RESUMO
BACKGROUND: Although alcohol risk is heritable, few genetic risk variants have been identified. Longitudinal electronic health record (EHR) data offer a largely untapped source of phenotypic information for genetic studies, but EHR-derived phenotypes for harmful alcohol exposure have yet to be validated. Using a variant of known effect, we used EHR data to develop and validate a phenotype for harmful alcohol exposure that can be used to identify unknown genetic variants in large samples. Herein, we consider the validity of 3 approaches using the 3-item Alcohol Use Disorders Identification Test consumption measure (AUDIT-C) as a phenotype for harmful alcohol exposure. METHODS: First, using longitudinal AUDIT-C data from the Veterans Aging Cohort Biomarker Study Cohort (VACS-BC), we compared 3 metrics of AUDIT-C using correlation coefficients: (i) AUDIT-C closest to blood sampling (closest AUDIT-C), (ii) the highest value (highest AUDIT-C), (iii) and longitudinal trajectories generated using joint trajectory modeling (AUDIT-C trajectory). Second, we compared the associations of the 3 AUDIT-C metrics with phosphatidylethanol (PEth), a direct, quantitative biomarker for alcohol in the overall sample using chi-square tests for trend. Last, in the subsample of African Americans (AAs; n = 1,503), we compared the associations of the 3 AUDIT-C metrics with rs2066702 a common missense (Arg369Cys) polymorphism of the ADH1B gene, which encodes an alcohol dehydrogenase isozyme. RESULTS: The sample (n = 1,851, 94.5% male, 65% HIV+, mean age 52 years) had a median of 7 AUDIT-C scores over a median of 6.1 years. Highest AUDIT-C and AUDIT-C trajectory were correlated r = 0.86. The closest AUDIT-C was obtained a median of 2.26 years after the VACS-BC blood draw. Overall and among AAs, all 3 AUDIT-C metrics were associated with PEth (all p < 0.05), but the gradient was steepest with AUDIT-C trajectory. Among AAs (36% with the protective ADH1B allele), the association of rs2066702 with AUDIT-C trajectory and highest AUDIT-C was statistically significant (p < 0.05), and the gradient was steeper for the AUDIT-C trajectory than for the highest AUDIT-C. The closest AUDIT-C was not statistically significantly associated with rs2066702. CONCLUSIONS: EHR data can be used to identify complex phenotypes such as harmful alcohol use. The validity of the phenotype may be enhanced through the use of longitudinal trajectories.
Assuntos
Álcool Desidrogenase/genética , Transtornos Relacionados ao Uso de Álcool/sangue , Transtornos Relacionados ao Uso de Álcool/genética , Glicerofosfolipídeos/sangue , Fenótipo , Polimorfismo Genético/genética , Transtornos Relacionados ao Uso de Álcool/psicologia , Estudos de Coortes , Feminino , Humanos , Estudos Longitudinais , Masculino , Veteranos/psicologiaRESUMO
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.