title: Multiple-domain predictors of problematic alcohol use in young adulthood.
abstract: OBJECTIVE
  The goal of this study was to identify predictors of problematic young adult alcohol use.
  
  
  METHODS
  The sample consisted of 141 subjects (81 females) participating in a national study of genetic risk factors for alcoholism. All subjects were evaluated first as children or adolescents, then approximately 5 years later as young adults. Outcome consisted of the number of alcohol symptoms (0-10) endorsed at this second time point. Predictors of outcome were drawn from five domains representing: (1) Demographic Characteristics, (2) Child/Adolescent Problematic Alcohol Use, (3) Biological Risk, (4) Externalizing Behaviors, and (5) Family Environment. A two-stage analytic strategy was used in which (1) separate multiple regression analyses were conducted within each of the five domains and (2) statistically significant predictors of problematic alcohol use from each domain were combined into one regression model to determine which remained significant.
  
  
  RESULTS
  In the final model, 31% of the variance in the number of alcohol symptoms in young adulthood was predicted by a high number of alcohol symptoms in childhood and adolescence, low initial sensitivity to alcohol, and a negative child/adolescent relationship with the father.
  
  
  CONCLUSIONS
  These results demonstrated that GABRA2--originally associated with a diagnosis of alcohol dependence in adults--also predicted the onset of symptoms among subjects in their 20s, confirmed specific hypotheses about three other predictors in the fi nal model, and suggested the utility of incorporating biological and nonbiological predictors to optimally predict young adult alcohol problems.
authors.affiliation: Department of Psychiatry, Medical Education Building, University of Iowa School of Medicine, 500 Newton Road, Iowa City, Iowa 52242-1000, USA. john-kramer@uiowa.edu
authors.forename: John R
authors.initials: JR
authors.lastname: Kramer
authors.forename: Grace
authors.initials: G
authors.lastname: Chan
authors.forename: Danielle M
authors.initials: DM
authors.lastname: Dick
authors.forename: Samuel
authors.initials: S
authors.lastname: Kuperman
authors.forename: Kathleen K
authors.initials: KK
authors.lastname: Bucholz
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Edenberg
authors.forename: Linnea A
authors.initials: LA
authors.lastname: Polgreen
authors.forename: Victor M
authors.initials: VM
authors.lastname: Hesselbrock
authors.forename: Marc A
authors.initials: MA
authors.lastname: Schuckit
authors.forename: John I
authors.initials: JI
authors.lastname: Nurnberger
authors.forename: Ellen S
authors.initials: ES
authors.lastname: Kapp
authors.forename: Bernice
authors.initials: B
authors.lastname: Porjesz
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Bierut
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000428:Alcohol Drinking / Q000453:epidemiology* / Q000235:genetics*; D019973:Alcohol-Related Disorders / Q000453:epidemiology* / Q000235:genetics*; D002648:Child; D000431:Ethanol / Q000008:administration & dosage / Q000494:pharmacology; D005260:Female; D005500:Follow-Up Studies; D005544:Forecasting / Q000379:methods; D020022:Genetic Predisposition to Disease; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010287:Parent-Child Relations; D011963:Receptors, GABA-A / Q000235:genetics; D012044:Regression Analysis; D012307:Risk Factors
chemical_list: C484841:GABRA2 protein, human; D011963:Receptors, GABA-A; D000431:Ethanol
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