nach oben

Erschienen in:

06.11.2016

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

verfasst von: Yao Zheng, Dustin Albert, Robert J. McMahon, Kenneth Dodge, Danielle Dick, the Conduct Problems Prevention Research Group

Erschienen in: Prevention Science | Ausgabe 1/2018

Einloggen, um Zugang zu erhalten

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.

Nur mit Berechtigung zugänglich

Achenbach, T. M. (1991a). Manual for the child behavior checklist and revised child behavior profile. Burlington: University of Vermont, Department of Psychiatry.

Achenbach, T. M. (1991b). Manual for the teacher’s report form. Burlington: University of Vermont, Department of Psychiatry.

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908.CrossRefPubMed

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.CrossRef

Bloom, H. S., & Michalopoulos, C. (2013). When is the story in the subgroups? Prevention Science, 14, 179–188.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.

Cardon, L. R., & Palmer, L. J. (2003). Population stratification and spurious allelic association. Lancet, 361, 598–604.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRef

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.CrossRefPubMedCentral

Conduct Problems Prevention Research Group. (2000). Merging universal and indicated prevention programs: The Fast Track model. Addictive Behaviors, 25, 913–927.CrossRefPubMedCentral

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.CrossRefPubMedCentral

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.CrossRef

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.

Kusche, C. A., & Greenberg, M. T. (1995). The PATHS curriculum. Seattle: Developmental Research and Programs.

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.

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.CrossRefPubMed

Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Authors.

Newcomb, M. D., & Bentler, P. M. (1988). Consequences of adolescent drug use: Impact on the lives of young adults. Thousand Oaks: Sage Publications.

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.CrossRefPubMedPubMedCentral

Oyserman, D., Sanchez-Burks, J., & Harrison, K. (1996). Social identity and possible selves in adolescence. Unpublished manuscript, University of Michigan.

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.CrossRefPubMed

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.

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.

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.CrossRef

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.CrossRef

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.CrossRefPubMed

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRefPubMed

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.CrossRef

Watt, T. T., & Rogers, J. M. (2007). Factors contributing to differences in substance use among Black and White adolescents. Youth & Society, 39, 54–74.

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.CrossRefPubMed

Titel: Glucocorticoid Receptor (NR3C1) Gene Polymorphism Moderate Intervention Effects on the Developmental Trajectory of African-American Adolescent Alcohol Abuse
verfasst von: Yao Zheng
Dustin Albert
Robert J. McMahon
Kenneth Dodge
Danielle Dick
the Conduct Problems Prevention Research Group
Publikationsdatum: 06.11.2016
Verlag: Springer US
Erschienen in: Prevention Science / Ausgabe 1/2018
Print ISSN: 1389-4986
Elektronische ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-016-0726-4

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

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

Abstract

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 1/2018

Commentary for Special Issue of Prevention Science “Using Genetics in Prevention: Science Fiction or Science Fact?”

Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth

Prevention of Early Substance Use Mediates, and Variation at SLC6A4 Moderates, SAAF Intervention Effects on OXTR Methylation

The Implications of Genetics for Prevention and Intervention Programming

Informing Prevention and Intervention Policy Using Genetic Studies of Resistance

Transactions Between Substance Use Intervention, the Oxytocin Receptor (OXTR) Gene, and Peer Substance Use Predicting Youth Alcohol Use