigenetic techniques may be able to predict the likelihood of AWS. Over the past 5
years using genome wide approaches, we and other have shown that heavy alcohol consumption is
associated with profound changes in DNA methylation status. Furthermore, we have recently refined the
signatures obtained using these expensive time-consuming methylation arrays to an easy to perform,
potentially clinically employable digital PCR panel that is highly sensitive and specific for heavy alcohol
consumption. These panels are now being used commercially for insurance underwriting. However, whether
this DNA methylation panel or any other DNA methylation panel could also be useful for determining likelihood
of AWS, alone or together with composite self-report/biomarker tools such as the Prediction of Alcohol
Withdrawal Scale (PAWSS) is unknown.
 In this high risk R21 application, we will test whether DNA methylation can aid current schemes for
predicting alcohol withdrawal. Specifically, we will solicit 150 subjects admitted to the University of Iowa for
alcohol detoxification. We will then characterize each of these subjects with a battery of tools including the
PAWSS, phlebotomize them to provide biomaterial for the methylation studies, then follow each of these
subjects to determine which of them went onto develop AWS. Finally, we will determine DNA methylation
status in each of these subjects. We hypothesize that the DNA methylation will predict AWS and that the
PAWS and DNA methylation predict AWS better than either measure alone. It is innovative because
generally accepted biomarkers for assessing risk for alcohol withdrawal do not exist and the use of DNA
methylation for these purposes has not been tested. The team is well prepared to conduct the research and
includes board-certified clinicians, statisticians and a leading expert on DNA methylation. The institution at
which the examination will be based admits thousands of intoxicated patients annually. As a direct result of
this research we will establish the feasibility of DNA methylation to predict AWS and gather the data to design
a well powered R01 investigation that specifically examines this new approach as compared to existing
measures.

## Key facts

- **NIH application ID:** 10447464
- **Project number:** 1R21AA029435-01A1
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Allan M Andersen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $183,469
- **Award type:** 1
- **Project period:** 2022-05-10 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10447464

## Citation

> US National Institutes of Health, RePORTER application 10447464, Predicting  Alcohol Withdrawal using DNA Methylation (1R21AA029435-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10447464. Licensed CC0.

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