ZSCAN25 methylation predicts seizures and severe alcohol withdrawal syndrome

Allan Andersen; Emily Milefchik; Emma Papworth; Brandan Penaluna; Kelsey Dawes; Joanna Moody; Gracie Weeks; Ellyse Froehlich; Kaitlyn deBlois; Jeffrey D Long; Robert Philibert

doi:10.1080/15592294.2023.2298057

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ZSCAN25 methylation predicts seizures and severe alcohol withdrawal syndrome

Journal article

Open access

Peer reviewed

ZSCAN25 methylation predicts seizures and severe alcohol withdrawal syndrome

Allan Andersen, Emily Milefchik, Emma Papworth, Brandan Penaluna, Kelsey Dawes, Joanna Moody, Gracie Weeks, Ellyse Froehlich, Kaitlyn deBlois, Jeffrey D Long, …

Epigenetics, Vol.19(1), 2298057

01/03/2024

DOI: 10.1080/15592294.2023.2298057

PMCID: PMC10766392

PMID: 38166538

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Abstract

Currently, clinicians use their judgement and indices such as the Prediction of Alcohol Withdrawal Syndrome Scale (PAWSS) to determine whether patients are admitted to hospitals for consideration of withdrawal syndrome (AWS). However, only a fraction of those admitted will experience severe AWS. Previously, we and others have shown that epigenetic indices, such as the Alcohol T-Score (ATS), can quantify recent alcohol consumption. However, whether these or other alcohol biomarkers, such as carbohydrate deficient transferrin (CDT), could identify those at risk for severe AWS is unknown. To determine this, we first conducted genome-wide DNA methylation analyses of subjects entering and exiting alcohol treatment to identify loci whose methylation quickly reverted as a function of abstinence. We then tested whether methylation at a rapidly reverting locus, cg07375256, or other existing metrics including PAWSS scores, CDT levels, or ATS, could predict outcome in 125 subjects admitted for consideration of AWS. We found that PAWSS did not significantly predict severe AWS nor seizures. However, methylation at cg07375256 ( ) and CDT strongly predicted severe AWS with ATS ( < 0.007) and cg07375256 ( < 6 × 10-5) methylation also predicting AWS associated seizures. We conclude that epigenetic methods can predict those likely to experience severe AWS and that the use of these or similar Precision Epigenetic approaches could better guide AWS management.

Alcoholism - genetics

DNA Methylation

Ethanol

Humans

Seizures - genetics

Substance Withdrawal Syndrome - genetics

Zinc Fingers

Details

Title: Subtitle: ZSCAN25 methylation predicts seizures and severe alcohol withdrawal syndrome
Creators: Allan Andersen - University of Iowa
Emily Milefchik - University of Iowa
Emma Papworth - University of Iowa
Brandan Penaluna - University of Iowa
Kelsey Dawes - University of Iowa
Joanna Moody - University of Iowa
Gracie Weeks - University of Iowa
Ellyse Froehlich - Department of Psychiatry, University of Iowa, Iowa City, IA, USA
Kaitlyn deBlois - University of Iowa
Jeffrey D Long - University of Iowa
Robert Philibert - University of Iowa
Resource Type: Journal article
Publication Details: Epigenetics, Vol.19(1), 2298057
DOI: 10.1080/15592294.2023.2298057
PMID: 38166538
PMCID: PMC10766392
NLM abbreviation: Epigenetics
ISSN: 1559-2294
eISSN: 1559-2308
Grant note: R44 AA022041 / NIAAA NIH HHS UM1 TR004403 / NCATS NIH HHS R21 AA029435 / NIAAA NIH HHS R43 AA027197 / NIAAA NIH HHS
Language: English
Date published: 01/03/2024
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Psychiatry; Iowa Neuroscience Institute; Biostatistics
Record Identifier: 9984539656202771

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