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Are prescription misuse and illicit drug use etiologically distinct? A genetically-informed analysis of opioids and stimulants

Published online by Cambridge University Press:  18 January 2021

Genevieve F. Dash Open the ORCID record for Genevieve F. Dash [Opens in a new window] ,
Nicholas G. Martin ,
Arpana Agrawal ,
Michael T. Lynskey  and
Wendy S. Slutske
Genevieve F. Dash*
Affiliation:
Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
Nicholas G. Martin
Affiliation:
Queensland Institute of Medical Research- Berghofer, Brisbane, QLD, Australia
Arpana Agrawal
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
Michael T. Lynskey
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
Wendy S. Slutske
Affiliation:
Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
*
Author for correspondence: Genevieve F. Dash, E-mail: genevievedash@mail.missouri.edu
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Abstract

Background

Drug classes are grouped based on their chemical and pharmacological properties, but prescription and illicit drugs differ in other important ways. Potential differences in genetic and environmental influences on the (mis)use of prescription and illicit drugs that are subsumed under the same class should be examined. Opioid and stimulant classes contain prescription and illicit forms differentially associated with salient risk factors (common route of administration, legality), making them useful comparators for addressing this etiological issue.

Methods

A total of 2410 individual Australian twins [Mage = 31.77 (s.d. = 2.48); 67% women] were interviewed about prescription misuse and illicit use of opioids and stimulants. Univariate and bivariate biometric models partitioned variances and covariances into additive genetic, shared environmental, and unique environmental influences across drug types.

Results

Variation in the propensity to misuse prescription opioids was attributable to genes (41%) and unique environment (59%). Illicit opioid use was attributable to shared (71%) and unique (29%) environment. Prescription stimulant misuse was attributable to genes (79%) and unique environment (21%). Illicit stimulant use was attributable to genes (48%), shared environment (29%), and unique environment (23%). There was evidence for genetic influence common to both stimulant types, but limited evidence for genetic influence common to both opioid types. Bivariate correlations suggested that prescription opioid use may be more genetically similar to prescription stimulant use than to illicit opioid use.

Conclusions

Prescription opioid misuse may share little genetic influence with illicit opioid use. Future research may consider avoiding unitary drug classifications, particularly when examining genetic influences.

Keywords

Illicit drug useopioidsprescription misusestimulantstwin study
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 14 , October 2022 , pp. 3176 - 3183
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Copyright
Copyright © The Author(s) 2021. Published by Cambridge University Press

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References

Agrawal, A., Neale, M. C., Jacobson, K., Prescott, C. A., & Kendler, K. S. (2005). Illicit drug use and abuse/dependence: Modeling of two-stage variables using the CCC approach. Addictive Behaviors, 30(5), 1043–1048.CrossRefGoogle ScholarPubMed
Brown, S. A. (2015). Stigma towards marijuana users and heroin users. Journal of Psychoactive Drugs, 47(3), 213–220.CrossRefGoogle ScholarPubMed
Carlson, R. G., Nahhas, R. W., Martins, S. S., & Daniulaityte, R. (2016). Predictors of transition to heroin use among initially non-opioid dependent illicit pharmaceutical opioid users: A natural history study. Drug and Alcohol Dependence, 160, 127–134.CrossRefGoogle ScholarPubMed
Chen, L.-Y., Strain, E. C., Alexandre, P. K., Alexander, G. C., Mojtabai, R., & Martins, S. S. (2014). Correlates of nonmedical use of stimulants and methamphetamine use in a national sample. Addictive Behaviors, 39(5), 829–836.CrossRefGoogle Scholar
Ciccarone, D. (2011). Stimulant abuse: Pharmacology, cocaine, methamphetamine, treatment, attempts at pharmacotherapy. Primary Care: Clinics in Office Practice, 38(1), 41–58.CrossRefGoogle ScholarPubMed
Ciccarone, D., Ondocsin, J., & Mars, S. G. (2017). Heroin uncertainties: Exploring users’ perceptions of fentanyl-adulterated and-substituted ‘heroin’. International Journal of Drug Policy, 46, 146–155.CrossRefGoogle Scholar
Cicero, T. J., Ellis, M. S., Surratt, H. L., & Kurtz, S. P. (2014). The changing face of heroin use in the United States: A retrospective analysis of the past 50 years. JAMA Psychiatry, 71(7), 821–826.CrossRefGoogle ScholarPubMed
Compton, W. M., & Volkow, N. D. (2006). Abuse of prescription drugs and the risk of addiction. Drug and Alcohol Dependence, 83, S4–S7.CrossRefGoogle ScholarPubMed
Cumming, G. (2009). Inference by eye: Reading the overlap of independent confidence intervals. Statistics in Medicine, 28(2), 205–220.CrossRefGoogle ScholarPubMed
Daniulaityte, R., Falck, R., & Carlson, R. G. (2012). “I'm not afraid of those ones just ‘cause they've been prescribed”: Perceptions of risk among illicit users of pharmaceutical opioids. International Journal of Drug Policy, 23(5), 374–384.CrossRefGoogle ScholarPubMed
Darke, S. (2013). Pathways to heroin dependence: Time to re-appraise self-medication. Addiction, 108(4), 659–667.CrossRefGoogle ScholarPubMed
Darke, S., Torok, M., & Ross, J. (2017). Developmental trajectories to heroin dependence: Theoretical and clinical issues. Journal of Applied Social Psychology, 47(3), 165–171.CrossRefGoogle Scholar
Degenhardt, L., Barker, B., & Topp, L. (2004). Patterns of ecstasy use in Australia: Findings from a national household survey. Addiction, 99(2), 187–195.CrossRefGoogle ScholarPubMed
Degenhardt, L., Roxburgh, A., Dunn, M., Campbell, G., Bruno, R., Kinner, S. A., … Topp, L. (2009). The epidemiology of ecstasy use and harms in Australia. Neuropsychobiology, 60(3–4), 176–187.CrossRefGoogle ScholarPubMed
Dick, D. M., & Kendler, K. S. (2012). The impact of gene–environment interaction on alcohol use disorders. Alcohol Research: Current Reviews, 34(3), 318–324.Google ScholarPubMed
Fischer, B., Fischer, B., Patra, J., Fischer, B., Patra, J., Firestone Cruz, M., … Gittins, J. (2008). Comparing heroin users and prescription opioid users in a Canadian multi-site population of illicit opioid users. Drug and Alcohol Review, 27(6), 625–632.CrossRefGoogle Scholar
Gillespie, N. A., Bates, T. C., Hickie, I. B., Medland, S. E., Verhulst, B., Kirkpatrick, R. M., … Benotsch, E. G. (2019). Genetic and environmental risk factors in the non-medical use of over-the-counter or prescribed analgesics, and their relationship to major classes of licit and illicit substance use and misuse in a population-based sample of young adult twins. Addiction, 114(12), 2229–2240.CrossRefGoogle Scholar
Gillespie, N. A., Neale, M. C., & Kendler, K. S. (2009). Pathways to cannabis abuse: A multi-stage model from cannabis availability, cannabis initiation and progression to abuse. Addiction, 104(3), 430–438.CrossRefGoogle ScholarPubMed
Julious, S. A. (2004). Using confidence intervals around individual means to assess statistical significance between two means. Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry, 3(3), 217–222.CrossRefGoogle Scholar
Karkowski, L. M., Prescott, C. A., & Kendler, K. S. (2000). Multivariate assessment of factors influencing illicit substance use in twins from female-female pairs. American Journal of Medical Genetics, 96(5), 665–670.3.0.CO;2-O>CrossRefGoogle ScholarPubMed
Kendler, K. S., Aggen, S. H., Tambs, K., & Reichborn-Kjennerud, T. (2006). Illicit psychoactive substance use, abuse and dependence in a population-based sample of Norwegian twins. Psychological Medicine, 36(7), 955–962.CrossRefGoogle Scholar
Kendler, K. S., & Eaves, L. J. (1986). Models for the joint effect of genotype and environment on liability to psychiatric illness. The American Journal of Psychiatry, 143(3), 279–289.Google ScholarPubMed
Kendler, K. S., Gardner, C., Jacobson, K. C., Neale, M. C., & Prescott, C. A. (2005). Genetic and environmental influences on illicit drug use and tobacco use across birth cohorts. Psychological Medicine, 35(9), 1349–1356.CrossRefGoogle ScholarPubMed
Kendler, K. S., Jacobson, K. C., Prescott, C. A., & Neale, M. C. (2003). Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. American Journal of Psychiatry, 160(4), 687–695.CrossRefGoogle ScholarPubMed
Kendler, K. S., Karkowski, L., & Prescott, C. A. (1999). Hallucinogen, opiate, sedative and stimulant use and abuse in a population-based sample of female twins. Acta Psychiatrica Scandinavica, 99(5), 368–376.CrossRefGoogle Scholar
Keyes, K. M., Cerdá, M., Brady, J. E., Havens, J. R., & Galea, S. (2014). Understanding the rural–urban differences in nonmedical prescription opioid use and abuse in the United States. American Journal of Public Health, 104(2), e52–e59.CrossRefGoogle ScholarPubMed
Loehlin, J. C. (1996). The Cholesky approach: A cautionary note. Behavior Genetics, 26(1), 65–69.CrossRefGoogle Scholar
Lord, S., Brevard, J., & Budman, S. (2011). Connecting to young adults: An online social network survey of beliefs and attitudes associated with prescription opioid misuse among college students. Substance Use & Misuse, 46(1), 66–76.CrossRefGoogle ScholarPubMed
Lynskey, M. T., Agrawal, A., Henders, A., Nelson, E. C., Madden, P. A., & Martin, N. G. (2012). An Australian twin study of cannabis and other illicit drug use and misuse, and other psychopathology. Twin Research and Human Genetics, 15(5), 631–641.CrossRefGoogle ScholarPubMed
McHugh, R. K., Nielsen, S., & Weiss, R. D. (2015). Prescription drug abuse: From epidemiology to public policy. Journal of Substance Abuse Treatment, 48(1), 1–7.CrossRefGoogle ScholarPubMed
Mezquita, L., Sánchez-Romera, J. F., Ibáñez, M. I., Morosoli, J. J., Colodro-Conde, L., Ortet, G., & Ordoñana, J. R. (2018). Effects of social attitude change on smoking heritability. Behavior Genetics, 48(1), 12–21.CrossRefGoogle ScholarPubMed
Miech, R., Johnston, L., O'Malley, P. M., Keyes, K. M., & Heard, K. (2015). Prescription opioids in adolescence and future opioid misuse. Pediatrics, 136(5), e1169–e1177.CrossRefGoogle ScholarPubMed
Muthén, L. K., & Muthen, B. (2017). Mplus user's guide: Statistical analysis with latent variables, user's guide. Muthén & Muthén.Google Scholar
Neale, M. C., Eaves, L. J., & Kendler, K. S. (1994). The power of the classical twin study to resolve variation in threshold traits. Behavior Genetics, 24(3), 239–258.CrossRefGoogle ScholarPubMed
Netherland, J., & Hansen, H. B. (2016). The war on drugs that wasn't: Wasted whiteness, “dirty doctors,” and race in media coverage of prescription opioid misuse. Culture, Medicine, and Psychiatry, 40(4), 664–686.CrossRefGoogle ScholarPubMed
Reid, L. W., Elifson, K. W., & Sterk, C. E. (2007). Ecstasy and gateway drugs: Initiating the use of ecstasy and other drugs. Annals of Epidemiology, 17(1), 74–80.CrossRefGoogle ScholarPubMed
Rigg, K. K., & Monnat, S. M. (2015). Comparing characteristics of prescription painkiller misusers and heroin users in the United States. Addictive Behaviors, 51, 106–112.CrossRefGoogle ScholarPubMed
SAMHSA. (2019). Key substance use and mental health indicators in the United States: Results from the 2018 National Survey on Drug Use and Health (HHS Publication No. PEP19–5068, NSDUH Series H-54). Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from https://www.samhsa.gov/data/.Google Scholar
Slutske, W. S., Deutsch, A. R., & Piasecki, T. M. (2019a). Neighborhood alcohol outlet density and genetic influences on alcohol use: Evidence for gene–environment interaction. Psychological Medicine, 49(3), 474.CrossRefGoogle ScholarPubMed
Slutske, W. S., Deutsch, A. R., & Piasecki, T. M. (2019b). Neighborhood density of alcohol outlets moderates genetic and environmental influences on alcohol problems. Addiction, 114(5), 815–822.CrossRefGoogle ScholarPubMed
Szalavitz, M., & Rigg, K. K. (2017). The curious (dis)connection between the opioid epidemic and crime. Substance Use & Misuse, 52(14), 1927–1931.CrossRefGoogle ScholarPubMed
Van den Bree, M. B., Johnson, E. O., Neale, M. C., & Pickens, R. W. (1998). Genetic and environmental influences on drug use and abuse/dependence in male and female twins. Drug and Alcohol Dependence, 52(3), 231–241.CrossRefGoogle ScholarPubMed
Verhulst, B. (2017). A power calculator for the classical twin design. Behavior Genetics, 47(2), 255–261.CrossRefGoogle Scholar
Via, K. D. (2019). Preventing the next epidemic: Prescribed stimulant abuse. The Journal for Nurse Practitioners, 15(3), 232–235.CrossRefGoogle Scholar
Votaw, V. R., Geyer, R., Rieselbach, M. M., & McHugh, R. K. (2019). The epidemiology of benzodiazepine misuse: A systematic review. Drug and Alcohol Dependence, 200, 95–114.CrossRefGoogle ScholarPubMed
Williams, C. T., & Latkin, C. A. (2007). Neighborhood socioeconomic status, personal network attributes, and use of heroin and cocaine. American Journal of Preventive Medicine, 32(6), S203–S210.CrossRefGoogle ScholarPubMed
Zhou, H., Rentsch, C. T., Cheng, Z., Kember, R. L., Nunez, Y. Z., Sherva, R. M., … Polimanti, R. (2020). Association of OPRM1 functional coding variant with opioid use disorder: A genome-wide association study. JAMA Psychiatry, 77(10), 1072–1080.CrossRefGoogle ScholarPubMed
Zimmerman, G. M., & Farrell, C. (2017). Parents, peers, perceived risk of harm, and the neighborhood: Contextualizing key influences on adolescent substance use. Journal of Youth and Adolescence, 46(1), 228–247.CrossRefGoogle ScholarPubMed
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