Validation and characterisation of a DNA methylation alcohol biomarker across the life course.


Journal

Clinical epigenetics
ISSN: 1868-7083
Titre abrégé: Clin Epigenetics
Pays: Germany
ID NLM: 101516977

Informations de publication

Date de publication:
27 11 2019
Historique:
received: 24 05 2019
accepted: 23 09 2019
entrez: 29 11 2019
pubmed: 30 11 2019
medline: 28 7 2020
Statut: epublish

Résumé

Recently, an alcohol predictor was developed using DNA methylation at 144 CpG sites (DNAm-Alc) as a biomarker for improved clinical or epidemiologic assessment of alcohol-related ill health. We validate the performance and characterise the drivers of this DNAm-Alc for the first time in independent populations. In N = 1049 parents from the Avon Longitudinal Study of Parents and Children (ALSPAC) Accessible Resource for Integrated Epigenomic Studies (ARIES) at midlife, we found DNAm-Alc explained 7.6% of the variation in alcohol intake, roughly half of what had been reported previously, and interestingly explained a larger 9.8% of Alcohol Use Disorders Identification Test (AUDIT) score, a scale of alcohol use disorder. Explanatory capacity in participants from the offspring generation of ARIES measured during adolescence was much lower. However, DNAm-Alc explained 14.3% of the variation in replication using the Head and Neck 5000 (HN5000) clinical cohort that had higher average alcohol consumption. To investigate whether this relationship was being driven by genetic and/or earlier environment confounding, we examined how earlier versus concurrent DNAm-Alc measures predicted AUDIT scores. In both ARIES parental and offspring generations, we observed associations between AUDIT and concurrent, but not earlier DNAm-Alc, suggesting independence from genetic and stable environmental contributions. The stronger relationship between DNAm-Alcs and AUDIT in parents at midlife compared to adolescents despite similar levels of consumption suggests that DNAm-Alc likely reflects long-term patterns of alcohol abuse. Such biomarkers may have potential applications for biomonitoring and risk prediction, especially in cases where reporting bias is a concern.

Sections du résumé

BACKGROUND
Recently, an alcohol predictor was developed using DNA methylation at 144 CpG sites (DNAm-Alc) as a biomarker for improved clinical or epidemiologic assessment of alcohol-related ill health. We validate the performance and characterise the drivers of this DNAm-Alc for the first time in independent populations.
RESULTS
In N = 1049 parents from the Avon Longitudinal Study of Parents and Children (ALSPAC) Accessible Resource for Integrated Epigenomic Studies (ARIES) at midlife, we found DNAm-Alc explained 7.6% of the variation in alcohol intake, roughly half of what had been reported previously, and interestingly explained a larger 9.8% of Alcohol Use Disorders Identification Test (AUDIT) score, a scale of alcohol use disorder. Explanatory capacity in participants from the offspring generation of ARIES measured during adolescence was much lower. However, DNAm-Alc explained 14.3% of the variation in replication using the Head and Neck 5000 (HN5000) clinical cohort that had higher average alcohol consumption. To investigate whether this relationship was being driven by genetic and/or earlier environment confounding, we examined how earlier versus concurrent DNAm-Alc measures predicted AUDIT scores. In both ARIES parental and offspring generations, we observed associations between AUDIT and concurrent, but not earlier DNAm-Alc, suggesting independence from genetic and stable environmental contributions.
CONCLUSIONS
The stronger relationship between DNAm-Alcs and AUDIT in parents at midlife compared to adolescents despite similar levels of consumption suggests that DNAm-Alc likely reflects long-term patterns of alcohol abuse. Such biomarkers may have potential applications for biomonitoring and risk prediction, especially in cases where reporting bias is a concern.

Identifiants

pubmed: 31775873
doi: 10.1186/s13148-019-0753-7
pii: 10.1186/s13148-019-0753-7
pmc: PMC6880546
doi:

Substances chimiques

Genetic Markers 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

163

Subventions

Organisme : Department of Health
ID : RP-PG-0707-10034
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0902144
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_19009
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12013/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00011/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_15018
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 19169
Pays : United Kingdom
Organisme : Medical Research Council
ID : G9815508
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/I025263/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 102215/2/13/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12013/2
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/I025751/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00011/5
Pays : United Kingdom

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Auteurs

Paul Darius Yousefi (PD)

MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK. paul.yousefi@bristol.ac.uk.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. paul.yousefi@bristol.ac.uk.

Rebecca Richmond (R)

MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Ryan Langdon (R)

MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Andrew Ness (A)

National Institute of Health Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.
Bristol Dental School, University of Bristol, Bristol, UK.

Chunyu Liu (C)

Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Daniel Levy (D)

The Framingham Heart Study, Framingham, MA, USA.
The Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA.

Caroline Relton (C)

MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Matthew Suderman (M)

MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Luisa Zuccolo (L)

MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

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Classifications MeSH