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Neighborhood Deprivation Moderates Shared and Unique Environmental Influences on Hazardous Drinking: Findings from a Cross-Sectional Co-Twin Study.

Isaac C RhewCharles B FlemingSiny TsangErin HornRick KostermanGlen E Duncan
Published in: Substance use & misuse (2020)
Background: There has been increased interest in the interplay of genetic and environmental factors in the development of problematic alcohol use, including socioeconomic conditions of the neighborhood. Using a co-twin design, we examined the extent to which contributions of genetic, shared environmental, and unique environmental influences on hazardous drinking differed according to levels of neighborhood socioeconomic deprivation. Method: Data came from 1,521 monozygotic (MZ) and 609 dizygotic (DZ) twin pairs surveyed in Washington State. A measure of neighborhood deprivation was created based on census-tract-level variables and the Alcohol Use Disorders Identification Test 3-item instrument was used to assess level of hazardous drinking. We tested a series of nested structural equation models to examine associations among hazardous drinking, neighborhood deprivation, and the variance components (genetic [A], shared [C] and unique environmental [E] influences) of these two constructs, testing for both main effects and moderation by neighborhood deprivation. Results: Neighborhood deprivation was significantly associated with increased hazardous drinking, after accounting for A and C variance common to both phenotypes. Adjusting for within-pair differences in income and education, neighborhood deprivation moderated the magnitude of variance components of hazardous drinking, with the variance attributable to shared environment and non-shared environment increasing in more deprived neighborhoods. Conclusions: Findings point to amplification of early childhood as well as unique adulthood environmental risk on hazardous drinking in areas of greater deprivation.
Keyphrases
  • physical activity
  • alcohol consumption
  • human health
  • healthcare
  • genome wide
  • life cycle
  • copy number
  • depressive symptoms
  • dna methylation
  • machine learning
  • climate change
  • big data
  • deep learning
  • early life
  • nucleic acid