AUDIT‐C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations. (1st August 2018)

• Record Type: Journal Article
• 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.
• Is Part Of: Addiction. Volume 113:Number 12(2018)
• Page Start: 2214
• Page End: 2224
• Publication Date: 2018-08-01
• Subjects: ADH1B -- African American -- alcohol use disorder diagnostic codes -- Arg369Cys -- Arg48His -- AUDIT‐C -- electronic health record data -- European American -- trajectory analyses; Alcoholism -- Periodicals; Drug addiction -- Periodicals; 616.86
• DOI: 10.1111/add.14374
• Languages: English
• ISSNs: 0965-2140

Abstract

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.