AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations.
- Authors
- Justice, Amy C; Smith, Rachel V; Tate, Janet P; McGinnis, Kathleen; Xu, Ke; Becker, William C; Lee, Kuang-Yao; Lynch, Kevin; Sun, Ning; Concato, John; Fiellin, David A; Zhao, Hongyu; Gelernter, Joel; Kranzler, Henry R; VA Million Veteran Program
- Year
- 2018
- Journal
- Addiction (Abingdon, England)
- PMID
- 29972609
- DOI
- 10.1111/add.14374
- PMCID
- PMC6226338
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.
Proportion of Sample with AUD Codes by Age-Adjusted Mean AUDIT-C and Population Group
Proportion of Sample with AUD Codes by AUDIT-C Trajectories and Population Group
Proportion of Sample with 1 or 2 Copies of the Minor Allele by Candidate Phenotype and Population GroupFigure 2a. Age-Adjusted Mean AUDIT-C in African Americans Figure 2b. AUDIT-C Trajectories in African AmericansFigure 2c. AUD Diagnostic Codes in African AmericansFigure 2d. Age-Adjusted Mean AUDIT-C in European AmericansFigure 2e.AUDIT-C Trajectories in European AmericansFigure 2f. AUD Diagnostic Codes in European Americans
Proportion of African-American Sample with Minor Allele at rs2066702 by Age-Adjusted Mean AUDIT-C Score and AUD Codes (n=57,677)
Figure 3b. Proportion of European Americans with Minor Allele at rs1229984 by Age-Adjusted Mean AUDIT-C Score and AUD Codes (n=110,044)
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