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Prevention Science

, Volume 19, Issue 1, pp 79–89 | Cite as

Glucocorticoid Receptor (NR3C1) Gene Polymorphism Moderate Intervention Effects on the Developmental Trajectory of African-American Adolescent Alcohol Abuse

  • Yao ZhengEmail author
  • Dustin Albert
  • Robert J. McMahon
  • Kenneth Dodge
  • Danielle Dick
  • the Conduct Problems Prevention Research Group
Article

Abstract

Accumulative evidence from recent genotype × intervention studies suggests that individuals carrying susceptible genotypes benefit more from intervention and provides one avenue to identify subgroups that respond differentially to intervention. This study examined the moderation by glucocorticoid receptor (NR3C1) gene variants of intervention effects on the developmental trajectories of alcohol abuse through adolescence. Participants were randomized into Fast Track intervention and control groups self-reported past-year alcohol abuse annually from grade 7 through 2 years post-high school and provided genotype data at age 21 (69% males; European Americans [EAs] = 270, African-Americans [AAs] = 282). Latent growth curve models were fit to examine developmental trajectories of alcohol abuse. The interactions of 10 single nucleotide polymorphisms (SNPs) in NR3C1 with intervention were examined separately. Both EAs and AAs showed significant increases in past-year alcohol abuse with substantial inter-individual differences in rates of linear growth. AAs showed lower general levels and slower rates of linear growth than EAs. Adjusting for multiple tests, one NR3C1 SNP (rs12655166) significantly moderated intervention effects on the developmental trajectories of alcohol abuse among AAs. Intervention effects on the rates of linear growth were stronger among AAs carrying minor alleles than those not carrying minor alleles. The findings highlight the importance of taking a developmental perspective on adolescent alcohol use and have implications for future intervention design and evaluation by identifying subgroups that could disproportionally benefit from intervention.

Keywords

Differential susceptibility Glucocorticoid receptor genes Alcohol use Adolescent Developmental trajectory Genotype-environment interaction 

Notes

Acknowledgements

This work used data from Fast Track project (for additional information concerning Fast Track, see http://www.fasttrackproject.org). We are grateful for the collaboration of the Durham Public Schools, the Metropolitan Nashville Public Schools, the Bellefonte Area Schools, the Tyrone Area Schools, the Mifflin County Schools, the Highline Public Schools, and the Seattle Public Schools. We appreciate the hard work and dedication of the many staff members who implemented the project, collected the evaluation data, and assisted with data management and analyses.

The Conduct Problems Prevention Research Group

Karen L. Bierman, Pennsylvania State University; John D. Coie, Duke University; Kenneth A. Dodge, Duke University; Mark T. Greenberg, Pennsylvania State University; John E. Lochman, University of Alabama; Robert J. McMahon, Simon Fraser University and BC Children’s Hospital; and Ellen E. Pinderhughes, Tufts University.

Compliance with Ethical Standards

Funding

This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, R18 MH50953, K05MH00797, and K05MH01027; National Institute on Drug Abuse (NIDA) Grants DA016903, K05DA15226, and P30DA023026; and Department of Education Grant S184U30002. The Center for Substance Abuse Prevention also provided support through a memorandum of agreement with the NIMH. Additional support for the preparation of this work was provided by a LEEF B.C. Leadership Chair award, Child & Family Research Institute Investigator Salary and Investigator Establishment Awards, and a Canada Foundation for Innovation award to Robert J. McMahon.

Conflict of Interest

Drs. Bierman, Coie, Dodge, Greenberg, Lochman, McMahon, and Pinderhughes are the principal investigators on the Fast Track Project and have a publishing agreement with Guilford Publications, Inc. Royalties from that agreement will be donated to a professional organization. They are also authors of the PATHS curriculum and donate all royalties from Channing-Bete, Inc. to a professional organization. Dr. Greenberg is a developer of the PATHS curriculum and has a separate royalty agreement with Channing-Bete, Inc. Bierman, Coie, Dodge, Greenberg, Lochman, and McMahon are the developers of the Fast Track curriculum and have publishing and royalty agreements with Guilford Publications, Inc. Dr. McMahon is a coauthor of Helping the Noncompliant Child and has a royalty agreement with Guilford Publications, Inc.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

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Supplementary Figure 1 (DOCX 42 kb)
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Supplementary Figure 2 (DOCX 120 kb)
11121_2016_726_MOESM3_ESM.docx (39 kb)
Supplementary Figure 3 (DOCX 39 kb)
11121_2016_726_MOESM4_ESM.docx (25 kb)
Supplementary Table 1 (DOCX 24 kb)

References

  1. Achenbach, T. M. (1991a). Manual for the child behavior checklist and revised child behavior profile. Burlington: University of Vermont, Department of Psychiatry.Google Scholar
  2. Achenbach, T. M. (1991b). Manual for the teacher’s report form. Burlington: University of Vermont, Department of Psychiatry.Google Scholar
  3. Albert, D., Belsky, D. W., Crowley, D. M., Bates, J. E., Pettit, G. S., Lansford, J. E., Dick, D., & Dodge, K. A. (2015a). Developmental mediation of genetic variation in response to the Fast Track prevention program. Development and Psychopathology, 27, 81–95.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Albert, D., Belsky, D. W., Crowley, D. M., Latendresse, S. J., Aliev, F., Riley, B., Sun, C., Conduct Problems Prevention Research Group, Dick, D. M., & Dodge, K. A. (2015b). Can genetics predict response to complex behavioral interventions? Evidence from a genetic analysis of the Fast Track Randomized Control Trial. Journal of Policy Analysis and Management, 34, 497–518.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2015). The hidden efficacy of interventions: Gene × environment experiments from a differential susceptibility perspective. Annual Review of Psychology, 66, 381–409.CrossRefPubMedGoogle Scholar
  6. Beach, S. R. H., Brody, G. H., Lei, M. K., & Philibert, R. A. (2010). Differential susceptibility to parenting among African American youths: Testing the DRD4 hypothesis. Journal of Family Psychology, 24, 513–521.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908.CrossRefPubMedGoogle Scholar
  8. 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
  9. Bloom, H. S., & Michalopoulos, C. (2013). When is the story in the subgroups? Prevention Science, 14, 179–188.CrossRefPubMedGoogle Scholar
  10. Brody, G. H., Beach, S. R., Philibert, R. A., Chen, Y. F., Lei, M. K., Murry, V. M., & Brown, A. C. (2009a). Parenting moderates a genetic vulnerability factor in longitudinal increases in youths’ substance use. Journal of Consulting and Clinical Psychology, 77, 1–11.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Brody, G. H., Beach, S. R., Philibert, R. A., Chen, Y. F., & Murry, V. M. (2009b). 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.CrossRefPubMedGoogle Scholar
  12. Brody, G. H., Beach, S. R., Hill, K. G., Howe, G. W., Prado, G., & Fullerton, S. M. (2013a). Using genetically informed, randomized prevention trials to test etiological hypotheses about child and adolescent drug use and psychopathology. American Journal of Public Health, 103, S19–S24.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Brody, G. H., Chen, Y. F., & Beach, S. R. (2013b). Differential susceptibility to prevention: GABAergic, dopaminergic, and multilocus effects. Journal of Child Psychology and Psychiatry, 54, 863–871.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Brody, G. H., Chen, Y. F., Beach, S. R., Kogan, S. M., Yu, T., DiClemente, R. J., Wingood, G. M., Windle, M., & Philibert, R. A. (2014). Differential sensitivity to prevention programming: A dopaminergic polymorphism-enhanced prevention effect on protective parenting and adolescent substance use. Health Psychology, 33, 182–191.CrossRefPubMedGoogle Scholar
  15. Bureau of Labor Statistics. (2002). National Longitudinal Survey of Youth 1997 Cohort, 1997–2001. Chicago/Columbus, OH: University of Chicago, National Opinion Research Center/Ohio State University, Center for Human Resource Research.Google Scholar
  16. Cardon, L. R., & Palmer, L. J. (2003). Population stratification and spurious allelic association. Lancet, 361, 598–604.CrossRefPubMedGoogle Scholar
  17. Cleveland, H. H., Schlomer, G. L., Vandenbergh, D. J., Feinberg, M., Greenberg, M., Spoth, R., Redmond, C., Shriver, M. D., Zaidi, A. A., & Hair, K. L. (2015). The conditioning of intervention effects on early adolescent alcohol use by maternal involvement and dopamine receptor D4 (DRD4) and serotonin transporter linked polymorphic region (5-HTTLPR) genetic variants. Development and Psychopathology, 27, 51–67.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Conduct Problems Prevention Research Group. (1992). A developmental and clinical model for the prevention of conduct disorder: The FAST Track Program. Development and Psychopathology, 4, 509–527.CrossRefGoogle Scholar
  19. Conduct Problems Prevention Research Group. (1999). Initial impact of the Fast Track prevention trial for conduct problems: I. The high-risk sample. Journal of Consulting and Clinical Psychology, 67, 631–647.CrossRefPubMedCentralGoogle Scholar
  20. Conduct Problems Prevention Research Group. (2000). Merging universal and indicated prevention programs: The Fast Track model. Addictive Behaviors, 25, 913–927.CrossRefPubMedCentralGoogle Scholar
  21. Conduct Problems Prevention Research Group. (2011). The effects of the Fast Track preventive intervention on the development of conduct disorder across childhood. Child Development, 82, 331–345.CrossRefPubMedCentralGoogle Scholar
  22. Conduct Problems Prevention Research Group. (2015). Impact of early intervention on psychopathology, crime, and well-being at age 25. American Journal of Psychiatry, 172, 59–70.CrossRefGoogle Scholar
  23. DeRijk, R. H., van Leeuwen, N., Klok, M. D., & Zitman, F. G. (2008). Corticosteroid receptor-gene variants: Modulators of the stress-response and implications for mental health. European Journal of Pharmacology, 585, 492–501.CrossRefPubMedGoogle Scholar
  24. Desrivieres, S., Lourdusamy, A., Muller, C., Ducci, F., Wong, C. P., Kaakinen, M., Pouta, A., Hartikainen, A. L., Isohanni, M., Charoen, P., Peltonen, L., Freimer, N., Elliott, P., Jarvelin, M. R., & Schumann, G. (2011). Glucocorticoid receptor (NR3C1) gene polymorphisms and onset of alcohol abuse in adolescents. Addiction Biology, 16, 510–513.CrossRefPubMedGoogle Scholar
  25. Dick, D. M., Latendresse, S. J., & Riley, B. (2011). Incorporating genetics into your studies: A guide for social scientists. Frontiers in Psychiatry, 2, 1–11.CrossRefGoogle Scholar
  26. Dick, D. M., Agrawal, A., Keller, M. C., Adkins, A., Aliev, F., Monroe, S., Hewitt, J. K., Kendler, K. S., & Sher, K. J. (2015). Candidate gene–environment interaction research: Reflections and recommendations. Perspectives on Psychological Science, 10, 37–59.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Duncan, L. E., & Keller, M. C. (2011). A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. American Journal of Psychiatry, 168, 1041–1049.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Duncan, S. C., Alpert, A., Duncan, T. E., & Hops, H. (1997). Adolescent alcohol use development and young adult outcomes. Drug and Alcohol Dependence, 49, 39–48.CrossRefPubMedGoogle Scholar
  29. Duncan, S. C., Duncan, T. E., & Strycker, L. A. (2006). Alcohol use from ages 9 to 16: A cohort-sequential latent growth model. Drug and Alcohol Dependence, 81, 71–81.CrossRefPubMedGoogle Scholar
  30. Foxcroft, D. R., Ireland, D., Lister-Sharp, D. J., Lowe, G., & Breen, R. (2003). Longer-term primary prevention for alcohol misuse in young people: A systematic review. Addiction, 98, 397–411.CrossRefPubMedGoogle Scholar
  31. French, K., Finkbiner, R., & Duhamel, L. (2002). Patterns of substance use among minority youth and adults in the United States: An overview and synthesis of national survey findings (National Evaluation Data Services [NEDS] Technical Report, Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration [SAMHSA], contract no. 270-97-7016). Fairfax, VA: Caliber Associates.Google Scholar
  32. Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., Liu-Cordero, S. N., Rotimi, C., Adeyemo, A., Cooper, R., Ward, R., Lander, E. S., Daly, M. J., & Altshuler, D. (2002). The structure of haplotype blocks in the human genome. Science, 296, 2225–2229.CrossRefPubMedGoogle Scholar
  33. Hawes, D. J., Brennan, J., & Dadds, M. R. (2009). Cortisol, callous-unemotional traits, and pathways to antisocial behavior. Current Opinion in Psychiatry, 22, 357–362.CrossRefPubMedGoogle Scholar
  34. Howe, G. W., Beach, S. R., & Brody, G. H. (2010). Microtrial methods for translating gene-environment dynamics into preventive interventions. Prevention Science, 11, 343–354.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2010). Monitoring the future: National survey results on drug use, 1975–2009. Volume I: Secondary School Students (NIH Publication No. 10–7584). Bethesda, MD: National Institute on Drug Abuse.Google Scholar
  36. Kusche, C. A., & Greenberg, M. T. (1995). The PATHS curriculum. Seattle: Developmental Research and Programs.Google Scholar
  37. Mason, W. A., Kosterman, R., Hawkins, J. D., Haggerty, K. P., & Spoth, R. L. (2003). Reducing adolescents’ growth in substance use and delinquency: Randomized trial effects of a parent-training prevention intervention. Prevention Science, 4, 203–212.CrossRefPubMedGoogle Scholar
  38. Mason, W. A., Hitch, J. E., Kosterman, R., McCarty, C. A., Herrenkohl, T. I., & Hawkins, J. D. (2010). Growth in adolescent delinquency and alcohol use in relation to young adult crime, alcohol use disorders, and risky sex: A comparison of youth from low- versus middle-income backgrounds. Journal of Child Psychology and Psychiatry, 51, 1377–1385.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Masyn, K. E., Petras, H., & Liu, W. (2014). Growth curve models with categorical outcomes. In Encyclopedia of criminology and criminal justice (pp. 2013–2025). New York: Springer.Google Scholar
  40. Metzger, I., Cooper, S. M., Zarrett, N., & Flory, K. (2013). Culturally sensitive risk behavior prevention programs for African American adolescents: A systematic analysis. Clinical Child and Family Psychology Review, 16, 187–212.CrossRefPubMedGoogle Scholar
  41. Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Authors.Google Scholar
  42. Newcomb, M. D., & Bentler, P. M. (1988). Consequences of adolescent drug use: Impact on the lives of young adults. Thousand Oaks: Sage Publications.Google Scholar
  43. Nyholt, D. R. (2004). A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. American Journal of Human Genetics, 74, 765–769.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Oyserman, D., Sanchez-Burks, J., & Harrison, K. (1996). Social identity and possible selves in adolescence. Unpublished manuscript, University of Michigan.Google Scholar
  45. Rehm, J., Mathers, C., Popova, S., Thavorncharoensap, M., Teerawattananon, Y., & Patra, J. (2009). Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet, 373, 2223–2233.CrossRefPubMedGoogle Scholar
  46. SAMHSA. (2002). Alcohol and cigarette use by race/ethnicity and age in the NHSDA Survey (NEDS fact sheet 136). Rockville, MD: Center for Substance Abuse Treatment.Google Scholar
  47. SAMHSA. (2013). Results from the 2012 National Survey on Drug Use and Health: Summary of national findings (NSDUH Series H-46, HHS Publication No. (SMA) 13–4795). Rockville, MD: SAMHSA.Google Scholar
  48. Schlomer, G. L., Cleveland, H. H., Vandenbergh, D. J., Fosco, G. M., & Feinberg, M. E. (2015). Looking forward in candidate gene research: Concerns and suggestions. Journal of Marriage and Family, 77, 351–354.CrossRefGoogle Scholar
  49. Schulenberg, J. E., & Maggs, J. L. (2002). A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood. Journal of Studies on Alcohol, S14, 54–70.CrossRefGoogle Scholar
  50. Smit, E., Verdurmen, J., Monshouwer, K., & Smit, F. (2008). Family interventions and their effect on adolescent alcohol use in general populations: A meta-analysis of randomized controlled trials. Drug and Alcohol Dependence, 97, 195–206.CrossRefPubMedGoogle Scholar
  51. Taylor, B. J., Graham, J. W., Cumsille, P., & Hansen, W. B. (2000). Modeling prevention program effects on growth in substance use: Analysis of five years of data from the Adolescent Alcohol Prevention Trial. Prevention Science, 4, 183–197.CrossRefGoogle Scholar
  52. Tobler, N. S., Roona, M. R., Ochshorn, P., Marshall, D. G., Streke, A. V., & Stackpole, K. M. (2000). School-based adolescent drug prevention programs: 1998 meta-analysis. Journal of Primary Prevention, 20, 275–336.CrossRefGoogle Scholar
  53. van de Wiel, N. M., van Goozen, S. H., Matthys, W., Snoek, H., & van Engeland, H. (2004). Cortisol and treatment effect in children with disruptive behavior disorders: A preliminary study. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1011–1018.CrossRefGoogle Scholar
  54. van Ijzendoorn, M. H., & Bakermans-Kranenburg, M. J. (2015). Genetic differential susceptibility on trial: Meta-analytic support from randomized controlled experiments. Development and Psychopathology, 27, 151–162.CrossRefPubMedGoogle Scholar
  55. Wallace, J. M. (1999). Explaining race differences in adolescent and young adult drug use: The role of racialized social systems. Drugs & Society, 14, 21–36.CrossRefGoogle Scholar
  56. Watt, T. T., & Rogers, J. M. (2007). Factors contributing to differences in substance use among Black and White adolescents. Youth & Society, 39, 54–74.Google Scholar
  57. Werthamer-Larsson, L., Kellam, S., & Wheeler, L. (1991). Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585–602.CrossRefPubMedGoogle Scholar

Copyright information

© Society for Prevention Research 2016

Authors and Affiliations

  • Yao Zheng
    • 1
    • 2
    Email author
  • Dustin Albert
    • 3
  • Robert J. McMahon
    • 1
    • 2
  • Kenneth Dodge
    • 4
    • 5
  • Danielle Dick
    • 6
    • 7
  • the Conduct Problems Prevention Research Group
  1. 1.Institute for the Reduction of Youth Violence, Department of PsychologySimon Fraser UniversityBurnabyCanada
  2. 2.BC Children’s HospitalVancouverCanada
  3. 3.Department of PsychologyBryn Mawr CollegeBryn MawrUSA
  4. 4.Center for Child and Family PolicyDuke UniversityDurhamUSA
  5. 5.Department of Psychology and NeuroscienceDuke UniversityDurhamUSA
  6. 6.College Behavioral and Emotional Health InstituteVirginia Commonwealth UniversityRichmondUSA
  7. 7.Departments of Psychology, African American Studies, and Human & Molecular GeneticsVirginia Commonwealth UniversityRichmondUSA

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