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Addiction. 2018 Dec;113(12):2214-2224. doi: 10.1111/add.14374. Epub 2018 Aug 1.

AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations.

Author information

1
Yale School of Medicine, New Haven, CT, USA.
2
Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
3
Yale School of Public Health, New Haven, CT, USA.
4
University of Louisville School of Nursing, Louisville, KY, USA.
5
Department of Statistical Science, Temple University, Philadelphia, PA, USA.
6
VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA.
7
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Abstract

BACKGROUND AND AIMS:

Longitudinal electronic health record (EHR) data offer a large-scale, untapped source of phenotypical information on harmful alcohol use. Using established, alcohol-associated variants in the gene that encodes the enzyme alcohol dehydrogenase 1B (ADH1B) as criterion standards, we compared the individual and combined validity of three longitudinal EHR-based phenotypes of harmful alcohol use: Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) trajectories; mean age-adjusted AUDIT-C; and diagnoses of alcohol use disorder (AUD).

DESIGN:

With longitudinal EHR data from the Million Veteran Program (MVP) linked to genetic data, we used two population-specific polymorphisms in ADH1B that are associated strongly with AUD in African Americans (AAs) and European Americans (EAs): rs2066702 (Arg369Cys, AAs) and rs1229984 (Arg48His, EAs) as criterion measures.

SETTING:

United States Department of Veterans Affairs Healthcare System.

PARTICIPANTS:

A total of 167 721 veterans (57 677 AAs and 110 044 EAs; 92% male, mean age = 63 years) took part in this study. Data were collected from 1  October 2007 to 1 May 2017.

MEASUREMENTS:

Using all AUDIT-C scores and AUD diagnostic codes recorded in the EHR, we calculated age-adjusted mean AUDIT-C values, longitudinal statistical trajectories of AUDIT-C scores and ICD-9/10 diagnostic groupings for AUD.

FINDINGS:

A total of 19 793 AAs (34.3%) had one or two minor alleles at rs2066702 [minor allele frequency (MAF) = 0.190] and 6933 EAs (6.3%) had one or two minor alleles at rs1229984 (MAF = 0.032). In both populations, trajectories and age-adjusted mean AUDIT-C were correlated (r = 0.90) but, when considered separately, highest score (8+ versus 0) of age-adjusted mean AUDIT-C demonstrated a stronger association with the ADH1B variants [adjusted odds ratio (aOR) 0.54 in AAs and 0.37 in AAs] than did the highest trajectory (aOR 0.71 in AAs and 0.53 in EAs); combining AUDIT-C metrics did not improve discrimination. When age-adjusted mean AUDIT-C score and AUD diagnoses were considered together, age-adjusted mean AUDIT-C (8+ versus 0) was associated with lower odds of having the ADH1B minor allele than were AUD diagnostic codes: aOR = 0.59 versus 0.86 in AAs and 0.48 versus 0.68 in EAs. These independent associations combine to yield an even lower aOR of 0.51 for AAs and 0.33 for EAs.

CONCLUSIONS:

The age-adjusted mean AUDIT-C score is associated more strongly with genetic polymorphisms of known risk for alcohol use disorder than are longitudinal trajectories of AUDIT-C or AUD diagnostic codes. AUD diagnostic codes modestly enhance this association.

KEYWORDS:

ADH1B; AUDIT-C; African American; Arg369Cys; Arg48His; European American; alcohol use disorder diagnostic codes; electronic health record data; trajectory analyses

PMID:
29972609
PMCID:
PMC6226338
[Available on 2019-12-01]
DOI:
10.1111/add.14374

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