Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T08:48:27.428Z Has data issue: false hasContentIssue false
  • English
  • Français

Life stress, the dopamine receptor gene, and emerging adult drug use trajectories: A longitudinal, multilevel, mediated moderation analysis

Published online by Cambridge University Press:  11 July 2012

Gene H. Brody ,
Yi-Fu Chen ,
Tianyi Yu ,
Steven R. H. Beach ,
Steven M. Kogan ,
Ronald L. Simons ,
Michael Windle  and
Robert A. Philibert
Gene H. Brody*
Affiliation:
University of Georgia Emory University
Yi-Fu Chen
Affiliation:
University of Georgia
Tianyi Yu
Affiliation:
University of Georgia
Steven R. H. Beach
Affiliation:
University of Georgia
Steven M. Kogan
Affiliation:
University of Georgia
Ronald L. Simons
Affiliation:
University of Georgia
Michael Windle
Affiliation:
Emory University
Robert A. Philibert
Affiliation:
University of Iowa
*
Address correspondence and reprint requests to: Gene H. Brody, University of Georgia, Center for Family Research, 1095 College Station Road, Athens, GA 30602-4527; E-mail: gbrody@uga.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

This study was designed to examine the prospective relations of life stress and genetic status with increases in drug use. African Americans (N = 399) in rural Georgia (Wave 1 mean age = 17 years) provided three waves of data across 27.5 months and a saliva sample from which the dopamine receptor D4 (DRD4) gene was genotyped. Multilevel growth curve modeling analysis indicated that emerging adults manifested the highest escalations in drug use when they reported high life stress and carried an allele of DRD4 with 7 or more repeats (7 + R allele). In addition, emerging adults who reported high life stress and carried the 7 + R allele evinced the largest increases in two proximal risk factors for drug use: affiliations with drug-using companions and drug use vulnerability cognitions. Furthermore, when the Gene × Environment interaction effects on the increases in affiliations with drug-using companions and vulnerability cognitions were entered into the model forecasting drug use, the Life Stress × DRD4 Status interaction on drug use became nonsignificant in the presence of the risk mechanisms. This finding provides an example of “second generation” Gene × Environment interaction research in which the interaction's effects on proximal risk mechanisms account for its effects on outcomes.

Type
Articles
Information
Development and Psychopathology , Volume 24 , Issue 3: Multilevel Approaches Toward Understanding Antisocial Behavior: Current Research and Future Directions , August 2012 , pp. 941 - 951
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.Google Scholar
Aseltine, R. H. Jr., & Gore, S. L. (2000). The variable effects of stress on alcohol use from adolescence to early adulthood. Substance Use and Misuse, 35, 643–668.CrossRefGoogle ScholarPubMed
Baumeister, R. F., & Scher, S. J. (1988). Self-defeating behavior patterns among normal individuals: Review and analysis of common self-destructive tendencies. Psychological Bulletin, 104, 3–22.CrossRefGoogle ScholarPubMed
Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2007). For better and for worse: Differential susceptibility to environmental influences. Current Directions in Psychological Science, 16, 300–304.CrossRefGoogle Scholar
Boatright, S. R. (2005). The Georgia county guide. Athens, GA: Center for Agribusiness and Economic Development.Google Scholar
Bradley, S. L., Dodelzon, K., Sandhu, H. K., & Philibert, R. A. (2005). Relationship of serotonin transporter gene polymorphisms and haplotypes to mRNA transcription. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 136, 58–61.CrossRefGoogle Scholar
Brody, G. H., Beach, S. R. H., Philibert, R. A., Chen, Y.-F., & Murry, V. M. (2009). Prevention effects moderate the association of 5-HTTLPR and youth risk behavior initiation: Gene × Environment hypotheses tested via a randomized prevention design. Child Development, 80, 645–661.CrossRefGoogle Scholar
Brody, G. H., Chen, Y.-F., & Kogan, S. M. (2010). A cascade model connecting life stress to risk behavior among rural African American emerging adults. Development and Psychopathology, 22, 667–678.CrossRefGoogle ScholarPubMed
Brody, G. H., Chen, Y.-F., Kogan, S. M., Smith, K., & Brown, A. C. (2010). Buffering effects of a family-based intervention for African American emerging adults. Journal of Marriage and Family, 72, 1426–1435.CrossRefGoogle ScholarPubMed
Brody, G. H., Chen, Y.-F., Murry, V. M., Ge, X., Simons, R. L., Gibbons, F. X., et al. (2006). Perceived discrimination and the adjustment of African American youths: A five-year longitudinal analysis with contextual moderation effects. Child Development, 77, 1170–1189.CrossRefGoogle ScholarPubMed
Brody, G. H., Murry, V. M., Gerrard, M., Gibbons, F. X., Molgaard, V., McNair, L. D., et al. (2004). The Strong African American Families Program: Translating research into prevention programming. Child Development, 75, 900–917.CrossRefGoogle ScholarPubMed
Brody, G. H., Yu, T., Chen, Y.-F., Kogan, S. M., Evans, G. W., Beach, S. R. H., et al. (in press). Cumulative socioeconomic status risk, allostatic load, and adjustment: A prospective latent profile analysis with contextual and genetic protective factors. Developmental Psychology.Google Scholar
Brody, G. H., Yu, T., Chen, Y.-F., Kogan, S. M., Evans, G. W., Windle, M., et al. (2012). Supportive family environments, sensitivity genes, and allostatic load among rural African American emerging adults: A prospective analysis. Journal of Family Psychology. Advance online publication.Google ScholarPubMed
Caspi, A., Hariri, A. R., Holmes, A., Uher, R., & Moffitt, T. E. (2010). Genetic sensitivity to the environment: The case of the serotonin transporter gene and its implications for studying complex diseases and traits. American Journal of Psychiatry, 167, 509–527.CrossRefGoogle Scholar
Caspi, A., & Herbener, E. S. (1990). Continuity and change: Assortative marriage and the consistency of personality in adulthood. Journal of Personality and Social Psychology, 58, 250–258.CrossRefGoogle ScholarPubMed
Castro, F. G., Maddahian, E., Newcomb, M. D., & Bentler, P. M. (1987). A multivariate model of the determinants of cigarette smoking among adolescents. Journal of Health and Social Behavior, 28, 273–289.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention. (2000). Youth risk behavior surveillance—United States, 1999. Morbidity and Mortality Weekly Report, 49.Google Scholar
Chassin, L. A., Tetzloff, C., & Hershey, M. (1985). Self-image and social-image factors in adolescent alcohol use. Journal of Studies on Alcohol, 46, 39–47.CrossRefGoogle ScholarPubMed
Cicchetti, D., & Blender, J. A. (2006). A multiple-levels-of-analysis perspective on resilience: Implications for the developing brain, neural plasticity, and preventive interventions. Annals of the New York Academy of Sciences, 1094, 248–258.CrossRefGoogle ScholarPubMed
Cleveland, M. J., Gibbons, F. X., Gerrard, M., Pomery, E. A., & Brody, G. H. (2005). The impact of parenting on risk cognitions and risk behavior: A study of mediation and moderation in a panel of African American adolescents. Child Development, 76, 900–916.CrossRefGoogle Scholar
Cloninger, C. R. (1987). A systematic method for clinical description and classification of personality variants: A proposal. Archives of General Psychiatry, 44, 573–588.CrossRefGoogle ScholarPubMed
Connell, A. M., & Dishion, T. J. (2006). The contribution of peers to monthly variation in adolescent depressed mood: A short-term longitudinal study with time-varying predictors. Development and Psychopathology, 18, 139–154.CrossRefGoogle Scholar
Cooper, M. L., Shapiro, C. M., & Powers, A. M. (1998). Motivations for sex and risky sexual behavior among adolescents and young adults: A functional perspective. Journal of Personality and Social Psychology, 75, 1528–1558.CrossRefGoogle Scholar
Daley, S. E., & Hammen, C. (2002). Depressive symptoms and close relationships during the transition to adulthood: Perspectives from dysphoric women, their best friends, and their romantic partners. Journal of Consulting and Clinical Psychology, 70, 129–141.CrossRefGoogle ScholarPubMed
Dishion, T. J., Eddy, J. M., Haas, E., Li, F., & Spracklen, K. M. (1997). Friendships and violent behavior during adolescence. Social Development, 6, 207–223.CrossRefGoogle Scholar
Dishion, T. J., Spracklen, K. M., Andrews, D. W., & Patterson, G. R. (1996). Deviancy training in male adolescents' friendships. Behavior Therapy, 27, 373–390.CrossRefGoogle Scholar
Eisenberg, D. T. A., MacKillop, J., Modi, M., Beauchemin, J., Dang, D., Lisman, S. A., et al. (2007). Examining impulsivity as an endophenotype using a behavioral approach: A DRD2 TaqI A and DRD4 48-bp VNTR association study. Behavioral and Brain Functions, 3, Article 2.CrossRefGoogle Scholar
Farber, E. A., & Egeland, B. (1987). Invulnerability among abused and neglected children. In Anthony, E. J. & Cohler, B. J. (Eds.), The invulnerable child (pp. 253–288). New York: Guilford Press.Google Scholar
Flory, K., Brown, T. L., Lynam, D. R., Miller, J. D., Leukefeld, C., & Clayton, R. R. (2006). Developmental patterns of African American and Caucasian adolescents' alcohol use. Cultural Diversity and Ethnic Minority Psychology, 12, 740–746.CrossRefGoogle ScholarPubMed
Fuligni, A. J., & Hardway, C. (2004). Preparing diverse adolescents for the transition to adulthood. Future of Children, 14, 99–119.CrossRefGoogle Scholar
Gabel, S., Stallings, M. C., Schmitz, S., Young, S. E., & Fulker, D. W. (1999). Personality dimensions and substance misuse: Relationships in adolescents, mothers and fathers. American Journal on Addictions, 8, 101–113.CrossRefGoogle ScholarPubMed
Glueck, S., & Glueck, E. (1950). Unraveling juvenile delinquency. Oxford: Commonwealth Fund.Google Scholar
Hammen, C. (2003). Risk and protective factors for children of depressed parents. In Luthar, S. S. (Ed.), Resilience and vulnerability: Adaptation in the context of childhood adversities. New York: Cambridge University Press.Google Scholar
He, K., Kramer, E., Houser, R. F., Hacker, K. A., & Chomitz, V. R. (2004). Defining and understanding healthy lifestyles choices for adolescents. Journal of Adolescent Health, 35, 26–33.CrossRefGoogle ScholarPubMed
Heath, A. C., Madden, P. A. F., Slutske, W. S., & Martin, N. G. (1995). Personality and the inheritance of smoking behavior: A genetic perspective. Behavior Genetics, 25, 103–107.CrossRefGoogle ScholarPubMed
Hoffman, L., & Stawski, R. S. (2009). Persons as contexts: Evaluating between-person and within-person effects in longitudinal analysis. Research in Human Development, 6, 97–120.CrossRefGoogle Scholar
Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (2000). Monitoring the Future national survey results on drug use, 1975–1999: Vol. 1. Secondary school students (NIH Publication No. 00-4802). Bethesda, MD: National Institute on Drug Abuse.Google Scholar
Kim-Cohen, J., & Gold, A. L. (2009). Measured gene–environment interactions and mechanisms promoting resilient development. Current Directions in Psychological Science, 18, 138–142.CrossRefGoogle Scholar
Kotchick, B. A., Shaffer, A., & Forehand, R. (2001). Adolescent sexual risk behavior: A multi-system perspective. Clinical Psychology Review, 21, 493–519.CrossRefGoogle ScholarPubMed
Laucht, M., Becker, K., Blomeyer, D., & Schmidt, M. H. (2007). Novelty seeking involved in mediating the association between the dopamine D4 receptor gene exon III polymorphism and heavy drinking in male adolescents: Results from a high-risk community sample. Biological Psychiatry, 61, 87–92.CrossRefGoogle ScholarPubMed
Levitan, R. D., Masellis, M., Lam, R. W., Kaplan, A. S., Davis, C., Tharmalingam, S., et al. (2006). A birth-season/DRD4 gene interaction predicts weight gain and obesity in women with seasonal affective disorder: A seasonal thrifty phenotype hypothesis. Neuropsychopharmacology, 31, 2498–2503.CrossRefGoogle ScholarPubMed
Li, D., Sham, P. C., Owen, M. J., & He, L. (2006). Meta-analysis shows significant association between dopamine system genes and attention deficit hyperactivity disorder (ADHD). Human Molecular Genetics, 15, 2276–2284.CrossRefGoogle ScholarPubMed
Meeus, W., Branje, S. J. T., & Overbeek, G. (2004). Parents and partners in crime: A six-year longitudinal study on changes in supportive relationships and delinquency in adolescence and young adulthood. Journal of Child Psychology and Psychiatry, 45, 1288–1298.CrossRefGoogle ScholarPubMed
Moffitt, T. E., Caspi, A., & Rutter, M. L. (2006). Measured gene–environment interactions in psychopathology: Concepts, research strategies, and implications for research, intervention, and public understanding of genetics. Perspectives on Psychological Science, 1, 5–27.CrossRefGoogle ScholarPubMed
Muthén, L. K., & Muthén, B. O. (1998–2010). Mplus user's guide (6th ed.). Los Angeles: Author.Google Scholar
Offner, P., & Holzer, H. (2002). Left behind in the labor market: Recent employment trends among young Black men. Washington, DC: Brookings Institution.Google Scholar
Paschall, M. J., Flewelling, R. L., & Faulkner, D. L. (2000). Alcohol misuse in young adulthood: Effects of race, educational attainment, and social context. Substance Use and Misuse, 35, 1485–1506.CrossRefGoogle ScholarPubMed
Patrick, D. L., Cheadle, A., Thompson, D. C., Diehr, P., Koepsell, T., & Kinne, S. (1994). The validity of self-reported smoking: A review and meta-analysis. American Journal of Public Health, 84, 1086–1093.CrossRefGoogle ScholarPubMed
Patton, G. C., Hibbert, M., Rosier, M. J., Carlin, J. B., Caust, J., & Bowes, G. (1996). Is smoking associated with depression and anxiety in teenagers? American Journal of Public Health, 86, 225–230.CrossRefGoogle ScholarPubMed
Pérez de Castro, I., Ibáñez, A., Torres, P., Sáiz-Ruiz, J., & Fernández-Piqueras, J. (1997). Genetic association study between pathological gambling and a functional DNA polymorphism at the D4 receptor gene. Pharmacogenetics, 7, 345–348.Google Scholar
Pomery, E. A., Gibbons, F. X., Reis-Bergan, M., & Gerrard, M. (2009). From willingness to intention: Experience moderates the shift from reactive to reasoned behavior. Personality and Social Psychology Bulletin, 35, 894–908.CrossRefGoogle ScholarPubMed
Proctor, B. D., & Dalaker, J. (2003). Poverty in the United States: 2002 (Current Population Reports, P60-222). Washington, DC: US Bureau of the Census.Google Scholar
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.Google Scholar
Rutter, M. L., & Silberg, J. (2002). Gene–environment interplay in relation to emotional and behavioral disturbance. Annual Review of Psychology, 53, 463–490.CrossRefGoogle ScholarPubMed
Shaw, B. A., Agahi, N., & Krause, N. (2011). Are changes in financial strain associated with changes in alcohol use and smoking among older adults? Journal of Studies on Alcohol and Drugs, 72, 917–925.CrossRefGoogle ScholarPubMed
Simons, R. L., Lei, M.-K., Beach, S. R. H., Brody, G. H., Philibert, R. A., & Gibbons, F. X. (2011). Social environment, genes and aggression: Evidence supporting the differential susceptibility perspective. American Sociological Review, 76, 883–912.CrossRefGoogle Scholar
Simons, R. L., Stewart, E. A., Gordon, L. C., Conger, R. D., & Elder, G. H. Jr. (2002). A test of life-course explanations for stability and change in antisocial behavior from adolescence to young adulthood. Criminology, 40, 401–434.CrossRefGoogle Scholar
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.CrossRefGoogle Scholar
StataCorp. (2011). Stata statistical software: Release 12. College Station, TX: Author.Google Scholar
Sum, A., Khatiwada, I., Pond, N., Trubsky, M., Fogg, N., & Palma, S. (2002). Left behind in the labor market: Labor market problems of the nation's out-of-school, young adult populations. Chicago: Northeastern University, Center for Labor Market Studies.Google Scholar
Unger, J. B., Hamilton, J. E., & Sussman, S. (2004). A family member's job loss as a risk factor for smoking among adolescents. Health Psychology, 23, 308–313.CrossRefGoogle Scholar
Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21, 213–228.CrossRefGoogle Scholar
Whalen, C. K., Jamner, L. D., Henker, B., & Delfino, R. J. (2001). Smoking and moods in adolescents with depressive and aggressive dispositions: Evidence from surveys and electronic diaries. Health Psychology, 20, 99–111.CrossRefGoogle ScholarPubMed
Wimberly, R. C., & Morris, L. V. (1997). The Southern Black belt: A national perspective. Lexington, KY: TVA Rural Studies Press.Google Scholar
Windle, M., Mun, E. Y., & Windle, R. C. (2005). Adolescent-to-young adult heavy drinking trajectories and their prospective predictors. Journal of Studies on Alcohol, 66, 313–322.CrossRefGoogle ScholarPubMed
Zucker, R. A., Wong, M. W., Puttler, L. I., & Fitzgerald, H. E. (2003). Resilience and vulnerability among sons of alcoholics: Relationship to developmental outcomes between early childhood and adolescence. In Luthar, S. S. (Ed.), Resilience and vulnerability: Adaptation in the context of childhood adversities (pp. 76–103). New York: Cambridge University Press.CrossRefGoogle Scholar